From Compute Overhang to Compute Crunch - by Steve Newman

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From Compute Overhang to Compute Crunch<br>The state of AI in Q2 2026, part 2

Steve Newman<br>May 19, 2026

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In the first installment of this “state of AI” series, I reviewed the pace of change. In this installment, I’ll review the factors driving that change. Understanding the forces behind AI progress can help us understand where things will go from here.

A punctured equilibrium

I was in my 20s when the World Wide Web burst onto the scene. I remember being startled at how quickly it went from an obscure technical standard to a household name. On reflection, I realized the web spread rapidly because the ingredients had already been in place. Computers were widespread in the workplace and home. Many were already connected to modems or networks to access AOL, CompuServe, or corporate e-mail. The modems tapped into an existing worldwide network of telephone cables. And there was pent-up demand for services the Web could offer. Like a boulder perched at the edge of a cliff, the world of the 1990s was poised for change.<br>The AI wave is being driven by another dramatic overhang of untapped potential. Prior to the release of ChatGPT in November 2022:<br>Semiconductor manufacturing was already an enormous industry, well accustomed to meeting surges in demand.

Close to 100 million “GPU” chips were being manufactured per year. They were primarily used for video game graphics, which involves massive numbers of numerical calculations, meaning these chips could easily be repurposed for training large language models.

Semiconductor factories can easily pivot to new chip designs. As a result, capacity could be shifted from other chips to GPUs, and GPU designs could rapidly be altered to more efficiently execute the “deep learning” algorithms used in LLMs.

Vast quantities of text existed in digital form, ready to be fed into AI training pipelines.

Most non-physical work had long since moved from pen and paper to computers, leaving it poised for automation.

One result is that, since the beginning of 2022, the world’s supply of AI computing capacity has increased by a factor of 1000!

Notice how steeply this graph rises. Then note that it only 3.5 years! (source)<br>Eventually, some of these tailwinds will peter out, as I’ll discuss below. But for now, all of this untapped potential is driving an insane pace of change. Everyone in the AI industry feels like they’re in a race. Some are racing to beat the competition; some are just racing to rake in money as quickly as possible. The most pivotal race is that between OpenAI and Anthropic.<br>Two companies are setting the pace – and choosing the course

An unfathomable number of companies are working to develop and deploy AI. Among startups alone, hundreds have raised $100M or more, including many I’ve never heard of.

ChatGPT Pro claims that each of these AI companies has raised $100M or more.<br>Despite all this activity, most current action is being driven by progress in two areas:<br>General-purpose models like ChatGPT and Claude, that are trained on a vast array of data and can take on a wide range of bite-sized tasks.

“Agent harnesses” like Codex and Claude Code, that tackle large projects by breaking them up into a series of tasks that can be handled by a model.

Models, on their own, can do things like answering questions or summarizing documents. Incorporated into an agent harness, they can carry out an increasing range of valuable tasks, from writing software to preparing presentations. Together, they are central to the present-day impacts and expansive future scenarios of AI.<br>Developing a cutting-edge model is now a multi-billion-dollar proposition. Only five companies have been able to spend that kind of money, and three of them are not getting success for their money.<br>AI efforts at Meta (Facebook) have been plagued by dysfunction and leadership churn. Meta has never managed to produce a cutting-edge general-purpose model, and at this point, there’s no obvious reason to believe they will. They’ve pushed the state of the art in some areas, and their AI efforts have apparently paid off by improving ad targeting, but they’re not a central player in the most important race.<br>xAI – Elon Musk’s AI company, now part of SpaceX – has built a pair of gigantic data centers in record time, aptly dubbed Colossus 1 and 2. However, their “Grok” model is best known for contradicting crazy things said by Musk, creating racy pictures of nonconsenting women, and briefly referring to itself as MechaHitler. xAI could conceivably have a future in robotics (a focus for another Musk company, Tesla). But for now, they are such an unimportant player in the AI world (experiencing ongoing departures of key staff) that they can’t make use of the Colossus 1 data center and are handing it to Anthropic.<br>The most surprising failure, of course, is Google . In a familiar story in tech, Google invented the key...