First, Anthropic updated Fable and Mythos. Then came model enhancements from Meta and Google. OpenAI followed suit by releasing GPT-6 Astra.

[F]or the users of AI models and services, it’s created complexity and chaos as CEOs and IT managers spend an outsized amount of time and resources comparing costs and capabilities to avoid getting left behind. — Read More

Four years ago, I was pissed. I knew I wasn’t doing product management, but I couldn’t name what I was doing. Eventually, the term found me: bullshit management. I wrote exactly what I felt, published it, and moved on. Barely did I know what was about to happen.

That single article got over 1 million reads combined across LinkedIn, Substack, Medium, and my website. I ended up speaking about it at conferences in 12 countries. For a long time, I asked myself why this one traveled so far.

The answer is uncomfortable. People were tired of doing bullshit management, but they didn’t have a name for it. I gave this name and people embraced it. — Read More

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

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

Have you ever seen any transformation working?

If you have, please share what made it successful because more people need to learn from it. Most transformations fail. And they fail for predictable reasons.

… Today, we hear about AI transformation. How does that differ from the transformations we’ve been experiencing for decades now?

If I had to bet, I’d say 90%+ of “AI Transformations” will create no different results from the poor digital transformations or failed agile ones. And the reason they will fail is the same as always: Lack of courage to address the foundations that enable any transformation to succeed. — Read More

For thirty years we built data systems for human analysts, who supply the context, judgment, and skepticism to work around data that’s incomplete or wrong. Autonomous agents supply none of that. They act on whatever they’re handed, confidently. For data to be AI-ready we need to build a series of layers: a data foundation that makes data trusted, a context layer to apply proper meaning, and an access layer that supports and controls how agents operate on that data. While doing this we need continuous attention to observability that ensures the data is properly governed and we have an auditable trace of its use in decision-making. — Read More

#strategy

Had a lot of fun chatting again with my twin brother Dylan Patel.

We went through lab economics over the next few years – the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone).

And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities.

One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry – the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI. — Read More

What happens when AI changes not only how companies compete, but the very game they are competing in?

[V]iewing AI through the traditional mechanisation/automation lens traps us in looking for operational benefits (much of the discourse today), when the real advantage lies in understanding its impact on the rules of competition. It changes where scarcity lies, how industries are organized, and what constitutes a defensible advantage.

As AI makes intelligence within individual modules abundant, advantage increasingly moves to those who can connect activities, learn across their boundaries, and improve the whole system together.

Vertical integration may therefore be returning, but in a new form. The prize is no longer necessarily ownership of every asset. It is ownership of the feedback loops that connect them. — Read More

Everyone suddenly wants to tell you SaaS is dead.

Even Gartner now estimates that as much as $234 billion in enterprise application spending could be exposed to what it calls “agentic arbitrage” by 2030, roughly 20 percent of enterprise SaaS spending.

But Gartner itself hedges the apocalypse. It calls what is coming less an apocalypse than a metamorphosis.

I think that’s right.

The next SaaS company may run one service underneath ten thousand different applications. — Read More

Four years into the generative AI revolution, consulting giant McKinsey reckons we’ve finally started the engine and are officially “on the road to ROI.” Whether that road leads to actual profit-making and how long it takes to travel is anyone’s guess, because the firm’s data suggests most respondents still aren’t reporting an enterprise-level earnings contribution from AI.

McKinsey surveyed 1,719 professionals and business leaders from around the world and across industries for its report on the State of AI in 2026, and what it found sounds a lot like what similar studies have determined in the past couple of years. According to the report, more businesses are deploying more AI in the belief that their investments will start paying off, but the number of people reporting an actual earnings boost from their AI initiatives has remained flat.  — Read More