With the GPT-6 announcement today, we immediately thought of two things. First, the model matters less and less than the application of the model. The frontier labs seem to be increasingly focused on the best models for particular applications (which we posted about recently). That reminded us of a post of ours from 2.5 years ago, which we feel is more relevant than ever.
If the application matters more than anything, then the open question (in 2026 terms) becomes whether the harness can be separated from the intelligence engine. Our bet would be yes. — Read More
Recent Updates
AI, tools and transformation
It’s very tempting to imagine that AI turns everyone into a tool-builder – now everyone can just ask the model to make the software they need, and apps as we know them are dead. I think that misunderstands how most people think and where software actually comes from, and more importantly, it isn’t a path to change how companies actually work.
The typical big American company today has hundreds, and perhaps thousands, of different pieces of software. … It can be very tempting to think that AI will sweep most of this away. … The hard part is knowing that you need a tool for this in the first place, and then knowing what the tool should do. — Read More
‘Welcome to the AGI era’: OpenAI launches GPT-6 Astra
The rumors were true, all of them (and then some): OpenAI today is releasing GPT-6 Astra, a new frontier model that the company says likely marks the onset of artificial generalized intelligence (AGI), its long sought goal of “highly autonomous systems that outperform humans at most economically valuable work.”
In a closed a press briefing earlier today, OpenAI co-founder and president Greg Brockman offered an unusually direct formulation of that message, ending the session with: “Welcome to the AGI era.”
That is an unusually consequential framing even by the standards of frontier AI launches. But for enterprises, the more immediate significance of Astra may be considerably more concrete: OpenAI is positioning GPT-6 Astra as a new era of computing in which users, including employees, no longer have to click around a mouse or type on a keyboard ever again (if they don’t want).
… Astra is designed to navigate software much as a person does — working across browsers, spreadsheets, websites and desktop applications, producing finished documents and presentations, and carrying out multistep workflows rather than merely telling a user how to complete them. — Read More
An Organizational Second Brain: Building an AI That Learns From Experts
We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon.
This is not a typical domain-specific agent. Its novelty comes from integrating two layers:
— A structured, auditable knowledge architecture separates what the agent knows from how it reasons.
— A self-improvement loop then compiles expert feedback into verified, regression-tested updates without model retraining.
Together, these two layers turn one-off expert corrections into permanent, compounding institutional memory, and the pattern is designed to generalize to other domains governed by retrievable text rather than model weights. — Read More
LLMs: Intelligence vs. cost
ArtificialAnalysis is a website that benchmarks the intelligence of various LLM models. They publish a headline Intelligence Index, which is calculated as the mean output of the curated selection of benchmarks they run on each model. It’s a decent finger-in-the-air measure of how smart a model is overall.
AA also records useful information — namely, how much it cost them to run the benchmarks. Since the benchmarks are the same across all models, this offers a good indicator of how much it will cost a user to run each model, in relative terms.
One of their main plots is the Intelligence vs. cost plot, which shows the Pareto frontier, i.e. the cheapest model that can achieve each intelligence score. This frontier is important, because using a super-intelligent and super-expensive model to accomplish menial tasks that could be done by a much dumber and cheaper one is just a waste of money.
Over time, I’ve become progressively more irritated by this plot, for a few reasons. — Read More
The race to engineer new knobs for the human brain
I almost dropped my phone when I saw the news that chemogenetics was in human clinical trials. Chemogenetics is a powerful technique that modifies specific neurons so they can be controlled remotely by normally inert drugs. Bryan Roth, one of the technique’s inventors, told the BRAIN Initiative conference audience on August 13th1 that he had found seven ongoing clinical trials of chemogenetics in China. The disclosure set off a flurry of coverage in the trade media: We’re translating our sci-fi basic neuroscience tools to humans! We’re engineering new knobs for biology! We might have new ways of treating epilepsy, Parkinson’s disease, and pain! — Read More
How we make AI coding more cost efficient without sacrificing task quality
Output quality is important when working with AI coding agents, but true efficiency comes from getting work done quickly, efficiently, and with the right context.
That’s why token count of individual interactions alone isn’t a meaningful measure of efficiency. The goal shouldn’t be to use fewer tokens, but to tap into the right amount of context to move a task forward. — Read More
Atlas: A World Model for Spatial Intelligence
World models generate, reconstruct, and simulate any possible world. They understand how worlds appear, behave, and evolve so that we can render imagined worlds for creative users, simulate the real world in high fidelity, and help robots plan actions. At World Labs, we build these general purpose world models in pursuit of spatial intelligence.
Today we are introducing Atlas, our next-generation world model. Atlas is an omni model that we pretrained from scratch to natively operate on text, images, video, and 3D. It is a multimodal autoregressive diffusion transformer: all inputs are combined into a shared spatial context. Atlas uses that context to generate what comes next, staying consistent in 3D with everything it has seen and imagining what lies beyond it. — Read More
The Pentagon now has its own version of ChatGPT and Grok
The Pentagon has launched versions of OpenAI’s ChatGPT and xAI’s Grok, giving 3 million civilian and military personnel access to generative AI tools that have been tailored to “warfighter needs.”
… These latest additions are part of the Pentagon’s broader effort to use AI tools to accelerate work and give the Department of Defense an edge without sacrificing security. They also highlight the absence of Anthropic’s Claude model and the Pentagon’s effort to work with other companies following its dispute with the frontier lab. — Read More
Who gets to decide? The CIO and the new architecture of enterprise authority
For most of my career, technology governance began with a familiar set of questions: Is the system secure? Is it resilient? Does it meet the architecture standard? Can we afford it? Those questions still matter. But they are no longer enough.
AI is moving rapidly from producing content and recommendations to initiating actions. It can route work, change code, approve exceptions, communicate with customers, trigger transactions and coordinate other systems. In that environment, the most important question may not be what the technology can do. It is who, or what, has the authority to do it. — Read More
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