Does the Bitter Lesson Have Limits?

The last time I was reminded of the bitter lesson was when I read about Guidance & Control Networks, after seeing them used in an autonomous drone that beat the best human FPV pilots [0]. Basically it's using a small MLP (Multi Layer Perceptron) on the order of 200 parameters, and using the drone's state as input and controlling the motors directly with the output. We have all kinds of fancy control theory like MCP (Model Predictive Control), but it turns out that the best solution might be to train a relatively tiny NN using a mix of simulation and collected sensor data instead. It's not better because of huge computation resources, it's actually more computationally efficient than some classic alternatives, but it is more general.

[0] https://www.tudelft.nl/en/2025/lr/autonomous-drone-from-tu-d...

https://www.nature.com/articles/s41586-023-06419-4

https://arxiv.org/abs/2305.13078

https://arxiv.org/abs/2305.02705

I was not at all a fan of "The Bitter Lesson versus The Garbage Can", but this misses the same thing that it missed.

The Bitter Lesson is from the perspective of how to spend your entire career. It is correct over the course of a very long time, and bakes in Moore's Law.

The Bitter Lesson is true because general methods capture these assumed hardware gains that specific methods may not. It was never meant for contrasting methods at a specific moment in time. At a specific moment in time you're just describing Explore vs Exploit.

I don’t think people understand the point sutton was making; he’s saying that general, simple systems that get better with scale tend to outperform hand engineered systems that don’t. It’s a kind of subtle point that’s implicitly saying hand engineering inhibits scale because it inhibits generality. He is not saying anything about the rate, doesn’t claim llms/gd are the best system, in fact I’d guess he thinks there’s likely an even more general approach that would be better. It’s comparing two classes of approaches not commenting on the merits of particular systems.

The question is when price/performance hits financial limits. That point may be close, if not already passed.

Interestingly, this hasn't happened for wafer fabs. A modern wafer fab costs US$1bn to US$3bn, and there is talk of US$20bn wafer fabs. Around the year 2000, those would have been un-financeable. It was expected that fab cost was going to be a constraint on feature size. That didn't happen.

For years, it was thought that the ASML approach to extreme UV was going to cost too much. It's a horrible hack, shooting off droplets of tin to be vaporized by lasers just to generate soft X-rays. Industry people were hoping for small synchrotrons or X-ray lasers or E-beam machines or something sane. But none of those worked out. Progress went on by making a fundamentally awful process work commercially, at insane cost.

I see elements of the bitter lesson in arguments about context window size and RAG. The argument is about retrieval being the equivalent of compute/search. Just improve them, to hell with all else.

However, retrieval is not just google search. Primary key lookups in my db are also retrieval. As are vector index queries or BM25 free text search queries. It's not a general purpose area like compute/search. In summary, i don't think that RAG is dead. Context engineering is just like feature engineering - transform the swamp of data into a structured signal that is easy for in-context learning to learn.

The corollory of all this is it's not just about scaling up agents - giving them more LLMs and more data via MCP. The bitter lesson doesn't apply to agents yet.

The problem with the Bitter Lesson is that it doesn't clearly define what is a computational "hack" and what is a genuine architecturally breakthrough. We would be no where without transformers for example.

One odd thing is that progress in SAT/SMT solvers has been almost as good as progress in neural networks from the 1970s to the present. There was a time I was really interested in production rules and expert system shells and systems in the early 1980s often didn't even use RETE and didn't have hash indexes so of course a rule base of 10,000 looked unmanageable, by 2015 you could have a million rules in Drools and it worked just fine.

I'm not so sure Stockfish is a good example. The fact it can run on an Iphone is due to Moore's law, which follows the same pattern. And Deepmind briefly taking its throne was a very good example of the Bitter Lesson.

This brings about a good point:

How much of the recent bitter lesson peddling is done by compute salesmen?

How much of it is done by people who can buy a lot of compute?

Deepseek was scandalous for a reason.

> This views organizations as chaotic “garbage cans” where problems, solutions, and decision-makers are dumped in together, and decisions often happen when these elements collide randomly, rather than through a fully rational process

Only tangentially related, but this has to be one of the worst metaphors I’ve ever heard. Garbage cans are not typically hotbeds of chaotic activity, unless a raccoon gets in or something.

It would not be surprising if a bitter lesson 2.0 comes about as a bitter lesson to the bitter lesson.

A better lesson: https://rodneybrooks.com/a-better-lesson/

Does anyone else see the big flaw with the chess engine analogy?

When AlphaZero came along it blew stockfish out of the water.

Stockfish is a top engine now because besides that initial proof of concept there's no money to be made by throwing compute at Chess.

This article focuses on the learning aspect of The Bitter Lesson. But The Bitter Lesson is about both search and learning.

This article cites Leela, the chess program, as an example of the Bitter Lesson, as it learns chess using a general method. The article then goes on to cite Stockfish as a counterexample, because it uses human-written heuristics to perform search. However, as you add compute to Stockfish's search, or spend time optimizing compute-expenditure-per-position, Stockfish gets better. Stockfish isn't a counterexample, search is still a part of The Bitter Lesson!

> The bitter lesson is dependent on high-quality data.

Arguably, so is the alternative: explicitly embedding knowledge!

Nothing is immune to GIGO.

I think it's a little early (even in these AI times) to call HRM a counterexample of the bitter lesson.

I think it's quite a bit more likely for HRM to scale embarrassingly far and outstrip the tons of RLHF and distillation that's been invested in for transformers, more of a bitter lesson 2.0 than anything else.

When The Bitter Lesson essay came out it was a bunch of important things: addressing an audience of serious practitioners, contrarian and challenging entrenched dogma, written without any serious reputational or (especially) financial stake in the outcome. It needed saying and it was important.

But its become a lazy crutch for a bunch of people who meet none of those criteria and perverted into a statement more along the lines of "LLMs trained on NVIDIA cards by one of a handful of US companies are guaranteed to outperform every other approach from here to the Singularity".

Nope. Not at all guaranteed, and at the moment? Not even looking likely.

It will have other stuff in it. Maybe that's prediction in representation space like JEPA, maybe its MCTS like Alpha*, maybe its some totally new thing.

And maybe it happens in Hangzhou.

Not familiar with the cited essay (added to reading list for the weekend), but the post does make some generally good points on generalization (it me) vs specialization, and the benefits of an optimized and scalable generalist approach vs a niche, specialized approach, specifically with regards to current LLMs (and to a lesser degree, ML as a whole).

Where I furrow my brow is the casual mixing of philosophical conjecture with technical observations or statements. Mixing the two all too often feels like a crutch around defending either singular perspective in an argument by stating the other half of the argument defends the first half. I know I'm not articulating my point well here, but it just comes off as a little...insincere, I guess? I'm sure someone here will find the appropriate words to communicate my point better, if I'm being understood.

One nitpick on the philosophical side of things I'd point out is that a lot of the resistance to AI replacing human labor is less to do with the self-styled importance of humanity, and more the bleak future of a species where a handful of Capitalists will destroy civilization for the remainder to benefit themselves. That is what sticks in our collective craw, and a large reason for the pushback against AI - and nobody in a position of power is taking that threat remotely seriously, largely because the owners of AI have a vested interest in preventing that from being addressed (since it would inevitably curb the very power they're investing in building for themselves).

The Neuro-Symbolic approach is what the article describes, without actually naming it.

I should know better than to speak anything too enthusiastically about the humanities or feminism on this particular forum, but I just want to say the connection here to Donna Haraway was a surprise and delight. Any one open to that world at all would behoove themselves to check her out. "The Cyborg Manifesto" is the one everyone knows, but I recently finished "Living with the Trouble" and can't recommend it enough!

All links render as blue strike-through line in Firefox (underline in Chrome), hurting legibility :(

This is about AI, despite the title being ambiguous.