Comment by SwellJoe
5 days ago
The moat looks deep today but it's going to become more shallow every year.
Training a new model from scratch takes serious resources. Post-training/fine-tuning an existing model, dramatically less. The knowledge for the process was esoteric two years ago, now you can ask a current model (one of several) to walk you through it, while building the tools to do it as you go. Several of my recent weekend projects have been exactly that sort of thing, just so I understand it better. "Let's make a LoRA", "let's generate a corpus of training data for fine-tuning a model for X task", "how can I put my face in a text-to-image model?" stuff like that. All of this is do-able on kinda modest local hardware (a couple of old GPUs or a Strix Halo or DGX Spark or big Mac Studio), or for a few bucks or a few hundred bucks or a few thousand bucks of cloud compute, depending on scale.
Scale that up to corporate or startup scale, with the money that's been flowing into AI for the past couple/few years, and it's obviously there's going to be a lot of competition just as the top model makers need to start ringing the cash register. That's a lot of opportunities for people to look at their ballooning Claude usage costs and find other ways to do the same thing for drastically less money. $100/month or $200/month is a no-brainer for Claude Code with probably the best model for coding, but they're pushing more users to usage-based billing which becomes cost-prohibitive real fast.
So, they desperately need to continue to be among the only ways to solve the hardest problems, and they need the alternatives to cost a similar amount. They can count on OpenAI and Google to ratchet up prices, too. They probably can't count on everybody, especially the vendors in China with different economics, to do it. And, they can't count on companies to look at their own usage and not ask, "Can we train a smaller specialist model that does this one thing we're using the Anthropic API most heavily for?"
I'm hoping they just mean stuff like using Claude for distillation by e.g. Chinese model makers, and not "how do I fine-tune Gemma 4 to write more like me?" or whatever.
What moat? There are multiple companies providing pareto-optimal frontier models, and it takes O(10) people to build one of these things.
The rest is capital intensive, and the price will approach the cost of production over time.
Thinking this is a profitable endeavor is equivalent to claiming coal plants have good margins because boilers are expensive.
I think we agree?
What moat? You answered yourself: "capital intensive"
But, history says the supercomputer of today will fit in your pocket in a few years.
They've bought up all the RAM and GPUs, which pushes the capital requirements upward for everyone else. But, they can't corner the market forever, there are too many competing interests. AMD and Intel keep making new GPUs and APUs. The memory makers can't just sell to only AI companies forever, if they do Chinese manufacturers will move in and eventually eat them from below (as has happened many times before).
They have a moat today, and it's just that it's really expensive to train and host frontier models, especially at commercial scale. It used to be there was also some secret sauce to making it fast and efficient. But, secret sauce is being published daily by all sorts of researchers, folks are figuring out how to do more with less and it often finds its way into llama.cpp or vLLM or SGLang within days or weeks.
> But, history says the supercomputer of today will fit in your pocket in a few years.
I don't think this will be true in the same time span anymore. Each miniaturization is costing more and more money.
Perhaps they'll come up with exotic fundamental improvements, but I don't think the rate of improvement of compute/watt will match the previous decades.
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> I think we agree?
That is such a crazy way to start a response to someone trying to argue with you. I should try this. That's amazing. I know you didn't mean it as a trick, at least I'm pretty sure you meant it sincerely, but I'm just struck by the power of it to defuse and redirect the conversation. And this was a very low-grade example, but I could imagine this being useful in much more heated contexts.
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The other half of the moat is the data they stole from everyone else, some of it illegally. So, be sure they will do everything in their power to stop others from getting that data freely.
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They’ve bought up all the RAM and GPUs…
Is there an endgame where even this is considered overly complex? Instead of starving the competition by buying up all the compute, why not just buy up… all the money!? Hoover up as much investment capital as possible so that your competitors can’t get funding.
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>But, history says the supercomputer of today will fit in your pocket in a few years.
That was Moore's law saying that. And it seems Moore's law slowed down quite a bit for now.
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"But, history says the supercomputer of today will fit in your pocket in a few years."
hmm nooo ??, physic says otherwise
> it takes O(10) people to build one of these things
To build a working prototype, sure. To operate at production scale, definitely not. The same rule would apply to WhatsApp and many other world-scale products. Turns out that, the moment you need to monetize these machines, your O(10) stops working.
O(10) people?
So, a constant number of people.
(less facetiously, I think they mean "5 to 50")
Other models arent even close except for gpt 5.5. You're dead wrong on that. You read too many benchmarks and/or chinese propaganda. There hasn't been a serious contender in agentic SWE besides OAI and anthropic for a long time, and no chinese model has even reached opus 4.5 performance yet. The moat isnt insurmountable but it is very solid for at least a 12 month lead time. Which is such an insane amount of time in this landscape and industry. The moat is stretching, not shrinking, on agentic SWE. And that is literally the only moat that matters for RSI.
DeepSeek 4 Pro is performing agentic SWE tasks for me quite well. It can't do everything Opus can do, but if OpenAI and Anthropic disappeared tomorrow, I'd figure out ways to make it work with harness improvements and other optimizations.
Anthropic can stretch the moat all they want, but in the department of trust, they put a final nail in their coffin today. Anthropic is pure evil at this point.
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I use gpt 5.5 at work (because they pay for it) and DeepSeek at home (because I pay for it) and while I do agree one is better than the other, I think you’re really overstating how far apart they are. Just my take.
What's 12 months lead time worth? Not much from what I can tell. Contrary to what these AI companies might tell you, if an AI model can't do it, a human can still do the work.
Honest question, is it possible that since might be using the latest/best model to analyze and improve the existing ones, the moat will expand exponentially, making the models better and more efficient at each iteration until there is no point in competing?
All models from the past two years are close in the general case.
This is just another incremental improvement, rushed out to boost the ipo, AI has the capacity to aid an engineer but this minor bump in performance will have essentially zero impact on the productivity of an engineer working on real world solutions when compared with any other major model.
We are trending towards asymtotic and it can't happen fast enough, that's when the true cost of this will become evident.
Most of HN is stuck in this fantasyland where they insist their local LLM setup is comparable to Opus 4.8 or GPT 5.5. It's like a collective delusion, I've never seen anything like it.
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> The moat looks deep today
Does it? What can this model do that I both want and cannot already do?
Anthropic made a nice little post saying how dangerous it is, because it is good enough to eat their own business. But I don't want to eat their business. They also said it was good at playing Slay the Spire, but I can't think of anything more insulting than have a machine do that in my place. That's MY comfort game, not something for a stupid Clanker to take away.
They did not provide any other use case.
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<The memory makers can't just sell to only AI companies forever, if they do Chinese manufacturers will move in and eventually eat them from below (as has happened many times before)>
Unfortunately this has been happening almost forever. You can spend 10s of thousands of $$ design, prototyping, building & marketing anything, whether a physical product or software & some company where the wages are lower are going to come along, build it cheaper (not necessarily better quality either) and ship it to the world.
As a result, the other countries import more stuff "because it is cheaper" and eventually local manufacturing dwindles away to virtually nothing. That is the case here in Australia. Our manufacturing base has shrunk to stuff all compared to what we had 30 years ago & as a result we are poorer as a nation for it
>Unfortunately this has been happening almost forever. You can spend 10s of thousands of $$ design, prototyping, building & marketing anything, whether a physical product or software & some company where the wages are lower are going to come along, build it cheaper (not necessarily better quality either) and ship it to the world.
>As a result, the other countries import more stuff "because it is cheaper" and eventually local manufacturing dwindles away to virtually nothing. That is the case here in Australia. Our manufacturing base has shrunk to stuff all compared to what we had 30 years ago & as a result we are poorer as a nation for it
Then the companies in that country need to learn how to be more competitive and governments need to learn how not to overregulate, overtax and raise barriers.
> overregulate, overtax and raise barriers
Also known as labor and environmental protections. I am in favor of labor and environmental protections, but when producers are allowed to avoid them simply by moving production abroad, well, the incentives are clear.
> some company where the wages are lower are going to come along, build it cheaper (not necessarily better quality either) and ship it to the world.
Yeah, it's called competition. It existed even in the socialist countries (where is was called "socialist competition/emulation").
Given that Anthropic has never released anything open weights I wouldn’t count on the fact that they view finetuning Gemma 4 as something allowable. I think they think nobody other than Anthropic should have AI
> The moat looks deep today but it's going to become more shallow every year.
Unless the frontier labs start nerfing their models, which is exactly what seems to be happening.
The counter-point to your argument is a future where less and less un-nerfed open-source frontier models exist. Sure, China/Meta might keep commoditizing their complement by releasing un-nerfed models, but these come with their own limitations too.
I am worried that the door to great open-source frontier models might be closing by the day now.
A nerfed model is a useless model which makes the moat shallower, not deeper.
Not if the model is part of a broader product that happens to be very useful/relevant to private companies willing to pay a lot for it (e.g. a coding agent that can do many things but won't help you build a frontier LLM model).
My intuition is that Claude and the likes are going gung-ho after this, along all the verticals that will generate money without threatening their moat.
The moat is not the model, it's the harness. I wager that's one of the main reasons why Google made Antigravity closed source.
I don't feel strongly about anything most folks are arguing back and forth about, but this one is obviously wrong.
Everybody and their brother has made an agent. There are toolkits. You can whip one up in an afternoon.
Not only that, I've found models often perform worse, or at least cost more and take longer, in a big complicated agent like Claude Code, including Anthropic models. They want proprietary doodads hanging off the side (multi agent orchestration, memory, things of that nature) to matter, because they can lock you into tools like that. But, top models can do everything with bash.
But harness is relatively easy to code yourself?
They're just system prompt composer, with some tool functions that the LLM can invoke. I've vibe coded my own in just one day.
I don't understand why this is being presented as an either/or thing.
The moat is actually the harness AND the model, and one of the reasons that Claude works so well is because the model is actually trained with its usage in that specific harness in mind, and the harness is designed to deal with Claude model's idiosyncracies. Easy to validate, just run Claude through some other harness and compare, then just run some other model through Claude's harness and compare
But is there anything preventing them from putting their own proprietary wolfram alpha/prolog/super duper expert system in there?
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Could you explain your analogy here, what does a moat have to do with a harness?
There is no training from scratch though. It's kind of, "first create the universe" framing pretention. All models rely heavily on the large corpus that humanity built through large span of time. And of course humanity didn't create the condition of its emergence.
What makes you say usage billing is cost prohibitive? I use as much flagship model as I could possibly want and it's like four figures a month. That's totally doable compared to SWE pay.