When Google prepared to go public in 2004, Larry Page, a co-founder of the company, wrote a letter to shareholders describing the internet firm’s responsibility to the world.
“We believe a well-functioning society should have abundant, free and unbiased access to high-quality information,” Mr. Page said.
“Google fulfilled that responsibility by acting as a gateway to the internet. It answered people’s search queries with lists of hyperlinks, pushing users out to what is known as the “open web” — the millions of websites run by merchants, publishers, universities and others — for more information. In the ensuing decades, Google became one of the planet’s richest and most powerful companies by directing people to the vastness of the open web.
“Now in the age of artificial intelligence, Google appears to be shrinking back from the open web — and may be imperiling it. — Read More
Daily Archives: July 21, 2026
Who’s Afraid of Chinese Models?
There’s a story I tell about my first day in STRT-431 at Kellogg School of Management, the introductory class that every first-year MBA was required to take; I leafed through the readings and case studies and was dismayed that there weren’t any tech companies on the docket. Me being me, I spoke to the professor after class wondering why, and was told that the goal of the course was not to necessarily learn about specific industries, but rather to uncover broadly applicable universal principles that could be applied to any company in any industry.
I did not, as I usually tell the story, find this very satisfactory: to me the nature of tech, particularly the fact that software and distribution had zero marginal costs (and zero transaction costs), was something fundamentally different; putting in zeroes in formulas tends to wreak havoc! I soon realized, however, that that was my opportunity. The fundamental insight undergirding Aggregation Theory is that zero marginal costs leads to fundamentally different value chains than people once expected from the Internet: centralization and scale in a world where controlling demand mattered more than distributing supply.
What is fascinating about AI, however, is the extent to which those old universal principles are coming back to the forefront. That was never more apparent than this past weekend, when arguments raged on X about the implications of Kimi K3, another open weights model out of China, approaching the state-of-the-art in terms of capabilities. The long and short of it is this: marginal costs are back in a big way, both in terms of short-term implications of state-of-the-art free models, and in terms of the long-term structure of the industry. — Read More
The Human-in-the-Loop is Tired
… Programming with LLMs is genuinely useful and genuinely destabilizing. These two things coexist. If we pretend the second one isn’t happening, we will all burn out.
…[I]n the last few months, there have been days when I have spent close to two full days writing a plan for an LLM to execute: obsessively clarifying, specifying, re-specifying, only to have it still do something inexplicably stupid. Port a React hook into a Storybook story file. Read from the wrong plan. Invent components that don’t exist. And these aren’t errors of capability; they’re errors of coherence. The models are smart enough to produce plausible code, but not always smart enough to maintain a coherent intent across a complex change.
This creates a peculiar new kind of fatigue, the fatigue of supervision: of holding the intent in your head while the machine generates volumes of mostly-correct output that still needs your eyes, your judgment, and your taste. Douwe put it well: he used to get a dopamine hit from collaborating with a real person on a cool feature in open source. Helping someone become better at their craft. Now, he said, “everything I write goes into some AI black hole. There’s no person on the other side actually learning anything.” That loss is real and it’s worth naming: the intensity trap. — Read More
Why is AI bad at design?
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
The Prototype Is a Question, Not a Product
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
A Practical Guide to Reducing Token Spend
Embedding complex workflows and logic into AI Agents through skills alone is a dramatically inefficient way to work. By using a swamp workflow instead of the skill alone, I dropped the token usage 8x, and decreased the run time by 2x for a complex code review workload. For a lot of use cases, it’s the single best thing you can do to reduce your token spend. — Read More
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