This is about AI, despite the title being ambiguous.

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

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.

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.

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.

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

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.

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 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 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

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

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.

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.

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.

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!

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!

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 bitter lesson is dependent on high-quality data.

Arguably, so is the alternative: explicitly embedding knowledge!

Nothing is immune to GIGO.

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.

> 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.

Well hardware has limits so I guess so. Humans evolved with faculties not a massive organic generic compute engine.

    43% of American workers have used AI at work, they are mostly doing it in informal ways, solving their own work problems. Scaling AI across the enterprise is hard 

A lot of firms starting into this business are "betting the farm" on "scaling AI across the enterprise"

In my experience LLMs are incredibly useful from a simple text interface (I only work with text, mainly computer code). I am still reeling from how disruptive they are, in that context.

But IMO there is not a lot of money to be made for start ups in that context (I expect there is not enough to justify the high valuations of outfits like Open AI). There should be a name for the curse - revolutionary technology that makes many people vastly more productive, but there is no real way to capture that value. Unless "Scaling AI across the enterprise" can succeed.

I have my doubts. I am sure there will be niches, and in a decade or so, with hindsight, it will be clear what they are. But there is no reliable way to tell now

The "Bitter Lesson" seems like a distraction to me. The fundamental problem is related: this technology is generally useful, much more than it is specifically useful.

The "killer app" is a browser window open to https://chat.deepseek.com. There is not much beyond that. Not nothing, just not much.

But so long as you have not bet your farm on "scaling AI across the enterprise" nor been fired by someone else who is trying, we should be very happy. We are in a "steam engine" moment. Nothing will ever be the same.

And if Open AI and the like all go belly up and demote a swathe of billionaires to be normally rich, that is the cherry on the top

In my experience, there's an opposing "bitter lesson" when trying to make incremental, tactical progress in user-facing AI / ML applications: _you're not a researcher_. Stay to tried-and-true, boring ML methods that have been proven at scale and then add human knowledge and rules to make it all work.

Then, as the article mentions, some new fundamental shift happens, and practitioners need to jump over to a completely new way of working. Monkeypatching to make it all work. Rinse repeat.

> The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin.

Be careful when anyone, even a giant in the field such as Sutton, posits a sweeping claim like this.

My take? Sutton's "bitter lesson" is rather vague and unspecified (i.e. hard to pin down and test) for at least two reasons:

1. The word "ultimately" is squishy, when you think about it. When has enough time passed to make the assessment? At what point can we say e.g. "Problem X has a most effective solution"?

2. What do we mean by "most effective"? There is a lot of variation, including but not limited to (a) some performance metric; (b) data efficiency; (c) flexibility / adaptability across different domains; and (d) energy efficiency.

I'm a big fan of Sutton's work. I've read his RL book cover-to-cover and got to meet him briefly. But, to me, the bitter lesson (as articulated in Sutton's original post) is not even wrong. It is sufficiently open-ended that many of us will disagree about what the lesson is, even before we can get to the empirical questions of "First, has it happened in domain D at time T? Second, is it 'settled' now, or might things change?"

The Bitter Lesson assumes Moore's law is alive and well. It may still be alive, but not as full of vim and vigor as it once was.

Por que no los dos?

Thirty-five years ago they gave me a Ph.D. basically for pointing out that the controversy du jour -- reactive vs deliberative control for autonomous robots -- was not a dichotomy. You could have the best of both worlds by combining a reactive system with a deliberative one. The reactive system interfaced directly to the hardware on one end and provided essentially a high-level API on the other end that provided primitives like "go that way". It's a little bit more complicated than that because it turns out you need a glue layer in the middle, but the point is: you don't have to choose. The Bitter Lesson is simply a corollary of Ron's First Law: all extreme positions are wrong. So reactive control by itself has limits, and deliberative control by itself has limits. But put the two together (and add some pretty snazzy image processing) and the result is Waymo.

So it was no surprise to me that Stockfish, with its similar approach of combining deliberative search with a small NN computing its quality metric blows everything else out of the water. It has been obvious (at least to me) that this is the right approach for decades now.

I'm actually pretty terrified of the results when the mainstream AI companies finally rediscover this. The capabilities of LLMs are already pretty impressive on their own. If they can get a Stockfish-level boost by combining them with a simple search algorithm the result may very well be the GAI that the rationalist community has been sounding the alarm over for the last 20 years.