[hotpotat]

Lots of in-depth analysis, but I think the author is very clearly emotionally invested to the point that they are only drawing conclusions that justify and support their emotions. I agree that we’re in a bubble in the sense that a lot of these companies will go bankrupt, but it won’t be Google or Anthropic (unless Google makes a model that’s an order of magnitude better or order of magnitude cheaper with capability parity). Claude is simply too good at coding in well-represented languages like Python and Typescript to not pay hundreds of dollars a month for (if not thousands, subsidized by employers). These companies are racing to have the most effective agents and models right now. Once the bottleneck is clearly humans’ ability the specify the requirements and context, reducing the cost of the models will be the main competitive edge, and we’re not there yet (although even now the better you are at providing requirements and context, the more effective you are with the models). I think that once cost reduction is the target, Google will win because they have the hardware capabilities to do so.

[UltraLutra]

> Claude is simply too good at coding in well-represented languages like Python and Typescript to not pay hundreds of dollars a month for (if not thousands, subsidized by employers).

I think the cost is more in thousands to cover inference. And, no, I don’t think it’s been proven out that an engineer is so much more productive to justify thousands of dollars a month cost. The models are great for greenfield projects. But a lot of engineering is iterating and maintaining an existing code base——a code base that the engineer is fluent in. So the time savings is writing code specific enough to implement a new feature vs writing a prompt specific enough that the AI can write code specific enough to implement a new feature. The difference between those two tasks is the time savings.

Say that difference is like 10%. You save 10% of your time by using AI, meaning you have 4 more hours a week than you did before. Are you going to spend 4 more hours writing code? No. Some will be spent in meetings. Some will be spent reading Hacker News. Maybe you’ll get two hours a week of additional coding time. So you’re really only increasing your output by 5%.

The so the employer gets 5% more from you if you have AI. If your salary is 10k per month, they wouldn’t pay more than $500. Per month. And you’re probably costing Anthropic >$10k in inference costs per _week_. The economics just don’t make sense.

You can sub out the numbers here and play around with the scenario. I think the cost of inference needs to drastically fall. And I don’t think that happens soon. What might happen 10 years from now is developers are given a laptop with a built-in GPU for AI inference that does much better code auto-complete using AI. That’s something an employer can pay 3k-5k for _once_ as a hardware investment. But the future of AI coding won’t be agents. It won’t be prompt-engineering. The models aren’t going to get much better. It will be simple and standard and useful but unimpressive. It’s going to feel boring. It’s going to feel boring. When it’s working, when it’s mature, when it becomes economical, it always feels boring. And that’s a good thing.

[UltraLutra]

* And you’re probably costing Anthropic >$1k in inference costs per _week_. Not >$10k. Typo.

[o_nate]

Hard to justify the valuations of these companies based on the potential market for what is essentially an IDE plugin.

[danenania]

OpenAI was arguably an oom ahead at one point, and competitors caught up in about a year. So I’m not sure even an advantage like that is insurmountable. Like we saw with Anthropic, you just need a group of key researchers to leave the incumbent and start their own thing—they’ll then have a pretty good shot at catching up.

[cwmma]

It is not clear at all if Claude will actually be profitable, are there enough people who will actually pay the subsidized costs especially if they end up being a significant fraction of an additional dev's salary.

[827a]

I think there's enough people willing to pay Claude's token rates today, either via subscription or via proxy through e.g. Cursor, that they can effectively turn the "new model R&D cost" knob to whatever their financials can support, and they'll survive for as long as their models can remain competitive.

The challenge all these frontier labs have is: Their existing models can have high token profitability, but they have to invest everything they've got (and everything they're given) into new model R&D, because if they don't xAI will beat them, or Anthropic will beat them, or Google will. That's the nature of frontier spaces like this.

But the flipside is, model capability will plateau, they probably are already, and as that happens it becomes safer to aim for profitability. And I have zero doubt that OpenAI and Anthropic can find profitability. xAI, Perplexity, Mistral, and the other labs, I'm less sure about.