Speculation: open models is what will kill Anthropic and OpenAI. Hyperscalers can run the models without a licensing fee. Apple can make them smaller and put them on the device.

The frontier models are an edge and a liability. They're astronomically expensive to train. Without them, their models will fade into obscurity. Their marketing depends on people believing the models are meaningfully different, as people have sweatily argued on this forum. Personally, I'm not convinced there's much of a difference between these models at this point. The harness is what takes these random and hallucinogenic models and make them into something deterministic and useful.

I'm a bit skeptical of the token cost/ROI for all models, but sunk costs are sunk.

It has the feel of self-improving super-intelligence or bust to me. If you get that, the frontier model(s) run away with a faster exponential. It's a bit like semi with Moore's Law with silicon, GaAs could never catch up. If you don't get it, the fast followers crush the high investment and there's no moat. Not like they can enforce copyright!

Open models are probably also comparatively astronomically expensive to train - just less so than the frontier models because they’re somewhat smaller, +/- the creators are more incentivised to focus on getting more from less compute because they’re have to, +/- they rely on distillation of the frontier models and this is more efficient.

But efficiencies aside; creation of open models still requires a lot of money and compute from a large organisation which is willing to accept zero return for that spend. This largesse is unlikely to continue forever; so the question is which will crack first, the frontier models’ business model or the fast followers’ generosity?

> Apple can make them smaller and put them on the device

Someone can, but Apple has essentially admitted defeat and handed the reigns over to Google.

The real moat aren't the models, but the tooling around the models that allow them to perform specific tasks/goals. That's what really sets apart frontier vs open. Open only has the model itself, closed have the tooling to enhance the model.

Completely agree. Once I can reliably get open models doing what I am on Fable ultra I imagine I will switch for good. I am fortunate to have access to a decent bit of local RAM, 192GB of DDR5 at an OK speed. It is not enough and costs are well past absurd. In a few years time I envisage a setup that is sub $10k which can accomplish such tasks. The pace so far has been breakneck. That is all I personally need. That may change, but until true AGI I do think there will be a ceiling to how much I will pay for something frontier if it is only marginally better.

Eventually they will kill the hyperscalers too because of privacy issues. It's better for a company to pay an uprfont cost and then run everything on premise that uploading their entire codebase to a third party service.

> Their marketing depends on people believing the models are meaningfully different, as people have sweatily argued on this forum.

They are noticeably different. Benchmarks, anecdotes, all say the same thing.

Now, is a ~6 month lead actually worth 1 gajillion dollars? Maybe not.

You can run the same harness on fable, opus, sonnet, and see a huge difference between them. It is true the harness is important, and openai has begun encryption its instructions to swarmed sub-agents instead of just encrypting the chain of thought, but the model is still important at this stage.

I still strongly believe Google Gemini has the best position for one simple reason: model maintenance. Accurate information is a moving target.

Open models are indeed very capable, but they will eventually become more specialized to the application to keep an edge. It makes perfect sense that the future shape of AI conforms to the landscape it was born out of.

Open models are 4k TV (or maybe 1080p tv now and 4k TV soon) and SOTA frontier models are 8k TV. Can I or the average user tell the difference? Not really. Would they pay for that difference? Not a chance. Our entire economy is teetering on some future hope that this fragile and immaterial difference will pay off, when the reality is that LLMs are a race to the bottom and eventual razor thin margins. Maybe a tiny vocal subset of programmers can use it for work and make paying for it worth it to them, but that can't prop up an entire economy, especially when said programmers are phased out, jobless, and replaced by AI with each better iteration...

The thing about not much difference between models and the harness making them deterministic and useful is wrong. Also models have different strengths and weaknesses and some are better at almost everything by a large margin compared to others.

As for your speculation, I think it's hinging on some companies releasing models for free or no big differences between models. In a world with hyperscalers and companies training models you can quickly recreate Anthropic or OpenAI by having an hyperscaler ally with a model training company, train a good/a better model, and not release it.

Just like opensource search engines killed google

oh wait

Even in the world where all models are basically equivalent (a thesis I don’t buy, but will grant you for arguments sake) - I believe there is much more to the AI business than just training and running models.

It’s a very new set of technologies, and understanding what is useful to customers and what isn’t is the whole game. Call it, product taste. There were a million cell phones before the iPhone took over the world. Why iPhone? Product taste. There are a million startups, and only a select few become unicorns. Why? Product taste.