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

Roblox is built for scale. In August 2025, the platform reached a peak of 45 million concurrent users. Few game engines can support that kind of demand.

But Roblox itself isn’t the one making most of those games. It gives creators the tools to build them, then focuses on the infrastructure beneath them. Now, Roblox is exploring how a video world model can work alongside its game engine to make those experiences photorealistic without compromising scale or multiplayer performance.

This hybrid approach offers valuable lessons for engineers building real-time systems at scale. To explore those lessons, we sat down with Anupam Singh, senior vice president of engineering at Roblox, to hear from him about the world model that Roblox is using to make its multiplayer games look photorealistic, the key insights that have come from taking that approach, and the next big thing that the Roblox team is focusing on. We thank Anupam for sharing with us. — Read More

If there’s anything that AI is particularly good at, it’s writing code. The whole software engineering profession has been turned upside down as more and more code is written by agents, with humans supervising and reviewing. If LLMs have product-market fit in anything, it’s software engineering.

… No matter how much context you give it, AI tends to produce design work that looks plausible, but is very generic and on further inspection full of (obvious to us humans) flaws. Turns out that, no, it’s not just me. There are some fundamental technical reasons why AI is nowhere near as good at designing as it is at coding. — Read More

A product lead wants a natural-language setup flow for a developer tool. One engineer thinks it could remove most of the friction from the first integration. Another worries that authentication, error recovery, and the current SDK contracts will make the experience brittle. A staff engineer sees a different risk: if the demo looks convincing, it may become a roadmap commitment before the team understands what it would take to operate.

The team could spend another week improving the design document, or it could build a narrow prototype and put the disagreement in front of real evidence.

AI changes the economics of that choice. For throwaway prototypes, I have felt the speed gain directly: a proof of concept that once took two or three days can now land in an afternoon. That makes implementation cheap enough to use during the decision process, not only after the decision has already been made. — Read More

In the past six months, engineers at Replit have nearly tripled code output. Review times held steady. Reversions and product incidents have stayed flat. Quality metrics improved, and releases have accelerated. All the typical trade-offs you might expect have not occurred.

… Agents now investigate production incidents, review pull requests, answer questions, analyze business data, triage support tickets, research sales accounts, and improve the systems that power Replit Agent itself.

It feels like a single master intelligence threaded through every employee, even though it is not. It is an expanding system of agents operating across the company: taking goals from people, gathering context, performing work, checking the results, and escalating when human judgment is needed.

We think this represents the beginning of a new kind of organization: the self-driving company. — Read More

Three men who compete for the same enterprise budget said the same thing within two weeks of each other. There’s a new narrative around information and domain knowledge as a competitive advantage that’s important to explain. Some parts are accurate, but what they all leave out is a deception that could cost enterprises everything.

… Models are commoditizing, and the moat is moving from the model to the proprietary knowledge that makes models and agents valuable. Nadella calls that knowledge tacit, stored in private evals and corrections. Benioff calls it context and grounding. Karp calls it the ontology and the alpha. It’s the same asset and competitive advantage with three different brands that suddenly find they have a common interest. — Read More

Last week I wrote my longest newsletter ever, 8,500 words long, which covered the essentials of AI software and hardware, from the very basic key intuitions about AI models (what they are, how they learn), to the key intuitions in AI hardware that helped readers understand why GPUs and other accelerators are used, why memory is so important, and other key ideas the industry holds dear.

And finally, we discussed the “interesting” world of AI finance, particularly some numbers to understand how this entire industry aims to make money.

Today, I bring you the second part of the guide. In this one, I’ve focused more on the product, detailed AI inference maths, and future trends. — Read More

[E]vals are becoming essential for AI.

…Organizations need metrics that show whether work is producing the intended result.

OKRs should not be activity lists.

Key results should not simply measure work completed.

They should define visible, tangible outcomes.

… If an organization cannot define what good AI output looks like, AI will create more output without necessarily creating more value.

Evals, metrics, and key results all serve the same deeper purpose. — Read More

AI was supposed to replace human labor.

It did the opposite.

For the first time in history, humans are cheaper than software.

And AI is creating more jobs than it eliminates. — Read More