> it lacks the meta cognition to ask 'hey, is there a better way to do this?'.

Recently had an AI tell me this code (that it wrote) is a mess and suggested wiping it and starting from scratch with a more structure plan. That seems to hint at some meta cognition outlines

I mean, not always. I've seen Claude step back and reconsider things after hitting a dead end, and go down a different path. There are also workflows, loops that can increase the likelihood of this occurring.

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I feel like if you're really spending a ton of time on off by one errors after twenty years in the field you haven't actually grown much and have probably just spent a ton of time in a single space.

Otherwise you'd be senior staff to principle range and doing architecture, mentorship, coordinating cross team work, interviewing, evaluating technical decisions, etc.

I got to code this week a bit and it's been a tremendous joy! I see many peers at similar and lower levels (and higher) who have more years and less technical experience and still write lots of code and I suspect that is more what you're talking about. In that case, it's not so much that you've peaked, it's that there's not much to learn and you're doing a bunch of the same shit over and over and that's of course tiring.

I think it also means that everything you interact with outside your space does feel much harder because of the infrequency with which you have interacted with it.

If you've spent your whole career working the whole stack from interfaces to infrastructure then there's really not going to be much that hits you as unfamiliar after a point. Most frameworks recycle the same concepts and abstractions, same thing with programming languages, algorithms, data management etc.

But if you've spent most of your career in one space cranking tickets, those unknown corners are going to be as numerous as the day you started and be much more taxing.

That's just sad. Right when I found love in what I do, my work has no value anymore.

Imagine a senior dev who just approves PRs, approves production releases, and prioritizes bug reports and feature requests. LLM watches for errors ceaslessly, reports an issue. Senior dev reviews the issue and assigns a severity to it. Another LLM has a backlog of features and errors to go solve, it makes a fix and submits a PR after running tests and verifying things work on its end.

Even right at this very moment where we have a high-tenacity AI, I'd argue that working with the AI -- that is to say, doing AI coding itself and dealing with the novel challenges that brings requires a lot of stubborn persistence.

First time I've seen this idea, I have a tingling feeling it might become reality sooner rather than later.

That’s far-fetched. It’s in the interest of the model builders to solve your problem as efficiently as possible token-wise. High value to user + lower compute costs = better pricing power and better margins overall.

I was thinking more of deliberate backdoor in code. RCE is an obvious example, but another one could be bias. "I'm sorry ma'am, computer says you are ineligable for a bank account." These ideas aren't new. They were there in 90s already when we still thought about privacy and accountability regarding technology, and dystopian novels already described them long, long ago.

The free market proposition is that competition (especially with Chinese labs and grok) means that Anthropic is welcome to do that. They're even welcome to illegally collude with OpenAi such that ChatGPT is similarly gimped. But switching costs are pretty low. If it turns out I can one shot an issue with Qwen or Deepseek or Kimi thinking, Anthropic loses not just my monthly subscription, but everyone else's I show that too. So no, I think that's some grade A conspiracy theory nonsense you've got there.

Is this from a page of dating apps playbook?

And after manually coding I often have an LLM review the diff. 90% of the problems it finds can be discounted, but it’s still a net positive.

you are using it on easy questions. some of us are not.

interesting post. i wonder if these people go back and introspect on how incorrect they have been? do they feel the need to address it?

As a user of LLMs since GPT-3 there was noticeable stagnation in LLM utility after the release of GPT-4. But it seems the RLHF, tool calling, and UI have all come together in the last 12 months. I used to wonder what fools could be finding them so useful to claim a 10x multiplier - even as a user myself. These days I’m feeling more and more efficiency gains with Claude Code.

> The "hitting a wall" / "plateau" people will continue to be loud and wrong. Just as they have been since 2018[0].

Everybody who bet against Moore's Law was wrong ... until they weren't.

And AI is the reaction to Moore's Law having broken. Nobody gave one iota of damn about trying to make programming easier until the chips couldn't double in speed anymore.

Repairing grandfather clocks isn't more expensive now because it's gotten any harder; it's because the popularity of grandfather clocks is basically nonexistent compared to anything else to tell time.

"repairing a unique clock" getting costlier doesn't mean technology hasn't gotten cheaper.

check out whether clocks have gotten cheaper in general. the answer is that it has.

there is no economy of scale here in repairing a single clock. its not relevant to bring it up here.

Instead of advancing tenuous examples you could suggest a realistic mechanism by which costs could rise, such as a Chinese advance on Taiwan, effecting TSMC, etc.

Time-keeping is vastly cheaper. People don't want grandfather clocks. They want to tell time. And they can, more accurately, more easily, and much cheaper than their ancestors.

No. You don't get to make "technology gets more expensive over time" statements for deprecated technologies.

Getting a bespoke flintstone axe is also pretty expensive, and has also absolutely no relevance to modern life.

These discussions must, if they are to be useful, center in a population experience, not in unique personal moments.

Or riding in an uber?

10x more tokens today cost less than than half of X tokens from ~mid 2024.

ok but the capabilities are also rising. what point are you trying to make?

> bitcoin

so close! that is a commodity

For Bitcoin that’s by design!

Yes, because the margin is like 80% for Nvidia, and 80% again for the manufacturers like Samsung and TSMC. Once the fixed cost like R and D is amortized the same node technology and hardware capacity could be just few single digit percent of current.