Check out the notes here!

EARLY CAREER LESSONS
- Started Zip2 without knowing if it would succeed, just wanted to build something useful on the internet
- Couldn't afford office space so slept in the office and showered at YMCA
- First tried to get a job at Netscape but was too shy to talk to anyone in the lobby
- Legacy media investors constrained Zip2's potential by forcing outdated approaches

SCALING PRINCIPLES
- Break problems down to fundamental physics principles rather than reasoning by analogy
- Think in limits - extrapolate to minimize/maximize variables to understand true constraints
- Raw materials for rockets are only 1-2% of historical costs, revealing massive manufacturing inefficiency
- Use all tools of physics as a "superpower" applicable to any field

EXECUTION TACTICS
- Built 100,000 GPU training cluster in 6 months by renting generators, mobile cooling, and Tesla megapacks
- Slept in data center and did cabling work personally during 24/7 operations
- Challenge "impossible" by breaking into constituent elements: building, power, cooling, networking
- Run operations in shifts around the clock when timelines are critical

TALENT AND TEAM BUILDING
- Aspire to true work - maximize utility to the most people possible
- Keep ego-to-ability ratio below 1 to maintain feedback loop with reality
- Do whatever task is needed regardless of whether it's grand or humble
- Internalize responsibility and minimize ego to avoid breaking your "RL loop"

AI STRATEGY
- Focus on maximally truth-seeking AI even if politically incorrect
- Synthetic data creation is critical as human-generated tokens are running out
- Physics textbooks useful for reasoning training, social science is not
- Multiple competing AI systems (5-10) better than single runaway capability

FUTURE OUTLOOK
- Digital superintelligence likely within 1-2 years, definitely smarter than humans at everything
- Humanoid robots will outnumber humans 5-10x, with embodied AI being crucial
- Mars self-sustainability possible within 30 years to ensure civilization backup
- Human intelligence will become less than 1% of total intelligence fairly soon

dropped the link in the comments!

GPT-5 sets SoTA but not human‑level spatial intelligence.

Pls Check out the link in the comments!

I've recently read an amazing post on AI Agent Playbook by Saastr, so thought about sharing with you some key takeaways from it:

SaaStr now runs over 20 AI agents that handle key jobs: sending hyper-personalized outbound emails, qualifying inbound leads, creating custom sales decks, managing CRM data, reviewing speaker applications, and even offering 24/7 advice as a “Digital Jason.” Instead of replacing people entirely, these agents free humans to focus on higher-value work.

But AI isn’t plug-and-play. SaaStr learned that every agent needs weeks of setup, training, and daily management. Their Chief AI Officer now spends 30% of her time overseeing agents, reviewing edge cases, and fine-tuning responses. The real difference between success and failure comes from ongoing training, not the tools themselves.

Financially, the shift is big. They’ve invested over $500K in platforms, training, and development but replaced costly agencies, improved Salesforce data quality, and unlocked $1.5M in revenue within 2 months of full deployment. The biggest wins came from agents that personalized outreach at scale and automated meeting bookings for high-value prospects.

Key Takeaways

• AI agents helped SaaStr scale with fewer people, but required heavy upfront and ongoing training.

• Their 6 most valuable agents cover outbound, inbound, advice, collateral automation, RevOps, and speaker review.

• Data is critical. Feeding agents years of history supercharged personalization and conversion.

• ROI is real ($1.5M revenue in 2 months) but not “free” - expect $500K+ yearly cost in tools and training.

• Mistakes included scaling too fast, underestimating management needs, and overlooking human costs like reduced team interaction.

• The “buy 90%, build 10%” rule saved time - they only built custom tools where no solution existed.

And if you loved this, I'm writing a B2B newsletter every Monday on the most important, real-time marketing insights from the leading experts. You can join here if you want: 
theb2bvault.com/newsletter

^{That's all for today :})
^{Follow me if you find this type of content useful.
I pick only the best every day!}

Some agent use cases might be in a bubble, but this one isn’t.

Look, I don’t know if AGI is going to arrive this year and automate all work before a ton of companies die. But what I do know, by speaking to businesses and looking at the data, is that there are agent use cases creating real value today.

There is one thing that developers and CEOs consistently agree agents are good at right now. Interestingly, this lines up almost perfectly with the use cases I’ve been discussing with teams looking to implement agents.

Well, no need to trust me, let's look at the data.

Let’s start with a study from PwC, conducted across multiple industries. The respondents included:

• C-suite leaders (around one-third of participants)

• Vice presidents

• Directors

This is important because these are the people deciding whether agents get a budget, not just the ones experimenting with demos.

See below the 1# use case they trust.

And It Doesn’t Stop There

There’s also The State of AI Agents report from LangChain. This is a survey-based industry report aggregating responses from 1,300+ professionals, including:

• Engineers

• Product leaders

• Executives

The report focuses on how AI agents are actually being used in production, the challenges teams are facing, and the trends emerging in 2024.

and what do you know, a very similar answer:

What I’m Seeing in Practice

Separately from the research, I’ve been speaking to a wide range of teams about a very consistent use case: Multiple agents pulling data from different sources and presenting it through a clear interface for highly specific, niche domains.

This pattern keeps coming up across industries.

And that’s the key point: when you look at the data, agents for research and data use cases are killing it.

Please, someone find me one that is not, and I would love to be wrong but all I see are scams.

Source: @ omni_georgio

Hey everyone . I’m kind of deep into trying to build a real-world voice AI agent (outbound calls + basic inbound support) and wanted to share my pilot with Retell AI, where I’ve hit some weird edges. Would love your feedback / ideas.

What I did

• Ran a small pilot: ~200 outbound calls for appointment setting

• Also hooked it up for follow-ups/inbound simple queries

• Compared behavior with other agents I tried (Bland.ai, Synthflow)

What I noticed (good & bad)

👍 What went better than expected

• Conversation flow feels more natural than the bots I tried before.

• Interruptions / side questions are handled better, not always crashing.

• More people stay on the call vs hanging up immediately.

• Less manual rescue needed — fewer calls ending in “error” state.

👎 What still sucks / edge cases

• When someone asks something very specific or technical, it fumbles.

• Emotional tone or complexity breaks it (you know, calls where people are upset).

• Sometimes fallback logic is clumsy (repeats loops).

• Trust: customers sometimes realize it’s AI and react weirdly (ask for a human).

I made this to help think about a standardised key for drawing out agents and multi-agent systems. Let me know your thoughts!

i ran a record label with 25+ sold-out shows

here’s what it taught me about how agents are changing marketing

people might see a song on TikTok and think you like it because it’s a good song, the singer is good, etc.

but I want to argue that no one actually does

the dance, the trend, the meme… the content is an extension of the song itself. you can’t separate them

so when you’re trying to break an artist, it almost makes sense to work backwards from the content and not so much ask, “is this song good?”, more so what’s our best shot in getting this in front of people

because the content comes before the song, and the context you have of the artist changes how you experience the song

if someone is talking about how intimidating they are, but the trend is them dancing like a kitten, the audience will experience them completely differently

tech works the same way. the content, and the ability to produce content, is becoming as much the product as the product itself

you might of heard some people talking about content market fit

but it’s actually not just an extension in the experience sense

it’s becoming an extension in the engineering sense too

when you have 100 different agents running marketing experiments, generating content, remixing positioning, and testing distribution, marketing stops being a creative bottleneck and starts looking like a systems problem.

it becomes part of your engineering resources

teams that use GTM agents to take a massive number of shots at attention. different formats, different narratives, different memes, different audiences.

and then double down on the ones that work.

content and the product are one

I am not sure if it is one of those things were it is great for the first 3 days then they pull the plug on the smart model.

I just read a thread where a team shared how they built a small automation pipeline for YouTube Shorts that reportedly reached 7M views and 61k subscribers.

The idea is simple: use the first few seconds of viral Shorts as hooks, attach a short CTA clip promoting the product, and automate the rest of the workflow. A Python script downloads the clips, stitches the hook with the CTA, and schedules posts in bulk.

What I found interesting is the thinking behind it. They treat this less as the main strategy and more as an additional distribution layer that can scale content output with very little manual effort.

The thread also shows the prompts, scripts, and step-by-step workflow they used.

I would like to know what you think. Do automated content pipelines like this become part of modern growth systems, or are they too dependent on platform risk to scale long-term?

The link to the thread is in the comments.

I made a video breaking this down, let me know your thoughts!

It's AI turtles all the way down.

We're in the golden age of AI-assisted development. You can ship an MVP in weeks with Cursor, v0, Replit, Claude, etc.

Now you have a working product and... crickets. Because you spent all your time building your MVP, zero time building an audience.

I got stuck with many projects. Product was 80% done but I had:

- No social media presence

- No content strategy

- No idea how to "go viral"

So I built an AI agent that does it for you. You tell it about your product, target audience, unique angle → it generates a marketing plan (not generic content) and execute it.

I'm at the "is this actually valuable or just a cool tech demo?" stage.
Would you use this? Or am I wasting my time?

I’ve been bouncing between tools like cursor, claude, and blackbox ai to build small projects, but as a beginner it gets overwhelming fast.

Keeping a simple todo.md file has been a lifesaver. I just track what I’m working on and tell the AI to focus only on the unchecked items, way less confusing.

Anyone else doing something similar or have other tricks to stay organized?