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Comment by jackb4040 | Hacker News Reader

How is this different from arguing that Microsoft Clippy was RSI? An AI tool being involved in the process of work can't be the bar for RSI.

I don't think there can be a coherent definition of RSI unless people lay out their theory for how intelligence scales. LLM-assisted coding is great but respectfully optimizing pytorch features or whatever is not gonna lead to exponential improvements. That approach to scaling diminished years ago, leading all the labs to switch to reasoning.

Now it seems reasoning is also yielding diminishing returns, so all the labs are pivoting to specializing in particular fields like math / infosec / biology. They're improving due to accessing new proprietary training data and doing RL with human experts. Again I don't really see any amount of "AI research interns" leading to an exponential improvement to this strategy, they're not the bottleneck in the first place.

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>Now it seems reasoning is also yielding diminishing returns

Is the diminishing returns in the room with us?

>so all the labs are pivoting to specializing in particular fields like math / infosec / biology.

They're not pivoting to anything. The goal has always been creating a machine that could automate all or nearly all human work. They're just coming along on that mission.

As for RSI...I think the term is a bit odd in the modern context. It was created at a time when conventional wisdom was that generally intelligent machines would be these logic automatons that could "alter their own code". Instead we have massive neural networks that take months to train.

In this paradigm, the ways a LLM could "improve itself" would be altering its own weights directly or creating and training better, vastly more efficient architectures for the next generation of models.

The former is probably not happening but the latter is possible.

Yes, diminishing returns. Not overall, they've still been able to create more intelligent models even up to today. But the strategy for scaling that intelligence has shifted. From the initial ChatGPT release to GPT-4.1, they were basically scaling up compute training compute / model size. Then 4.5 flopped, while o1 demonstrated that gains could continue by reasoning (scaling up compute at inference time). o1 is now the ancestor of all their flagship models from GPT-5 on.

This is why I'm trying so hard to drill down on the theory of scaling, and not just talk about improvement in general, hand-wavy terms. If the bottleneck of current scaling strategies is training data, or something fundamental about the model architecture, then just throwing more harnessed chatbots at it won't lead to an exponential increase in performance.

Now you could argue that the AI we have now will help us find that change in architecture, and I would agree. But that means we're firmly outside the singularity for the time being, and what people are in fact talking about is a hypothetical.

>Then 4.5 flopped, while o1 demonstrated that gains could continue by reasoning (scaling up compute at inference time). o1 is now the ancestor of all their flagship models from GPT-5 on.

That's not quite right. They are still scaling model size and have had several new base pre-trains, just nothing so big as 4.5 (as far as we're aware). o1/4o has not been the base for some time now.

Data is obviously a bottleneck for some regimes and LLMs will have to get their hands dirty experimenting but it doesn't look like an insurmountable wall either.

> "get their hands dirty" > "insurmountable wall"

This is gibberish, you may as well tell me you've found a load-bearing seam.

Okay?

There's no reason the reinforcement learning that is getting them better at computer use can't be applied to other domains, like biology, chemistry etc. It's just expensive, because the environment often becomes the physical world, it requires creating labs like anthropic are doing here, and gathering a lot of data, it requires llms attempting their own experiments(that's what 'getting their hands dirty' means).

Getting the data and setup will be expensive, but not impossible, and labs are clearly gearing up to do just that. If you can't understand that then that seems like a you problem.

> Now it seems reasoning is also yielding diminishing returns

Not true. On the contrary, LLMs are developing faster than predicted. They were expected to solve a Millennium Prize by 2030... and here we are in 2026. Release cycles are getting faster. Just compare the most recent GPT or Claude with what they were an year ago.

> How is this different from arguing that Microsoft Clippy was RSI?

We can argue about semantics, but that's not really the point. The point is that what started now - which no doubt is in its infancy - will result in full autonomy quite soon (they project an year or so), with the risk of RSI causing agent development to slip (long term) outside human cognitive control/capacity.

Again, can you lay out your theory for how intelligence scales? You're using a lot of terms like "full autonomy" without definitions. Why do you think that just throwing more harnessed LLMs at (something?) will lead to an increase rate of improvement?

I feel like I laid out several cases where other things were the limiting factor on improvement and more agents wouldn't have helped, and I didn't get a response to those cases.

What "they project" (the labs) is of minor interest to me. Aside from their incentives and track record of lying, in recent months they are laying out a story that is pretty much just the plot of Terminator, and directly referencing rationalist beliefs that were published long before LLMs even existed.

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