… 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