AI eats the world (Spring 26) [pdf]

One of the graphs has two series: net revenue for one company, gross revenue for another. Absolutely ridiculous.

And that's just one example. You also haven't adjusted for inflation in your graphs that span multiple decades. Not to mention that the graphs themselves are not related to what you're discussing most of the time. You're just pointing at random historical developments and seemingly claiming they imply something for AI. They don't.

Also you don't name your sources. You just say "Companies" for most of them. Or a single name. Ridiculous. Those are not sources. You should identify the documents.

This is incredibly low quality work. A college freshman would do better.

Evidence point to the same type of scaling law. Compute for a training run grows 4-5x every year.[0] I'm sure this will slow down but the premise remains that weaker competitors will not be able to maintain this pace. We already see labs like Cohere, Mistral, Inflection AI, Adept, Character.ai, and others bow out of the frontier race. I'm also skeptical that Meta, xAI can catch up. Even Google has trouble keeping up.

Even if this isn't true, comparing telecom bits to tokens is wrong. Bits are the same no matter what telecom transfers them. Tokens are not all the same. The quality varies.

We're already seeing a massive divide between frontier models and lesser models in growth rates. Anthropic is adding $10b - $15b every month in ARR. This figure likely dwarfs open source labs. This is all because its models are maybe 10-15% better.

The cost to inference a 1T param frontier model is the same as a 1T param open source model. Therefore, if the frontier model is even 10-15% better, it will gobble up the market over time.

Lastly, even though Claude Code and Codex are the biggest revenue drivers for Anthropic and OpenAI today, I don't believe this will be true 2-5 year from now. I believe selling their tokens via API will be their biggest. The sum of applications in the world will dwarf coding in market size. For example, biotech, finance, physics, engineering, robotics, sensor data, etc. This is why I think OpenAI and Anthropic are becoming more like iOS and Android than AT&T and Verizon. Applications will build on top of OpenAI and Anthropic just like iOS and Android.

[0]https://epoch.ai/blog/training-compute-of-frontier-ai-models...

What in the AI slop is the Yogi Berra “AI predications” duplicate slide?

In business there's 52 (4*13) weeks in a year and as a result, 2080 regular working hours in a year (40*52). I think these are just generally agreed upon ways to define time for simplicity. In some (most?) systems your 'hourly wage' is simply your salary divided by 2080, trying to divide your salary by other metrics to determine hourly wage tend to wonk the numbers a bit.

> Imagine asking “What will be changed by the internet?” in 1997

Pretty much all of the stuff that was suggested back then or earlier: Shopping, advertising, video conferencing, collaboration, software distribution, media consumption, banking, finance and of course communication overall.

Most of these ideas weren't exactly new in 1997, but go back to services like CompuServe and even Douglas Engelbart's Mother of All Demos. The bottlenecks were bandwidth and personal computer performance (both of which were then predictably following Moore's law), not human imagination.

A few examples that a lot of people correctly extrapolated from: NLS (1968), PictureTel (1987) and later LiveShare, IndyCam (1993), CUSeeMee (1995), RealAudio (1995), RealVideo (1997).

Perhaps the core business problem with LLM:s isn't finding a product-market fit, but that our imaginations have been running wild with expectations on "AI" since at least the 1950s, and now we have something that quacks - but doesn't quite walk - like a duck.