The costs do exist somewhere though, and must be paid by someone. There's no free lunch, and the human lunch is very likely far more costly than the LLM lunch.

> Add to that the fact that we can't blindly trust LLM output just yet

Can't blindly trust human output either. That's why there are various tiers in roles, from junior-equivalent to senior-equivalent, and the actual user of the product is always the final arbiter. There's ultimately nothing different, except that the LLM iterates on issue resolution in seconds to minutes, whereas the human equivalent takes hours to days.

The political climate won't allow that to happen. The US will do everything to stay ahead of China, and a rise in prices means a sizeable migration to Chinese models, giving them that much more data to improve their models and pass the US in AI capability (if they haven't already).

But also it'll happen in a way, as eventually models will become optimized enough that run cost become more or less negligible from a sustainability perspective.

Same thing applies to humans: the better someone knows a codebase, the better they will be at resolving issues, etc.

Calculate the approximate cost of raising a human from birth to having the knowledge and skills to do X, along with maintenance required to continue doing X. Multiply by a reasonable scaling factor in comparison to one of today's best LLMs (ie how many humans and how much time to do Xn, vs the LLM).

Calculate the cost of hardware (from raw elements), training and maintenance for said LLM (if you want to include the cost of research+software then you'll have to also include the costs of raising those who taught, mentored, etc the human as well). Consider that the human usually specializes, while the LLM touches everything. I think you'll find even a roughly approximate answer very enlightening if you're honest in your calculations.

Very likely not nearly as well, unless there are many open source libraries in use and/or the language+patterns used are extremely popular. The really huge win for something like the Linux kernel and other popular OSS is that the source appears in the training data, a lot. And many versions. So providing the source again and saying "find X" is primarily bringing into focus things it's already seen during training, with little novelty beyond the updates that happened after knowledge cutoff.

Giving it a closed source project containing a lot of novel code means it only has the language and it's "intuition" to work from, which is a far greater ask.

> Every developer on Linux already knows both.

I've been developing on Linux for over 10 years and I don't. It's like exiting vim: whenever I want to do anything beyond running a command or basic variable use, I have to go lookup how to do it online. Every time.

This sounds like where lat.md[0] is headed. Only thing is it doesn't do task constraint. Generally I find the path these tools are taking interesting.

[0] https://github.com/1st1/lat.md

ChatGPT on the other hand is annoyingly wordy and repetitive, and is always holding out on something that tempts you to send a "OK", "Show me" or something of the sort to get some more. But I can't be bothered with trying to optimize away the cruft as it may affect the thing that it's seriously good at and I really use it for: research and brainstorming things, usually to get a spec that I then pass to Claude to fill out the gaps (there are always multiple) and implement. It's absolutely designed to maximize engagement far more than issue resolution.

And it doesn't matter. To each their own. Take one example: cooking. Some may choose to be a gourmet chef, whether professionally or just on their own time. Some will just regularly cook their food. Some will cook only when they have to. And some will avoid cooking no matter what, leaving it to family or going out to buy food, etc.

Now apply to every task and endeavor that one may be involved in. It doesn't matter if any particular thing sticks or not. Some may care and dive deeply, and some may prefer a hands-off approach. Nothing changes either way; life goes on.

The primary reason to get into anything deeply before was because it contributed directly to survival, eg studying and building a career to provide a product/service others needed. Things had to stick because living depended on it. Now with AI, well it just doesn't matter anymore with the essentials and everything beyond increasingly being automated away.

In the end eventually everyone will be at the same industry earning potential level (or whatever it's called), and then there will literally be no more "potential for earning" because there would be 0 economic value to human labor (but there will always be aesthetic value). And by then the greatest collective decision the majority of mankind will have to make in its existence would already have been made: do away with this highly flawed and unsustainable economic system, or be wholly at the mercy and whims of those unreasonably trying to keep it in place. It's up to us whether the inevitably fully automated future is a dystopia, or utopia. There is no viable middle ground.

https://marshallbrain.com/manna