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This idea has failed to pan out time and time again - people have an instinct that hand-crafted finely-tuned specialized AI systems must be optimal, but throwin

This idea has failed to pan out time and time again - people have an instinct that hand-crafted finely-tuned specialized AI systems must be optimal, but throwing more scale and compute to something more generally smart always wins out. It's especially palpable just looking at the last few years of LLM's: a frontier model with all the world knowledge you can stuff in it and every tool at its disposal has always performed the best at all tasks. Suggesting otherwise has become an extraordinary claim requiring extraordinary evidence. http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html Recent comment touching on this in relation to LLM's in more depth: https://news.ycombinator.com/item?id=49322695#49323341 https://news.ycombinator.com/item?id=49322695#49323341

applfanboysbgon 2mo ago
This idea has not failed to pan out at all. I work for a startup that is exactly what GP described, and am set for life because of how wildly successful it is. Notably, we are successful, in a genuine sense of the word: we bootstrapped from running tiny models to larger and larger models on our own slowly improving fleet of GPUs, and now have millions in revenue without a single dime of outside investment. Conversely, you cannot call taking on ~1 trillion in debt and purchase commitments to scale "success". OpenAI and Anthropic are underwater financially. To be precise, they're in the Mariana Trench.
- wild_egg 2mo ago
  Wait, you actually found a viable counter to The Bitter Lesson? Please say more
  - applfanboysbgon 2mo ago
    This is a misunderstanding of either the bitter lesson or what was being claimed, on multiple accounts. Firstly, the bitter lesson is merely about human expertise-tuned algorithms vs. throwing raw compute at a domain. But, notably, it is still domain-specific. No matter how much compute you throw at training an LLM, it is never going to beat a Chess engine at Chess. If you give a Chess engine 1,000,000 compute units and a general-purpose LLM 1,000,000 compute units, the Chess engine is obviously superior at Chess; ergo, there is value in throwing compute units into training models for specific tasks. This is true for within several orders of magnitude of compute, in fact. It's also true that if you give the Chess engine 1000 compute units it'll still beat the all-purpose model with 1,000,000 units, so actually there's a lot of value in training for specific tasks. Secondly, the bitter lesson is predicated on compute being cheap. There was a period where a hand-tuned algorithm informed by human expertise would outperform a raw alpha-beta search at Chess. Then compute got cheaper, and DeepBlue ascended to the top. Compute is now expensive again relative to the tasks being performed. We are absolutely still in a period where human expertise in training LLMs will outperform a naive approach with more raw compute.
    - CamperBob2 2mo ago
      I don't know much about chess engines; do they still use hand-tuned algorithms, or are they more like AlphaZero, where they learn through self-play to beat any/all possible human contenders? I don't believe DeepBlue was automated to that extent, but it may have been. In the latter case, the chess example would tend to support the Bitter Lesson, rather than refute it. I would also be VERY slow to claim that general-purpose models will never be competitive at chess. It wasn't so long ago that transformers couldn't add two-digit numbers reliably without resorting to tool use. They are now as good at "mental arithmetic" as any human savant. It wouldn't surprise me at all to see someone come up with a model that just happens to be really, really good at leveraging the portions of its general training data having to do with chess. In fact you could argue that AGI demands such a model, if we are to assume that LLMs are a guidepost in that direction.
      - dmoy 2mo ago
        I don't know anything about the last 8 years of chess engines, but yea maybe 8-10 years ago AlphaZero shit all over e.g. stockfish.
        brucehoult 2mo ago
        I guess you missed Leela then. And in 2020 Stockfish 12 adding some NN evaluation. And then in 2023 Stockfish 16 entirely removing the classical position evaluation code. https://stockfishchess.org/blog/2023/stockfish-16/ https://stockfishchess.org/blog/2023/stockfish-16/
      - applfanboysbgon 2mo ago
        DeepBlue beat Kasparov with essentially raw compute thrown at alpha-beta search. That does support the premise of the bitter lesson in general. But that does not mean the bitter lesson is correctly being applied here. The point is that even if throwing raw compute at a task is better than careful human-crafted algorithms, it's still task-dependent. The current trend with the people blowing hundreds of billions of dollars is developing an all-purpose model that is everything to everyone, but you don't need hundreds of billions of dollars to create a task-specific model that outperforms their model at a given task. > I would also be VERY slow to claim that general-purpose models will never be competitive at chess. This is not the claim. The claim is that for the same amount of compute, a general-purpose language model will never beat a Chess model. I'm dubious, but allow for the possibility that a language model could eventually compete at a top level against humans with enough compute. However, it will never compete with a dedicated Chess model with similar resources. Training a model for a specific task with the same amount of compute will outperform training a general-purpose model with the same amount of compute. This should be common sense, right? The bitter lesson was only about compute over human algorithms, not at throwing compute at a generalised domain over throwing compute at a specific domain. You made arguments against two claims that I did not make (that I was trying to refute the bitter lesson or that I claimed that LLMs could never be competitive against humans at Chess), so I'd like to ask you read my statements a little more carefully this time.
  - klipt 2mo ago
    Perhaps an analogy to Moore's law? Bitter lesson #1: don't waste time optimizing code when a faster processor is around the corner. What countered it: Moore's law stopped working. Bitter lesson #2 similarly relies on scaling laws that might have diminishing returns wrt model runtime vs intelligence. Runtime matters for turnaround on the problem you're solving.
    - wild_egg 2mo ago
      Moore's Law has nothing to do with processors getting faster. Dennard scaling stopped working but Moore just slowed somewhat, not stopped.
      - bsder 2mo ago
        You are technically correct. The best kind of correct. However, what most people think of as Moore's Law--CPU speed doubles every 18 months--broke somewhere between 90nm and 22nm. And even the actual Moore's Law--2x the transistors every 18 months--doesn't hold for all types of chips anymore. Memory only gained 2x density over 10 years.
  - HDThoreaun 2mo ago
    The issue is that GP is misusing the bitter lesson. Yes, search + learn tends to be more effective than human rules based strategies, but that's not what's being considered here. The original claim is effectively that AGI isn't needed for most tasks and more value can be created by using search + learn to solve specific problems instead of applying general models to every problem. Then GP commented a non sequitur
  - joe_the_user 2mo ago
    I think the gp found a viable counter to the ggp's version of the bitter lesson, which seems so extreme as to certainly exceptions. IE, they seem to say nothing but the latest frontier model is ultimately viable as an AI business. The actual argument of the Bitter Lesson essay is pretty limited but people's interpretation of it has gradually drifted until it's seen as prediction that current LLM will reach AGI at a large enough scale.
- z3t4 2mo ago
  Do you have a website?
- shimman 2mo ago
  Are you willing to say the same of the startup or are you still stealth?
CamperBob2 2mo ago
Suggesting otherwise has become an extraordinary claim requiring extraordinary evidence. VibeThinker 3B constitutes extraordinary evidence, IMO. The first such evidence I've seen myself. Very small model, very low literacy, almost no world knowledge, but it is as good at math and logical reasoning as models a hundred times larger. The Bitter Lesson is a valid and trenchant observation about how about we got here, but I think it's a mistake to assume it tells us very much about where we're going. Too much has changed recently and is still doing so.
- algo_trader 2mo ago
  > VibeThinker 3B constitutes extraordinary evidence.. math and logical reasoning Any similar model aimed at coding? A >10B model for mass spawning/swarming and reporting back to a larger model
  - CamperBob2 2mo ago
    To some extent VT 3B is intended as a coding model (see https://old.reddit.com/r/LocalLLaMA/comments/1u7dzdr/scaling_former_vibethinker15b_to_3b_now_it/ https://old.reddit.com/r/LocalLLaMA/comments/1u7dzdr/scaling... ). It reportedly does well on leetcode-type problems, although I didn't check that myself. I wouldn't use it for anything important without heavy supervision, as it's very weak outside its specialty. Not ideal for instruction-following tasks.
- spockz 2mo ago
  So theoretically, if you give that model the means to find information, ascertain the quality of said information, it could still reason its way to an proper answer? Is this whole thing than maybe a read vs write optimisation again? Spent more time and effort training more knowledge into the model upfront and get it out in a single question instead of training a small model and needing more steps to answer the same question?
nickysielicki 2mo ago
The Bitter Lesson is very popular right now. It seems true right now. It’s having its moment right now. That doesn’t actually mean it’s axiomatically true. Commenter below gets it absolutely correct: stockfish, which runs on your 5 year old phone, is dramatically better at chess than Fable. Like, so much better that it’s not even remotely comparable. The theory of the Bitter Lesson, and it’s only a theory, is that LLMs could eventually outperform stockfish. It’s not true today and it remains to be seen whether it will ever be true. For now, specialized models are absolutely better at specialized tasks.
- Evidlo 2mo ago
  This seems really backwards. The Bitter Lesson is all about large data-based approaches vs hand-crafted ones, it doesn't say anything about language models not trained specifically for chess. I can't find the comment you're referring to, but the latest versions of stockfish are based on neural networks trained on millions of games, so if anything the Bitter Lesson turned out true here.
  - nickysielicki 2mo ago
    The conclusion of the bitter lesson would be that a large language model trained on chess commentary as well as being trained on millions of chess games would outperform stockfish which is only trained on millions of chess games. There’s no evidence at this point that this is true.
    - iainmerrick 2mo ago
      I think you have it backwards. The common mistake is to think “maybe if we use a blend of raw data and hand-crafted heuristics, we’ll get the best of both worlds!” But the bitter lesson says no, beyond a certain point it’s better just to use the data. Thinking that an LLM might be able to improve on purely “big data” machine learning seems to me to be the same incorrect idea. Its “intelligence” is no more useful than human intelligence. The LLM is based on a massive data corpus, sure, but the amount of data specifically about chess in there pales in comparison to just playing billions of games of chess.
srcreigh 2mo ago
No. The bitter lesson is about capabilities. GP is talking about efficiency. GP isn’t suggesting that focused narrow model(s) will be more capable than large model, but that many small focused models can have sufficient capability while being more optimal. Also, the bitter lesson is just wrong. The bitter lesson is about hand tuned AI vs computational general methods. However in truth today’s AI uses both. We have general compute heavy models which require narrow expert instructions (eg tools internet docs). LLMs would not be as good without expertly written context, and expert context without LLMs aren’t as good either.
- anon373839 2mo ago
  > The bitter lesson is about hand tuned AI vs computational general methods. However in truth today’s AI uses both. We have general compute heavy models which require narrow expert instructions The models are not even really trained bitter lesson-style anymore. That concept peaked during the era of pre-train scaling, back when it was thought that making a bigger and bigger GPT-3 would automatically solve all problems through prompting. In 2026, the most important part of training is post-training, which uses vast quantities of niche, hand-curated data to fit the models for specific tasks in domains like tax law.
ZainRiz 2mo ago
I'd respectfully push back on the framing here. If you look at value as purely the LLM output, then there's a valid argument that the best frontier models will always be better than fine tuned specialists. (I'm not convinced personally, but it's a defensible claim) But that misses two dimensions: 1. The cost of acquiring that output 2. What is actually "good enough" for that specialist domain Not every output needs to be the best to produce value. And as specialist models increase in cost, their cost/value proposition goes down. At some point, there's a threshold where cheaper, fine tuned models are "good enough" at the task and also substantially cheaper than the expert models. That's where fine tuning helps. Personally, I became a believer in fine tuning after fine tuning a 1B Qwen model as a second pass over my local voice transcription app, achieving excellent accuracy at ~zero token cost and waaaay lower latency than if I'd invoked my Claude subscription under the hood.
- DrewADesign 2mo ago
  At some point, the idea of cost/benefit analysis in the software business turned into the benefit analysis. The amount of money going into the frontier LLM model game is fantastically ridiculous. Being much better than the free resources doesn’t even touch how much better they will have to be to justify the expense of creating them, let alone continually maintaining these services.
joefourier 2mo ago
> It's especially palpable just looking at the last few years of LLM's: a frontier model with all the world knowledge you can stuff in it and every tool at its disposal has always performed the best at all tasks. Suggesting otherwise has become an extraordinary claim requiring extraordinary evidence. Absolutely false. At least when it comes to multimodal inputs, even a simple classifier will outperform the largest LLMs who still hallucinate details or don’t describe audio and images accurately. And there’s also the issue of cost/inference speed. Running a trillion parameter model for all tasks will be incredibly costly, require a cloud API, while a tiny CNN can be run locally or at a cost multiple orders of magnitude lower.
lqstuart 2mo ago
It’s not hard to fine tune a 4B parameter model to outperform GPT-5.x or Claude on a domain-specific task. You don’t even need GRPO or whatever the flavor of the month RL technique is either. I suspect what you’re saying is that massive frontier models are the best for doing big slow tasks on human time horizons—and I emphatically agree—but keep in mind LLMs are useful for quite a bit more than just multi-turn chatbots; and in those instances, throwing infinite compute and latency budgets at a problem is generally not a great way to find the “best” solution :)
miki123211 2mo ago
I think large-scale systems are optimal when the number of tasks is relatively bounded and costs don't matter. When you're doing something "at scale", you want smaller models, weaker heuristics and rules written in code, as they're much cheaper, much faster, much more predictable and much easier to reason about. If your task is "tell me which pages on the internet are most relevant to this search query", running Claude Fable to evaluate every single page would be ridiculous. You may run something like it at the end, to synthesize information from a small result set for example, as that's a "human-sized" task.
OtherShrezzing 2mo ago
I think if you start taking cost into account for tasks which models have saturated a long time ago, the bitter lesson stops being applicable. If your task is "write a function to sort a list of 1mn entries alphabetically", a small coding model will exactly match the performance of a frontier model (it'll 1-shot it, with 100% accuracy). So the only differentiator is cost, and the frontier model will cost orders of magnitude more than the specialised model. For most intelligence based tasks, you don't (and never have) needed the tool which "performs best at all tasks". You need the cheapest one which performs adequately for your immediate task. This doesn't mean the bitter lesson is incorrect. At the frontier, it's still correct. It means that it's not applicable at all to lots of tasks.
schrodinger 2mo ago
If anyone else finds that BitterLesson link difficult to read because it wraps full-width, you can type "javascript:" in your URL bar and paste: document.body.style.cssText='margin:auto;width:600px;' (Chrome strips the "javascript:" for security if I were to include it myself.)
- xnx 2mo ago
  Or use reader mode or resize the browser window.

applfanboysbgon 2mo ago

This idea has not failed to pan out at all. I work for a startup that is exactly what GP described, and am set for life because of how wildly successful it is. Notably, we are successful, in a genuine sense of the word: we bootstrapped from running tiny models to larger and larger models on our own slowly improving fleet of GPUs, and now have millions in revenue without a single dime of outside investment. Conversely, you cannot call taking on ~1 trillion in debt and purchase commitments to scale "success". OpenAI and Anthropic are underwater financially. To be precise, they're in the Mariana Trench.

wild_egg 2mo ago
Wait, you actually found a viable counter to The Bitter Lesson? Please say more
- applfanboysbgon 2mo ago
  This is a misunderstanding of either the bitter lesson or what was being claimed, on multiple accounts. Firstly, the bitter lesson is merely about human expertise-tuned algorithms vs. throwing raw compute at a domain. But, notably, it is still domain-specific. No matter how much compute you throw at training an LLM, it is never going to beat a Chess engine at Chess. If you give a Chess engine 1,000,000 compute units and a general-purpose LLM 1,000,000 compute units, the Chess engine is obviously superior at Chess; ergo, there is value in throwing compute units into training models for specific tasks. This is true for within several orders of magnitude of compute, in fact. It's also true that if you give the Chess engine 1000 compute units it'll still beat the all-purpose model with 1,000,000 units, so actually there's a lot of value in training for specific tasks. Secondly, the bitter lesson is predicated on compute being cheap. There was a period where a hand-tuned algorithm informed by human expertise would outperform a raw alpha-beta search at Chess. Then compute got cheaper, and DeepBlue ascended to the top. Compute is now expensive again relative to the tasks being performed. We are absolutely still in a period where human expertise in training LLMs will outperform a naive approach with more raw compute.
  - CamperBob2 2mo ago
    I don't know much about chess engines; do they still use hand-tuned algorithms, or are they more like AlphaZero, where they learn through self-play to beat any/all possible human contenders? I don't believe DeepBlue was automated to that extent, but it may have been. In the latter case, the chess example would tend to support the Bitter Lesson, rather than refute it. I would also be VERY slow to claim that general-purpose models will never be competitive at chess. It wasn't so long ago that transformers couldn't add two-digit numbers reliably without resorting to tool use. They are now as good at "mental arithmetic" as any human savant. It wouldn't surprise me at all to see someone come up with a model that just happens to be really, really good at leveraging the portions of its general training data having to do with chess. In fact you could argue that AGI demands such a model, if we are to assume that LLMs are a guidepost in that direction.
    - dmoy 2mo ago
      I don't know anything about the last 8 years of chess engines, but yea maybe 8-10 years ago AlphaZero shit all over e.g. stockfish.
      - brucehoult 2mo ago
        I guess you missed Leela then. And in 2020 Stockfish 12 adding some NN evaluation. And then in 2023 Stockfish 16 entirely removing the classical position evaluation code. https://stockfishchess.org/blog/2023/stockfish-16/ https://stockfishchess.org/blog/2023/stockfish-16/
    - applfanboysbgon 2mo ago
      DeepBlue beat Kasparov with essentially raw compute thrown at alpha-beta search. That does support the premise of the bitter lesson in general. But that does not mean the bitter lesson is correctly being applied here. The point is that even if throwing raw compute at a task is better than careful human-crafted algorithms, it's still task-dependent. The current trend with the people blowing hundreds of billions of dollars is developing an all-purpose model that is everything to everyone, but you don't need hundreds of billions of dollars to create a task-specific model that outperforms their model at a given task. > I would also be VERY slow to claim that general-purpose models will never be competitive at chess. This is not the claim. The claim is that for the same amount of compute, a general-purpose language model will never beat a Chess model. I'm dubious, but allow for the possibility that a language model could eventually compete at a top level against humans with enough compute. However, it will never compete with a dedicated Chess model with similar resources. Training a model for a specific task with the same amount of compute will outperform training a general-purpose model with the same amount of compute. This should be common sense, right? The bitter lesson was only about compute over human algorithms, not at throwing compute at a generalised domain over throwing compute at a specific domain. You made arguments against two claims that I did not make (that I was trying to refute the bitter lesson or that I claimed that LLMs could never be competitive against humans at Chess), so I'd like to ask you read my statements a little more carefully this time.
- klipt 2mo ago
  Perhaps an analogy to Moore's law? Bitter lesson #1: don't waste time optimizing code when a faster processor is around the corner. What countered it: Moore's law stopped working. Bitter lesson #2 similarly relies on scaling laws that might have diminishing returns wrt model runtime vs intelligence. Runtime matters for turnaround on the problem you're solving.
  - wild_egg 2mo ago
    Moore's Law has nothing to do with processors getting faster. Dennard scaling stopped working but Moore just slowed somewhat, not stopped.
    - bsder 2mo ago
      You are technically correct. The best kind of correct. However, what most people think of as Moore's Law--CPU speed doubles every 18 months--broke somewhere between 90nm and 22nm. And even the actual Moore's Law--2x the transistors every 18 months--doesn't hold for all types of chips anymore. Memory only gained 2x density over 10 years.
- HDThoreaun 2mo ago
  The issue is that GP is misusing the bitter lesson. Yes, search + learn tends to be more effective than human rules based strategies, but that's not what's being considered here. The original claim is effectively that AGI isn't needed for most tasks and more value can be created by using search + learn to solve specific problems instead of applying general models to every problem. Then GP commented a non sequitur
- joe_the_user 2mo ago
  I think the gp found a viable counter to the ggp's version of the bitter lesson, which seems so extreme as to certainly exceptions. IE, they seem to say nothing but the latest frontier model is ultimately viable as an AI business. The actual argument of the Bitter Lesson essay is pretty limited but people's interpretation of it has gradually drifted until it's seen as prediction that current LLM will reach AGI at a large enough scale.
z3t4 2mo ago
Do you have a website?
shimman 2mo ago
Are you willing to say the same of the startup or are you still stealth?

CamperBob2 2mo ago

Suggesting otherwise has become an extraordinary claim requiring extraordinary evidence. VibeThinker 3B constitutes extraordinary evidence, IMO. The first such evidence I've seen myself. Very small model, very low literacy, almost no world knowledge, but it is as good at math and logical reasoning as models a hundred times larger. The Bitter Lesson is a valid and trenchant observation about how about we got here, but I think it's a mistake to assume it tells us very much about where we're going. Too much has changed recently and is still doing so.

algo_trader 2mo ago
> VibeThinker 3B constitutes extraordinary evidence.. math and logical reasoning Any similar model aimed at coding? A >10B model for mass spawning/swarming and reporting back to a larger model
- CamperBob2 2mo ago
  To some extent VT 3B is intended as a coding model (see https://old.reddit.com/r/LocalLLaMA/comments/1u7dzdr/scaling_former_vibethinker15b_to_3b_now_it/ https://old.reddit.com/r/LocalLLaMA/comments/1u7dzdr/scaling... ). It reportedly does well on leetcode-type problems, although I didn't check that myself. I wouldn't use it for anything important without heavy supervision, as it's very weak outside its specialty. Not ideal for instruction-following tasks.
spockz 2mo ago
So theoretically, if you give that model the means to find information, ascertain the quality of said information, it could still reason its way to an proper answer? Is this whole thing than maybe a read vs write optimisation again? Spent more time and effort training more knowledge into the model upfront and get it out in a single question instead of training a small model and needing more steps to answer the same question?

nickysielicki 2mo ago

The Bitter Lesson is very popular right now. It seems true right now. It’s having its moment right now. That doesn’t actually mean it’s axiomatically true. Commenter below gets it absolutely correct: stockfish, which runs on your 5 year old phone, is dramatically better at chess than Fable. Like, so much better that it’s not even remotely comparable. The theory of the Bitter Lesson, and it’s only a theory, is that LLMs could eventually outperform stockfish. It’s not true today and it remains to be seen whether it will ever be true. For now, specialized models are absolutely better at specialized tasks.

Evidlo 2mo ago
This seems really backwards. The Bitter Lesson is all about large data-based approaches vs hand-crafted ones, it doesn't say anything about language models not trained specifically for chess. I can't find the comment you're referring to, but the latest versions of stockfish are based on neural networks trained on millions of games, so if anything the Bitter Lesson turned out true here.
- nickysielicki 2mo ago
  The conclusion of the bitter lesson would be that a large language model trained on chess commentary as well as being trained on millions of chess games would outperform stockfish which is only trained on millions of chess games. There’s no evidence at this point that this is true.
  - iainmerrick 2mo ago
    I think you have it backwards. The common mistake is to think “maybe if we use a blend of raw data and hand-crafted heuristics, we’ll get the best of both worlds!” But the bitter lesson says no, beyond a certain point it’s better just to use the data. Thinking that an LLM might be able to improve on purely “big data” machine learning seems to me to be the same incorrect idea. Its “intelligence” is no more useful than human intelligence. The LLM is based on a massive data corpus, sure, but the amount of data specifically about chess in there pales in comparison to just playing billions of games of chess.

srcreigh 2mo ago

No. The bitter lesson is about capabilities. GP is talking about efficiency. GP isn’t suggesting that focused narrow model(s) will be more capable than large model, but that many small focused models can have sufficient capability while being more optimal. Also, the bitter lesson is just wrong. The bitter lesson is about hand tuned AI vs computational general methods. However in truth today’s AI uses both. We have general compute heavy models which require narrow expert instructions (eg tools internet docs). LLMs would not be as good without expertly written context, and expert context without LLMs aren’t as good either.

anon373839 2mo ago
> The bitter lesson is about hand tuned AI vs computational general methods. However in truth today’s AI uses both. We have general compute heavy models which require narrow expert instructions The models are not even really trained bitter lesson-style anymore. That concept peaked during the era of pre-train scaling, back when it was thought that making a bigger and bigger GPT-3 would automatically solve all problems through prompting. In 2026, the most important part of training is post-training, which uses vast quantities of niche, hand-curated data to fit the models for specific tasks in domains like tax law.

ZainRiz 2mo ago

I'd respectfully push back on the framing here. If you look at value as purely the LLM output, then there's a valid argument that the best frontier models will always be better than fine tuned specialists. (I'm not convinced personally, but it's a defensible claim) But that misses two dimensions: 1. The cost of acquiring that output 2. What is actually "good enough" for that specialist domain Not every output needs to be the best to produce value. And as specialist models increase in cost, their cost/value proposition goes down. At some point, there's a threshold where cheaper, fine tuned models are "good enough" at the task and also substantially cheaper than the expert models. That's where fine tuning helps. Personally, I became a believer in fine tuning after fine tuning a 1B Qwen model as a second pass over my local voice transcription app, achieving excellent accuracy at ~zero token cost and waaaay lower latency than if I'd invoked my Claude subscription under the hood.

DrewADesign 2mo ago
At some point, the idea of cost/benefit analysis in the software business turned into the benefit analysis. The amount of money going into the frontier LLM model game is fantastically ridiculous. Being much better than the free resources doesn’t even touch how much better they will have to be to justify the expense of creating them, let alone continually maintaining these services.

joefourier 2mo ago

> It's especially palpable just looking at the last few years of LLM's: a frontier model with all the world knowledge you can stuff in it and every tool at its disposal has always performed the best at all tasks. Suggesting otherwise has become an extraordinary claim requiring extraordinary evidence. Absolutely false. At least when it comes to multimodal inputs, even a simple classifier will outperform the largest LLMs who still hallucinate details or don’t describe audio and images accurately. And there’s also the issue of cost/inference speed. Running a trillion parameter model for all tasks will be incredibly costly, require a cloud API, while a tiny CNN can be run locally or at a cost multiple orders of magnitude lower.

lqstuart 2mo ago

It’s not hard to fine tune a 4B parameter model to outperform GPT-5.x or Claude on a domain-specific task. You don’t even need GRPO or whatever the flavor of the month RL technique is either. I suspect what you’re saying is that massive frontier models are the best for doing big slow tasks on human time horizons—and I emphatically agree—but keep in mind LLMs are useful for quite a bit more than just multi-turn chatbots; and in those instances, throwing infinite compute and latency budgets at a problem is generally not a great way to find the “best” solution :)

miki123211 2mo ago

I think large-scale systems are optimal when the number of tasks is relatively bounded and costs don't matter. When you're doing something "at scale", you want smaller models, weaker heuristics and rules written in code, as they're much cheaper, much faster, much more predictable and much easier to reason about. If your task is "tell me which pages on the internet are most relevant to this search query", running Claude Fable to evaluate every single page would be ridiculous. You may run something like it at the end, to synthesize information from a small result set for example, as that's a "human-sized" task.

OtherShrezzing 2mo ago

I think if you start taking cost into account for tasks which models have saturated a long time ago, the bitter lesson stops being applicable. If your task is "write a function to sort a list of 1mn entries alphabetically", a small coding model will exactly match the performance of a frontier model (it'll 1-shot it, with 100% accuracy). So the only differentiator is cost, and the frontier model will cost orders of magnitude more than the specialised model. For most intelligence based tasks, you don't (and never have) needed the tool which "performs best at all tasks". You need the cheapest one which performs adequately for your immediate task. This doesn't mean the bitter lesson is incorrect. At the frontier, it's still correct. It means that it's not applicable at all to lots of tasks.

schrodinger 2mo ago

If anyone else finds that BitterLesson link difficult to read because it wraps full-width, you can type "javascript:" in your URL bar and paste: document.body.style.cssText='margin:auto;width:600px;' (Chrome strips the "javascript:" for security if I were to include it myself.)

xnx 2mo ago
Or use reader mode or resize the browser window.