AI is the first technology that can finally close that gap, which is why we're launching Claude for Small Business

The problem with this explanation is that it's very incomplete. In reality, we should expect to see big differences in pay even if superstars were only a tiny bit better than your average postdoc.

The benchmark tasks were meticulously constructed to be realistic, involving the hard work of hundreds of experts and likely millions of dollars — placing it among the most expensive economics papers of all time.

When our engineers no longer spend time supervising Codex sessions, the economics of code changes completely. The perceived cost of each change drops because we're no longer investing human effort in driving the implementation itself.

Will smarter models be increasingly expensive because of greater accuracy or less expensive because they're smarter?

The structural cost problem in AI inference that makes Apple's on-device bet defensible, not just defensive.

GPT‑5.5 is priced higher than GPT‑5.4, it is both more intelligent and much more token efficient. In Codex, we have carefully tuned the experience so GPT‑5.5 delivers better results with fewer tokens than GPT‑5.4 for most users

This is the part people miss about AI-native companies - the $113k is not a cost, it is your headcount budget allocated differently.

They don't mind paying the AI labs for tokens — but the agent itself, they'd much rather have outside of the labs' infrastructure.

The ending of the pause took an entire new paradigm to kick off. Railroads became a new driver of labor demand that took the slack that the industrial revolution created.

There were 1 billion commits in 2025. Now, it's 275 million per week, on pace for 14 billion this year if growth remains linear

We just started the prepaid billing rollout which means you have to pay ahead of time to use the Gemini API, this is rolled out to all new US billing accounts as of yesterday

except API tokens are currently sold at a LOSS. That "$20,000 scan" probably cost closer to $100,000+ in real gpu time

Raising prices will for sure decrease demand and that risks killing the growth story. And even if revenue keeps growing, it doesn’t matter if there are no margins

AI infrastructure developers cannot wait five years. In many cases, they cannot wait six months, because waiting six months costs billions of dollars of lost opportunities.

As we are on the precipice of a very large wave of lending, I also have to ask myself, is capitalism itself ready for it? More thoughts behind a paywall

incentive-misalignment problem

Help is coming in the form of specialized AI processors that can execute computations more efficiently and optimization techniques, such as model compression and cross-compilation, that reduce the number of computations needed. But it’s not clear what the shape of the efficiency curve will look like. In many problem domains, exponentially more processing and data are needed to get incrementally more accuracy. This means – as we’ve noted before – that model complexity is growing at an incredible rate, and it’s unlikely processors will be able to keep up. Moore’s Law is not enough. (For example, the compute resources required to train state-of-the-art AI models has grown over 300,000x since 2012, while the transistor count of NVIDIA GPUs has grown only ~4x!) Distributed computing is a compelling solution to this problem, but it primarily addresses speed – not cost.

We live in an age of paradox. Systems using artificial intelligence match or surpass human level performance in more and more domains, leveraging rapid advances in other technologies and driving soaring stock prices. Yet measured productivity growth has fallen in half over the past decade, and real income has stagnated since the late 1990s for a majority of Americans. Brynjolfsson, Rock, and Syverson describe four potential explanations for this clash of expectations and statistics: false hopes, mismeasurement, redistribution, and implementation lags. While a case can be made for each explanation, the researchers argue that lags are likely to be the biggest reason for paradox. The most impressive capabilities of AI, particularly those based on machine learning, have not yet diffused widely. More importantly, like other general purpose technologies, their full effects won't be realized until waves of complementary innovations are developed and implemented. The adjustment costs, organizational changes and new skills needed for successful AI can be modeled as a kind of intangible capital. A portion of the value of this intangible capital is already reflected in the market value of firms. However, most national statistics will fail to capture the full benefits of the new technologies and some may even have the wrong sign