563 karma · joined December 14, 2023
They own the careers of all but a tiny handful of US politicians
If the author is contending that AI models will improve to a point that they actually make no mistakes while coding, and have that same level of reliability, then they're on exactly the same side as AI doom trolls just wearing a different costume.
AI improves over time, sure, but it has differences and advantages and disadvantages over human intelligence. It's just a fundamentally different thing. In the same way calculators are superhuman, and improving, but can't write books. You yourself acknowledge to some degree the uneven nature of that improvement when you referenced formalizability.
One thing I will add: I'm sure the optimistic side will try to take the quantization that your (and other work like SlopCodeBench) are introducing and claim that we'll be able to fix the problem by training on that data, producing coding models that are rewarded for coherence.
I invite them to try! But my suspicion is that this will require a rather deep form of intelligence (world models) that the current language/data-based paradigm isn't really equipped to reach without requiring more energy than the Sun.
1. The Decoherence Trap: https://maevyn.codes/decoherence 2. SlopCodeBench, a benchmark that quantifies how models decohere over repeated change requests rather than a single one-shot challenge: https://news.ycombinator.com/item?id=50108717
I think the most important takeaway is that it takes time for these problems to manifest. If you start with a particularly coherent codebase it will take years for slop code to degrade it to a point where engineers are noticeably less productive.
I didn't downvote you, but if I had to guess why you were downvoted it's because it's very obvious to many working software engineers that decoherence is a real problem. In many ways that's what this OP was all about; not just loss of joy in programming for its own sake but loss of the ability to design deterministic, fully-understood systems that won't require constant off-the-books maintenance eating into your free time.
If the imperial core spontaneously disappeared, they would begin developing - and that development would take the form of centralization and specialization and state-driven investment in underdeveloped sectors. Look at what Burkina Faso is doing right now for example: they went from contracts with French companies to export gold and uranium for a thousandth of their market value to now climbing the value chain by developing their own gold refineries.
To keep the example but circle back to the original point: If you lived in a future Burkina Faso that successfully developed its economy, proposing that they go back to small-scale production with worse economies of scale would be a step backwards, and yes the logical conclusion of that in any country is hunter-gatherers. There can be other important learnings from doing this as a constrained experiment within a development model, like resiliency. But thinking this is a development model in itself is a cargo cult mentality that misunderstands industrial production as some evil external imposition on humanity rather than the system that proved most effective at feeding the most people.
It's disappointing that the prevailing sentiment now is that architecture is less important than we thought. Software is always going to be getting more important, and all of it should be well thought out. If these people had these tools in 2004 they might've made thousands of fragmented buggy social media startups and actually slowed progress on that front.
"The AI labs are scared of their own models, surely they must be credible sources on how powerful those models are"
"They are asking the government for regulation, they must be even more responsible than the average company"
"They are claiming to be a software engineer who automated their job with AI, surely I have to buy their course"
There are many boilerplate pieces of software that an LLM can generate faster than a human, but nearly half a decade into the LLM era there is no evidence that it's leading to the apocalyptic end of software organizations. And that's all with the subsidy in place that is now disappearing. Everyone online is lying for money, and that money is coming out of the same investor-subsidized marketing budgets.
It's not necessarily a given that the person you're replying to agrees on the latter, and therefore they might not be stoked on you starting off by agreeing with them but going on to make an argument that supports the companies responsible for the very thing they're complaining about.
I think there's a contradiction in you stating that management thinks LLM-generated output is "maintainable". They went to business school, they don't know what that means. It's your job to explain to them the consequences of shipping code faster than the people whose job it is to understand it can do so.
> solipsist
This is exactly the point they're making though. This is an exercise only available to the privileged. It is not available to people who haven't worked in tech for some amount of time, if not actively working in tech or holding equity like the homesteader above us.
It takes for granted the concentrated specialization of production that is modern, "on-grid" cities to produce things like microprocessors and food safety and insulin. This lifestyle is not available to masses of people, and we have a word for a society where everyone lives off-grid: "hunter-gatherers".
There can be other value in doing this, like Hundred Rabbits' exploration of how tech itself could be made more resilient given different macroeconomic conditions / incentives. But talking about this as if it's about how The Man just doesn't want you to be an independent free thinker is a misunderstanding of why most people live in the societies that they do.
They are fundamentally based in language, and achieving deeper models of the world through language alone is deeply inefficient compared to the way humans model the world for years without any language at all. They do not learn at inference time. They don't have semantic understanding of the difference between their own output and other sources. etc etc.
That depth is the key for me. Of course they are capable of producing novel sentences that aren't in their training data, but the depth of that novelty is basically within the bounds of language itself. They are capable of more serious depth and more abstract reasoning than that, but I have experienced limits, which it then tries to surpass with tools to convert things it can't understand back into language (unit tests, LEAN) upon which it is trained.
Because I'm not an AI booster, my account is limited to 5 comments a day. So this is the last reply I'll be able to make today, if you want to continue the conversation we'll have to wait for tomorrow.
I'm glad both apis exist.
The deeper architectural difference is still there, which manifests whenever you try to get the models to apply known techniques to modalities and problems outside their training data.
When they speak of "existential consequences", it is not as members of humanity. It is a projection of their control over humanity. Personal risk for them is just being made into a regular human (no longer being ultrawealthy). They can't even contemplate physical risk. Case in point, this exact same class of people are buying up private islands and literally purchasing the blood of poorer people.
They would never accept your wager. It's the kind of thing Elon Musk would say "sounds interesting" to on Joe Rogan ten years ago, and then the only consequence after that would be his fanboys reminding everyone how cool it was that he said that.
"we need the government to halt model training for us (and potential competitors who are currently behind us) due to hypothetical sci-fi future bad things"
on the one hand, and then
"we should not face consequences for that time we helped a teenager plan a mass shooting"
on the other so effortlessly, so instantaneously. And virtually the entire media helps them do it!