… I am on the record as being skeptical that LLMs will take us to superintelligence, but I think there is a real shot that World Models will drive superhuman, complementary machines that do things that we can’t, or don’t want to, do.

… The world is a place where unexpected futures unfold, but in somewhat predictable ways. As humans, we can envision almost all of them with roughly the same amount of effort with a very similar amount of time given to each thought. Computers can’t.

It’s no wonder traditional computing struggles with this complexity. Imagine anticipating and coding each and every action, as well as the interactions between all of those actions. Mathematically, in a traditional engine, simulating N fans is at least an O(N) or O(N²) problem. Each person, flag, chair, and ball must be explicitly calculated — and really, the interactions between them need to be calculated, too.

In robotics, machines must respond to situations in the real world in the same amount of time, regardless of their complexity, even though, in traditional computing, different situations can take wildly different amounts of time to simulate. This has been a major bottleneck for robotics and embodied AI progress.

World Models are a solution to that problem. — Read More

… Today, AI sits adjacent to the work. It assists. It suggests. It drafts. But it doesn’t run the operating room, underwrite the loan, or manage the supply chain. Not in production. Not yet.

… “You can’t just slot [AI] in to a critical workflow in health care and all of a sudden show up where if you make a misdiagnosis or if you make a mischaracterization of a procedure, you can get fined and go to jail. If you’re in financial services and you make a mistake about somebody’s portfolio, or you make a misallocation and you point to a model, you will get sued and you will be in trouble.”

So what does every responsible enterprise do? They experiment at the edge. They run pilots. They check the box. They wait. — Read More

Why do some people become enthusiastic, consistent adopters of AI, while others give it a try and shrug? We collaborated with Stanford University researchers to find out.

Over the last 18 months, we took the researchers behind the curtain at Google to observe how Googlers were learning and using AI in their day-to-day work. The timing of the study allowed us to observe firsthand how the rapid pace of AI was fundamentally challenging and changing how we build, collaborate and lead.

The published study found that while most people were eager to find value in AI tools, many were stuck in what the researchers called “simple substitution”: swapping existing tasks for AI alternatives. But many found the effort it took to learn the AI tool and get to a good result was often greater than the payoff. Crucially, the researchers found that successful adopters didn’t just focus on prompt engineering or its more recent sibling, context engineering. Instead, deep AI adopters completely changed how they approached AI — taking inspiration from product management. — Read More

Bill Gates published Business @ the Speed of Thought in 1999. I read it for the first time this summer, which is a bit like watching a prophet’s sermon after most of the prophecies have already come true.

It’s a strange reading experience. You keep nodding along, thinking “yes, obviously,” and then you remember that when he wrote this, most people were still using dial-up and the idea of checking your bank balance on a phone would have sounded like science fiction.

… I thought this book was interesting right now, as Gates was trying to answer a question in 1999 that we’re trying to answer again in 2026, just with different technology. Everyone is wrestling with, well what happens to business when information becomes fast, cheap, and ubiquitous? — Read More.

In the emerging effort to make the full TAM of labor addressable via software and robotics, there is a growing appreciation for how complex the world actually is.

That complexity is not a reason to sit on our hands and wait for things to play out.

We are finally at a moment where there is enough understanding around how model capabilities intersect with data, RL, evaluations, and the broader vendor stack that ambitious organizations can begin to behave differently.

The strategies are increasingly legible. The vendor ecosystem has matured. Open-source models have proliferated. The infrastructure required to pursue durable advantage has now emerged.

And so the implication is fairly straightforward: this is a moment for concentrated research ambition in every vertical. — Read More

Your computer can do anything. Seriously.

Every modern computer is Turing complete. That’s a technical way of saying it can perform any computation that is physically possible.

… It always could. From the moment you bought it.

So why have most people spent decades using computers for a handful of things — browsing websites, editing documents, compressing images?

The answer is software. And it’s more interesting than it sounds. — Read More

Sivesh and I recently hosted a roundtable on the future of software engineering with Anthropic’s Ash Prabaker and we were joined by engineering leaders from Stripe, NVIDIA, Microsoft, Google DeepMind, xAI, Apple, Scale AI, as well as the legend Peter Steinberger of OpenClaw/OpenAI.

… A major thread throughout the discussion was “closed-loop” development. One participant described a setup at their company where bug reports are automatically triaged by an agent, bucketed by severity, checked against an eval set, and then a fix PR is opened — much of it running with minimal human touch. The room broadly agreed that this kind of loop is where compounding gains actually come from: better coding tools improve the models, better models improve the coding tools. Several people noted their companies are prioritizing coding specifically because of this dynamic.

… The room converged on long-horizon tasks as the real frontier problem. One participant noted that product engineering has started to go exponential for them, but closing the loop on more complex research workflows isn’t there yet. The open questions everyone shared: what do you actually assign an agent for a four- or five-hour run? How do you observe it? How do you keep a human in the loop without babysitting? Nobody had a clean answer. — Read More

Language models can solve tough math problems at research grade but struggle on simple computational tasks that involve reasoning over many steps and long context. Even multiplying two numbers or solving small Sudokus is nearly impossible unless they rely on external tools.

But what does it take for an LLM itself to be as reliable and efficient as a computer?

We answer this by literally building a computer inside a transformer. We turn arbitrary C code into tokens that the model itself can execute reliably for millions of steps in seconds. — Read More

Ask an AI coding assistant to, say, “build a React component with a dropdown menu,” and you’ll probably get something impressive in seconds—clean code, proper hooks, accessible markup. It’s the kind of demo that makes CTOs lean forward in their chairs.

Now ask that same AI about your company’s internal API for user authentication. Ask it to integrate with your legacy billing system. Ask it why your team deprecated a particular approach last quarter. Watch it hallucinate with confidence, suggesting endpoints that don’t exist, recommending patterns your architecture explicitly forbids, and generally ignoring the hard-won institutional knowledge that makes your systems actually, you know, work.

This is the enterprise AI paradox: Foundation models know everything about public libraries but precious little about the specifics that matter for your business. They’re trained on millions of open source repositories, but they’ve never seen your codebase. They can regurgitate best practices from popular engineering blogs, but they fail to grasp why those practices might be impossible in your environment. Without context—the community-vetted, institutional knowledge behind business decisions—AI assistants remain dangerously confident when they shouldn’t be. — Read More

In 2025, Google, Amazon, Microsoft and Meta collectively spent US$380 billion on building artificial-intelligence tools. That number is expected to surge still higher this year, to $650 billion, to fund the building of physical infrastructure, such as data centers (see go.nature.com/3lzf79q). Moreover, these firms are spending lavishly on one particular segment: top technical talent.

Meta reportedly offered a single AI researcher, who had cofounded a start-up firm focused on training AI agents to use computers, a compensation package of $250 million over four years (see go.nature.com/4qznsq1). Technology firms are also spending billions on “reverse-acquihires”—poaching the star staff members of start-ups without acquiring the companies themselves. Eyeing these generous payouts, technical experts earning more modest salaries might well reconsider their career choices.

Academia is already losing out.  — Read More