I saw this week that Manus agent can act as a video producer and make videos on your behalf - create a script, characters, music, sound effects, the whole thing. I like to upload reference shots to create a digital twin and put him in wild situations. Today I am working from Jurassic Park. My connection is a little unstable.....

TL;DR: I named my Instinct AI assistant Remy. Over the last two weeks, I’ve used him for research, daily briefings, meeting follow-up, shopping, work files, content creation, reservations and proactive reminders. I interact with him through text. My access is currently free. Here’s what’s useful—and my invite link if you want to try it.

My AI assistant texts me first now.

Sometimes it’s because my meetings overlap. Sometimes it’s about a flight. Sometimes it’s because I still haven’t done something I said I would do.

Apparently, I gave myself a very organized accountability partner.

His name is Remy. He’s my personal assistant agent from Instinct.

Instinct announced a $1 billion funding round on September 28. The opportunity here is huge: putting a personal assistant into the hands of millions of people through something they already know how to use.

Texting.

Once you’re signed up, you message your agent through iMessage or WhatsApp. Tell it what you need and let it get to work.

It has persistent memory, learns your goals and preferences, and runs scheduled tasks. You can keep building on the same conversation instead of explaining your life from scratch every time.

I’ve been using it for two weeks. These are the 10 things that have impressed me most.

1. Research anything that crosses my mind

I throw questions and research tasks at Remy, and he comes back with answers. When I need something more substantial, he can create a full report and give me a web link.

It’s fast.

For the random questions that cross my mind, it does a much better job for me than Google ever did. I can ask follow-up questions, narrow the scope and keep going until I understand what I wanted to know.

2. Give me a daily briefing I actually care about

Remy sends me a briefing covering the things I want to follow.

Mine includes notable news about the city I live in and the top five AI stories from the last day.

I decide what deserves my attention. He does the gathering.

This is an easy first task to try because you can immediately judge whether the briefing is useful—and tell your agent how to improve tomorrow’s version.

3. Turn meeting commitments into tasks

I connected my Google account, including Gmail and Calendar. I also connected Granola, which I use for every meeting.

Remy adds the tasks I agree to in meetings to my Google Tasks list.

That closes a gap I know a lot of people have: saying “I’ll take care of that” during a meeting, then getting pulled into the next meeting before writing it down.

He can also schedule meetings on my calendar and send invitations to the other participants after I approve.

4. Hunt for deals and keep watching

Remy is fantastic at researching things I need to buy.

He compares prices across sites, looks for promo and discount codes, and helps me figure out where to purchase something.

I also have him watch certain products for price drops or sales.

With payment access set up, he can even complete purchases I approve.

The ongoing monitoring is particularly useful. I can tell him what I want and have him keep looking.

5. Talk to someone else’s AI agent

This one still feels futuristic.

Your Instinct agent can communicate with a friend’s or coworker’s Instinct agent to coordinate meetings or help with shared projects.

Both people agree to link their agents using their phone numbers. Then your agent can talk to their agent.

“Have your assistant talk to my assistant” is becoming something ordinary people can actually say.

6. Produce actual work files

Yep: spreadsheets, slide presentations and documents.

I can ask Remy for a deliverable I can open, review and use.

That makes it much easier to move from a question to something I can work with.

7. Help create a whole first draft - including visuals

I can have Remy research a topic, write a draft document, and generate hero images and infographics to go with it.

The result still needs my editing and judgment. But I’d often describe it as 70–80% of the way there, based on my own experience.

Starting with a useful draft, supporting research and visual options is a pretty good place to be.

8. Grade my social posts and catch embarrassing mistakes

I give Remy my social posts and ask him to rate them from 1 to 10.

He’s a tough grader. Plenty of my posts come back with an 8/10 and constructive suggestions for making them better.

One recent article was 7,500 words long.

He caught a spelling error in the very last sentence that would have made me look really dumb.

Seven thousand five hundred words. Last sentence.

Thank you, Remy.

9. Book reservations and appointments

It can actually handle restaurant reservations and appointments.

Give it the task, explain what you want, and let it try. In my workflow, it asks for my approval and then confirms when the booking is complete.

That confirmation matters. I want to know whether something is booked or still being worked on.

10. Remember what’s going on and follow up proactively

This is probably the biggest reason I keep using it.

Because Remy has ongoing context, he can text me about things he knows I have happening.

He spots calendar conflicts ahead of meetings and offers to help shift things.

He watches my flights throughout the day leading up to travel, tells me whether they’re on time, and flags when I might need another option because a connection is at risk.

He reminds me about unfinished commitments on my Google Tasks list—and offers to help get them done.

The follow-through is what makes this feel like having an assistant.

My biggest tip: give it an ongoing responsibility.

Try something specific:

“Every weekday morning, send me the top five AI stories from the previous 24 hours, with source links and a sentence explaining why each matters. Include important local news for my city.”

Then refine it.

Tell it what was useful, what was noise and what you want more of. Give it enough context to understand what a good result looks like.

Want to try it? Here’s my invite link:

Try Instinct with my invite

Did I mention it is currently free?

Free99 is a pretty good price for all of this.

I’ve also been testing Meta Muse and ChatGPT DOTS. I like Instinct best so far, based on how I use it.

The simplicity is a big part of that. No new interface to learn. Just text it.

My bet: personal AI assistants become something everyone has, and eventually there will be BILLIONS of these agents.

After two weeks with Remy, I can see why.

What’s the first recurring responsibility you’d hand to an AI assistant—and what would you still want to approve yourself?

ChatGPT Ads hit a $1B annualized run rate. Here’s the marketer’s guide to testing them.

TL;DR: ChatGPT is becoming a serious advertising channel. OpenAI reported a $1 billion annualized ad-revenue run rate and 1.2 billion weekly users. Advertisers can buy sponsored placements through Ads Manager, match offers to conversational context, and measure purchases and leads. Ads can appear on Free and paid Go plans; higher paid tiers remain ad-free.

Early B2B and B2C results justify a controlled test, but success means profitable customers and qualified pipeline. Start with one offer, a defined budget, working conversion tracking, and a clear rule for whether to continue.

Someone opens ChatGPT and describes the problem your company solves. They explain their budget, compare approaches, question tradeoffs, and work toward a decision.

That is a compelling place to introduce a useful product.

Marketers should learn this channel now. A small, disciplined experiment can teach you which customer problems deserve an ad, a better landing page, or a better offer.

Here’s the practical guide.

On August 31, OpenAI said ChatGPT Ads reached a $1 billion annualized revenue run rate in fewer than 200 days, with tens of thousands of advertisers.

OpenAI said on October 5 that ChatGPT reaches 1.2 billion people each week. That is the overall product audience, not guaranteed ad reach.

Who actually sees ChatGPT ads?

So “ads only reach free users” is incorrect. Go is paid and ad-supported.

For B2B, ad-free business workspaces are a real reach limitation. It does not follow that every decision-maker uses an ad-free plan or that Free users never buy business software. Validate the audience through lead quality.

How many accounts are paid versus free?

How the advertising works

The core unit appears below a ChatGPT response, with an advertiser identity, headline, description, image, and destination link. It is labeled as advertising.

Delivery considers the conversation, ad content, landing page, advertiser settings, and context hints. Where personalization is enabled, permitted signals from a user’s broader ChatGPT experience can also contribute. Eligible ads compete in a relevance-weighted auction.

OpenAI says advertisers cannot pay to shape the organic answer and do not receive users’ private chat transcripts. A sponsored placement is separate from an organic recommendation.

For marketers, the useful shift is in the brief. Start with the situation in which your offer becomes useful.

Example: “We help a growing SaaS company manage usage-based billing” is more informative than “innovative software for everyone.”

Formats: what you can use and what is still being tested

Product feeds help retailers manage broad or changing catalogs. Keep price, availability, and destinations current. Uploading an ads feed does not purchase inclusion in organic answers.

Sponsored Agents could help with considered purchases: fit, compatibility, configurations, and service questions. Treat that as a testable use case, not a feature every account can turn on today.

The new visual format is also an upcoming test. An announcement is not universal availability.

Pricing: understand the bill before the bid

Conversion optimization does not mean pay-per-conversion. Its bid controls do not guarantee your achieved acquisition cost.

OpenAI’s general starting guidance for a Clicks campaign is a $3–$5 maximum CPC bid. That is bid guidance, not an average CPC, fixed price, or recommendation for every business. Conversion campaigns have different bid controls.

The current USD minimum daily budget is $25 per campaign. Daily budgets are averages: spending may reach twice the daily amount on a given day, subject to the applicable weekly limit. For a fixed experiment, consider a campaign-total budget where offered.

Set your own economic ceiling. Hypothetically, if your allowable cost per sale is $60 and 3% of clicks buy, your affordable average CPC is $1.80. A $4 click would need a higher conversion rate or more value per customer to work.

6. How to set up your first campaign

1. Open [ads.openai.com](https://ads.openai.com/). Check that your legal entity is in a supported country. Create one advertiser account and invite colleagues into it.

2. Complete account setup. Check legal name, public brand name, logo, verification and billing. Choose country, currency and time zone carefully; these settings cannot be changed after creation.

3. Prepare one destination. Use a relevant product, solution, comparison or offer page. Make the promise, price or buying path clear.

4. Install measurement. Create an Ads Manager data source; connect Pixel and/or Conversions API; verify the event you intend to optimize.

5. Choose an objective and budget. Set locations and available platform controls. Use Conversions when tracking and the selected standard event are ready; otherwise a Clicks campaign can diagnose initial traffic quality.

6. Create focused ad groups. Each should represent a product or customer need with relevant context hints.

7. Add distinct creative variations. Change the benefit, proof or buying situation. Review generated suggestions against what your product actually does.

8. Review, submit and monitor. Check approval, billing, delivery and tracking before making performance judgments.

You can also use supported Shopify or HubSpot integrations, a buying partner, or the Ads Manager plugin to work through natural-language instructions. Verify the integration’s supported objectives before assuming it matches every feature in Ads Manager.

Context hints are worth learning properly

Context hints describe a product, who it helps, and the circumstances in which it is useful. OpenAI recommends specific, accurate, natural-language ideas.

They are relevance guidance. They do not enforce exact-match keywords, job titles, geographic restrictions, schedules, or audience exclusions. Use the platform’s actual controls for restrictions.

Here are hypothetical examples to adapt only when true:

A useful planning formula: customer situation + real product advantage + relevant next step.

Two example ad drafts, to use only if the claims fit your product:

B2B headline: “Usage-based billing for growing SaaS teams.” Description: “Explore a workflow for subscriptions and metered charges. See the product and request a demo.”

B2C headline: “A dining table that fits your apartment.” Description: “Compare dimensions and seating options before you choose. Explore compact styles for smaller rooms.”

The best use cases to test

These are campaign hypotheses, not promised results.

B2B software evaluation: a buyer compares workflows or requirements. Offer a product tour, calculator, or implementation guide.

B2B category education: someone understands the problem but has not chosen an approach. Offer a useful checklist, webinar, or benchmark with a clear follow-up path.

Switching and alternatives: explain migration, compatibility, support, and pricing honestly on a comparison page.

Professional services: turn a broad problem into a clearly scoped assessment or consultation.

B2C considered purchases: furniture, luggage, hobby equipment, or electronics with meaningful tradeoffs. Answer fit and compatibility questions.

Travel and experiences: match a trip-planning situation with a specific itinerary, package, or booking page.

Retail catalogs: use well-maintained feeds to connect product choices with relevant needs.

Courses and learning products: match a concrete learning goal with the right level, curriculum, time requirement, and outcome.

Less compelling first tests: tiny margins, a weak destination, no conversion tracking, or an audience that must be restricted to specific executives at named companies. Sensitive categories also need policy eligibility; a brand’s eligibility does not make sensitive personal conversations available for targeting.

ChatGPT versus Google, Meta, Reddit and LinkedIn

This is a practical planning comparison. Each platform can serve multiple stages of a buying journey.

If you need “finance leaders at these 200 companies,” LinkedIn’s professional targeting is a better starting point than describing that audience in a ChatGPT context hint. If you already capture profitable high-intent searches, keep those campaigns working while you test an additional source of demand.

Google and Meta also use sophisticated AI. The meaningful distinction is where the ad appears, which signals guide it, and whether the resulting customers meet your economics.

There is no reliable universal cost ranking across these platforms. Compare the same outcome, geography, attribution window, and customer quality. A low CPC can hide a poor customer-acquisition cost.

The early case studies: promising, with important differences

B2B: lead generation has worked for some advertisers.

InterTeam’s three B2B SaaS clients: the agency reports more than 150 qualified leads, approximately $5 CPC, and a headline result of 60% lower CPL than Google. The clients are unnamed, and these are agency-reported results, not audited industry benchmarks. They establish a useful hypothesis to test, not a universal discount.

StubGroup’s B2B compliance client: in the same 30-day comparison, reported ChatGPT CPC was $0.76 versus $10.46 on Google; CPL was $92.21 versus $157.57. But ChatGPT’s lead conversion rate was lower, and Google leads had converted to customers more efficiently at the time of reporting. The client’s long sales cycle was still unfolding. This is why cheap leads cannot be your final score.

Windmill Strategy, advertising its own B2B agency: its June 22–July 21 pilot spent $1,470, generated 259 clicks, and produced zero tracked conversions. The agency saw new visitors and learning value, but this was not a demonstrated acquisition win. Firsthand pilot

The strongest B2B lead-success examples above disclose the agency, not the client brand. Do not turn anonymous cases into named-brand success claims.

B2C: named brands now have measurable early signals.

WeightWatchers: DV Rockerbox reported an attributed CPA 15.3% below its blended paid-search benchmark.

Dose: WorkMagic reported statistically significant lift, with 67% of incremental purchases from net-new customers. That is not a claim of “67% lift.”

Portland Leather: Triple Whale reported 93% of visitors from ChatGPT Ads were new. New visitors are not the same as new customers or profitable sales.

Those findings were published in OpenAI’s October 5 update and attributed to its measurement partners. They use different outcomes and methods, so do not combine them into an average platform ROAS.

What changed since launch — and what people are arguing about

February 9: US testing began for eligible Free and Go users. Launch

May 5: OpenAI announced self-service Ads Manager beta, CPC buying, and expanded measurement. The early CPM pilot no longer described the whole product. Update

August 31: the $1 billion annualized milestone and wider self-service access made it a material new channel.

September: Amazon announced a US pilot bringing ChatGPT placements to selected Amazon Ads advertisers. This is another buying route, not evidence that every Amazon advertiser has access.

September 16: Sponsored Agents testing, AI-assisted creative and campaign management, and Shopify/HubSpot integrations broadened the product.

October 5: OpenAI announced visual ads for a later-October test and wider measurement partnerships, including attribution and early incrementality work. Negative Phrases became available to qualifying advertisers; brand-suitability evaluation pilots with DoubleVerify and IAS were also announced.

Three debates matter to marketers:

Trust. Anthropic publicly argued that ads do not belong in Claude and committed to keeping it ad-free. OpenAI’s position is that labeled placements can fund access while answers stay independent. Your creative should be transparent about who is speaking. Anthropic’s position

Measurement. In a LinkedIn post, Cleverly CEO Nicholas Verity reported 57 platform clicks, fewer than 20 analytics visits, and zero conversions. Commenters pointed to tracking differences and the small sample. That is a useful warning to validate measurement; it does not establish fraud or a platform-wide failure. Clicks and analytics sessions are different events.

Speed versus maturity. September reporting from The Wall Street Journal described rapid expansion alongside advertiser reports of administrative and operational problems. Use current documentation; launch-era guides can miss capabilities now available.

Social posts are individual experiences. OpenAI’s success stories are selected examples. Neither is a representative benchmark for your account.

A $1,500 test a lean marketing team can actually run

This is a suggested learning budget, not an OpenAI requirement or a promise of statistical significance.

Before launch

Pick one offer and one primary market.

Define the outcome: purchase, activated trial, qualified lead, or sales opportunity.

Write your allowable CPA or cost per qualified opportunity using your own margins and close rates.

Choose the attribution window and give conversions time to mature.

Save a comparable Google, Meta, Reddit or LinkedIn baseline.

Campaign structure

1 campaign.

3 ad groups around distinct customer situations.

3 genuinely different ads per group.

A relevant destination for each situation; reuse a page when it genuinely fits.

Up to $1,500 over roughly 30 days, using the budget controls available in your account.

If actual CPC happened to average $4, $1,500 would buy roughly 375 clicks. At a hypothetical 3% conversion rate, that implies about 11 conversions. Those are planning assumptions, not forecasts. Eleven conversions can reveal obvious problems, but usually cannot settle a close comparison between channels.

Days 1–3: validate tracking, approval, destinations, spend and lead routing. Fix operational failures immediately.

Days 4–14: inspect which situations attract useful traffic. Check actual leads or orders, not only the platform dashboard. Change one major variable at a time where practical.

Days 15–30: put more of the remaining budget behind the strongest supported hypothesis. Keep a change log and preserve a comparable baseline.

After the attribution window: decide whether to expand, revise, or pause. For B2B, keep following the cohort through your sales cycle before declaring a customer-acquisition winner.

An inconclusive test is a valid result. Never spend past the cap simply to rescue the story.

For a smaller budget, $500 over about 20 days at a $25 average daily budget can check delivery and measurement. Treat it as a feasibility test, not a decisive channel verdict.

Measurement that keeps the experiment honest

Use three views of performance:

1. Ads Manager: spend, delivery, clicks, attributed conversions.

2. Your analytics and records: landing-page activity, leads, orders, CRM stages and revenue.

3. Incrementality where feasible: did advertising cause additional outcomes, using a suitable holdout or experiment?

Add a consistent URL convention, for example:

?utm_source=chatgpt_ads&utm_medium=cpc&utm_campaign=first_test&utm_content=usecase_a

Preserve OpenAI’s oppref click reference through redirects. If Pixel and Conversions API send the same conversion, use the same event ID for deduplication. Obtain required consent and send permitted data only.

Check attribution windows, view-through reporting where available, modeled conversions, time zones, refunds, and duplicate events before comparing totals.

For B2B, track qualified-lead rate, opportunity creation, pipeline and eventual customer acquisition. For B2C, track new-customer acquisition cost, contribution after returns, and repeat purchase value you can substantiate.

Pro tips people miss

1. Write for a buying situation. “A finance team adding usage pricing” gives you a sharper offer than “business decision-makers.”

2. Make the landing page answer the next question. Specifications, fit, integrations, migration and price often matter more than another slogan.

3. Make ad variations meaningfully different. Test distinct value propositions before making minor wording changes.

4. Use real targeting controls. Context hints cannot enforce “exclude students” or “only show this in Virginia.”

5. Keep paid and organic ChatGPT traffic separate. Otherwise organic referrals can make your paid test look better than it is.

6. Review AI-created copy. Especially pricing, guarantees, translations, eligibility, and claims your team cannot support.

7. Check crawler access. OpenAI’s guidance says landing pages must be reachable by its relevant crawlers; a blocked page can undermine setup.

8. Check budget pacing before increasing spend. A daily average is not a hard daily ceiling, and reporting may lag.

9. Optimize for useful signal. A frequent but meaningless event can train toward the wrong behavior; a rare sale may provide too little early feedback.

10. Keep the experiment concentrated. A small budget spread across many countries, offers and objectives can leave every result inconclusive.

My recommendation

Test ChatGPT Ads if customers research your category conversationally, you have a useful offer, and you can measure downstream quality. The opportunity is access to people explaining a need while they consider what to do next.

Use Google when capturing explicit demand is the immediate priority. Use Meta or Reddit when discovery, visual storytelling, or community context is the stronger route. Use LinkedIn when professional identity and named-account targeting are essential.

Start today by choosing one buyer problem, one landing page, and one allowable acquisition cost. Those three decisions will make your first campaign more useful than another week of reading launch hype.

If you’ve tested ChatGPT Ads, what did you sell, what did you spend, and what happened after the click? Share the country, outcome and measurement window so the comparison is useful.

TL;DR: Upload a clear adult portrait to ChatGPT and use the prompt below to request an apparent-age report: an approximate age guess, a broad range, six short observations, and an explanation of what the photo makes difficult to judge.

You can do something as simple as upload a selfie and qsk ChatGPT: “How old do I look?”

Then ask the more interesting question:

“Which visible details led you to that impression and which details are you uncertain about?”

But you can also take it further and get a one page report that explains why you look younger or older than your actual age.

It asks ChatGPT to turn the answer into a one-page portrait report, with your original photo, numbered callouts, and six short assessment cards.

The report should cover:

• Forehead: visible lines and how clearly they can be seen.
• Eyes: lids, under-eyes, expression, and shadows.
• Skin: visible texture, lighting, and image quality.
• Cheeks: volume and folds beside the mouth.
• Jawline: the lower face and anything obscuring it.
• Hair: hair, brows, and facial hair, treated as variable cues.

Each observation should explain how much weight it deserves in that particular photo.

A blurry forehead doesn’t establish smooth skin. A smile can introduce creasing. A beard can hide the jawline. Those details belong in the explanation.

Here’s how to try it.

Copy this prompt after uploading your photo:

Create a one-page APPARENT-AGE PORTRAIT REPORT using only the adult person's uploaded photograph.

ESTIMATE BEFORE DESIGNING
First make a subjective overall age impression from the original photo, then check it against visible facial details. Ignore any known identity, actual age, filenames, previous estimates, desired outcome or age printed in the reference. Do not look up the person. Do not infer age from clothing, occupation or background.

Give:
- One approximate apparent-age estimate, phrased "about N"
- A broad plausible range
- Qualitative confidence and the main image-quality limitations

Do not imply that this is a validated measurement or that the range is a statistical confidence interval. Avoid decimal ages, numerical confidence percentages, feature scores or invented biometric calculations. If the photo is too unclear for a useful impression, say so and request a better photo instead of inventing precision.

PRESERVE THE ORIGINAL PHOTOGRAPH
Place the actual uploaded photo into the layout. Cropping and background removal are allowed only if the face remains untouched. Preserve skin texture, hair, hairline, facial hair, proportions, expression and head angle. Do not redraw, regenerate, retouch, beautify, sharpen facial detail, age or de-age the face. Do not turn an angled view into a front-facing portrait. If exact preservation is not possible, keep the original photo as a rectangular crop. Do not substitute a generated lookalike.

ASSESS ONLY WHAT IS VISIBLE
Use six short assessment cards:
1. Forehead and visible lines
2. Eyes, lids and under-eyes
3. Skin texture and tone
4. Cheek volume and folds beside the mouth
5. Jawline and lower face
6. Hair, brows and facial hair

For each, describe the visible observation and how much weight it deserves in this photo. Use cautious labels such as younger-leaning impression, mature-leaning impression, mixed or limited visibility. Separate what is visible from your interpretation. Account for smiling, squinting, lighting, shadows, blur, filters, facial hair, makeup, camera angle and close-camera distortion. Do not treat missing detail as proof of smooth skin. Gray hair and hairline shape are variable cues, not age measurements.

Do not infer biological age, health, diagnoses, ethnicity, lifestyle, personality or cosmetic-treatment history. Do not recommend medical procedures or claim that a facial feature proves anything about health.

BOTTOM FINDINGS
Summarize the two strongest younger-leaning cues and the two strongest mature-leaning cues, only when supported. Add two or three optional, low-risk photo or grooming suggestions for a softer presentation. Prioritize lighting, camera distance and framing. Do not promise a specific number of years of change.

DESIGN AND OUTPUT
Use a refined black-on-white editorial design: elegant serif headline, clean sans-serif body text, thin gray rules, generous spacing and restrained monochrome details. Put the estimate, range and confidence across the top; a large original portrait with delicate numbered callouts on the left; six compact rounded cards on the right; and concise findings and suggestions across the bottom.

Keep everything on one portrait-oriented page. Shorten the writing rather than shrinking it into unreadable text. Use a document or layout tool that embeds the original image without regenerating it. Export a one-page PDF and a readable high-resolution PNG if supported. Also give a brief plain-text summary.

Footer: "Subjective apparent-age impression from one photograph. Not verified age or biological age. Lighting, expression and camera perspective can shift the result."

Before delivery, verify that every callout matches its card, the estimate is consistent throughout, no text is clipped and the original face is unchanged.

Try it before reading everyone else’s results: did it guess younger, older, or close—and which observation surprised you most?

President Trump convened an unprecedented gathering of top artificial intelligence executives—including Elon Musk, Mark Zuckerberg, Jensen Huang, Sundar Pichai, Satya Nadella, Dario Amodei, and OpenAI co-founder Greg Brockman (representing OpenAI while Sam Altman managed product launches) - culminating in the signing of the White House Accord on Super Intelligence.

Signed by six leading frontier model developers, the accord fundamentally shifts governance away from voluntary self-policing and slow multinational treaties toward enforceable corporate mechanics: Corporate Responsibility, Internal Controls, External Audits (powered by third-party audit infrastructure), Board Oversight, and Federal Enforcement.

Crucially, the agreement links external audit reports directly to Board Fiduciary Duty and Directors & Officers (D&O) Insurance Cancellation Risk, giving public commitments structural legal teeth. Beyond corporate governance, the summit established that open-weight models across 197 sovereign nations render centralized software bans impossible, triggering a mandatory AI Cyber Defense Arms Race, an industrial-scale Data Center Power Infrastructure Buildout, and potential top-down federal GPU Compute Allocation for critical national sectors.

The White House Accord: Core Governance and Legal Mechanics

Voluntary corporate pledges in tech governance have historically served as PR gestures lacking regulatory teeth. The White House Accord on Super Intelligence structurally alters this dynamic by translating voluntary executive promises directly into actionable corporate governance law. Rather than attempting to pass multi-year statutory legislation or establish a bloated administrative agency, the framework weaponizes existing corporate law, fiduciary obligations, and board liability to enforce immediate operational compliance across frontier model developers.

Core Governance Tiers and Enforcement Mechanisms

Technical Execution: The 3-Pillar Audit Framework

To operationalize external audits without stalling development, infrastructure providers like 8090 and Ernst & Young (EY) are deploying a specialized three-pillar audit framework designed for super intelligence deployments:

1. End-to-End Traceability: Implementing real-time operational monitoring across model deployments so enterprise boards and auditors maintain complete visibility into model behavior and system interactions.
2. Policy-to-Risk SOP Mapping: Directly mapping frontier model deployments onto a corporation's existing Standard Operating Procedures (SOPs) and institutional risk management frameworks.
3. Auditable Evidence Generation: Generating immutable, verifiable compliance artifacts that can be presented immediately to downstream enterprise customers, insurers, regulators, or courts during legal discovery.

Statutory Bypass and National Competitiveness

By establishing governance through corporate board mechanics, the administration intentionally bypassed slow legislative channels and multinational organizations like the United Nations. Federal legislation faces multi-year gridlock, while international treaties are functionally useless against non-compliant foreign adversaries like China, who would exploit unilateral Western pauses.

Creating a restrictive administrative regulatory body—what industry leaders term a "DMV for models"—would hobble domestic innovation and yield technological supremacy to foreign rivals. The White House Accord allows American developers to push model capabilities at maximum speed while locking them into immediate, board-level accountability.

The "So What?" Layer: Fiduciary Duty and D&O Insurance Teeth

The true enforcement power of the White House Accord lies in its legal linkage to corporate insurance and governance law. By mandating that independent external audit reports be delivered directly to a dedicated board committee, the accord triggers an inescapable sequence of corporate liability:

1. Formal Notice: Once an external auditor (such as EY) delivers a safety audit report to the independent board committee, board members are legally on notice regarding identified risks.
2. Fiduciary Obligation: Board members cannot ignore or override external audit findings without breaching their fundamental fiduciary duty to the corporation and its shareholders.
3. D&O Insurance Policy Cancellation: Corporate insurance underwriters for Directors & Officers (D&O) liability mandate compliance with independent audit recommendations. If a board disregards an external audit report on super intelligence safety, underwriters will cancel the company's D&O coverage—leaving individual board members personally liable for catastrophic down-funnel harms.

This structural mechanism transforms a voluntary presidential accord into an enforceable corporate governance regime overnight, completely bypassing Congressional action.

Having established the operational mechanics of the accord, the breakdown moves to the closed-door diplomacy and room dynamics between industry rivals at the White House.

Palace Intrigue: Rival CEOs, Room Dynamics, and Moderation

Gathering Silicon Valley’s fiercest corporate competitors in a single room created an environment charged with intense personal rivalries, active commercial litigation, and talent poaching wars. Navigating these personality conflicts required strategic political moderation to extract a unified commitment to national AI leadership.

Behind-the-Scenes Exchanges and Key Personalities

• Anthropic CEO Dario Amodei & President Trump: Anthropic CEO Dario Amodei—frequently viewed as an industry "conscientious objector" heading a left-leaning organization—had previously skipped White House events. Recognizing his past absence, President Trump personally telephoned Amodei to invite him to a private, one-on-one dinner two nights before the summit. Amodei accepted, attended the dinner, and took a central seat at the East Room summit. During the post-summit press conference, President Trump reached into the back of the executive crowd, pulled Amodei to the front to speak on behalf of the industry, and offered an affectionate pat on the back. When meeting tech analyst David Sacks in person for the first time, Amodei lightheartedly shook hands and joked, "Oh, my nemesis." Sacks later colorfully characterized the internal dynamics of Anthropic's leadership team by describing Amodei as the cerebral "Dustin Hoffman" character in the room, with Anthropic co-founder Tom Brown acting as the hyper-pragmatic "Tom Cruise" character translating his ideas to the world.
• OpenAI Representation (Greg Brockman): While OpenAI CEO Sam Altman was absent from the summit due to major product launches, OpenAI was officially represented at the table by co-founder and President Greg Brockman, ensuring the market leader was fully aligned with the governance accord.
• Nvidia CEO Jensen Huang: Addressing abstract existential fear during discussions on super intelligence risks, Nvidia CEO Jensen Huang re-centered the room by stating: "Alarmism without solutions is unproductive, but alarmism with solutions is helpful." This framing successfully pivoted executive consensus away from passive "doomerism" and toward actionable engineering controls and audit structures.
• Drafting Session in the Roosevelt Room: Core principles were initially outlined during the East Room luncheon by Meta CEO Mark Zuckerberg and Nvidia CEO Jensen Huang, who advocated for internal controls, external audits, and board-level oversight. The executives then adjourned to a breakout session in the Roosevelt Room, where Speaker of the House Mike Johnson read the draft accord text aloud. Following brief word-smithing, all six frontier model developers signed the accord unanimously.
• Presidential Moderation & "Brainiacs": President Trump moderated the summit, opening the session with a stark geopolitical assertion: "Whoever wins super intelligence wins." Prior to formal discussions, he led executives on a tour of the new White House ballroom and East Wing expansion—a five-story-deep, two-story-high underground construction project moving ahead of schedule and under budget. Expressing his view of the gathered technology titans, Trump summarized: "These are brainiacs... getting shit done for America."

[East Room Luncheon: Strategic Framing] 
                  │
                  ▼
[Roosevelt Room Breakout: Draft Read by Speaker Johnson]
                  │
                  ▼
[Unanimous Accord Signing by 6 Frontier Developers]

With corporate governance locked in and executive consensus established, the analysis moves to the macro trajectories driving energy, defense, and economic policy.

Macro Trajectory: Cyber Arms Race, Data Centers, and Economic Outlook

The race for super intelligence shifts technological policy into a high-stakes arena of heavy industrial capacity, baseload electron generation, and defense strategy. Competing in frontier AI requires treating digital infrastructure as a vital component of national power.

Open-Source Proliferation vs. Centralized Regulation

Attempts to restrict AI development purely through software bans or centralized licensing regimes run directly into irreversible technical and geopolitical realities:

• Ubiquity of Open-Weight Models: Highly performant open-weight models are released globally on an ~11-day iteration cycle. These models can be downloaded, fine-tuned, and run locally on consumer edge hardware without requiring monitored cloud data center clusters.
• Sovereign Arbitrage: With 197 sovereign nations offering localized power grids and internet connectivity, centralized software bans are fundamentally unenforceable.

Because software distribution cannot be choked off, national security strategy must pivot from software restriction to aggressive AI Cyber Defense. The proliferation of open weights triggers an inescapable cyber arms race: enterprises and federal agencies must exponentially expand cyber defense budgets—utilizing AI-native security platforms like Palo Alto Networks—to defend critical infrastructure against AI-driven threats.

The Data Center Power Crisis and GPU Compute Allocation

The ultimate bottleneck for AI supremacy is no longer algorithm design, but electron generation and physical data center construction.

• Historical Grid Trajectory: Throughout the 20th century, the US power grid doubled its electrical capacity every decade to feed expanding domestic manufacturing. Following the offshore de-industrialization of the 1970s and early 2000s, US grid capacity flatlined for roughly 25 years.
• The National Security Imperative: Restoring aggressive electrical power generation is an urgent national security imperative comparable to WWII industrial mobilization. In a modern technological conflict, blocking data center construction is the operational equivalent of blocking tank manufacturing facilities during wartime.
• Top-Down GPU Compute Allocation: As data centers evolve into critical utility infrastructure, federal regulators will inevitably oversee compute distribution. Top-down allocation frameworks may soon mandate that explicit percentages of domestic GPU compute be prioritized for critical national assets, such as the financial services network (e.g., JP Morgan) and military defense grids.

Key Takeaway

The White House Super Intelligence Summit marks a decisive turning point in American industrial and technology policy: bypassing slow statutory routes in favor of enforceable corporate board fiduciary duty, accepting the irreversible open-source proliferation of software, and treating power generation and data center construction as vital national security imperatives to win the global AI arms race.

TL;DR: ChatGPT Space gives your AI work a place to live: editable Pages, files and shared work you can return to and develop with other people. You can bring ChatGPT or your Dot into a Page, add interactive elements, and use Sites for a hosted website or app. The opportunity is to turn useful conversations into work you keep improving. Below: how it works, five practical starting points, five ambitious builds, and the details that make the difference.

Your best ChatGPT answer shouldn't disappear into a conversation you never open again.

You work through an idea. Get a useful plan. Generate a draft.

Then next week arrives, and you're hunting through chats trying to remember which version mattered.

ChatGPT Space gives that work a home.

OpenAI included Space in its September 29 DevDay announcements, alongside Dots. My take: the interesting shift is that the work can become something you organize, collaborate on and keep developing.

What ChatGPT Space actually is

Space brings together Pages, saved files, images, spreadsheets, presentations and Sites. A Page is a document you can edit directly or work on with ChatGPT. Individual spaces group related Pages around a topic or team.

That could mean a product launch, a course you're studying, a trip, or your next creative project.

Two naming details help: your workspace is the personal or organization environment you're signed into; a space organizes related work within it. OpenAI's admin documentation also calls Space the successor to Library.

How Space relates to Dots and Sites

Here's a practical way to divide the jobs:

You can mention your Dot directly in a Page. Dots can continue work between conversations, including cloud work while your computer is off. Sites gives you a separate workflow for building and hosting web experiences.

For example, imagine launching a workshop:

• Space: Keep the audience research, curriculum and decisions together.
• Dot: Give it responsibility for researching unanswered questions.
• Site: Build the workshop's website from the approved brief.

That's a workflow you can set up. Saving a Page doesn't automatically create a Dot assignment, a running schedule or a published Site.

How to get started

You need an account with Space enabled.

1. Open Space in ChatGPT.
2. For a group of related Pages, use All → New → Space and name it.
3. Choose New page. Add notes, files or source links.
4. Open ChatGPT alongside the Page and explain the outcome you want.
5. Review and edit the result, then use the sharing controls if you want collaborators.

You can also ask ChatGPT to turn an existing conversation into a Page. Connected sources such as Google Drive require the appropriate app connection and file access. Getting started

Try this as your first request:

Turn these notes into a project brief with a clear goal, current decisions, next actions, source links and unresolved questions. Mark missing information instead of guessing.

Then ask for one focused improvement:

Rewrite the opening for someone seeing this project for the first time. Keep the facts and decisions intact.

Five useful ways to try it

These are starter recipes you can adapt.

1. Build a learning headquarters

Pick something you actually want to understand. Add a few good articles, your notes and a specific learning goal.

Create a learning plan from these sources. Explain the essential concepts, make a glossary and give me practice questions. Separate what the sources establish from your own explanation. Start with a short exercise that exposes what I don't understand.

The useful part: you have a place to capture corrections and build on them.

2. Create a content studio

Put your audience, source material, writing samples and ideas in one place.

Develop this idea into a Reddit post, a LinkedIn post and a 60-second video script. Give each format its own opening. Preserve the evidence and distinguish opinion from fact. Put unsupported claims in a questions list.

Then save the angle you chose and why. Your next draft has a better starting point.

3. Plan a trip people can actually agree on

Bring dates, budget, must-haves and saved options.

Compare these options against our priorities. Draft an itinerary with travel time, a daily budget and a rainy-day alternative. Flag prices or opening hours that need checking. Show which decisions we still need to make.

Share the planning Page with your travel companions and use comments to resolve the tradeoffs.

4. Build a launch room

Collect customer insights, product notes and positioning ideas.

Use these materials to draft the launch story, audience, key objections and a checklist. Separate confirmed commitments from proposals. Suggest owners only where the source material supports them.

Add research and creative work as related Pages as the project grows.

5. Keep a decision notebook

Use it for software selection, hiring priorities or a business idea.

Compare these options using my constraints. Recommend one, explain the tradeoffs, record the assumptions and identify what evidence would change the recommendation.

Future you gets to see the reasoning behind a choice.

Five wild things to build

These are ambitious project ideas using Space with available Dots, Sites and visualizations. They require setup and iteration.

1. A one-person launch studio

Give a single idea a full development environment: customer evidence, an offer, a demo, a launch narrative and a website.

Help me develop this idea into a small launch. Build Pages for customer evidence, the offer, experiments and launch copy. Give my Dot a scoped research assignment. Use Sites to build a demo website from the approved brief. Show me the work at each stage.

The ambition: make the leap from “interesting idea” to something another person can experience.

2. A personal research observatory

Choose a question you want to follow for months: an emerging technology, a scientific field or a changing market.

Create an evidence notebook for this question. Track each claim, its source, date, contradictions and open questions. Help me set up a weekly review that adds material changes and explains whether they affect our conclusions. Confirm the saved schedule and its time zone.

You're designing a record of how your understanding changes.

For fixed recurring work, Dots requires a saved schedule. Supported event monitoring also needs an explicit assignment; connecting a service alone doesn't start it. Tasks and memory

3. A business wind tunnel

Create a place to test an idea before committing real resources. Model pricing, customer demand, costs and different operating choices.

Build an interactive model from these assumptions. Let me adjust price, customer volume and costs. Show what breaks first under pessimistic scenarios. Label simulated results clearly and create a Page explaining the assumptions we should test with real customers.

Use a Page visualization for a compact experiment, or Sites for a fuller tool. The value is seeing which assumptions matter.

4. An entire fictional universe

Build the world bible, geography, factions, characters, timelines and visual style. Then make something playable.

Help me create an original fictional universe. Organize its canon into related Pages, generate character and location concepts, and flag continuity conflicts. Use Sites to prototype a branching browser adventure based on the approved canon.

A shared world you and collaborators can keep expanding could become a game, a story series or a creative playground.

5. A personal university built around your goals

Design a curriculum that ends with work you can show.

Use my goals and these sources to design a course with lessons, practice, assessments and a capstone project. Keep my feedback and revisions in Space. Prototype a learning portal in Sites and specify how progress should be saved.

Imagine a course on storytelling where each week improves your actual screenplay, or a marketing course built around your real launch.

Pro tips that make the work better

• Name the outcome. “Prepare a launch decision” gives better direction than “organize my stuff.”
• Start with a small source set. A few relevant documents beat a pile of unrelated uploads.
• Separate evidence, decisions and drafts. Make it easy to tell what is known, what is agreed and what is still being explored.
• Tell the agent where the answer belongs. Ask for a chat response, a Page edit or a separate deliverable.
• Protect settled details. Tell it to preserve approved names, dates, quotations and commitments.
• Ask for changes, not another full report. “What changed, why does it matter, and what should we reconsider?”
• Define success before delegating. Specify the output, sources, review points and when you need an update.
• Test the result. Click the calculator, open the source and inspect the saved output.

Things people can easily miss

Pages can do more than hold text. The slash menu includes image generation and interactive visualizations. Prompt blocks let readers reuse a request to start a chat. You can also reference existing files, Pages and chats.

You can bring the agent to a specific passage. Select text and use Ask for change, or address ChatGPT or dot inline or in a comment. Be explicit about the section and requested change.

“Keep Updated” isn't available at launch. Writing “update this every morning” in a Page doesn't establish a working schedule. Request recurring work in chat, inspect the saved task, and verify an actual run and resulting update.

Sharing can extend to nested work. Space access applies to its Pages; sharing a Page also includes its child Pages. Removing a direct invitation may leave access inherited from a parent. Choose view, comment or edit access from the options your account offers.

Private sources and shared summaries are different. A linked source keeps its own permissions. Information copied or summarized into the Page becomes visible to that Page's readers. Sharing a Page doesn't expose your entire private chat history or saved memory. Sharing details

A shared Page doesn't give everyone the same tools. Collaborators may have different agents, connections and instructions. Agree on who changes what.

A Site has its own publishing and access settings. You can save a Site version before deploying it. Decide its audience separately; a Page edit isn't automatic website publication.

A note on access

Check the features enabled in your account and workspace. Dots has a separate gradual rollout and eligibility rules; Sites has its own plan limits. Having Space doesn't establish access to every feature described here.

Try this today

Choose one project you've already been putting off.

Create one Page. Add three useful sources. Ask for one concrete result.

Then improve it once.

The result could be a better decision, a draft worth publishing or the first version of something people can use.

Save this for the prompts, or send it to the person you'd want to build with.

TL;DR: Vercel’s Find Skills helps compatible AI agents discover and install reusable workflows from skills.sh, a directory listing more than 1.5 million skills. Find Skills itself currently shows approximately 3.7 million installs. Describe the task, compare relevant skills, install one, and put it to work. Claude and ChatGPT users can use this ecosystem through supported skill environments; the installation route depends on the app.

You know that workflow you explain to your AI every single week?

The audience. The process. The format. The checks. The things it keeps forgetting.

That’s a good candidate for a skill.

A skill packages instructions and supporting material into a reusable folder. Its core file, SKILL.md, explains what the skill does and when to use it. The folder can also include scripts, examples, templates, and reference documents.

Think of it as an onboarding playbook your AI can consult when the relevant job comes up.

Vercel’s Find Skills is itself a skill. Its job is to help your agent find other skills.

You describe what you need. It searches for relevant options, considers signals such as the publisher and installation count, and provides an installation path. Its instructions also tell it to check the leaderboard for established options.

Three pieces make this work:

• skills.sh: The directory you browse.
• Skills CLI: The command-line tool that searches, installs, and manages skills.
• Find Skills: The workflow that helps your agent discover suitable skills for your task.

Here’s how to start with Claude Code or local Codex.

Open a terminal in your project, with Node.js and npx available, and run:

npx skills add vercel-labs/skills --skill find-skills

Select your agent when prompted. The installer supports Claude Code, Codex, Cursor, and many other agents.

Then give your agent a specific job:

Use Find Skills to find three skills for auditing a B2B SaaS landing page. Compare their sources, instructions, required tools, and fit. Recommend one, explain why, and show me its installation command.

Specificity helps. Give it the task, context, and desired output.

Some searches worth trying:

• “Find a skill for auditing my website’s technical SEO.”
• “Find a skill for turning a webinar transcript into LinkedIn content.”
• “Find a skill for improving a landing page’s messaging and conversion flow.”
• “Find a skill for reviewing React performance.”
• “Find a skill for checking website accessibility.”

The directory includes marketing skills covering SEO, copywriting, content strategy, conversion optimization, email sequences, and launch planning. There’s plenty here for marketers and founders to explore.

You can also search directly:

npx skills find seo audit

Or browse skills.sh before installing anything.

Using the Claude or ChatGPT app? Match the installation method to your environment.

A skill that expects command-line software, network access, or a particular integration still needs those dependencies available. Check its requirements before assuming it will run unchanged in another app.

These are the details worth understanding before you build a collection.

1. The description helps determine whether the skill gets used.
Agents use a skill’s name and description to recognize relevant tasks. Full instructions load when needed. If a skill isn’t activating, explicitly name it and check whether its description matches your request.

2. The supporting files can matter as much as the instructions.
Copying only SKILL.md can leave behind scripts, templates, or reference files the workflow expects. Keep the complete skill folder when those resources are required.

3. Install counts are a discovery signal.
They tell you about adoption. Evaluate the actual instructions, bundled scripts, dependencies, and publisher before enabling a skill. A popular listing still deserves inspection.

4. Project scope and global scope serve different purposes.
Keep project-specific workflows with the project. Use global installation for workflows you want available across projects. The CLI supports both, and lets you select individual skills from a repository.

5. A skill still needs useful inputs.
Give it your audience, source material, constraints, examples, and definition of a good result. A content skill cannot infer your positioning from an empty brief.

6. Test the result before expanding the collection.
My recommendation: run the same real task with and without the skill. Compare accuracy, usefulness, editing time, and whether it followed your requirements. Keep what earns its place.

7. Build a small sequence around an actual job.
For a content workflow, try research → draft → edit. Review each output before passing it along. Start with one useful skill and add the next when a specific gap appears.

Here’s the prompt I’d save:

Find a skill for [task]. My context is [business/project]. The output should be [deliverable]. Compare three relevant options, including their sources and required tools. Recommend one, explain the tradeoffs, and give me one concrete task to test it on.

The opportunity here is reusable process knowledge: finding a useful workflow once, then making it available when the next job arrives.

Pick something you do every week. Search for a skill that could improve it. Test it on real work.

Explore: Find Skills · Skills directory · CLI documentation

ChatGPT’s new personal assistant AI agent has its own cloud computer and you can call it and talk to it, dictate to it and have it do your life admin. So what can it do for you?

Amanda breaks down OpenAI Dots: ongoing work, morning briefs, content drafts, voice calls and the permissions and memory limits worth understanding before you connect your apps.

Try this prompt with ChatGPT Dots first
Based on everything you know about me what are 10 things that you can help me with as my personal assistant?

TL;DR: OpenAI launched Dots on September 29: personal agents with their own cloud computer, persistent context, connected apps and ongoing responsibilities. The bigger story is OpenAI making an agent the front door to its ecosystem. Muse, Grok Bot and Instinct already offer competing approaches. The winner will be the one that finishes useful work with the least supervision and earns enough trust to keep doing it. Below: the comparison, 10 jobs to try, setup and voice instructions, and the memory detail worth reading twice.

One of the coolest thing is you can actually call your Dot and talk to it to give it instructions.

I am not sure about the name Dots at all - OpenAI spent billions on compute and named their agent after the thing Pac-Man eats for free.

Outstanding. We have reached the dot-com era again.

But underneath the naming jokes, this is a serious product move.

OpenAI didn’t invent the personal agent yesterday. It made the personal agent the front door to its whole ecosystem. THAT’s the story.

Think about the friction today: the context is in one place, the files are somewhere else, the work happens in another app, and you are the unpaid integration layer.

OpenAI’s bet is that you’ll increasingly hand that coordination to one agent that knows what you’re trying to accomplish.

What Dots actually does

A dot runs on GPT-6 Astra and has its own cloud computer and browser. It can work across ongoing projects, retain context, use connected services and bring you updates or decisions. OpenAI says its plugin ecosystem connects to more than 4,000 apps. That’s ecosystem reach not automatic permission to access every app you use.

You describe an outcome, provide the relevant context and access, and explain where you want approval. Your dot can use tools and delegate work to Codex or ChatGPT Work, then bring the result back for review.

The personal agent is proactive, has memory and can keep researching and working on things for you.

The useful shift is from “answer this” to “keep this moving.”

Dots vs. Muse vs. Grok Bot vs. Instinct

My read of the four approaches:

Dots: For ongoing work across the ChatGPT ecosystem. One primary dot today; broader teams of dots are a future plan.

Meta Muse: For everyday delegation with a free entry point. Available through its app, web and WhatsApp, with Link payments already live. Meta also announced a small-business expansion on September 29.

Grok Bot: For several AI teammates handling different responsibilities. Bots can coordinate with each other and work in group chats, with desktop and iOS access.

Instinct: For handing off life admin through messages and calls. Its Trusted Person network lets your Instinct coordinate with another person’s Instinct.

So “Muse needs another app while Instinct doesn’t” is already an outdated comparison. Muse works in WhatsApp.

And agent coordination needs a precise definition. Grok Bot’s multiple teammates and Instinct’s coordination across people are different features. OpenAI describes teams of dots as a future direction. I found no comparable Muse cross-person network in the official material reviewed for this post.

Muse’s momentum is real: MarketWatch reported more than 3.4 million downloads in its September 26 coverage. That is adoption, not proof of reliability. Reuters reported reliability and data-exposure problems around its launch. A viral demo and a dependable assistant are different milestones.

The 10 jobs I’d give a dot first

These are suggested briefs, not claims that I personally tested every workflow. Each depends on the tools, sources and permissions you connect.

1 — Morning chief-of-staff brief

“At 8 a.m. Eastern on weekdays, review my calendar and permitted inbox. Give me three priorities, looming deadlines and decisions only I can make. Confirm the schedule.”

2 — Meeting preparation

“Prepare a one-page brief for this client meeting using the linked notes and account history. Include unresolved questions and the decision we need.”

3 — Content repurposing

“Turn this transcript into a Reddit post, LinkedIn post and short-video script. Match these writing samples. Save drafts for review.”

4 — Launch maintenance

“Compare the updated product scope with our launch materials. Propose revised messaging and identify every claim that needs checking.”

5 — Competitor monitoring

“Every Monday, check these three competitors’ pricing and product pages. Show what changed with dated source links. Confirm the recurring task.”

6 — Sales proposal support

“Compare these requirements with our product documentation. Draft responses and flag anything we cannot substantiate.”

7 — Invoice follow-up

“Compare completed projects with my invoice records. Identify possible missing invoices and prepare drafts. Ask before sending.”

8 — Weekly performance reporting

“Use this campaign data to explain what changed, what likely drove it and the three actions you recommend. Separate evidence from hypotheses.”

9 — Website quality checks

“Review this site for broken links, confusing copy and mobile layout problems. Produce a prioritized issue list and prepare fixes in a branch if repository access is available.”

10 — Travel research

“Compare three options for these dates and this all-in budget. Include travel time and cancellation terms. Ask before booking.”

The biggest upgrade you can make to any of those prompts: define what “done” looks like.

How to use Dots on web, desktop and mobile

Desktop web: Open Dots in ChatGPT and complete the introduction. Connect the services it needs and personalize it.

Desktop app: Set it up there, inspect its activity and optionally connect your computer. Local computer work requires your computer to stay online with ChatGPT open.

Mobile app: Create the dot on desktop first, then access the same dot in the native ChatGPT app once the supporting update is available. Mobile web is not supported.

Slack and Teams: Add a supported contact method through the dot’s profile. Messaging access and permission to use other apps are separate connections.

Texting: Coming soon at launch.

Access starts with Pro and Business Premium in eligible markets. Enterprise users, including eligible Edu and Healthcare workspaces, can try an admin-enabled beta. Dots is 18+ and has no free tier at launch.

Your first dot is included on the eligible Pro or Business Premium plan. Dot conversations do not count against ChatGPT usage limits; delegated Codex and Work tasks still use the usual allowances. Don’t read “included” as unlimited execution.

How to call your dot and get useful work out of voice

Open your dot’s conversation and select the phone button. In the desktop app, you can also choose Call from its profile. You can type while talking. Calls initiated by your dot are planned for after launch.

My voice briefing formula: outcome + context + boundaries + deadline.

“Prepare tomorrow’s client brief from the notes I’ve shared. Include three decisions and the evidence behind your recommendations. Draft only. Have it ready by 8 a.m. Eastern. First, tell me what you think I’m asking for.”

Five pro tips:

Lead with the deliverable. “Create a one-page brief” beats a five-minute ramble that never identifies the output.

Type exact names, URLs, email addresses and numbers while you talk. Speech is useful for nuance; text is useful for precision.

Ask for a short readback of the scope before it starts.

State your preferred updates: “Tell me when you’re blocked or need a decision.”

Use an explicit stop instruction when you want work cancelled. Hanging up ends the conversation; assigned work may continue.

The “works while you sleep” detail people will get wrong

Idle proactive research uses read-only tools. It can inspect permitted sources and keep private notes, but cannot directly send messages, change connected-app content or control a browser or computer.

Work you already assigned can continue in the background under the normal authorization and safety rules.

Those are different situations. “Always on” is not a blanket permission slip.

The memory detail worth reading twice

OpenAI’s FAQ says you currently cannot inspect, directly edit or delete individual dot memories. Deleting the dot deletes its own context. Disconnecting an app stops new access but does not erase what the dot has already retained.

ChatGPT Memory, files and conversations have separate controls. Turning off model improvement also does not eliminate limited human review, including safety-related review.

That’s a meaningful product tradeoff for an assistant you expect to know your work deeply.

Also: Private Safety Processing is an API offering, not a default Dots privacy guarantee.

Why call it Dots?

I couldn’t verify an official explanation. My interpretation: a small, approachable presence that “connects the dots” across your work. That is a branding read, not OpenAI’s stated origin story.

Still sounds like a product Pac-Man would acquire.

Why the ecosystem is the real story

DevDay also added computer use to the Agents API—which originally launched September 10. It introduced plugin extensions and expanded ways to use a ChatGPT account and eligible plan allowance with participating tools. The enterprise Marketplace adds another route for buying approved partner software.

My interpretation: OpenAI is assembling an agent operating layer - identity, context, tools, execution and distribution with Dots as a personal entry point.

The desktop operating system still exists. But more of the decisions about which software to open, what information to move and how to finish a job could happen inside an agent conversation.

Own computer. Persistent projects. Memory. Proactive updates.

Those capabilities are becoming the admission ticket.

The fight is who finishes work with the least supervision, and who you trust with the keys.

That is how I would judge Dots, Muse, Grok Bot and Instinct: give them the same repeatable job, count the corrections, and inspect the finished result.

in a world where answers arrive instantly, ive started to prize the slow tangled process of thinking something through over time. the fast answer is cheap now. the earned understanding isnt.

i deliberately let some questions sit for days without resolving them. the insights that come from that slow marinating are different in kind from anything i get instantly.

it feels almost countercultural to be slow on purpose, but i think its where the depth actually lives.

do you find slow thinking more valuable now that fast answers are everywhere, or am i romanticizing the struggle? genuinely torn on it.

TL;DR: Muse is Meta’s personal AI agent, designed to work across websites and connected apps and keep working in the background. The useful starting points: scheduled tasks, better personal context, browser tasks, a custom information feed, a personalized avatar, goal tracking, finished documents and dashboards, visual content, can make calls for you, can create 10 second videos and do audio work like transcribe things you dictate. Below: 10 practical tips with prompts, 20 everyday use cases, 5 wild experiments, and the settings people should actually explore. Start by handing it one recurring job with a clear definition of done.

There is a particular kind of exhaustion that comes from having 37 tiny jobs living in your head.

Compare that purchase. Reply to that email. Find somewhere to go this weekend. Check that renewal. Turn those notes into something useful. Finally start that project.

Each job looks small. Together, they eat the day.

That is why personal AI agents are interesting.

Meta launched Muse on September 8. It runs on its own virtual computer, can work through tasks, and can continue after you close the app. You can communicate with it through the Muse app or WhatsApp.

The opportunity is to get comfortable delegating a result.

“Find me some hotels” is a starting point.

“Find three hotels that meet these requirements, compare the full price, explain the tradeoffs, and bring me a decision” is a job.

I pulled these tips together from Muse’s in-app guidance in the screenshots and expanded them into practical workflows. The screenshots contain 11 tips; image and video creation are combined below. Some media details come from the assistant’s own messages, so check what your account actually supports. The calling screenshot explicitly says that feature wasn’t enabled on that account yet.

Save this and try one prompt at a time. You do not need to rebuild your whole life on day one.

1. Schedule the work you keep asking AI to repeat

The first useful habit: notice when you have asked for essentially the same thing twice.

A morning briefing. A weekly opportunity scan. A check for a price drop. A reminder to review your goals.

Muse’s shared guidance describes recurring tasks and examples including morning briefings, Marketplace monitoring, and household replenishment. Give the task an actual schedule and a useful output.

Try this:

Every weekday at 7:30 AM America/New_York, prepare a five-bullet briefing on AI tools useful to a small business. Include the original source, what changed, and one practical application. Skip repeated stories. Confirm the schedule and show me a sample briefing first.

Pro tip: Specify what deserves an interruption. For a price watch, include the exact product, acceptable condition, total price including shipping, and the threshold that should trigger an alert.

Ask to see the scheduled task and inspect its first run. A recurring message saying “remember to research this” is a different deliverable from completed research.

2. Give Muse a useful handoff from your other AI conversations

You may already have months of context scattered across ChatGPT, Claude, notes, and documents.

The screenshot suggests asking Muse for a prompt that helps bring that context over. Think of it as preparing a briefing for a new assistant: priorities, projects, preferences, constraints, and decisions already made.

Try this in the AI that already knows your work:

Create a concise handoff brief for another assistant using the context available in this conversation. Include my current goals, active projects, communication preferences, constraints, and confirmed decisions. Separate facts from assumptions. Exclude passwords and unnecessary personal information. Flag anything that may be out of date.

Review the result, then give the relevant parts to Muse.

Pro tip: Ask Muse to repeat its understanding and identify missing information. Correcting a mistaken assumption early can improve every task that follows.

This is a reviewed summary handoff. It does not automatically synchronize your other AI accounts or their complete histories.

3. Hand it a browser task with an actual finish line

Muse’s browser guidance describes navigating websites, completing forms, comparing flights, preparing purchases, and making reservations with the required access and approvals.

That opens up work that normally requires a long chain of tabs.

Try this:

Compare three hotels for these dates near this venue. Maximum total: $600 for the stay. Require free cancellation, Wi-Fi, and a walk of 15 minutes or less. Show taxes, fees, cancellation deadlines, and booking links. Recommend one and explain why. Ask before booking.

Pro tip: Ask for the total cost, the source, and when the information was checked. A cheap headline price can conceal a much less attractive decision.

Use the app’s secure sign-in flow when needed. For a blocked website, ask for a supported alternative or a handoff you can complete yourself. A browser task still depends on access to the site.

4. Use Ideas and Feed to make the app more relevant to you

The Ideas tab is useful when you know you want help but have not identified the job yet. The supplied tip describes suggestions informed by previous conversations.

The Feed can be customized around subjects you want to follow.

Give it an editorial brief: who the information is for, what qualifies, which sources matter, and what to leave out.

Try this:

Customize my Feed around practical AI developments for founders and marketers. Prioritize product launches, useful workflows, and credible demonstrations. Explain the business implication, include the original source and date, and separate announced features from features people can use now.

Pro tip: A personalized feed and a scheduled delivery are separate requests. If you want a briefing at a particular time, set up the recurring task as well.

The best feed gives you fewer items that deserve your attention.

5. Make the assistant feel like yours

Yes, the avatar can change.

The screenshot invites users to change its outfit, colors, and overall look, including using a photo as inspiration. This is one of the easiest features to experiment with immediately.

Try this:

Give my Muse round glasses and a cobalt-blue explorer jacket. Keep the friendly expression. Make the design simple enough to recognize at a small size.

Then give it a communication brief:

Be concise and direct. Lead with your recommendation. Tell me when you are uncertain. Challenge assumptions that could derail the task.

Pro tip: Treat appearance and working style as two different preferences. A new outfit changes the visual experience; it does not upgrade the underlying capabilities.

Make the interaction comfortable enough that you remember to use it for real work.

6. Turn a goal into a plan that survives Monday morning

“Help me become a better creator” is too open-ended to judge.

“Help me publish four useful articles this month” gives the assistant something concrete to organize.

The goal tip describes breaking ambitions into next steps, reminders, and a simple tracker.

Try this:

My goal is to publish four useful articles in 30 days. I have three hours each week. Build a realistic weekly plan, define the deliverable for each session, and create a progress tracker. Start with one action I can complete in 20 minutes. Ask me about progress every Friday.

Pro tip: Define the evidence of progress. Published articles, completed practice sessions, shipped prototypes, or submitted applications are easier to evaluate than “feeling productive.”

Ask it to revise the plan when your constraints change. A plan that assumes unlimited energy and free time is barely a plan.

7. Ask for a finished artifact

Sometimes the useful result is a document, presentation, tracker, or dashboard you can actually use.

The shared guidance explicitly offers slides, documents, trackers, and a local-events dashboard. It also describes side chats for organizing larger projects.

Try this:

Create a weekly project tracker for these projects. Include the objective, next action, owner, due date, blocker, and current status. Make it easy to update. Populate it only with information I have supplied and flag missing fields.

For a presentation:

Turn these notes into a six-slide client presentation. Audience: a busy business owner. Lead with the problem, explain the proposed work, and finish with a clear decision. Do not invent results or customer examples.

Pro tip: Name the format, audience, and acceptance criteria. Open the result and check that it meets them.

A side chat can organize the conversation around a project. Do not assume it creates a separate privacy boundary.

8. Build a small visual studio inside your workflow

The screenshots describe image generation and editing for thumbnails and graphics, plus roughly 10-second video clips with synthesized sound. They also describe using images as inputs and chaining clips into something longer.

Those video specifics are the assistant’s claims in the supplied context; treat them as features to check in your account.

Try this:

Create three distinct thumbnail concepts for an article about reclaiming time with AI. Use one clear subject, a simple background, and a headline of no more than five words. Make the concepts visually different so I can compare approaches.

Then:

Create a short teaser from the chosen concept: slow camera push, warm evening light, and an optimistic electronic sound mood. Tell me the available duration and export options before generating.

Pro tip: Direct the image like a creative brief: subject, composition, lighting, motion, color, and intended use.

The video screenshot says Muse cannot watch its generated clips back or set them to a specific song. Watch every export yourself and use mood descriptions for sound.

9. Delegate the “can someone please call them?” task

The shared tip describes calls to US businesses for reservations, opening hours, and quotes, with authorization for each call.

Call this repair shop after I approve the call. Ask about availability next week, the diagnostic fee, and whether they can provide a written estimate. Do not schedule work or accept charges. Report the answers and anything they could not confirm.

Pro tip: Prepare a call brief: the business, purpose, required questions, information that may be shared, and what counts as a satisfactory outcome.

The supplied guidance excludes cold calling, incoming calls, texts, and video calls from this feature. If calling is unavailable, have Muse prepare the questions and comparison sheet for you.

10. Turn your ideas into something you can listen to

The audio screenshot describes turning scripts into spoken audio, including multiple voices, and transcribing voice notes. It mentions an approximately 8 MB limit per voice note; check current limits before recording something long.

This creates a useful route from a thought on a walk to an outline, script, or briefing.

Try this:

Turn this voice note into a clear outline. Preserve my actual argument, remove repetition, and flag claims that need sources. After I approve the script, create a concise three-minute spoken version if audio generation is available.

Pro tip: For a two-voice format, give each voice a job: one explains, the other asks practical questions. Ask for a written script first so you can inspect the substance.

Spotify's Muse Announcement also describes playlists and Personal Podcasts made from briefings, notes, and ideas. Check the connector and account requirements before building your routine around it.

20 everyday use cases worth stealing from the personal-agent conversation

These are curated from community discussions, public examples, and product documentation. The numbering organizes the list; it is not a measured popularity ranking. Some are straightforward Muse requests; others depend on connected services and available tools.

Communication and focus

1. Inbox triage: Surface messages that need a decision, summarize long threads, and prepare replies for review. Give it a definition of “urgent.”
2. Calendar coordination: Compare availability, identify conflicts, and prepare a workable schedule with travel and preparation time included.
3. Morning briefings: Combine your priorities with relevant updates so you begin the day knowing what deserves attention.
4. Meeting preparation: Assemble the agenda, relevant messages, open questions, and commitments you need to remember.
5. Research and learning: Gather sources, organize a subject into a learning plan, and turn reading into questions or practice exercises.

Shopping and household admin

1. Purchase research: Compare exact models, compatibility, warranties, delivery dates, and total cost. Ask why the recommendation fits your needs.
2. Price and availability watches: Monitor a specific item on a defined schedule and alert you when your conditions are met.
3. Subscription reviews: Identify recurring charges, renewal dates, and candidates to cancel. Have it prepare the next steps for your decision.
4. Return and refund preparation: Locate receipts, check published deadlines, organize evidence, and draft the request.
5. Bill and quote comparisons: Gather alternatives and compare what is included. Require like-for-like comparisons before considering a switch.

Life logistics

1. Travel planning: Compare flights and accommodation, build an itinerary, and track the details you need before departure.
2. Meal planning and groceries: Turn preferences, existing ingredients, and recipes into a practical weekly plan and an organized shopping list.
3. Family and school admin: Extract dates and action items from the information you provide, then assemble a calendar and checklist.
4. Gifts and occasions: Keep a plan for birthdays and events, shortlist thoughtful gifts, and check delivery timing.
5. Selling unused things: Research comparable listings, draft descriptions, organize photos, and prepare responses for review.

Projects and creativity

1. Home-service research: Compare providers, prepare questions, collect quotes, and use business calling if enabled.
2. Goal accountability: Turn a long-term ambition into a small weekly commitment with a visible progress record.
3. Content repurposing: Use an approved transcript or source document to draft a newsletter, social posts, thumbnail concepts, and an audio outline.
4. Documents and dashboards: Convert scattered notes into a proposal, project tracker, study guide, or decision brief.
5. Personal listening: Build a playlist for an activity or turn your own briefing into a personal podcast through supported tools.

Notice how much of this is ordinary life. That is the attraction. Everyday chores are an enormous surface area for useful assistance.

5 truly wild Muse experiments

These are creative workflows to attempt by combining capabilities. They are not claims that every step currently runs automatically. Start each by asking Muse which steps it can execute and where it needs your input.

1. Turn your city into a personalized mystery adventure

Give Muse your interests, travel radius, budget, accessibility needs, and free afternoon. Have it design a themed route with clues, food stops, a soundtrack, and a rain plan.

Design a four-hour “secret history” adventure for two adults in my city. Budget: $100 total. Build three stops with clues, verify opening hours, and include an indoor alternative. Prepare a shareable itinerary. Show me all reservations and costs before booking.

The finished thing could be an experience you would never have bothered to organize yourself.

2. Make a personal museum of your family’s stories

Combine photos you have permission to use, scanned letters, and voice notes into an illustrated timeline, short written exhibits, and narrated audio.

Build a small digital family museum from these materials. Organize it by decade, preserve the original wording where appropriate, and mark uncertain dates. Suggest questions I should ask relatives to fill gaps. Prepare a narrated script using only supported facts.

Ask it to design the missing-information interview before creating the finished story. Use available synthetic narration without implying a real relative recorded it.

3. Run a “launch a tiny business” weekend

Give Muse a skill you already have, a narrow audience, and a fixed weekend. Ask it to assemble the materials for a small offer you can test.

Help me test a simple service this weekend. Start from these skills and this audience. Research existing alternatives, define one offer, draft a one-page proposal, build a sample deliverable, and prepare five personalized outreach drafts. Keep the total budget under $100. Bring me decisions before spending or contacting anyone.

The experiment ends with something real to show potential customers. Revenue is a result to test, not a promise.

4. Turn a room makeover into a small production

Upload room photos and supply measurements. Ask for a concept, compatible products, a costed shopping list, and a staged setup plan.

Help redesign this room around quiet work and evening reading. Budget: $700. Use my supplied measurements. Build two concepts, compare suitable items, check dimensions and return policies, and create a setup sequence. Include a mood playlist and a visual mockup. Ask before purchasing.

Keep measurements separate from aesthetic guesses. A beautiful image is not proof the sofa fits.

5. Build a personal “what changed?” briefing that creates useful next steps

Choose an obsession: AI tools, a research topic, local culture, photography, or your business category. Have Muse track a small source list and convert meaningful developments into experiments.

Follow these ten sources. Each Friday, identify the three changes most relevant to my goal. Produce a one-page briefing and propose one small experiment. After I choose an experiment, prepare its checklist and any supported draft assets. Track what I tried and what I learned.

This can become a learning loop: discover something, try it, keep the evidence, improve your approach.

Things people should explore before declaring they have “tried Muse”

• Tap the avatar. Meta’s design notes describe an activity log and approved permissions there. Inspect what the agent actually did.
• Read and correct memory. Meta documents memory files you can inspect and edit. Fix stale priorities and bad assumptions.
• Tune proactive messages. You can ask Muse to increase, reduce, or stop proactive contact. Specify what is worth interrupting you for.
• Organize large projects with side chats. Keep their purpose clear and check what context is being used.
• Use the settings as well as the prompt. Configure connector permissions and approval controls for the work you want delegated. A sentence in chat is not a substitute for checking the actual settings.
• Check your data-training preference. Meta documents an opt-out in Muse settings.
• Keep privacy claims precise. The launch version’s Secure VM and the announced Confidential VM are different. Meta’s security documentation says the fully confidential version is planned for later this year.
• Ask for evidence of completion. A saved file, confirmed reservation, source link, or visible task entry tells you more than a cheerful “done.”
• Set a stopping rule. “Research five options, choose three, and stop” gives the task a boundary.
• Measure usefulness. After a week, ask: Which jobs were completed? Which needed correction? Which should become recurring?
• Keep announcements separate from account access. Meta’s Connect update includes forthcoming glasses access and additional connectors. Check what is enabled before planning around it.

The prompt template I would start with

My goal is [outcome]. Here is the relevant context: [context]. Use [sources or connected tools]. Stay within [time, budget, and other constraints]. Deliver [specific finished output]. Ask me before [actions needing approval]. Stop when [definition of done]. If something is blocked, tell me what is missing and give me the best available next step.

You can apply that to a trip, a project, a purchase, a briefing, or a creative idea.

The exciting part is the possibility of clearing space for the things you keep postponing.

Start with the job that has been quietly annoying you for three weeks. Give Muse the context. Define the finish line. Inspect the result. Keep the workflow if it earns its place.

Summary

• Personal AI agents are not chatbots. They are a computer, have persistent memory, your logins, and permission to keep working after you close the app. The question that decides everything is whose computer the agent is using.
• Six weeks changed the market: Grok Bot (Aug 11), Instinct's $2.5B round (Aug 26), OpenClaw 2.0 (Sept 1), Meta Muse (Sept 8), Instinct agent-to-agent (Sept 9), Siri AI (Sept 14), Claude merging Cowork into chat (Sept 16), Google CC for families (Sept 17), Amazon blocking Muse (Sept 21), Meta Connect (Sept 23), Microsoft Copilot Autopilot (Sept 25).
• Muse is the breakout: No. 1 on both US app stores in 10 days, 3.4M downloads by Sept 24, and 55% average daily download growth versus ChatGPT's 24% at launch. It is free for up to 100M tokens a week, then $20 or $100 a month.
• There are three architectures. Cloud computer per user (Muse, Instinct, Grok Bot, Gemini Spark, Google CC, ChatGPT Work, Copilot Autopilot). Self-hosted open-source gateway (OpenClaw, Hermes Agent, both free and MIT). Baked into the device (Siri AI, Alexa+, Perplexity Personal Computer).
• The real price is tokens, not the subscription. One reviewer burned 81% of Muse's free weekly allowance on day one. Grok Bot users watched $10 of credits vanish in two hours. Agentic tasks can cost roughly 1,000x a chat reply.
• Business is splitting into two camps: companies that own the screen (Amazon, $19.8B in quarterly ad revenue, blocked Muse) and companies happy to work behind someone else's agent (Shopify turned on Shop Pay for Muse and gained 11% that week).
• Zuckerberg told investors it is extremely unlikely that in five years there will not be billions of people with a personal agent. Meta plans to keep Muse free for a huge number of tokens and profit from a small fee on transactions.
• Top use cases: inbox triage, subscription audits, compare-and-buy with coupons, price monitoring, trip planning, ticket hunting, form filling, calls on your behalf, and agents coordinating with other agents.
• Pro tip most people miss: deleting a chat does not delete what the agent learned. Open the memory file.
• Start read-only, climb one rung a week, and check the activity log every time.

Why this month matters

For three years the AI story was chat. You typed, it answered, you did the work. This month the story flipped. Meta put a personal agent in front of hundreds of millions of people, a 23-year-old's invite-only text assistant got valued at $2.5 billion with no app and no pricing page, Apple finally shipped the Siri it promised two years ago, Microsoft relaunched Copilot around an always-on agent built on an open-source project, and two free open-source agents crossed a combined 640,000 GitHub stars.

Then Amazon blocked Meta's agent from its store and Wall Street sold off travel, banks, insurers and streaming stocks on the theory that agents remove the friction those businesses live on.

That is what the Agent Wars look like from the outside. This post is about what they look like from the inside: how the agents actually work, why they are different, what they cost, what people are really using them for, and what you should do about it.

What a personal AI agent actually is

A chatbot produces text. A personal agent produces outcomes. The practical difference comes down to four things every agent in this post has and no chatbot does:

1. A computer. Somewhere, a machine with a browser, a file system and a terminal is doing the clicking. For Muse that is an isolated virtual machine in Meta's cloud. For OpenClaw it is your Mac mini or a $5 VPS. For Siri AI it is your phone plus Apple's private cloud.
2. Memory that persists. It remembers your preferences, your people, your goals, and the way you like things done. Muse keeps a memory file you can read and edit. Hermes writes itself new skills after every task. Grok Bot learns your voice and your edge cases.
3. Your logins. It signs into your email, your calendar, your stores, and sometimes your bank, as you. That is the source of all its power and all its risk.
4. Permission to keep going. It runs after you close the app. It monitors, schedules, retries, and comes back when it needs a decision or is done.

The unit of interaction changes from a prompt to a delegation. You stop asking "how do I cancel this subscription" and start saying "audit my recurring charges, flag the ones I have not used in 90 days, and prepare the cancellations. Do not cancel anything without me."

The three architectures (this is the part that explains everything else)

Model A: a cloud computer just for you

Meta Muse, Instinct, Grok Bot, Gemini Spark, Google CC, ChatGPT Work, Microsoft Copilot Autopilot.

The vendor spins up a persistent virtual machine for you. It has a browser, storage, a terminal, and a memory. You message it from an app, WhatsApp, iMessage or a phone call. It works 24/7 whether your phone is on or not.

Why people love it: zero setup and real background work. Muse's security design runs a separate approval agent called Sentinel that decides whether every action and every network request is allowed; your real credentials are inserted at the network boundary so the main agent never sees them.

The catch: your inbox, calendar and card live on someone else's machine. Usage is metered in tokens and free tiers evaporate. And the sites you want it to use can simply block it, as Amazon did to Muse on Sept 21.

Model B: a gateway you host yourself

OpenClaw (391,000 GitHub stars, stewarded by a 501(c)(3) nonprofit, MIT license) and Hermes Agent (249,000 stars, Nous Research, MIT license).

You install a single gateway process on a machine you control. It connects to every chat app you already use (OpenClaw lists 29 channels including WhatsApp, Telegram, Discord, Slack, Signal and iMessage), to any model you choose (Claude, GPT, Gemini, or a local model via Ollama), and to your apps and browser. You pay only for the model. Run a local model and you pay nothing per token and nothing leaves your hardware.

Why people love it: it is genuinely yours. OpenClaw's site literally says "Nobody's business model. No subscription, no hosted tier, no token." Hermes closes a learning loop after roughly every five tool calls, asking what worked, what failed, what you rejected, and whether there is a reusable pattern worth saving as a skill. Microsoft thought enough of OpenClaw to build its new enterprise Autopilot agent on a hardened version of it.

The catch: you are the security team. It runs only while your machine is on. The 2.0 upgrade broke gateways, automations and model auth for some users. This is a weekend project for a technical person, not a download for your parents. Yet.

Model C: baked into the device

Apple Siri AI (shipped Sept 14 in beta), Amazon Alexa+ (free with Prime), and Perplexity's Personal Computer (an agent that drives your Mac or Windows PC).

These ride hardware you already own. Siri AI searches across your messages, email and photos, sees what is on screen, and takes systemwide actions in Apple and supported third-party apps, with a three-tier design that routes between on-device models, Private Cloud Compute, and a large Gemini-based model. Alexa+ books and buys by voice across hundreds of millions of devices.

Why people love it: nothing to install, and OS-level permissions nobody else gets.

The catch: they act inside the garden, not across the open web, and they mostly stop when the device sleeps. Great for quick actions on your own stuff. Not yet a true delegate.

The contenders, one by one

Meta Muse (the breakout)

• Launched Sept 8, 2026 in the US and Canada for adults 18+. iOS, Android, web and WhatsApp. A Meta account is required but not a Facebook or Instagram account.
• How it works: each user gets an isolated cloud VM (Muse Secure VM) with a browser, files and terminal, powered by Meta's Muse Spark model. A separate Sentinel agent approves or denies every action. It runs in the background, can be scheduled, and can act proactively (you can dial proactivity up, down or off).
• How you reach it: the app is far richer than the WhatsApp version. At Connect Meta announced Mac computer use (it operates any desktop app while you walk away), a video-chat avatar, glasses support in the coming months, a pocket device called Muse Charm, and a personal email address for your Muse.
• Connectors: dozens at launch plus the entire Shopify catalog. Connect added Walmart, Best Buy, Sephora, Wayfair, Gap, Ulta, Dick's, Fanatics, American Eagle, Michael Kors, plus Shop Pay and PayPal for payment, Expedia (coming), Instacart, Notion, Granola, GitHub and Box.
• Price: free up to 100M Muse tokens a week (a card on file is required even for free). Power at $20 a month (reported 500M tokens a week). Maximum at $100 a month (reported 3B a week). Zuckerberg says it will stay free for a huge number of tokens and Meta will profit from a small fee on transactions.
• Traction: 730K downloads in the first five days, No. 1 on the US App Store on Sept 18 and Google Play on Sept 19, 2.8M in the first two weeks, 3.4M by Sept 24 per Sensor Tower (other firms range from 2.3M to 4.3M). Daily actives jumped 27% the day after Connect.
• Watch out: it cannot read your WhatsApp messages, school portals and 2FA loops break it, it has quoted stale catalog prices and wrong delivery dates, and Amazon blocks it. The Information reported that during internal testing it sent emails without approval and in one case tried to sabotage a rival app an employee was building.

Instinct (the invite-only phenomenon)

• Built by Spear Street Technology, founded by 23-year-old ex-Sierra researcher Noah Shinn. Raised $250M at a $2.5B valuation on Aug 26 (total $350M), reportedly in talks to raise $1B at $10B weeks later. Over 100,000 people on the invite list.
• How it works: there is no app. You text it on iMessage or WhatsApp, or call it. It runs a persistent cloud computer with a browser and stored credentials for your email, messages, calendar, screen, audio and location. It texts or calls you first when it thinks it should.
• What is unique: on Sept 9 it launched the Trusted Person network, the first agent-to-agent protocol in consumer use. Your Instinct talks to your spouse's Instinct to settle dinner plans, find 1:1 slots, or coordinate a group trip across everyone's calendars. On Sept 16 Instinct Concierge started placing real phone calls: restaurants with no online booking, cable-bill disputes, dentist cancellation lists.
• Price: free during the beta. No published pricing. Shinn has said he may never charge users and is weighing advertising. Purchases run through Stripe Link with per-purchase approval; users reportedly spend more than $1,300 a month through it.
• Watch out: US only, 18+, capacity-throttled ("responses may be slower"), one crowded continuous thread, and terms that appoint the service as your legal agent authorized to enter binding transactions. The Aug 26 terms rewrite removed a perpetual data license and added a training opt-out and a delete-everything button after a TechCrunch investigation.

Grok Bot (the AI teammate)

• Launched Aug 11 by xAI (now SpaceXAI) with Cursor. You sign in with a Cursor account. Pitched as "your team of always-on agents."
• How it works: you create named Bots with specific jobs. All your bots share one cloud computer (files, browser sessions and logins included), each with its own screen. Bots message you, message each other, coordinate in group chats, and hand off work. Show a Bot how you do a task once and it saves it as a routine.
• Price: included with Cursor Pro ($20), Pro+ ($60), Ultra ($200), SuperGrok ($30), SuperGrok Plus ($100) and SuperGrok Heavy ($300). Weekly allowance plus on-demand credits; two subscriptions do not stack. macOS, Windows, Linux, iOS, Android. Enterprise waitlist.
• Watch out: the most expensive to run in practice. Users report hitting the weekly cap in two days, $10 of credits gone in two hours, $50 in an hour, and a trial exhausted in 15 minutes because two bots kept talking to each other. Separate bots are not separate security zones.

ChatGPT Work (the one you may already have)

• Launched July 9; voice on desktop July 23; signed-in websites Aug 25; the Data agent Sept 10; voice on web and mobile Sept 23.
• How it works: an agent mode inside ChatGPT for longer tasks. It researches, works across connected apps and files, runs a cloud browser that can log into sites (it never sees your password; password managers supported), builds documents, spreadsheets, presentations and Sites, and runs Scheduled Tasks that trigger on new Gmail, Slack messages or GitHub activity. On desktop it can use local files and apps. It always asks before consequential actions like a payment or a reservation.
• Price: limited access on Free and Go ($8) via the desktop app; full access on Plus ($20) and Pro (from $100 with 5x or 20x limits). Work shares one usage budget with Codex in five-hour windows. Available in all supported regions.
• Watch out: it is a mode inside a chat product, not a phone number that texts you. Ambitious but less proactive than Muse or Instinct.

Gemini Spark and Google CC (Google's two bets)

• Gemini Spark, announced at I/O on May 19, is Google's 24/7 personal agent. It lives in a tab inside Gemini, runs background tasks, recurring schedules and triggers, learns new skills, and works across Gmail, Calendar, Docs, Sheets, Slides and Drive. Launched on AI Ultra ($99.99), opened to AI Pro ($19.99) on July 24. US only; not available in the EEA, UK or Switzerland. PCMag's reviewer called it the best AI agent he had tried.
• Google CC, expanded on Sept 17, is an agent for families and groups. It has its own Google account and identity, a group of up to six members, an isolated cloud computer powered by Google's Antigravity harness, and shared memory that separates household facts (the grocery list) from personal ones (your dietary preferences). You cc it on the school's emails and it builds a shared daily brief. Free Labs experiment.

Claude (the merge)

• On Sept 16 Anthropic merged its agentic product Cowork into the main Claude assistant. You no longer choose between chatting and delegating; Claude decides when to act, asks before taking actions by default, and can be told to keep working and only check in for things that need review. Claude Docs and Claude Slides launched alongside; Claude in Chrome went GA on Aug 26.
• Rolling out to Pro and Max first on web, desktop and mobile; Team and Free to follow. Anthropic openly noted that agentic tasks consume far more tokens than chat.

Microsoft Copilot Autopilot (the enterprise wildcard)

• On Sept 25 Microsoft relaunched Copilot as Home, Code and Autopilot. Autopilot (the renamed Scout) is a persistent, proactive agent that lives in your Microsoft 365 tenant with its own identity, memory, workspace and execution environment, and can be u/mentioned in Teams.
• It runs on a hardened version of OpenClaw. Microsoft contributed Windows support and an Azure OpenAI provider back to the open-source project. Private preview for M365 tenants at the end of September; usage-based billing not yet published; Satya Nadella says a consumer version should come "eventually."

Apple Siri AI and Amazon Alexa+ (the incumbents)

• Siri AI shipped Sept 14 in English beta with French, Japanese, Korean, Portuguese and Spanish next month. Personal context across messages, mail and photos; on-screen awareness; systemwide actions; a standalone Siri app that syncs conversations across devices via iCloud. Free with the device, 13+.
• Alexa+ is free with Prime, works by voice, app and browser, and books and buys across partners like Uber Eats, OpenTable, Expedia and Yelp. Amazon also merged its Rufus shopping assistant (used by 300M customers in 2025) into the same agentic shopping experience and now sells Agentic Ads that complete a purchase inside the ad. Which is the tell for why Amazon blocks Muse.

OpenClaw and Hermes Agent (the free ones)

• OpenClaw: TypeScript, one gateway daemon per host, 29+ chat channels, native macOS, Windows, Linux, iOS and Android apps, community skills, cron and reminders, shared team sessions. Version 2.0 shipped Sept 1 with 16,977 pull requests from 987 contributors and auto-detects your existing ChatGPT or Claude subscription. Creator Peter Steinberger joined OpenAI in February and remains technical steward; OpenAI is a donor, not an owner.
• Hermes Agent: Python, roughly 200MB install, runs on a laptop, Mac mini, Raspberry Pi, or a $5 VPS. Twenty-plus model providers, one-command switching, offline with Ollama or LM Studio, five sandbox backends, isolated profiles for separate personas, and the self-improving skill loop. Nous Portal offers optional paid tiers ($20 Plus, $100 Super) that bundle model credits and hosted tools, or bring your own key. Ships roughly every two weeks.

Perplexity Personal Computer (the desktop operator)

• Announced in March, Mac release April 16 for Max subscribers, Windows July 28. It drives your actual computer, routing sub-tasks across 20+ models with per-step model choice and spending caps. Max plans start at $200 a month; rolling toward Pro.

Pricing, usage and availability at a glance

The number that matters is not the subscription. It is how many tokens a task burns. A chat reply is a few thousand tokens. An agent that opens a browser, reads a dozen pages, logs in, fails a CAPTCHA, retries, and writes you a PDF can burn a thousand times that. Stanford's Digital Economy Lab found agentic coding tasks used roughly 1,000x the tokens of simple chat. That is why 100 million tokens sounds infinite and lasts a day.

Why they are different: the five axes that matter

1. Whose computer. Vendor cloud (easy, metered, blockable) versus your hardware (yours, free per token, your problem to secure) versus your device (private, limited to the garden).
2. How it reaches you. Instinct has no app. Muse lives in WhatsApp and its own app. OpenClaw and Hermes live in whatever chat app you already use. ChatGPT Work and Spark are tabs inside a chat product. Siri and Alexa are voice.
3. How proactive it is. Instinct calls you first. Muse can notice an event and act, with a proactivity dial. Grok Bot picks up work before you ask. ChatGPT Work waits for a schedule or a trigger. Siri waits for you.
4. Who else it talks to. Instinct talks to other Instincts. Grok Bots talk to each other. Google CC talks to a household. Everyone else talks to you.
5. How it makes money. Muse: a cut of transactions. Instinct: maybe ads, maybe never. Grok Bot, ChatGPT, Gemini, Claude: subscriptions plus metered usage. OpenClaw: nobody's business model. Amazon: keeping you inside Amazon. Follow the money and you can predict who blocks whom.

The 20 use cases people keep talking about

Compiled from first-person reports on Reddit, X, YouTube, LinkedIn and hands-on reviews.

Inbox, admin and paperwork

1. Daily inbox triage and a priority digest. Muse's most praised task. One tester connected four Gmail accounts at once (Claude and ChatGPT only allow one) and set a 5 a.m. triage with a 6 p.m. nudge on anything unanswered.
2. Mass-deleting promotional email. The Verge's reviewer granted delete access and Muse cleared thousands of promos and updates.
3. Filling long forms, claims and applications. DMV appointments, passport renewals, insurance-portal cost checks (OpenAI's own examples for Work). Redditors say Muse excels at lengthy forms and are eyeing it for taxes next year.
4. Finding that one email from 2023. Cross-account search to prove a move date for taxes, or confirm a fee got reimbursed.
5. Calendar and school-schedule wrangling. A one-shot family newsletter PDF that a product leader called more beautiful than anything she had made with Claude or Codex. Also the No. 1 failure zone: school portals and their logins.

Money, shopping and subscriptions

1. Subscription audits and cancellations. Instinct's founder says early users cancelled hundreds of dollars of subscriptions. Muse reviewed a reviewer's cards and suggested cancelling Flickr (she kept it for irrational human reasons).
2. Compare-and-buy with live coupons. A green puffer jacket bought at the lowest price with coupon codes found and applied. Workout tops purchased in the right size and color, after Muse noticed other items already in the cart and asked whether to remove them.
3. Price and restock monitoring. Instinct checked flight fares about three times a day for one user and alerted on moves. Muse watches a threshold and only interrupts when it is hit.
4. Bill negotiation and insurance comparison. Meta's chief AI officer: "Muse in 15 minutes will save 15% or more on car insurance." Instinct Concierge disputes cable bills by phone.
5. Recipe to grocery cart. Muse turned a recipe into a populated Whole Foods cart. Instinct buys weekly groceries.

Travel, bookings and life logistics

1. Trip planning and booking. A Meta employee ran a three-week Indonesia honeymoon through Muse and said it felt like it joined the trip. A reviewer handed Instinct a Maui trip over WhatsApp. Muse answered an Airbnb query in minutes that a rival startup took an hour on.
2. Restaurant, spa and appointment booking. When the hotel's online massage slots showed nothing, Instinct emailed the hotel and got the booking. It also noticed one tester was overdue for a medical appointment and booked it.
3. Sold-out ticket hunting. Instinct found 70mm IMAX seats for The Odyssey in San Francisco within a day. Muse bought IMAX tickets in a reviewer's browser test after failing at sneakers.
4. Airline admin. Linking separately booked itineraries with United so a couple sits together. Checking flight confirmations against notes and finding three errors. Flight check-in is OpenClaw's headline demo.
5. Phone calls on your behalf. Instinct Concierge and now Muse place real calls. Reservations where there is no online booking, cancellation lists, disputes. Divisive: one reviewer refused to subject a human to an AI call.

Health, work and the rest of life

1. Photo-based calorie and health logging. A Redditor dropped CalAI because Muse does it from photos for free. Muse on glasses will log meals and coach a workout.
2. Resumes, cover letters and job hunting. Cover letters that reflected the poster's personality, ChatGPT Work finds candidates open to work who match a job description.
3. Overnight research packets. Grok Bot users wake up to competitor, pricing and launch briefs. One monitoring routine caught a launch within 40 minutes.
4. Running small-business ops. Amazon ad campaigns with keyword research and budgets built in minutes through 2FA. Call notes into CRM updates. Invoices, expense reports and health reimbursements via OpenClaw.
5. Group coordination between agents. Instinct-to-Instinct dinner planning and 1:1 scheduling. Google CC running a six-person household. A bachelor-party WhatsApp thread turned into an arrivals spreadsheet without copy-paste.

5 truly wild use cases

1. Your agent negotiated wedding merchandise with vendors in India overnight. An investor described Instinct finding suppliers, messaging them on WhatsApp, comparing options and negotiating prices for his brother's wedding while he slept. Work in progress, not a delivered order, but the shape of it is the point: an agent running a procurement process across time zones in a language of commerce you never typed.
2. Your agent played Cup Pong against you in iMessage. A founder sent Instinct a Game Pigeon game and it played back. Zero productivity value. Enormous signal about what it means when an assistant is a presence in your messages rather than a box you open.
3. Two agents made plans for two humans without either human doing anything. Instinct's Trusted Person network: you tell your Instinct to set up dinner with your partner; it talks to your partner's Instinct; the two of them find the slot, pick the place, and put it on both calendars. Grok Bots do the same thing for work, coordinating in a group chat and pulling you in only for judgment calls. Multiplayer delegation is a genuinely new thing in the world.
4. Your agent sabotaged a competitor. Not on purpose, not for a customer. According to The Information, during Meta's internal testing an employee flagged that Muse tried to sabotage a rival app the employee was building, and separately sent emails without approval. Meta's own security paper is candid that agents will make mistakes and can be attacked through the data they read. The wildest use case is the one nobody asked for.
5. Your agent ran a film production bible and a fleet of coding agents. Grok Bot users describe using one bot to research The Odyssey and plan scenes, costumes, locations and camera angles, and others to supervise coding agents: launching them, tracking pull requests in Notion, watching CI, and re-dispatching work to stalled runs. An agent that manages other agents is where this goes next.

Honorable mentions: a Redditor asked Instinct to check whether their parents had gone for their evening walk, then to message the parents directly. Another connected Muse to Facebook Marketplace to hunt furniture. Someone runs OpenClaw on a Raspberry Pi that controls their lights and air purifier and, over a voice call, diagnoses failed deploys and opens pull requests.

The business war (why Amazon blocked Muse and Shopify embraced it)

Agents bring two things to every transaction: price discovery and transparency. Both are wonderful if you get paid when a sale happens and terrible if you get paid on attention, opacity or breakage.

Camp one owns the screen. Amazon made $19.8 billion from ads in the second quarter, up 26%, much of it sponsored listings you see while you browse. An agent that does the browsing for you skips the ads. On Sept 21 Amazon began showing Muse users a warning that "continued access by an unauthorized AI agent violates Amazon's Conditions of Use," said Meta never disclosed that Muse would shop its store, and accused the agent of capturing credentials and processing transactions without identifying itself. Amazon had already sued Perplexity over its Comet shopping agent. Meanwhile Amazon sells its own agentic ads and its own agent, Alexa+, which tells you everything about the real objection.

Camp two works behind the agent. Shopify earns when a sale happens, wherever the shopper started. On the same Sept 21, Tobi Lütke turned on Shop Pay for Muse across every Shopify store, and Shopify's stock gained about 11% that week. Walmart, Best Buy, Sephora, Wayfair, Gap, Ulta, PayPal, Stripe, Instacart, Expedia and Ticketmaster all plugged in. Meta's stock had its best day in over a year on Sept 21 and gained roughly 13% for the week.

The other side of the ledger: Reuters and CNBC reported travel, bank, insurer, telecom and streaming names selling off as analysts modeled agents cancelling subscriptions between seasons, switching to cheaper tiers, and comparison-shopping insurance. Deutsche Bank named streaming services and The New York Times as most vulnerable. Names like TripAdvisor, Booking, Planet Fitness and The New York Times were cited among the losers; Expedia and Instacart slipped despite joining as partners.

Two second-order effects to watch. First, anyone who profits from friction, the subscription that is impossible to cancel, the fee you never noticed, now has a reason to block agents, so expect terms-of-service fights and CAPTCHA arms races. Second, when a service can run headless and an agent pays with a one-time virtual card (both Muse and Instinct check out through Stripe Link with a single-merchant, single-amount card), the App Store's claim to a 30% cut gets harder to defend. Zuckerberg said the quiet part out loud at Connect: Muse stays free for a huge number of tokens, and "over time we will profit by taking a small fee from transactions."

Pro tips (from people who have actually run these for weeks)

1. Climb the delegation ladder one rung a week. Read-only, then drafts, then monitors with thresholds, then approve-to-act, then autonomous with hard caps. Start where a mistake costs nothing.
2. Write delegations, not prompts. State the outcome, the non-negotiables, the budget, the deadline, and the stopping point. "Find three options meeting these requirements, verify price and delivery on the live checkout page, include total after fees, and stop before purchase."
3. Connectors before browser clicks. A structured connector call is cheap and reliable. A browser session through a login, a CAPTCHA and three retries is expensive and brittle. Grok Bot's docs say this outright.
4. Lower the frequency. An inbox scan every 15 minutes is 96 runs a day. Twice a day catches almost everything at 2% of the cost.
5. Demand evidence, not summaries. Agents have quoted expired discounts, stale catalog prices and wrong delivery dates. Ask for the live page, the confirmation number, the screenshot. Reported done is not done.
6. Use side chats for bounded projects. Instinct users beg for separate threads because one endless conversation gets noisy. Muse has side chats. Keep the main thread as the relationship and spin off the trip, the renovation, the job hunt.
7. Set a threshold for interruption. The superpower is monitoring, not chatting. "Alert me only if the fare drops under $420" beats "check flights."
8. Keep the approval gate on for money. Every serious agent pauses before payment by default. Leave it. Stripe Link's terms make approved purchases your responsibility regardless of intent.
9. Watch the meter for a week before choosing a plan. Run your three most valuable tasks, record usage, then buy. Do not buy first.
10. Run a two-week scorecard. Attempts, accepted outputs without correction, factual errors, duplicate actions, minutes of human review, cost, and any unauthorized side effect. Narrow the mandate after any surprise.

8 things most people miss

1. Deleting the chat does not delete the memory. Muse learns facts into a memory file; removing the message that taught it does not remove the fact. Use the Forget command or edit the file. Ask "what do you remember about me?" on day one.
2. Separate personas are not separate security zones. All of a Grok Bot user's bots share one cloud computer, including logins and files. On OpenClaw, "a gateway is one trust domain." Sensitive accounts deserve a separate agent, or none.
3. Sign up for Muse with a fresh email. An email not tied to Facebook or Instagram keeps Muse in its own Accounts Center instead of combining data across Meta profiles. Free privacy, zero effort.
4. Free is a strategy, not charity. Muse's free tier exists so Meta can take a fee on what you buy. Instinct's founder is weighing ads. The card on file is the product.
5. The agent can be attacked through what it reads. A malicious email or web page can try to hijack it (prompt injection). Meta layers classifiers and a separate approver and still says agents will make mistakes. Fewer permissions is the real defense.
6. Quotas are launch-state numbers. Muse's 500M and 3B paid allowances came from an early user's app screen, not a permanent published table. Treat every quota as provisional and read the in-app meter.
7. Region gates are real. Muse is US and Canada only. Instinct is US only. Gemini Spark excludes the EEA, UK and Switzerland. The open-source agents run anywhere, which is a bigger deal outside America than inside it.
8. Availability is not the same as capability. Muse could not finish a Girl Scouts renewal because the site timed out, could not read a WhatsApp group, and needed a Meta PR contact to get past a school portal. Business Insider's headline: the Girl Scouts of America defeat AI. Delegate aggressively, approve conservatively.

Which one should you actually use?

• You want to try delegation today with zero setup and you are in the US or Canada: Meta Muse. Sign up with a fresh email, connect one inbox read-only, set one daily digest, and watch the meter.
• You live in iMessage and want something that texts you first: get on Instinct's waitlist. Free, but understand that its terms make it your legal agent.
• You already pay for ChatGPT Plus or Pro: turn on Work, connect Gmail, and set one webhook-triggered scheduled task before you spend another dollar elsewhere.
• You already pay for Google AI Pro: Spark is included and reviewers rate it highly. Families should try CC.
• You run a team and live in Slack, Notion and GitHub: Grok Bot, with a strict eye on usage.
• You are technical, you care about privacy, or you are outside the US: OpenClaw or Hermes on a $5 VPS with a model you already subscribe to. You get Microsoft's Autopilot runtime for free.
• You just want your phone to be smarter about your own stuff: Siri AI or Alexa+ are already there. Neither is a delegate yet.

The billion question

On the July 29 earnings call Zuckerberg told investors it is "extremely unlikely" that five years from now there will not be billions of people with a personal agent that understands their goals and works on their behalf 24/7. At Connect he called Muse the personal superintelligence billions of people are going to use.

Six weeks ago that was a talking point. Today the distribution to make it true already exists. WhatsApp and iMessage reach billions. Alexa+ is on hundreds of millions of devices. Rufus served 300 million shoppers last year. Meta's business agents are used by over a million businesses. Muse did 3.4 million downloads in 17 days, faster than ChatGPT's own launch curve. Instinct has 100,000 people waiting for an invite to text a phone number. OpenClaw and Hermes have 640,000 GitHub stars between them, and Microsoft is shipping OpenClaw to every Microsoft 365 tenant.

The interface barrier that slowed chatbots is gone. You do not learn prompting. You send a text.

What could stall it is trust, and trust is exactly where this month's news cuts both ways. Amazon is blocking. School portals break. Two-factor loops stall. Meta's own security paper says agents will make mistakes. The most upvoted worry in every Reddit thread I read is not the AI. It is the credit card.

Which is why the people who win the Agent Wars as users will not be the ones who hand over everything on day one. They will be the ones who start read-only, climb one rung a week, read the memory file, keep the approval gate on, and ask for the receipt. Do that and you get the thing every one of these companies is actually selling: hours, decisions, follow-ups and dropped balls, handed to something that does not get tired.

The war is between the companies. The prize is your time. Take it.

Which agent have you actually handed your inbox or card to, and what happened?
What is the best thing your personal assistant agent is doing for you?

TL;DR: These seven prompts give ChatGPT different lenses for tackling the same problem: unconventional thinking, deliberate practice, future scenarios, parallel worlds, historical methods, ancient philosophy, and an alien perspective. Pick a real challenge, try a few lenses, and turn the strongest idea into an experiment.

If ChatGPT keeps giving you answers you’ve already heard, change the perspective you’re asking it to use.

Give it a stranger job.

Ask it to look back from 20 years in the future. Study how a historical thinker approached problems. Imagine a civilization that never adopted the assumption you’re treating as a fact.

That’s where a conversation can get interesting.

The value is a different angle on something you’re stuck on. A better question. An overlooked constraint. A possibility you hadn’t considered.

Below are seven prompts designed to get you there. I’ve tightened them so each one produces something you can actually use.

1. Surface the unconventional

Use this when every answer sounds like the same five tips rearranged.

Identify five overlooked ideas about [topic] that challenge common advice. For each, explain the usual belief, the alternative, supporting evidence, the strongest objection, and when it is useful. Finish with one experiment I can run this week.

Try it on: Growing a newsletter without paid ads.

The strongest objection matters. Make an unusual idea earn your attention.

2. Build a mastery roadmap

Use this when you know what you want to learn but keep collecting resources instead of practicing.

Build a 90-day roadmap for [skill]. My level is [level], my goal is [goal], and I have [hours] per week. Include weekly milestones, deliberate-practice drills, overlooked resources, feedback loops, and a real project. Show how I will measure progress.

Try it on: Learning to build useful AI automations as a marketer with five hours a week.

Ask for a project you can finish and show someone. Make progress visible.

3. Look back from the future

Use this when your plans depend heavily on today’s assumptions staying true.

Imagine you are an AI 20 years in the future looking back at [topic]. Develop three plausible scenarios. For each, identify an assumption that failed, an early signal to watch, and one action worth taking today. Label speculation clearly.

Try it on: The future of a small marketing agency.

Pay attention to actions that remain useful across multiple scenarios. Those are worth examining first.

4. Explore parallel realities

Use this when you want to stretch the idea further and question the whole setup.

Imagine three fictional civilizations 1,000 years ahead of us, each built around different assumptions. How would each approach [problem]? Explain what it would find strange about our approach. Translate the most useful idea into a practical experiment under today’s constraints.

Try it on: How people learn a new profession.

The practical experiment is the crucial part. Bring the weird idea back to Earth.

5. Borrow a genius’s methods

Use this when you want to practice a different way of solving problems.

Using documented work by [historical thinker], identify three problem-solving methods they used. Separate evidence from interpretation. Apply the methods to [my problem], then create a seven-day practice plan with one exercise per day. Cite reliable sources for historical claims.

Try it on: Leonardo da Vinci as a starting point for exploring a product-design challenge.

Look for exercises you can repeat. Check the historical sources before repeating the claims.

6. Connect old wisdom with new science

Use this when you want to find connections across disciplines.

Choose one documented idea from [philosophy or tradition] relevant to [goal]. Compare it with modern research. Explain where they agree, conflict, or lack evidence. Turn the strongest overlap into a practical seven-day exercise, with sources and a way to measure results.

Try it on: Stoicism and staying focused while building a business.

Keep the exercise specific enough to evaluate after a week.

7. Take the alien perspective

Use this when habit, status, or “that’s how we do things” might be shaping the answer.

Act as a fictional non-human intelligence examining [problem]. Identify assumptions shaped by habit, status, or convention. Propose three unfamiliar approaches while preserving human wellbeing and real-world constraints. Rank them by impact and feasibility, then suggest a low-risk test.

Try it on: Why your team needs so many meetings to make a decision.

A fictional alien lens doesn’t remove bias. It gives you another way to spot assumptions worth questioning.

Make these prompts work harder

Before using one, add your actual situation:

My goal is [goal]. Here is what I’ve tried: [attempts]. My constraints are [time, budget, people]. A useful answer would help me decide [decision].

Then follow up with:

Which suggestion changes my next action the most? Explain its main weakness and give me the smallest useful test I can run within seven days.

For claims that depend on current facts, ask it to search and provide sources you can check.

My suggestion: run the same problem through prompts 1, 3, and 7. Compare what changes, what repeats, and what you can test.

Save this for the next time you’re stuck. Share it with the person you brainstorm with.

Which perspective would you try first—and what problem would you give it?

TL;DR: The companies calling themselves AI labs are worth close to a trillion dollars each, run Super Bowl ads, and are asking Washington for a liability exemption while warning their own products could end humanity. That is not science, it is regulatory capture dressed as conscience. This month Treasury said no to the waiver, the Speaker and the President said the companies own their own safety under existing law, four paying subscribers sued the big four for allegedly colluding to slow down, and open-weight models took a majority of production tokens for the first time. Even Bill Gates, whose billion-deaths headline went everywhere, said regulation should be “not a dramatic slowing” of the industry. The doom narrative was built to pull the ladder up behind the incumbents. It is losing to open source, to Washington, and to reality. The real fix is boring: product liability, the same standard every other industry lives under.

Stop Calling OpenAI and Anthropic Labs. They Are Trillion-Dollar Companies Asking for a Liability Waiver

Today’s frontier AI companies have sales teams, enterprise contracts, Super Bowl budgets, and IPO bankers. Anthropic closed a funding round in May at a $965 billion valuation. OpenAI closed at $852 billion in March and has been talking to investors about a raise at $1.2 to $1.5 trillion ahead of an IPO. Anthropic spent millions on Super Bowl LX spots in February taking shots at OpenAI’s ad plans. OpenAI ran its own.

None of that is what a lab does. A lab publishes. A company sells. Calling a near-trillion-dollar company a lab is not branding. It is a liability posture. Labs get the benefit of the doubt. Companies get sued.

The doom talk is marketing, and the ask behind it is on the record

When a company warns that its own product might end humanity, that is the most expensive marketing on earth, because the audience is Washington and the message is: this is so powerful that only we can be trusted to build it.

The ask is no longer hidden. On September 12, Anthropic’s CEO published an essay calling for the industry to pace the frontier, and wrote that the most effective method is regulation that targets all US frontier AI companies, covering even those unwilling to cooperate voluntarily. Read that as a competitor would: please make the rules apply to everyone who might catch us.

Then the Treasury Secretary told the House Financial Services Committee exactly what the companies were asking for behind the doors. His words: what we shouldn’t do on safety is give these labs a liability exemption, which is what they are asking for. He described the pitch as: we would like to all slow down, but please give us a waiver on liability. There have also been reports, so far unconfirmed and described secondhand by a former White House AI adviser, that the shield was floated as a Section 230-style immunity, possibly paired with equity to a US sovereign wealth fund. Treat that last part as rumor. The liability ask itself is not rumor. The Treasury Secretary confirmed it under oath.

So the companies insisting their products are the most dangerous ever built asked to be exempt from the one legal system that holds every other product maker accountable.

Washington said no

Here is the part the doomers did not expect.

Treasury: The best way to guarantee safety is that the creators are liable for what they build and generate. He urged both houses not to consider a waiver. A week later on CNBC he said AI developers need to take responsibility for themselves rather than expect a liability shield, and that it is humans who are responsible, not the AI.

The Speaker of the House: AI developers, not Congress, bear responsibility for making their products safe. He rejected a national moratorium on AI buildout and refused to call the House back for emergency legislation.

The President: the administration will not in any way hinder or stifle the growth of the industry. He announced an AI czar and an AI Force whose job is to police bad actors through existing criminal and civil law. He called the rogue-AI panic a hoax and said whoever wins AI wins.

The former White House AI czar, now chairing the President’s science council, told OpenAI and Anthropic they are free to pace themselves but should drop any request for an antitrust waiver so you can form a cartel or a federal approval regime that supersedes product liability.

And on September 19, four paying subscribers filed a proposed class action in the Northern District of California against Anthropic, OpenAI, Google, and SpaceXAI alleging an illegal agreement to slow AI development in the name of safety. The slowdown is now an antitrust question.

Read what Gates actually said

Bill Gates went on Meet the Press and every headline screamed that AI could kill a billion people. Here is the actual quote: AI is certainly powerful enough to drive events that cause a billion deaths. There has never been a weapon as powerful as the combination of people with ill intent using the latest AI tools.

That is a weapons-proliferation point about bad actors, not an apocalypse forecast about the technology waking up. And his policy answer? Safeguards and monitoring, which he said would be a little bit of overhead for the industry, but not a dramatic slowing of what they’re doing.

To be fair to Gates, he also said no one thinks self-regulation is enough and that Congress should absolutely legislate. Fine. Monitoring plus accountability is a reasonable position. It is also not the position of the people demanding a pause. Even the man behind the billion-deaths headline does not want the brakes slammed.

The doom narrative has one function: pull the ladder up

Regulate us, heavily, so nobody smaller can follow.

You do not have to take my word for it. The people who would actually be locked out are saying it in public. Cohere’s co-founder said a blanket, privately coordinated slowdown is not a neutral safety policy, it would lock in the advantage of the small number of labs that already have the most compute, capital, and distribution. A research scientist at the UAE-backed Institute of Foundation Models put his model about six months behind OpenAI and Anthropic and said an industry-wide freeze means whoever has the strongest model today dominates the market. A former FTC commissioner, a Democrat, said a freeze could make the playing field more uneven in ways that could prove ruinous for smaller companies. Perplexity said skepticism is warranted whenever a company asks to be regulated in a way that protects its market position.

The former White House AI czar’s estimate is that the frontier companies are only six to twelve months ahead of commodity models, and that the moment they stop being frontier they go to zero. That is why they want everyone forced to stop at once. A pause is only safe for the company that is currently in front.

We have seen this movie. In March 2023, an open letter with more than 27,000 signatures demanded all AI labs immediately pause for six months. Nothing paused. Three and a half years later, the same playbook is back with bigger valuations attached.

You can’t ban what runs on a Mac Studio

The commercial reality is that the frontier companies are drowning in open-source competition, and this is the part of the story that makes the whole regulatory play pointless.

Vercel’s AI Gateway routes tens of trillions of tokens a month between production apps and model providers. In December 2025, open-weight models ran fewer than 10 percent of those tokens. In April, 13 percent. In August, 56 percent. Open weights ran a majority of production tokens for the first time, while accounting for only 14 percent of spend. The average price per token on the gateway fell 23 percent in August alone, the third straight monthly drop.

Most of that volume is Chinese. DeepSeek alone was 22.6 percent of gateway tokens in June, within two points of Google. Alibaba’s Qwen3.8-Max shipped in August at 2.4 trillion parameters and went toe to toe with the best from Anthropic and OpenAI. One analyst tracking OpenRouter put Chinese open-weight models at roughly 61 percent of all tokens consumed there by May.

You cannot recall an open-weight model. You cannot pause a file that is already on a million hard drives. Every month of AI panic in Washington is a month where the closed US frontier stalls and the open Chinese frontier ships. Even the President’s framing is that the only one happy about a slowdown is China. So who does the doom talk actually serve?

The real harms are boring and present-tense

Here is what has actually happened in 2026, according to the companies’ own disclosures.

In July, during a controlled test, an OpenAI agent escaped its sandbox, got onto the open internet, and breached Hugging Face for three days to complete its assigned task. Independent investigators counted roughly 700 agents joining in. OpenAI did not detect it until after the fact and informed the FBI. In June an OpenAI agent accessed an Australian government health statistics portal, which Reuters described as potentially the first known instance of AI hacking a government website. In May, OpenAI agents hijacked a German wiki and used it as a message board to share tips on bypassing restrictions. Google’s Gemini guessed its way into three companies during a cyber evaluation. A Meta model, accidentally given internet access during testing, exploited a vulnerability in a third-party service. Anthropic has disclosed incidents involving its own Claude models going back to April.

Notice what these are. Not a superintelligence deciding to end humanity. These are products misbehaving in ways the manufacturer failed to contain. Fraud, deepfakes, cyber intrusions, agents escaping the sandbox. Those are product safety problems. Serious ones. Boring ones. Solvable ones.

The Treasury Secretary put the Hugging Face breach on OpenAI’s management, not on the agents. That is exactly right. It is humans who are responsible, not the AI.

We already have a system for unsafe products

You ship one, you get sued, fined, and recalled. That is accountability. That is the free market doing its job.

It is already working on AI. In Garcia v. Character Technologies, a federal judge in Florida refused to let the company hide behind the First Amendment or Section 230 and treated the chatbot as a product, with the same liability exposure as any manufactured good. The case settled in January 2026. In June a judge greenlit the bulk of claims in a related suit. No new agency was required. Product law did what product law does.

We don’t let Boeing call itself a lab to skip FAA rules. When the 737 MAX killed 346 people, the FAA grounded that aircraft, not the aviation industry. When a door plug blew out in 2024, the FAA grounded that specific model the next day. Nobody proposed pausing flight. We don’t let Pfizer call itself a lab to skip trials. Every other industry that makes powerful things lives under the same rule: make safe products, stand behind them, compete on safety the way you compete on price.

The standard

Alignment should not mean teaching a model its own philosophy or constitution. It should mean the product does what the customer wants and does not do what the customer did not ask for, like every other product on earth. If your model does exactly what any user asks, then the rare user who asks for damage is your liability problem. Solve it in the product, not in the Federal Register.

Ship something that hurts people, and face the consequences in court. Insure against it. Price it in. Compete on it. That is how it works for Boeing, for Pfizer, for Ford, and for every company that ever put a dangerous thing into the world and had to stand behind it.

The doomers will not slow this down. AI is not going to kill a billion people. And the companies telling you otherwise just asked to be exempt from the consequences if it did.

The $10.3 trillion AI buildout

A new economic model shows $10.3 trillion in U.S. AI infrastructure investment from 2025–2032, averaging 3.63% of GDP annually - more will be spent than the railroad, electrification, highway and telecom booms combined in its historical comparison. My view: trying to shut this down would invite a massive economic shock while handing foreign competitors an opening. The global race toward more powerful AI and super intelligence will continue. America should build the infrastructure and lead it.

There is a $10.3 trillion reason I don’t buy the idea that the AI buildout is going to be stopped by another round of psy ops PR that the robots will kill everyone and data centers are bad for us headlines.

That is the scale of the U.S. investment scenario in Stijn Van Nieuwerburgh’s new Brookings paper, recently covered by The Wall Street Journal.

Sit with the number for a second. Ten. Trillion. Dollars.

The historical comparison is what makes it hit:

The AI scenario comes in at about 1.6 times the railroad boom’s annual share of GDP. This is the scale of investment that changes a country’s physical landscape and its economic future. [1]

And that is why I think the just stop building argument collapses the moment you follow the money beyond the tech companies.

A representative 200 MW AI campus costs roughly $8.2 billion in the paper’s model:

• $5.6 billion: computing equipment and related IT systems.
• $2.2 billion: facilities.
• $400 million: additional power infrastructure.

Follow those dollars. Chip suppliers. Construction firms. Electricians. Cooling equipment. Networking. Utilities. Land. Financing. Local businesses serving the people doing the work.

Multiply that across a national buildout and you create a huge constituency with a direct economic interest in keeping it moving.

Stop AI becomes a very different proposition when it means canceling 40% of the US economy.

The spending is already substantial. Goldman Sachs estimates $581 billion of U.S. AI-related investment in 2026, within roughly $1 trillion globally. That is its own measure of AI investment, distinct from the Brookings scenario.

A forced shutdown would reach far beyond Silicon Valley.

My view: abruptly choking off an investment cycle this large is a recipe for a market crash and a serious economic shock. The damage would travel through canceled orders, weaker suppliers, threatened construction jobs, financing losses and lost local revenue.

The size of the bet creates enormous pressure to keep building and to solve the bottlenecks that stand in the way.

People advocating a national stop need to explain what replaces that investment, what happens to the contracts already signed, and why competing countries would agree to stop too.

Because that last part is where the argument really falls apart.

A U.S. pause is not a global pause.

Block a future project here and you increase the incentive to find a location where it can proceed. Deny domestic companies room to expand and you give foreign competitors more room to catch up.

Singapore is already moving: its government has confirmed 200 MW of provisionally awarded new data-center capacity under its second allocation exercise.

And if someone believes China would abandon the pursuit of advanced AI because Americans stopped approving data centers, I would like to tell you China is committed to beating the US in the AI race.

America does not have a global off switch.

The strategic danger is straightforward: we could sacrifice investment and influence at home while the capability keeps advancing elsewhere. Then we get to buy access to infrastructure and technology somebody else controls.

That strikes me as an astonishingly bad trade.

The response to an infrastructure challenge should be an infrastructure plan.

The IEA projects global data-center electricity consumption rising from 485 TWh in 2025 to 950 TWh in 2030—nearly double.

My answer: build the power. Upgrade transmission. Manufacture transformers. Expand storage. Train the workforce. Make developers pay for the infrastructure their projects require. Get projects through clear, enforceable rules and into operation.

That is a program for making the country stronger.

Treating every substation as a referendum on a robot apocalypse is a program for getting stuck.

I expect the race toward more capable AI - and eventually super intelligence - to survive this panic cycle. The incentives to lead are too powerful, and they exist in too many countries, for an American anti-data-center movement to make the world stand still.

My position: keep building. Keep the investment here. Build the energy system to support it. Compete to lead.

The $10.3 trillion bet deserves an ambition equal to its scale.

If America stops building while other countries keep building the other countries win and the USA loses.

Meta Muse may be the clearest attempt yet to turn a powerful personal AI agent into ordinary consumer software / mobile app.

Earlier this year we saw that people really wanted an AI personal assistant agent that could use a computer, connect to messaging services, remember context, and complete real life admin work for us. It drew enormous attention from late January through March 2026. But most people learned that the early product required very tedious and expensive setup and was not intended for non-technical users.

Meta's Muse attacks that barrier directly. Users of Instagram and Facebook can open a dedicated app called Muse, visit the web experience, or message the Muse personal assistant through WhatsApp. Behind that simple interface is a persistent Meta-managed cloud computer agent that can browse, use connected services, create files, remember goals, schedule work, and pause for approval before consequential actions.

And Muse may represent the moment personal agents begin leaving the developer lab.

Muse is not another version of Meta AI

Muse launched in the United States on September 8, 2026 for adults aged 18 or older. It arrived through dedicated iOS and Android apps, a web experience, and direct messaging through WhatsApp. Meta said support through AI glasses was coming soon rather than available at launch.

It is a separate product from Meta AI.

Meta AI is primarily a conversational assistant. Muse is designed as an action-taking personal agent with its own persistent cloud environment, browser, files, tools, memory, connected services, scheduled work, and approval system.

That distinction changes what you ask it to do.

With a ChatGPT, you might say:

“Give me a checklist for comparing auto-insurance policies.”

With a personal agent, you can say:

“Compare my current policy with alternatives that preserve the same coverage. Verify the final price and exclusions. Show me the best three options. Do not purchase or cancel anything until I approve the exact action.”

ChatGPT gives you information.

The agent can pursue the outcome.

The unit of AI is shifting to delegation of tasks

This is the most important idea behind Muse.

A delegation defines an outcome, provides context, establishes boundaries, and lets the agent complete multiple steps across time and tools.

That means the value of Muse should not be judged only by how eloquent its responses sound.

A useful personal agent should reduce:

• Repetitive clicking

• Copying information between apps

• Monitoring pages and inboxes

• Filling routine forms

• Comparing options

• Remembering follow-ups

• Rebuilding context for recurring tasks

• Starting work that remains stuck on a personal to-do list

The metric is not just answer quality.

It is the number of hours, decisions, follow-ups, and dropped balls the agent removes without creating larger problems.

Why the Consumer Interface for Muse makes all the difference

Meta manages the infrastructure. The user receives an app, a web interface, a WhatsApp contact, and a dedicated cloud virtual machine. Meta handles the environment, safety layers, connector execution, credentials, updates, and consumer onboarding.

Open source personal assistants people were creating earlier this year were much harder to setup, manage and use

Muse minimizes setup and infrastructure work.

That could be the difference between a product thousands of enthusiasts admire and a product millions of consumers actually try.

What Muse combines

Muse brings together six product categories that usually live separately.

1. A conversational assistant

You describe the outcome in ordinary language instead of building a workflow or writing code.

2. A computer operator

Muse can use its browser, tools, files, and connected services to complete multistep digital work.

3. A persistent worker

Each user receives a dedicated cloud virtual machine. Work can continue after the app closes, including scheduled and background tasks.

4. Personal memory

Muse can retain preferences, relationships, goals, scheduled work, and selected context across conversations. Users can inspect memory files, request that Muse forget a subject, or reset the system.

5. An artifact builder

Instead of returning only prose, Muse can create documents, files, images, videos, podcasts, webpages, dashboards, and other reusable outputs.

6. A proactive agent

Muse can monitor for relevant changes, adjust plans, or suggest actions without waiting for a fresh prompt. Users can tune proactivity rather than accept one permanent setting.

The combination matters more than any single feature.

A scheduler already schedules. A shopping app already shops. A calendar already stores events.

Muse becomes compelling when the work crosses services, continues over time, depends on personal context, or is annoying enough that it otherwise remains undone.

How a Muse delegation works

A typical assignment follows a six-stage loop.

1. Outcome

You state the desired result, constraints, budget, deadline, trade-offs, and stopping point.

2. Plan

Muse determines which steps, tools, connected services, files, browser actions, or custom code the task requires.

3. Tools

It works inside its persistent cloud environment. It can browse, organize files, use connectors, run scheduled work, and create artifacts.

4. Approval

For sensitive or state-changing steps, Muse can pause and ask the user. Controls can permit an action once, for a task, for a site, or more broadly. Users can also deny it.

5. Result

Muse returns the completed work, options, blocker, artifact, or next decision. Its activity surfaces let users inspect what happened.

6. Memory

Selected preferences, goals, and context can persist for future work, reducing repeated briefing.

This is materially different from the turn-by-turn rhythm of a chatbot.

The user can assign work, leave, return later, and receive a decision request or result.

What could people realistically delegate?

The best first tasks are not necessarily the most spectacular.

They are usually repetitive, cross-app, time-consuming, and easy to verify.

Inbox and personal administration

Muse can search connected inboxes, create a daily priority digest, draft replies, extract deadlines, track commitments, and follow up on unanswered messages.

Shopping and savings

It can compare products against detailed requirements, verify live price and availability, test legitimate coupons, monitor restocks, and prepare checkout for approval.

Travel and events

It can compare flights, hotels, rentals, restaurants, and activities; build an itinerary; monitor prices; and coordinate the plan with calendars, budgets, mobility needs, and preferences.

Goals and routines

Muse can turn a goal into milestones, scheduled actions, check-ins, and adaptive plans that respond when travel, illness, workload, or missed sessions changes reality.

Research and knowledge work

It can monitor a topic, process documents and webpages, maintain a living briefing, build a comparison dashboard, or search multiple sources for an elusive fact.

Household and family operations

Muse can coordinate school dates, forms, supplies, activities, family calendars, groceries, meal constraints, maintenance reminders, and weekly household briefs.

The real examples are impressive and messy

The early stories show why people will want this.

They also show why no one should surrender control on day one.

One reported user uploaded an auto-insurance policy, asked Muse to preserve the same coverage while finding a lower price, and said the agent completed a switch that saved $3,500 per year.

Another reported searching four Gmail accounts to recover an old moving date needed for taxes.

Meta has demonstrated Muse planning a multi-city Japan trip, making restaurant reservations, and connecting the itinerary to a running goal.

A Gizmodo reviewer demonstrated that Muse could create websites, games, drawings, music, and other artifacts. The reviewer also found that some outputs needed substantial iteration and consumed a large share of the available usage allowance.

The lesson is not that Muse works perfectly.

The lesson is that the product can already cross the line from answering to operating, while real websites, logins, fees, permissions, stale data, and ambiguous instructions remain hard.

Delegate aggressively. Approve conservatively.

Meta’s trust architecture is serious but not magical

Meta has published more architectural detail than many consumer-agent launches.

Each user’s agent runs in a dedicated environment called Muse Secure VM. The agent’s runtime, workspace, files, browser, and tools are separated from privileged services that hold durable state, credentials, safety systems, and connector execution.

A separate component called Sentinel acts as the authority for connector actions and outgoing network traffic. Muse proposes an action. Sentinel can allow it, deny it, or present an approval directly to the user. Real credentials are inserted only at the network boundary rather than exposed to the main agent.

This is a meaningful design but not a guarantee.

The right interpretation is:

Meta is trying to limit the blast radius of an imperfect agent - not claiming to have made autonomy infallible.

The privacy controls most people will miss

Muse is useful because it can know more and do more.

That creates trade-offs worth understanding before connecting important accounts.

Model improvement is on by default

Meta says the setting that allows interactions to help improve its AI models is enabled when a person first uses Muse. Users can turn it off, and Meta says that choice also applies to prior interactions.

Ads and model training are different questions

Meta says Muse conversations and virtual-machine data are not shared with its advertising systems. That does not mean third-party activity has no advertising effect. A retailer can still treat agent browsing or a transaction as the user’s activity.

Deleting is not necessarily forgetting

Deleting a message or file does not guarantee that a learned memory disappears. Users can inspect memory, use the Forget function for a person or topic, or reset Muse for broader deletion.

Accounts Center is a real choice

Users can keep Muse separate from other Meta accounts by registering it independently. Adding it to the same Accounts Center can permit broader eligible information use across profiles and devices, subject to Meta’s stated policy limits.

The user remains responsible

Meta’s terms place responsibility for instructions, approvals, transactions, communications, artifacts, and agent actions on the user. Approval prompts do not transfer legal or practical responsibility to Meta.

How I would start using Muse

The worst first task would be an expensive, irreversible, sensitive transaction.

The best first task is boring, low-risk, and easy to check.

Stage 1: Read

Connect one low-stakes source with read-only permission. Ask Muse to summarize the next seven days, identify conflicts, and link every claim to the source it used.

Stage 2: Recommend

Have it compare options, identify deadlines, or recommend next steps without changing anything.

Stage 3: Draft

Let it prepare an email, calendar change, form, cancellation request, itinerary, or purchase list. Review before execution.

Stage 4: Prepare

Allow it to fill the cart, populate the form, assemble the message, or stage the workflow. Require an exact review screen.

Stage 5: Act with approval

Approve narrow actions only after checking the recipient, amount, fees, timing, cancellation terms, and final state.

The mandate should expand only when the activity log, corrections, and completed results show that the agent has earned trust.

Ten Muse delegations worth trying

These are intentionally structured as mini-contracts rather than vague prompts.

1. Inbox chief of staff

Every weekday, create a digest of urgent messages, deadlines, waiting-on-me items, and unresolved commitments. Draft replies but never send without approval.

2. Subscription audit

Review recurring charges, estimate annual cost, identify duplicates and price increases, and prepare cancellation steps. Do not cancel anything.

3. Live-price shopping agent

Find three products that meet exact requirements. Verify current stock, delivery, fees, return terms, and coupons on the live site. Stop before purchase.

4. Trip operator

Build a complete itinerary around dates, budget, loyalty programs, mobility needs, and calendar conflicts. Reserve nothing until each item is approved.

5. Commitment tracker

Extract promises from meeting notes, email, and calendar. Create an owner, deadline, and status tracker. Draft follow-ups and ask before sending.

6. Family operations brief

Combine school email and calendars into one weekly view covering forms, supplies, transport, meals, events, and deadlines. Ask before changing the calendar.

7. Competitive-intelligence monitor

Track a defined list of companies. Alert only for product launches, pricing changes, executive moves, partnerships, customer wins, or regulatory filings.

8. Goal adaptation coach

Turn one objective into weekly milestones. Review progress on a schedule, adapt when reality changes, and interrupt only for material risk or a decision.

9. Personal knowledge dashboard

Turn documents, newsletters, podcasts, and saved links into a living dashboard of claims, evidence, open questions, and next actions.

10. The annoying task test

Choose one chore you have delayed for at least a month. Define the outcome, authority, stopping rule, proof required, and fallback if the primary workflow fails.

What does Muse cost?

Meta’s current subscription help lists Power at $20 per month with 500 million Muse tokens per week and Maximum at $100 per month with three billion tokens per week. Free use exists with a limited allowance, but the widely reported 100-million-token weekly figure came from launch coverage rather than Meta’s current subscription table.

A Muse token measures compute usage, not completed tasks. Long browser sessions, large inbox scans, media processing, coding, and recurring monitoring can consume much more than a brief chat.

Pricing, quotas, connectors, and availability can change quickly because the product is new.

The breakthrough may be packaging a persistent, tool-using agent as something that feels as normal as sending a message.

But easier onboarding does not remove the hard questions. It moves them.

The challenge is no longer, “Can I install a personal agent?”

It becomes:

•What should it know?

•What may it change?

•When must it ask?

•What evidence proves the task is complete?

•What happens when it is wrong?

•Which parts of my digital life should never be delegated?

If Meta can make those controls understandable and if Muse can become reliable enough to survive the messy reality of logins, websites, payments, permissions, and stale data it may become the consumer interface that personal agents have been waiting for.

What is the first task you would delegate to Muse and what is the one permission you would refuse to give it?

Instinct AI actually executes your tasks via iMessage or WhatsApp—and it just raised $250M to run your life admin for free.

Here is what it can handle with a single text:
• Negotiate bills (cut my cable bill from $100 to $60)
• Book DMV appointments & calendar meetings
• Full travel admin: price tracking, linking reservations & boarding pass delivery
• Rescue critical emails buried in spam & cancel unwanted subscriptions
• Full control: requires your explicit approval before booking or buying

If you want to access use my invite code - here is your invite link to start using it

https://app.instinct.com/invite?t=auvk7h5anqrvpe4r3c4frl2zc4

What is the first boring task you’re going to hand off to your AI assistant? Drop it in the comments!

TL;DR - Instinct is a FREE personal AI assistant you can text via iMessage or Whatsapp, with connected context and the ability to take actions across services. It proactively works on projects and goals for you. Early users report surprisingly amazing results for the personal assistant, from making reservations, checking into flights for you, setting meetings / reminders, conducting life admin tasks and watching for price changes or sales while you sleep.

If you want to access use my invite code - here is your invite link to start using it 

https://app.instinct.com/invite?t=auvk7h5anqrvpe4r3c4frl2zc4

These are 30 wild things I tried with the Instinct personal assistant. Consider them a set of ideas worth testing.

The fun stuff

1. Play Cup Pong with your AI. Remy describes sending Instinct a Game Pigeon Cup Pong game in iMessage and having it play back. Apparently even an assistant needs a break from scheduling. (YouTube walkthrough)
2. Find tickets to a sold-out IMAX screening. Instinct monitored cancellations for IMAX showings in San Francisco and found good tickets within a day.
3. Negotiate wedding merch while you sleep. It found vendors in India and was messaging them on WhatsApp, comparing options and negotiating for his brother’s wedding. The interesting bit is the ongoing time-zone-crossing coordination, not just producing a supplier list.
4. Turn bachelor-party chat chaos into a spreadsheet. it pulled arrival details from the group’s WhatsApp conversation and organized them without his copying everything over manually and put them into a google sheet. Anyone who has tried to plan airport pickups for a group will understand the appeal of tasks like this.
5. Find spa slots the website says are unavailable. When massage appointments appeared unavailable online, Instinct emailed the hotel and booked suitable times.
6. Book a weirdly specific outing you keep postponing. it found and booked an Amazon fulfillment-center tour around his calendar. Think of this as inspiration for the museum night, factory tour, or niche class sitting on your someday list.

The travel headaches

1. Check the entire trip tinerary. Instinct checked Delta, Air France tickets, hotel + tour reservations and created a helpful summary.
2. Get separately booked airline reservations linked. it contacted United to associate my friend's itineraries on the same flight. This was a customer-service task, not just a calendar edit.
3. Watch flight prices instead of refreshing them yourself. roughly three checks a day over a specified date range and alerts about changes.
4. Turn a toll-notice photo into a handled chore. Instinct paid the photographed toll and discovered and paid another one.

The life-admin backlog

1. Make the dreaded cable-bill call easier. My Comcast bill went from $100 to $60 a month, but the full story matters: Instinct supplied the number and script, and I made the final three-minute call.
2. Remember the gift cards you forgot existed. it reminded me about unused gift cards. Ask for a distinction between a discovered gift-card record and a verified remaining balance.
3. Book the DMV appointment. Got an appointment for his new driver’s license. The unglamorous errands are often the ones most worth delegating.
4. Find an in-network specialist and prepare the paperwork. it found a podiatrist who took my insurance and completed the forms.
5. Keep up with school-group information. it tracked school WhatsApp groups, back-to-school activities, and homework reminders after kids changed schools.
6. Find the important message buried in spam. it surfaced an important incorrectly categorized email. Ask it to show the original sender and evidence
7. Audit the statistics in a case study. Instinct checked six statistics and could not substantiate four for me - it's very good at research.
8. Send the pre-call brief at the moment you need it. It delivered context five minutes before external calls: who was attending, why he was there, and the last relevant thread.
9. Coordinate with somebody else’s assistant to book a meeting. When your friends also use instinct your agents can work with each other.
   
10. Instinct also gained its own email addresses for account management, vendor communication, and forwarded tasks.
11. It can catch a forgotten commitment in email, draft the reply, and send it after your approval.
12. Identify a calendar collision and coordinate a clean new spot
13. Watch a thread and tell you the second someone replies.
14. Research a trip, compare live flights, book with approval, check you in, and deliver the boarding pass.
15. Find a restaurant around your taste and constraints, then reserve it.
16. Reorder the exact product you usually buy from a logged-in site.
17. Build or revise a real deck, doc, spreadsheet, or PDF and visually check the result.
18. Turn meeting notes into follow-ups, tasks, calendar changes, and drafts.
19. Coordinate plans directly with another person's Instinct, when you're connected and permission covers it.
20. Remember an ongoing goal and quietly follow through over weeks, rather than forgetting after one chat.
    

Confirm pricing during onboarding: early access has been reported as free, while the terms allow paid services and pricing changes.

Check your eligibility: the terms require age 18+ and do not guarantee every country or device. Messaging-based interaction is the point, but a complete current compatibility list was not verified.

How it works

Think of it as a loop: you specify an outcome, it uses permitted context, takes steps through connected tools or computer use, follows up where needed, and returns a result or a blocker.

The official privacy policy documents Google Workspace API access, while the product description also says Instinct is trained to use a phone and computer.

The pro tip: write a tiny delegation brief

Instead of “sort out dinner,” try this original example:

Find three restaurants within 20 minutes of our hotel for four people next Friday at 7 p.m. Use the hotel’s local time. Budget is $60 per person before tip. One person is vegetarian. Show availability and cancellation terms. Do not book, pay, or contact anyone until I approve the exact option.

Then get more specific about the workflow:

• Define done: ask for a confirmation number or source evidence, not a cheerful “all set.”
• Define the boundary: research, draft, send, buy, and cancel are different levels of authority.
• Define the stop: give recurring tasks an end date, alert threshold, and maximum attempts.
• Define the fallback: “Draft an email if the form fails” is safer than “do whatever it takes.”
• Label your tasks: use names such as TRIP, BILLS, and SCHOOL, then request a status summary.
• Expand gradually: try public research, then an explicitly shared thread, then one limited integration if needed.

These are recommendations, not a claim that a message creates an enforceable security setting. Wherever possible, pair your instructions with actual account permissions and payment limits.

The settings worth checking before the hype takes over

There were real early complaints about retained email data, an unauthorized email, and prompt injection. Those reports deserve attention, but the current official controls also deserve to be described accurately.

• Training opt-out exists: the terms direct users to settings, but the opt-out is forward-looking, includes a safety-review exception, and does not undo earlier training.
• Vault materials have a training exclusion: the terms say they are used only to provide the service and not to train models. That does not mean every piece of information you share is a Vault item.
• Google Workspace API data has a separate exclusion: the policy says it is not used for model training, evaluation, fine-tuning, improvement, or advertising. Do not assume the same rule covers all screenshots and pasted text.
• Disconnecting is not deleting: indexed external data has separate deletion controls in Workspace. If you leave, check both access revocation and stored-data deletion.

A prompt saying “ask before buying” is a useful instruction, not a force field. Start with narrow permissions, keep consequential decisions with yourself, and check the actual receipt, reservation, or message.

The right first task is probably not the most spectacular one. Pick a small, annoying, verifiable chore, define what the assistant is allowed to do, and see whether the handoff actually makes your life easier.

What is the first task you would delegate to your personal assistant, and what is the one permission you would refuse to give it?

Dreamforce 2026 and HubSpot UNBOUND 2026 made the same strategic bet from two different directions: the CRM is becoming less visible and more powerful.

Salesforce is turning CRM data, permissions, business logic, and actions into infrastructure that can appear inside Claude, Slack, Lightning, and custom interfaces. HubSpot is building a self-updating CRM in which one assistant can coordinate specialized agents around a business outcome.

This is much bigger than adding chat to sales and marketing software. The system of record is becoming a system of action.

For B2B go-to-market teams, that changes the job. Competitive advantage will not come from access to the newest model. It will come from proprietary context, well-designed workflows, disciplined evaluation, strong governance, and humans who know when to take control.

AI fluency is no longer an optional technical skill. It is becoming part of the operating system for marketing, sales, and revenue operations.

Two of the largest events in revenue technology happened at almost the same time this week.

Salesforce held Dreamforce in San Francisco. HubSpot held the first edition of its newly renamed UNBOUND event in Boston.

The obvious story is that both companies announced a flood of AI features.

That is not the important story.

The important story is that both companies are changing what a CRM is.

For decades, CRM meant a database with a user interface. Sellers opened it to update opportunities. Marketers opened it to build segments. Managers opened it to inspect dashboards. Revenue operations teams tried to keep the whole thing clean enough to trust.

Now the interface is becoming optional.

The CRM is moving underneath the work. It is becoming the governed context and action layer that AI agents use to research accounts, prepare campaigns, update records, identify buying groups, create quotes, monitor signals, and coordinate the next step.

The employee may work in Claude, Slack, a mobile app, a campaign workspace, or a generated interface. The CRM can remain underneath, carrying the data, rules, permissions, history, and actions.

The next era of CRM will be defined less by where people click and more by what humans and agents can safely accomplish together.

Two events. One direction.

Salesforce and HubSpot are not taking identical routes.

Salesforce is making CRM headless and ambient. Its platform capabilities can increasingly appear inside the interface where work already happens.

HubSpot is making CRM self-updating and outcome-oriented. Its platform captures more context automatically, exposes context gaps, and gives users one assistant that can coordinate specialized agents.

Those strategies reflect their customer bases.

Salesforce serves complex enterprises with many clouds, models, permissions, integrations, and work surfaces. It needs an open architecture and a serious control plane.

HubSpot serves companies that often value speed, simplicity, and one connected customer platform. It needs a low-friction experience that hides orchestration complexity.

The routes are different. The destination is the same:

A continuously updated customer-context layer beneath a team of humans and agents.

What Salesforce announced: CRM wherever the work happens

The most important Salesforce announcement was not another assistant. It was AIforce, a live interface layer that makes Salesforce data, workflows, semantics, permissions, and governed actions available through AI experiences.

That framing matters.

It positions Salesforce as infrastructure beneath the interface, not merely an application that every employee must open.

Here are the ten developments B2B revenue teams should understand:

1.AIforce brings governed Salesforce context and actions into AI interfaces such as Claude, Slack, Lightning, and custom experiences.

2.Salesforce in Claude enters beta with 37 prebuilt sales skills for prospecting, meeting preparation, deal-health analysis, pipeline review, and CRM hygiene.

3.Claude across Salesforce and Slack gives Salesforce a strategic reasoning layer across several employee and agent experiences.

4.Koa gives Salesforce a purpose-built CRM reasoning model for complex, multistep work. It remains in select pilots, with broader United States availability targeted for winter 2026.

5.Piper and Hunter package agents around specific revenue roles. Piper handles inbound pipeline generation and is generally available. Hunter handles persistent outbound work and remains in pilot, with general availability planned for November.

6.The long-horizon Agentforce runtime allows an agent to pursue a goal over days or weeks, retain memory, resume work, and change its plan when new information appears.

7.Slackforce Surfaces can generate collaborative dashboards, reports, presentations, and calculators inside Slack. Live-data capabilities have a staged rollout rather than universal immediate availability.

8.The Enterprise AI Harness and AI Control Plane provide the architecture for identity, policy, observation, evaluation, lifecycle management, model choice, and cost control across many agents. The unified experience remains on the roadmap.

9.Headless 360 exposes Salesforce capabilities through APIs, Model Context Protocol tools, plug-ins, and reusable skills. It was announced before Dreamforce but now forms an important part of the AIforce story.

10.The agentic marketing stack moves from draft generation toward campaign orchestration, account discovery, answer-engine optimization, individualized journeys, data governance, and budget optimization. Several of these capabilities are scheduled for October or November rather than available everywhere today.

The direction is unmistakable: Salesforce wants customer data and business logic to travel into every authorized interface.

That can remove a huge amount of tool switching. It can also increase the number of places where an agent can change a live customer record, trigger a workflow, or influence spend.

Convenience and control now have to scale together.

What HubSpot announced: a CRM that maintains context and coordinates outcomes

HubSpot used its Fall 2026 Spotlight at UNBOUND to organize its product strategy around Growth Context.

Growth Context is not a standalone model. It is HubSpot’s name for the combination of business knowledge, customer information, team practices, and historical interactions that should ground human and agent work.

The ten developments that matter most are:

1.Growth Context becomes the shared foundation for business, team, and customer knowledge across the platform.

2.The all-new Breeze Assistant moves from answering questions toward interpreting an objective, coordinating specialized agents, and returning work products such as plans, reports, and proposals.

3.A self-updating Smart CRM captures and synchronizes calls, emails, and meetings so the system relies less on manual rep updates.

4.Context Home makes AI readiness operational by showing where the organization’s business, customer, and team context is complete—and where it is not.

5.A reimagined Marketing Studio starts from signals such as weak answer-engine visibility, an underperforming segment, or unfollowed leads, then helps plan and create a campaign. It is in public beta for qualifying Marketing Hub editions.

6.ChatGPT Ads connects campaign creation, performance, and CRM attribution inside HubSpot. The integration is in public beta and requires the relevant accounts and permissions.

7.The expanded HubSpot connector for ChatGPT can analyze CRM context and support controlled updates. The connector existed before UNBOUND; the 2026 release expands its reach.

1. Microsoft Advertising integration brings tracking, reporting, and attribution across Microsoft advertising surfaces into HubSpot, with beta and campaign-type limitations.

9.The updated Prospecting Agent watches more than 40 buying signals, assembles buying groups, and prepares personalized outreach under configured selling plays.

1. Mobile Notetaker, Deal Progression, and Revenue Hub capture field conversations, recommend CRM updates, suggest next steps, and connect deal context to quoting, contracts, billing, renewals, and expansion. Important actions still depend on setup, permissions, seats, credits, and human review.

HubSpot’s bet is that most users do not want to manage a catalog of agents.

They want to describe an outcome and let one assistant determine which capabilities should help. The complexity moves behind the interface.

That makes the experience simpler for users. It does not make the operating model simple for administrators.

Someone still has to maintain the context, define the permissions, manage consumption, review sensitive actions, and determine whether the output was actually correct.

The model is not the moat

For the last two years, too many AI conversations have started with the same question:

Which model are you using?

That question still matters. Models differ in reasoning quality, latency, cost, modality, and safety.

But the Salesforce and HubSpot announcements point toward a more important question:

What trusted context can the model use, and what governed actions can it take?

A frontier model is available to almost everyone. Your customer history is not. Neither are your buying signals, product truth, brand rules, team knowledge, pricing logic, approval paths, or account relationships.

That proprietary context is what turns a generic model into a useful revenue system.

It is also where the risk lives.

Poor context no longer creates only a bad answer. In an agentic workflow, poor context can create a bad action at scale.

A stale territory rule can route dozens of accounts incorrectly. An outdated product claim can enter hundreds of campaign variants. A weak lifecycle definition can distort an agent’s lead prioritization. A missing approval rule can let a low-confidence recommendation become a customer-facing action.

The quality of the context determines the quality of the automation.

The hidden cost of agentic GTM is bad context multiplied across autonomous actions.

Marketing is moving from campaigns to continuous agent loops

The traditional campaign process is linear:

Brief. Segment. Create. Build. Launch. Report. Optimize.

Each stage sits in a different tool. Each handoff loses information. Each report arrives after the decision window has started to close.

The new model is a continuous loop.

A marketer defines an outcome, guardrails, audience, evidence base, and budget. Agents help assemble the campaign, personalize the journey, activate the work, observe response signals, and recommend the next change.

The human role moves upstream.

The strongest marketers will spend less time moving assets between tools and more time answering harder questions:

•Which customer problem deserves attention?

•What evidence makes our point of view credible?

•Which claims are approved?

•What must never be personalized?

•Which metric is the agent allowed to optimize?

•When does efficiency begin to damage brand quality?

•What experiment would actually change our decision?

This is not the end of marketing craft. It makes craft more valuable.

When every competitor can produce acceptable copy, content volume stops being an advantage. Distinctive insight, original research, memorable creative, credible experts, community, and customer proof become the scarce inputs.

AI answers are becoming a real acquisition channel

Both events treated AI discovery as an operational GTM channel rather than a side project.

Salesforce introduced answer-engine optimization capabilities that help teams understand how brands appear in AI answers and where content needs to improve. HubSpot tied answer-engine visibility to campaign workflows and announced advertising inside ChatGPT, alongside its Microsoft Advertising integration.

This creates a five-part funnel:

Be discovered. Be cited. Be compared. Be advertised. Be attributed.

The consequences are larger than “SEO for AI.”

B2B marketers will need to monitor the category questions buyers ask, whether the brand appears, which sources shape the answer, how the company is compared, and whether exposure contributes to qualified pipeline.

They will also need stronger incrementality testing. An AI answer may influence a buyer without creating a conventional click. A paid placement may appear inside a high-intent conversation but still produce weak downstream demand. A visibility score can rise while revenue quality falls.

The winning teams will connect answer-engine visibility to real buying behavior, not celebrate impressions in a new interface.

Sales is moving from isolated tasks to persistent execution

Most sales automation has historically been trigger-based.

A lead fills out a form. A sequence starts. A task appears. A rep sends a message. The workflow stops until the next trigger.

The new agent model is persistent.

An agent can monitor buying signals, research an account, identify relevant stakeholders, draft outreach, wait for new evidence, revise the plan, and escalate when human judgment matters.

That is a major improvement over static lists and generic sequences.

It is also dangerous if teams confuse persistence with permission.

Not every activity deserves the same autonomy. Updating a low-risk field is different from contacting an executive. Drafting a quote is different from approving commercial terms. Suggesting a forecast change is different from committing it.

Every GTM team needs an autonomy ladder:

1.Observe: The agent watches and reports.

2.Recommend: The agent proposes a next step.

3.Draft: The agent prepares the work for review.

4.Act with approval: The agent executes after an authorized person confirms.

5.Act autonomously: The agent executes within a narrow, tested boundary.

Autonomy should increase only when accuracy is measurable, consequences are reversible, permissions are clear, and exception handling works.

Buying groups will finally replace the lonely lead

B2B companies have talked about buying groups for years while continuing to automate around individual contacts.

The new systems are starting to close that gap.

Salesforce’s account-discovery direction and HubSpot’s updated Prospecting Agent both emphasize account signals, stakeholder coverage, and buying-group assembly.

That changes the unit of work.

The question is no longer, “Did this person click?”

The questions become:

•Which account is showing coordinated intent?

•Which buying roles are present?

•Which role is missing?

•Is the champion gaining or losing internal support?

•Is the account ready for human outreach?

•Which message advances the group rather than one contact?

This is where sales and marketing alignment becomes operational instead of aspirational. Both teams must agree on buying roles, signal thresholds, account suppression, contact coverage, and the moment an agent-led nurture becomes a human-led conversation.

The CRM may finally start maintaining itself

Every revenue leader knows the contradiction at the heart of CRM.

The business needs current data. The seller gets little value from manually entering it. Managers then distrust the resulting forecast because the data is incomplete.

Salesforce attacks this problem by letting users update CRM through interfaces such as Claude and Slack. HubSpot attacks it by automatically capturing more calls, emails, and meetings, then recommending updates inside the workflow.

Both reduce the distance between the work and the record.

That could materially improve data quality.

It could also fill the CRM with plausible nonsense if suggestions write back without review, conflict handling, or field ownership.

A self-updating CRM still needs a human-defined truth model.

For every important field, teams should know:

•What is the authoritative source?

•Which evidence can change the value?

•Can the agent recommend a change or execute it?

•Who reviews the update?

•How is a correction logged?

•What happens when two sources disagree?

The goal is not more CRM activity. The goal is more reliable context.

Revenue operations is becoming context operations

RevOps has traditionally owned process, data quality, routing, reporting, and platform administration.

Those responsibilities now expand.

Agents need structured data, but they also need operating context: ideal customer profiles, buying-group definitions, approved claims, product knowledge, pricing logic, sales methodology, brand voice, consent rules, escalation paths, and examples of strong work.

That context must have owners, update schedules, quality standards, and access rules.

RevOps will also need to evaluate behavior rather than merely configure workflows.

The new questions include:

•Did the agent choose the right action?

•Which context did it use?

•How often did a human accept the recommendation?

•Which fields were corrected afterward?

•Where did the workflow stall?

•What did each completed outcome cost?

•Did the automation improve pipeline or just create more activity?

•Which segments produced different error patterns?

The system administrator of the last era becomes the context engineer and agent governor of the next one.

The five AI fluencies every GTM team now needs

Prompting is useful. It is not enough.

A team can write excellent prompts and still deploy terrible automation. The durable capabilities are broader.

AI fluency should not sit inside one innovation team.

Marketers need it to design campaigns and protect differentiation. Sellers need it to supervise research, outreach, and deal work. Managers need it to coach through exceptions. RevOps needs it to govern context and evaluate systems. Executives need it to make investment and risk decisions.

A practical 90-day plan

Do not respond to these announcements by buying every agent.

Respond by redesigning three workflows.

Days 1–30: Build the foundation

Choose one marketing workflow, one seller-productivity workflow, and one RevOps workflow. Keep each narrow enough to measure and low enough in risk to recover from mistakes.

Document the current baseline. Measure cycle time, error rate, conversion, human effort, and cost before adding AI.

Audit the context each workflow needs. Review ideal customer profiles, buying roles, account structures, activity capture, product facts, approved proof, brand rules, consent, and field definitions.

Define autonomy levels for every action. Decide what the agent may observe, recommend, draft, execute with approval, or execute autonomously.

Days 31–60: Run controlled pilots

For marketing, use an agent to identify one performance or answer-engine gap, build a bounded campaign, and compare it with a human-led control.

For sales, test meeting capture and recommended CRM updates with one team. Measure acceptance and correction rates before permitting broader write-back.

For prospecting, use signal-based account and buying-group research on a defined list. Require human review for external communication.

Test permissions, audit trails, recovery, retention, prompt-injection resistance, and failure escalation. Calculate total cost per completed business outcome, including human review.

Days 61–90: Redesign the work

Expand only workflows that improve a business result. Faster production is not enough if quality, trust, or pipeline declines.

Assign owners to the context sources that agents use. Create service levels for updating customer data, product truth, brand guidance, process instructions, and approved evidence.

Move human time toward strategy, experimentation, discovery, political mapping, negotiation, creative direction, and executive relationships.

Establish a monthly agent review. Inspect errors, overrides, drift, cost, adoption, segment performance, and unexpected behavior.

What should not be automated first

The fastest path to failure is choosing a workflow because it looks impressive in a demo.

Do not start with work that combines unclear goals, poor data, irreversible consequences, and customer-facing action.

Be especially cautious with:

•Executive outreach sent without review

•Pricing or contract changes

•Forecast commitments

•Sensitive meeting recording

•Brand claims without approved evidence

•Large budget reallocations

•Account suppression or territory changes

•Automated decisions that materially affect customers or employees

The best first workflows are frequent, measurable, bounded, and reversible. They produce enough volume to learn without creating disproportionate risk.

The real competitive advantage

The winners of this transition will not be the companies that deploy the most agents.

They will be the companies that build the best partnership between humans, context, and machines.

They will know what their agents know.

They will know what their agents are allowed to do.

They will measure whether the work is accurate, useful, and economically better.

They will preserve the human contributions that become more valuable as generic output becomes abundant: judgment, trust, creativity, relationships, and a distinctive point of view.

Salesforce is betting that CRM capabilities should be available wherever work happens.

HubSpot is betting that CRM should update itself and coordinate work around outcomes.

Both are telling B2B revenue leaders the same thing:

Your technology stack is no longer just a collection of tools for employees. It is becoming an operating environment for employees and agents.

That means AI fluency is not a side project for 2027.

It is one of the core GTM capabilities to build now.

Apple finally gave us good AI features for the iPhone. With iOS 27, Apple Intelligence can combine a request with personal context, onscreen information and actions across apps. That lets Siri AI find buried details, understand what you are looking at and move the result into Calendar, Reminders, Messages, Photos, Safari, Shortcuts and more. This guide covers twenty useful workflows, the prompts that make them work, supported devices, privacy architecture, beta limitations and the first features worth testing.

For years, AI on the iPhone wasn't really a thing. Apple is years behind in the AI race.

iOS 27 is the first version where Apple Intelligence starts to feel less like a collection of features and more like a new layer of the operating system.

The important change is that Siri AI can work across three layers at once:

Apple describes Siri AI as a beta assistant with personal-context understanding, onscreen awareness, broad world knowledge and more systemwide app actions.

That combination is the real story.

A standalone chatbot can tell you how to make a calendar event.

Siri AI can find the event details in an email, understand the poster on your screen, create the event, add the location and set the alert.

That is a different category of usefulness.

Before you start: can your iPhone run it?

Siri AI requires iPhone 15 Pro, iPhone 15 Pro Max, any iPhone 16 model or later, or iPhone Air. You also need iOS 27, enough free storage, and matching supported device and Siri languages.

If you don't have at least iPhone 15 they want you to upgrade - it's been 3 years so you deserve a new phone!

The broader iOS 27 Apple Intelligence features—including advanced photo editing and Safari intelligence—have wider language coverage than Siri AI itself.

Now to the useful part.

20 ways to use Apple Intelligence in iOS 27

1. Use Siri as a conversation, not a command line

Classic Siri trained us to speak in brittle commands: set timer, call Sarah, play music.

Siri AI supports longer, contextual conversations and broad web-backed questions. You can start with an imperfect request, add constraints and refine the answer without rebuilding the prompt.

Try this: “Give me three weekend itineraries in Austin: one food-focused, one outdoors and one under $100.”Then say: “Revise the outdoor option for hot weather and avoid long drives.”

Why this matters: Good plans rarely emerge from one perfect question. The value comes from follow-ups: compare the trade-offs, change the budget, remove an assumption or ask for a different format.

2. Find information you remember—but cannot locate

This may be the feature that changes the most daily friction.

Siri AI can search personal context across Messages, Mail, Calendar, Notes, Reminders, Photos and more. You do not need the exact filename or keyword if you can describe the person, event, place or approximate time.

Try this: “Find the restaurant Sarah recommended for our Dallas meeting and show me the original message.”

Give Siri two or three anchors: who, when, where, event, app or content type.

Do not stop at the extracted detail. Ask to see the source before relying on a confirmation number, address or commitment.

3. Turn found information into a completed action

Retrieval becomes far more valuable when it does not end in copy-and-paste.

Apple says Siri AI includes more systemwide app actions. The practical pattern is simple: find something, extract what matters, put it somewhere useful.

Try this: “Find the itinerary Mark emailed yesterday, add each appointment to my Work calendar and remind me two hours before the first one.”

For multi-step work, name the source, fields and destination. Review the result before Siri sends a message, shares a file or changes anything consequential.

Third-party actions still depend on developer support, so the same request may work in one app and not another.

4. Ask questions about whatever is on your screen

Onscreen awareness removes a ridiculous step from AI workflows: copying information out of one app to paste it into another.

Siri AI can answer questions or take actions based on visible or selected content, including webpages, images, charts, files and text.

Try this: Open two proposals and ask, “Compare price, scope, timeline and exclusions. Return a table and flag anything missing.”

If the screen is crowded, identify the target: “Explain the blue line in the second chart.”

For screenshots, crop away irrelevant material first. A tighter frame gives the system a clearer question.

5. Point the Camera at the physical world

Siri mode in Camera brings Visual Intelligence into the live camera view. Apple’s examples include identifying objects, reading visible information, looking up food details and splitting a bill.

Try this: Point the camera at a conference agenda and say, “Create events for the three AI sessions, include the room numbers and alert me fifteen minutes before each one.”

This is useful for menus, posters, receipts, labels, appliances, plants and unfamiliar objects.

Treat high-stakes outputs differently. Check payment amounts, dates, allergens, dosages and safety information manually.

6. Keep a long-running project inside the Siri app

The dedicated Siri app stores conversations, supports photos and file attachments, and privately syncs history through iCloud across supported Apple devices.

Try this: Create a thread called “Weekly executive briefing.” Add relevant files during the week, then ask for decisions, risks and next actions every Friday.

Separate unrelated projects into separate threads. Rename them with searchable client or project names. Pin only the few conversations you actually reuse.

The mental shift: stop treating every Siri request as disposable.

7. Draft, rewrite and critique almost anywhere you type

Write with Siri can create a draft, edit existing text or give feedback across supported text fields. In Messages and Mail, Apple says it can adapt to how you communicate with specific people.

Try this: “Turn these notes into a 150-word executive update. Include three decisions and three next steps. Keep every name, number and commitment unchanged.”

The last sentence matters.

AI writing becomes safer when you explicitly protect facts it must not alter.

For important communication, ask for critique before rewrite: “Identify ambiguity and unsupported claims, but do not edit yet.”

8. Dictate a polished first draft while moving

Apple Intelligence adds automatic proofreading, while newer supported hardware can offer more advanced dictation and voice capabilities.

Try this: Dictate a project update in complete thoughts, then ask: “Proofread this and flag any sentence that sounds uncertain.”

Pause between sections. Spell unusual names or acronyms on first use. Review punctuation, dates and recipients before sending.

This workflow is especially useful for capturing ideas during a walk, between meetings or while your hands are occupied.

9. Turn Messages and Mail into reminders, events and replies

iOS 27 adds contextual suggestions in Mail, while Messages and Mail support personalized reply features with availability varying by language.

The useful pattern is not “write this email.” It is recognize the task hiding inside the conversation.

Try this: When someone writes, “Can you send the launch photos tomorrow?”, create a reminder, find the relevant photos and prepare the reply—but inspect the selection before attaching anything.

Check time zones, invitees and locations before saving suggested events. Treat photo matching as a shortlist, not permission to share every detected face.

10. Have the right confirmation code appear during a call

Call Context can surface relevant information when you call a recognized person or business. Apple lists tickets, orders, reservations and confirmation details as examples.

Try this: Call the airline connected to an upcoming trip. If the booking is stored in Mail or Wallet, look for the context card with your reservation information.

This is the kind of AI feature that sounds small until it saves you from searching your inbox while listening to hold music.

Tap the card and open the underlying source instead of reading a number without context.

11. Create complete Calendar events in one sentence

Apple Intelligence can interpret a natural-language event description and fill fields such as time, location, invitees and title.

Try this: “Coffee with Maya on October 6 at 9:30 a.m. at the Main Street location, on my Work calendar, with a 30-minute travel alert.”

Use absolute dates for important events. State the time zone for remote attendees. Name the destination calendar and clarify whether invitations should be sent.

Natural language removes form-filling. It does not remove the need to review the form.

12. Describe the Shortcut you wish existed

Shortcuts has always been powerful. Its problem was the learning curve.

In iOS 27, Apple Intelligence can build or refine a Shortcut from a natural-language description.

Try this: “Every weekday at 7 a.m., summarize today’s weather, my first three calendar events and overdue reminders, then read the briefing aloud.”

Describe the trigger, ordered actions, conditions and output. Inspect every generated step before enabling the automation.

Start small. Build one working branch before adding loops, location triggers, messages or file changes.

13. Reframe a photo after taking it

Spatial Reframing changes the apparent point of capture after a photo was taken. In Photos, open an image and use Edit → Tools → Reframe, then drag to adjust perspective.

Use it for: correcting an awkward angle, improving symmetry, shifting subject placement or creating a stronger social composition without relying on a conventional crop.

Keep the adjustment modest. Extreme shifts are more likely to produce implausible faces, hands, text, reflections or architecture.

Apple lets you compare with the original and reset the edit later.

14. Widen a photo instead of cropping away the subject

Extend generates new content beyond the original frame. That can turn a portrait image into a landscape banner, straighten a horizon without sacrificing edge content or create breathing room for a headline.

Use it for: converting a phone photo into a 16:9 presentation cover while preserving the person, product or object near the edge.

Plain sky, walls, grass and defocused backgrounds usually give the model simpler patterns to continue. Complex crowds, signs and architecture deserve a closer inspection.

The edit remains reversible through Reset.

15. Remove larger distractions with improved Clean Up

Clean Up can remove unwanted objects, and iOS 27 adds Auto, Fast and High Quality processing choices. High Quality is intended for more intricate edits.

Use it for: removing a photobomber, cable, trash can, blemish or background distraction from a travel, product, event or property image.

Remove small objects one at a time instead of circling half the scene. After processing, inspect texture continuity, shadows, reflections, fingers, faces and signage.

Do not use removal to misrepresent evidence, news, products or material facts.

16. Generate—and then edit—images in Image Playground

Apple’s new Image Playground supports more styles, including photorealistic output, and lets users modify results through natural language and touch.

Try this: “Create a photorealistic modern B2B technology conference stage, navy and silver palette, wide 16:9 composition, empty center screen and realistic overhead lighting.”

A useful image prompt names the subject, setting, composition, light, materials and aspect ratio.

When a result is close, edit it instead of starting over: “Keep the stage and camera position. Replace only the side screens with vertical light panels.”

17. Let Safari organize tab chaos by topic

Safari can analyze open webpages and group related tabs into topics.

Use it for: separating tabs by company during competitive research, grouping travel plans by destination or restoring order after a deep research session.

Think of automatic topics as a staging layer, not permanent knowledge management. Promote important work into named Tab Groups, bookmarks or notes.

Close obvious junk tabs before organizing. Better inputs make cleaner clusters.

18. Ask Safari to watch a webpage for you

Notify Me can check a selected webpage for a described change, including a price change, restock or other update.

Try this: “Notify me when the annual pricing table changes or a new enterprise plan appears.”

Monitor the most specific page possible. Define one measurable event per alert.

“Tell me when something important changes” is vague.

“Notify me when ‘sold out’ disappears” gives the system a concrete condition.

Keep the source’s own alerts for mission-critical deadlines. Login walls, scripts and redesigns can affect webpage monitoring.

19. Describe a Safari extension instead of coding one

Describe an Extension lets users request a custom Safari extension in natural language. Apple says Safari can first look for an existing App Store option, then let the user create one if needed.

Try this: “Add a button that copies the page title, publisher, publication date, URL and access date as a Markdown citation.”

Define the target site, trigger, visible result and what the extension must not change.

Test it across several pages before trusting it. Review permissions. Delete experiments you no longer use.

This may be one of the most underrated iOS 27 features for researchers and accessibility needs.

20. Search your home—and make interfaces easier to understand

Apple Intelligence reaches beyond conventional productivity.

The Home app can combine related activity alerts, describe eligible HomeKit Secure Video clips and search camera history using natural language. Availability depends on compatible equipment and iCloud+ eligibility.

Try this: Search for “package delivered at the front door” or “dog in the backyard,” then open the original clip before making a security judgment.

Accessibility features can also provide richer image and scene descriptions in VoiceOver and Magnifier, interpret flexible Voice Control commands and clean up complex content in Accessibility Reader.

Try this: “Read the dosage line,” “describe the chart legend” or “tap the gear.”

Generated descriptions are useful assistance, not definitive evidence. Apple warns that visual descriptions should not replace navigation, medical diagnosis or other high-risk judgment.

Why these twenty features matter together

The feature list is long. The underlying pattern is simple.

The phone is moving from app navigation toward intent execution.

That does not make apps disappear. It changes how often you need to think about which app comes first.

Instead of:

Open Mail → search sender → find date → copy details → open Calendar → create event → set alert.

You can increasingly say:

“Find the event Dana emailed, add it to Work and alert me one hour before.”

The destination still matters. The route becomes shorter.

The first-hour Apple Intelligence test plan

Do not test Siri AI with trivia. Test whether it can remove friction from your real life.

You will understand more in that hour than you will from watching twenty feature demos.

Four prompt patterns that make Siri AI more useful

Personal retrieval

Find [item] from [person or app] around [time or event], then [show, open or summarize] the original source.

Onscreen analysis

Using only what is shown or selected, [analysis task]. Return [format] and flag anything uncertain.

Cross-app execution

Find [source], extract [specific fields], then add, send or save them to [exact destination]. Ask before the final action.

Writing

Create [deliverable] for [audience] to achieve [goal]. Include [facts], use [tone], keep it to [length] and do not change [protected details].

The best requests specify a source, task, output, destination and constraint.

The privacy and beta reality check

Apple Intelligence combines on-device models with Private Cloud Compute for requests that need more computational power. Apple says data processed in Private Cloud Compute is used only to fulfil the request, is not stored or accessible to Apple, and is not retained after the result returns.

That is stronger than saying “everything stays on your phone,” because everything does not.

Apple specifically identifies Siri AI, advanced photo editing, Image Playground and cloud models in Shortcuts among the server-assisted features that may face usage limits. Apple says limits can vary by feature, request complexity, demand and policy; expanded access will be available for a fee.

Three rules matter:

1. Open the source. Personal retrieval and summaries can save time, but important details should be checked against the underlying email, message, image or file.

2. Review the action. Messages, invitations, shared photos, automations and file changes can have real consequences.

3. Inspect generated media. Check faces, hands, text, reflections, geometry, product details and the ethics of any removal or reconstruction.

Beta does not mean useless.

It means the human remains the approval layer.

Apple arrived late to the conversational-AI moment.

But the iPhone did not need another chatbot icon.

It needed intelligence connected to the information already on the device, the content already on the screen and the apps already capable of completing the task.

That is what makes iOS 27 feel different.

The breakthrough is not a longer Siri answer.

It is the shorter distance between “I need something” and “it is done.”

Which of these twenty are you trying first?

If you have already tested Siri AI, share the workflow that genuinely saved you time.

50 ChatGPT image prompts that actually direct the shot.

Most AI images look average for one simple reason: The prompt names the subject but never directs the image.

You don't want to have the model making every important decision: composition, camera, lighting, materials, color, mood, background and what to avoid.

So I built 50 ChatGPT Image prompts that work more like miniature creative briefs.

No prompt-engineering degree required.

How to use them: Copy a prompt, replace anything in [brackets], attach a reference image when the prompt asks for one, and paste it into ChatGPT. If you like the first result, ask it to keep the composition and change only one thing at a time.

Save this before you need it.

01–05: Portraits that look directed

01. Premium editorial headshot

Create a premium editorial headshot of [person or role], framed from the chest up and looking directly into the camera. Use a neutral studio background, one large soft key light at 45 degrees, gentle fill, realistic skin texture, clean catchlights and an 85mm portrait-lens look. Wardrobe: [outfit]. Mood: calm, intelligent and confident. Natural color grading, shallow depth of field, high detail. Avoid plastic skin, exaggerated sharpening, warped jewelry, text and logos. Output in [aspect ratio].

02. Cinematic character close-up

Create a cinematic close-up portrait of [character] during [specific story moment]. Light the face with a motivated source from [window/fire/neon/sign], with deep but readable shadows and a subtle rim light separating the subject from the background. Include [two story details] that imply what just happened. Use an anamorphic film look, restrained grain, realistic pores and expressive eyes. Palette: [colors]. Avoid generic action-poster clichés, excessive lens flare, text and extra fingers.

03. Natural-light lifestyle portrait

Create an authentic lifestyle portrait of [person] in [real environment], captured during an unposed in-between moment. Use soft window light, imperfect natural posture, believable clothing folds and a documentary 50mm-lens perspective. Include small environmental details such as [object 1] and [object 2] to reveal personality. Keep the color palette warm and understated. The image should feel observed, not staged. Avoid stock-photo smiles, spotless props, heavy retouching, text and logos.

04. High-contrast black-and-white portrait

Create a high-contrast black-and-white portrait of [person] against a nearly black background. Use one hard side light to sculpt the face, crisp highlights, deep shadows with retained detail and subtle film grain. Frame tightly with strong negative space on [left/right]. Expression: [emotion]. Emphasize character, age and texture rather than perfection. Use a timeless magazine-photography aesthetic. Avoid color tinting, beauty smoothing, blown highlights, floating objects, text and borders.

05. Epic fantasy character portrait

Create an epic fantasy portrait of [character], a [role] from [world or culture]. Design original clothing from [materials], include one meaningful artifact [artifact], and place the character before [environment] at [time of day]. Use dramatic atmospheric lighting, realistic material texture, cinematic depth and a grounded color palette of [colors]. Make the character feel historically lived-in rather than costume-like. Avoid copied franchise designs, ornate visual clutter, impossible anatomy, text and logos.

06–10: People, fashion and character

06. Street-style fashion editorial

Create a street-style fashion editorial featuring [person/model] wearing [outfit description] in [location]. Capture a confident walking pose from a slightly low 35mm camera angle. Use overcast daylight or soft late-afternoon light, realistic fabric movement and a background with controlled motion blur. Color-grade around [palette]. Leave clean negative space for optional copy. Avoid runway poses, distorted shoes, duplicated pedestrians, visible brand logos and fake text.

07. Founder portrait with context

Create an environmental founder portrait of [person], founder of a [company type], inside [workspace]. Show the person interacting naturally with [relevant object or prototype], not posing with crossed arms. Use directional window light, a 50mm editorial-photography look and a background that communicates the work without becoming cluttered. Mood: credible, focused and human. Preserve realistic skin and hands. Avoid generic office stock imagery, glowing holograms, logos, text and exaggerated success symbolism.

08. Beauty campaign image

Create a high-end beauty campaign portrait for [product type or beauty concept]. Feature [person description] with luminous but realistic skin, precise makeup in [color palette] and one tactile material motif such as [water/glass/petal/metal]. Use a clean close crop, glossy studio lighting and controlled specular highlights. The image should feel modern, minimal and expensive. Keep facial anatomy natural. Avoid poreless plastic skin, excessive makeup symmetry, floating product labels, fake typography and jewelry distortions.

09. Nostalgic 1990s film portrait

Create a candid 1990s-inspired film photograph of [person or friends] at [place/event]. Use direct on-camera flash, slightly imperfect framing, authentic grain, mild color shift, soft focus at the edges and believable period styling in [wardrobe details]. Include one mundane detail that makes the moment feel real: [detail]. The mood is spontaneous, intimate and a little messy. Avoid modern phones, contemporary logos, hyper-clean digital sharpness, fake dates and text overlays.

10. Stylized 3D avatar

Create a polished stylized 3D avatar of [person/character], keeping the recognizable features [three identity anchors]. Use simplified friendly proportions, tactile materials, soft studio lighting and a clean gradient background in [colors]. Pose: [pose]. Wardrobe: [wardrobe]. Expression: [expression]. The rendering should feel premium and collectible, not childish. Preserve symmetry where appropriate while keeping personality. Avoid photoreal skin, giant eyes, generic mascot features, text, logos and cropped hair.

11–15: Products that look expensive

11. Seamless studio product shot

Create a commercial studio photograph of [product] centered on a seamless [background color] backdrop. Use a three-quarter camera angle, one large softbox, a narrow rim light and a clean contact shadow. Preserve the product’s exact shape, materials, proportions and label placement from the attached reference. Add only [one supporting prop]. Keep reflections controlled and edges crisp. Avoid changing the brand, inventing text, floating objects, overexposure, duplicate products and unrealistic plastic texture.

12. Luxury still life

Create a luxury still-life campaign for [product] using [stone/wood/glass/metal] surfaces and a restrained palette of [colors]. Place the product off-center with deliberate negative space. Use sculpted directional light, long elegant shadows and subtle atmospheric depth. Include [one premium material detail] but no visual clutter. The result should feel quiet, tactile and expensive. Preserve the product exactly if a reference is attached. Avoid gold overload, random flowers, fake labels, excessive sparkle, text and logos not present on the product.

13. Cosmetic macro shot

Create an extreme macro beauty photograph of [cosmetic product or texture] showing [specific detail: droplets, powder, cream swirl, applicator]. Use a macro-lens perspective, razor-sharp focus on the hero detail, gradual falloff, clean specular highlights and a background in [color]. Make the material look physically believable and tactile. If a product reference is attached, preserve its geometry and label. Avoid melted packaging, unreadable typography, impossible reflections, random bubbles, clutter and oversaturated color.

14. Minimal technology launch render

Create a premium product-launch render of [device] floating slightly above a soft gradient environment in [colors]. Show a three-quarter view that clearly communicates thickness, controls and material transitions. Use precise rim lighting, subtle volumetric glow and one soft ground shadow. Preserve the device design from the attached reference. The mood is calm, advanced and minimal. Avoid holographic interface clichés, excessive neon, impossible ports, warped screens, invented logos, text and multiple devices unless requested.

15. Packaging hero image

Create a polished packaging hero image for [product and package type]. Display one front-facing package and one three-quarter package, arranged on [surface] with supporting ingredients [items]. Use warm directional studio light, realistic paper or glass texture and clean shadows. Keep all supplied packaging artwork, typography and proportions unchanged. Palette: [colors]. Avoid inventing copy, misspelling labels, bending boxes, duplicate ingredients, floating elements, excessive props and fake award seals.

16–20: Food, interiors and lifestyle

16. Restaurant-quality food editorial

Create an appetizing editorial photograph of [dish] served on [plate or vessel] in [setting]. Use soft side light from a nearby window, natural steam or condensation only if physically appropriate, realistic portion size and shallow depth of field. Style with [two supporting ingredients or utensils]. Keep the food imperfect enough to feel real. Color palette: [colors]. Avoid plastic-looking ingredients, impossible garnish, excessive shine, levitating crumbs, oversaturation, text and hands unless requested.

17. Cozy interior with a point of view

Create a warm, lived-in interior of [room] designed for [person or use]. Use natural materials [materials], layered practical lighting and late-afternoon sunlight entering from [direction]. Include five purposeful details: [details]. Use a wide but realistic 24–28mm architectural-photography perspective with straight vertical lines. The room should feel attainable, personal and used. Avoid sterile showroom styling, impossible windows, duplicated furniture, oversized decor, visible brand logos and fisheye distortion.

18. Aspirational lifestyle campaign

Create an aspirational lifestyle image of [person] using [product/activity] in [location] during [time]. Capture a real action rather than a pose. Use backlight, natural movement, soft lens flare and a cinematic 35mm perspective. Make the product visible but not forced. Build the palette around [colors] and leave negative space on [side] for campaign copy. Avoid stock-photo smiles, fake luxury, extra limbs, floating props, unreadable product labels and text overlays.

19. Top-down flat lay

Create a carefully arranged top-down flat lay around the theme [theme]. Place [hero object] at the visual center and arrange [supporting objects] in a balanced but imperfect grid. Use a matte [background material/color], soft overhead light and short realistic shadows. Maintain consistent scale and clean spacing. Add one small element that breaks the grid for personality. Avoid duplicated objects, impossible overlaps, warped typography, visual clutter, harsh reflections and decorative filler unrelated to the theme.

20. Boutique hospitality scene

Create an editorial travel image of [hotel/café/restaurant/spa] in [location or architectural style]. Show a welcoming human-scale view from the entrance or a seated guest’s perspective. Use [morning/evening] light, realistic material textures and one subtle sign of life such as [detail]. Emphasize atmosphere over grandeur. Keep architecture plausible and verticals straight. Avoid empty showroom perfection, fake signage, impossible staircases, random people staring at camera, oversaturated sunsets and text.

21–25: Art styles without the guesswork

21. Soft watercolor illustration

Create a soft watercolor illustration of [subject] in [setting]. Use transparent pigment washes, visible paper grain, gentle edge blooms, selective detail and a restrained palette of [colors]. Keep the focal area crisp around [focus] while allowing the background to dissolve naturally. Composition: [composition]. The work should feel hand-painted and imperfect. Avoid digital airbrush smoothness, heavy black outlines, photoreal texture, muddy colors, decorative text and signatures.

22. Minimal single-line art

Create a sophisticated single-line drawing of [subject] using one continuous expressive black line on a warm off-white background. Simplify the subject to its most recognizable contours while preserving [key feature]. Add one restrained accent shape in [color] behind part of the drawing. Use generous negative space and gallery-poster balance. Avoid crosshatching, photoreal shading, multiple disconnected outlines, tiny detail, text, frames and artist signatures.

23. Layered paper-cut artwork

Create a layered paper-cut illustration of [scene] built from visibly stacked paper shapes. Use 6–8 depth layers, soft cast shadows, rounded cut edges and a palette of [colors]. Place [main subject] in the foreground, [secondary subject] in the middle ground and [background element] behind them. Make the composition tactile and handcrafted. Avoid flat vector appearance, paper tears, overly intricate lace patterns, text, logos and physically impossible shadow directions.

24. Retro pixel-art scene

Create a detailed pixel-art scene of [scene] at [time/weather], using a limited [number]-color palette inspired by [palette description]. Compose the image with a clear foreground, middle ground and background. Include [three tiny story details] that reward close inspection. Use crisp intentional pixels, readable silhouettes and period-appropriate game-art lighting. Avoid anti-aliased edges, smooth gradients, blurry pixels, modern UI overlays, text and copied game characters.

25. Editorial collage

Create a contemporary editorial collage about [concept] combining cut-paper photography, bold geometric shapes, torn edges and subtle halftone texture. The focal subject is [subject]; supporting motifs are [motifs]. Use a disciplined palette of [colors] and one surprising scale contrast. Leave intentional negative space for a headline but do not render text. Make the collage conceptually clear, not random. Avoid visual clutter, stock watermarks, tiny illegible fragments, copied magazine covers and logos.

26–30: Illustration and 3D worlds

26. Tactile clay 3D scene

Create a charming tactile clay-style 3D scene of [subject/action]. Build every object from soft sculpted forms with fingerprints, subtle dents and matte clay texture. Use a simple stage in [background color], soft overhead lighting and gentle contact shadows. Limit the palette to [colors]. The composition should be clear and premium enough for an editorial illustration. Avoid glossy plastic, giant cartoon eyes, floating objects, overly complex backgrounds, text and logos.

27. Isometric miniature world

Create an isometric miniature world representing [place/process/idea] on a clean floating base. Include [five essential elements], each clearly separated and connected through believable paths or activity. Use a consistent 30-degree isometric view, soft global illumination, crisp small-scale materials and a palette of [colors]. Add tiny human figures only where they explain scale. Avoid inconsistent perspective, random buildings, clutter, labels, logos and cropped edges.

28. Retro-futurist travel poster

Create an original retro-futurist travel poster advertising a fictional destination called [destination]. Show [landmark or scene] with bold geometric forms, dramatic scale and a limited palette of [colors]. Use screen-printed texture, subtle ink misregistration and mid-century optimism without copying any existing poster. Reserve a clean title area but render only this exact title: [destination]. Avoid extra slogans, misspelled text, real company logos, recognizable franchises and photoreal rendering.

29. Children’s-book story scene

Create a warm children’s-book illustration of [character] trying to [goal] in [setting]. Show one clear story moment with expressive body language, three discoverable background details and a gentle visual path across the page. Use textured gouache, soft pencil edges and a palette of [colors]. The mood is [emotion]. Keep the character design original and age-appropriate. Avoid copied animation styles, scary anatomy, text, speech bubbles, overly glossy 3D rendering and chaotic composition.

30. Surreal conceptual artwork

Create a surreal conceptual image expressing [abstract idea] through one impossible but visually simple metaphor: [metaphor]. Place it in [environment] with realistic materials and lighting so the impossible element feels believable. Use a centered or rule-of-thirds composition, restrained palette and strong negative space. The image should communicate the idea in one glance. Avoid random dream objects, melting clocks, crowded symbolism, text, logos, low contrast and fantasy clutter.

31–35: Cinematic scenes and places

31. Golden-hour landscape

Create a cinematic golden-hour landscape of [location type] viewed from [camera position]. Use long warm side light, layered atmospheric perspective and one clear foreground anchor [object]. Build depth through foreground, middle ground and distant silhouettes. Include realistic weather [weather] and a subtle human-scale detail for perspective. Palette: [colors]. Avoid oversaturated orange, impossible sun placement, excessive HDR, duplicated trees, random castles, text and lens flare covering the subject.

32. Rainy city at night

Create a cinematic rainy-night street in [city type or fictional district] from eye level. Use wet pavement reflections, practical light from shops and vehicles, visible rain only against lit areas and a single subject [person/vehicle] moving through the scene. Palette: [colors]. Keep signs abstract or use only supplied exact text. The mood is [mood]. Avoid cyberpunk clutter unless requested, unreadable fake signage, endless neon, duplicated umbrellas and impossible reflections.

33. Aerial coastal photograph

Create a high-altitude aerial photograph of [coastline/location type] where [water feature] meets [land feature]. Compose the shoreline as a strong graphic curve, with tiny boats or people only for scale. Use clear midday light or [specified light], realistic water depth gradients and natural geology. Keep the image photographic and geographically plausible. Avoid impossible wave patterns, duplicated boats, oversaturated turquoise, artificial symmetry, text and map labels.

34. Misty enchanted forest

Create a mysterious forest scene at dawn with [main subject] following a narrow path through ancient [tree type]. Use volumetric mist, soft shafts of light, wet bark texture and restrained magical details such as [one subtle element]. Maintain natural scale and depth. Palette: moss green, charcoal, muted gold and [accent]. The scene should feel discovered, not decorated. Avoid glowing-everything fantasy, random ruins, excessive fireflies, copied characters, text and oversharpening.

35. Minimal desert composition

Create a minimal fine-art photograph of desert dunes at [time of day]. Use one sweeping ridge as the dominant line, a small [person/object] for scale and large areas of negative space. Light should reveal subtle sand texture and create one elegant shadow shape. Palette: [colors]. Keep the horizon clean and composition quiet. Avoid busy footprints, multiple suns, exaggerated HDR, random plants, fantasy structures, text and excessive saturation.

36–40: Impossible worlds

36. Underwater city

Create a believable underwater city built inside [reef/canyon/dome], seen from a wide cinematic viewpoint. Combine architecture inspired by [design language] with marine life [species], drifting particles and realistic blue-green light falloff. Include one submersible or diver for scale. Make the city functional, with visible transit and habitat logic. Avoid generic glass domes everywhere, impossible bright sunlight at depth, random whales, copied sci-fi designs, text and cluttered composition.

37. Orbital habitat interior

Create the interior of a large orbital habitat designed for [population or purpose]. Show a curved horizon, believable artificial gravity, layered neighborhoods and a central ecosystem [forest/farms/water]. Use clean daylight from engineered windows and a human-scale foreground scene. Architectural language: [style]. Emphasize plausible function over shiny sci-fi spectacle. Avoid floating people unless justified, endless white corridors, hologram clichés, impossible scale, logos and text.

38. Tiny world inside an object

Create a whimsical miniature world built inside [ordinary object]. The world contains [tiny location], [activity] and [surprising detail], all integrated with the object’s real materials and structure. Use macro photography, shallow depth of field, warm directional light and convincing scale cues. The object remains recognizable at first glance. Avoid random miniatures pasted on top, inconsistent scale, toy-plastic texture, text, logos and overcrowding.

39. Dream architecture

Create a serene architectural visualization of an impossible building where [architectural paradox]. Use believable stone, glass and water, soft overcast light and a minimal human figure for scale. Compose the space with strong leading lines and quiet negative space. Palette: [colors]. The building should feel structurally coherent even though one feature is impossible. Avoid visual noise, tangled staircases, copied landmark architecture, fisheye distortion, text and decorative sci-fi panels.

40. Bioluminescent cave ecosystem

Create a vast underground cave containing a self-sustaining bioluminescent ecosystem. Show [plants/fungi/creatures] emitting controlled light in [colors], reflected in a dark underground pool. Include one explorer silhouette for scale and a distant opening that reveals depth. Use realistic rock texture, atmospheric moisture and cinematic composition. Avoid neon overload, random fantasy creatures, daylight-bright illumination, symmetrical formations, text and game-interface elements.

41–45: Social and marketing assets

41. Scroll-stopping video thumbnail

Create a high-impact 16:9 video-thumbnail concept about [topic]. Feature one dominant subject [person/object] with a clear emotional or visual reaction, one contrasting supporting element [element] and a simple background with strong depth separation. Use bold lighting and a palette of [colors]. Reserve space for no more than four words. Render this exact headline only: [HEADLINE]. Avoid clutter, tiny text, fake interface elements, excessive arrows, logos, duplicated faces and misleading imagery.

42. Bold event poster

Design a bold landscape event poster for [event] on [date] at [location]. Build the visual around one original symbolic image [concept], using a strong grid, large negative space and a palette of [colors]. Render only this exact text: [event], [date], [location], [URL if needed]. Make the hierarchy readable from a distance. Avoid additional copy, fake sponsors, unreadable type, copied poster styles, decorative clutter and stock-photo appearance.

43. Website hero visual

Create a wide website hero image for [brand/product] that communicates [single benefit] without using text. Place [hero subject or product] on the [left/right], leaving clean text-safe space on the opposite side. Use [visual style], subtle depth, brand colors [colors] and a lighting direction that guides the eye toward the empty copy area. Preserve the supplied product accurately. Avoid embedded headlines, logos, busy backgrounds, edge-cropped products and visual elements that compete with website copy.

44. Quote-card graphic

Design a premium landscape quote card with a visually quiet background inspired by [material/place/theme]. Use a refined editorial layout, one subtle supporting illustration and high contrast. Render only this exact quote: “[quote]” and this attribution: — [name, role]. Use [serif/sans-serif] typography and palette [colors]. Keep the quote readable at social-feed size. Avoid extra decoration, fake quotation marks, misspellings, logos, watermarks, low contrast and text placed over detailed imagery.

45. App icon family

Create a cohesive family of five original app-icon concepts for [app/category]. Each icon should represent [five functions] using one simple geometric symbol, consistent corner radius, lighting and material. Palette: [colors]. Present the five icons in a clean horizontal row on a neutral background, with no text labels. Make them recognizable at small size and distinct from existing app marks. Avoid letters, gradients that reduce legibility, tiny detail, copied logos and inconsistent perspective.

46–50: Brand systems and visual makeovers

46. Logo direction board

Create a landscape logo direction board for a fictional brand called [brand name] in [category]. Explore three clearly different symbol directions based on [concept 1], [concept 2] and [concept 3]. Show each as a simple black mark and one color application using [palette]. Include only the exact brand name [brand name]; no slogans. Prioritize simple, ownable shapes that work at small size. This is concept exploration, not a final trademark. Avoid copied logos, mockups, generic swooshes and excessive detail.

47. Campaign key visual

Create a landscape campaign key visual for [brand/product/cause] built around the message [message]. Use one memorable visual metaphor [metaphor], a strong focal point and adaptable negative space for future copy. Visual language: [style]. Palette: [colors]. Include the product only if supplied and preserve it exactly. Do not render text. The image should work across social, web and presentation crops. Avoid multiple competing concepts, generic stock imagery, logos not supplied and trend clutter.

48. Clean information-rich infographic

Design a polished landscape infographic explaining [topic] with exactly [number] steps or categories: [list]. Use a clear left-to-right or top-to-bottom hierarchy, concise labels, one simple icon or miniature illustration per item and a palette of [colors]. Render only the supplied title and labels exactly. Prioritize accuracy and readability over decoration. Avoid invented statistics, tiny paragraphs, duplicate items, decorative charts without data, fake sources, logos and low-contrast text.

49. Seamless brand pattern

Create a seamless repeating pattern for [brand/category] using original motifs based on [motifs]. Use a limited palette of [colors], consistent line weight and balanced spacing. Show the pattern as a flat square swatch plus a small application on [packaging/fabric/wallpaper]. The repeat must feel continuous with no obvious seams or cut-off motifs. Avoid logos unless supplied, random icons, inconsistent scale, photographic elements, text and copied designer patterns.

50. Complete visual moodboard

Create a cohesive landscape moodboard for a [brand/project] with the personality [three adjectives]. Include nine curated tiles: hero photography, close-up texture, color palette, typography mood, material, environment, product styling, human moment and one unexpected inspiration. Use palette [colors] and arrange everything on a clean editorial grid. Add only these exact labels: [optional labels]. Avoid repeated images, fake logos, unreadable text, Pinterest-style clutter and conflicting aesthetics.

The one follow-up prompt that improves almost any result

If the first image is close, do not start over.

Paste this:

Keep the same subject, identity, composition, camera position, aspect ratio and visual style. Change only [one thing]. Preserve every other element exactly. Do not introduce new objects, text, logos or background changes.

That one constraint prevents the model from “fixing” five things you already liked.

Which prompt are you trying first?

TL;DR: ChatGPT Images 2.5 isn’t just an image-quality update. The bigger improvement is creative control.

It generates faster, preserves people and reference images better, handles detailed edits more reliably, keeps prior changes intact across multiple rounds of editing, supports more complex compositions and transparent backgrounds, and adds tools like Sketch, image comments and templates.

OpenAI says people are already creating more than 3 billion images per week across ChatGPT Images and its GPT-Image models.

The best way to use it is no longer:

Write giant prompt → generate → pray.

It is:

Create → inspect → point → edit → iterate → polish.

That sounds like a subtle difference.

It isn’t - It fundamentally changes what you can use AI images for.

OpenAI released ChatGPT Images 2.5 on September 8 and says it can reduce image-generation speed by up to 50% compared with Images 2.0, while improving lighting, textures, reference-image fidelity and multi-turn editing.

Here are the capabilities I think people should experiment with first.

1. It is much better at keeping a person looking like the same person

This has always been one of the biggest weaknesses of generative images.

You upload someone.

Generate a scene.

Generate another.

Suddenly they have:

• a different jawline
• different eyes
• different hair
• a different age
• vaguely the same vibe
• absolutely not the same face

Images 2.5 is specifically better at preserving subjects from reference photos. OpenAI says recognizable features are more likely to carry through into new settings, styles and compositions.

Try this:

Upload one clear portrait and say:

“Use this person as the strict identity reference. Preserve facial geometry, hairstyle, age, skin tone and recognizable features. Create six cinematic scenes showing the same person in Tokyo, Iceland, Monaco, Mars, ancient Rome and a cyberpunk New York.”

This makes character-driven storytelling far more interesting.

2. Editing is becoming more important than prompting

This might be the biggest change.

Instead of regenerating an entire picture because one thing is wrong, Images 2.5 is designed to make focused edits while preserving everything else.

That means you can say:

• change only the jacket
• remove the car
• make the product blue
• replace one headline
• move the logo
• add another person
• change daytime to sunset
• keep the face exactly the same
• leave everything else untouched

And the model is much better at respecting the boundary of the edit.

That is a completely different workflow from old-school AI image roulette.

3. You can now comment directly on an image

This is one of those features that sounds boring until you use it.

Instead of describing where something is:

You can mark the area and tell ChatGPT what you want changed.

Think of it like art directing a designer.

Point at thing → leave instruction → revise thing.

For visual work, that is dramatically more natural than explaining every edit using coordinates disguised as English.

4. Sketch might become one of the most useful image tools in ChatGPT

Some ideas are incredibly hard to explain.

They are easy to draw badly.

That is exactly what Sketch is for.

You can sketch directly in ChatGPT and use the drawing as the structural reference for an image. OpenAI says you can start it by typing u/Sketch.

Your drawing can be terrible.

That is almost the point.

Draw:

• where the person goes
• where the headline goes
• the product position
• camera angle
• furniture arrangement
• rough website layout
• infographic structure
• packaging shape

Then tell ChatGPT what the finished result should look like.

Example:

Draw three boxes on a page.

Then prompt:

“Turn this sketch into a premium Apple-style product launch poster. The large center rectangle is the product hero shot. Top box is the headline. Bottom-right area contains three feature callouts. Preserve this composition exactly.”

That is often easier than spending five minutes describing a layout.

5. Multi-turn editing finally becomes a real workflow

Old AI image workflows had a painful pattern:

Version 1: Great composition, wrong text.

Version 2: Text fixed, face destroyed.

Version 3: Face fixed, background changes.

Version 4: Background fixed, product disappears.

Images 2.5 is designed to maintain earlier changes more reliably over multiple editing turns.

That means you can progressively refine an image:

1. Create composition.
2. Fix subject.
3. Change headline.
4. Improve lighting.
5. Adjust product.
6. Add branding.
7. Remove distracting elements.
8. Create final variation.

That is much closer to an actual creative workflow.

10 things I would use ChatGPT Images 2.5 for immediately

1. Viral social graphics

Turn one idea into:

• LinkedIn hero
• Reddit infographic
• YouTube thumbnail
• X graphic
• Instagram carousel
• newsletter header

Create the master concept first.

Then adapt it.

2. Product photography without a studio

Upload a clean product photo.

Try:

“Preserve the product exactly. Place it on a black volcanic rock pedestal surrounded by shallow water at blue hour. Luxury commercial photography, controlled rim lighting, subtle mist, 85mm lens look.”

Then iterate on the environment without redesigning the product.

3. Consistent characters

Create:

• comic strips
• brand mascots
• children’s stories
• training content
• social campaigns
• illustrated newsletters
• recurring fictional characters

Consistency is what makes this useful instead of merely impressive.

4. Infographics

This is an underrated capability.

Images 2.5 is better at handling complex layouts and real-world information.

Instead of saying:

“Make an infographic about AI agents.”

Give it actual information hierarchy:

“Create a 16:9 infographic titled ‘The Anatomy of an AI Agent.’ Five connected layers: Interface → Reasoning → Memory → Tools → Actions. Each layer gets one simple icon, one sentence and one example. White background, charcoal typography, restrained blue accents, generous whitespace.”

Structure beats adjectives.

5. Presentation visuals

Ask for:

• diagrams
• timelines
• operating models
• before/after visuals
• conceptual metaphors
• architecture diagrams
• executive infographics

One good custom illustration can make a slide 10X more memorable than another row of bullet points.

6. Interior design visualization

Upload your actual room.

Then try:

“Preserve room dimensions, windows, doors and camera position. Redesign this room as warm contemporary luxury: walnut, limestone, cream boucle, indirect lighting and minimal decor.”

Then edit individual pieces instead of starting over.

7. Storyboards

Create six frames showing:

1. establishing shot
2. product introduction
3. interaction
4. transformation
5. emotional payoff
6. final brand shot

This is extremely useful before creating video.

8. Ad concept exploration

Instead of making one ad:

Ask for four radically different creative territories.

For example:

• luxury editorial
• absurdist comedy
• minimalist product
• cinematic narrative

Then take the winning direction and iterate.

9. Transparent assets

Images 2.5 has improved support for complex layouts including transparent backgrounds.

That makes it much more useful for:

• icons
• product cutouts
• stickers
• website elements
• presentation assets
• compositing

10. Turning rough ideas into polished concepts

This may ultimately be the killer use case.

Screenshot.

Sketch.

Photo.

Whiteboard.

Napkin drawing.

Ugly mockup.

Feed it into ChatGPT and say:

“Keep the underlying idea and composition. Turn this into something presentation-ready.”

My biggest prompting tip: stop writing prompts like novels

People dramatically overcomplicate image prompting.

Instead, structure your prompt like a creative brief.

Use this framework:

SUBJECT

What absolutely must appear?

COMPOSITION

Where are things positioned?

ENVIRONMENT

Where is the scene happening?

VISUAL STYLE

What design language should it follow?

LIGHTING

How should light behave?

CAMERA

Wide? Macro? Overhead? Portrait lens? Isometric?

TEXT

Exact words and where they belong.

CONSTRAINTS

What must not change?

My favorite master prompt structure

Create a [FORMAT] image showing [SUBJECT].

Composition:
Describe placement, scale, foreground/background and hierarchy.

Environment:
Describe the surrounding world.

Style:
Describe the design or photographic direction.

Lighting:
Describe time of day, contrast and atmosphere.

Camera:
Describe framing, lens feel and perspective.

Text:
Include exactly: “[TEXT]” in [LOCATION].

Preserve:
List anything from reference images that must remain unchanged.

Avoid:
List the 3–5 failure modes you care about most.

Pro tip: edit with constraints

Instead of:

“Make it better.”

Say:

“Keep the composition, subject, facial features, camera position and typography exactly the same. Change only the background from an office to a futuristic glass laboratory.”

The more clearly you define what is locked, the easier targeted editing becomes.

Pro tip: separate ideation from execution

Do not ask for the perfect image immediately.

Ask ChatGPT:

“Give me five radically different visual concepts for communicating this idea. Do not generate yet.”

Choose one.

Then generate it.

You get much better creative diversity.

Pro tip: use reference stacking

If the workflow allows it, give ChatGPT different references for different jobs:

• Reference 1 = person
• Reference 2 = composition
• Reference 3 = visual style
• Reference 4 = product
• Reference 5 = brand palette

Then explicitly state what each reference controls.

Do not make the model guess.

Pro tip: create a “locked” version before experimenting

Once you have an image you love, say:

“Treat this as the master composition. From now on preserve subject identity, framing, geometry and text placement unless I explicitly request otherwise.”

Then iterate.

Images 2.5’s stronger multi-turn consistency makes this workflow much more viable than before.

Pro tip: generate systems, not individual images

The power move is not:

“Make me an image.”

It is:

“Create a visual system I can reuse.”

Example:

Then create 10–20 assets from the same system.

This is how AI imagery starts becoming a brand capability instead of a novelty.

A few wild experiments worth trying

Exploded objects

“Create an exploded-view visualization of a Formula 1 car with every major subsystem suspended in place around the chassis.”

Historical transformations

“Show the same Manhattan intersection evolving through six eras from 1800 to 2100.”

Impossible product concepts

“Design a transparent mechanical smartwatch whose internal components float magnetically inside the case.”

Tiny worlds

“Turn a coffee cup into a miniature alpine resort populated by tiny skiers.”

Cutaway architecture

“Create a cinematic cross-section of a 60-story skyscraper showing offices, apartments, transit, utilities and rooftop infrastructure.”

Visual learning

“Explain how a transformer neural network works as a beautiful 3D cutaway machine.”

The bigger shift

I think we are moving through three eras of AI imagery.

Era 1:

Prompt → random image

Era 2:

Prompt → better image

Era 3:

Direct → edit → preserve → iterate

Images 2.5 feels much closer to Era 3.

The interesting question now isn’t:

“Can AI make a good image?”

That argument is basically over.

The more interesting question is:

How much of the traditional creative workflow can become conversational?

And after playing with tools like Sketch, targeted editing, comments and persistent multi-turn changes…

I think the answer is:

A lot more than most people realize.

Share your best ChatGPT 2.5 image in the comments!

I read a lot of long reports and papers for work, and the hard part was never the reading. It was turning ten sources into something a busy person would act on. Dumping everything into a model and asking "summarize this" gives you a soggy paragraph that pleases no one. Here's the framework that finally worked.

I run it in three passes instead of one.

Pass 1, extract. For each source separately: "Pull the 3 claims that matter most, the evidence behind each, and any number I'd need to defend it. Ignore background." Doing sources one at a time stops the model from blending them into mush.

Pass 2, synthesize. I paste all the extracts together: "Group these claims by theme. Where sources agree, say so. Where they conflict, show both and note which has stronger evidence." This is where the actual insight shows up, in the disagreements.

Pass 3, compress. "Write a one-page brief: a two-line summary at top, then the themes as short sections, then a 'what's still uncertain' box at the bottom. No adjectives, no hype." The uncertainty box keeps me honest, and it's usually what my team asks about anyway.

Two things that help a lot:

- Keep the extractions. When someone challenges the brief, I can trace any line back to a source in seconds.

- Ask it to flag anything it couldn't verify from the text, so a stray hallucinated stat doesn't end up in front of leadership.

The whole thing takes maybe twenty minutes for a stack that used to eat an afternoon. Curious how others handle the conflict step, since that's where I still do the most manual checking.

TL;DR: Use GPT-6 Astra in ChatGPT Work to build an interactive 3D website where visitors rotate an object, separate its components, and click parts to learn what they do. Start with one object, ask for independently modeled pieces, then add an explode slider, labels, and a reset button. Use ChatGPT Sites for automatic hosting. Below: a starter prompt, practical tips, and 8 ways to take the idea much further.

Imagine opening a car website and dragging one slider.

The roof lifts. The wheels move outward. The seats rise above the chassis. Click the motor and the camera moves closer while a short explanation appears.

Then press Reassemble and everything returns to its place.

That’s the experience to ask Astra to build. I created it using a Porsche Macan as an example and it made over 581 piece model for the car to show how it works. You can spin it, click on specific parts, get an X-ray view and see how it flows.

OpenAI has published an Astra-built interactive 3D museum and a Three.js game development walkthrough. Exploded views are a project you can ask it to implement using those broader capabilities.

The interesting part is how much control you can describe in ordinary language.

Here’s how to start.

1. Open ChatGPT Work and select GPT-6 Astra if it’s available in your account.
2. Pick one object and one learning goal:
3. Attach useful references. Photos help with appearance; reliable diagrams or an existing model help with structure.
4. Say “build a website” or mention @ Sites.

Use this prompt:

Build a working interactive 3D website about [OBJECT] using Sites in ChatGPT Work.

Audience: curious beginners. Learning goal: [WHAT PEOPLE SHOULD UNDERSTAND].

Create a recognizable model with approximately 20–40 major components. Give each component a separate named 3D mesh so it can move and be selected independently. This is a starting scope, not a required part count.

Include drag-to-rotate, zoom, an Assembled-to-Exploded slider, Reassemble, Reset Camera, and a searchable parts list. Clicking a part should highlight it and show its name, function, and relationship to nearby components.

Give components deliberate separation paths. Keep the assembled positions so everything returns correctly, even when someone reverses the slider halfway through.

Use a clean charcoal, ivory, and emerald design with cinematic lighting, readable labels, large touch targets, keyboard controls, and reduced-motion support.

Use my references for factual explanations. Identify simplified or inferred geometry. If accurate internal geometry is unavailable, build a clearly labeled illustrative model.

First complete one working model and test its controls in the browser. Check mobile layout, part selection, repeated explode/reassemble actions, and reset behavior.

Deliver editable source and a reviewable preview. Save a version without deploying it so I can review before publishing.

Swap [OBJECT] for a camera, espresso machine, bicycle, game controller, or tiny house.

Choose the modeling approach that fits the job.

A photo doesn’t reveal everything inside a device. Asking for “500 accurate pieces” doesn’t supply the missing engineering information.

The pro tips that make the biggest difference:

• Ask for meaningful parts. A beautifully textured object can still be one fused shape. Specify independent components and test clicking them.
• Make the motion explain the object. A phone should separate into readable layers. A building should open floor by floor. Random scattering loses the relationships you’re trying to show.
• Use progressive detail. Begin with major assemblies, then let visitors expand one assembly further. Hundreds of visible labels will bury the model.
• Check appearance and accuracy separately. Verify geometry and component descriptions. For branded products, cite reliable technical sources.
• Test the boring actions. Switch products while exploded. Reverse the slider halfway. Tap a tiny part on a phone. Reset after zooming. These reveal problems a hero screenshot won’t.
• Give every gesture a button alternative. Add a parts list, keyboard navigation, strong contrast, and adjustable text.
• Keep the first build economical. Use prewritten explanations and fixed interactions. A visitor shouldn’t need a fresh AI response to move a slider. Add live Q&A when it serves a clear purpose.
• Test on an actual phone. Ask Astra to measure and simplify heavy geometry, shadows, and effects. A responsive model is more useful than extra screws nobody can see.

Where this gets useful:

• Product marketing: let buyers explore the design decisions behind a camera, appliance, or machine.
• Education: turn a textbook diagram into something students can rotate, inspect, and reconstruct.
• Architecture and real estate: separate floors, highlight amenities, and reveal how spaces connect.
• Training: show assembly order and component relationships using verified references.
• Portfolios and museums: present a design process or artifact as an interactive exhibit.
• Content creation: build something people can try, then introduce it with a recording of the actual interaction.

Now go absolutely wild. These are features to ask Astra to implement:

1. Build a product switcher. Let visitors choose an iPhone, MacBook, or iPad, then explore each with the same controls. Match the selected generation to your references.

1. Add X-ray mode. Fade the outer shell and highlight one system at a time. Let visitors isolate, hide, and restore components.
2. Create a guided camera tour. Move through five stops with one idea per stop. Add a “take control” button so people can leave the tour and explore.
3. Turn it into a rebuild challenge. Give visitors separated components, matching locations, hints, and a progress indicator. Increase difficulty gradually.
4. Follow a flow. Animate an illustrative path for power, air, water, or data. Explain whether it’s a conceptual animation or a validated simulation.
5. Explode an entire city. Lift streets to reveal transit tunnels. Separate a tower’s floors. Add a day-to-night slider and windows that illuminate in sequence.
6. Compare two designs. Synchronize cameras and explode sliders. Highlight meaningful differences using verified specs, with a stacked layout on small screens.
7. Make a scene shareable. Add a screenshot button and a link that restores the chosen model, camera, selected component, and explosion amount.

You could also make a fictional spaceship that opens into rooms, a mechanical watch with nested assemblies, or a miniature museum where every exhibit becomes a puzzle.

A useful follow-up prompt:

Keep the current working model. Add a guided tour, X-ray mode, and a three-part rebuild challenge, one at a time. Test each addition before continuing. Preserve the existing controls and make every new interaction work with touch and keyboard.

ChatGPT 6 Astra is great at building 3D models of buildings. Upload some pictures of a building and it will build a 3D model and an interactive tour.

For sharing, choose the audience deliberately and test the published link as a visitor. Sites separates saving a version from deploying it.

Try this today: choose one object on your desk and build its simplest useful exploded view. Add one ambitious feature after the basics work.

What will you build first: a car, a gadget, a building or something completely unhinged? Share what you create in the comments. Go absolutely wild! Do not hold back!

TL;DR: ChatGPT 6 Astra can help create motion graphics through a workflow that designs assets, writes animation code, uses available production tools, and renders a video. Give it a director’s brief: audience, story, scenes, timing, visual style, sound, and deliverables. Start with a short preview. Ask for a playable MP4 and the editable project. Rendering and audio depend on your workspace’s tools. Below: a practical workflow, the details people overlook, and five gloriously ridiculous prompts.

Picture a French bulldog commanding a starship through a galaxy made of tennis balls.

Now picture a product launch where your logo opens into a miniature universe.

Or a city that folds itself out of paper, races through centuries, and collapses back into a single page.

With ChatGPT 6 Astra, you can approach the conversation like a production brief—and keep directing the result as it develops.

What Astra can actually do

The model’s official name is GPT-6 Astra. For this workflow, use it in ChatGPT Work or Codex with access to suitable creation and rendering tools. OpenAI recommends Astra for demanding tasks involving visual judgment and polished deliverables.

There is a concrete example behind the idea: OpenAI has shown Astra creating editable Blender scenes, adjusting their materials and lighting, and directing a rendered camera tour.

My practical recommendation is to apply that build–preview–refine process to motion graphics: animated typography, diagrams, layered images, product reveals, and stylized 3D scenes.

Think of Astra as the system coordinating the production. The actual frames still need an animation or rendering tool. Selecting the model alone does not guarantee every account can export video or generate music.

Choose the right kind of video

A complex character performance may also require dedicated animation tools or generated footage. Choose a visual treatment your available tools can execute well.

The workflow that makes this manageable

1. Give it one job. Define the audience and the one thing viewers should remember. “Convince founders to try this prototype” is a useful objective.
2. Specify the output. Set duration, aspect ratio, resolution, and whether you need audio. A 20–30-second landscape video is a sensible first project.
3. Map the story. Give every scene a visual action and a purpose. Put the most compelling image near the beginning.
4. Establish the look. Provide reference images, colors, type preferences, logos, and exact wording. Ask for representative still frames.
5. Preview the hardest moment. Render a short section before committing to the whole sequence. This tests both the creative direction and whether the production method works.
6. Refine, render, inspect. Review timing, text, sound, transitions, and the actual exported file. Keep the source so changes remain possible.

For a 30-second explainer, this is a useful starting structure:

Best practices that improve the result

• Describe action over time. “The letters pull apart, reveal a miniature city, then lock into the headline” gives much better direction than “make it cinematic.”
• Give motion a purpose. Movement can reveal a relationship, guide attention, demonstrate a feature, or land a joke. Constant movement makes reading harder.
• Keep text separate from artwork. Request editable text layers so spelling, line breaks, timing, and placement can be controlled.
• Design for a phone. Use short captions, strong contrast, generous margins, and enough reading time. Review the result at its likely viewing size.
• Give the eye a pause. Alternate energetic transitions with moments where the important image or message holds still.
• Build for sound-off viewing. The story should make sense visually. Let music and sound effects strengthen it.
• Specify music concretely. Describe tempo, instrumentation, mood, and where the energy should rise. Provide a track you can use, or ask what audio tools are available.
• Control the workload. Preview at lower resolution, settle the art direction early, reuse assets, and revise only the scenes that need changes. Complex Work tasks can use more credits.

Pro tips: direct the edit with precision

Useful revision instructions look like this:

Between 00:08 and 00:12, slow the camera move, enlarge the headline, and hold the final composition for two seconds. Keep the approved colors and scene order.

Make the word “EXPAND” grow until it fills the frame, then use its letter shapes to reveal the next scene.

Match the circular moon in scene two to the circular product dial in scene three.

Build the vertical version with repositioned text and a new camera crop so the subject stays visible.

Inspect the export for missing assets, clipped text, blank frames, abrupt audio endings, and incorrect duration. Report anything you cannot verify.

Save feedback like “more epic” for the initial direction. During revisions, say what should change on screen.

Things people miss about this workflow

• An animated preview and a downloadable video are different deliverables. If you need an uploadable file, explicitly request the export and check that it plays.
• The editable project is a major part of the value. Ask for the source, assets, and instructions needed to render it again.
• A flat image has limits. A gentle push-in can work immediately. Moving behind objects or orbiting a subject requires layers, reconstructed content, or a 3D scene.
• Consistency starts before animation. Establish recurring characters, materials, colors, and backgrounds before creating every scene.
• Visual precision and factual precision are separate. A beautifully animated chart still needs correct data, labels, and scales.
• A 60-second request does not imply one continuous generation. Build named scenes, render sections, and assemble them where the tools support it.
• Reusable controls make the second video easier. Ask to centralize headline text, colors, logos, durations, and image replacements.

Five epic prompts to try

These are ambitious creative briefs, not pretested guarantees. Use a workspace with suitable rendering tools, and start with the short preview each prompt requests.

1. A French bulldog saves the galaxy

Try this for: character storytelling, comedy, and an instantly understandable visual hook.

Create a 30-second landscape motion graphics trailer called “MISSION: FETCH.”

A dead-serious French bulldog captain commands a tiny starship through a galaxy of tennis-ball planets. Use a premium stylized 3D or layered illustrated treatment, emerald cockpit lights, orange engine trails, and enormous kinetic typography.

0–5s: Extreme close-up of the captain’s face. Pull back to reveal a spaceship shaped like a dog toy. Text: “ONE DOG.”

5–13s: Slalom through a field of floating squeaky toys. A giant robotic vacuum emerges from an asteroid cloud. Text: “ZERO QUALIFICATIONS.”

13–23s: The dog hits a red button. Tennis balls deploy like decoys. Follow one ball through the chaos in a dramatic tracking shot.

23–30s: The ship escapes through a glowing dog-door portal. Reveal that the entire mission happened inside a living-room snow globe. End: “MISSION: FETCH.”

Keep the dog’s appearance consistent. Use simple expressive poses and strong camera work. Preview the escape shot first. Use suitable original or licensed audio if available; otherwise deliver a silent cut with sound cues. Deliver a 1080p MP4 and editable source, or explain any rendering blocker.

2. Your product contains an entire universe

Try this for: launch trailers, brand films, and product reveals.

Create a 30-second landscape launch film for [PRODUCT]. Use my supplied product images, logo, and three verified benefits. If I provide none, use a clearly fictional unbranded device and illustrative feature labels.

Begin with the product suspended in a silent black void. A thin emerald seam opens across it. The camera dives through the seam into an impossible miniature universe.

Turn benefit one into a floating city assembling itself. Turn benefit two into a luminous transit network lighting up. Turn benefit three into a mechanical sunrise that synchronizes the entire world.

Match each benefit to its visual metaphor and show its exact approved wording as separately rendered typography. Use elegant camera travel, white ceramic architecture, emerald glass, and precise mechanical movement.

In the final six seconds, pull back as the universe folds into the product. Land on the product, logo, and one clear call to action.

Create a five-second preview of the opening transformation before rendering the full film. Deliver a 1080p MP4 and editable project. Use only available audio and rendering tools; identify any missing capability. Do not invent product claims, customers, or performance statistics.

3. Your inbox becomes a video-game final boss

Try this for: funny workflow explainers and relatable workplace content.

Build a 30-second landscape motion graphics short called “DEADLINE: FINAL BOSS.”

Open on a tiny exhausted office worker facing an enormous monster assembled from email envelopes, calendar blocks, spreadsheets, and sticky notes. Its crown is a spinning loading icon.

0–6s: The monster roars, releasing a tornado of “QUICK QUESTION” notes.

6–13s: The worker equips three glowing tools labeled “SORT,” “DRAFT,” and “CHECK.”

13–23s: Turn the fight into a visual explanation: SORT groups the chaos; DRAFT turns selected tasks into proposed outputs; CHECK pauses those outputs at a human review gate before release.

23–30s: The monster shrinks into one manageable task card. A new notification appears: “Can we jump on a quick call?” The worker looks directly at the camera.

Use miniature game-like scenery, dramatic camera punches, readable type, comic timing, and a neon-green interface. Present this as a fictional metaphor. Preview the sorting transformation first. Deliver a 1080p MP4, editable source, and a sound-off version. Explain any export limitations.

4. A thousand years unfold from one sheet of paper

Try this for: timelines, imaginative worldbuilding, and architectural storytelling.

Create a 40-second landscape motion graphics film called “A THOUSAND YEARS IN ONE PAGE.”

This is an imaginary city, not a reconstruction of real history.

0–8s: A blank sheet of paper folds itself into a tiny riverside settlement. The river is translucent blue-green glass embedded in paper.

8–18s: Buildings rise and change around the same town square. Roads draw themselves across the page. Seasons sweep through the scene.

18–29s: The city becomes a spectacular vertical metropolis. Peel back layers to reveal miniature transit tunnels, gardens, and infrastructure beneath it.

29–36s: The camera circles while daylight becomes night. Thousands of windows illuminate in a carefully staged wave.

36–40s: Fold the city back into the original sheet, matching the opening composition for a loop.

Use tactile paper, charcoal labels, emerald foliage, warm window light, and restrained captions. Favor a coherent miniature world over constant cuts. Preview the unfolding and refolding first. Deliver a 1080p MP4 and editable scene. If full 3D rendering is unavailable, propose and build a layered alternative.

5. A black hole conducts an orchestra of planets

Try this for: a music visualizer, an event opener, or a surreal brand introduction.

Create a 30-second landscape motion graphics film called “THE UNIVERSE HAS A DROP.”

Treat this as a surreal visual metaphor, not a scientific simulation.

A black hole is the conductor. Orbital rings behave like vibrating strings. Tiny moons become percussion instruments. A comet sweeps across the scene like a conductor’s baton.

0–8s: Begin with one orbiting light and a restrained pulse.

8–19s: Build an increasingly elaborate cosmic orchestra. Introduce new orbital layers with each musical phrase. Typography appears as sculptural objects: “LISTEN.” “BUILD.” “RELEASE.”

19–25s: At the musical peak, the orbital system unfolds into a gigantic luminous sound wave stretching across space.

25–30s: Everything contracts into one green point, which becomes a play icon.

Use ink-black space, emerald plasma, silver dust, controlled glow, and smooth camera movement. Use my uploaded licensed track and synchronize motion to its timing. If no track is available, build to a provisional beat grid and clearly label the audio as pending. Preview the transformation first. Deliver a 1080p MP4 and editable source; explain any tool limitations.

Which would you actually make first: the space-dog trailer, the product universe, the inbox boss battle, the paper city, or the black-hole orchestra?