> There are certain tasks, like improving a given program for speed, for instance, where in theory the model can continue to make progress with a very clear reward signal for a very long time.
This makes me think: I wonder if Goodhart's law[1] may apply here. I wonder if, for instance, optimizing for speed may produce code that is faster but harder to understand and extend. Should we care or would it be ok for AI to produce code that passes all tests and is faster? Would the AI become good at creating explanations for humans as a side effect?
And if Goodhard's law doesn't apply, why is it? Is it because we're only doing RLVR fine-tuning on the last layers of the network so all the generality of the pre-training is not lost? And if this is the case, could this be a limitation in not being able to be creative enough to come up with move 37?
> The fundamental challenge in AI for the next 20 years is avoiding extinction.
That's a weird thing to end on. Surely it's worth more than one sentence if you're serious about it? As it stands, it feels a bit like the fearmongering Big Tech CEOs use to drive up the AI stocks.
If AI is really that powerful and I should care about it, I'd rather hear about it without the scare tactics.
> The fundamental challenge in AI for the next 20 years is avoiding extinction.
So nice to see people who think about this seriously converge on this. Yes. Creating something smarter than you was always going to be a sketchy prospect.
All of the folks insisting it just couldn't happen or ... well, there have just been so many objections. The goalposts have walked from one side of the field to the other, and then left the stadium, went on a trip to Europe, got lost in a beautiful little village in Norway, and decided to move there.
All this time though, the prospect of instantiating a something smarter than you (and yes, it will be smarter than you even if it's at human level because of electronic speeds...) This whole idea is just cursed and we should not do the thing.
> Creating something smarter than you was always going to be a sketchy prospect.
Sure, but not so sure that this has any relevance to the topic at hand. You seem to be taking the assumption that LLMs can ever reach that level for granted.
It may be possible that all it takes is scaling up and at some point some threshold gets reached past which intelligence emerges. Maybe.
Personally, I'm more on board with the idea that since LLMs display approximately 0 intelligence right now, no amount of scaling will help and we need a fundamentally different approach if we want to create AGI.
> And I've vibe coded entire ephemeral apps just to find a single bug because why not - code is suddenly free, ephemeral, malleable, discardable after single use. Vibe coding will terraform software and alter job descriptions.
I'm not super up-to-date on all that's happening in AI-land, but in this quote I can find something that most techno-enthusiast seem to have decided to ignore: no, code is not free. There are immense resources (energy, water, materials) that go into these data centers in order to produce this "free" code. And the material consequences are terribly damaging to thousands of people. With the further construction of data centers to feed this free video coding style, we're further destroying parts of the world. Well done, AGI loverboys.
It’s interesting that half the comments here are talking about the extinction line when, now that we’re nearly entering 2026, I feel the 2027 predictions have been shown to be pretty wrong so far.
>* For years, despite functional evidence and scientific hints accumulating, certain AI researchers continued to claim LLMs were stochastic parrots: probabilistic machines that would: 1. NOT have any representation about the meaning of the prompt. 2. NOT have any representation about what they were going to say. In 2025 finally almost everybody stopped saying so.
Man, Antirez and I walk in very different circles! I still feel like LLMs fall over backwards once you give them an 'unusual' or 'rare' task that isn't likely to be presented in the training data.
> Even if LLMs make mistakes, the ability of LLMs to deliver useful code and hints improved to the point most skeptics started to use LLMs anyway
Here we go again. Statements with the single source in the head of the speaker. And it’s also not true. The llms still produce bad/irrelevant code at such rate that you can spend more time prompting than doing things yourself.
I have programmed 30K+ hours. Do LLMs make bad code: yes all the time (at the moment zero clue about good architecture). Are they still useful: yes, extremely so. The secret sauce is that you'd know exactly what to do without them.
> There are certain tasks, like improving a given program for speed, for instance, where in theory the model can continue to make progress with a very clear reward signal for a very long time.
Super skeptical of this claim. Yes, if I have some toy poorly optimized python example or maybe a sorting algorithm in ASM, but this won’t work in any non-trivial case. My intuition is that the LLM will spin its wheels at a local minimum the performance of which is overdetermined by millions of black-box optimizations in the interpreter or compiler signal from which is not fed back to the LLM.
> * Programmers resistance to AI assisted programming has lowered considerably. Even if LLMs make mistakes, the ability of LLMs to deliver useful code and hints improved to the point most skeptics started to use LLMs anyway: now the return on the investment is acceptable for many more folks.
Could not agree more. I myself started 2025 being very skeptical, and finished it very convinced about the usefulness of LLMs for programming. I have also seen multiple colleagues and friends go through the same change of appreciation.
I noticed that for certain task, our productivity can be multiplied by 2 to 4. So hence comes my doubts: are we going to be too many developers / software engineers ? What will happen for the rests of us ?
I assume that other fields (other than software-related) should also benefits from the same productivity boosts. I wonder if our society is ready to accept that people should work less. I think the more likely continuation is that companies will either hire less, or fire more, instead of accepting to pay the same for less hours of human-work.
I'm not sure that it will scale to other fields other than coding and math. The approach with RLVR makes it more amenable to STEM fields in general and most jobs believe it or not aren't that. The level of open source software with good test suites effectively gave them all the training material they needed; most professions won't provide that knowing that they will be giving their moat away. LLM's to other fields from my understanding still exhibit the same hallucination rates if only mildly improved especially if there isn't public internet material in that field.
We have to accept in the end that coding/SWE is one of the most disrupted fields from this breed of AI. Disruption unfortunately probably means less jobs overall. The profession is on trend to disrupting and automating itself I think; plan accordingly. I've seen so many articles claiming its great we didn't learn to code now; that's what the AI's have done.
Where to understand more about how chain of thoughs really affects LLMs performance? I read the seminal paper but all it says is that it's basically another prompt engineering tecnique that improves accuracy.
> Programmers resistance to AI assisted programming has lowered considerably. Even if LLMs make mistakes, the ability of LLMs to deliver useful code and hints improved to the point most skeptics started to use LLMs anyway: now the return on the investment is acceptable for many more folks.
I'm not a fan of this phrasing. Use of the terms "resistance" and "skeptics" implies they were wrong. It's important we don't engage in revisionist history that allows people in the future to say "Look at the irrational fear programmers had of AI, which turned out to be wrong!" The change occurred because LLMs are useful for programming in 2025 and the earliest versions weren't for most programmers. It was the technology that changed.
> 1. NOT have any representation about the meaning of the prompt.
This one is bizarre, if true (I'm not convinced it is).
The entire purpose of the attention mechanism in the transformer architecture is to build this representation, in many layers (conceptually: in many layers of abstraction).
> 2. NOT have any representation about what they were going to say.
The only place for this to go is in the model weights. More parameters means "more places to remember things", so clearly that's at least a representation.
Again: who was pushing this belief? Presumably not researchers, these are fundamental properties of the transformer architecture. To the best of my knowledge, they are not disputed.
> I believe [...] it is not impossible they get us to AGI even without fundamentally new paradigms appearing.
Same, at least for the OpenAI AGI definition: "An AI system that is at least as intelligent as a normal human, and is able to do any economically valuable work."
LLMs have certainly become extremely useful for Software Engineers, they're very convincing (and pleasers, too) and I'm still unsure about the future of our day-to-day job.
But one thing that has scared me the most, is the trust of LLMs output to the general society. I believe that for software engineers it's really easy to see if it's being useful or not -- We can just run the code and see if the output is what we expected, if not, iterate it, and continue. There's still a professional looking to what it produces.
On the contrary, for more day-to-day usage of the general pubic, is getting really scary. I've had multiple members of my family using AI to ask for medical advice, life advice, and stuff were I still see hallucinations daily, but at the same time they're so convincing that it's hard for them not to trust them.
I still have seen fake quotes, fake investigations, fake news being spreaded by LLMs that have affected decisions (maybe, not as crucials yet but time will tell) and that's a danger that most software engineers just gross over.
Accountability is a big asterisk that everyone seems to ignore
> LLMs have certainly become extremely useful for Software Engineers
They slow down software delivery on aggregate, so no.
They have a therapeutic effect on developer burnout though. Not sure it's worth it, personally. Get a corporate ping-ping table or something like that instead.
Doesn't really matter when this is a human problem. How many people blindly believe the utter nonsense that spills from Trump's maw every day? Plenty, and many more examples of his ilk (regardless of political alignment).
The use of LLMs in software does not stop at code generation. With function calling, the prompt becomes the program and the LLMs acts as an intelligent interpreter/runtime that excutes complex business logic using primitives (the functions) they have access to (MCP) and that's the real paradigm shift for software engineering.
Adults can cope somehow...
But what about children? In schools, where the majority society (teachers) probably won't tell them that hallucinations occur in 60 percent of cases.
What will they grow up to be?
I compare it to the situation before Google - with Google.
Sure, we function somehow as a society... but still, I am worried.
> For years, despite functional evidence and scientific hints accumulating, certain AI researchers continued to claim LLMs were stochastic parrots: probabilistic machines that would: 1. NOT have any representation about the meaning of the prompt. 2. NOT have any representation about what they were going to say. In 2025 finally almost everybody stopped saying so.
It's interesting that Terrence Tao just released his own blog post stating that they're best viewed as stochastic generators. True he's not an AI researcher, but it does sound like he's using AI frequently with some success.
"viewing the current generation of such tools primarily as a stochastic generator of sometimes clever - and often useful - thoughts and outputs may be a more productive perspective when trying to use them to solve difficult problems" [0].
I get the impression that folks who have a strong negative reaction to the phrase "stochastic parrot" tend to do so because they interpret it literally or analogously (revealed in their arguments against it), when it is most useful as a metaphor.
(And, in some cases, a desire to deny the people and perspectives from which the phrase originated.)
They are very advanced stochastic parrots that allow AI invested authors to suddenly write in perfect English.
If Antirez has never gotten an LLM to perform an absolutely embarrassing mistake, he must be very lucky or we should stop listening to him.
Programmers' resistance has not weakened. Since the ORCL drop of 40% anti-LLM opinions are censored and downvoted here. Many people have given up, and we always get articles from the same LLM influencers.
50 comments
[ 5.5 ms ] story [ 77.6 ms ] threadThis makes me think: I wonder if Goodhart's law[1] may apply here. I wonder if, for instance, optimizing for speed may produce code that is faster but harder to understand and extend. Should we care or would it be ok for AI to produce code that passes all tests and is faster? Would the AI become good at creating explanations for humans as a side effect?
And if Goodhard's law doesn't apply, why is it? Is it because we're only doing RLVR fine-tuning on the last layers of the network so all the generality of the pre-training is not lost? And if this is the case, could this be a limitation in not being able to be creative enough to come up with move 37?
[1] https://wikipedia.org/wiki/Goodhart's_law
> The fundamental challenge in AI for the next 20 years is avoiding extinction.
That's a weird thing to end on. Surely it's worth more than one sentence if you're serious about it? As it stands, it feels a bit like the fearmongering Big Tech CEOs use to drive up the AI stocks.
If AI is really that powerful and I should care about it, I'd rather hear about it without the scare tactics.
This reminded me of the Don’t look up movie where they basically gambled with the humans extinction.
[0] https://redis.io/redis-for-ai/
So nice to see people who think about this seriously converge on this. Yes. Creating something smarter than you was always going to be a sketchy prospect.
All of the folks insisting it just couldn't happen or ... well, there have just been so many objections. The goalposts have walked from one side of the field to the other, and then left the stadium, went on a trip to Europe, got lost in a beautiful little village in Norway, and decided to move there.
All this time though, the prospect of instantiating a something smarter than you (and yes, it will be smarter than you even if it's at human level because of electronic speeds...) This whole idea is just cursed and we should not do the thing.
Sure, but not so sure that this has any relevance to the topic at hand. You seem to be taking the assumption that LLMs can ever reach that level for granted.
It may be possible that all it takes is scaling up and at some point some threshold gets reached past which intelligence emerges. Maybe.
Personally, I'm more on board with the idea that since LLMs display approximately 0 intelligence right now, no amount of scaling will help and we need a fundamentally different approach if we want to create AGI.
Around the world people ask an LLM and get a response.
Just grouping and analysing these questions and solving them once centrally and then making the solution available again is huge.
Linearly solving the most asked questions and then the next one then the next will make, whatever system is behind it, smarter every day.
I'm not super up-to-date on all that's happening in AI-land, but in this quote I can find something that most techno-enthusiast seem to have decided to ignore: no, code is not free. There are immense resources (energy, water, materials) that go into these data centers in order to produce this "free" code. And the material consequences are terribly damaging to thousands of people. With the further construction of data centers to feed this free video coding style, we're further destroying parts of the world. Well done, AGI loverboys.
Man, Antirez and I walk in very different circles! I still feel like LLMs fall over backwards once you give them an 'unusual' or 'rare' task that isn't likely to be presented in the training data.
Here we go again. Statements with the single source in the head of the speaker. And it’s also not true. The llms still produce bad/irrelevant code at such rate that you can spend more time prompting than doing things yourself.
I’m tired of this overestimation of llms.
For me LLMs are a waste of time.
Super skeptical of this claim. Yes, if I have some toy poorly optimized python example or maybe a sorting algorithm in ASM, but this won’t work in any non-trivial case. My intuition is that the LLM will spin its wheels at a local minimum the performance of which is overdetermined by millions of black-box optimizations in the interpreter or compiler signal from which is not fed back to the LLM.
Could not agree more. I myself started 2025 being very skeptical, and finished it very convinced about the usefulness of LLMs for programming. I have also seen multiple colleagues and friends go through the same change of appreciation.
I noticed that for certain task, our productivity can be multiplied by 2 to 4. So hence comes my doubts: are we going to be too many developers / software engineers ? What will happen for the rests of us ?
I assume that other fields (other than software-related) should also benefits from the same productivity boosts. I wonder if our society is ready to accept that people should work less. I think the more likely continuation is that companies will either hire less, or fire more, instead of accepting to pay the same for less hours of human-work.
We have to accept in the end that coding/SWE is one of the most disrupted fields from this breed of AI. Disruption unfortunately probably means less jobs overall. The profession is on trend to disrupting and automating itself I think; plan accordingly. I've seen so many articles claiming its great we didn't learn to code now; that's what the AI's have done.
I'm not a fan of this phrasing. Use of the terms "resistance" and "skeptics" implies they were wrong. It's important we don't engage in revisionist history that allows people in the future to say "Look at the irrational fear programmers had of AI, which turned out to be wrong!" The change occurred because LLMs are useful for programming in 2025 and the earliest versions weren't for most programmers. It was the technology that changed.
This one is bizarre, if true (I'm not convinced it is).
The entire purpose of the attention mechanism in the transformer architecture is to build this representation, in many layers (conceptually: in many layers of abstraction).
> 2. NOT have any representation about what they were going to say.
The only place for this to go is in the model weights. More parameters means "more places to remember things", so clearly that's at least a representation.
Again: who was pushing this belief? Presumably not researchers, these are fundamental properties of the transformer architecture. To the best of my knowledge, they are not disputed.
> I believe [...] it is not impossible they get us to AGI even without fundamentally new paradigms appearing.
Same, at least for the OpenAI AGI definition: "An AI system that is at least as intelligent as a normal human, and is able to do any economically valuable work."
But one thing that has scared me the most, is the trust of LLMs output to the general society. I believe that for software engineers it's really easy to see if it's being useful or not -- We can just run the code and see if the output is what we expected, if not, iterate it, and continue. There's still a professional looking to what it produces.
On the contrary, for more day-to-day usage of the general pubic, is getting really scary. I've had multiple members of my family using AI to ask for medical advice, life advice, and stuff were I still see hallucinations daily, but at the same time they're so convincing that it's hard for them not to trust them.
I still have seen fake quotes, fake investigations, fake news being spreaded by LLMs that have affected decisions (maybe, not as crucials yet but time will tell) and that's a danger that most software engineers just gross over.
Accountability is a big asterisk that everyone seems to ignore
They slow down software delivery on aggregate, so no. They have a therapeutic effect on developer burnout though. Not sure it's worth it, personally. Get a corporate ping-ping table or something like that instead.
What will they grow up to be?
I compare it to the situation before Google - with Google.
Sure, we function somehow as a society... but still, I am worried.
It's interesting that Terrence Tao just released his own blog post stating that they're best viewed as stochastic generators. True he's not an AI researcher, but it does sound like he's using AI frequently with some success.
"viewing the current generation of such tools primarily as a stochastic generator of sometimes clever - and often useful - thoughts and outputs may be a more productive perspective when trying to use them to solve difficult problems" [0].
[0] https://mathstodon.xyz/@tao/115722360006034040
(And, in some cases, a desire to deny the people and perspectives from which the phrase originated.)
If Antirez has never gotten an LLM to perform an absolutely embarrassing mistake, he must be very lucky or we should stop listening to him.
Programmers' resistance has not weakened. Since the ORCL drop of 40% anti-LLM opinions are censored and downvoted here. Many people have given up, and we always get articles from the same LLM influencers.