Design-system maturity frameworks often follow a linear progression: you start by building components, move to driving adoption, hit the growing pains of scale, and eventually reach a steady state of governance and evolution.

But design systems don’t mature in a straight line — the reality of design-system work is much messier and more multifaceted than a ladder can capture. This article proposes an alternative that treats design-system maturity as a multidimensional assessment rather than a sequential journey. — Read More

…More and more companies are sharing cautionary tales about what happens when tokens are used as a productivity metric. Pushing developers to consume as many tokens as possible can encourage AI adoption, but it also opens the door to catastrophic expenditure.

But is tokenmaxxing over as many suggest?… Now there seems to be a shift in the industry away from high token consumption toward “valuemaxxing.” — Read More

Opposition to AI data centers has emerged as a primary theme in US politics, one that—surprisingly—doesn’t fall along party lines. We applaud people coming together for constructive debate on any issue, and agree that communities need to evaluate whether any economic benefits these data centers bring is worth their costs. Still, we worry that a focus on data centers obscures the larger impacts of AI on people’s lives: the concentration of power of AI companies, and their widespread political and financial influence. — Read More

#strategy

Tokens have become the practical accounting unit for modern foundation model services, linking information processing, computation, memory use, energy expenditure, pricing, and economic value. This paper develops a framework for AI tokenomics: the study of how tokens are generated, consumed, priced, allocated, and optimized across AI systems. We connect token-level technical costs to workflow-level production functions, enterprise resource allocation, measurement and instrumentation methods, and emerging market-design questions. The framework shows that token expenditure and economic value are distinct: value depends on marginal productivity, workflow position, hidden reasoning activity, risk, and downstream propagation effects. The paper concludes by identifying open research directions in hidden-token measurement, empirical calibration, token productivity, dynamic allocation, and token-based markets. — Read More

#strategy

AI didn’t level the playing field. It raised the floor while making the ceiling harder to reach for most people.

For years, the story was simple: learn valuable skills, get ahead. Then the internet made information abundant. Then no-code tools made building easier. Now AI has made output abundant. You can generate writing, code, designs, strategies, and research at a level that would have been impressive just a few years ago.

The problem is that abundance changes the game. When something becomes easy to produce, it loses its value. What used to be an edge becomes table stakes. And the people who treat AI as the end of the leverage game are going to be disappointed. — Read More

As you read this sentence, circuits in your brain are adjusting your posture, controlling your breathing, and transforming lines and curves on the screen into recognizable words. Most of this processing is invisible to you. But some of what takes place in your brain you do have access to—an image that pops into your head, or a deliberate plan you make about where to go shopping. Neuroscientists and philosophers sometimes refer to the latter type of brain activity as “consciously accessible,” to distinguish it from all the other processing that goes on unconsciously. This activity has special properties: we can describe it, control it, and use it for deliberate reasoning, in contrast to all the automatic processing that goes on without our awareness.

In a new paper, we present evidence that a similar distinction has emerged in modern language models like Claude. We find that Claude has developed a small collection of internal neural patterns that, compared to all its other internal processing, play a special role. — Read More

The annual prediction market conference was earlier this month. This was the year prediction markets went from an obscure hobby to a multi-billion dollar industry; from semi-illegal to having the President’s son as an advisor. I can’t remember if anyone talked about any of that. It didn’t even register. All eyes were on the AI superforecasters.

The extending-lines-on-graphs community has long predicted that AIs would beat the best human forecasters sometime in 2026 – 2027. What did you expect the bots-finally-beat-humans-at-predicting-the-future moment to look like? Vibes? Papers? Essays? In retrospect, sure: it will look like AIs making crazy profits on prediction markets and beating the stock market by some comfortable amount.

But what happens next? — Read More

… The old advice was clear. Grind LeetCode, memorize debounce and throttle, be able to reproduce a virtualized list from memory. That advice is now actively wrong for these formats, not just outdated.

Here is the shift in one sentence, and it comes straight from the people running these loops: these loops grade whether you can judge code, not whether you can produce it from a blank editor. Interviewers are listening for whether you own the output or outsource the thinking.

That distinction sounds soft until you see how it gets measured.  — Read More

In the last couple of years, I’ve increasingly been asked questions that boil down to: will AI benefit from new kinds of programming languages? My answer has been “probably not” and, so far at least, that answer has held up well: AI is now able to generate large quantities of code in just about any programming language you or I can think of.

Now that the technology has advanced, and its characteristics have started to become clearer, my answer has changed. My experience is that AI – at least as it stands right now – often generates high-quality local (e.g. a function) chunks of code, but often struggles when asked to generate code that requires a global understanding of the program. The easiest way to see this is a proliferation of unnecessary defensive checks: these seem benign, but can cause an exponential increase in the number of states later readers of the code believe can occur, with all the deleterious effects that implies.

Perhaps this struggle will soon be overcome, but if it isn’t, we might once again look to programming language design for help. — Read More

Research proposal for measuring how coding LLM perplexity scales with codebase context size, using Lean as a test case for whether formal languages have better predictability exponents and could lead to safer, more secure software worldwide.

Research idea: empirically measure the scaling of coding LLM perplexity over codebase size to estimate the scaling laws of ‘predictability’ by programming language or other factors. This should translate into overall security and safety. — Read More