thinking like a trader in a polarized world

a month or so back i was at a mixer with a trading firm talking to a couple of their quant traders, doing the whole “what should i do if i want your job?” thing when i asked them for book recommendations and what reading list they were assigned when they got their initial offers.

i expected a list of overly dense, academic quant finance texts on things like options pricing, stochastic calculus, processes, & modeling, or idk, maybe even just a 900-page tombstone of greek letters.

instead:

not a single finance book
one trader told me the firm explicitly told him not to learn anything from his university derivatives class before his internship

they essentially were told “we’ll teach you what you need, just don’t come in with the wrong mental models.”

and then their actual recommendation?

superforecasting by philip tetlock and dan gardner.

no pricing PDEs or crazy finance texts. just a book about how to... forecast?

that conversation sparked something into my focus that had been floating around in the background of how I see the world: the real bottleneck in high-signal environments isn’t the domain-specific knowledge, it’s mindset. how you learn, how you update, and how you handle uncertainty, etc.

finance is just one example but this exact problem is everywhere right now, especially in tech.

let’s unpack further.

why trading firms might not want you to learn from a university derivatives course

quant trading firms already assume certain attributes of you like:

fast learner
probabilistic intuition when under pressure or scrutiny combined with a sustained & consistent ability to rubber-duck with good-to-brilliant levels of quantitative fluency
you won’t melt like a 10 y/o playing call of duty as soon as your PnL swings against you (also partially the reason why strong poker players can also make good traders, except for maybe phil hellmuth... sorry phil)

assuming those, it is less about teaching you the mechanical finance knowledge. there’s plenty of textbooks, internal docs/wikis, and training programs, and given that you’ve been persistent and diligent enough to make it thus far, then you will have done something to show potential of strong performance here.

what isn’t straightforward, is teaching someone how to translate ambiguous problems into smaller, more-manageable subproblems (i like to call this ”framing the problem statement”). having the willingness to say “~71-73% likely” over “definitely gonna happen bro... trust” is a key determinant in your success as a trader. and last but not least, you’ll need the ability to change your mind in public without experiencing ego death (”when the facts change, so does my mind...” type)

but that’s the stuff superforecasting is about.

tetlock’s research starts from a brutal finding that made waves when it first came out in 2005:

the average expert was roughly as accurate as a dart-throwing chimpanzee.

however, he emphasizes that this infamous quote is misleading. the most valuable finding from the study was that he identified a small group of people ("superforecasters") who consistently beat everyone else in large forecasting tournaments, and this is including intelligence analysts who literally had [classified information](Stanford University) and what’s (midly) surprising is that they’re not all geniuses. while they mean superporefaster tends to have "above-the-mean IQ" and more moderately will come from a STEM background, what isn’t so suprising is that they are all forever learners and operate instinctively and exhaustively within first principles using confident bayesian intuition.

furthermore, these are the key traits highlighted by tetlock that you see over and over in this special group:

they think like foxes, instead of hedgehogs: lots of small models rather than focusing on one big idea.
they’re comfortable living in gray area
they like to keep score (brier scores, calibration curves, etc.) and let feedback hurt their ego enough to improve, but not so much they quit.
they update belief constantly based on information instead of doubling down on vibes

so when SIG assigns traders 0 finance books, what they’re implicitly communicating is:

"if your brain is conditioned in this manner, we can teach you how to drive the boat. if it doesn’t, there isn’t a strong enough expectation that you may succeed when being handed the keys to managing the risk of a portfolio worth a few hundred million, to outweigh the risk."

which honestly really clicked for me because i already naturally live life through those principles. i’m obsessive over semantics and i absolutely will argue you of the face of the earth about whether exact hexcode of gray is E6E6E6 or E8E8E8, and so superforecasting resonated directly with my soul and personal perspectives.

foxes, hedgehogs, and late-stage capitalism

tetlock borrows this metaphor from isaiah berlin:

hedgehogs: ideological, prevailingly confident, emotional and mental attachment one big idea, everything is a nail for their favorite hammer, additionally coined as "big-idea people"
foxes: many small ideas (eye of a dragonfly), willing to mix and match, update, and generally able to admit when reality doesn’t match initial expectation

this maps cleanly onto how people talk about... basically everything now?

i was in berkeley visiting a friend recently, and we were talking about the rising presence of polarization today and he mentioned he hears a lot of ppl chalk these symptoms up to being a product of “late-stage capitalism”.

now it is important to note that (imho) a lot of people tend to use it as a catch-all explanation: housing crisis? late-stage capitalism. weird dating dynamics? late-stage capitalism. nobody reads long-form anymore? late-stage capitalism.

and ofc, there are real structural problems:

shrinking middle class
odd, bimodal labor markets (either low-skill + low-pay or insanely “elite” roles with zero middle ground)
people postponing or skipping kids because life is too expensive
everything mediated by algorithms that reward outrage and spectacle

but the way we talk about this stuff is almost always hedgehog-brained:

pick a single story (capitalism, patriarchy, immigration, ai tech bros, whatever)
crank the narrative dial to 11
ignore any data that doesn’t fit
double down on whatever the initial perspective was and aggressively diminish any counter POVs in whatever way possible.

the more chaotic and complex the world gets, the more people cling to big simple stories. they want black and white. they crave good guys and bad guys. they want to feel validated and as though their perspective is the right one. and if you’re the type who genuinely enjoys arguing over the exact hex code of gray something is, you tend to come across to this group as obnoxious, boring, overly serious, pedantic, or even downright maniacal. but what made me feel validated with my argumentative a*se, which tetlock shows with actual data, is that those “semantics sickos” (the people obsessed with nuance, calibration, and conditional statements) are the ones who end up consistently less wrong about the world over long time horizons.

the gap between:

the world is collapsing because late-stage capitalism!!!

and

i’ve outlined 9 overlapping forces, their base rates, and a 10-year probability distribution over the 15 outcomes i was able to map out within the realm of possibilities.

...is the same gap between hedgehogs and foxes.

and that brings us to everyone’s favorite topic, the state of the job market in tech.

tech as a case study in polarization

if you want a live demo of late-stage capitalism + hedgehog thinking + miscalibrated forecasting, look at the tech job market right now.

on one side:

largest number of CS majors and bootcamp grads in history
endless chatgpt-generated linkedin posts about “breaking into tech in 90 days”
viral advice threads from people who’ve written like 3 scripts total

on the other:

a tiny fraction of people who are actually more cracked than ever
- deep understanding of fundamentals, systems, and their own personal toolbox
- can build nontrivial things that solve a problem end-to-end
- can reason from first principles instead of just parroting frameworks
- capable of learning things faster due the technology and sheer volume of information at our disposal today

and there’s basically no middle. it’s all polarized:

tons of people who can talk about tech but can’t ship
a small group who are terrifyingly competent
and a hiring landscape that’s somehow both oversaturated and starved of talent at the same time

AI pours gasoline on this:

makes it easier than ever to sound competent
makes it harder to distinguish between “ctrl-c + ctrl-v straight from chatgpt” and “fundamentally understood and could derive from scratch using first-principles”
punishes those who spend the time required on the difficult, yet most important parts to the learning process b/c then they don’t have the same to show for their work when evaluated against the next guy, who spent all of his time chasing resume points but cheated himself out of the true learning.

thus, you end up with an obnoxious amount of noise b/c it’s become more profitable to sell hedgehog stories (“AI will kill all dev jobs” vs “AI is just autocomplete lol”) than to just engage with honesty. and we see the impact of this through various mediums. hiring managers overcorrect in dumb ways because they don’t have good mental models for the new landscape, competitive programming becoming a lost art due to a structural inability to keep up w/ the amount of cheating, and above all, a societal inability to have genuine discourse because the space has become so clouded with the egregious statements, playground arguments, and repulsive, clout-chasing behavior.

it’s always:

tech is dead for juniors

no, tech is fine you just need to grind leetcode bro

hedgehog brain vs hedgehog brain, yelling to see whose voice is louder and measuring their accuracy by the volume of attention their opinion receives.

why this made me want to trade

one of the big reasons i’m drawn to trading is the daily incentives line up with what i’m looking for in a long-term career:

you are forced to put numbers on your beliefs
you get marked to market daily
being confidently wrong costs real money, and fast
updating beliefs is mandatory, not optional

it’s basically superforecasting with higher stakes and better feedback loops (among many other things).

also, selfishly, the traits the industry optimizes for are the same ones i’ve valued and coveted since i was in kindergarten trying to solve the equation to becoming the fastest kid on the playground:

championing curiosity over credentialism
deeply introspective & obsessive deliberation over lazy, hot takes
love for the granularlity in the details and the process of sifting through the gray area, rather than worshipping one big story

when those SIG traders were told “don’t learn about derivatives, read superforecasting,” they were effectively told:

we care more about how you see the world than how well you know the closed-form solution for an option pricing model.

i find that very reassuring. and also a little damning for how the overwhelming majority of the world operates.

trying to be slightly better less wrong

so what do you actually do with all of this if you’re a student / early career person staring at the abyss of polarized chaos?

this a methodology i’ve been trying out recently for a few of my personal projects & goals, albeit very much still work-in-progress:

1. log everything

it’s hard to argue with a spreadsheet and numbers. if you want to achieve something:

set out a plan of action by breaking things down into smaller chunks that you can tackle on a daily basis. the goal is to eliminate as much daily friction as possible.
establish metrics that you can look back on to objectively assess your progress and performance.
log your daily & weekly progress and take notes on how you did each day/week like:
- when did you bite off more than you could chew?
- when did it feel like you were just going through the motions?
- or when was the work was too easy?
frequently reassess in retrospect not only your progress, but also your metrics and success parameters to optimize what truly drives your success, and throwing out things that don’t actually move the needle.

this approach sucks because again, you can’t argue with what the numbers say about your laziness, inabilities, etc., and that’s the whole point. you’re taking the quantitative approach, removing subjectivity from the feedback loop, and ruthlessly prioritizing weak points for maximum ROI with each rep.

2. force yourself out of hedgehog mode

whenever i catch myself saying “it’s because of X”:

i pause and force out at least 2-3 other plausible underlying drivers, ideally more
then i ask: “if X were false, how would that really change the outcome, and by how much?”
if the answer is “not much at all or enough to change the landscape of this decision problem,” then X wasn’t the core driver and was more than likely just my own personal narrative or projection.

this is especially important when talking about “late-stage capitalism” or “AI will...” type topics because they’re magnets for lazy explanations. because we humans crave the easy explanation, just as we crave the donut sitting right next to the salad. and boy do i love donuts! and that’s totally normal & human! but this requires me to be intentional with how i approach my life by implementing structure with strict guardrails and objective feedback loops to prevent me from falling back to my monkey-brained primal desires and becoming gluttonously obese.

3. frame skills within a distribution, and treat them not as badges

in a polarized market:

the middle gets hollowed out (shrinking middle class)
the bottom becomes crowded
the top gets smaller but with extreme/intensified rewards

if you’re serious about making something of your life and doing something real, your only real option is to sprint as far up the right tail as you can.

concretely, for tech this looks like:

attacking the fundamentals (systems, math, statistics) instead of memorizing interview patterns and obsessing over what you think a non-technical HR recruiter will find impressive on a resume
building things that hurt a little. stuff that interacts with the real world, has users, hits limits, and ACTUALLY SOLVES A PROBLEM. this is where you should try some things you don’t think will work out and see what happens. if it works, why? what contributed to you formulating that false assumption? if it didnt work out, did it still turn out exactly in the manner you expected? if not, how did it differ from expectation? is this noise or something bigger? this process will serve as fundamental in your ability to dig deeply into a problem and go further than your (or even society’s) current knowledge boundary.
for each career path or niche you build towards, constantly asking yourself “could GPT-7 do this better than me?”. if the answer is “yes, and honestly GPT-6 might even be able to do it”, then it’s probably time to move upstack or find a different moat for your personal skill set.

4. keep your identity small

superforecasters generally don’t wrap their identity around a single ideology or prediction.

this is the opposite of twitter/linkedin-brain, where every take is tied to “personal brand”.

the smaller amount of care you place into your identity, the easier it is to say:

yeah, i was wrong. perspective now updated & onto the next.

because the faster you update, the less damage you do to your future self.

a bayesian mindset is probably one of the most important habits to being successful in anything you do in life.

closing thoughts

i honestly did not expect a casual book rec to send me into a spiral about late-stage capitalism, polarization, and the death of the competent middle, but it did force a pretty clean separation in my head:

there’s the part of the world that rewards being loud, simple, and certain
and there’s the part that rewards being calibrated, nuanced, and willing to say “i don’t know 100% for certain, but i’d probably start with a rough ballpark of around 60-65%, so maybe an initial guess of 63% since i’m leaning ever-so-slightly in the direction of the upper bound based on my experience/inituiton/etc.”

right now, the first part is winning the attention war and is why presidential debates feel like reality TV, why everyone speaks in absolutes, and why every problem is viewed through the lens of big-idea people.

the second part quietly runs the trading desks, research labs, and the few teams in tech still doing non-clown-like work.

and i’m confident which side i’d like to end up on for the rest of my career.

if you’re a fellow logic-obsessed semantics goblin who enjoys arguing about the exact hexcode of gray for hours… good news: the world is polarizing! and that makes your nerd brain more valuable! not less! just as long as you lean into the fox side, keep score, and stay just a little bit less wrong year-by-year, day-by-day.