[NitpickLawyer]

> 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.

[agumonkey]

One thing though, if the slowdown is too abrupt, it might forbid openai, anthropic etc to keep financially running datacenters for us to use.

[imiric]

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".

[NitpickLawyer]

> 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

[imiric]

I don't understand. You're highlighting a project that implements an "agent" as a counterargument to my claim that the bulk of improvements are from "agents"?

Sure, the models themselves have improved, but not by the same margins from a couple of years ago. E.g. the jump from GPT-3 to GPT-4 was far greater than the jump from GPT-4 to GPT-5. Currently we're seeing moderate improvements between each release, with "agents" taking up center stage. Only corporations like Google are still able to squeeze value out of hyperscale, while everyone else is more focused on engineering.

[losvedir]

They're pointing out that the "agent" is just 100 lines of code with a single tool. That means the model itself has improved, since such a bare bones agent is little more than invoking the model in a loop.

[imiric]

That doesn't make sense, considering that the idea of an "agentic workflow" is essentially to invoke the model in a loop. It could probably be done in much less than 100 lines.

This doesn't refute the fact that this simple idea can be very useful. Especially since the utility doesn't come from invoking the model in a loop, but from integrating it with external tools and APIs, all of which requires much more code.

We've known for a long time that feeding the model with high quality contextual data can improve its performance. This is essentially what "reasoning" is. So it's no surprise that doing that repeatedly from external and accurate sources would do the same thing.

In order to back up GP's claim, they should compare models from a few years ago with modern non-reasoning models in a non-agentic workflow. Which, again, I'm not saying they haven't improved, but that the improvements have been much more marginal than before. It's surprising how many discussions derail because the person chose to argue against a point that wasn't being made.

[losvedir]

The original point was that the previous SotA was a "heavily harnessed" agent, which I took to mean it had more tools at its disposal and perhaps some code to manage context and so on. The fact that the model can do it now in just 100 LoC and a terminal tool implied the model itself has improved. It's gotten better at standard terminal commands at least, and possibly bigger context window or more effectively using the data in its context window.

Those are improvements to the model, albeit in service of agentic workflows. I consider that distinct from improvements to agents themselves which are things like MCP, context management, etc.

[IanCal]

I think the point here is that it’s not adding agents on top but the improvements in the models allow the agentic flow.

[emp17344]

But that’s not true, and the linked agentic design is not a counterargument to the poster above. The LLM is a small part of the agentic system.

[IanCal]

LLMs have absolutely got better at longer horizon tasks.

[jameslk]

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