Okay, and what about calibrating? By calibrating, I mean something like "bringing into alignment with reality," i.e. making it so that your gut feelings seem to be good predictors of real-world outcomes. But words like intuition and instinct seem to imply a somewhat immutable relationship to your gut – what is there to do about it?

My answer to this is good, old-fashioned, high-quality, educated speculation grounded in a sample size of one. As with many things, it starts with the art of noticing. That is, to "listen to your gut," but not in the sense of do-what-your-gut-tells-you, more in the sense of try-to-notice-that-your-gut-is-saying-anything-at-all-even-if-you-have-no-idea-what-it's-going-on-about. In fact, you should be careful of the first sense, because at this stage your gut might still be wrong.

Noticing

There's a video game series I'm fond of called Fire Emblem. It's a tactical RPG where you try to strategically hit things and not get hit back too badly. If you do this well, eventually you get to hit a dragon until it dies and you win. There are various game mechanics involved, but the most basic one is your hit chance. For this, Fire Emblem uses a system called True Hit. Naturally, it lies to you.

The details of how exactly it lies to you aren't that important. The important effect is that displayed hit chances that are above 50% are actually higher in practice, and those below 50% are actually lower in practice. Fire Emblem does this because most players, on a gut level, feel that a 95% chance event ought to happen much more often than 95% of the time, and that a 99% chance event is an almost certainty – it feels like an injustice when it doesn't happen! In that sense, Fire Emblem calibrates the game mechanics to better fit a player's gut feelings to make the game feel more intuitive. But what this really does is just hide what your gut is trying to say, even if it's wrong.

Reality typically won't recalibrate itself to match your gut feelings, but that means we can notice what happens when our gut is saying something. The exact "motions" (mostly involuntary and mental rather than physical) happening aren't super important, only the noticing of them. There might be a certain way that we plan around an event, taking it for granted when we shouldn't. We might mentally dwell too much on an irrelevant fact or gloss over something important. We might flinch away from an uncomfortable thought or fidget or feel various physical sensations. There might be a specific flavor of mental unease that pushes us to defer entirely to spreadsheets. Whatever it is, there's something there to attend to.

Calibrating yourself to your gut

Last year, I attended an ad hoc workshop on improving your calibration for prediction markets. There was surprisingly little quantitative analysis – it was mostly about learning to pay conscious attention to the specific gears moving in our brains that led to them spitting out a probability. The point was to be able to correctly associate these gut feelings / gears / mental motions to reality. That is, suppose you think something is the right call 90% of the time and that comes with some kind of internal subjective experience composed of various feelings and motions. Actually though, suppose it turns out that's only true 70% of the time. If we're paying attention, we can learn to re-associate our mental connection with that cluster of emotions from being "what we feel when something is right 90% of the time" to "what we feel when something is right 70% of the time."

That exercise was limited to prediction markets and assignment of probabilities, but I suspect this is much more broadly applicable. If we're listening to a new argument for something, we can pay attention to where we feel resistance, if our nervous system gets activated, or where our mind tries to pattern match, and later cross-check that against a more objective judgment. When presented with an unfamiliar situation, we can notice the things that happen internally as we move through the situation and make decisions and later review if our internal feelings seemed to correspond well with what turned out to be the right decision or not.

As a personal example, I've historically struggled with writing the bottom line first and working backwards to justify it. I recently started noticing – feeling in my gut – what it feels like when I'm rationalizing something, before I consciously realize I'm doing so (and I then try to follow that up by explicitly acknowledging it and positively reinforcing myself for successfully noticing). This has lots of smaller, day-to-day consequences, but it was also a larger scale version of this noticing (that I was rationalizing my trajectory at the time) that became a catalyst for my recent career shift to AI safety.

Actually trusting your gut

So far we've been talking about how to listen to and interpret what your gut is saying. That seems pretty valuable to me already, but sometimes you might have to do the opposite of what your gut is saying if you've noticed it's consistently getting something wrong (such as being uneasy with hard conversations despite them typically turning out well). I claim that at this point, most of the difficult work is done, and you just have to give it time.

If you maintain this explicit mental attention / feedback loop between your internal motions and outcomes, then your gut feelings will gradually evolve. They'll tend to ease their discomfort in situations where you notice that such discomfort is unnecessary and to strengthen their conviction where you notice it is warranted. If you stay in tune with these changes, then better calibration – and being able to trust your gut – is something that just naturally follows.

I've had this conversation a bunch of times now, and I'm happy to talk about it each time, but it seems like it would be more convenient for all parties if there were just a compiled list of my advice somewhere, so here's an attempt at such a compilation. Apologies in advance for any gaps, please feel free to email me with questions, and answers may get added to this reference, though I think I'm approximately aiming for an 80/20 rule here.

Why leave?

Whatever reasons there are will be highly personal. I can list some of my own reasons below, as well as some that I've heard, but if you're reading this, you might have some of your own as well. Note that this is unashamedly biased towards leaving academia, but I don't think someone is wrong if they conclude that staying in academia is right for them, so long as the decision is well-informed.

But my first lukewarm take is that the discoverability of other options is generally terrible, and the friction of figuring out what options there are and how to actually achieve those options is enough to stop many people from seriously considering an exit.

I'll also note a personal mental obstacle that I had as a young grad student. For whatever reason, I had a sort of cached mental impression that not "making it" to an academic job was a failure. It's hard to pinpoint exactly where that impression came from. I'm sure some of it was internal, but there's also a degree to which this view is baked in around us – for example, it's common to refer to leaving academia as selling out, which has some negative connotation. I don't think holding the view that it's a failure is healthy, nor is it true.

In reality, I think most of the reasons why someone might remain in academia are superseded by the appropriate arrangement outside of academia (of which there are many), up to and including being able to do more research outside of academia. Read on for more details.

Reasons to leave

• You make (a lot) more money outside of academia
  • By the time you would get tenure in academia, you could counterfactually be a senior or staff level researcher/engineer (see later on for a note on "levels") in tech and earn 300-500k+ total compensation (combined salary + equity grant, which is as good as cash)
  • The opportunity cost of staying in academia is well into the six figures per year, just from the raw salary difference, not even accounting for compounding growth of investments. If you weren't already in academia would you pay $200k+ per year in order to be in academia? (if you look further down, my estimation of the opportunity cost at the tenured professor level is much higher)
• The job demands, stress, and time commitment are typically lower
  • The demands are often low enough that you can hold a full time job and still have more time for research than in academia
  • The Google employees in the tweet above are probably on the low end, but not that far from the true median.
• You have more flexibility in where you live (especially with remote work)
• Remote work in tech specifically also comes with extremely flexible hours
• Less administrative burden, zero course preps, no outreach/service expectations
• If you learn certain practical skills, you are incredibly employable and don't need the job security guarantees of tenure (and make enough money anyways that you can absorb unlikely periods of unemployment, or just straight up retire after ~10-20 relatively chill years and just do whatever the hell you want after)
• There may be a personal situation tying you to your job. Depending on that situation (e.g. with visa status), you may find a solution anyways outside of industry (many tech companies sponsor visas)

Where do I go?

Ok, so let's say you're considering leaving. What are your career options? When I first Googled "math major career options" in undergrad, the top result was being an actuary. Nothing against actuaries, they're probably cool people, but I don't want to personally find out. Your career options are much more varied than this.

• Tech / Silicon Valley
  • This can mean researcher (e.g. machine learning, data science, etc) or software engineer
  • This is broadly split into Big Tech (FAANG companies) and startups
  • Big Tech has the highest compensation between the two and the lowest job demand (which is paradoxical, but FAANG makes a lot of money, and it's hard to be ultra efficient at their scale)
    • As an intuition pump: Elon caught a lot of flak for firing like, 80% of X (f.k.a. Twitter), but it's still running (albeit with some performance degradation here and there). I was among the doubters, but so far it seems like he was right on this point. Many big tech companies could probably survive >50% cuts if they had the guts to do it.
  • Startups have the most exciting work, but lower compensation (higher than academia) and higher job demand (comparable to academia). If you're an early employee at a startup that eventually IPOs, you're probably going to walk away very rich, but don't count on it. The attraction here is working on problems that feel like they're either building the future or really fixing existing problems in a meaningful way. Be careful not to choose a startup whose mission you don't care about. The entire point of working at a small startup is mission alignment. Working on a mission you do care about feels amazing. Some real examples of startups that you can really work on:
    • Flying houses (mission: tiny houses that can be airlifted into disaster relief areas)
    • Lab grown meat (mission: end animal suffering due to factory farming)
    • Alternative energy (mission: solve world energy supply)
    • Wearable tech (biometrics: live healthier lives / AI assistants: extend our cognition with new tools)
    • Self-driving cars (lots of potential missions, e.g. solve car accident mortality)
    • Literal space travel (mission: go to SPACE and maybe COLONIZE MARS)
  • Those are just the eye-catching ones. If you can think of a real problem in the world, even if it's incredibly unsexy, there's probably a startup somewhere working on it (lots of startups also suck though and are working on fake problems, but evaluating startups is an entire 5k word guide on its own)
• Quant finance
  • Pay is similar to big tech
  • Great if you are specifically extremely competitive, resilient to stress, and can be happy with high variance positive EV decision making even if the realized consequence was negative (think activities like poker)
  • There is a particular type of person for whom this is a good fit, but most people are not, and that is fine
• NSA
  • The NSA employs a lot of mathematicians, but I'm not familiar with the precise kind of work they do. Presumably a lot of cryptography related work.
  • I hear the work-life balance is great at the NSA
• CCR campuses
  • Apparently the NSA also has more "relaxed" research centers called Center(s) for Communications Research
  • Examples: Princeton, La Jolla (there are more if you Google, for some reason they don't seem to be aggregated anywhere)
• NASA
  • You can do cool space stuff!
• Government labs
  • There are a variety of national labs that fund fundamental research
  • If such a lab happens to fund the type of research you do, you can enjoy what is essentially an academic job but without the teaching
• Healthcare / biostats
  • A close friend of mine does work in epidemiology using statistics and gets to do covid modeling
• Honestly, almost anything
  • Your most powerful tool is being agentic. You can, in some sense, really do whatever you put your mind to – most people are limited by artificial walls. A PhD is proof that you can do hard stuff on your own. The degree of career freedom you have outside of academia is incredible. Not all artificial walls are worth tearing down, but before you think "I could never do that," look with fresh eyes and ask if that's really true.
  • There are plenty of careers that there aren't degrees for. America employs ~160M people, and my undergrad institution had like ~120 majors. Lots of people have jobs that they didn't formally study for, and you can do that too.
• Things that I did or almost did at various points (in the span of only 2.5 years!) that no one had to give me permission or a formal list of prereqs for: education tools, music tech, science communication, game design, sports betting (for American football – and I still don't even know what a tight end is), algorithmic trading, software engineering, blockchain/web3, tech consulting/advising for early startups, data science, supply chain optimization, AI safety research, and probably a lot more that I've forgotten already
• Other thoughts
  • Perspectives that people have on PhDs are mixed, but there's more than enough people who look at it and say "oh shit, they have a PhD, they're probably really smart" rather than "their thesis isn't relevant, let's not hire them" that I think it's net positive (you only need one good hiring manager to think positively towards a PhD)
  • There is a ton of interesting work that happens outside of academia.

How do I actually, concretely get a job?

[This is from the perspective of a student, but postdocs and professors can adapt the same general strategies.]

For most of the common career paths, the primary new skill you need to pick up is some degree of programming (coding combinatorics experiments is a good starting point, but not representative of the skills you need) and/or some degree of statistics/data science/machine learning (these three all blend together to different degrees at different jobs). Everything else you can learn on the job (and don't worry – everyone does). If you're picking the "choose-your-own-adventure" option in the previous section, then I'll leave it up to you to determine how much of what skill you need to learn and how you learn it.

• You probably don't need to learn much programming, if any, for jobs like at the NSA or at government labs (or if you do, they might just teach you on the job)
• You need to learn a little programming (probably leaning towards statistical languages, e.g. Python, R) but mostly data science concepts for things like biostats and data science
• You need to learn a decent amount of programming if you want to do software engineering
• You need both programming (less than for software engineering) and data science/ML for quant finance
• You need both if you want to specialize to certain parts of software engineering, such as machine learning engineering
• Lukewarm take, but I think basically any job in the realm of knowledge work will benefit from some proper programming experience, even if the job description doesn't explicitly call for it (and even if none of your coworkers know any)

From here on, the main focus will be on getting tech/quant/data science jobs. The reason for this is that these are 1) what I'm most familiar with and 2) what I expect will be the most involved processes / require the most domain specific prep knowledge.

Learning data science and machine learning

Your core resource is Kaggle. Start with the Titanic and Housing Prices intro competitions (in that order). Find some example notebooks, or if you're braver, try to analyze the dataset and generate predictions on your own without hints. These notebooks exist to familiarize you with the common tools, languages, and techniques that are used in data science. Pay attention to the questions asked, but also the heuristics and rigor. Notice the types of questions which are not asked. Data science is a different field from mathematics with different standards for what is or isn't a good answer to a question.

Work your way up to trying some live competitions, including competitions with real prize money. Pick one that seems interesting, maybe find a few friends who are also trying to learn data science and form a team. Give yourself somewhere from a few weeks to a couple months and dig really deep into that competition. There is no substitute to learning by doing.

As a reference point for the level of difficulty of questions you might get in interviews:

• One of my onsite questions at a quant hedge fund was essentially a series of questions about the housing prices dataset
• Another ML/data science role asked me to code the k-nearest neighbors algorithm from scratch (I later learned that they didn't mean literally from scratch, it was meant to be a test of how well I knew the numpy library in Python)
• Various questions on linear regression. These types of questions did not inherently require knowledge beyond the undergraduate level, but they did require a depth of understanding of those concepts that wouldn't normally be expected of an undergraduate.
  • E.g. suppose you have a dataset on the 2D plane, where data is uniformly distributed in a rectangle. What happens to the linear regression line as you rotate that rectangle 90 degrees clockwise or counter-clockwise?
  • Similar questions about normal distributions are common

Learning to program

Programming experience in the software industry is typically split into "production" and "not production" level experience. "Production" means work that was done in a commercial setting or otherwise at a commercial level. This typically implies things such as: real money is on the line, code is run live without supervision most of the time, and coding is done as a team in a shared repository with peer reviews, among other things. Unfortunately, within the software industry, things like personal projects or coding experiments for math research are not typically considered production-level coding experience.

At first glance, there is an apparent catch-22 that it's hard to find a job if you don't have production-level coding experience, but you can't get production-level coding experience if you never get a job. Internships can be one solution, but maybe you're applying in your last year and you don't have any summers left, or maybe you're a postdoc/professor already. In that case, here is a "minimum viable solution" to getting out of the catch-22:

• Watch the MIT OCW Introduction to Algorithms videos. Doing problem sets is optional.
  • Become comfortable with the data structures and algorithms presented as well as in the analysis of time and space complexity of solutions.
• Doing problem sets in the previous step is optional because doing problems in this step is not optional. Make a LeetCode account, consider if a premium subscription is useful to you (I bought one month of premium at one point and found it useful enough for one month but not more), and then do a ton of problems.
  • Start with easy problems, then move up to medium once you feel good about easy problems.
• There is a (now somewhat infamous maybe) list of 75 LeetCode problems that aim to be representative of the types of problems you'll run into in interviews. If you're pressed for time (e.g. graduating soon), this is the minimal set of LC problems you should aim to solve
• LeetCode is seriously the core of the current meta of (entry level) tech interviews. It is a fake skill in the sense that the connection with ability to do real work is dubious (though I don't think it's totally indefensible), but it is a real skill in the sense that you can really study to the exam and get good at LC style problems and this is highly instrumental for getting a job in the first place.
• A good benchmark is being able to consistently do LeetCode medium problems on your own, with no hints, in 15-30 minutes. The vast majority of interview problems are not harder than LeetCode medium. Past this point, you start to get diminishing returns on the time spent on LeetCode, but it is (IMO) quite hard to accidentally get to a point where marginal practice is pointless.
• Quick plug for a fun alternative to LeetCode: Advent of Code. This is an annual advent calendar of coding problems that increases in difficulty as it approaches Christmas and has a silly story to go along with it.
• Finally, work on some actual personal projects. Find something interesting and just do it.
  • You can sometimes get a 2 for 1 with data science/ML experience by doing some kind of coding-intensive competition on Kaggle.
  • The first project I ever worked on that made it onto my resume was an automated arbitrage bot for Magic The Gathering: Online. The code was trash, it was a buggy mess (in our defense, this was partly because MTGO itself was a buggy mess), and we got banned from the platform shortly after. We made some actual money off of running the bot, but the real value was returns on career capital – it has one of the highest return rates of any single week I've ever spent coding. It is the most common talking point in my interviews and is still proudly displayed on my resume.
  • Check out this Hacker News thread for examples of long-tail outcomes of interesting projects
• Another avenue to show off some of your coding (and to actually get some real code review/feedback) is to make open source contributions on Github. I won't get into how to do this, but some employers view this very favorably and like to see actual code examples as a way to de-risk hiring you.
• A longer path (which you can take if you decide well in advance that this is the path you'd like to take) is to take some computer science classes, and maybe even get a masters in CS. This seems effective if you're willing to put in the time commitment and can plan ahead for it.

Misc quant questions

There is a small industry built around certain types of quantitative problems and brain teasers that get asked almost exclusively in quant interviews. Getting into these problems is probably out of scope for this document, but you can find plenty of examples online.

General interview advice

Interviewing is a skill, too. Your first few interviews will probably feel like garbage. If I haven't interviewed for a while, my interview skills get rusty, and it takes a few warm-up interviews to get comfortable with them again. Bias towards interviewing earlier in your interview prep process and just get the crappy first few interviews out of the way. I promise it gets better, and there are enough companies out there that flubbing a few interviews for middle-range targets isn't an issue.

For most of the quantitative jobs above, the vast, vast majority of your interviews will be technical interviews. Other interviews (such as with HR or recruiters) will be more like formalities in the process, mostly just checking that you're reasonably easy to talk to. I didn't have a single real behavioral interview until one point where I was speculatively interviewing for a totally different kind of job.

For technical interviews, a full guide is also out of scope for this reference, but here are some general pointers:

• Think out loud (seriously, it is sometimes a red flag for interviewers if you are too quiet)
• Ask clarifying questions (green flag if you ask them, red flag if you don't + were wrong about interpreting the question)
• Don't be afraid to start with naive solutions to programming problems where the time complexity sucks and is obviously not optimal. As long as you say that out loud and then iterate from there, it's totally fine and counts as partial progress towards answering the question
• If a question seems really unusually hard, it might be your unlucky day, but it might also be the case that it's hard for everyone. Many places rate interview performance on problems "on a curve"
• For some questions, you can get more "points" by sharing high quality thinking out loud without arriving at the right answer than by just jumping to the right answer. What this looks like is: asking the right questions, thinking about the right things, coming up with tractable, reasonable sounding ideas (that may not ultimately work)
• If you know someone who is in tech, call in some favors and do practice interview sessions and get feedback
• The interview process can be pretty long; I think my longest interview processes had 5 rounds (where the last one is an "onsite," though these are often over Zoom now instead of physically onsite), and I've heard of more. Have a constant pipeline of interviews, and don't wait for one interview process to end before looking for another.
• Don't get too attached to any one job. I made this mistake a few times, and still ultimately ended up with a job I was very happy to get each time (and which was likely better than the counterfactual offer anyways)

A note on "levels"

One of the great (IMO) features of tech careers is the notion of a "level." Although they aren't fully standardized between different companies (and it takes some experience to know what to expect from a "senior" engineer coming from different types of orgs), I still find them a pretty useful framework for understanding jobs in tech. A typical sequence of levels at tech companies (or quant sometimes, for larger firms) looks something like the following, where if you're on say, a data scientist track, you might replace "software engineer" with "data scientist" everywhere.

• Junior
  • Sometimes can be a title like "Software Engineer 1" or "SDE 1" or "SWE 1"
  • Entry level for bachelors
  • Very low expectations, your job at this level is mostly to learn. Companies typically view juniors as an investment more than a clear source of value.
• Mid
  • Often comes without a title prefix, e.g. just "Software Engineer" and not "Mid-level Software Engineer"
  • Otherwise might look like "Software Engineer 2" or "SDE 2" or "SWE 2"
• Senior
  • Typically considered a "terminal" role in tech – there are many levels beyond this, but this is where you're considered good enough for companies to be happy to have you sit here indefinitely and provide this level of value without growing further. Many people do just that.
  • Typical expected time is ~5 yrs to reach this level (for the median engineer that makes it to senior level, not for the median engineer with a PhD that makes it to senior – you can make it to senior much faster if you want but you do not have to)
  • When interviewing for this level and above in software, you will start to need to know system design. Expect at least one technical interview to be on system design problems.
• Staff
  • This is where there's an option to move into the management track, which has its own levels that parallel the "individual contributor" (IC) track
    • Most people actually move into management instead of moving up the IC ladder, see notes on that below
  • It is harder to define what your role is at this level and above, staff+ ICs tend to get increasingly specialized roles based on their individual capabilities and the needs of the company
  • Compensation was already stupid, but this is where it gets real dumb. 500k+ total compensation is common at FAANG
• Principal
  • Here levels start to break down further at different companies. Sometimes there is a "senior staff" before this level and sometimes there is a "senior principal" after this level. Sometimes staff is skipped, sometimes principal doesn't exist.
  • Compensation gets downright offensive: FAANG principal+ can earn 7 figures

Management track has similar levels (and compensation) starting with "Engineering Manager" or something similar being the equivalent of something like "Staff Software Engineer." It then has its own titles like "Senior Engineering Manager," and may go towards titles like "VP of Engineering." All of these levels (for both IC and manager track) have their own expectations, which I won't cover here. There are good/comprehensive resources online, like this one.

Benchmarking levels against academia

Ok, here's where the real hot takes begin. I'm not going to bother justifying these, so take them with a grain of salt, but I think this is what I believe from my limited perspective.

• If you don't have a PhD in computer science or machine learning, expect to enter tech or quant at the junior or mid-level. You might be able to get a data science role at a senior level if you did e.g. probability and do well on your interviews.
  • The startup I first worked for didn't have levels in a formal sense until a year after I joined, but I think I was retroactively initially placed at the junior level and then promoted to mid-level after a year.
• If you can get a PhD in math, you can reach senior level in tech, full stop. Tech jobs are high paying because 1) tech companies make a shitload of money, and marginal engineers really do actually bring a lot of value to the company at scale and 2) because the jobs are competitive and hard and have high expectations. But crucially, they are competitive and hard and have high expectations relative to the typical job in America. They are not more competitive or harder or have higher expectations than earning a PhD (modulo personal fit for tech, some people may just find software easier than others).
• Postdocs up through tenured professors are probably somewhere around the "staff" level equivalent for academia.
• I'm less sure about this, but I'd guess that full professor in academia is comparable to principal level in tech or to the equivalent management track role (hey, remember that 7 figure compensation number above?)
• These don't mean they're where your cap is, but I would confidently place money at 1:1 odds (and probably much worse odds, though I don't want to commit to figure those out here) that someone with the given level of academic accomplishment can first reach the given comparable tech level in a typical amount of time
• In general, the number of people who reach each progressive level in the tech ladder tends to decrease exponentially, and the difficulty and expectations of the levels also increase substantially past senior. It is totally fine to hit ~senior level and just chill there forever and do other stuff with your time, like fun side projects or your math research or just having more time to enjoy life in general.

A personal request

If you've gotten this far, hopefully this resource has been useful to you. I am happy if this improves your life in any way. My only request to you is that you please try to avoid working directly on AI capabilities as a result of this advice.

I've written about AI safety previously and have pivoted my career to technical safety research because I think this is such an important problem. This is not asking you to say, turn down the only offer you get and not be able to support yourself or your family, but to please think about this if you find yourself having a choice in the matter. Most job opportunities don't fall under this umbrella, and it'll probably be clear if a given job is aimed at significantly advancing AI, so you mostly have to be specifically looking for such jobs.

Dear reader, it was foggy and overcast. There was no sunrise. I'm not upset, but thanks for asking.

While I was walking, I thought back to the conversations I had in Oxford and to previous work retreats before that. I've only experienced remote work, and a lesson that I keep relearning is how impactful meeting in-person is. It's clearly helpful for productivity – for about a week, everyone's primary focus is on planning, on sharing knowledge, on aligning the big picture. But I don't think this is actually the most important part. There's a key social cohesion created, and not just from intentionally designed icebreaker activities.

When you first interact with someone, you get the broad strokes that everyone gets. He's a teacher, she's an engineer, they're a parent, a spouse, a sibling. Work intros often start with everyone rattling off education history, past jobs, and maybe a well-rehearsed fun fact. Basic knowledge is of course a necessary starting point, but how much of a picture do you really get of someone from that?

These are the things that often come to mind when we think of our identity, but billions of other people in the world are, say, cat or dog people, and I'd take short odds that there's more variance within one of those groups than between them. You can learn a dozen facts about a person and still be no closer to understanding them or building a close relationship.

Most people never see the the small, mundane, in-between moments in our lives, but I think this is where intimacy lies. I don't identify with a foggy morning; it doesn't feel like a part of who I am in any way. And yet, I'd only share this kind of story with a few people (and you of course, dear reader). I don't broadcast this to the world (kindly ignore that I'm publicly writing about it) because it isn't that important, and most people just won't care. For the people that do, they care because it's a story about me, because someone in their tribe experienced it, and because it means something to be one of the first people that comes to mind when anything happens.

I'm not saying that spending a week in person gets you anywhere near that level of closeness. But having such dense overlap in organic moments sure seems like a bigger step forward than as many video calls to hash out business logistics. There's no choice but to experience the ordinary together and much more space to fill with mundane questions (how was your morning?) and mundane stories (the funniest thing happened...). Maybe there's something like the Ben Franklin effect happening too – if sharing is caring, then perhaps caring follows sharing as much as the reverse.

I also notice that a drop-off in mundane sharing predicts an eventual drop-off in closeness**. I think this makes sense; most of what happens in our lives isn't news-worthy, so not talking about the boring stuff means much less contact overall. But beyond this, there's probably something powerful in gradually losing the urge to share and in the worry that you might be bothering someone with something uninteresting rather than feeling secure in the idea that anything is interesting to them if you're the main character. Sometimes the last things to develop are the first to go.

*Technically this happened a few days ago by time of publishing, but I wrote that line on the day it happened. Don't sue me.

**I'm also noticing while writing that I'm generally not sharing much of the boring stuff with anyone right now (so for my close friends reading this, don't go off thinking I secretly hate you in particular because I didn't tell you about a non-sunrise). This seems like a sign that I'm getting too isolated and need to give more attention to my social wellbeing.

It's been an eventful year! I quit my job, tried to start a company, had a summer fling with freelancing (it was awesome), and did a ton of soul searching. It's also been a bit of an emotional rollercoaster: lots of stress, lots of confusion, lots of fun (from New England sunset boat rides to laser tag in a castle). The year's not over yet, so there's still time for curveballs, but it looks like some dust is finally settling.

The dominating consequence of my soul search was the conviction that AI safety is the most important thing I can work on right now. I spent the better part of the last 6 months exploring how rapid AI development will transform our world, and the world in turn kept smacking me in the face with how wide the cone of possibilities was. Unfortunately, not all outcomes are positive – I now believe that this is the most perilous century for humanity, with AI being the primary driver.

The hype around AI – that it could solve cancer, poverty, conflict, disease, even aging and death – massively accelerates its development. To be clear, AI safety shouldn't be conflated with the belief that approaching Reedspacer's Lower Bound is inherently bad. I hope we cure all disease! I hope we get functionally infinite prosperity! I am so on board with solving death! It would be tragic if we never achieved safe artificial superintelligence.

But hype breeds recklessness. Almost all of our forecasted extinction risk this century comes from AI – many times more than from nuclear war, climate change, and bio-risk combined. And development goes on accelerating anyways. I want all the great things that AI promises, I just think we can get them for a better price.

So what's next for me? I'm excited to work towards a safer future with Timaeus, a brand spankin' new technical AI safety research org. I'm hopping on board to help scope out a new field of interpretability research as a research assistant/engineer for at least the next ~6 months. I'm also frantically cramming Spanish for the AI Futures Fellowship in Mexico City early next year, where I'll hopefully develop a broader base of knowledge, meet brilliant people, and eat far too much food.

The future is unclear

I think more people should spend time thinking hard about AI. Whatever consequences you think there will be, it seems hard to get away from the conclusion that they'll be transformative in some direction. But sensemaking about AI is really hard, especially if you don't have a technical background. Even as someone with the relevant background, I spent months highly confused about the right path to take and still feel fairly uncertain. We can and should at least still strive to be directionally correct though, and voting for safe policies is just as critical as technical research (if not more).

Finally, I think this is the ~correct direction (or a correct direction) for me to be moving in right now, but it's all pretty nuanced, and I'm always open to more perspectives. Please reach out if you're curious or have thoughts you'd like to share. Don't be a stranger, odds are I'd love to chat – everyone who's let me talk their ear off so far can attest to that.

This thought keeps coming back to me lately: everything is about to get really weird. For the past two years, I’ve had a front-row seat to the primordial soup of AI research and startups. I’ve built a couple of products based on language models, consulted on generative music tools, and dabbled across the board of generative AI, including image, audio transcription, voice, and more. A massive wave of commercial products is coming, for which ChatGPT is just the tip of the iceberg.

AI has been fairly consistent in progressing exponentially. The thing about exponential growth is that for a long time, it looks like nothing is happening, then it looks like something is finally happening, and then while you’re still processing the something that’s happening, the growth explodes and… uh oh, suddenly everything is weird now. We’re still in the “huh, it looks like something is finally happening” stage with AI.

A certain recent pandemic demonstrated that we’re not so good at dealing with the next phase of exponential growth. But covid didn’t care what was or wasn’t intuitive to us; the world just changed overnight anyway. We scrambled to react and flatten the curve, but not before it was already a foregone conclusion that there would be a huge curve. When it comes to AI, it’s fortunately not all downside like it is with a global pandemic. AI has incredible potential to bring prosperity – some have even compared it to the discovery of fire or electricity. But there’s a darker side, and that side grows exponentially too.

While writing this, I hopped on X (formerly known as Twitter, RIP) and saw a post on practical acoustic side-channel attacks on keyboards. The model in that paper can correctly identify 93% of your keystrokes from the audio that a Zoom call picks up. I see news like this more often than is comfortable, and then I predictably think to myself that everything is about to get really weird. You have to really stand out these days to be noteworthy – a few months ago, a research team managed to read the human mind(!) with >90% accuracy by training a model on fMRI scans. [Note: since writing this, we've managed to reconstruct music by reading brainwaves too.]

The optimistic among us would hope in vain that AI tech isn’t abused. FraudGPT and WormGPT are two cousins of ChatGPT, repurposed for enabling cyberattacks and scams. ChaosGPT was a (fortunately ineffective) bot whose task was literally to destroy humanity. What happens as we progress along the exponential growth curve and the people building these harmful AIs get access to shiny new toys? Dario Amodei, CEO of Anthropic, testified to the US Congress that a straightforward extrapolation of today’s systems suggests that large-scale biological attacks could be possible in only two or three years. He went on to say that if we don’t have mechanisms in place to restrain AI systems by 2025-26, “we’re gonna have a really bad time.”

Two to three years isn’t a lot of time, and we don’t even know what the right restraining mechanisms are yet! It would be great to have a bit more time to figure things out before the exponential progress curve blows past us. Fortunately, unlike with the pandemic, we can actually make that happen for AI. The Future of Life Institute wrote an open letter with over 33,000 signatures calling for a six-month moratorium on AI development, signed by many of the biggest names in AI research and tech. It’s not clear that the specific plan of a six-month moratorium would be most effective, but this seems directionally correct. Policymakers might consider prophylactic legislation that would allow such a moratorium to come into effect if certain conditions are met. Compute governance is a similar slowing mechanism, where large training runs of AI models are limited in the amount of compute power they’re allowed to use. The threshold can be adjusted over time to have some control over the pace of development without it being all or nothing. These aren’t long-term solutions, but they would buy time to figure out more permanent measures.

Besides short timelines to serious risks, another core challenge with making AI safe is the asymmetry between offense and defense. There are simply too many angles of attack to be able to cover all vulnerabilities – but policy can reshape the AI landscape into something more defensible. When advanced AI is too openly accessible, any individual can choose to take one of those many angles of attack, so an obvious solution might be to prevent advanced AI from being too openly accessible.

We can accomplish that via regulatory action, but this is a pretty controversial topic in the tech world; open-sourcing (making publicly available) your research and your code is usually synonymous with nice things such as transparency, collaboration, and even security since you have more eyes on fixing vulnerabilities. Furthermore, a common counterargument to closing off access to AI technology is that it centralizes too much power in the hands of the few major AI labs and that we can only have a fair balance of power if the technology is available to everyone.

But the world is not always a safer place when people have more access to technology. The US has a lot of guns and – surprise – a lot of mass shootings. If everyone had a nuke in their pocket, it would only be a matter of time before most urban cities disappeared in a mushroom cloud. All it takes is one person doing the wrong thing. And if everyone has a large-scale-biological-attack-capable AI on their home computer, Dario Amodei put it well: we’re gonna have a really bad time.

It doesn’t seem likely that we can entirely close Pandora’s box on AI, but we can make it manageable. Everything is about to get really weird, but we still have the power to decide if that’ll be a good weird or a bad one.

To get that number, I made some salary approximations, calculated taxes and cost of living, and was naively surprised at S&P 500 returns in a zero interest rate environment, which managed to account for a substantial amount of the difference. The total gap will increase over time too, through the interminable power of compounding returns. Your ultimate career path might not have quite such inflated salaries, and zero interest rates probably aren’t coming back any time soon, but the opportunity cost for a PhD may still be much higher than you expect.

Some people hold the position that they should do their best to deter anyone and everyone they can from getting a PhD. Their belief is that you’re fit for one if and only if you ignore that advice and boldly forge ahead anyways.

I think this is unnecessary. There are plenty of real reasons not to do a PhD (I have at least 1.17M of them), and one should have a better reason to do one than an inability to absorb advice. These considerations, and my own story, are the best I can offer to you if you're a prospective student; the final decision is up to you.

The happy path of grad school

Whatever path you choose in life, you’ll take some form of opportunity cost along the way; the important thing is just to notice the tradeoff. For example, last I checked, it was quite expensive to raise children in the US. Most people go on to have children anyways, and many go on to be happier with their kids than the money would make them. An econ PhD friend of mine talks about a concept of “Life EV”: sometimes we do things that are financially suboptimal because they sufficiently improve our life along other dimensions (he often yells this as he's halfway through leaping into a clearly bad decision because it seems fun).

But just as you shouldn’t have kids for the sole reason of hoping you have someone to visit you in your nursing home, you shouldn’t only think of a PhD in terms of what comes after. The best reason to do a PhD, and one which is both necessary and sufficient, is that you simply want to experience the journey.

That journey can be a beautiful one, if you’re lucky. You’ll meet other like-minded, bright-eyed grad students. You’ll meet generous mentors who pay things forward like it is their moral duty. You’ll travel to conferences (on someone else’s dime!) across the world, and you’ll meet more people that care about your tiny corner of reality than you could have imagined existed (there were dozens of us).

You can work on the kind of problem that follows you to the shower. The kind that itches your brain at night. The kind that jolts you awake as you lay in bed, because a piece finally clicked into place and you can’t hope to sleep until you write it down. The kind that gives you an adrenaline high for days when you figure it out, because you’ve been working on it for months or years.

There are other reasons that help, too. It’s hard to believe you can tackle a challenge that no one else has, especially if the pressure of completing your thesis hangs over you. A PhD is training and proof of your ability to grapple with the unknown.

You might want to do something you can only do with a PhD, or which a PhD can open doors for. In the world of math, this could mean working at a university, but it could also mean working at a hedge fund, at a national lab, or at an AI lab of a big tech company. Most things don’t need a PhD in the strictest sense, but people do pay attention when you have one.

These things are icing on the cake, but at the end of the day, the surest way to make it a positive, worthwhile experience is to want the experience. A PhD should not feel like paying your dues, and it is not noble to suffer through one. An important corollary is that if you are decidedly not enjoying the experience, it’s ok to leave!

A note on "slack"

It can be a little paradoxical to describe grad school as a time with a lot of slack. After all, grad students have a reputation for being over-worked, under-paid, over-caffeinated, and under-rested. But it isn't always like that, and there is a unique thread of intellectual freedom through all of it.

For me, that freedom manifested as space to grow. Once I finished most of my coursework (first two years), the only expectation seemed to be that I spend a lot of time thinking. In theory, that means thinking about your research, but in practice, my mind still managed to wander for many hours a day.

That space expanded considerably in my last two years; the pandemic happened, I'd already decided to leave academia, and I had enough research to graduate. I had few expectations to fulfill, and any remaining research work was mainly tying up loose ends. This was an unusual abundance of slack, but it led to substantially more hours being alone in my head.

I used to think this time was a bit wasted. The amount of concrete things that came out of those last couple of years felt pretty dismal in number. I was probably burnt out for a substantial portion of it, and the social isolation was hard.

Looking back, those years were probably some of the richest years of personal growth that I've ever had. Among other things, I developed a lot more self-awareness and confronted large chunks of emotional baggage that I never previously noticed.

This constitutes a large part of why I would make the same choice to do a PhD, if I could go back and choose again. It also highlights another kind of opportunity cost, which is of time. Whether I spent 5 years in grad school or at Google, those 5 years would pass either way. I only got to spend my early 20s once, and I'm glad that I spent them the way I did. Just as money in the bank would compound year over year, I can benefit from that space to grow for the rest of my life.

The darker side of grad school

The PhD journey can be a beautiful one, if you’re lucky. If you’re unlucky, academia is a meat grinder. There are various opportunity costs to consider, but there are real costs too, mostly in the form of stress to the mind and spirit. From my limited view, the following things were the main culprits.

Impostor syndrome

Academia has a high density of brilliant people. Many academics, including ones you will think are far more brilliant than yourself, feel they don’t belong. They do, and you will too if you choose to join them. There is an entire post that can be written about this topic, but the short version is that it is important to know that you may feel this way, to know that it is a normal and well-worn path, and to know that there are resources for this.

Social isolation

It can be intoxicating to be consumed by your work, but the darker side is that you can end up disconnected from and left behind by your social network. PhD programs are selective, so you can’t be picky about which city you end up in. I think this is especially hard for overseas students, where even phone calls are difficult to coordinate thanks to time zones.

You’ll see other people moving on to the next stage of their life, starting their careers, starting families, buying houses, cars, vacations, and other shiny things. Your family and friends may valiantly try to understand your work, but they probably won’t. Your cohort might end up a bit south of social and outgoing (n.b. you are allowed to be the one that organizes social events!).

My advice for this is to find a hobby with a strong community around it (preferably a physical one; you get exercise and get to meet people in person). I took up rock climbing in my first year, and it changed my life.

A bad advisor relationship

Professors are people too, and in any group of people there will be unsavory individuals. Some rare professors have a reputation for dealing in politics, gossip, and lies. It is not always easy for a fresh grad student to figure out who these are, but one should do everything in their power to avoid such professors.

Some other professors have very particular (and antagonistic) approaches to mentorship. One who shall not be named would intentionally fail students on their quals. Their alleged motivation for doing so was to use the pressure to pass quals as a motivational whip or forcing function on their students. By doing so, they could compel their students to study additional foundational material for the second attempt. Think twice before working with someone like this.

Setting aside extreme examples, you might just not get along well with your advisor through no fault of yours or theirs. Working chemistry is fickle, and you’re allowed to decide it isn’t a good match. Shop around for advisors aggressively until you find a fit (or at least do the best you can with the size of your department), since from the point of picking an advisor onwards, they’ll be the biggest factor in your journey. It can be worth picking your second favorite topic if it means working with your favorite professor.

Academia or bust

One of my undergrad math professors once told me how they got their job. While preparing to defend their thesis, their advisor got a phone call from a colleague asking if he had any good students finishing soon. That was it. No interview, no postdoc needed (typically a 3 year contract), they just discovered the next day that they had a job waiting for them. That was some 60 odd years ago.

Today, you’ll fight to earn 1-2 postdocs, then fight even harder to earn a tenure track position, then work on earning tenure (usually a 6 year process). Although it’s uncommon to be denied tenure once you have a tenure-track position, that’s still a total of ~16 years from the start of grad school before you can be completely sure of stable employment. A pretty low (but not insignificant) portion of PhD students that want tenure eventually make it.

I believe in chasing dreams; if tenure is your dream, then aim for tenure. Just don’t hinge all of your expectations and happiness on that outcome. Luck is enough of a factor that brilliancy won’t inoculate you. Yitang Zhang proved a partial result of one of the most famous open problems in math in 2013. He was incredibly persistent, but prior to this major result, still found himself living out of his car and working at Subway (I think this is exceptionally unlucky though).

At the end of the day, there are plenty of other places you can do research or teach.

Unexpected regrets

Few journeys end with no regrets. 5 years is a long time for regrets to find you, and they may not be what you expect. I am happy to have done a PhD, and I wouldn’t change that decision. But my biggest regret was that it kept me away from home for what ended up being the last years of our family cat’s life. She unexpectedly passed away shortly after defending my thesis, and before I could make it home to say goodbye.

Appendix: miscellaneous advice + other articles

External links

• Read Terry Tao’s career advice, it generalizes well. Some standouts for me:
  • Does one have to be a genius to do maths?
  • Continually aim just beyond your current range
• Sara Billey’s advice on getting a PhD in a timely fashion.
  • Choose your advisor systematically. Talk to current and former grad students. Are they happy? Do they have the kind of job that you want? Do they have jobs at all?
  • 20 hours per week of focus time is enough. Hours in front of the TV don’t count. Hours interrupted by texting and checking your phone don’t count.
• John Baez’s advice for young scientists.
• A great list of questions before starting a philosophy degree.
• A list of resources by Matt Might.

On teaching

• Put your heart into it when you do it, because you have a responsibility to your students, but set reasonable boundaries, because you have a responsibility to yourself, too.
• It’s easy to put infinite time into teaching. It feels productive compared to slamming your head against a research problem with no visible progress. Resist this urge. In the language of the Eisenhower Matrix, teaching is frequently important and urgent, while research is frequently important but not urgent. The latter category contributes most to long-term success.
• Grade efficiently. If you can choose the grading rubric, simpler point rubrics are easier and faster to grade. The difference between an answer that is 2 out of 3 points and 3 out of 3 points is much easier to spot than 17 or 18 out of 20.
• Grade fairly and predictably. Students will compare grades and answers and bring papers back to you with complaints (even if you grade perfectly, students will do that anyway, it'll just cost you less time).

Other notes

• Develop connections with people outside of your department. If you're gunning for academic positions, aim for at least one recommendation letter from someone outside. Your advisor can and should help you via warm intros.
• People who brag about how much they work don’t work as much as they say they do. Don’t let it get in your head, and don’t try to catch up. Grad school is a marathon and not a sprint.
• People who brag about how little work they do often work more than they say they do. Some people really are built different, but for the most part, it isn't the norm to work very little and make visible progress.
• There’s some folk wisdom that you should go to a different institution at each stage of your career. The idea behind this is that each department (or research group/lab) has its own school of thought. This might hold true beyond grad school, but it’s overblown at the undergrad -> grad transition. Don’t be afraid to stay at your undergrad institution for your PhD.
• Prestige isn’t everything, but it still matters more than it should. If you want to stay in academia, then the prestige of your advisor can open doors for you. If you want to leave academia, people still pay attention to the university brand next to your degree.

Thank you to those who volunteered to read drafts: Alex, Felix, Ross

Feedback appreciated!

There might be some collective wisdom on this. I honestly expected a lot more concern and muted skepticism whenever I revealed that I’d quit my job. What I received instead was congratulations, excitement, and curiosity. My guess is that good things seem to happen when someone voluntarily makes a big life decision. Maybe they have an amazing opportunity waiting for them, maybe leaving their comfort zone creates amazing opportunities. Maybe I’m just surrounded by relatively fortunate young people without enough financial commitments to make unemployment truly scary.

Anyways. The point of this isn’t to convince you to quit your job. This was a fairly lucky outcome, but I didn’t plan for that, and the point isn’t to brag about it either. I’ve had a lot of lucky outcomes in my life, and the point of this is to share how I made that happen and how you can be luckier, too.

What do you need to be lucky?

Luck is random, but it’s not purely random. If we think of being lucky as having net positive impact from random chance in your life, then we can be skillful by selecting more opportunities where the odds are stacked in our favor. This is hard because the odds are usually too fuzzy to compute.

This implies a few prerequisites for practicing luck as a skill. First and foremost, you must have an appropriate amount of belief that you can have an impact on the seemingly random outcomes in your life. Our belief in this is heavily conditioned on how lucky we’ve been, and we tend to over attribute skill when good things happen and over attribute random chance when bad things happen. As a result, there are people for whom this feels obvious and people for whom this feels impossible, but both parties are off the mark. Underconfidence means you never take enough shots on goal, but overconfidence means you’ll bite off more than you can chew, and you’ll really be gambling more than practicing luck as a skill.

You must also have an appetite and tolerance for risk. The goal here is to select more opportunities with odds stacked in your favor, but that still means taking a lot of opportunities where odds are involved. That means you will still lose a lot of bets. Humans are naturally risk-averse, and it will feel bad to lose bets. Dealing with this over time requires a high tolerance for failure, and you must have tolerance both in your mental game and in having sufficient resources to absorb the negative outcomes. Resources here can mean things like time, energy, money, social status, etc, and having more resources makes the mental game easier. Quitting my job was made much easier by knowing how long my savings could last and that I was fortunate to have a valuable skill set.

It’s a rigged game

Fortune is not evenly distributed. Some people are born richer, some with more social status, some are more neurotypical, some more attractive, and on and on. People with more of these resources have access to more opportunities, and as a result, access to more opportunities with favorable odds. It is easier to afford the necessary risk of these opportunities if you have more resources. Although this post is not about the distribution of these resources, make no mistake: this is the truth and this is not fair.

That being said, you can make a meaningful impact on your future luck. Whatever your starting point is, you can beat a majority of people who don’t try, and most people don’t try. It also gets easier over time; luck begets luck. Quitting my job was easier because of my savings and skill set, but the size of my nest egg mostly came from a couple of unique bets that turned out well. I chose my career from the overlap of things I like and things that pay well, which increased my access to good opportunities. Those in turn came from earlier, smaller things. Wherever you start, your luck can gradually snowball and change your life.

A closely related concept is playing to your outs, and nearly every life situation has outs, however improbable. These might only serve to make a terrible situation into a slightly less bad one, but they exist for those with the desire to find them. I sincerely hope you are not in such a terrible situation, but if you are, you probably aren’t an exception. As harsh as that sounds, it’s actually a good thing. The upshot is that you can make a meaningful impact on your future luck. The game is rigged, and the game is hard. We’re stuck playing it anyway, so let’s play with our eyes open.

Luck is making chances to dig for outliers

The first hard thing about being lucky is realizing that you can just do things. This is like the extreme version of noticing you can just choose to have cake for dinner. You have ownership over your life, and you are allowed to do more things than you realize to make it better.

The second hard thing is noticing all the places in your life where you might want to do something. When I say all, I really mean all. Noticing that you can quit your job isn’t hard. Neither is noticing that you can go on a backpacking trip through Europe, that you can have kids, that you can exercise more, or that you can move to that city you’ve always dreamed of living in. People talk about these things all the time. They’re worth thinking about, but far from the only things worth thinking about. Sometimes exploring the full space of Bets You Can Make looks like staring into the abyss. Sometimes it looks like ordering a dish you’ve never tried before that sounds kind of weird.

The next step is evaluation; not all of these bets are good bets to take. I’m going to focus on a particular type of bet, which I think is fortunately both the easiest to talk about and the most useful. That class is one where there’s potential for huge outliers in outcomes. This is what that looks like as a graph:

This type of distribution is called long-tailed, heavy-tailed, or right skewed. Line 1 shows roughly where the median outcome is. If you only take a few of these long-tailed bets, then you’re likely to only end up with approximately median outcomes, and your luck will feel average. Line 2 shows roughly where the mean outcome is. The key here is that the downside outcomes are minimal and bounded, and the upside outcomes are massive and practically unbounded. If you take enough bets with this distribution of outcomes, then you’ll eventually pick up enough outliers (line 3) to bring your net outcome well above the median. This is also a theory for how diversified portfolios manage to beat treasury returns. Most individual stocks end up with lower return than treasuries, but they’re incredibly long-tailed, and it’s the few outliers that cause a diversified portfolio to outperform treasuries.

Ben Kuhn has a great post on systematically searching for outliers within specific domains, such as job selection, blog post writing, or startup investing. These tasks have common features:

• You can keep resampling from the distribution by taking new jobs, writing new posts, or investing in more startups
• It’s hard to tell how good the outlier outcomes can actually be, especially with low sample size
• Scoring a few outliers covers the cost of searching and then some (and usually a lot more than some)

One of the key takeaways is that finding success in those domains is largely a function of how well you can repeatedly sample from their outcome distribution.

My addition to this process is that you don’t have to compartmentalize it to each specific domain. Suppose you have 1 domain with a specific outcome distribution and 100 other domains that all happen to have a similar outcome distribution. Assuming you’re equally happy to get outliers in any of these domains, you can get as many expected outliers by sampling from the first, single domain 100 times, or by sampling from each of the 100 unique domains once each. In either case, you get to the same number of attempts at scoring outliers.

Long-tailed results also typically take a long time to be realized. You don’t get immediate feedback on whether the job you picked or the partner(s) you’re with is an outlier. There’s a serial nature to these selection tasks that limits the number of shots you have in your lifespan. If it takes a couple of years to figure out if the job you’re at is really exceptional, then focusing solely on resampling new jobs might leave you with only a couple dozen shots on goal in your entire life (and the longer it takes to find an outlier, the less you can reap the benefits). You can roughly double your shots on goal to find (any) outliers in life if you parallelize the process of resampling your job with resampling your romantic partner.

Why stop at two parallel processes? There are more unique domains than you realize that have long-tailed outcomes and that you can parallelize your sampling from all these domains.

Unfortunately, this is where the whole thing gets much harder. The biggest source of parallelization is going to be from decisions that are very specific to your life path and that you might only get to make a few times in your life. You might miss them, and if you see them, you might misevaluate them. There are a lot of these types of decisions, and not all of them have this long-tailed outcome distribution. Here is a quick, non-exhaustive list of things you might not get to do very often and which have varying degrees of difficulty, impact, and endorsement.

• Picking an advisor in grad school
• Getting married
• Having an expensive wedding
• Getting divorced
• Having kids or not
• The number of kids to have
• Adopting a kid
• Adopting a pet
• Choosing a career (especially a high-commitment one, like being a doctor)
• Trade on a potential Black Swan event
• Committing major financial crimes
• Buying a house
• Emigrating to a new country
• Donating a kidney
• Undergoing gender-affirming care
• Following a particular religion
• Taking a particularly unique job abroad
• Whistleblowing

These are still things that are common or high profile enough that anyone can think of them, but access to opportunities is highly path dependent.

Your full list of opportunities will look very different from mine, and it will be much longer than the list above. Your list will be unique and constantly changing, because it depends on your skills, resources, connections, and the state of the world around you. To be luckier, you must be able to frequently and accurately update your list and pick which ones to take the leap on (please don't pick major financial crimes).

A recent example that comes to mind are these beautiful, functional(!) QR codes generated by ControlNet. I’m familiar with Stable Diffusion and other generative AI models, but my brain contains basically zero knowledge about QR codes (it turns out QR codes are quite malleable). I wouldn’t have considered this to be on my personal list of opportunities, but for this person, it was and they took it. This wasn’t just a once-in-your-lifetime opportunity, this was once-in-anyone’s-lifetime. Now they get to be “that person that made super cool QR codes” forever, and no one else does. Depending on what you’re optimizing for (visibility, career opportunities, internet fame), this could be a fantastic outlier outcome for a speculative project.

Going for those one-off chances doesn’t lend itself to a clean framework of resampling on the same decision, but it does expose you to many more potential outliers over a lifetime. You can get lucky in a particular domain by understanding how to search for outliers in that domain and by repeatedly sampling from that domain. This is good, but this still limits your shots on goal. You also might not find a life-changing outlier until late in life.

You can get even luckier, and sooner in life, by being able to accurately identify all the domains of long-tailed decisions that are accessible to you and learning to search for these outliers everywhere. Even if some of them are once-in-a-lifetime opportunities, once-in-a-lifetime opportunities abound.

“But X is already pretty good”

At the job that I left, I previously held the belief that it was an exceptionally good place to work. My basis for believing this was that many of my coworkers frequently commented on it being the best place they’ve ever worked at. Surely if so many people think so, there must be some truth to it being exceptional?

I still believe it was a great place to work, but (as Ben Kuhn also discusses in his post) there’s a difference between being at the 90th percentile (great) and being at the 99.9th percentile (exceptional). If you’re at a 90th percentile company and most of your colleagues have worked at around 5 places in total, there’s a 65.6% chance that your 90th percentile company is better than all of the previous 4 places they’ve been. So being at a 90th percentile company might look like two thirds of your coworkers truthfully being able to say it’s the best job they’ve ever had, even though 1 in 10 places are better.

Truly strong evidence of an exceptional outcome looks stronger than two thirds of people with something comparable saying it’s the best outcome they’ve ever had. Evidence of an exceptional outcome looks more like zero people around you with something comparable. Without this kind of evidence, chances are that a given situation in your life is closer to a median outcome than you think it is. If so, the risk for rolling the dice again is also lower than you think it is. In general, we bias away from change, but we should try to embrace it to become lucky. In 2020, a study asked 20,000 people to make some difficult decision on their mind with a coin flip:

For important decisions (e.g. quitting a job or ending a relationship), individuals who are told by the coin toss to make a change are more likely to make a change, more satisfied with their decisions, and happier six months later than those whose coin toss instructed maintaining the status quo.

(n.b. If you're a former coworker, don't take this as advice or judgement. I haven't even worked at 5 places yet!)

What you can do to get luckier

Network more. Every new person that you develop a connection with, either socially or professionally, can be a long-tailed outcome. That might sound a little sociopathic, but the quality of our relationships predict a lot of our life happiness, so we should be more intentional in creating chances for outliers. Most people will be acquaintances, but some can be lifelong friends, romantic partners, and/or collaborators. Furthermore, a rich and healthy network might also spot long-tailed opportunities that are uniquely suited for you and bring them to you. Noticing good bets is easier when you have more bets to choose from, and having a strong network is much faster than searching alone (pay this forward!).

Be curious, too. It costs less energy to spend time learning about things that excite you. Doing great work often looks like following your curiosity to places where people haven't tread before. This can mean going deeper than anyone else has, but it can also mean going broad enough that you find a unique combination of knowledge, as with the earlier QR code art.

In the long term, it’s crucial to develop a strong probabilistic mindset and intuition, which makes it easier to reason about whether a decision is potentially long-tailed or not. This also helps avoid certain types of opportunities, such as the lottery, which is arguably long-tailed due to massive outliers, but not actually worth it. Finally, this helps identify downside risk, so that you can avoid making a decision that leads to a complete blow up. As you’re exposed to more chances, you’ll develop a better sense of what makes an opportunity good or bad in this framework, including making more accurate assessments for more unique long-tailed decisions that you only get to make once or twice.

There isn’t a lot of general advice that I can offer beyond this. The ideal situation for developing a skill is one where you can do deliberate practice, but this framework can’t be applied to the domain of luck. There are no well-established training methods, and as far as I can tell, no one officially coaches for being lucky. A lot of my personal skill in this domain falls under gut feeling, or tacit knowledge, and I haven’t thought explicitly about this enough to make that gut feeling explicit. If you’ve thought about this and have ideas, please share them. In lieu of a perfectly written guide, here are some things you might start with:

• Interview occasionally at jobs that sound interesting, even when you aren’t looking for a new job
• If you’re introverted, get adopted by an extroverted friend
• Say yes to invitations as a default
• Do more things where the worst that can happen is nothing
• Do fewer things that look like sitting at home and watching Netflix (I am guilty of sitting at home and playing video games)
• Go to random events or activities on Meetup or Eventbrite
• Start saving up some resources to be ready for opportunities when they come; remember that your resources are resources, and you should spend them (wisely)
• Don’t do pyramid schemes

Mileage is the best teacher available, so find some risk you can tolerate, and start taking some shots.

This is my first post. It was inspired by this unfinished draft by David R. MacIver and by this post on starting a blog by Ben Kuhn. I recently re-read it for the third or fourth time and finally got off my ass to try my luck with it.

Thank you to those who volunteered to read drafts: AJ, Cyrus, Felix, Ross.

Any feedback is appreciated!