I have strong math for the question they’re asking but f them.
How does Jane Street skim money from those who hold passive index funds?
Can you imagine human potential if it was somehow applied to crop harvesting efficiency, new medicines, etc?
Not everything has to be perfectly efficient but it just saddens me to see all these great minds doing what, adversarially harvesting margin from the works of others?
No doubt many of us agree with you, but this is not the kind of comment that should be stuck at the top of a thread, choking out more specific and interesting conversation.
Generic-indignant comments always get heavily upvoted, which is a failure mode of the upvoting system (and perhaps the human brain, who knows).
We already have very efficient crop harvesting and Eli Lilly is nearly a $1 Trillion dollar company. Interestingly, the new medicine is designed to keep us from eating so many cheap calories (new weight loss drugs).
> Not everything has to be perfectly efficient but it just saddens me to see all these great minds doing what, adversarially harvesting margin from the works of others?
The traders and investors who work in this space also go to where they are need, aka where the big money is. So few of these folks are trading corn and soybeans, though some do, rather most are trading drug stocks, tech stocks, and recently sovereign debt related trading (e.g. things like gold and bonds). The focus is around the big questions of our time, like "Are AI investments going to pay off?", or "Is the US going to default/soft default?", and so on.
Deciding how a society allocates its resources, or places its bets, is an important function. Otherwise, you end up with planned economies by disconnected leaders, which often leads to massive failures and large social consequences. Unfortunately, the US is trending in that direction to some degree with it's giant fiscal deficits, tariffs, and tribal politics creeping into economic policy. Nevertheless, traders will weigh these outcomes in their trades, and you'll see a quick reflection from any major change in policy almost immediately, which is a helpful feedback mechanism. For example, the tariff tantrums caused by trump proposing 100%+ china tariffs where he crashed the markets last spring, leading to a moderation in policy.
But don’t fool yourself, they don’t make their money with intelligence.
They just do fees and insider trading.
[1] https://www.reuters.com/sustainability/boards-policy-regulat...
[2] https://www.bloomberg.com/news/articles/2026-02-24/jane-stre...
If these sectors offered competitive salaries - sure, talent would flock to them. As a former chemist, I struggled to find a job that didn't pay scraps, no matter the industry - from big pharma to advanced materials. Eventually, I just gave up and went into the IT, which is 3x-10x better paid (at the very least).
If you want to solve meaningful problems you need a different kind of intelligence; you need to be open to risk, have a lot of naivety, not status orientated, and a rare ability to see the forest among the trees (i.e. an interesting problem isn't necessarily a important one).
What they could achieve in spending their attention on real problem would be massive.
Which begs the question: what would actually be a good field to apply human potential towards? I agree that finance, sales and ads are very low on that list.
We were blown away when LLMs came on the scene, because 'whoa, man! Machine can talk like a human', but really, stupid people have been posting online for decades. They talk like humans too! Yet they don't add any value (viz shit posters and trolls).
To be honest, this system seems pretty good.
Who says its not? Look up Rentosertib which is underground the different trial phases.
Have no doubt that workers at these companies could do a better job than an out of touch executive + board team that more resemble communist dictatorships than sound business practices.
At this point tech is probably worse than finance, at least in finance they dont pretend to be saving the world no matter what the giant squid says.
if the greatest minds of earth, in their wisdom, have all collectively concluded that the smartest thing for them to do is make as much money as humanly possible, then evidently the greatest calling for mankind is... to be wealthy!
and the older i get the harder it becomes to argue with such a perspective... hmm... maybe i am getting closer to wisdom? haha!
How is finance not exactly that?
"Eschew flamebait. Avoid generic tangents."
"Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something."
"Have curious conversation; don't cross-examine."
https://x.com/1914ad/status/2026757796390449382
Haven't read it yet but seems spicey
Feels to me like it’s similar to dumping a binary with an image, the format being entirely custom.
And/or trying to decode a language or cipher, trying to recognize patterns.
> After looking at the final two layers I was somewhat quick to intuit that this was some sort of password check, but wasn’t entirely sure where to go from there. I tried to reverse it, but it was proving to be difficult, and the model was far too deep. I started evaluating the structure and saw the 64 repeated sections of 84 layers that each process 4 characters at a time. Eventually I saw the addition and XOR operations, and the constants that were loaded in every cycle, and the shift amounts that differed between these otherwise identical sections.
> I thought it was an elaborate CTF cryptography challenge, where the algorithm was purposely weak and I had to figure out how to exploit it. But I repeatedly was getting very stuck in my reverse-engineering efforts. After reconsidering the structure and the format of the ‘header' I decided to take another look at existing algorithms...
Basically it took a lot of trial and error, and a lot of clever ways to look at and find patterns in the layers. Now that Jane Street has posted this dissection and 'ended' this contest I might post my notebooks and do a fuller post on it.
The trickiest part, to me, is that for about 5 of the days was spent trying to reverse-engineer the algorithm... but they did in fact use a irreversible hash function, so all that time was in vain. Basically my condensed 'solution' was to explore it enough to be able to explain it to ChatGPT, then confirm that it was the algorithm that ChatGPT suggested (hashing known works and seeing if the output matched) and then running brute force on the hash function, which was ~1000x faster to compute than the model.
They make a shit ton of money because of this. If you're working at a place where the code matters more than the decisions that went into it, you're basically working at a sweatshop for people who are desperate for a win and will throw away you and all your code once the MVP stage is over, and that's the only way this "works".
Generative probabilistic AI is not equivalent to a compiler and never will be until we can do this kind of thing completely deterministically. No matter how much you reduce the error in the "model", it's still more error than the error rate of the logic gates. It's completely futile considering the sheer depth of indirection at play, and that indirection is the whole point of software.
> Alex had actually tried to brute force the hash earlier, but had downloaded a list of the top 10,000 most popular words to do it, which turned out not to be big enough to find it. Once he had a big enough word list, he got the answer.
They don't reveal the answer.
https://huggingface.co/spaces/jane-street/puzzle
It's not "hot dog". I wrote in another comment how I found the solution but to give you a clue it is AI related.
https://gist.github.com/alexspurling/598366d5a5cf5565043b8cd...
Knowing the input text was two words separated by a space, I was able to use hashcat and the unix wordlist (/usr/share/dict/words) to find the solution almost immediately. It's a shame that Alex didn't find it this way on his first attempt as the two words are fairly common.
I don’t think I’m close to making progress on stuff like this. Interesting to note. Glad they wrote out this behind the scenes thing.
I hope they next blog about that.
