Despite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech. Revenue per worker is rising for the Mag 7 but is flat for the S&P 493, see the first chart below. Profit margins are rising for the Mag 7 but are flat for the S&P 493, see the second chart below. And there are still no signs of AI boosting productivity growth, see the third chart below. With no impact on the bottom line, mainstream corporate America is pulling back and starting to view token spending not as a source of growth but as a cost center that weighs on margins.
VC-backed companies are spending more and more on tokens, but mainstream corporate America does not need the latest model and is unwilling to pay for it. … The question looking ahead is whether, even as models become commoditized, mainstream corporate America can turn increasingly cheap intelligence into higher revenue growth and higher profit margins.
The bottom line is that the AI story will be decided in the S&P 493, not the Mag 7, and so far, there is no evidence that revenue growth or margins are improving outside the tech sector. — Read More
Recent Updates
The State of Context Engineering
The Job is Knowing When to Be Deterministic & When to Be Probabilistic.
… Giving clear instructions about what to do and what not to do, and ensuring those instructions are consistent and not noisy, will forever be a challenge with AI, no matter how good it gets.
Context engineering will continue to be hard. But it will be hard in unique ways, because models aren’t people. You can make assumptions about how models think or interpret things (use whatever anthropomorphizing word you like), but it helps to understand how they’re built, optimized, and trained. Their unique attributes and construction create a unique set of failure modes we need to reckon with. — Read More
How to build the AI platform your company needs
… You use ChatGPT or Claude every day, and nobody at your company built anything for it. So why does your tech team want a platform? Look at the two ways your company uses AI.
In the first, you’re the user. You type into ChatGPT or Claude. OpenAI or Anthropic built that product, picked the model and decided what it may say.
… In the second, your customer is the user. They type into an AI feature your team built inside your product. …Once several teams build features like these, they all need the same things around their model calls. That shared layer sits between all your AI features and all the models they call. — Read More
OpenAI and Anthropic cut their LLM prices by up to 80% within a few months. Why?
Let’s look at a quick timeline of the major LLM model announcements from both OpenAI and Anthropic over the past few months. … [T]here is a clear tit-for-tat between Anthropic and OpenAI, and it is reasonable to assume that these particular price cuts from OpenAI and Anthropic are in reaction to each other. However, no one cuts prices by 80% unless there is some externality in play. So, what gives?
… OpenAI and Anthropic will have to differentiate from these open weight models with higher-quality models—they are currently doing a good job with that particularly with Opus 5.5—but they need to keep doing that, which is why billions of dollars are being invested into R&D. Open-weight models are catching up fast. — Read More
The End of the Internet
It was the summer of 2003, just weeks before I was set to move to Taiwan, that I decided to create a blog to document my journey. … I was thinking back to those days last week when news broke that every version of PHP had a critical bug that allowed for remote code execution on the host machine; Stratechery today still runs on WordPress, but that WordPress is managed by Pressable, an Automattic company. That meant that my site was patched before the vulnerability was disclosed, and it would be crazy for me — or anyone else — to do it any other way. It’s kind of insane to think that twenty-some years ago I was doing everything by hand without having any idea what I was doing, and that it was totally fine. That’s the Internet, though: an insane invention from a time that is, sadly, gone forever. — Read More
The AI Bubble Is Real, But Not Where You Are Looking
Every few weeks someone asks me whether AI is in a bubble. My honest answer is “yes and no,” which sounds like a dodge, so let me explain why it isn’t. Most people try to answer the bubble question at the technology layer. They look at model capabilities, GPU orders, or the valuation of a foundation model company and decide whether it all feels too expensive. I think that’s the wrong layer. The bubble question can only be answered at the customer layer.
… So today I want to break AI demand into five usage markets, based on who’s buying, where their money comes from, and whether they can measure a return. Two of them look like a bubble to me. One is shaky. Two are about as far from a bubble as it gets. — Read More
We’re in “Black October”. Here’s Why
[W]e always discuss how the world will change. We’ve often discussed how things in AI are very Hemingway-esque: things happen gradually, then suddenly.
And for some people, the cybersecurity cohort, to be precise, the sudden face is now here for reasons that people now describe as “Black October.”
And, to be clear, it’s changing for the worse. — Read More
Using thousands of AI agents to uncover new leads in biological data
What if scientists could uncover new clues about Alzheimer’s and Parkinson’s in minutes instead of months? Working together, OpenAI, PerturbAI(opens in a new window), and Scripps Research(opens in a new window) used thousands of AI agents powered by GPT‑Rosalind(opens in a new window) to analyze and interpret existing experimental data from millions of mouse brain cells(opens in a new window) in just 32 minutes. The agents connected the effects of gene disruptions with findings from scientific literature, uncovering unexpected biological connections that can help scientists understand disease and identify research directions for new treatments. The work shows how AI can help researchers explore more of what large biological experiments reveal. — Read More
Read the paper
AI Is Changing Everything About Hacking. No One Knows What Happens Next
For much of the last month, many of the cybersecurity experts I talk to and follow have been posting like they are very scared. Many have been warning of an AI-powered “Black Swan” event, have been alarmed by the number of vulnerabilities suddenly discovered, and of the apparent power of AI agents and large language models more broadly to find and exploit previously unknown bugs.
The rhetoric has ranged from worried-but-measured (things are about to get weird), to run-of-the-mill advice like update your software and make sure you have two-factor authentication on, to vagueposting about AI-powered attacks, to things like ‘the internet was nice while it lasted,’ to outright prepper-level terror.
.. [I]t has become clear that AI, broadly defined, has fundamentally changed both offensive hacking and defensive cybersecurity. This is not because AI has become sentient or because human beings have lost control of it or because of “swarms” of “rogue agents,” but because AI is proving something that we have always known: much of the software that runs the internet, our devices, our accounts, and our lives is vulnerable, or out of date… — Read More
Why AI Compute Comes in Rectangles — Erik Bernhardsson, Modal CEO
Two years ago, we spoke with Erik Bernhardsson about his vision for truly serverless AI infrastructure: developers should be able to run code without provisioning clusters, predicting load, or paying for idle resources. CTO Akshat then joined us to talk about Modal’s infra primitives and stack to offer those primitives and agent experience.
A lot has changed in the past 5 months. Since their $5B series C, Modal now reports it crossed $1 billion in annualized run rate, raised a $750 million Series D at a $16B valuation, and has spun up nearly three billion sandboxes. In this conversation, Erik joins Vibhu to explain the infrastructure and economics behind that growth—and why demand for compute rarely looks like the contracts used to buy it. — Read More
