Charlie O'Neill

speaker

259 appearances 2 recordings 1 series first heard Jul 2026 last heard 15 Sep

Charlie O'Neill’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 2 in all, peaking in Sep 2026 with 1.

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Prof G Markets · Gas Is Back Above $4 — And Could Keep Rising · 21 Jul 2026 podcast

And so Anthropic is very clearly doing this.

They're going after the verticals of finance and legal, open AI as well.

And so I think companies are really feeling this pressure.

If you're a startup or a company in legal or finance and you're using LLM to do these particular things and you have previously just been an Anthropic wrapper,

You've just got some logic calling anthropic models.

You don't have a distinguishing moat between you and anthropic, and so you're starting to think about, okay, what's the one thing I have that anthropic doesn't have?

And that's a really nice feedback cycle.

I have users who love and hate my product for various reasons, and they will tell me what they love and hate, and I can use that to improve the intelligence of a model.

And again, you do that through training.

And the only real way to do that is with open source models.

And so I think it's this combination of margin pressure and companies wanting to develop their own mode to protect themselves against their vertical being eaten by these closed source frontier labs.

The answer to this used to be simply that the Chinese and open source models were much smaller.

So the big labs were the only ones that had the compute to be able to train the really large models.

And of course, like the scaling laws that we have predict that intelligence increases, but with diminishing returns in model size.

And so, yes, of course, the big labs had better and bigger models, but you often could use a much smaller model to do the task.

I think now it's more of a case of like, okay,

some of these open source models are actually very large.

And I think K3 was a massive shifting point because, you know, previously we'd gone into the, just for it into the one trillion parameter model range with the previous Kimi models and DeepSeq very, very recently.

But this is, you know, almost three trillion parameters.

Like this is a big boy.

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