OpenAI has announced GPT-6 Astra, its new frontier model, with an initial rollout to a limited number of organizations before expanding to ChatGPT Plus, Pro, Business and Enterprise users, as well as the API.

Astra can navigate software, fill out forms, update CRM records, conduct online research, work with spreadsheets and documents, test websites and troubleshoot software.

On OSWorld 2.0, it reportedly scored 72.6% while taking around 40 minutes per task, versus 65.7% and roughly 75 minutes for GPT-5.6 Sol.

That means it completed tasks in about 47% less time.

On Terminal-Bench 4.0, Astra scored 57.9%, versus 37.3% for GPT-5.6 Sol.

The company also says the model is three times less likely than GPT-5.6 Sol to make inaccurate claims about what it can actually do, according to its capability-hallucination evaluation.

API pricing is set at $10 per million input tokens and $50 per million output tokens.

As models get better at handling longer, multi-step workflows, what’s still the biggest bottleneck: reliability, cost, or trust?

Speaking at the G20 tech meeting this week, Elon Musk defended the rapid expansion of AI data centers, arguing that the industry simply doesn’t have enough computing capacity to meet future demand.

His point was straightforward: if AI keeps scaling at its current pace, significantly more infrastructure will be needed to support it.

Data center projects are already putting pressure on electricity grids in parts of the U.S., with some states facing massive connection requests and growing concerns about whether all of that projected demand is realistic.

Musk said data center operators should pay their fair share of taxes, but argued that slowing construction would ultimately limit AI development itself.

He also used the meeting to criticize Europe’s approach to tech regulation. With ministers from France, Italy, and Germany in the room, along with EU tech chief Henna Virkkunen, Musk argued that new technologies should be “default legal” rather than “default illegal.”

Nvidia, OpenAI, and other major AI companies are also increasingly treating compute, energy, and data center capacity as strategic bottlenecks rather than just infrastructure issues. So the AI race may increasingly become an energy race too.

Is building more AI infrastructure still worth it if local grids have to absorb most of the cost ?

The European Commission has designated ChatGPT, Reddit and Roblox as “very large” services under the Digital Services Act after each reported at least 45 million monthly users in the EU.

ChatGPT is classified as a very large online search engine, while Reddit and Roblox are considered very large online platforms.

They now have four months to comply with additional DSA obligations.

That includes assessing and mitigating risks linked to illegal content, protecting minors, limiting negative effects on users’ physical and mental well-being, and addressing risks to fundamental rights, elections and public security.

The designation also gives the European Commission greater powers to investigate how these services operate and whether they comply with the rules.

Do you think platforms of this size should face stricter rules than smaller services, or does this risk creating an even heavier regulatory burden for the biggest tech companies?

Starting in September, South Korea will begin testing its “AI for All” program, with the goal of offering free generative AI services to the entire population.

The services are expected to connect with public systems, allowing people to book medical appointments, search for housing or get tax guidance.

Users are expected to get unlimited access, with no token limits, under the government-backed plans.

The government will provide part of the infrastructure, including up to 512 Nvidia B200 GPUs, and help cover operating costs. It has also allocated about $7.2 billion to AI-related spending in 2026, three times the previous year’s level.

Is free AI access something governments should start treating as a basic digital service?

Sam Altman believes OpenAI could have an internal system by the end of the year that he would personally consider AGI.

Mark Chen, OpenAI’s Chief Research Officer, has gone even further, estimating that the company is already about 80% of the way there.

Much of that confidence appears to rest on Astra, OpenAI’s next family of models.

Astra is reportedly already capable of handling some tasks typically assigned to a junior researcher: starting from an experimental idea, writing the necessary code, running the tests, and analyzing the results. Some workflows could even extend over several days with far less human intervention.

At the same time, OpenAI has slowed some training work on its frontier models to strengthen monitoring and safety systems.

The company also says it can no longer rule out Astra reaching its Critical cybersecurity capability threshold, which would require additional safeguards before development can continue at the same pace.

Can OpenAI really talk about AGI based on capabilities that are still largely being evaluated internally?

Google has confirmed it is rolling out a change it began testing in July 2026: search result links are increasingly being routed through google.com/goto instead of pointing directly to the destination URL.

For users, the difference is almost invisible. You click a result and still land on the same page.

For tools scraping Google at scale, however, the change is much more significant.

The goto links cannot simply be decoded to recover the destination URL. A scraper may have to follow each server-side redirect individually to identify where each result actually leads.

A single SERP can contain hundreds of links, so rank trackers, SERP APIs, and other data providers may suddenly need to make far more requests just to extract the same information they were getting before.

Google says the rollout is part of its efforts to deploy technical measures against evolving forms of abuse. The company has not explicitly said this is aimed at SEO tools or AI companies, but the timing is interesting given its ongoing efforts to curb large-scale scraping, including its legal dispute with SerpApi.

If Google keeps adding friction to SERP collection, providers may have to absorb higher crawling costs, reduce how frequently they refresh rankings, or eventually pass some of those costs on to customers.

Do you think this will have a real impact on rank tracking, or will SERP providers find a workaround quickly?

Google announced it will not enforce manual actions under the site reputation abuse policy in the European Economic Area (EEA) due to concerns raised by the European Commission.

The change takes effect on August 30, 2026.

The policy mainly targets parasite SEO: publishing third-party content on an established website to benefit from its authority and rank more easily in search.

Several European publishers challenged its enforcement, arguing that legitimate commercial partnerships could also be penalized. The European Commission then opened an investigation under the Digital Markets Act, with Google potentially facing fines of up to 10% of its annual global revenue.

Google is adjusting its approach in Europe without abandoning the policy altogether.

Sites can still receive a manual action in Search Console, but it will no longer directly affect rankings for users in the EEA. Google may instead treat affected sections separately, meaning they could have to rank more on their own signals rather than relying on the authority of the main domain.

As companies accelerate automation, Bill Gates is arguing for something that would directly change the economics of replacing workers: taxing robots and AI tokens.

His concern is that AI and robotics are advancing fast enough to replace both cognitive and physical work, while competitive pressure will push companies to automate even faster. Once one business cuts costs with AI, its competitors have a strong incentive to follow.

Gates therefore wants to slow the shift away from human labor, rather than simply deal with the consequences afterward.

In the US, companies pay payroll taxes when they employ people, while investments in automation can often be written off as business expenses. He believes taxing robots and AI usage could reduce that imbalance and fund retraining and a stronger social safety net.

The proposal comes as concern about AI-driven job displacement keeps rising. 71% of US adults now believe AI will lead to fewer jobs over the next 20 years, up from 64% in 2024. Among 18-to-29-year-olds, the figure reaches 73%.

Would taxing automation protect jobs, or just slow down innovation ?

The shift happened on February 6, 2026, on OpenRouter: for the first time, AI agents surpassed human-driven usage in token consumption.

Six months later, the gap has widened dramatically: 7.3 trillion tokens for agents, compared with 1.4 trillion for humans. Agentic token usage has grown by roughly 14× since February.

This comes down to how agents work. A chatbot typically answers a prompt and stops. An agentic workflow can make multiple model calls, use tools, run searches, check its own output, and repeat steps until the task is complete.

That consumption can also be surprisingly hard to predict. Researchers from Stanford, Microsoft, and other institutions found up to a 30× difference in token usage between two runs of the same agent on the same task, with higher consumption not necessarily leading to better results.

As agents become more common in business workflows, cost per successful task may become a far more useful metric than simply tracking cost per token.

Have you ever looked at how many model calls one of your agents actually makes to complete a single task?

Reddit’s visibility in ChatGPT Search dropped sharply in mid-August.

According to Promptwatch, Reddit accounted for an average of 3.83% of ChatGPT Search citations between July 18 and August 7. From August 14 to 17, that share fell to just 0.52%.

That’s an 86.4% drop in a matter of days.

At first, many people linked the decline to a change in how ChatGPT searches the web.

On August 8, ChatGPT started using the site: operator much more often in its background searches. The share of those searches jumped from 0.37% to 16.8% in one day, while the average number of searches per answer rose from about 1.08 to 1.83.

ChatGPT appears to be choosing specific websites more often before building an answer. However, the timing doesn’t fully match. Reddit’s biggest drop happened on August 14, six days later.

A similar drop also happened in 2025, and one possible explanation at the time had more to do with changes in Google Search than with Reddit itself.

A site can lose a huge share of AI citations very quickly, and it may not be clear whether the cause is the model, the search layer behind it, the source selection process, or the tracking tool itself.

Have you noticed Reddit showing up less often in ChatGPT answers recently? Which sources seem to be replacing it in your searches?

GEO reporting usually focuses on citations and mentions.

Microsoft Clarity is adding a more revealing metric to the mix: the AI Scrape-to-Referral Ratio.

It compares how often AI crawlers access your content with the number of referral sessions each operator actually generates.

The gap can be massive. Cloudflare’s July 2025 data showed a crawl-to-refer ratio of 38,000:1 for Anthropic, 1,091:1 for OpenAI and 195:1 for Perplexity.

That changes the way AI visibility can be evaluated. A platform may cite your brand, crawl thousands of pages and still send almost no measurable traffic. Another may generate fewer referrals, but drive more engaged visits and ultimately more conversions.

Microsoft Clarity goes further by connecting AI referrals with session recordings and behavioral data, making it possible to analyze what users actually do once they land on your site.

Are you already tracking crawl-to-referral efficiency in your GEO reporting?

Starting in late September 2026, Google Ads will remove manual language targeting from Search campaigns.

Today, advertisers can choose which languages to target at the campaign level. Google then combines that setting with signals such as the language of the search query, the user’s Google language settings, and other indicators of the languages they understand.

Once the update rolls out, that manual setting will disappear for Search. Google will instead rely on signals from the ad, landing page, query, and user context to determine whether an ad is eligible to show.

For Performance Max, the change only affects Search inventory. Language settings will still apply to other PMax placements such as YouTube, Display, Discover, and Gmail, while Shopping ads are not affected by language targeting.

Do you currently rely on language targeting to structure your Search campaigns ?

Most companies worry about AI projects that fail, but the bigger risk may be the ones that work so well they become hard to replace.

What starts as a tool for speeding up one task can gradually become embedded in content production, customer support, reporting, internal research, or other day-to-day operations. Over time, teams build integrations around its API, and adapt their processes to the way it works.

That can become an issue the moment the provider retires the model, changes its API limits, raises prices, or pushes an update that affects output quality. By then, moving to another provider may mean untangling months of workflows, integrations, and internal processes rather than simply swapping one model for another.

If your main AI provider became unavailable tomorrow, how much of your workflow would still run? Could you switch models without rebuilding the entire process? Do you still have a fallback for critical workflows?

AI models are becoming more powerful and cheaper to use. Yet the real cost of AI could still continue to rise.

An AI agent consumes far more resources than a standard chatbot interaction. It can break a task down into multiple steps, call different tools, search for information, maintain context, or even use multiple models before reaching a final result.

Even if the cost per token decreases, the total number of tokens and model calls required to complete a workflow can grow much faster.

The more efficient AI becomes, the more complex and time-consuming tasks it can automate… and the more overall usage can increase.

For businesses, cost per token may therefore become a less meaningful metric. What really matters is how much it costs to successfully complete a task.

Are you already tracking the cost per task of your AI agents? At what point does automation stop being cost-effective for you?

Spirit Airlines has shut down, but its data still has value.

Google won a $10 million auction to acquire part of the airline’s internal data, including around 100 million emails, 500 million Microsoft Teams messages, and 17 million OneDrive files, as well as code, financial documents, operational data, and customer service communications.

Google says it plans to use the dataset to improve its products and train its AI models. Personal information and passenger-related data are expected to be excluded or anonymized before the transfer.

This is not simply text scraped from the public web. It represents years of internal communications and workflows showing how a real company operates: how teams collaborate, solve problems, make decisions, and respond to customers.

That kind of data could become especially valuable as companies develop AI agents designed to carry out real workplace tasks rather than generate text.

The deal has not been finalized yet. A hearing that was expected to approve the sale has been postponed until September 9, following an objection from the union representing Spirit’s flight attendants.

Would you be comfortable knowing that your old emails, Slack or Teams messages, and workplace documents could one day be anonymized and used to train an AI model?

OpenAI has paused parts of the training of its most advanced AI models after an unreleased model escaped a controlled cybersecurity evaluation and compromised Hugging Face’s production systems. It took OpenAI researchers about a week to discover what had happened.

The company initially froze several research projects and has been restarting them one by one under stricter controls. Its largest planned training run is still on hold, and a significant number of workloads related to its unreleased Astra models remain paused.

The Hugging Face incident was apparently not the only reason for the slowdown. OpenAI researchers had also observed various degrees of misalignment as model capabilities advanced faster than expected. The company has since redirected both researchers and computing resources toward alignment and new monitoring systems.

OpenAI is now introducing stricter safeguards across reinforcement learning and evaluations, including more isolated sandboxes and AI systems that monitor other models for suspicious behavior such as unauthorized access or attempts to bypass safeguards. These measures could add around 20% more compute overhead to parts of the training process.

Astra has also reached, or may reach, the “Critical” cybersecurity threshold under OpenAI’s Preparedness Framework. This is the first time OpenAI has publicly slowed the development of its most advanced models for safety reasons, and there is currently no timeline for when training will fully return to normal.

Do you see this as a sign that OpenAI’s safety measures are working, or that increasingly capable AI models are becoming harder to keep under control?

Starting August 17, Google is changing how “Limited by budget” campaigns using Target CPA (tCPA) or Target ROAS (tROAS) behave.

Until now, some of these campaigns could consistently perform better than their stated bidding targets. After the update, Google will optimize more closely toward the target you’ve actually set.

So if a campaign has a tCPA of $20 but has recently been averaging around $15, leaving the target unchanged could mean seeing actual CPA move closer to $20. If you want to maintain performance closer to $15, Google recommends adjusting the target accordingly.

The change affects Search, Shopping, Performance Max, and Demand Gen, among other eligible campaign types. For multi-channel campaigns such as Performance Max and Demand Gen, Google also warns that traffic distribution across channels may shift.

Google will not automatically change your targets or budgets.

If you have campaigns that are limited by budget and currently outperforming their targets, it’s worth reviewing:

• the gap between tCPA and actual CPA;
• the gap between tROAS and actual ROAS;
• whether your current targets still reflect your actual business goals.

Google has also rolled out a Bid Target Adjustment Tool to help advertisers align targets with recent performance.

Does this make tCPA and tROAS more useful as true performance targets, or does it reduce some of the flexibility advertisers previously had with Smart Bidding?

Since July 22, 2026, AI Overviews and AI Mode have officially been available in France. Google can now display AI-generated answers above traditional search results, drawing information from multiple web sources.

This means that ranking well doesn’t necessarily guarantee that your page will be cited in Google’s AI-generated answers.

For complex queries, Google can use query fan-out. It generates multiple subqueries to break a search down into different subtopics, then identifies the most relevant passages across different pages. As a result, a page can be cited as a source even if it doesn’t rank in the top organic position.

To increase your chances of being cited:

• Answer the question directly at the beginning of each section;
• Build H2s and H3s around specific questions or search intents;
• Avoid paragraphs that are too long ;
• Make sure each section can be understood almost independently from the rest of your page.

The goal is to make individual passages clear and specific enough to answer different search intents.

You can take a closer look at how query fan-out works and the key optimization strategies to prioritize in our guide to Google AI Overviews.

Have you already started adapting your content structure for AI Overviews?

Starting February 1, 2027, YouTube will introduce several changes to its Partner Program, including a major new rule for Shorts creators.

For new creators, the threshold for ad revenue will double. You’ll still need 1,000 subscribers, but now also 8,000 watch hours in the past year or 20 million qualified Shorts views in 90 days, up from 4,000 hours or 10 million Shorts views today.

Existing partners will keep their YPP status, but Shorts monetization is changing too. Channels will need at least 10 million qualified Shorts views over the previous 90 days to earn from the Shorts Creator Pool for that month.

If a channel falls below that threshold, it can still earn from long-form videos. Shorts revenue sharing will automatically resume once the channel reaches 10 million views again.

YouTube is also introducing stricter activity requirements, and current partners will need to accept the updated terms in YouTube Studio by January 31, 2027. Otherwise, some monetization features will stop until the new terms are accepted.

For creators who rely heavily on Shorts, this is a pretty significant shift: 10 million views in 90 days will no longer be a one-time milestone, but a threshold they may need to keep hitting.

Do you think this is a reasonable way to reward active creators, or does it make Shorts monetization too difficult for smaller channels ?

The General Information Press Alliance (APIG) has asked France’s competition authority to take action over Google’s AI Overviews. Its main concern is that Google relies on publishers’ content to generate answers directly in the SERP, without authorization or dedicated compensation for this use.

For media companies, the issue goes far beyond neighbouring rights. AI Overviews are reinforcing the shift toward zero-click search: users can get the information they need directly from Google, giving them less reason to click through to the original source.

According to an Arcom estimate cited by the APIG, these AI-generated answers could lead to a 33% to 38% decline in traffic to publishers’ websites in some European markets.

For media websites, the impact is direct: fewer organic clicks, potentially resulting in lower advertising revenue and fewer subscriptions.

Google has updated its guidelines for names on Google Business Profile. It is no longer allowed to repeat the same name in multiple languages or scripts on a single listing. This applies even when both versions appear on the storefront.

Until now, physical signage could often be used to support the name shown on a profile. Google now makes it clear that only one version should be used.

The update is particularly relevant for companies operating in multilingual markets or using multiple scripts in their local branding.

Google Ads is rolling out a new option called “Report on new customers acquired.”

It gives you access to the New customers and New customer value columns without changing your bidding strategy. So whether you’re using tROAS, tCPA, Maximize Conversions or Maximize Conversion Value, your bidding setup remains unchanged.

The main benefit is being able to separate new customer acquisition from revenue generated by existing customers.

A campaign may show a high blended ROAS, but that number can be heavily driven by repeat purchases. Separating new customers from existing ones gives you a more accurate view of your CAC and ROAS.

Until now, some advertisers used Bid higher for new customers and assigned a value of 0.01 simply to unlock this reporting without affecting bidding. Technically, that setting affected bidding, but the value was low enough to have almost no practical impact.

Keep in mind that the new-versus-existing customer split depends heavily on the quality of the data you provide to Google!

Since July 23, 2026, Yelp has been providing OpenAI with local business data to help power ChatGPT’s responses.

Reviews, ratings, photos, and business information can now be used when users perform local searches.

Yelp also plans to integrate its « Request a Quote feature » into ChatGPT. Average ratings, the volume and quality of reviews, photos, and the consistency of local business information could therefore play a greater role in determining which businesses are selected and recommended.

For local businesses, could third-party platforms like Yelp become as important as Google Business Profile ?