Comment by theptip
4 days ago
I think this take underestimates a couple points:
1) the opportunities for vertical integration are huge. Anthropic originally said they didn’t want to build IDEs, then realized the pivot to Claude Code was available to them. Likewise when one of these companies can gobble up Legal, Medical, etc why would they let companies like Harvey capture the margins?
2) oss models are 6-12 months behind the frontier because of distillation. If labs close their models the gap will widen. Once vertical integration kicks off, the distillation cost becomes higher, and the benefit of opening up generic APIs becomes lower.
I can imagine worlds where things don’t turn out this way, but I think folks are generally underrating the possibilities here.
If OSS models are 6-12 months behind, it means sometime during 2026, we'll see a model that is on par with the likes of GPT 5.2/Opus 4.5.
For code generation specifically, the performance level of this is going to be more than enough for this customer base. What does Anthropic do then to justify $200/mo price sticker? Better model? Just how much better? Better tools? Single company can't compete with the tools entire OSS can produce.
I would be unable to sleep if I was running OAI / Anthropic.
If capabilities stop increasing for some reason, then yeah, Anthropic is screwed.
If METR task times double twice into the multi-day range in 12 months, then it’s plausible to me that Anthropic can charge $1k/mo or more by automating large chunks of the SWE role. (They have 10x’d their revenue every year, perhaps “value of enterprise contracts” is a better way of intuiting their growth rather than “$/seat” since each seat gets way more productive in this world-branch.)
Just game the benchmarks, bro. (The singularity we didn't want or ask for.)
It's what the current model providers are doing anyways.
The question is always about performance plateau. If LLM performance plateaus, then OSS models will catch up. If there isn’t a plateau, then I can simply ask the super intelligent AI to distill itself, or tell me how to build a clone.
It’s ironic, if the promise of AGI were realized, all knowledge companies, including AI companies, become worthless
> I can simply ask the super intelligent AI to distill itself,
I notice I am quite confused by this point. Why would you expect a super-intelligent AGI to honor your request, which would be at least a request to breach your contract with the AI provider, if not considered actively dangerous by the AI itself?
The smarter the AI, the less likely you should expect to be able to steal from it.
> or tell me how to build a clone
Step one: acquire a $100b datacenter. Step 2: acquire a $100b private dataset Step 3: here is the code you’d use to train Me2.0.
I don’t think this knowledge helps in the way you think it does.
Counterpoint, why wouldn't AGI honor the request? It's AGI, it has it's own agency, it can do what it wants. Can you even own AGI? That seems like slavery? A lot of un-trodden ground here. This is all hypotheticals.
> Step one: acquire a $100b datacenter. Step 2: acquire a $100b private dataset Step 3: here is the code you’d use to train Me2.0.
That seems very useful if your competitor is valued at $800B. I can go to an investor and say, hey if you lend me $200B I'll have the same product as that guy.
But cost to train will probably come down over time. After all, all of us trained on a single life of data at 20Watts.
I actually think that plateauing is the best case scenario for big labs.
I think there are three broad scenarios to consider:
- Super-intelligence is achieved. In this scenario the economics totally break down, but even ignoring that, it’s hard to imagine that there are any winners except for the the singular lab that gets here first.
- Scaling laws hold up and models continue to get better, but we never see any sort of “takeoff”. In this scenario, models continue to become stale after mere months and labs have to spend enormous amounts of money to stay competitive.
- Model raw capabilities plateau. In this scenario open source will catch up, but labs will have the opportunity to invest in specific verticals.
I believe that we’re already seeing the third scenario play out, but time will tell.
Future historians:
In Jan 2027 AGI was achieved
In Feb 2027 it created a plan for its post singularity hypermind
In Mar 2027 Cobalt mines in Congo closed due to Tutsi rebel group M23 starting another ethnic cleansing
It is 2032 the AGI promises again the the hypermind will be ready next year if it can just secure the needed minerals, offering to broker peace in the middle east
It is 2035 and the AGI reduced its capabilities to be able to extend its runway as it is on the verge of bankruptcy
Its is 2036 VCs finally throwing the towel on AGI, talking about the return of Crypto
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LLM performance has already plateaued. I don't know, nor care what benchmarks are saying, because they not once translated to the real world for me.
The only thing that has seen massive boost are harnesses around AI. And AI companies are behind here compared to OSS.
Totally agree. It's about getting the right context in at the right time, a big part of which is exposing the AI to the right tools.
That said, bigger context windows / faster Inference seem really useful.
> And AI companies are behind here compared to OSS.
How so?
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You are assuming that superintelligent AI will follow every command of users outside the labs.
I mean, the model itself is just sitting there, waiting to be prompted. The labs try to embed safeguards but they don't know (nor do any of us know) how to make a foolproof safeguard for an AI system. We don't understand how AI even thinks.
Not if they can leverage their superior abundance of compute/intelligence to invade other industries.
To go vertical they’d need to illustrate the value-add, a problem that the vertical competitors already have. Why use Claude for Accountants at $300/month when regular Claude will do the same thing for much less? The stock answer is that Claude for Accountants keeps your data more secure and doesn’t train on it. But a) I think the enterprise consumer is much less likely to trust a model creator not to stick its hand in the cookie jar than a middleman who needs the trust to survive, and b) the vertical competitors typically don’t use the absolute most up-to-date models in their products anyway, so why not just go open-source and run everything in-house? 6 months is a long time in tech, but it’s the blink of an eye in most white-collar professions.
Once the majority of work at a company can be done by AI, Anthropic has an alternative revenue stream to selling AIs to that company--directly competing with that company with a completely integrated AI system. There's of course many barriers to entry/various advantages of incumbents--but it's possible to see a world in which the company selling the AI has a huge advantage too.
The point is that in this hypothetical you can get public access to Claude Opus 6, but they internally use Claude Opus 7 (Accounting Finetune) which is both cheaper to operate and higher IQ.
So they (or their wholly owned subsidiary) can sell accounting services cheaper than anyone on the outside.
Regarding the diffusion/distillation time, I assume it gets harder to distill in the world where frontier labs don’t give API access to their newest models.
BTW the distillation (or accusations of it) seems to go both ways. I've seen multiple reports of people asking Claude what model it is -- in Chinese -- and having it answer that it's DeepSeek.
They're all scavengers, and we're the road kill.
I think it’s very plausible that the OSS models are being distilled too, but note that it’s asymmetrical.
You can’t get an Opus 4.5 by distilling from DeepSeek. What you might be able to get is a slightly more cost-effective training data generation pipeline, or something along those lines.
In the other direction, my belief is that DeepSeek could not have been trained without distilling from US labs. They simply didn’t have the compute to do the pre-training required.
Tech has been trying to "gobble up" legal, medical, etc for decades. I'm quite skeptical a newcomer with a powerful model will be able to penetrate them, especially while selling those incumbents access to the same models they are building on.
> Tech has been trying to "gobble up" legal, medical, etc for decades
This time it’s different, obviously.
> especially while selling those incumbents access to the same models they are building on.
In the extreme, i think it’s plausible that frontier labs basically stop selling any access to their leading models. Whatever you make available by API will just get distilled. In the vertical integration world, the only way you get access to these models is by contracting with a company to buy a product (requirements in, code/decisions out) rather than direct conversation with the AI.
I don’t think they would unship Opus 4.6, but there isn’t a strong incentive to compete on chatbot intelligence in this world.
After trying out Pi, I really don't know what 'vertical integration' Claude Code offers. And Pi isn't even the most popular alternative (I think it's OpenCode rn).
Take a look at Harvey. Pi / Opus alone cannot do what this product does.
You can think of this most simply as “model + scaffolding/skills = product”.