Amy Tam

Every technical person I know is doing the same math right now. They won’t call it that. They’ll say they’re “exploring options” or “thinking about what’s next.” But underneath it’s the same calculation: how much is it costing me to stay where I am? Not in dollars. In time. There’s a feeling in the air that the window for making the right move is shrinking, that every quarter you spend in the wrong seat, the gap between you and the people who moved earlier gets harder to close. A year ago, career decisions in tech felt reversible. Take the wrong job, course correct in eighteen months. That assumption is breaking down. The divergence between people who repositioned early and people who are still weighing their options is becoming visible, and it’s accelerating. I see this up close. I’m an investor at Bloomberg Beta, and I spend most of my time with people in transition: leaving roles, finishing programs, deciding what’s next. I’m not a career advisor. But I sit at the intersection of “what are you leaving” and “what are you chasing.” The valuable skill in tech went from “can you solve this problem” to “can you tell which problems are worth solving and which solutions are actually good.” The scarce thing flipped from execution to judgment: can you orchestrate systems, run parallel bets, and have the taste to know which results matter? The people who figured this out early are on one arm of a widening K-curve. Everyone else is getting faster at things that are about to be done for them. The shift from execution to judgment is happening everywhere, but the cost of staying and the upside of moving look completely different depending on where you’re sitting. ## FAANG Here’s the tradeoff people at big tech companies are running right now: the systems are built, the comp is great, and the work is... fine. You’re increasingly reviewing AI-generated outputs rather than building from scratch. For some people that’s a gift. It’s leverage, it’s sustainable, it’s a good life. The tradeoff is that “fine” has a cost that doesn’t show up in your paycheck. The people leaving aren’t unhappy. They’re restless. They describe this specific feeling: the hardest problems aren’t here anymore, and the org hasn’t caught up to that fact. The ones staying are making a bet that the stability and comp are worth more than being close to the frontier. The ones leaving are making a bet that the frontier is where the next decade of career value gets built and every quarter they wait is a quarter of compounding they miss. Both bets are rational. But only one of them is time-sensitive. ## Quant Quant still works. Absurd pay, hard problems, immediate feedback. If you’re good, you know you’re good, because the P&L doesn’t lie. The tradeoff that’s emerging: the entire quant toolkit (ML infrastructure, data obsession, statistical intuition) turns out to be exactly what AI labs and research startups need. Same muscle, different problem. The difference is surface area. In quant, you’re optimizing a strategy. In AI, you’re building systems that reason. Even the quant-adjacent world is feeling it: the most interesting work in prediction markets and stablecoins is increasingly an AI infrastructure problem. One has a ceiling. The other doesn’t, or at least nobody’s found it yet. Most quant people are staying, and they’re not wrong to. But the ones leaving describe something specific: they hit a point where the intellectual challenge of finance felt bounded in a way it didn’t before. They’re not chasing money. They’re chasing the feeling of working on something where the upper bound isn’t visible. ## Academia This is where the tradeoff is most painful, because it shouldn’t be a tradeoff at all. Publishing novel results used to be the purest form of intellectual prestige. You did the work because the work was beautiful. That hasn’t changed. What changed is that the line between what you can do at a funded startup and what you can do in a university lab is blurring, and not in academia’s favor. A 20-person research startup can now do in a weekend what takes an academic lab a semester, because compute costs money that universities don’t have. The most ambitious PhD students I talk to aren’t choosing between academia and industry. They’re choosing between theorizing about experiments and actually running them. The pull toward funded startups and labs isn’t about selling out. It’s about wanting to do the science, and the science requires resources that academia can’t provide. The people staying in academia for the right reasons (open science, long time horizons, genuine intellectual freedom) are admirable. But they should know that the clock is ticking differently for them too: the longer the compute gap widens, the harder it becomes to do competitive work from inside a university. ## AI Startups (Application Layer) If you’re building products on top of models, you already know the feeling: the clever feature you shipped in March gets commoditized by a model update in June. The ground moves every quarter and your moat evaporates. The tradeoff here is between chasing what’s exciting and building what’s durable. The founders who are thriving right now stopped caring about model capabilities and started caring about the things models can’t take away: data moats, workflow capture, integration depth. It’s less fun to talk about at a dinner party. It’s where the actual companies get built. The people making the sharpest moves in this world are the ones who got excited about plumbing. Not the demo, not the pitch, not the capability. The ugly, boring infrastructure that makes a product sticky independent of which model sits underneath it. ## Research Startups: The New Center of Gravity This is where the K-curve is most visible. Prime Intellect, SSI, Humans&. 10-30 people doing genuine frontier research that competes with organizations fifty times their size. This would have been impossible three years ago. It’s happening now because the tools got good enough that a small number of people with great judgment can outrun a bureaucracy with more resources. The daily workflow here is the clearest picture of what the upper arm looks like in practice. You’re kicking off training runs, spinning up experiments, letting things cook overnight. You come back in the morning and your job isn’t to write code. It’s to know what to do with what came back. To have the taste to distinguish signal from noise when the system hands you a wall of results. It's passive leverage. You set the experiments in motion, and the compounding happens whether or not you’re at your desk. The tradeoff people are weighing: these companies are small, unproven, and many will fail. The bet is that being at the center of the frontier, with your judgment directly touching the work, compounds faster than the safety of a bigger organization, even if the specific company doesn’t make it. The skills transfer. The network transfers. The three years you spend reviewing someone else’s outputs at a big company don’t transfer the same way. ## Big Model Labs: The Narrowing Frontier The pitch, “we’re building AGI,” still works. It might always work on a certain type of person. But the experience inside has shifted. The most interesting research is concentrated among a small number of senior people. Everyone else is doing important supporting work (evals, infra, product) that doesn’t feel like the frontier they signed up for. You joined to touch the thing and you’re three layers removed from it. The tradeoff is prestige vs. proximity. A big lab on your resume still opens every door. But the people leaving are making a specific calculation: the resume value of “I was at [top lab]” is depreciating as the labs get bigger and more corporate, while the value of “I did frontier research at a place where my judgment shaped the direction” is appreciating. The window where big-lab pedigree is the best credential is closing, and the people who see it are moving. ## The Clock Every one of these tradeoffs has the same variable hiding inside it: time. A year ago, you could sit in a comfortable seat and deliberate. The cost of waiting was low because the divergence was slow. That’s no longer true. The tools are compounding. The people who moved early are building on top of what they learned last quarter. The difference between someone who moved six months ago and someone still weighing their options is already compounding. The upper arm isn’t closed. People are making the jump every week, and the people who are hiring them don’t care where you’ve been. They care whether you can do the work. But the math is directional: the longer you optimize for comfort, the more expensive the switch becomes. Not because the opportunities disappear, but because the people who are already there are compounding and you’re not. The companies winning the talent war right now aren’t the ones with the best brand or the highest comp. They’re the ones where your judgment has the most surface area, where the distance between your taste and what actually gets built is zero, and where you’re surrounded by people who know things you don’t yet. The best people want to be close to other people who have tricks they haven’t learned yet, at places with enough compute to actually run the experiments. The question isn't whether you're smart enough. It's that you've already done the math. You just haven't acted on it.