> The fact that AI models can be so easily distilled and replicated is such a stroke of luck.

Sort of. It means the country on the verge of monopolizing all aspects of hardware production (China) doesn't need to rely on outsiders for the software. So while that weakens one monopoly it strengthens another.

For what it’s worth, “race to the bottom” typically refers to a scenario that we absolutely do not want as a consumer. We do want a highly competitive market that drives prices down, but “race to the bottom” specifically refers to a scenario where firms compete by minimizing quality, regulatory oversight, consumer/labor/environmental protection, etc.

It's a mistake to think only OpenAI and Anthropic are actually spending the big bucks on pretrain, and the others just distill that.

The Chinese models are pretrained on large clusters just like OpenAI ones are. Yes, they use outputs of the frontier models to further improve the final model, but even without those outputs they'd still have very strong models.

It's not like in a world without distillation things would be much different as you claim.

Intelligence ended up being an equalising force. Kurzweil kind of predicted this, but SV was too obsessed with total world domination.

It reminds me conceptually of the idea of using a ST:TNG replicator to just give you another replicator of your own, or asking a stereotypical genie for "infinite wishes". The genie is indeed out of the bottle in many ways.

Even before LLMs, ML folks were already aware that you can use a model to teach another model. I doubt this is something AI companies put at the top of their investor materials, but it's been nice to see it play out.

That said, there are other moat factors like, a US company needing to use a US AI provider, sticky customers due to corporate onboarding friction, and others. Not nothing, but not as large a moat as some imagined.

The internet created lots of monopolies with network effects and economies of scale.a low margin commoditized business that still attracted a trillion dollars of investment to get off the ground was not how I envisioned it happening either.

Where is the actual evidence of distillation? I keep seeing this repeated ad nauseam but I must have somehow missed the evidence.

It reminds me of the seo antics out there. The search results page is the engine, much like how distilling is the "intelligence" for your chinese room machine

Hmm, don't people think that if the frontier labs really put enough engineering effort into preventing distillation that they would be able to do that, or at least diminish it significantly? I'm sure there are variety of additional techniques they could use on top of what they already do, but I suspect it just hasn't been at the top of their priorities yet. Maybe that will change soon. Worst case they could add additional hurdles to account creation ("know your customer" type of thing).

OpenAI could still have a significant moat. ChatGPT occupies most consumers’ minds when they think about AI and has become a household name. Google won because search became a habit-forming product people grew accustomed to using. Bing was once effectively indistinguishable from Google Search, yet still failed to achieve mass adoption because users had already become accustomed to “Googling” things. The same could be said for people "ChatGPT-ing" things. If OpenAI and Anthropic are smart, they will maintain similar pricing rather than aggressively undercutting each other, allowing the market to resemble Home Depot and Lowe’s, or cloud computing, where AWS, Google Cloud, and Azure coexist as highly profitable competitors. Unfortunately, I doubt OpenAI or Anthropic will pursue this strategy, as both companies appear to be acting as though the race to AGI is winner-take-all even if the market may ultimately support several highly profitable competitors.

> Rather, it seems that selling intelligence might end up as a race to the bottom.

Personally, I came to this conclusion early this year. To acquire the data that AI Companies are using to train their models is low cost and once they have it, they can refine and store it. Creating the LLM takes a bit of money but it is not a serious blocker. Clearly, the Chinese companies can make AI so they will drive down costs. There is a need for good AI (Not just Great AI) and it is not cost prohibitive to make good AI (The same with specialized AI).

My prediction is that AI will spilt into two categories, Great AI (High Cost) and Good Enough AI (Low Cost). Which for the long run of AI and companies that use AI, this is good.

I wouldn’t quite call it a “race to the bottom” because the costs to produce the models aren’t actually decreasing.

Altman specifically has said in an interview that I listened to once that he envisions AI being as cheap as electricity.

the moat is real. the big expensive base models are like the data collected from huge particle accelerators - there's enough unknown structure to be mining for years. you can extract features with more and more generation loss but access to the raw weights is a real advantage, and literally a moat if the interesting behaviors are fenced off

Was it not obvious that the value and advantage was going to be in AI-adjacent services?

The quality of the harness UX, and random fun crap like Sora, it's a shame that OpenAI killed that so soon, and also Group Chats in ChatGPT.. they risk running a Googlelike reputation at this rate

Maybe ultimately whomever can be the "Apple of AI" will win

I think the only moat in the future will be the scale of hardware deployment. If one company is able to deploy an order of magnitude more silicon, they'll have a firm grip on a SOTA model and massive inference usage.

China or SpaceX seem like the 2 likely candidates in 5 years, but who knows.

well, a stroke of luck until the whole US stock market crashes & everyone's retirement funds get cut 40% I guess when people internalize this. it will have to happen sooner or later though I suppose