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Comment by aurareturn

6 days ago

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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aurareturn

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benedictevans  6 days ago

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.

  • aurareturn  6 days ago

    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.

musebox35  6 days ago

Most of your analysis I can easily relate to except “There is evidence that the Chinese models are falling further behind, not gaining.” Where is that evidence? Deepseekv4 claims to be trailing front runners by six months. I read people agreeing with this. I watched Eric Schmidt to recently make similar comments. Is he just scaremongering? Why do you claim they are falling behind?