"Do children need to learn math anymore?" Now that AI solves hard math and writes long-horizon code, people ask: what's the point of learning what a machine can do for you? I think early training in verifiable domains matters more than ever. Here's why. 🧵
the most common failure case I see is when people are working outside their domain of expertise and don't know how to be precise with their prompts and plans, so they have to spend a lot of turns iterating imprecisely
This is why universities are just so unnecessary. Either teach foundational knowledge, or go into the real world and exploit that knowledge. We do not need to go to universities to "learn how to apply" things. Just go into industry!
I am so sick of my workplace (a university if you can believe it!) force-feeding AI into everything we do - "so that you know how to apply AI responsibly". Given this technology is based on IP theft and is an environmental disaster - how about not using AI...period!!
We created gyms because modern day work no longer required physical activity. Without exercise, our muscles atrophy.
I predict we’ll need gyms for our brains too, once AI starts doing our knowledge work.
Uhh this is just not true in our experience in prop shops and quant funds. The opposite in fact, because it's impossible to robustly differentiate between a bs-er and an "idea guy"
https://t.co/0srs2BOPEu
Quant firms are hiring idea guys, not quants
I've been skeptical about the whole - software is commoditized, ideas are the moat argument
But I've seen first hand the best quant and technical firms in the world hire thinkers, liberal arts grads, and generalists
I predict the trend accelerates
Letter from @AQRCapital below
Or life passions like AI-proofed childhood education. The cool thing is these quant passion projects won't be trying to grift for money (plug for us: @quant_mom)
One of the things that’s so crazy about my time at HRT was seeing the young traders make $10M+ before 30 while the rest of us grind our entire lives for fractions of that.
Multiple traders would retire to pursue life passions like dancing and cooking before being old enough to get married and have kids.
So deciding to leave is a rational probability weighting for them that they can actually make billions outside the firm vs millions inside it.
Trading helps them realize they can monetize genius, and the superambitious ones decide they want super scale.
We all need a quant-mom. Sets up the agents to do the toddler meal plan, tax optimization, venture deal flow, buying baby's first scooter (and helmet), organizes nanny's calendar, gets baby into preschool, and *works*
Silicon Valley keeps building me a husband when what I really want is a wife lol
I don't want an agent that only does one-off, random tasks that are nice but not life-changing: booking flights, making dinner reservations, or buying tickets
I want an AI wife who knows what’s on the family calendar, remembers there’s a birthday party Saturday and we haven’t bought a gift then sends me options, realizes we’re almost out of diapers and buys them, knows which bills are coming up, remembers someone needs a dentist appointment and books it, figures out what the hell we’re eating all week and what we need from the grocery store, and generally keeps track of the 47 things that need to happen before anyone else even realizes they need to happen.
So, who's building me an AI wife that carries the mental load instead of an AI husband that waits for me to give it a chore??
@trq212 I wrote a whole article about how you have to learn lower levels of knowledge first. Higher levels of abstraction (ie decision-making) are less backtestable and smaller sample size than the lower levels.
https://t.co/FnU1JzM2BZ
So competition math isn't just important because it teaches "grinding" and gives you a network. It's the best kind of gym for the brain because it's verifiable, like an RL gym for an LLM:
https://t.co/ITXBdaoEwR
AI lowers the value of a verifiable output, but raises the value of verifiable training and signaling. The shift: start rigorous education earlier, and diversify it — math, programming, chess, experimental science — to train search, abstraction, proof, planning, statistical intuition. Then go build. Follow @quant_mom for more.
https://t.co/ITXBdapcmp
"Do children need to learn math anymore?" Now that AI solves hard math and writes long-horizon code, people ask: what's the point of learning what a machine can do for you? I think early training in verifiable domains matters more than ever. Here's why. 🧵
As AI boosts every essay and extracurricular, traditional credentials lose signal. Skill in verifiable domains becomes more important, not less — the only robust way to distinguish human marginal value-add. Even the anti-college academies will select for it; they'll just do it in high school.