Let's give that one a SCP* re-up: https://news.ycombinator.com/item?id=47778922

(* explained at https://news.ycombinator.com/item?id=26998308)

That post doesn't address the human factor of cost, and I don't mean that in a good way. Even if AI costs more than a human, it's tireless, doesn't need holidays, is never going to have to go to HR for sexual harassment issues, won't show up hungover or need an advance to pay for a dying relative's surgery. It can be turned on and off with the flip of a switch. Hire 30 today, fire 25 of them next week. Spin another 5 up just before the trade show demo needs to go out and fire them with no remorse afterwards.

> It was easy to get comfortable with using the best model at the highest setting for everything for a while, but as the models continue to scale and reasoning token budgets grow, that's no longer a safe default unless you have unlimited budgets.

For a while I used Cerebras Code for 50 USD a month with them running a GLM model and giving you millions of tokens per day. It did a lot of heavy lifting in a software migration I was doing at the time (and made it DOABLE in the first place), BUT there were about 10 different places where the migration got fucked up and had to manually be fixed - files left over after refactoring (what's worse, duplicated ones basically), some constants and routes that are dead code, some development pages that weren't removed when they were superseded by others and so on.

I would say that Claude Code with throwing Opus at most problems (and it using Sonnet or Haiku for sub-agents for simple and well specified tasks) is actually way better, simply because it fucks things up less often and review iterations at least catch when things are going wrong like that. Worse models (and pretty much every one that I can afford to launch locally, even ones that need around ~80 GB of VRAM in the context of an org wanting to self-host stuff) will be confidently wrong and place time bombs in your codebases that you won't even be aware of if you don't pay enough attention to everything - even when the task was rote bullshit that any model worth its salt should have resolved with 0 issues.

My fear is that models that would let me truly be as productive as I want with any degree of confidence might be Mythos tier and the economics of that just wouldn't work out.

Human dev labor cost is still the high pole in the tent, even multiplying today's subsidized subscription cost by 10x. If the capability improvement trajectory continues, developers should prepare for a new economy where more productivity is achieved by fewer devs by shifting substantial labor budget to AI.

The problem is half the time you don't know you need the better model until the lesser model has made a massive mess. Then you have to do it again on the good model, wasting money. The "auto" modes don't seem to do a good job at picking a model IME.

> I know not everyone wants this mental overhead, though.

I’m curious how to even do it. I have no idea how to choose which model to use in advance of a given task, regardless of the mental overhead.

And unless you can predict perfectly what you need, there’s going to be some overuse due to choosing the wrong model and having to redo some work with a better model, I assume?

Isn't that essentially GPT Pro Extended Thinking?

Except developers can’t even do that. Estimation of any not-small task that hasn’t been done before is essentially a random guess.

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I agree, and I'm also quite skeptical that Anthropic will be able to remain true to its initial, noble mission statement of acting for the global good once they IPO.

At that point you are beholden to your shareholders and no longer can eschew profit in favor of ethics.

Unfortunately, I think this is the beginning of the end of Anthropic and Modei being a company and CEO you could actually get behind and believe that they were trying to do "the right thing".

It will become an increasingly more cutthroat competition between Anthropic and OpenAI (and perhaps Google eventually if they can close the gap between their frontier models and Claude/GPT) to win market share and revenue.

Perhaps Amodei will eventually leave Anthropic too and start yet another AI startup because of Anthropic's seemingly inevitable prioritization of profit over safety.

They're also getting into cloud compute given you can use the desktop app to work in a temporary sandbox that they provision for you.

I was about to call it reselling but so many startups with their fingers in the tech startup pie offer containerised cloud compute akin to a loss leader. Harking back to the old days of buying clock time on a mainframe except you're getting it for free for a while.

The "real cost" of running near-SOTA models is not a secret: you can run local models on your own infrastructure. When you do, you quickly find out that typical agentic coding incurs outsized costs by literal orders of magnitude compared to the simple Q&A chat most people use AI for. All tokens are very much not created equal, and the typical coding token (large model, large noisy context) costs a lot even under best-case caching scenarios.

> Is there an equivalent ultra-high-end LLM you can have if you’re willing to pay? Or does it not exist because it would cost too much to train?

I guess at the time that was GPT-4.5. I don't think people used it a lot because it was crazy expensive, and not that much better than the rest of the crop.

taalas is amazing. id gladly spend 5-15k on something that matched that performance with opus 4.6 quality

> It wouldn't surprise me if in 2-3 years the "max" plan is $800 or $2000 even.

I'd rather be surprised if they are still doing business by then.