Jakob Nielsen wrote his law in 2000, and it has outlasted almost everything else from that era of the web. Many of his commandments still hold, and not because technology stopped moving — it never does — but because people don’t change nearly as fast as their tools.

The implication: users expect your site to work like the ones they already know in the same way we expect automobiles the act a certain way i.e. if the steering wheel is a bunch of levers, I’m not going to want to rent or buy that car.

More and more tasks no longer start at a website or an app — they start in an assistant.  The number of interfaces a person touches is falling, and the assistant is becoming the front door to tasks that each used to have their own destination. — Read More

A billion machines will become autonomous or intelligent over the next ten years. Cars, trucks, tractors, mining haulers, defense systems, warehouse robots, humanoids—the physical economy will be rebuilt around software that perceives, decides, and acts.

The prevailing assumption about how we get there goes something like this: models keep improving, world models mature, foundation models for robotics arrive, and autonomy falls out the other end. Intelligence is the whole game; scale the intelligence and the machines will follow.

Deployed physical AI is a product of two variables: the capability of the models, and the capacity of the engineering system around them—i.e. how requirements become software, how software gets validated, and how validated systems get deployed, monitored, and improved. The industry has largely poured everything into the first variable while the second sits roughly where it was a decade ago. The contrarian bet, then, isn’t against intelligence. It’s that the next order of magnitude in physical AI comes from making the engineering system as intelligent as the models it carries.  — Read More

IT is going remote while sucking up more power.

Most corporate IT is now off-premises for the first time. … Uptime’s Global Data Center Survey 2026 reveals how the industry is managing to adapt to challenging circumstances, with the usual evergreen concerns over rising costs plus staffing and skills shortages.  — Read More

The release of Kimi K3 has opened a fresh round of angst and confused discourse. There’s a loud cohort of journalists, business leaders, and politicians arguing that open source AI is a dangerous threat.

Freely available AI for anyone? The horror!

Frontier labs’ case against open source AI is essentially: Open source models¹ are dangerous (and un-American!). We should open the AI Pandora’s Box, but only with responsible gatekeepers (toll collectors, preferably us!). Only trusted users (our most profitable customers) should be able to use it. — Read More

I have been working with a lot of companies that are trying to become AI-first.

At first, that usually means helping individuals get much better at using AI.

Soon the AI knows that when someone says MRR, they do not just mean the generic finance definition.

… It is no longer just automation. It is institutional memory – the company’s knowledge, history, processes, workflows, and way it uniquely does work.

And it likely will be the new backbone and most significant software infrastructure in the AI era. … AI company brains will become the next version of indispensable software. — Read More

#strategy

You’ve been told AI can’t make money. I’ve said it myself, and that thinking AI will make money one day was more a matter of faith than numbers.

But now, I can confidently say I would be wrong to keep thinking that, because something has changed.

Over the next months, we’re going to see markets adjusting to this new reality: AI is no longer about whether it can make money, but how much and by whom. — Read More

Poolside’s co-CEO on how his small team of top researchers built a model factory capable of training Laguna S – a 118B MOE beating Thinky’s ~1T open weights model… and this is just the beginning.

… Poolside’s recent tech report got a lot of praise due to their level of detail. … From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. — Read More

#strategy

Major benchmarks measure what AI can do. None measure whether it does what you mean: the distance between what you ask an AI to do and the unspoken assumptions about how you want the AI to do it. We propose a new metric: the Genie coefficient.

There’s often a gap between one person’s request and another’s understanding. Most of the time, we bridge it using general knowledge. For example, if you ask a friend to get you coffee, they’ll pour a cup from the pot or buy one from a coffee shop. They won’t bring you a bag of raw beans or snatch a cup from a stranger and hand it to you. You never specified any of this. You never had to.

One might think the fix is just to specify tasks, questions, and intent better. … This situation has major implications for AI agents that are increasingly being given requests by humans and expected to fulfill them. They have enormous latitude to get it wrong. — Read More

For the past two years, enterprise AI success has been measured in consumption. Companies celebrated how many employees used AI tools, how many queries were run and how many tokens (the basic measure of AI use) flowed through their systems. “Token” became a sort of corporate vanity metric, a proxy for being AI-forward.​

I think that era is ending. In conversations with founders and operators, I’ve noticed a clear shift in how enterprises think about intelligence, from what is colloquially referred to in the industry as “token-maxxing”—consuming as much AI as the budget allows—to “value-maxxing,” where all AI spend is expected to justify itself in business outcomes. Understanding why this shift is happening and what disciplines it demands may be the most important issue technology leaders face this year. — Read More

In the spirit of 12-factor apps, and 12-factor agents – I propose 12-factor companies. This document is a living document – I’ll continue to update it.

Organizations shape technology and technology shapes organizations. Double-entry bookkeeping, the printing press, steam power, fax, the internet and software of the past 30 years have each changed what it means to build and run great organizations. AI will do the same.

Though we are still very early in this arc – it will come swiftly with a lot of creative destruction. It is worth trying to predict how organizations of the future will operate. — Read More