Comment by frays
2 days ago
Interesting to see that you work at OpenAI but had to build a skill like this yourself.
Surprised that you don't have internal tools or skills that could do this already!
Shows how much more work there is still to be done in this space.
My theory is that even if the models are frozen here, we'll still spend a decade building out all the tooling, connections, skills, etc and getting it into each industry. There's so much _around_ the models that we're still working on too.
Agree completely. It's already been like this for 1-2 years even. Things are finally starting to get baked in but its still early. For example, AI summaries of product reviews, gemini youtube video summaries, etc..
Its hard to quantify what sort of value those examples generate (youtube and amazon were already massively popular). Personally I find it very useful, but it's still hard to quantify. It's not exactly automating a whole class of jobs, although there are several youtube transcription services that this may make obsoete.
> Shows how much more work there is still to be done in this space.
This is why I roll my eyes every time I read doomer content that mentions an AI bubble followed by an AI winter. Even if (and objectively there's 0 chance of this happening anytime soon) everyone stops developing models tomorrow, we'll still have 5+ years of finding out how to extract every bit of value from the current models.
One thing though, if the slowdown is too abrupt, it might forbid openai, anthropic etc to keep financially running datacenters for us to use.
The idea that this technology isn't useful is as ignorant as thinking that there is no "AI" bubble.
Of course there is a bubble. We can see it whenever these companies tell us this tech is going to cure diseases, end world hunger, and bring global prosperity; whenever they tell us it's "thinking", can "learn skills", or is "intelligent", for that matter. Companies will absolutely devalue and the market will crash when the public stops buying the snake oil they're being sold.
But at the same time, a probabilistic pattern recognition and generation model can indeed be very useful in many industries. Many of our problems can be approached by framing them in terms of statistics, and throwing data and compute at them.
So now that we've established that, and we're reaching diminishing returns of scaling up, the only logical path forward is to do some classical engineering work, which has been neglected for the past 5+ years. This is why we're seeing the bulk of gains from things like MCP and, now, "agents".
> This is why we're seeing the bulk of gains from things like MCP and, now, "agents".
This is objectively not true. The models have improved a ton (with data from "tools" and "agentic loops", but it's still the models that become more capable).
Check out [1] a 100 LoC "LLM in a loop with just terminal access", it is now above last year's heavily harnessed SotA.
> Gemini 3 Pro reaches 74% on SWE-bench verified with mini-swe-agent!
[1] - https://github.com/SWE-agent/mini-swe-agent
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Useful technology can still create a bubble. The internet is useful but the dotcom bubble still occurred. There’s expectations around how much the invested capital will see a return and growing opportunity cost if it doesn’t, and that’s what creates concerns about a bubble. If a bubble bursts, the capital will go elsewhere, and then you’ll have an “AI winter” once again
Cobbler’s children…
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