> AI keeps getting better and better until it can work around big AI slop code bases
The belief in this is a form of AI psychosis, I think.
Maybe in the future but certainly no evidence of this anytime soon
The belief in this is a form of AI psychosis, I think.
Maybe in the future but certainly no evidence of this anytime soon
Here's some anecdotal evidence from me - I cleaned up multiple GPT 4.x era vibecoded projects recently with the latest claude model and integrated one of those into a fairly large open source codebase.
This is something AI completely failed at last year.
Maybe you should try something like this or listen to success stories before claiming 'certainly no evidence' in future?
I don't know what happens in a decade when there are no junior engineers, skilled senior engineers are becoming rare, and the only data left the train LLMs on is 200th-generation slop. But AI slop being qualitatively slop is not enough of a obstacle to prevent that future from coming to pass. And billions of dollars will be "saved" along the way.
That's what makes this whole house of cards dangerous. The prescription to psychosis is profitable. Aka, selling a grift.
1) same business logic implemented in two different places, with extra code to sync between them
2) fixing apparently simple bugs results in lots of new code being written
It’s a sign I need to at least temporarily dedicate more effort to overseeing work in that area.
I somewhat agree with the AI psychosis framing of the OP. It takes some taste and discipline to avoid letting things dissolve into complete slop.
What evidence is there that we're not at or close to a plateau of what LLMs are capable of? How do you know the growth rate from 2023 to present will continue into 2029? eg. Is it more training data? More GPUs? What if we're kind of reaching the limits of those things already?
And the answer appears to be that the improvement is accelerating. So how could it be stopping?
I don’t think that the current AI paradigm has infinite headroom for improvement, similar to how every other AI approach before it eventually hit a limit.
And the link I posted shows the amount of work a query can do increasing non linearly. You can explore the site for more detail and a graph that shows error rates getting halved every couple of months.
No one said anything about infinite. It doesn't mean we don't have headroom to spare.
Software itself took 80-120 years to get where it is today depending on how you count. Time is on AIs side here.
I don't see why we would assume that we are at a plateau for RL. In many other settings, Go for instance, RL continues to scale until you reach compute limits. Some things are more easily RL'd than others, but ultimately this largely unlocks data. We are not yet compute/energy/physical world constrained. I think you would start observing clear changes in the world around you before that becomes a true bottleneck. Regardless, currently the vast majority of compute is used for inference not training so the compute overhang is large.
Assuming that we plateau at {insert current moment} seems wishful and I've already had this conversation any number of times on this exact forum at every level of capability [3.5, 4, o1, o3, 4.6/5.5, mythos] from Nov 2022 onwards.
The (leading) LLMs work by consensus, like Wikipedia, Openstreetmap, web search engine or opensource movement.
What I mean is if I ask LLM "create a linked list", its understanding (of what I want) is already close to the expected ideal. Just like Wikipedia article on linked list, for example.
But the LLMs will continue to improve in breath and depth of understanding the world, although technically (what they CAN do) they probably already peaked. Similarly, OSS movement technically peaked in the 90s with the creation of compiler, operating system and a database; doesn't mean that new opensource isn't being created.
LLMs (or specifically GPT algorithm) are 8 years old. It has matured as a technology. I am not sure how you imagine it being significantly improved, from a user point of view, without some kind of paradigm shift (i.e. something significantly different from GPT or LLM).
Although I can imagine one important social innovation yet to come - a generally available big public LLM, that "anybody can train". We had a technology of "encyclopedia" for years (famously Brittanica); yet the concept of Wikipedia has been a truly new take on encyclopedia.
Also, new kinds of AI might emerge - for example we might formalize all types of human reasoning and build a reasoning AI, as well a model of human language, from scratch rather by training via GPT (and thus, more understandable and potentially smaller). But that won't be an LLM.
I proposed how. New harness techniques and new training data/techniques, so the harness gets better and the LLM can be trained to work better with the harness. There's no reason to believe we're out of momentum for improvement in that direction.
However, they also make mistakes like humans, I don't think a better harness or better training will fix that, because fundamentally, they cannot read your mind, if you put in an ambiguous prompt.
I like to compare the process of turning inexact text to formal language to an error-correcting code. If you haven't made too much mistakes or have been precise in the specification, it will self-correct and do what you want. But if your input is too ambiguous, it will never do exactly what you want, but something close to it. And people (who are using AI) are still learning where is the boundary and how to tell.
The companies building these models are training them to react to typical expectations. If you have some special need, you will always have to tell the model, otherwise it will not know your exact context. And the harnesses have many tools for that or try to do that automatically already.
* A belief that AI will keep getting better, presented without evidence, does not yield a lot of skepticism around these parts.
* Your comment saying it is wrong to believe AI will keep getting better, also presented without evidence, is downvoted.