Is it a bubble?

I've found Opus 4.5 in copilot to be very impressive. Better than codex CLI in my experience. I agree Copilot definitely used to be absolutely awful.

I just did a review and 16% of our committed production code was generated by an LLM. Almost 80% of our code comments are LLM

This is mission critical robotics software

Look, I'm not trying to dunk on your website for fun. The issue is that you're making a specific argument: you're an experienced developer who uses AI to be 5-10x more productive without downsides, and you properly audit all the code it generates. You then offered your project as evidence of this workflow in action.

The problem is that your project has basic performance issues - FOUC, render waterfalls - that are central concerns in modern React development. These aren't arbitrary standards I invented to be mean. They're fundamental enough that React's recent development has specifically focused on solving them.

So when you say I'm inventing quality standards (in your now-deleted comment), or that this is just a passion project so quality doesn't matter, you're missing the point. You can't argue from professional authority that AI makes you more productive without compromise, use your work as proof, and then retreat to "it's just for fun" when someone points out the quality issues. Either it demonstrates your workflow's effectiveness or it doesn't. You can't have it both ways.

The kids' artwork comparison doesn't work either. You're not a child showing me a crayon drawing - you're a professional developer using your work as evidence in a technical argument about AI productivity. If you want to be treated as an experienced developer making authoritative claims, your evidence needs to support those claims.

I'm genuinely not trying to be cruel here, but if this represents what your AI workflow produces when you're auditing the output, it raises serious questions about whether you can actually catch the problems the AI introduces - which is the entire crux of your argument. Either you just aren't equipped to audit it (because you don't know better), or you are becoming passive in the face of the walls of code that the AI is generating for you.

The thing to remember about the dotcom era was that while there were a lot of bad companies at the time with a lot of clueless investors behind them, quite a few companies made it through the implosion of that bubble and then prospered. Amazon, Google, eBay, etc. are still around.

More importantly, the web is now dominant for enterprise SaaS applications, which is a category of software that did not really exist before the web. And the web post–dot-com bubble spawned a lot of unicorns.

In short, there was an investment bubble. But the core tech was fine.

AI feels like one of those things where the tech is similarly transformational (even more so, actually). It’s another investment bubble predicated on the price of GPUs, which is mostly making Nvidia very rich right now.

Right now the model makers are getting most of the funding and then funneling non-trivial amounts to Nvidia (and their competitors). But actually the value creation is in applications using the models these companies create. And the innovation for that isn’t coming from the likes of Anthropic, OpenAI, Mistral, X.ai, etc. They are providing core technology, but they seem to be struggling to do productive things in terms of UX and use cases. Most of the interesting things in this space are coming from smaller companies figuring out how to use the models these companies produce. Models and GPUs are infrastructure, not end-user products.

And with the rise of open-source models, open algorithms, and exponentially dropping inference costs, the core infrastructure technology is not as much of a moat as it may seem to investors. OpenAI might be well funded, but their main UI (ChatGPT) is surprisingly limited and riddled with bugs. That doesn’t look like the polished work of a company that knows what they are doing. It’s all a bit hesitant and copycat. It’s never going to be a magic solution to everyone’s problems.

From where I’m sitting, there is clear untapped value in the enterprise space for AI to be used. And it’s going to take more than a half-assed chat UI to unlock that. It’s actually going to be a lot of work to build all of that. Coding tools are, so far, the most promising application of reasoning models. It’s easy to see how that could be useful in the context of ERP/manufacturing, CRM, traditional office applications, and the financial world.

Those each represent verticals with many established players trying to figure out how to use all this new stuff — and loads more startups eager to displace them. That’s where the money is going to be post-bubble. We’ve seen nothing yet. Just like after the dot-com bubble burst, all the money is going to be in new applications on top of the new infrastructure. It’s untapped revenue. And it’s not going to be about buying GPUs or offering benchmark-beating models. That’s where all the money is going currently. That’s why it is a bubble.

Wrong. 80% of code not being advanced is quite strictly not the same as 80% people not doing advanced programming.