In slide 22, it compares LLM labs (OpenAI/Anthropic) to mobile data telecoms (AT&T, Verizon, TMobile) in 2010s. The difference is that mobile telecoms follow a standard (3G, 4G LTE, 5G) and there is little to no differentiation. It's virtually the same no matter which company you choose or which country you travel to.

A better comparison is actually AWS/Azure/Google Cloud/NeoClouds to AT&T and Verizon. The data centers follow a standard (CUDA/PyTorch/etc.) while OpenAI and Anthropic are becoming more like iOS and Android. Both the clouds and telecoms had to spend a ton of capex to build out infrastructure first.

Because of what I think is a poor comparison, the the next few slides make the wrong conclusions. For example, it thinks that models will be a commodity like 5G data. I disagree. I think frontier models are a classic duopoly/monopoly scenario. The smarter the model, the more it gets used, the more revenue it generates, the more compute the company can buy, the smarter the next model and so on. It's a flywheel effect. This is similar to advanced chip nodes like TSMC where your current node has to make enough money to pay for the next node. TSMC owns something like 95%+ of all of the most advanced node market. Back in the 80s and 90s, you had dozens of chip fab companies. Today, there are only 3. There should only be 1 but national security saved Intel and Samsung fabs.

There is evidence that the Chinese models are falling further behind, not gaining. Consolidation will likely happen soon because many unprofitable open source labs will have to merge and focus on revenue generation.

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benedictevans 15 hours ago | parent [-]

I've made the semi comparison myself, but the amount of capital required to build a SOTA model today is clearly nowhere near enough to lead to a monopoly.

I'm aware that telecoms networks are standardised (I was once a telecoms analyst), but that isn't a precondition for a commodity.

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aurareturn 15 hours ago | parent [-]

Just like how starting a chip fab was relatively easy back in the 80s and 90s. There were dozens of chip fab companies in the 80s.

It turns out that fabs follow Rock's Law which is that the capital cost to build a new fab doubles every 4 years. This means it will quickly get rid of the less competitive players. This is not dissimilar to the LLM scaling laws where you need a magnitude more compute to get unlock a new tier of intelligence.

Today, Anthropic and OpenAI are clearly in the lead for models and then there is everyone else. Google is a close 3rd. No one else is challenging them anymore in SOTA models. Some models might beat them in one or two benchmarks but none can compete overall. I expect this gap to grow bigger as models cost more and more to train.

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benedictevans 13 hours ago | parent [-]

Yes, I wrote about Rock’s Law too, but we don’t know that this is how these models will develop

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aurareturn 6 hours ago | parent [-]

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...

	▲	menaerus 23 minutes ago \| parent [-]
		You lay out some good arguments but I agree with both: the models relative to few years back really did become the commodity because today you could take the non-frontier model, maybe self-host it or pay the much less price per M tokens to get the performance of a ~2-year old frontier model. At the same time I do think that we are getting into the monopoly/duopoly/tripoly with the frontier models for all the reasons you already mentioned, and this scares me a little bit.