AI makes it cheap (eventually almost free) to traverse the already-discovered and reach the edge of uncharted territory. If we think of a sphere, where we start at the center, and the surface is the edge of uncharted territory, then AI lets you move instantly to the surface.
If anything solved becomes cheap to re-instantiate, does R&D reach a point where it can’t ever pay off? Why would one pay for the long-researched thing when they can get it for free tomorrow? There will be some value in having it today, just like having knowledge about a stock today is more valuable than the same knowledge learned tomorrow. But does value itself go away for anything digital, and only remain for anything non-copyable?
The volume of a sphere grows faster than the surface area. But if traversing the interior is instant and frictionless, what does that imply?
It's nearly frictionless, not frictionless because someone has to use the output (or at least verify it works). Also, why do you think the "shape" of the knowledge is spherical? I don't assume to know the shape but whatever it is, it has to be a fractal-like, branching, repeating pattern.
You know this is a false dichotomy right? You can treat and consider LLMs statistical parrots and at the same time take advantage of them.
In a stage interview (a bit after the "sparks of agi in gpt4" paper came out) he made 3 statemets:
a) llms can't do math. They can trick us with poems and subjective prose, but at objective math they fail.
b) they can't plan
c) by the nature of their autoregressive architecture, errors compound. so a wrong token will make their output irreversibly wrong, and spiral out of control.
I think we can safely say that all of these turned out to be wrong. It's very possible that he meant something more abstract, and technical at its core, but in the real life all of these things were overcome. So, not a luddite, but also not a seer.
The harnesses have helped in training the models themselves (i.e. every good trace was "baked in" the model) and have improved in enabling test time compute. But at the end of the day this is all put back into the models, and they become better.
The simplest proof of this is on benchmarks like terminalbench and swe-bench with simple agents. The current top models are much better than their previous versions, when put in a loop with just a "bash tool". There's a ~100LoC harness called mini-swe-agent [1] that does just that.
So current models + minimal loop >> previous gen models with human written harnesses + lots of glue.
> Gemini 3 Pro reaches 74% on SWE-bench verified with mini-swe-agent!
This is orthogonal to the issue of whether all ideas are essentially "remixes." For the record I agree that they are.
They can and are improved (papered over) over time. For example by improving and tweaking the training data. Adding in new data sets is the usual fix. A prime example 'count the number of R's in Strawberry' caused quite a debacle at a time where LLM's were meant to be intelligent. Because they aren't they can trip up over simple problems like this. Continue to use an army of people to train them and these edge cases may become smaller over time. Fundamentally the LLM tech hasn't changed.
I am not saying that LLM's aren't amazing, they absolutely are. But WHAT they are is an understood thing so lets not confuse ourselves.
The subsequent argument that "LLMs only remix" => "all knowledge is a remix" seems absurd, and I'm surprised to have seen it now more than once here. Humanity didn't get from discovering fire to launching the JWST solely by remixing existing knowledge.
[1] http://bactra.org/notebooks/nn-attention-and-transformers.ht...
[2] Well, smoothing/estimation but the difference doesn't matter for my point.
Even acknowledging it is interpolation, models can extrapolate slightly without making things up, within the range where the model still applies. Whos to say what this range is for an LLM operating in thousand dimensional space? As far as I can tell the main limiters to LLM creativity are guardrails we put in place for safety and usefulness.
And what exactly is your proof that human ingenuity is not just pattern matching. Im sure a hypothesis can be put that fire was discovered by just adding up all known facts the people of those times knew and stumbling on something that put it all together. Sounds like knowledge remix + slight extrapolating to me.
It's a hypothesis at this stage, but I'm going to have a go at making it more quantitative. It seems the obvious explanation for "hallucinations", and it seems like it should also be rather straightforward to attribute particular inference results to the training data that influenced them. I'm expecting to encounter difficulties, though, since the idea seems so obvious it's vanishingly unlikely it hasn't been tried.
> And what exactly is your proof that human ingenuity is not just pattern matching.
Firstly, I'm not the one making a strong claim that needs to "proved". Secondly, "pattern matching" is ill-defined and not what I'm saying human intelligence isn't. I'm saying human intelligence isn't a kernel smoothing algorithm run over a corpus of text. This seems rather obvious. What's your proof that it is that?
Not every solution needs to be unique, in many cases "remixing" existing solutions in an unique way is better and faster.