It's a joint ignorance of how these frontier models get baked and what consumers want.

Many pundits think it's just a matter of scraping the internet and having a few ML scientists run ablation experiments to tune hyperparameters. That hasn't been true for over a year. The current requirements are more org-scale, more payoff from scale, more moat. The main legitimate competitive threat is adversarial distillation.

Many pundits also think that consumers don't want to pay a premium for small differences on the margin. That is very wrong-headed. I pay $200/month to a frontier lab because, even though it's only a few % higher in benchmark scores, it is 5x more useful on the margin.

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svnt an hour ago | parent | next [-]

It is the benchmark error rate, not the benchmark success %, that we actually trip up on.

Going from 85% to 90% is possibly 1/3 fewer errors or even higher, depending on the distribution of work you’re doing.

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nick32661123 2 hours ago | parent | prev [-]

You pay to OpenAI or which one do you use? Do you switch regularly?

	▲	energy123 an hour ago \| parent [-]
		I pay OpenAI but I would also be a happy Anthropic customer. My view is that OpenAI, Anthropic and Google have a good moat. It's now an oligopolistic market with extreme barriers to entry due to needed scale. The moat will keep growing as the payoffs from scale keep growing. They have internal scale and scope economies as the breadth of synthetic data expands. The small differences between the labs now are the initial conditions that will magnify the differences later. It wouldn't be surprising to also see consolidation of the industry in the next 2 years which makes it even more difficult to compete, as 2 or 3 winners gobble up everyone and solidify their leads.