16 observations on hospital culture and incentives

I recently shadowed a hospitalist during a 10-hour shift. A hospitalist is a generalist doctor responsible for patients while they are in the hospital. They go through similar training to the doctors you see for your annual check-ups.

Unlike ER doctors, who handle immediate crises hour-by-hour, hospitalists manage the day-to-day care of patients with serious but not immediately life-threatening conditions.

At any given time, a hospitalist usually manages 8–10 patients, most of whom are new to them. Their job is to quickly gather information from medical records, patient conversations, and test results in order to make diagnoses and eventually discharge patients.

Hospitalists function as medical coordinators. They are trained in high-level diagnosis but quick to refer to specialists for specific interventions. In many ways, they act like PMs of the hospital: aligning stakeholders, tracking progress, and ensuring patients are on course. They can order tests such as MRIs but typically leave interpretation and detailed recommendations to specialists (e.g. surgery).

The following are a select assortment of notes from my visit:

1. Everyone jokes about death.

Everyone including the doctors, nurses, and support staff all joke about death in a way that is jarring to an outsider. This is best interpreted as emotional compartmentalization as a survival mechanism, not callousness. This makes sense, as it enables them to create a barrier between themselves and death. I imagine it’s one of the most effective ways to create a positive work environment while enabling everyone to do their best work without being depressed all day seeing people who are about to die.

2. Verbal communication dominates for up-to-date knowledge transfer, while written notes are often an afterthought.

When I first arrived, four surgeons were holding a stand-up meeting at a nearby table. The overnight surgeon recounted the nuances of a surgery she had just performed, while the others tried to listen and skim the patient’s chart at the same time. She had to repeat herself multiple times as they asked questions she had already answered.

When doing the morning rounds, the hospitalist never took notes during the meeting. The two medical students doing their rotation were taking notes in their notebook. In the hallway after meeting with the patient, the doctor and the medical students would discuss diagnoses and recommendations for next steps. There was very little disagreement as the next steps were usually clear. In one instance, the medical student reminded the hospitalist of one of the symptoms that the hospitalist had forgotten about during the 15 minute conversation. The hospitalist usually would keep everything to memory but sometimes wrote a quick 1-5 word note on their phone for future charting.

In the afternoon, medical students wrote detailed reports on the patients they had seen, joking with doctors that it wasn’t the best use of their time. The hospitalist reviewed these write-ups as part of the students’ end-of-rotation evaluations.

I asked whether they ever revisited old cases to track patient progress. Both shook their heads: “No, that isn’t part of the curriculum—and we’re way too busy.”

3. Information asymmetry among staff leads to repeated explanations and lost context due to a primarily verbal communication culture.

Because documentation is written retroactively (typically at the end of the day or the next day), the most up-to-date information is acquired by asking around. This leads to manhunts, tracking down individuals around the building to acquire a specific piece of information that could have easily been written down.

4. Everyone hates Epic.

5. The quality of your medical care may be determined by carrier reception while a specialist is eating lunch.

The hospital I visited is part of a HMO system. This means hospitalists and specialists are employed by the same organization, enabling more regular communication and shared context.

There were multiple instances where the hospitalist called a specialist for a second opinion reading a chart or thinking through a recommendation. Often, we were in a wing of a building with very poor cell reception, likely due to the machinery and other cell signal limiting infrastructure. All calls were routed via standard cell carriers, not Wi-Fi (even though the Wi-Fi was consistently good throughout all points in the building). There were multiple instances of difficult to interpret segments of calls due to poor call quality.

On the call, a specialist would recommend a specific medication and the hospitalist would type it into Epic and see 8 medications populate in the search query. The hospitalist would follow up and ask which one it was and it was apparently the fourth option, masked behind the noise of munching down sandwich and chips and a relatively thick accent.

6. Hospitalists are fine-tuned extrapolation machines.

The job of a hospitalist is to make accurate diagnoses given all available information. Information is either test results or discussions with the patients themselves.

Test results give you lots of clues and are generally accurate. However, doctors are trained to treat patients, not test results.

Patients lie frequently, and the best doctors treat this as part of the negotiation, giving the best care possible within existing constraints (available tests, insurance, etc.). Patients often lie to doctors and tell half-truths. Years of training allow them to make rapid judgments based on both conscious and subconscious cues.

In many ways, they are part negotiators and part matching engine.

7. Doctor competence is highly variable, as there are few incentives for improvement.

The hospitalist I was shadowing had one of the lowest average number of days patients stayed in the hospital, evidence of his ability to efficiently make diagnoses and get the patient out of the hospital. Ceteris paribus, this is a good thing as being in a hospital is bad for your health as you are sedentary and stressed.

Subjectively, he was one of the better hospitalists present that day (I also shadowed two others briefly throughout the day during slow periods). The other two hospitalists operated with much less urgency and agency.

For a patient in their mid-60s, the high-agency hospitalist I was shadowing ordered a specific test not required given symptoms, coming back positive for cancer. This likely found the cancer at least 3-4 months earlier than it would have otherwise been found.

But all hospitalists are paid under the same schedule (based on years of experience), meaning that the high-agency hospitalist is getting paid the same as their counterparts. Greater intrinsic motivation and competence are not explicitly rewarded.

8. Doctors know who’s good, they just won’t tell you.

One orthopedic surgeon in the organization is in his 60s and has performed many knee surgeries. One of the most common procedures is for torn meniscus. This surgeon had been trained only to remove the entire meniscus, a practice no longer recommended because it can lead to earlier arthritis. This surgeon does not perform meniscus repairs, which have been the preferred method for the past 10–20 years.

Hospitalists and other doctors are aware of these differences. They generally recommend alternative surgeons trained in meniscus repair, while sending older patients – who would likely receive a full meniscus removal anyway – to the removal surgeon.

Hospitalists will always say platitudes like “X is a highly capable surgeon”, even if they aren’t at the top of their list.

9. Doctors are mostly incentivized to be highly agreeable and take little risk.

Being contrarian and right is unrewarded and increases the workload of your peers. Common diagnoses are safe and will not deplete your malpractice insurance.

Everyone gets to go home to their kids sooner.

10. The social divide between doctors and nurses is palpable.

While the HMO hospital creates a flatter structure between all hospital staff, there is still clear tension between doctors, nurses, and support staff. During break periods around their desks, nurses would frequently be chatting around each other in relaxed positions as they update each other about their lives.

When a doctor tried to join the conversation, the dynamic changed, and it never felt quite the same. Conversation topics would often shift, creating a sense of forced friendliness.

11. Most hospitalists neither discuss work at home nor actively encourage their children to pursue a career in healthcare.

Many enjoy the high-stress nature but do not push their children toward the same path. Work is seen as a means to an upper-middle class life.

12. Everyone wears the same shoes.

Scrubs are more or less standard for nurses. There is surprisingly little coordination in exact uniform shades; you can see slight variations of blue or purple among their outfits. All doctors wear the same standard white coat.

The only attire choices people have are their shoes. Every single shoe I saw was either Hokas or Ons. If one believes in efficient markets, these must be the most comfortable shoes.

13. Being obese is very, very bad.

Not surprisingly, overweight and obese patients made up the majority of patients from the people I saw.

Being obese comes with many practical limitations. Larger beds with stronger motors are often required, and multiple nurses may be needed to help reposition patients.

One person was too large to fit into an MRI machine and had to be transported 20 miles to another hospital with a larger machine. This delayed her care by over a day and led to many miscommunications internally and frustration for both the patient and their partner.

14. The patients without close family are the most dismissive.

Patients who have many family members present, or who reference close family when asked, tend to be the most optimistic and the most willing to absorb and act on new medical information from their doctor.

15. Security is surprisingly lax.

There were contracted security guards at the major entrances, but they only did cursory checks. I would classify the level of security as similar to what you’d find at a concert.

If you come in with a backpack, like I did, it is only checked once before you receive a name tag. You can leave and come back with the same backpack and it won’t be checked again.

16. It’s easy to lose perception of time.

Most areas of the hospital are lit entirely by artificial LED light, with no exposure to natural sunlight, making it very easy to lose track of the time of day. The hospitalist I was shadowing compared the hardest part of his job to simply getting to work, similar to how going to the gym is often the hardest part of exercising.

Once you’re there, the time flies by.

Prediction hash: 448147cd4747bafd4461c68ee7469679ff2427f4b16c6d79f8d99185a484759d

Great work always finds the people who matter

On his podcast with Scott Alexander and Daniel Kokotajlo, Dwarkesh makes the claim that everything that is good gets read by all the right people:

“I feel like this slow, compounding growth of a fan base is fake. If I notice some of the most successful things in our sphere that have happened; Leopold releases Situational Awareness. He hasn’t been building up a fan base over years. It’s just really good…I mean, Situational Awareness is in a different tier almost. But things like that and even things that are an order of magnitude smaller than that will literally just get read by everybody who matters. And I mean literally everybody.”

Scott responds with:

“Slightly pushing back against that. I have statistics for the first several years of Slate Star Codex, and it really did grow extremely gradually. The usual pattern is something like every viral hit, 1% of the people who read your viral hits stick around. And so after dozens of viral hits, then you have a fan base. But smoothed out, It does look like a- I wish I had seen this recently, but I think it’s like over the course of three years, it was a pretty constant rise up to some plateau where I imagine it was a dynamic equilibrium and as many new people were coming in as old people were leaving.”

Watch the full clip here: Dwarkesh Podcast

The underlying assertion that Dwarkesh is making is that the content market for ideas is very efficient. Scott agrees conceptually but to a much lesser degree, citing his own experience in the early days of Slate Star Codex, and indicates that he considers the market to be less efficient than Dwarkesh does.

As a recovering efficient markets believer, I am very skeptical of anyone claiming that any market is efficient. However, Dwarkesh is correct here. Stated precisely:

The content market for novel and interesting ideas is efficient, enabled by incentive-aligned market microstructure.

To avoid ambiguity, let me define exactly what I mean by that claim:

• Content markets refer to markets that operate on internet rails. They have zero or effectively zero marginal cost and are non-rivalrous.
• Efficient means optimally connecting suppliers (content creators) in such a way that maximizes consumer satisfaction. People are bounded by their time and care about consuming the best ideas and content.
• Market microstructure refers to the mechanisms, tools, and systems that govern how content is discovered and distributed. [1]

In simple terms, good work gets noticed by everybody who matters.

Doesn’t Scott Alexander disprove the efficient content market thesis? Slate Star Codex is one of the most influential blogs ever and it took years for it to take off.

Short answer: no.

Scott is one of the best writers on the internet. For the efficient content market thesis to hold, at least one of the following has to be true:

1. His early blog posts are not that impressive.
2. The blogging market microstructure was severely under-developed in the early 2010s.

After going through his early posts, while well-written, they hadn't yet developed the unique insights that would later define his influence.

Many posts are interesting in hindsight because they provide insight into what Scott was thinking about in the early days. But reading the blog posts in a vacuum, nothing particularly stands out.

What does stand out is Scott’s consistency. In his first year of blogging (2013), he published 157 posts totaling more than 150,000 words. His writing frequency meant that he had a large volume of attempts to improve and eventually land 2+ standard deviation banger posts:

• The Control Group Is Out Of Control
• I Can Tolerate Anything Except The Outgroup
• Meditations on Moloch

(There are too many to list)

Blogs are compression machines: quality content requires a minimum amount of contextual surface area to support in-group references and insights. Marc Andreessen, Paul Graham, and Tyler Cowen all had pre-existing social capital and reputations. Bootstrapping a blog, especially as a nobody, is difficult and typically requires a minimum number of words to demonstrate insight. Scott had little context or reputation to draw from (he had some reputation from prior forum posts) and had to derive everything from scratch.

Most posts by anybody who blogs with any regularity are not insightful and subsequently forgotten. Volume enables more shots on goal to generate truly quality posts that generate outsized discussion and shape downstream culture:

• Why Software is Eating the World
• Do Things That Don’t Scale
• The changes in vibes – why did they happen?

Regarding (2), I don’t have first-hand insight on the blogging scene and infrastructure during this time (I was a much younger lad). I am hesitant to draw too many conclusions based on second hand accounts. It is clear that the infrastructure is better and operates more efficiently today via better tools and clearer Schelling points than what existed twelve years ago.

Liquidity providers have aligned incentives

Content markets are only efficient insofar as there are liquidity providers to efficiently serve content to interested end users. Liquidity providers can either be people or an algorithm. Both operate with an implicit or explicit bid-ask: the ask is the quality threshold of what they are willing to promote while the bid is what the audience expects. Cross the spread to gain exposure to their audience.

There are two ways for your content to gain immediate traction: somebody references it or an algorithm serves it. Both provide liquidity to your content, distributing it to interested consumers.

If you write something amazing, a few emails to some key people in your field is all you need to start this process. These intellectual liquidity providers are highly incentivized to reference your work as they (1) prove to their audience that they are still capable of identifying good content from talented individuals and (2) receive goodwill from you. This may lead to downstream events such as letting them invest in your startup or meeting your peers with other interesting ideas.

Dwarkesh is popular because of his ability to have deep, frontier-pushing conversations with extraordinary people. Tyler Cowen is popular because of his ability to identify nascent ideas and early signs of talent. After filtering for any junk, he’s incentivized to throw a link up on Marginal Revolution for association and attribution. Alexey is popular because of his ability to provide compelling evidence that people in storied institutions are deceiving us and also that he’s good at identifying developing technical talent.

Algorithms like the YouTube algorithm seek to maximize platform engagement. Platform engagement is highly correlated with the amount of interesting material a user receives. Early versions of algorithms were heavily weighted to serve new content from large existing content creators. On average, a post from a creator with a large following is much more likely to be engaging versus the marginal creator with no track record. This is a tractable signal of a creator’s quality that is easy to implement as a model feature.

TikTok's innovation is its algorithm that serves content to end users without heavily indexing on a creator's pre-existing following. Substack is attempting to replicate this model for text, actively improving discovery via a similar algorithm. Aside from specific forms of content regulated by governments, algorithms are not incentivized to discriminate against you in the long run, as they would lose out to a more efficient algorithm that serves users with better content.

While your ideas and blog won’t market themselves, the actual effort required is extremely minimal. It takes literally 30 minutes to write a few emails and post on the platforms where your desired consumers are.

There are more than enough rational liquidity providers to make this market very efficient, which is why I don’t buy a survivorship bias argument. Instead, content doesn't take off because it either isn't as exceptional as initially believed or lacks sufficient initial distribution effort.

Increase your banger base rate

An aspiring content creator saying, “I want to build an audience” is as much of a countersignal as an entrepreneur saying, “I want to be rich”. It’s vain and demonstrates no insight on how to actually achieve their goal. Worse, they consciously create content based on this framing via backward induction, a one-way ticket to the 0-view content graveyard.

Any insights you generate can be efficiently and widely shared with attribution regardless of whether you have a pre-existing audience. [2] Popularity in content markets reflects the aggregate probability that you'll produce power law content. Each additional piece of evidence of your ability to generate new ideas or insights will increase your banger base rate. If you come out of nowhere and one-shot Situational Awareness, the market prices you as having a 1/1 hit rate. People are incentivized to follow you on Twitter, subscribe to your blog, and watch your podcast appearances to seek out any other insights you have.

The early days are the best

One of my favorite things to do is to read through the blogs and tweets from people before they became widely recognized (paying special attention to which posts they backlink to and who is in the comment section). For a select few, I’ve been fortunate to have a front row seat throughout their entire journey. Whether they are videographers, writers, or programmers, they dug themselves out of the early trenches, connecting with their audience and finding their voice by consistently producing content.

When you’re a nobody, you get a zero-noise signal via metrics including likes, comments, retweets, and views. When you gain followers, even terrible content gets likes and comments from your followers giving you unearned dopamine and a false sense of accomplishment. Worse, you might even start to write pandering slop posts that you'll have to pay back down the line, or you'll decay into obscurity. It makes me very sad to see some of my favorite writers follow this trajectory and actively decay in real time.

Lucky for me, I’m still an obscure nobody. Most of my posts get very few views, floating in the digital ether as unread bits in some us-east S3 bucket.

My third post, No One is Really Working, is the one exception, at least relative to my other posts. That post blew up primarily thanks to Alexey Guzey’s tweet. It was up for 135 days before his tweet with de minimis viewership.

You'll never guess what happpened after

(If any of my other posts were any good, they would have gained recognition. I can definitively tell you that lots of people read my earlier posts. They just weren’t very good.)

When I was messaging with Alexey a couple days after it blew up, he mentioned that he did not expect that level of virality. Furthermore, he said that after his many years of blogging and Twitter, he still is not able to predict which ones will blow up with any meaningful degree of certainty.

With all due respect to Alexey, I disagree. I believe I have enough data and taste to predict which pieces will do well and which won’t ex ante.

From this post forward, each post will include a hash of my prediction for each post’s performance to test this thesis. I will analyze the results in a future post.

375fe6f5ad8993c980f7661fea4839edb591100494d4a1b8f0271c3b9ad5752a

[1] The rise of Substack is the most influential development in the written content market, aggregating content into a centralized, searchable site. Algorithms can then be trained on its data to serve the best content to end users on an individual basis. Furthermore, Substack is built on permissionless, portable substrate: email. This limits their ability to create a walled garden and incentivizes long-term actions. Obviously this centralization comes with many drawbacks including increased homogeneity.

[2] This entire post can be summarized by the following graphic:

Work harder, bet bigger

I’ve taken a lot of career and financial risk, more than almost all of my peers. While I view my decisions as largely rational and effective, I tend to keep most of my decision rationales and work habits to myself.

Much of my decision to take early career risk can be attributed to select blogs, books, and talking with a lot of people, especially older mentors. Since most of my friends already think I'm pretty weird, I figured I'd open-source some of my work habits and decision rationales that have shaped my approach:

• Working multiple jobs for a period of time and using the earnings to have a period of unemployment (what I’m doing currently). This has enabled me to prevent myself from (1) being anesthetized by my job and becoming complacent, and (2) giving myself full latitude to work on projects in-depth, not as perpetual half-assed “side projects”.
• Two super intense deep work days per week where I go zero to one with no distractions. This is usually Sunday and Tuesday, where I work with the aim of having the highest output day possible. The other days are spent largely editing the work product from these days or otherwise lighter work modes.
• Doing way more and trying way harder; no one is even trying. Successful people just have way more output than their peers who are often equally as smart and capable. “Work smarter not harder” is some of the worst advice I’ve received. You need to put in the hours and understand the context to determine what’s important and what’s not. July was a particularly high-output month for myself, writing 50k+ lines of code and 10,000 words, averaging 4 hours of deep work for 6 days a week.
• Reading long-form books regularly, with 1-3 intensive subject deep dives per year on specific topics or classics. I schedule reading during lighter work periods since books become a major distraction during my high-output deep work sessions.
• Not working on the hardest thing I can. You can make large contributions to many fields and working on the hardest thing is often irrational. My driving thought process is asking myself where I think I can make a large marginal contribution with my skill set.
• Writing on the internet to sharpen my thinking and increase the surface area of serendipity. I write across a variety of topics and personas which allows me to have edges and peer groups at the frontiers of different subject areas. Take the initiative to meet people in person and learn about their production functions.
• Understanding the long-term games worth playing. Becoming the best badminton or chess player is impressive but very capped. Think about playing in positive-sum environments where you can own a unique spot on a growing frontier.

I certainly do not advocate for most people to follow my work habits or career choices. It is best interpreted as real work habits from your friendly internet anon who isn’t successful (yet) and who has no incentive to lie. (If I really was successful, would I really be toiling away writing this blog?)

Lessons from Kelly

The Kelly Criterion is utilized in binary outcome environments and describes how much to wager for a bet given certain constraints. It is used to optimize long-term expected value of your bankroll. The inputs are:

• The size of your current bankroll
• If your bankroll is replenishable
• The probability of an event occurring
• Your edge in the market

In kind environments where feedback is honest and frequent, Kelly is a useful model. You can place 1 million sports bets and determine if you are a winning or losing player on average and use Kelly or fractional Kelly to give pointers on how much you should bet.

Kelly formalizes the natural intuition that you should bet larger when you have a bigger edge and less when your edge is smaller. The less intuitive part is how the math works out:

For even money (1:1) odds, Kelly suggests:

1. 80% market probability, 10% edge → 50% of bankroll
2. 50% market probability, 10% edge → 20% of bankroll
3. 10% market probability, 10% edge → 11% of bankroll

The overwhelming majority of people would not bet 50% of their net worth on a single binary event that is priced at 80% with a 10% edge. These numbers are amplified when the payouts follow power law outcomes, as career outcomes do. If your YouTube channel or startup succeeds, the payout is more like 100:1 in finances and time reclaimed.

Under the same conditions but with 100:1 payout odds, Kelly suggests:

1. 80% market probability, 10% edge → 90% of "life bankroll”
2. 50% market probability, 10% edge → 60% of "life bankroll"
3. 10% market probability, 10% edge → 20% of "life bankroll"

Kelly tells us that we should bet 90% of our life bankroll on something the market prices at 80% when we have a 10% edge. “Life bankroll” refers to a combination of your finite time and resources.

To most, these numbers seem absolutely insane and it probably is. People willing to do this are generally regarded as insane and only retroactively lauded: Elon parlaying all his PayPal earnings into Tesla and Fred Smith playing blackjack to save FedEx from bankruptcy.

To be sure, I’m not advocating for these decisions, as there is massive survivorship bias here. Furthermore, there are practical limitations to directly applying Kelly to career decisions:

• Kelly assumes infinite future opportunities. Most opportunities in life are scarce and only available for a short period of time (job offers, university decisions, etc.).
• Kelly assumes independent bets, but career outcomes are highly correlated.
• Payouts are typically not defined or known in advance.
• You don’t know your edge. Worse, you may delude yourself for years thinking you have an edge when you don’t.

However, it is clear that if you are seeking outlier success, you will need to allocate a disproportionately large portion of your life bankroll relative to what most people’s intuitions suggest, increasing your variance and probability of outlier success. You can think of your career as a portfolio but any one position at a given point in time should be concentrated and high-conviction.

Generating Edge and Life Bankroll Bets

You are only able to generate outlier success when you have an edge. Most people either never develop an edge or waste it once they have it.

There are thousands of writers who write 1000 words per day on useless marketing copy, thousands of senior engineers pushing 1000 lines of code per day tweaking button sizes. Most will spend their life not really working, getting anesthetized, never expending any of their life bankroll.

The internet is one mechanism that enables you to develop an edge through quick feedback loops. Early achievements (high chess ELO, USTA ranking, League of Legends rating) are signals that (1) you have an aptitude for certain abilities and (2) you are capable of generating an edge.

Other forms of edge can also derive from increasing your skill set, which typically occurs as you get older, accrue more tacit knowledge, and gain a larger network. Most people don’t do the work over long time horizons – there is an edge to be had in every field by compounding long-term conscientious behavior.

Developing an edge is only half the battle. You’re probably not thinking hard enough about applying your edge. There are countless ways to make life bankroll bets:

• Write one of the most read tech blogs from Taipei instead of Silicon Valley, enabling a unique worldview and insight into Asian economies.
• Commit to making a marketing movie for Nike. Spend the entire budget flying around the world with a friend for 10 days to create an insanely viral YouTube video, then leverage that success to popularize a new content format called vlogging.
• Write a definitive critique of leading sleep research and leverage your network to start a parallel institution for funding New Science.
• Leave your cushy job and relocate your family from California to an abandoned island in Malaysia to start a new YC for dark talent (and maybe a new country).
• Take more flights. Relocate to a frontier hub for a few months when you're young, at high energy and at peak fluid intelligence. You might not have the financial capital, but you can often compensate by convincing benefactors, participating in fellowships, or raising VC money.

The Cost of Underbetting

When talking to mentors and people older than myself, they almost universally cite that they wish they had taken more risk. Often, this sense of regret manifests itself in leaving their previous job sooner, getting divorced earlier, or moving cities earlier.

This sentiment aligns with the universal takeaways from Kelly:

• People undervalue the cost of inaction (not moving cities, staying in bad relationships), wishing they had done it sooner or at all.
• Bet sizing scales with edge, not absolute opportunity size. Many people make the mistake of betting more on safer opportunities without considering their own edge.
• Care less what people think. You have worked to accrue context-specific edge, don’t let others talk you out of it.

Half the time I look back on my own decisions and cringe. But when I zoom out, the only choices I regret are the ones where I let fear or other people’s opinions shrink my bet size. The moments I went all-in still feel like the right calls.

Losing hurts less than never betting. Variance smooths out, regret doesn’t.

For many parents, hyper-optimization is the preferred method for brute-forcing their children out of mediocrity.

NBA

The NBA is one of the most competitive domains in the world. There are 450 active players in the NBA at any given point in time (15 players per team, 30 teams total). The distribution of annual income for the top 1000 basketball players in the world looks something like this:

Claude Notebook

In the 2024-2025 NBA season, the average salary was $9,191,285, and the median salary was $3,657,120.

With such a steep drop in income and prestige among the top 1000 basketball players in the world, it is no surprise that parents are doing all they can to surpass the 450 threshold and climb along the NBA salary curve to get as close as possible to the NBA supermax.

Young children who have a shot at making the NBA typically show a strong signal from a young age. The baseline attributes include height and athleticism. On-court IQ is becoming increasingly important and better measured with advanced statistics and cameras. These traits are correlated with parents with above-average genetics and the resources to provide their child with the best environment to develop. Parents who are 2+ standard deviations athletic and domain-intelligent are much more likely to have children capable of playing in the NBA.

This is intuitively true and becoming more and more empirically true every passing year in NBA data:

Naturally, these parents want their kids to be successful. When they see that their kid has the genetic makeup to be in the NBA one day, they pour in resources to have their kid achieve that goal. Parents center their lives around enabling their kids to develop their abilities and make the league.

The average NBA player is far more athletic, skilled, and smarter than NBA players just a decade ago. The average NBA player from the 80s would have no shot at being in the league today with how the game is played today. The game itself has been optimized to a local equilibrium of high-volume threes.

Because the skill floor is so much higher today, NBA players are expected to enter the league with a much higher baseline skill set. This is advantageous for kids who have been playing basketball for their entire lives and attending camps with retired NBA players. It is now normal to see kids play AAU, travel ball, and varsity basketball throughout the entire year.

Even the NBA referee pipeline is becoming increasingly optimized.

NBA referees are even more competitive by the numbers. In the 2024-2025 season, there were 75 full-time referees. Referee salaries range from $150,000 to $550,000 per year, not including playoff games and bonuses (or other highly liquid avenues of making money).

Referees spend years watching game film and deliberating on the correct calls for contested plays. With the baseline genetic requirements for a referee being much lower than players, their moat is social barriers and a strong union with storied ties to NBA owners. The NBA is an entertainment product, and the referees with a lifelong understanding of the culture, along with the rich social and tacit knowledge passed down via tight social circles, create a pipeline for the next generation of NBA referees.

At least 14 current or former NBA officials came from Delaware County, Pennsylvania, and strong clusters around metropolitan East Coast cities. Unsurprisingly, their lineage can be traced back to families and friends, and a conscious effort to track the future generation to become seasoned referees.

Ron Garretson was the son of Darell Garretson, the NBA’s first Director of Officials. Joey Crawford officiated over 2,900 NBA games, and his father was an MLB umpire. James Capers Jr. is an active official in the NBA, having officiated for over 28 seasons, and is the son of former NBA official James Capers Sr.

University Admissions

Gunn High School is located in Palo Alto, California, and is one of the most competitive high schools in the country. Located near Stanford, parents disproportionately represent high-status intellectual professions: professors, venture capitalists, software engineers, doctors, and lawyers. It is no surprise that they disproportionately attend prestigious universities with low acceptance rates:

Claude Notebook

Parents spend resources on SAT tutoring, grinding some sport, and doing some extracurricular activities (speech and debate, robotics, etc.). Parents frequently send their kids to special Pre-College Summer Programs during their high school years to demonstrate more signal to colleges. The next meta includes having their children nominally start companies and non-profits to further distinguish themselves from their peers.

Parents understand that baseline test markers are no longer sufficient for collegiate admission success. To give your child the best chance of success, they must demonstrate excellence beyond their peers in one or more dimensions. Parents’ search function often starts by casting a wide net of potential activities, then doubling down on areas they believe their child has a comparative advantage in. Comparative advantages can arise from natural aptitude (e.g. predisposition towards math) or low-competition environments (e.g. fencing).

Regardless of what Twitter says, universities are still the institutional gatekeepers of intellectual social status. They are where most frontier ideas are exchanged and where lifelong peer groups are formed. With AI researcher compensation now on par with top NBA athletes, one should expect increasingly formalized hyper-optimized funnels to extend beyond college admissions and to a prestigious PhD program or top research lab.

The above examples are cherry-picked and certainly not representative of the median case.

But they illustrate the lengths parents will go to for their children and the natural progression of the professionalization of talent funnels. When the pathway is legible, you can bet that professional pipelines will emerge on the promise of getting your kid into the desired elite.

These pipelines exist because of the following factors:

1. The number of high-status positions has remained relatively constant.

About 2,200 high schoolers were admitted to Harvard’s Class of 1982, 2,147 in 1992, 2,074 in the mid-2000s, and 1,980 for the freshman class of 2021. During this time, Harvard went from ~10,000 applicants to over 54,000. Cornell, Dartmouth, Brown, and Yale have only seen a modest 20% growth in class size (~400 students).

On the faculty side, Harvard has plateaued at 720-730 ladder faculty (tenure and tenure-tracked) since 2008 despite large endowment growth. Faculty growth stagnation continues to persist at the vast majority of institutions.

2. Returns are compressing to select elites, with a steep drop-off.

The 451st best basketball player and the 781st best baseball player are scraping by in the minor leagues. The best engineer at a low-tier contracting agency is making significantly less than the worst full-time engineer at a tech company.[1]

The penalty for being average has never been so severe, but the payout for being extraordinary has never been higher.

3. Parents are having fewer children and investing more per child.

The US fertility rate has fallen from 3.5 in 1962 to 1.6 in 2023. Fewer people are having children, and those who do have children are having fewer of them.[2] More resources are being invested per child.

High-achieving parents who are on the tail end of the power law outcome will naturally want to do everything they can to increase the likelihood that their kids are positioned to do the same. They are incentivized to give them every unfair advantage to stack the deck in their favor. This ever-increasing anxiety not to let their children fall through the cracks of society creates more demand for optimizing their children.

4. The incentives of parents and children are not 100% aligned.

Parents act as insurance for their children. They invest resources in the early years with the goal of having their kids be self-sufficient in their 20s. Parents are on the hook emotionally and often financially if their kid is a deadbeat or goes broke building a startup. Even in the success case, parents benefit sublinearly from a large increase in their children’s wealth.

Their children are more incentivized to go after the extreme power law outcomes. This is especially true for high-ambition kids heading down a path that leads to a similar life to their parents, as they naturally aspire to have a better life than their parents. If they succeed, they propel themselves to new social circles and financial heights. If they fail, their parents are there to catch them.

This creates the incentive for parents to go after clearly defined, high-probability opportunities. Their children are incentivized to take more risks in hopes of punching their ticket into the upper echelons of the elite. Unlike the current high-status thing, the signals to get into the next-generation elite are amorphous and typically don’t have pipelines. Nobody was sending their kids to creator camps in 2010 or teaching them how to be a good online poaster.

Kids grow up aspiring to be the next Michael Jordan; parents just want to make sure they land somewhere in the middle of Google’s org chart.

Hyper-Optimization is Individually Rational But Collectively Insane

From the outside, all this sounds pretty depressing and dystopian for what is supposed to be the most prosperous time in human history. Hyper-optimization creates cohorts of zombies operating as vessels for their parents’ ideas of success. This is why it’s always so refreshing to meet someone who didn’t start coding when they were 5, do three sports, speech and debate, all while “starting a nonprofit” for African kids from their modest home in Atherton.

Children intuitively know how much their parents are investing in them, even at a young age. They don’t want to let them down, but their parents’ expectations are so much higher and the competition is so much better that they get sent down this rabbit hole that they had no choice in selecting. Trapped by this large sunk cost, most do not stray from the prescribed path, choosing the path of least resistance as they are anesthetized into a replaceable, homogeneous career where no one is really working. Parents think they're brute-forcing their children out of mediocrity when they're actually brute-forcing them into it.

Talent can emerge from unexpected places: Nikola Jokic developed his game from northern Serbia, and many elite programmers bypassed the Bay/Waterloo/Boston/T-20 pipeline entirely. The failed simulation effect – when we can’t quite mentally simulate how a person got to where they are – sends such a strong signal because they acquired their skills under atypical circumstances. More importantly than the skill itself, they demonstrate genuine motivation, free from their parental anxieties and expectations. Anecdotally, they are more curious, oftentimes more friendly, and more likely to view the world as positive-sum.

Hyper-optimized children are a luxury and largely a developed world phenomenon. The countries with hyper-optimized children en masse and respective tracked industries are some of the least creative and least happy. These countries – Korea and Japan in particular – are insanely zero-sum and inward-looking. Furthermore, each generation’s worldview narrows to the tiny slice of the human experience that exists within their hyper-optimized bubble.

We've successfully built a machine that turns curious children into anxious adults. Everyone knows it’s broken. No one knows how to stop.[3]

[1] The financial payouts for SWEs are much more linear than professional athletes, but the point is that it is still a piecewise function.

[2] Among women aged 40-49, 16.9% have no children, 17.6% have one child, 32.7% have two children, 22.6% have three children, and 10.1% have four or more children.

[3] Well, Bryan Caplan has one solution, and it’s to just be more fatalistic about people’s potential. He argues that nature accounts for more than we think, and nurture via overbearing parenting is very overrated. The natural corollary is that one should have more kids for the highest chance of a positive outlier. I don’t think this ideology has a chance to become popular amongst any meaningful part of society, particularly those from immigrant backgrounds.

Justifying the High Salaries of Early-Career Professionals

The following are anecdotes of a typical work schedule for young professionals in established, tracked professions.

Following these profiles, I provide explanations for why young professionals command such high compensation, relative to what their work product would indicate.

Adam: SWE at a gaming company

Adam has been a SWE for four years. He first started coding when he was a young boy and quickly found that he had a knack for solving puzzles. He always loved video games and was excited to learn that he could have a lucrative career that included programming the very games he enjoyed growing up.

His day consists of pushing updates to backend servers in Go and writing relevant Typescript client code. Most problems don’t require much brainpower, meaning that he can push a change and spend the next 30 minutes on TikTok. Typically, when he refocuses himself, he finds that he one-shots the problem and moves on to the next problem. A good day would be merging a couple of PRs and some friendly Slack banter.

Adam has compounded his skills over the years, thanks to a strong culture, well-defined tasks, and a competent manager. He has a good work-life balance as he is able to finish projects quickly, though most do not even have hard deadlines. He makes sure to not work too fast or set expectations too high. This is an implicit learned behavior from his boss, who is also competent and not incentivized to ask for or create more work.

On average, Adam puts in 0-10 hours of deep work a week. The rest of his work hours are spent mindlessly coding, listening in on various meetings with his camera off, and on TikTok.

Adam went to a large engineering school. He was much sharper than his fellow students and didn’t have to work too hard to get good grades. He leveraged his connections and grades to eventually work at the gaming company he’d always dreamed of.

His typical week includes merging a couple of PRs and periodically managing a few interns. He generally prefers to work alone. He is conflicted about whether to go the IC route or the manager route. He’ll probably go the manager route because his manager told him it is the path of least resistance.

Adam thinks AI and AI-adjacent tools are crutches. He does not use Twitter.

Brenda: Writer at a marketing firm

Brenda is a writer at a marketing agency, helping top brands with positioning and providing materials for advertising campaigns.

Brenda works in a hybrid setting, working from the office three days a week. She likes the hybrid split as it enables her to see her peers and collaborate more effectively while allowing her to take it easy on Mondays and Fridays.

A standard week typically includes writing an internal memo and reviewing peer’s assignments. Some weeks are more skewed towards her individual contributions, writing out full reports and sometimes presenting the result. These reports are sometimes fully internal facing while others are external to clients.

The clients love Brenda – she is young and in tune with Gen Z culture. She offers unique insights like “Instagram DMs are out” and “Being cringe is cool”. Her boomer clients run every piece of marketing material by her to avoid the never-ending cultural landmines and to be perceived as cool.

Brenda went to a good school, with many friends working in similarly prestigious positions across various industries. She takes frequent bathroom breaks to catch up and react in her five active group chats.

Likely future career paths include climbing the corporate ladder, working at a client’s company, or going to graduate school.

Carl: Strategy Consultant at a Big 4

Carl is a consultant at a Big 4 firm. He has worked on two projects during his first year as a consultant.

His first project was not very demanding, with vague deadlines as the product was still years out. Carl took advantage of the additional time afforded to him by meeting other people at his firm as well as networking with the client in person. This proved to be very helpful as Carl is responsible for finding his own projects so that he is not idle and riding the bench.

He networked his way onto his second project, which is somewhat more demanding. Some days he has to get to the office at 9 and works until 7.

Carl often asks himself what his real skills are. Most of his work is produced by some combination of AI tools including ChatGPT, Claude, and Perplexity. Deep Research has been especially useful for creating well-written scripts that he can read off during virtual presentations. He happily pays for these tools out of his paycheck.

Carl’s company issues him a work laptop, which soft-prohibits AI tools. Most of his day includes prompting Claude on his phone and typing the results from his phone to his work laptop. He prefers to work at home so he can use his second laptop, making copying easier and enabling him to have a YouTube video in the background.

Carl’s most difficult decision is deciding which burrito to order for lunch. He excels at navigating cultural contexts and is now fluent in corpo-speak. He always makes sure to align on strategic north stars and leverage whatever framework is in vogue.

Carl has his eyes set on getting an MBA. It's both what he wants to do and what his parents, managers, and friends unanimously recommend. He is in talks with his company to see if they can pay for business school.

Adam, Brenda, and Carl are archetypes of co-workers one would encounter in a prestigious post-college environment in cities across the US. In fact, they are some of the most competent co-workers one might encounter in the corporate world and represent the top ~5% that society has to offer.

In any major city, compensation for these roles typically ranges from $100,000 to $300,000. We know that we have gotten better at running The Sort, matching individuals to occupations where they can maximize their productive potential, as measured by income. However, it is highly unlikely that individuals are producing work output commiserate with their salaries from an efficient labor market perspective.

The following are possible explanations of why young professionals command such high wages:

1. The productivity of outlier employees covers everyone else and they don’t negotiate higher salaries for themselves.

Assume Adam, Brenda, and Carl are your typical employees, each being a 1x employee. Each company has a small percentage of employees that are 1000x more productive and do all the work. The company can not determine ex-ante which person is a 1x or a 1000x employee.

1000x employees are present in all companies and do not negotiate for higher pay or leave.

Furthermore, in post-product-market fit companies, it is difficult for any individual contributor to make a meaningful difference to the bottom line. The barriers to basic maintenance of the product typically do not hinge on a select few, and companies are incentivized to structure their companies such that this is not true.

Compensation impact: Low

2. A single breakthrough covers everything.

A worker comes up with the idea of a widget that increases internal productivity 1000-fold or creates a new product that everyone wants. The firm asymmetrically benefits from capturing the economic value of this breakthrough and does not compensate the employee proportionally to the value they’ve created.

You don’t know who will do this ex-ante (and neither does the employee) so you have to pay everyone an inflated salary to attract the innovator.

Compensation impact: High in select industries, low otherwise

3. Talent is finite and firms are paying everyone they can so someone doesn’t start a competitor.

Every person implicitly decides whether to work for your company or start a competitor. If they start their own company, they have an X% chance of starting a company that puts your company out of business in 10 years.

Your company believes that people can be financially persuaded to work for you and not start their own company, even if it is economically rational for the person to do so. Risk aversion and expected utility exist, and you take advantage of this.

You’re happy to offer higher salaries across the board to reduce the risk that talented employees might leave to start competing businesses.

Compensation impact: Medium-high in tech/select other fields, low otherwise

4. Firms are very concerned with mitigating downside risk; high salaries are a form of insurance.

The worst employees impose a large negative equity value on the firm. This can be through pushing a change to production that nukes your product, incessantly distracting your earnest employees, or acting inappropriately with clients.

You systematically pay a premium to hire better employees who command higher wages. The people that you hire are on average less likely to carry out negative equity value events.

This model would explain most of the difference in salaries between a worker based in the US and India in an increasingly globalized world. While the work between a US and Indian employee is of similar quality for the vast majority of cases, US workers are more contextually competent and less likely to initiate these value-destroying events.

Compensation impact: Low-medium

5. Society wants to maximally incentivize people to join the elite labor force to find the next generation of new elites.

The greater the monetary delta between a low-skill and a high-skill job, the more people are incentivized to pursue the high-skill profession. In a world where everyone gets paid the same regardless of their profession, nobody has any incentive to work harder to get a high-skill job and befriend other elite talent.

The labor market works decently well at finding talented, ambitious people over long time horizons. It’s a sorting mechanism for finding, developing, and unlocking ambitious high-productivity talent.

Adept high-skill workers will outcompete their counterparts and accrue more capital, on average. Capital is one of the main determinants of power, and this is the best way to allocate financial power.

Firms would have to implicitly agree that this is a collective action problem worth paying a premium for. Coordinating this scale would be nearly impossible without defectors who would reap the benefits of larger profit margins.

Compensation impact: Low

6. Firms are one of the many actors complicit in a systemic status subsidy scheme.

Higher education is a complete sham and elite human capital does not exist. People in positions of power across industries are working together to keep the scheme going, financially for educational credit repayments and socially for elite formation.

Society has to justify the investment of 20+ years of education and we have determined it is better to cover the scheme up. People need to feel a sense of self-actualization and fulfillment that this scheme provides.

Firms knowingly add a premium to workers’ wages, increasing costs, and lowering profits to perpetuate the scheme. While this may occur in some niche, protected industries, it is unlikely that this is occurring at scale.

Compensation impact: Medium for specific jobs, negligible otherwise

7. High wages are the preferred intergenerational wealth redistribution mechanism.

High salaries for young professionals allow educated elites to maintain cultural capital while preventing social unrest that might result from more obvious inequality.

These jobs provide high enough compensation to sustain consumption-focused lifestyles without requiring genuine adult responsibility or productivity, keeping otherwise unproductive young professionals politically and socially compliant. UBI is already here, it’s just not evenly distributed.

While high wages help obfuscate inheritance mechanisms to perpetuate the illusion of fairness, this is likely not happening on a global level. A firm could easily decide not to pay the premium and hire similarly competent individuals.

Compensation impact: Negligible

Exercise for the reader: Which of these compensation rationales are durable in the context of AI?

Clinton Foreign Policy, $TRUMP and $MELANIA, and the Foundations of the Internet

This post is about how relationships, emotions, and incentives drive real-world outcomes over reason, ethics, lawful behavior, and market forces.

Foreign policy is guided by sex and approval

In 1995, Bill Clinton had a very public affair with Monica Lewinsky. In response, Hillary Clinton did not speak with Bill for many months due to the affair and the second-order effects of his infidelity.

The re-commencing of their relationship and the eventual forgiveness by Hillary is claimed to be traced back to her strong-arming her husband to bomb Serbian villages: [1]

Bombing Serbia was a family affair in the Clinton White House. Hillary Clinton revealed to an interviewer in the summer of 1999, “I urged him to bomb. You cannot let this go on at the end of a century that has seen the major holocaust of our time. What do we have NATO for if not to defend our way of life?” A biography of Hillary Clinton, written by Gail Sheehy and published in late 1999, stated that Mrs. Clinton had refused to talk to the president for eight months after the Monica Lewinsky scandal broke. She resumed talking to her husband only when she phoned him and urged him in the strongest terms to begin bombing Serbia; the president began bombing within 24 hours. - Source

The primary source here is shaky at best. But is something like this really beyond the realm of possibility for one of the most ambitious and socially intelligent Western female leaders with a long history of hawkish foreign policy tendencies? Hillary’s love language could very well be acts of war.

Founding stories are a psyop

Early-stage startups run on secrets and secrets often spread like wildfire. NDAs are only as good as the ability to enforce them. Your favorite application (Facebook, pump.fun) has a much more complex founding story than is publicly available, likely with some questionable espionage-esque behavior as a core propellant to reaching their heights.

Most participants understand this social contract and end up contributing and extending in-group dynamics. This includes connected individuals carefully crafting narratives for public consumption.

The founding stories of successful companies including Paypal, Flexport, and Notion are well-documented and leveraged as lore to attract customers and prospective employees. How much of these stories reflect reality versus a curated Girardian story to persuade others of their divine purpose?

Verkada God mode

Verkada is a security camera company founded in 2016 and is currently valued at $4.5 billion. They offer a full-service physical security solution to institutions, installing and managing the logistics of their security camera operations.

Clients include the Los Angeles state government, prisons, Tesla factories, Equinox, healthcare systems, and leading biotech firms.

It was reported that Verkada has a God mode – exposed by hackers – enabling anyone with the password to access every security camera in the Verkada network. As is typical in startup land, shortcuts were taken, and God mode was gated by a single hard-coded secret with rudimentary security practices. [2]

Trump and Melania memecoin launch

At midnight of January 18, during his inauguration ball, Trump released an official memecoin. The token achieves virality in hours, reaching a total valuation of over $70B. Trump re-ignites himself as the main character, kicking off his upcoming term with a soon-unlocked infusion of financial capital, and once again cements himself as the most powerful man in the world.

Melania, who cannot stand her husband on a personal level, and is driven by envy that her husband is one-upping her, decides that she wants to launch her own memecoin. She gets in contact with one of the most extractive token-launching cabals and launches $MELANIA 43 hours later.

She then delivers the knockout punch to cement her social dominance: a retweet from the president’s official X account. This legitimizes her token, and more importantly, transforms her persona from the socially submissive tradwife to a culturally adept individual who demands our respect in the attention economy:

Tweet

Within days, billions are extracted from retail participants worldwide – including via siphoning liquidity from Trump’s own token – by insiders to satisfy Melania’s insecurities. [3]

What should we be cynical about now?

Embryo selection for the powerful is underway

One can make a good argument that universities, institutions, and churches are wrappers around assortative mating. The incentive stems from many traits being highly hereditary.

Assortative mating – i.e. picking a partner – has historically been the only lever you can control. However, with the assistance of modern medicine, you can take this one step further with embryo selection.

Embryo selection and modification are likely already well underway today, but it is hard to determine the extent and state-of-the-art technical capabilities. It is typically categorized into two groups: disease prevention and enhancement.

Disease prevention is concerned with eliminating the likelihood of negative qualities that affect quality of life (e.g. HIV) and traits that typically put a strain on the public welfare system. This is generally less controversial of the two.

Enhancement means specifically selecting for or modifying genes to increase traits such as athleticism and IQ.

When one of the leading experts is ostracized for ethical transgressions, this should provide some clues that incentivize powerful groups to at least understand the technological capabilities.

Is this something Elon would consider? How should we model the next generation of powerful kids against the empirical evidence of some level of mean reversion in intelligence and ambition amongst families?

Elliptic-curve cryptography has always been broken

Elliptic-curve cryptography (ECC) underpins much of the internet today including public-key cryptography, digital signatures, and pseudo-random number generation.

Government, Russia, Mathematics as Propaganda, and other resources can all be used to piece together a more likely origin story for the foundational primitives and incentives of the early internet.

Bitcoin

--

The world is a museum of passion projects, stewarded by the powerful to serve their wants and needs. It has never been better to be a billionaire or similarly powerful person.

As a recovering efficient markets enthusiast, the base case that you are being adversely selected against by a group of well-connected and smart individuals is a good assumption in theory but incomplete and naive in practice. [4]

The truth is closer to: reality is path-dependent, messy, littered with incompetence, and guided by human relationships and emotions.

[1] The whims of the powerful are now felt worldwide via mechanisms including cryptography and drone strikes.

[2] Uber had a similar well-documented God mode, paying a paltry $20,000 fine for a tool used to spy on reporters and key individuals in a position to negatively affect the company.

[3] This is not meant to imply that Melania has unique insecurities but rather she has her own ambitions and desires to be viewed as a certain kind of person.

[4] Powerful individuals are increasingly interconnected. Previously, this meant physical colocation, living in the same country club, and frequenting the same events. Now, the world runs through networks of individuals who aren’t necessarily co-located, aligned by ideologies or shared goals. Group chats rule the world.

There’s a graph from Tim Urban that I think about often:

It never ceases to amaze me that you can literally see all the weeks of your entire life on a single sheet of paper. You have a very finite number of weeks in your life. [1] Every decision matters.

Time is the one scarce resource that affects everyone equally. Regardless of your background or socioeconomic status, everyone plays by these same constraints. [2]

However, reality is even more pernicious than what this graph shows. As you cross off each week of your life, you falsely believe that you are operating in a linear regime where each week is an equal proportion of your perceived life. In reality, your perception of time in life is better modeled on a logarithmic scale rather than a linear one.

At age 5, one additional year is another 20% of your life. At age 50, one additional year is only 2% of your life.

While our perception of time is not perfectly logarithmic, talking to people older than me leads me to believe that this heuristic is at least directionally true.

What should we do about this? How can we maximize our ability to get what we want in our lives?

Career implications

As early as possible, you should develop a thesis for what you want to do given your risk profile, work/life balance, and skillset. Your thesis can and should evolve as you grow older.

Intelligence is getting what you want out of life, and most people complain they aren’t getting what they want without any evidence of even a modicum of effort. You can get ahead of basically everyone in any field by putting in an ounce of effort. No one is even trying.

Don’t spread yourself too thin, but realize that there are unexpected returns to being a polymath and deriving cross-pollination insights. Choose 2-3 areas to do ten times as much in life. Deliberate practice over 2-3 areas for even a few weeks will put you at a massive advantage over the vast majority of the population. Compounding is still greatly underrated in practice, even though everybody knows about it.

Assuming you have some basic education, you can learn most things rewarded by the free market in a very short period of time. Most people dramatically underinvest in “temporal leverage points” – where small investments of time can radically alter the trajectory of your life. Examples include: spending time really understanding how the talent system works in your given industry, learning the basics of probability theory, having lunch with someone 30 years older than you in your field, or starting a podcast. All can be done within a week with unbounded upside.

If you’re reading this, you’re likely stressing too much about exactly what you’re working on. There is such a thing as decision paralysis. Anecdotally, it’s much more important to be able to let the compounding effects occur and reach the frontier of a given field than to always be jumping around and never coming close to the frontier.

Personal life implications

The standard YOLO advice about seizing the day fundamentally misunderstands time perception. The real arbitrage opportunity is doing things that seem boring to others but have massive long-term returns. Learning Portuguese or exploring the Dark Forest on a new chain might seem like wasting your precious weeks, but these detours can yield the highest returns through unexpected recombinations.

Most arguments boil down to different time preferences. Understand your temporal discount factor and find others that match it.

By age 18, you will have spent 93% of the actual time you'll ever spend with your parents. On a logarithmic perception scale, you’ll have experienced 98-99% of your perceived time with them.

Location decisions become increasingly sticky as we age, as social and professional roots grow deeper. The optimal time to experiment with living in different cities or countries is often earlier in life when our perception of time is more expansive (and our commitments are more flexible).

Hobbies and personal interests should be cultivated deliberately, as they often provide the texture and meaning that make our finite weeks feel richer. Starting a creative practice or learning an instrument at 25 means you could have decades of enjoyment ahead, while the same decision at 45 comes with a different psychological weight.

Health investments made early can have large exponential returns. The good habits we build in our youth compound over decades.

[1] I have seen no evidence that immortality efforts such as Bryan Johnson’s will increase your lifespan by more than ~20%.

[2] Sure, billionaires can buy time by flying private, having personal chefs, etc. but they still age at effectively the same rate as the rest of us.