I think this is good advice for those who struggle with perfectionism and/or analysis-paralysis, although some forget to caveat that this mostly applies for decisions that are both relatively low effort to execute and reversible. Otherwise, it contradicts another piece of advice the same people are likely to give you: “Selection is everything. Think carefully about what you spend your time on.”

Often when you’re stuck, a better cliche to follow than “just do something” is to double down on strengths. I didn’t use the framing of “double down on what’s working” because presumably if you’re stuck, things don’t seem to be working. If you’re at a low point, even the word “strengths” might fail to conjure up a long list.

Be that as it may, most of us have lived life. We’ve lived in certain places, done particular things, befriended particular people. That’s a good starting point.

The Framework

Say you’re trying to search for a job or think about how to acquire your next client as a solo entrepreneur.

Step 1: List down all the keywords you associate with yourself.

These could be places, people, skills, industries, other common nouns. Mine was something like: [Singapore, Finance, VC, Writing, Debate, Recruitment, MIT...]

Cities, universities, hobbies, businesses—anything I associate with myself or people associate with me

Step 2: Break them down using whatever dimension feels natural. Keep doing this.

In finance, maybe you worked in both Wealth Management and Venture Capital. In writing, maybe you did both poetry and long-form.

Step 3: When you’re done or when stuck, ask four questions:

1. Whom do I know? Who did I meet?

2. What do I know? What did I learn?

3. What have I done? What can I show?

4. What do I have?

These should capture every form of advantage: social capital, skills and knowledge, track record and credentials, money. Usually money is fungible, so I wouldn’t focus on it while breaking categories down.

Step 4: Order or think based on strength and quality.

Clients you have deep relationships with come before prospective clients. Close friends come before acquaintances. Skills you have come before skills you could be well-positioned to learn.

From Assets to Action

The last step is a bit harder to detail but I found that easier and more natural to do than getting the process started.

Take (a) an asset or a set of assets/advantages and (b) the thing you’re trying to unstuck yourself on and think with the intention of generating a next step.

The ideal way to translate assets to actions depends on your available mix of time vs money too.

Example 1:

• Goal: Looking for a job

• Asset: Former boss who thinks highly of you

• Thought: Maybe he can introduce me to people or vouch for me

• Action: Catch up for coffee (or just ask for introductions, depending on relationship)

Example 2:

• Goal: Go on dates

• Asset: Interest in ideas/politics/philosophy

• Thought: Where else can I meet similar people? Perhaps a discussion group

• Action: Search for discussion groups online

Note: Even though this worked for me, men are well-advised not to join philosophy discussion groups to meet women, due to the abysmal gender ratio in these meetups.

A Note on Process

I don’t know if this would work if treated as a regimented exercise focussed on finding the best option.

But it is a way to try to execute actions that are likely to generate some sort of feedback loop. For example, sending one email to someone who’s 100x more likely to reply is better than sending 100 carefully drafted cold emails.

This is really a way to avoid the quantity and measurement trap by introducing quality into the process.

There are two ways to be wrong about hiring. The first is to say "we want the best people" without defining "best" or acknowledging that each additional bullet point on a job description has a cost. It is a market after all.

The second is to treat hiring like buying software : spec out what you need, review a bunch of options, pick one.

The market for full-time employees is different in two important ways:

Talent is 100% rivalrous. The purchase of software is mostly nonrivalrous. It’s not important that you beat other buyers to the purchase—in fact, if you get there after someone else, you’ve probably de-risked your purchase. But only one company can hire any given full time employee at any given point in time.

Two-way selection process. Software vendors are unlikely to turn down your business so the buyer is the one with selection power. Hiring is a two-way street. You choose your employees, and your employees have to choose you. That means foreclosing on other people and opportunities.

Hiring is a lot more like dating (to monogamous ends) than it is like buying software.

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Startups face unique challenges

If the market for talent is hard for everyone, startups face a few additional challenges:

Good and bad hires are more consequential. At a 10-person company, each employee is 10% of the workforce. Their output, judgment, and behavior shape the trajectory of the company in ways that dwarf the marginal impact of the median employee at a larger company.

Weak brand recognition and adverse selection. Even well-funded, fast-growing startups are unknown to most candidates. The talent you want doesn’t know you exist. They’re probably not going to find you through a job board or inbound application. The pool that does find you through open applications has a higher share of the desperate and the habitual spray-and-pray-ers.

Novel problems are less legible. A startup exists to manifest novelty in the world. That might entail the creation of a new category or a slight but surgical tweak to an old one. Incumbents know what work needs doing, so they've carved it into jobs. You're still figuring out the shape of the problem—which means you need people before you can describe the role

So: the stakes are high, the people you want won't find you, and you can't find them by keyword matching.

The upshot: Illegibility runs both ways

As a startup, you’re illegible to the market. But the market is also failing to see and price talent that might be perfect for you.

Large companies are where legibility gets produced. A recognizable name and a standardized role give the rest of the market a shortcut for evaluation. So those signals become the default filter; not because they’re equally predictive everywhere, but because they’re easy to read.

Large companies filter the way they do because they need hiring to be scalable and defensible.

This creates systematic blind spots for everyone:

• Path dependency. The right school leads to the right first job leads to the next. Each signal opens the next door. But if you missed an early gate—or took a non-standard route—the market will price you at a discount

• Arbitrary domain restriction. Ability transfers across domains, but the market filters by domain and really, any keyword it can use. So people with relevant skills get filtered out because they lack the labels. For example, someone doing performance attribution or manager selection at an asset allocator is likely to have a bunch of relevant skills for data science at a tech startup—but I know that these resumes rarely get through filters.

• Adaptive for large companies = (sometimes) maladaptive for startups. Impatience with process. Intolerance for bureaucracy. Disagreeableness. Large companies filter out these traits or stall their progress. These are features not bugs in early startups.

• The not-looking. The people you want are probably doing something else and doing reasonably well—but that doesn't mean they're doing the thing they're most excited about, or that fits best with their skill set. “I’ll consider if something great comes along” is a common posture and the willingness to jump changes based on personal factors and specifics of the opportunity.

Time Pressure Kills Quality

If you need to hire for role X right now, you’re stuck: either resort to the adversely selected pool or compete for highly legible talent by paying a premium. The people you actually want—the illegible talent, the people who aren’t looking—require time and context to find. They take longer to evaluate and longer to convince. You need relationships to reach, evaluate and close.

Sourcing Strategy

The earlier you are as a startup, the more it makes sense to think of sourcing not as the process of filling specific roles but as one of building a network/community of talent around your organization.

Doing this successfully means that you can hire opportunistically when excellent talent becomes available, and it means that when a position needs to be filled, you have at least a few candidates whom you have quite a bit of context/signal on.

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Building your Own Talent Community

There are two complementary ways to build this community.

Inside Out: Start with your existing social capital—founders, employees, advisors—and expand outward by asking “who’s great?” Networks encode information that’s hard to articulate. When someone recommends a person, they’re implicitly filtering for taste, judgment, work style, cultural fit—things that can’t be captured in a job description.

Outside In: Make yourself visible in places with a disproportionate share of people who fit—in terms of both skills and disposition.

The two reinforce each other. The network points you to communities; the communities generate new nodes for the network.

Implementing the Inside Out Model

Start with your initial nodes: co-founders, early employees, advisors. From these nodes, you’re trying to generate two types of recommendations.

The first is meta-level: people with good taste, good judgment, and access to talent. These are sources of future recommendations. Call them talent scouts.

The second is object-level: actual talent. People who might be right for a role now or in the future.

Both are valuable. And once you’ve built a relationship with someone—whether they become a hire or not—they become a potential source of further recommendations.
The network expands outward.

One useful tactic, especially for roles you don't know well: approach senior people explicitly not as candidates, but as advisors. "Assume you're not looking, but I'd love your advice on how to think about talent in this space." This gets you context on where to look and what to look for—and it opens the door to recommendations without the awkwardness of asking if they know anyone like themselves, but earlier in their career.

When someone joins the company, one of the first things you should do is ask them for talented people they've worked with. They're excited, their network is fresh in their mind, and they want to help build the team they're joining. But just as important: give them an easy way to send names your way as they think of people later.

Managing the recommendation flow

As you start generating recommendations, three principles to keep in mind:

Protect reputation. People need to look good when they send someone to you. In the early days, this means a senior person—founder or close to it—meets everyone who gets referred. Have open-ended, transparent conversations: “We’re still figuring things out. I’d love to learn about what you’re looking for.” Don’t lead anyone on. And close the loop with the referrer so they know their recommendation was taken seriously. Over time, you can be more selective—but initially, treat every referral seriously, or people stop sending them.

Reduce friction. When you put people on the spot—”who do you know?”—most blank out. Don’t require immediate answers. Leave the door open: “Anytime you think of someone interesting, just text me.” No forms, no portals. The barrier should be as close to zero as possible.

Learn over time. As the network develops, you’ll see patterns: the employees with interesting friends, the scouts who generate high-quality introductions, the networks that align with your taste. This lets you prioritize the high-signal sources. But you need data before you can make these distinctions, which means keeping your scouts happy until you know who they are.

Implementing the Outside In Model

Inside-out builds from existing relationships. Outside-in is more deliberate: you go looking for specific types of talent.

The key is to think broadly about selection effects. The obvious starting point is communities organized around specific skills (data science meetup, RUST discord etc) But for talent that’s harder to define - imagine your ideal employee and ask: What newsletters do they read? What podcasts do they listen to? What events do they attend? What open-source projects do they contribute to? What weird corners of the internet select for the traits you actually want?

One practical starting point: survey your existing employees—what do they read, listen to, follow? Then use that to find adjacent communities. You can even paste the list into Claude and ask “if someone likes X, what else might they like?”—a rough way to surface places that might prove generative.

But once a place becomes a known talent source or a de facto job board, it loses most of its selection.

What you want are incipient communities—emerging podcasts, newsletters, meetups, reading groups, niche Discords. Finding them requires curiosity and time. But once you do, organizers are often eager to partner: these places are usually undermonetized. Speak at their event, sponsor a meetup, buy an ad in the newsletter. Most of these groups are happy to do something ad hoc if you just ask.

Sending the right signals

While engaging with these spaces, the goal is to make the company memorable to the right people for the right reasons.

Networking is sending hard-to-fake signals. You want to put something out there that reveals what makes you interesting. A contrarian position on a niche technical issue. A norm or cultural stance where you disagree with the general Silicon Valley consensus. Something with personality.

Instead, people default to:

• “My co-founder is exceptional” — of course you’d say that

• “We move fast” — what startup doesn’t say this?

• “We have high standards” — free to claim, costs nothing

It’s also become fashionable to brag about how intense your culture is. This does filter—but it turns away people who don’t like performative intensity without giving serious people anything specific or falsifiable to evaluate. Intense about what? Driven by what?

It’s useful to remember that bold claims without specificity or evidence are not neutral. They’re often read as a negative signal.

Keeping Talent Warm

Initial meeting. Someone in the company meets with them. The conversation is open-ended, exploratory. You’re learning about them, they’re learning about you. Be transparent and non-committal: “We’re still figuring things out. I’d love to understand what you’re looking for.”

Add them to the community. If you like them, they go into your talent community: invited to events, receiving occasional updates, on your radar for future roles.

Assign a point person (for those you’re particularly impressed with). For people you’re especially excited about, someone in the company owns the relationship. They check in periodically. They watch for timing—new manager, shifting circumstances, openness to moving.

Make it easy for them to refer others. Everyone you meet is a potential scout. Even if they’re not right for you, they might know someone who is. Keep the friction low: “If you ever meet someone great, just text me.”

Events as ongoing touchpoint. Quarterly (or whatever cadence), invite the community to something low-key—pizza and beers at the office. You get to see people in a relaxed context. They get to see you. It keeps the relationship alive without constant 1:1 outreach.

Who the fuck has time for this?

There’s no getting around it: founders and early employees have to be deeply involved in talent. What I’ve described requires systems, time, and taste.

If you have the funding and expect to hire more than 10 people in the next 18 months, it’s not crazy to hire someone to own this.

To beat the horse that’s long dead: Labels and categories can be unhelpful. Instead of calling this person a ‘recruiter’ or ‘talent ops’, we can start by listing what you want done:

Strategic: A thought partner who helps map problems to people. “Do we actually need a full-time hire here, or is this better contracted?” “Should we look for a generalist or a specialist?” This requires understanding your organizational context—not just executing a search.

Social finesse: Someone who can be a credible face of the company to candidates and build relationships before there’s a role to fill.

Content and communication: Outbound recruiting is fundamentally a copywriting problem. This person writes outreach emails (often on behalf of the CEO), makes contact with creators and communities, and may create content that makes the company visible to talent.

Managing external resources: For specialized roles, you’ll engage external recruiters. There’s broad variance across agencies. Someone needs to select the right partners and manage these relationships—without judgment here, you’ll waste time on the wrong ones.

One exception: if you’re hiring specialized technical talent from a narrow domain, a recruiter with deep networks in that space—or a technical person who can evaluate candidates—might matter more.

For most startups, what matters is the ability to leverage the organization’s existing social capital: mining the founders’ and employees’ networks, turning every new hire into a node for further connections, converting early wins into inbound interest.

Pure agency recruiters are probably not a good fit. In-house experience is better. But the best fit is often someone who’s built something themselves: a bootcamp, a community, a small business. They’ve done the work of attracting and evaluating people without a brand to lean on.


In the next part of this series, I discuss the incentives of external recruiters and evaluation tactics.

Soon after Karp’s interview, Palantir announced its neurodivergence fellowship.

I fully expected the usual suspects to pile on, including from within the tech industry.

This is either intellectual laziness, or just plain bad faith, from the CEO of Replit. The reasoning seems to go something like this: “neurodivergence is vaguely about “inclusion” and “diversity”; who makes special accommodations for the “dysfunctional” except the woke; Palantir is anti-woke; therefore, hypocrisy. QED.”

I don’t think so. Whether the Replit guy agrees or not, Alex Karp believes he’s neurodivergent. The most straightforward, not even charitable, interpretation is that he wants more people like him: neurodivergent and productive.

Having said that, one could reasonably ask: if neurodivergent people are just as productive as neurotypicals (or more) — why do they need a dedicated fellowship at all?

As a group, neurodivergent people underperform neurotypicals on nearly every measurable skill. There’s nothing special about them in aggregate — they just look worse. But aggregate comparisons obscure the fact that some categories of neurodivergence show higher variance between their strongest and weakest abilities.

For example, individuals with ADHD and dyslexia show preserved reasoning abilities alongside impaired working memory and processing speed. This means that tests that involve time pressure and allow limited scope for externalizing working memory, will tend to underestimate their cognitive potential.

Now, if you’re an air traffic controller, time pressure and working memory aren’t arbitrary requirements. But in many knowledge work settings, the time pressure built into hiring filters is a result of the need to cheaply and accurately score candidates at scale. Timed tests are easy to administer and score; open-ended evaluations of reasoning ability are not. Add some path dependency, and you get a market that systematically underprices the skills of people who reason better than their performance in standard assessments would indicate.

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The thornier problem is conscientiousness. Even among high-IQ neurodivergent people, conscientiousness tends to be lower. Meta-analyses find correlations of r = -0.40 to -0.50 between ADHD symptoms and conscientiousness — a large effect by personality psychology standards. Autism and conscientiousness are also negatively related. Conscientiousness, is by definition, a positive trait, all else equal. But I’d guess that large companies systematically overweight legible sub-facets (eg. orderliness) of conscientiousness to the exclusion of other traits that are harder to observe and measure.

Corporate environments tend to zoom in on legible inputs, since outputs can’t usually be measured and attributed correctly. People may not promote on the basis of punctuality and email hygiene, but they sure as hell will use the absence of these traits as reasons not to promote someone. Most high-functioning normies see these as table stakes since it comes easy to them. They go “if you can’t even do x, how will you do complicated thing y?”

This, I think, is at the core of what Palantir is getting at.

“If you can’t even do simple thing x, how will you do complicated thing y ?” is not a good heuristic if you care about value creation, and not simply minimizing downside variance.

Sometimes, the people who are bad at emails or exchanging pleasantries before meetings are capable of thinking deeply and thoroughly, and distilling complicated systems with clarity. It’s possible for someone who is less legibly “on top of it” to have much better conceptual intuition and produce quality that is significantly better, albeit harder to measure. (Ask my wife—I’m far better at helping her think through complex work issues by asking the right questions, than I am at folding laundry properly or remembering to add simple tasks to my to-do list.)

If my thesis is broadly correct, the specific mechanics of Palantir's fellowship matter less than what it signals: Palantir is an environment that welcomes spikes. They aren't interested in a holistic normative judgment of a candidate's "finesse" but whether this person is capable of producing outsized value when given autonomy and a team that complements them. Just as important is the implicit promise on workplace norms: we won't hire you and then slowly grind you down with expectations designed for someone else.

All sorts of people will apply to this program, including those whose resumes would have arguably passed the screen even without this dedicated fellowship- the right credentials, keywords that indicate relevant prior experience etc. These applicants may or may not be neurodivergent. (Neurodivergence is a spectrum anyway, and I don’t want to suggest that people to the left of me should automatically disqualify.)

If i were optimizing for the counterfactual impact of this program, I’d probably start by interviewing people I wouldn’t otherwise have, based on resume screening alone. Which is why “final interviews will be conducted by Dr Karp himself” sounds good, but doesn’t necessarily alleviate the failure mode I anticipate.

Assuming senior leaders at Palantir share Karp’s views, I wouldn’t worry too much that a talented neurodivergent person is rejected in the final round. It seems more likely that this person doesn’t get interviewed at all, since the initial screening is where scale and hard filters have the highest returns, and where it’s hardest to resist the appeal of standard tools.

We tend to casually think of a firm as just a bundle of agents performing tasks in exchange for money. But that frame is worse than just incomplete. The average white collar worker is at least as much an externality on his colleagues as he is an input into production.

The sign of this externality is the target for what management books talk about endlessly without ever quite defining: Culture

Why does the firm exist?

Why do we have these strange abstract entities where people show up at the same place, take orders from someone, and get paid the same amount regardless of what they produce?

The economist Ronald Coase answered this question in his seminal paper, The Nature of the Firm. Hierarchies and the firm emerge when transaction costs of contracting external parties exceed the benefits of price competition between these external parties.

The act of hiring a full-time employee is usually an admission of at least one of the following, often both:

(1) the employee needs context to create value for the organization, but this context either cannot or is too expensive to be shared with external parties

(2) Output and inputs don’t have a straightforward one to one mapping such that the value of the output can’t be straightforwardly attributed to specific inputs/workers.

As a result, the company’s preference is to monitor the inputs more closely. The worker commits time and attention to the firm. In return, the company absorbs the variability/volatility of outputs. In other words, the employee gets paid the same so long as they show and do things that need to be done to not get fired.

Firms create the problem of “shirking”

When outputs can't be properly attributed, the rewards for good work are capped, and punishments for laziness attenuated, we should generally expect everyone to do less. The COVID-19 remote work era served as a kind of Reductio ad absurdum of this dynamic—job-stacking, mouse-jiggling devices to fake presence, and naps that would embarrass even a Sunday afternoon. But shirking, if in less brazen forms, has always been a feature of corporate life. It is a predictable consequence of the interaction between human nature and the firm's raison d'être.

Shirking is a specific manifestation of a broader problem: a self-interested agent internalizing benefits (working less, collecting a paycheck) while externalizing harms.

So if you can’t get the image of a disgruntled, lazy employee out of your head, think about the manager that overworks his team, gets promoted himself but increases attrition in the medium term, which is no longer easily attributed to him. It’s the sales guy who overpromises the customer to close a sale, while degrading the credibility of the brand. The harm is usually diffused - no one is palpably worse off in the short term but it’s catastrophic for collective future output. In other words, it’s the corporate equivalent of the guy that plays music on a speakerphone on the A train.

The limiting case

Consider the limiting case — the CEO. Executive compensation is designed to solve exactly this problem: equity dwarfs salary, vesting schedules extend over years, the goal is to make the CEO's payoff approximate a long-term shareholder's.

But it’s possible to imagine a CEO making a decision that boosts shareholder value in the near to medium term but slightly erodes something harder to measure — reputation, innovation capacity, employee trust. If markets aren’t perfectly efficient and there exists some information asymmetry between management and long term/future shareholders, incentives aren’t perfectly aligned.

As one gets further away from the feedback loop with markets - deeper down the hierarchy of the organization, we should expect to find more misalignment, not less. So what does perfect alignment actually look like?

It probably looks like the interests of a perpetual shareholder with a very, very long, almost infinite time horizon - perhaps one that doesn’t exist but can be approximated. In other words, someone who is almost intrinsically interested in the long-term survival and flourishing of the firm.

Enter organizational culture

Culture has to step in to resolve the fundamental dichotomy in the firm: between intrinsic valuing and instrumental pursuit. If everyone relentlessly optimizes for personal financial gain, the collective suffers, unless incentives are perfectly aligned, which they never are. Yet the firm cannot reject optimization altogether; in a competitive market, consistent profitability is non-negotiable. Without it, the entity dies, and all talk of higher purpose becomes moot.

The challenge for culture, then, is not to eliminate instrumentality but to flip it: to make profit-seeking the critical means to a greater, metaphysical end—which can be some greater virtue or an interest in perpetuation of this particular organization and what it stands for as something worth existing beyond any individual’s tenure or payout.

This is why founder-led companies so often develop strong cultures: founders are uniquely positioned to embody and transmit this narrative. Legacy and posterity are psychologically potent motivators that tap into deep human desires for transcendence and immortality. When a founder genuinely believes and lives this, they can model the intrinsic valuing in a way that doesn’t seem performative.

Cultures can also be built on ideals/virtues/traits instead of narratives. Bridgewater was clearly a founder-led company for a long time but the emphasis was not necessarily on Ray or the impact of the company on the world but on truth-seeking as a virtue/ideal.

But narratives and virtues both fade unless reinforced through rigorous selection. Which means they need to be specific and demanding enough to serve as costly-to-fake signals. Mild, universally agreeable platitudes (“excellence, teamwork, collaboration”) offer no real identity, no tribe worth sacrificing for, and are easily gamed by pretenders and free-riders. Over time, they hollow out into the mocked handbook slogans we all know.

Culture endures through strong mutual loyalty. Narratives and ideals draw people in. But loyalty's fuel is the lived reinforcement: leaders repeatedly demonstrating willingness to leave money on the table in service of that ideal—refusing the easy hire, walking away from a lucrative deal, protecting an employee when it's costly to do so.

This creates a reciprocity loop: employees who witness sacrifice feel obligated to sacrifice in turn. Peer enforcement emerges organically: those who've sacrificed resent those who free-ride, and push them out. The culture becomes self-policing, no longer dependent on founder vigilance alone.

<Insert average humblebrag post>

Inferred intended signal: “I’m accomplished but modest about it.”

Decoded actual signal: “I crave validation and lack self-awareness.”

*CRINGE*

So, why is LinkedIn such fertile ground for cringe content?

Signaling, Deception and Asynchronous Communication

Signaling isn’t inherently adversarial, but the ever-present possibility of deception activates game-theoretic dynamics. We’d all press a button that makes others view us more favorably—and because everyone knows this, we’re perpetually scanning for fakers and posers.

This is why the most effective signals conceal their intent. The best ones don’t trigger your adversarial alarm system. Instead, you feel like you’re forming independent judgments based on organic observations, not processing evidence someone carefully staged to manipulate your opinion.

Real-time interaction offers some protection against this. Face-to-face, it’s harder to maintain a carefully crafted facade when you must respond to constantly evolving stimuli and social cues. The performance cracks under pressure. Asynchronous communication, especially one to many broadcasts - inserts more skepticism. When someone tweets or posts a LinkedIn update, it’s common knowledge they crafted this message knowing hundreds or thousands would see it. The act of posting becomes presumptive evidence of intent: a rational person wouldn’t broadcast something unless it reinforced the identity or reputation they want to project. The signal’s deliberateness is foregrounded.

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Scrolling through outrageous tweets or hilarious videos, you’re scarcely aware of the status games beneath. Entertainment and emotional engrossment are perfect signal carriers—they occupy your conscious attention while the status work happens in the background. You’re too busy laughing to accuse the comedian of “just signaling.” And if that thought does occur, it arrives as vindication: “Good. He made me laugh. He correctly signaled he’s funny.”

The same applies to rage bait. When someone triggers you on Twitter, you’re too angry to rationally analyze their ulterior motives. You’re triggered—but more importantly, you’re engaged. And engagement provides cover.

All Social Media is NOT Made Equal

LinkedIn, more than any other platform, deserves the label “Normistan”. The modal Linkedin user did not spend his 20s meditating in Tibet or being a closeted white nationalist. Moreover, the context places significant constraints on LinkedIn users with respect to how comfortable they feel tickling each other’s senses or provoking outrage.

But they know their colleagues are on LinkedIn, probably signaling away to the next promotion or job offer, so they should be there too and do something to be…visible? But how do you hold attention without the tools that work everywhere else? Perhaps by sharing genuinely useful insights…

A tiny minority of LinkedIn posts meet this standard. General insights about decision-making and management come from organizational psychology or economics departments, not VPs at Goldman Sachs. The most valuable practical wisdom is either proprietary or too context-dependent to survive abstraction. And the people who could add most to the discourse are posting strategically to attract clients—often through an intern—because the real game is money, not showcasing smarts.

So it’s not surprising that when this pool of doers are asked to post but post under tight editorial constraints, they resort to one of these:

• Platitudes that are easy to state and hard to do

• “Insights” which have been regurgitated about 15 million times already

• Didactic monologues that are really just personal anecdotes from which no general advice can possibly be derived

• Fabricated stories designed to deliver a pre-packaged “lesson” (“Last night at 1 am, a founder called me crying”)

[I’m shocked by how many people have wailing business associates who seem to use them as their personal midnight therapists, but these cry babies have no qualms about you using their tears as LinkedIn fodder ]

• Performative vulnerability posts that frame minor setbacks as profound struggles overcome through resilience

• Quotes from Captain Obvious disguised as wisdom (“Collaboration is everything”)

• Humble brags disguised as gratitude (”Blessed to announce...” )

When your post lacks both utility and entertainment, what remains visible is just the naked signaling attempt—and one of two failures. Either the audience watches you transparently try to gain status and fail, or you succeed at producing a signal so cheap and easy to fake that it communicates nothing credible. Both roads lead to the same destination: cringe

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Did you think I would end a post about LInkedIn without talking about AI?

There was a time when you could read this essay and accuse me of being a total asshole to people who are trying their best. At least they’re making an effort! But are they? Less than a third of the LinkedIn posts on my feed pass the bar of “not obviously AI generated”. As someone who spends an embarrassing amount of time hanging out with my machine friends, I’m not here to moralize about LLM usage. But low-effort LLM generated posts are almost guaranteed to be useless for the brownie points the poster is gunning for.

Once upon a time, writing a grammatically correct coherent paragraph was a credible signal - of both communication skills and effort; much like a cover letter tailored to a job used to be a credible signal of your interest in the job. We don’t live in that world anymore.

For most goods, if input costs go down, it’s rational to produce more. But everyone is producing more, and unfortunately, status is a positional good. More posts just means more noise competing for the same finite pool of attention and respect. So unless you’re using LLMs to produce something of much higher quality than you otherwise would have, you’re wasting the 90 seconds you spent on it.

As someone whose humor is often described by my less-than-refined friends as “optimized for volume,” I can attest that a raunchy one-liner that flops won’t ruin you. The momentary awkwardness of a failed joke usually fades quickly, especially if everyone acknowledges the misfire. But for those who love telling long-winded stories and consistently miss the mark, the cringe hits harder. The more air time you take up, the more social capital you’ve staked. More importantly, when the performer remains oblivious to their failure despite social signals, cringe will envelope the rest of the room, almost seeming to compensate for the subject’s lack of embarassment.

Consider an encounter I had on the New York City Subway: a 300-pound, middle-aged man sporting oversized sunglasses belted out a bizarre rendition of Cyndi Lauper’s Girls Just Want to Have Fun. The word “sing” might be generous—he’d pause for 30 seconds of silence, then bellow “GIRLS,” followed by a three-second pause and a whispered “…just wanna have fun.” The passengers held it together, sensing he wouldn’t appreciate laughter. Yet, despite the oddity, I didn’t cringe. Why?

Our subway characters aren’t exactly projecting a stellar image. They also happen to be some of the most self-assured people I’ve met, suggesting a gap between self image and image. But the subway performer isn’t in any serious way trying to climb the social ladder. Their performance can’t fail to win approval because winning approval was never plausibly on the table. Cringe only comes with failing at the game everyone else is playing.

The intensity of cringe scales with how much someone appears to care about their social standing and how deliberately they act to enhance it. When their effort misfires, the magnitude of the failure—and the cringe—depends on the degree of their social miscalibration. A specific blind spot, like overestimating your singing or athletic ability, can be written off as a quirk. “We’re all deluded about something”.

But when the failure reveals a broader lack of social intelligence or broken social radar—cringe is maximized. This happens when someone’s high-effort attempt to shine, meant to showcase their best self, has the opposite effect, exposing their obliviousness to how they’re perceived.

I sometimes experience flashbacks to things I’ve done in the past that I now recognize as “cringe” and the feeling is so overpowering that I have to will myself to stop. But qualitatively, the feeling is no different from cringing at someone else’s behavior. I wonder if the purpose of cringe then is to send a strong negative signal to oneself to avoid own-goals that result in a loss of social status.

With that primer on cringe out of the way, stay tuned for part 2 in which I’ll discuss incentives and signaling on LinkedIn, a platform which seems engineered to amplify almost every dimension of cringe.

They’re not working 20 hour days and contemplating self-harm, so the answer is not - “the hourly pay is low, stupid”. Many of them don’t know very much about markets when they are hired, but from the people I know who work there, they don’t seem to be pulling uniformly from across the IQ distribution.

So it must be that these firms have figured out how to turn raw intelligence into profits. It makes intuitive sense that if there is a type of job that could do this, it’d be some form of trading.

Ask why and you might hear the word “leverage”. This is begging the question. Why do traders have so much (access to) leverage, literally and figuratively? After all, all business is about buying low and selling high. What’s so special about financial markets?

Financial Markets as a Special Case of Markets

In essence, trading is giving up something that matters to you less for something that matters to you more. A farmer handing two sacks of wheat to a blacksmith for a sharpened ploughshare fits the definition just as a trader buying stocks for cash.

Barter collapses the first time you try to trade outside your circle: you must locate someone who simultaneously wants your wheat and owns the ploughshare you need. Money breaks the "coincidence of wants" bottleneck.

Most properties of money (fungibility, divisibility etc) are downstream of its ability to project multi-dimensional, fuzzy sources of value (of goods, services etc) onto one quantifiable dimension. Once value lives on one axis, we can better order preferences and make them legible to others.

However, division of labor creates a scaling problem: as specialization increases, you must trade with more counterparties who are increasingly distant and unknown to you. Money solves the 'how to pay' problem but not the 'what exactly am I getting' problem. When you can't inspect goods personally or rely on shared reputation networks, you need the goods themselves to be standardized enough that their key attributes can be specified and verified remotely.

The standardization of goods—grading coffee as "Arabica Grade 2" with specified moisture content, defect tolerance, and bean size—is itself a step toward financialization. It makes the non-money side of the transaction more like money by removing the most important sources of variance, creating fungibility (one supplier's Grade 2 beans become interchangeable with another's), and legibility to strangers.

More importantly, standardization reveals that the goods and services we want are not identical to our preferences, but a means to satisfy our preferences. The company purchasing coffee beans doesn’t necessarily want any particular bag (or carton or whatever these things come in) of coffee beans but coffee beans that meet properties x, y and z, because coffee is just a means to an end of selling to their customers - which is just a means to making money.

Similarly, the company cares not only that it receives beans above some quality threshold, but also that it can buy those beans below a particular cost to maintain profitability. The company is actually in the market for coffee beans above quality X, below price Y, delivered within timeframe Z. Unless the seller has the exact inverse preference set (wanting to sell precisely that quality, at precisely that price, at precisely that timing), we're back to the ‘coincidence of wants problem’. It’s easier to find a seller who wants to sell you the beans than a seller who will take the other side of each one of these preferences.

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The way financial markets solve this coincidence of wants problem is similar to how money solved the problem. Money represents an IOU, an asset that you can trade for pretty much anything else of equal value. Similarly, financial products also represent claims, but ones that are more specific and contingent, tailored to meet a commonly occurring preference.

These abstract preferences have an interesting relationship with scale. As you increase volume and market participants, you increase the likelihood that any specific preference finds someone who will trade the other side. Scale enables preference satisfaction. But new preferences also emerge from scale, by increasing the sensitivity of economic actors to contingencies.

This creates a reinforcing cycle: scale demands abstractions to handle complex preferences that physical goods can't accommodate, while abstractions enable even greater scale by eliminating transaction friction and enabling trustless trading between strangers. Financial markets represent the endpoint of this process—where abstractions eliminate almost all transaction costs, allowing markets to scale to whatever size the underlying preferences demand.

Crucially, these abstractions make risk quantifiable, which enables leverage. When uncertainty can be measured statistically rather than assessed case-by-case, lenders can price risk systematically and extend credit instantly. Apple stock represents a standardized claim with public information and price history that narrows uncertainty. Your corner bakery is a unique bundle of location, management, customer base, and operational risks that no algorithm can quickly assess.

When markets are maximally frictionless and abstract, value creation can only manifest itself in price. In other businesses, you create value through multiple channels (better products, customer service, operations, etc.) and then try to capture some of that value as profit. Your goal is to build something valuable, and profit is how you measure success. But financial markets strip away everything except price accuracy. Here, both the objective function and the means to getting there converge to profit maximization - there is no intermediate step where you optimize for customer satisfaction or product quality. Hence, the misplaced intuition accusing market participants of "just making money, not creating anything of value". Getting price right is value created.

Getting the price right is also a purely cognitive process. There's no separate implementation phase because the ‘market’ is designed to execute your insight instantly. When economic value can be created by and tracked back to better thinking, the compensation naturally reflects that unique relationship.

[Culture, Vibes, Charisma, Chemistry, Presence, Flow]

One answer is that they are all linguistic equivalents of "residual error" - the unexplained variance that remains after accounting for all the measured variables.

You build your best possible model using every factor you can identify and quantify. You run the regression. But your predictions still don't perfectly match reality - there's always some unexplained variance left over. That's the residual.

These words like "vibes" and "chemistry" are pointing to that same phenomenon in human experience. It's not that we're holding measurable factors constant to isolate the "vibe" effect. Rather, we've considered all the legible factors we can think of (salary, location, job responsibilities, etc.) and there's still something additional that we can sense or feel is decision relevant - something we can only gesture toward with words like "culture" or "fit."

When our conceptual understanding of something is near perfect, "residual" may well be a synonym for noise. But in a whole host of domains, the residual error is in fact a reminder of how little we know conceptually. But other times, it's a reminder that we can know more than we can quickly define, and certainly more than we can reasonably measure.

This general realization has changed my attitude towards topics I used to dismiss. For most of my life, I'd eye-roll whenever someone brought up "culture" or other concepts I associated with management guru platitudes. Now I'm convinced that almost everything interesting falls into this category of illegible-but-consequential phenomena.

Which is why I'm hosting a happy hour next week on unpacking “Organizational Culture”

Join Us at the Winslow near Union Square on Aug 28 (Thursday).

What is Organizational Culture?

Whether for organizations or countries, culture somewhat encapsulates the feeling of “the way we do things around here”. There’s a lot tangled up in that but it seems important.

(Some of you may not like this) but one way to make progress on this problem is to do what Jordan Peterson does in arguably the funniest videos of all time and try to get specific about each word in that sentence. What do you mean by “the”, “way”, “we”, “do”, “things”, “around”, “here”.

In this context, “we” refers to an organization, a for-profit company, to be specific. The raison d'êtreis for any such organization is maximizing shareholder return by allocating capital in areas where “it” is to earn the highest expected return. What “it” is best suited to do at least partially depends on the actual people who are part of the organization. This “it” is not a static set of specific people, but a group that’s always in flux, based on where the organization is currently positioned and where it intends to go.

We know that the market, the org structure, financial performance, and human capital of that organization can all impact and be impacted by what we intuit as “culture”. Yet, we perceive culture as capturing something additional even if not independent from these other concrete concepts.

To allocate capital where it is best positioned to do so, organizations need to understand both the market and itself, and then make a series of decisions, including decisions about who gets to make what decisions and who gets in and out of the company.

Any one decision or function can fall into the bucket of “strategy”, “hiring” or something else, but how it coordinates across people, functions and actions to move forward - that touches almost everything - seems like the core of what we intuit as “culture”.

I think this comes down to four key questions:

(1) Who are we, why are we doing “this", and what is “this” (Identity/Meta-Cognition)?

(2) How should information flow within the organization? (Communication Norms and Information Flow)

(3) How should decisions be made, by whom, and on what basis? (Decision Making Processes and Norms)

(4) What gets rewarded and punished, and why?

Even if you break each of these down to more granular factors (which LLMs are great at doing), it won't eliminate the 'residual error' quality of culture - you can still walk into an office and immediately sense something is off without being able to articulate why. One reason, beyond the problems of measurability, is the interdependence between these dimensions. How decisions get made shapes what information flows where, which affects who gets status, which reinforces the organization's sense of identity. The 'vibe' you're sensing is often emerging from these complex web of interactions, and thus hard to pin down.

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Observations about Stated and Actual Corporate Culture

Culture should but does not often capture tradeoffs. If your cultural description is something everyone would agree with - "we make decisions responsibly" or "we value both innovation and execution" - you're not describing culture, you're stating platitudes. Real culture lives in the tensions: for example, how willing are we to risk discomfort or hurt feelings in the pursuit of truth? This is why firms like Netflix and Bridgewater stand out. By biting bullets and broadcasting their culture, they aid self-selection in and out of the organization.

Most cultural dimensions are emergent properties - You can't just decree that your organization will now be more truth-seeking or meritocratic. But there are specific leverage points where leadership has direct control, mainly: compensation, promotions, hiring, and firing. When you promote someone, you're broadcasting to the entire organization what actually gets rewarded here, regardless of what the handbook says. People reverse-engineer the real rules from status hierarchies and the accordance of status.

Hypocrisy is common. Organizations routinely claim to value one thing while systematically rewarding another. They say they want candid feedback but promote the diplomatic consensus-builders. This isn't usually deception; it's that the stated values represent aspirations while the actual culture reflect the accumulated weight of thousands of small decisions and inherited patterns.

In 'The Nature of the Firm', Coase conceptualizes firms as arising when transaction costs make it more efficient to coordinate activities internally rather than through market mechanisms. Instead of negotiating contracts for every task, companies can simply assign work through hierarchies. And is there a better word to evoke memories of the Soviet Union than "hierarchies"? Your friend at Google who averages two and half working hours a day only reifies this belief. After all, why wouldn't these pockets of planning churn out classics like "process over outcomes" or endless bureaucratic drag?

Now, Coase is technically correct, but there is something unhelpful or even counterproductive about the framing. It's like when people say 'evolution does X or Y'—evolution doesn't do anything, it's the mechanism we deduced from observing which species survived and which went extinct. The Coasian view has a similar problem: it makes firms sound like conscious choices to replace markets with central planning (which we instinctively associate with “bad incentives” or “bad epistemics”)

However, the visible and alive firm was better at delivering value more efficiently than competing firms. Firm survival in competitive markets is evidence of superior price discovery - the ability to figure out which resource allocations create the most value relative to what competitors are doing.

Think about a simple software business. You create some software in your free time and sell it. For you to make money, you have to believe you have some edge - you're solving a problem in a way the market isn't currently doing, or you're reaching customers others aren't reaching.

As you're proven right and start making money, by definition, the market gives you more resources to allocate. Now you want to scale. You face a choice: should you build your own sales team or use external channel partners to sell your software? You believe you are an incredible salesperson and you want control over brand and reputation, and you can really succeed at doing that. So you decide to spend your resources doing that, even though you could do it cheaper "using the market." If you are successful and grow to be ten times the size in five years, the market has validated not only your business but also your decision making and ability to understand your own edge. We can’t attribute this successs neatly to any one decision but it’s evidence that the things that mattered - you got right.

The larger a firm gets, the more the market is not just validating its products, but its decision-making heuristics and ability to think about its own abilities - its meta-cognition. Your firm isn't centrally planned - it's the market continuously validating your capacity for self-awareness about where you can create value.

The Recursive Problem

Entrepreneurship typically starts with an intuitive conviction about an opportunity. Most startup "ideas" are bundles of interconnected beliefs—beliefs about the problem, customer behavior, incentives, and execution. But these beliefs don't come pre-organized in a testable format. The entrepreneur's challenge is figuring which of these beliefs to test, in what order and how, given resource contraints.

This creates a recursive optimization problem. You need to make decisions about how to make decisions. Should you research extensively before testing or learn through rapid iteration? Should you test beliefs individually or as integrated systems? How much analysis is worth doing before you just start building?

The recursion emerges because optimizing your decision-making process is itself a resource allocation problem that requires systematic thinking..

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Four Level Framework

Level 1: Belief Segmentation - How do you break down your idea into coherent, testable pieces? The segmentation itself determines what you can learn.

Level 2: Belief Prioritization - Which pieces matter most? This depends on dependencies, impact if wrong, and your current confidence levels.

Level 3: Testing Architecture - Do you test pieces individually or as an integrated system?

Level 4: Information Gathering vs. Acting - For each test, do you research first or just execute?

Dimensionalization

Now you can dimensionalize each of these further to understand what's right for your business. I recommend reading (and using in your LLM conversations) this piece by my friend . The idea is to dimensionalize with respect to the purpose of that level.

For each level, you're solving a different problem, so the dimensions that matter change:

Level 1: Belief Segmentation - You're trying to create testable, coherent units. The dimensions that matter are those that help you carve up your idea space effectively: testability (can this be isolated?), specificity (is this actionable?), and causal independence (does this interact with other beliefs?).

Level 2: Belief Prioritization - You're trying to allocate limited resources to highest-impact beliefs. Now the dimensions that matter are about impact and risk: foundational impact (how many decisions depend on this?), confidence gap (how uncertain are you?), and irreversibility (how expensive is being wrong?).

Level 3: Testing Architecture - You're trying to design efficient learning processes. The relevant dimensions shift to system properties: decomposability (can beliefs be tested separately?), resource constraints (what's your bottleneck?), and signal clarity (do you need clean attribution?).

Level 4: Information Gathering vs. Acting - You're trying to choose the optimal learning method. The dimensions become about information quality and timing: information asymmetry (research vs. action learning), competitive timing (does delay hurt?), and failure costs (can you afford to be wrong?).

As Jordan notes, good dimensionalization is about finding the axes that map to reality (fidelity), that you can actually influence (leverage), and that don't overwhelm your cognitive capacity (complexity). Each level of the decision hierarchy requires different dimensional priorities based on what you're optimizing for at that stage.

Come on, Is This Really How People Make Business Decisions?

Isn't the solution just using intuition? Yes, and you should start there—but you need to know where to apply your intuition and where to trust it, and check where it fits in this complete model.

The issue is that we increasingly combine actual intuition with legible heuristics, and these heuristics mostly come from places like Y Combinator or other sources that mean well but cannot possibly specify all the relevant assumptions and caveats that underpin their advice.

Take the common advice to "iterate fast and test one assumption at a time." This heuristic makes sense when you've segmented your beliefs into decomposable pieces, when you have low dependencies that favor sequential testing, and when high signal clarity on one piece means more than a noisy signal on the whole system. But it doesn’t work when your beliefs are highly interdependent. For example, supply and demand on a marketplace are not independent. Sometimes you need to test the whole system to understand any part of it. The framework helps you identify which features of your situation make standard advice more or less applicable, rather than following heuristics blindly simply because they're legible and widely repeated.

But you might say - the recursive optimization problem is genuinely complex, and most people don't have the bandwidth to systematically dimensionalize their belief systems while also trying to build a company. I would have been more inclined to agree…before LLMs.

Before LLMs, the cost of applying rigorous analytical frameworks to business decisions was astronomical. You'd need either exceptional personal discipline to hire expensive consultants. Most founders rightfully chose to rely on intuition and standard heuristics, because the alternative was simply too costly.

LLMs collapse the cost of systematic reasoning. You can now literally have a conversation with a system that can help you dimensionalize your belief system, identify dependencies between your assumptions, and audit whether standard startup advice applies to your specific situation. The cognitive overhead that made this framework theoretical for most people has been dramatically reduced.

When you're young and not in touch with yourself, you're less likely to invest excess money into a fulfilling life. I spent money on things that, in hindsight, weren't very fulfilling—mostly because mimesis dictated how I used discretionary income rather than any positive vision of how money could improve my life.

But as you learn what makes you happy - perhaps spending significant time outdoors, writing, or building things - money becomes increasingly powerful as a tool for enabling those activities. , a substacker I’ve been late to catch up on, sums it up in her post about money:

Money is liquid economic influence. One can do many amazing things with money, and the more creative and high-agency one is, the more one can do with it. More money means more ambitious projects. Money can fuel companies, political advocacy, art, and research. Money can be spent to perfect the furniture of one’s life in widening circles. Why wouldn’t a builder want lots of money? Money can be used, generally, to build, and more specifically, to build your own life, along many dimensions of that life.

This connects to another misplaced intuition. Right out of college, I optimized for money with the maturity you'd expect from a 21-year-old. I took a job everyone said would be mind-numbingly boring and a terrible temperamental fit because it paid 50% more than my peers made. I spent this money on things that made me miserable, then concluded money wasn't that important.

I now recognize this as a basic error of cognition. It’s my poor judgement, not the usefulness of money that should be apportioned all the blame. Even if you do know how to use money well, uses for money expand as you get older. What's "enough" for a single 25-year-old isn't sufficient to scale that lifestyle to support a family in your city of choice.

Moreover, being the top earner in your peer group in your 20s is, in absolute terms, insignificant. The difference between $110k and $80k might mean a bigger TV. The difference between millions in disposable income and a typical professional salary is the ability to completely redesign your life. The hedonic treadmill is no match for high agency. I know wealthy people who used excess money to construct fundamentally lower-stress, higher-joy lives. They spend time on meaningful projects rather than projects that pay bills. The utility jump from "comfortable" to "wealthy" is probably enormous. If you see my writing a post every day and spending more time on Substack, you won’t have to wonder why.

What does all this point to?

1. Money is good. It’s utility doesn’t diminish nearly as fast as pop psychology will have you believe. And it probably gets better with age. It’s worth thinking really hard before making career decisions that will foreclose on the possibility of higher incomes or picking career paths that have a hard ceiling.

2.Compounding is real. It’s spending that needs justification, not saving.

3. But money is not the only thing that compounds. Any early career delta in annual salary and thus excess savings is worth contextualizing. An extra $20,000 in annual savings compounded at 5% for 20 years, is an extra $50,000. Good but not life-changing. If the cost of that extra 20,000 was sticking to a career path despite poor fit, it’s very unlikely to be worth the trade, certainly in utility terms, but most likely in monetary terms too. More on that in my next post….

This is a chance to learn from each other and show off how you use LLMs to enhance productivity, well-being, or anything else you care about.

Attendees can come up and do a quick 5-minute demo of their preferred use case. If you'd like to present, please submit ideas here.

For those of you who want to debrief and mingle, We'll be heading to get a round of drinks at Radegast after the presentations.

Date: 26 June (Thursday)

Time: 6:30 pm

Location: Fractal Tech, 111 Conselyea Street, Brooklyn, NY

Please RSVP here.

Best,

Vaish

- The trade-offs between working at startups and big tech

- Why people problems are often process problems?

- The value of writing and thinking in public


I intend to do more of these so if you have suggestions for folks you’d like to hear me chat with, you can comment or email me at vaishnav@cloutcareers.com

"Hey, could you pass along X's number? You must have it since she was on your show."

My friend felt an immediate wave of irritation, though he couldn't quite articulate why. Yes, he technically had the contact. Yes, he could probably pass it along. But something about the way she'd asked for this favor struck him (and me) as quite off-putting.

I want to unpack the “Why?” by discussing social capital - specifically, how we accumulate, spend, and sometimes inadvertently destroy this invisible currency that governs our relationships.

What is Social Capital?

I like the definition proposed by Robinson, Schmid and Siles in this paper, in which the authors argue that social capital does indeed behave like capital. They define social capital as follows:

Social capital is a person's or group's sympathy toward another person or group that may produce a potential benefit, advantage, and preferential treatment for another person or group of persons beyond that expected in an exchange relationship.

In this paper, the researchers defend this concept against critics who argue that social capital doesn’t behave like capital, by listing the properties of capital shared by social capital:

Transformation capacity: Just as physical capital (like a factory) transforms inputs into outputs, social capital transforms sympathy into various services—economic benefits, social support, validation, and information sharing.

Durability: Like physical capital that retains its identity after use (a tractor remains a tractor), social capital—especially family bonds—can remain intact even after multiple uses.

Flexibility: Similar to how some machines can serve multiple purposes, social capital can be used for various benefits—from emotional support to economic assistance.

Substitutability: Just as tractors can be replaced by animals for pulling, social capital can substitute for other forms of capital. For instance, sympathy can substitute for monitoring costs in business relationships.

Decay: Like physical capital that deteriorates without maintenance, social capital can decay through lack of contact or overuse.

Think of social capital as a store of felt obligation that can be leveraged in different ways. Someone might help you because of emotional closeness, moral obligation, transactional reciprocity, or community norms. What matters is that your interests influence their decisions, even if mildly—you're included in their calculus.

Your total social capital can be roughly understood as a product of the breadth and depth of your social bonds.

Theory of Self-Interest and Relationships

Understanding social capital requires examining the underlying mechanics of all relationships: human interactions can be explained in terms of rational self-interest, though at varying timescales and with different levels of flexibility.

The relationships we call "transactional" are usually those that impose explicit and inflexible conditions. For example, if you pay for a cup of coffee, you expect a cup of coffee within the next few minutes. It's not acceptable for the barista to pay you back by being there for you when your girlfriend dumps you, nor is it okay for them to give you the coffee three months from now.

By contrast, most romantic relationships don't impose the same restrictions on the exchange of benefits. If your wife is going through a hard time at work, it's usually not wise to offer a shoulder to cry on and then immediately ask her to run errands for you as payback. Yet you still have expectations—you might want her to listen to your workplace drama later, or provide emotional support in other ways.

In long-term relationships and lasting friendships, the terms of exchange aren't set in stone. But people still understand they can't shirk providing value to the other party forever. It doesn't have to be today, and you have flexibility in how you contribute, but value must accrue.

This flexibility is valuable because it dramatically increases the potential for positive-sum exchanges. Good friends can do things that cost them little but benefit their friends hugely. We can get help during difficult times without worrying about immediate repayment. Close business associates can work together without contractually covering every possible disagreement, trusting they'll resolve issues in good faith. The mechanism that facilitates this flexibility is trust.

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How is trust built?

Trust necessitates some level of predictability and reduction in information asymmetry. This doesn't mean predictability in terms of how someone will return a favor, which would in fact be something that you need less of in a trusting relationship. But one must possess some knowledge of the other person's boundaries, values, dispositions, and preferences.

One piece of information that's particularly useful to gather about someone concerns their incentives. Alignment of incentives can build trust, but trust also makes the explicit alignment of incentives less necessary.

For example, you hire someone to work for you and decide to give them equity-based compensation to align incentives. All else equal, this enables you to delegate more work to them without having to forecast or monitor excessively. Now, after working with them for a couple of years, you realize they're the type of person who tries to operate as ethically as possible, even under duress and even when they have the ability to cheat or free ride. Now, you can afford to do projects with them where incentives aren't or can't be as tightly aligned, thereby increasing the set of possible interactions.

Over time, you start to develop a friendship with them and care about them. This isn't consciously mediated through some benefit they can provide, but because it just feels good to be good to people you like and care about. You do right by them because it feels good to you, just as they do right by you (and others) because they consider themselves to be a deeply ethical person, and it is inherently rewarding for them to be ethical.

This evolution illustrates how relationships can operate at different levels depending on the degree of trust and internalized care:

Level 1: I will do X for you because you have agreed to do Y for me (standard consulting contract)

Level 2: I will be generally nice and helpful to you because I expect you to be generally nice and helpful to me—whether because we both want the same things, because I know you know that I can hurt you if you hurt me, or because I know you to be an ethical person

Level 3: I will be generally helpful to them because it makes me feel good to be generally helpful to them (this could be because I like you, or because we share values such that it's intrinsically good for me to help you)

This progression leads us to what might be the least romantic but most beautiful definition of love: the significant internalization of an external party's utility function.

(Speaking of relationship models, my wife, explores this topic in her piece "Are all relationships transactional?" which I highly recommend reading)

Acquiring Social Capital

If you don't have family, ideological, or institutional bonds to grease the wheels, spending time with someone is the most straightforward way of building trust. The more time you spend with someone, the more confident you can be that they can't exercise full conscious control over the signals they're sending you. In other words, you can trust the information you're gathering about them.

This process is made easier by what people call "personality" or "charisma." If spending time with you is generally pleasant or has a positive payoff, this trust-building process becomes a benefit, not a cost, to the other party.

I won't try to break down charisma completely, but it's worth thinking about both how to avoid being unpleasant to others and how to actively provide positive value—whether through listening skills, humor, knowledge, or other contributions.

I've seen people make the mistake of trying to talk about the future value they can provide, sometimes in the form of what is known as "bragging." Remember that some signals are easy and cheap to fake, like saying you're a great negotiator or that you have charisma. If you make these claims without providing reliable evidence that can't be faked, people will mostly take away that you're keen to show them how valuable you could be to them—which is ironically counterproductive.

Some people—inspired by Ben Franklin—talk about how asking people to do favors for you can counter-intuitively be a way of building trust. However, there are two important pre-conditions: the favor probably can't be too burdensome and should preferably be flattering to them. More importantly, the favor needs to be acknowledged, setting up an expectation that benefits can accrue to them in the future.

Acknowledging reciprocity

There's something slightly paradoxical in most human relationships: people generally don't like making explicit the underlying transactional structure of relationships, but this structure is revealed when people fail to appreciate the principle of reciprocity that relationships are built on.

The extent to which Person A can reasonably value and invest in a relationship cannot be divorced from the extent to which Person B values and is willing to invest in the relationship. When one party acts in a way that signals they're in a one-shot or short-term game rather than a repeated game, it can shake the foundations of a relationship.

It's worth thinking through how you would behave if you were truly using someone as a means to an end for some immediate near-term benefit and nothing else. If someone was of no value to you apart from the fact that you want them to introduce you to someone else, you would just make the ask—no matter what it costs them, and regardless of how much trust had already been established.

This gets us to solving the puzzle that opened this post. If a close friend or family member was dealing with financial issues or needed significant help due to health problems, most of us would offer our help or at least want to help when asked. We do this because we care about their welfare and because the relationship has enough built-up trust and reciprocity to handle such requests.

But if you ask an acquaintance for a significant favor—something that does cost them something—it's crucial to acknowledge that they're doing a favor. You might say something like "I know this is asking a lot" or "I really owe you one for this." If you don't do this, the signal you send is that whatever you need from them matters more than preserving the quality of your relationship with them. The relationship that makes the favor possible is revealed to be secondary to the favor itself.

This is exactly why my friend was right to feel annoyed. The person asking for the phone number didn't even demonstrate that they cared about what this request might cost him—the awkwardness of approaching a professional contact with a personal ask, the potential strain on his reputation, or the fact that he'd be spending relationship capital with the celebrity on someone he barely knew. Her casual "You must have her number since she was on your show" treated their acquaintanceship as purely instrumental, signaling that their barely-maintained social bond existed solely for her benefit. She had, in essence, revealed that she thought of him not as a person whose welfare mattered, but as a means to an end.

Conclusion

Understanding social capital serves a dual purpose: it helps us avoid social faux pas while reminding us not to become calculating relationship accountants. The awkward phone number request reveals why awareness matters—recognizing that relationships have underlying structures of give and take helps us navigate them with grace. But this understanding should inform our approach, not dominate our thinking.

The healthiest relationships exist when we have a default of cooperation and kindness, combined with an intuitive understanding of reciprocity. We extend help and care freely, while still retaining enough social awareness to recognize when someone views the relationship differently than we do. This balance prevents both the kind of blindness that leads to exploitation and the transactional cynicism that kills genuine connection.

For those of you in the NYC area, I'm organizing another evening of drinks and discussion on May 5 at Fractal Tech in Williamsburg.

This time, I'm excited to have Jordan Rubin lead a conversation on Forecasting. Jordan’s background is in quantitative finance - he was previously Senior VP at Two Sigma. For this discussion, he’ll be drawing from his experience advising the online forecasting platform, Metaculus. Jordan will frame the discussion around answering questions like: Who should use forecasts? What makes forecasts more or less valuable? How should one consumer forecasts?

The evening will include a brief welcome, the forecasting discussion, and unstructured time to mingle. Beers and snacks provided. Feel free to bring additional beverages to share.

Click Here to Register

Event Details: May 5 (Monday)
6:30 - 9:00 PM
Fractal Tech (111 Conselyea St, Brooklyn)

Looking forward to seeing you guys there !

Best,
Vaish

The instinctive reaction to this might be: 'Who cares? Recruiters get paid too much anyway.' But this is precisely the point. In the current system, recruiters charge high fees precisely because they're internalizing costs that should ideally be borne by the company—the costs of uncertain outcomes and unclear requirements.

The Optimal Role Division in Recruiting

This is roughly my map of the recruiting process:

Hiring manager: Responsible for formulating and outlining what the organization is looking for and communicating those requirements to recruiters.

Recruiter: Responsible for comprehending the requirements (at some higher level of abstraction), tapping the most likely sources where these requirements are met, and performing basic filtering to qualify candidates.

Hiring manager/organization: Responsible for evaluating candidates at each stage:

• Do we have enough information/confidence to hire candidate X?

• If not, is candidate X worth investing more time to evaluate?

This evaluation is not something the recruiter has much control or visibility on, not to mention expertise. Yet, the recruiter's payoff in any one case is largely dependent on the company's decision-making. In the current paradigm, recruiters are paid for accepting this risk. But this is not a risk they have any expertise in pricing and accepting.

Moreover, employees are sometimes structurally incentivized to minimize their personal risk rather than maximize company outcomes. This doesn't show up in financial statements, but manifests as hours lost interviewing too many candidates, indecisiveness, and then overpaying when desperate to make a hire.

A New Paradigm: Pay to Evaluate

What would an alternative model look like?

Recruiters shouldn't be paid for bringing lousy or even mediocre candidates to companies. Recruiters should be paid for leads that the company is willing to pay for.

What we want to offer clients at Clout (in addition to the traditional model) is a "pay to evaluate" model:

1. Companies/hiring managers get a 30-minute screening call with any candidate we surface to you.

2. If you like what you see, you pay to evaluate them further. If you only want to progress 1 candidate from the 10 we brought you, you only pay for 1.

Here's a simple model that illustrates the savings at our starting rate - 0.50% of base salary per candidate assessed.

For a $150K role, traditional recruiting costs $30,000 per hire. If you believe you need to thoroughly assess and interview (after a quick screening call) 10 candidates to hire 1 candidate (in expectation), the pay to evaluate model, as we’ve currently priced it, will cost you $7,500. That’s a 75% reduction in recruiting costs.

To match the cost of traditional recruiting, you would need to assess 40 candidates after initial screening. If you find yourself approaching this number without making a hire, it's worth reconsidering your process. At that point, the opportunity cost in management time far exceeds what you're paying us, and signals a fundamental mismatch between your needs and market reality. Perhaps your compensation package is below market, or your requirements exceed what's realistically available. Either way, this becomes valuable information for refining your approach.

The Spectrum of Pricing Models

While I've presented the pure pay-to-evaluate model, it's worth noting that this exists on one end of a spectrum. The traditional 20% contingency fee sits at the opposite end. Between these poles, there's room for hybrid approaches that distribute costs and incentives differently.

For companies concerned about potential gaming of the screening process, a tiered model might make sense. In this approach, companies would:

1. Pay a small fee after an initial screening call (say 0.5% of salary)

2. Pay a slightly higher fee for candidates who progress to later rounds (perhaps 1%)

3. Pay a modest success fee upon hiring (5-7% instead of 20%)

4. Add a retention bonus if the candidate remains with the company for a year (1-2%)

This graduated structure maintains most of the cost advantages of the pure pay-to-evaluate model—perhaps reducing costs by 50-60% rather than 75%—while providing additional safeguards against potential misalignment. It creates multiple checkpoints where the interests of all parties can realign.

Incentives Under the “Pay-to-Evaluate” Paradigm

Incentives are no more aligned for the recruiter in the traditional model, even if it appears that way. The fact that the recruiter gets paid on hiring doesn't make them any more likely to give you good candidates than if they got paid upon you choosing to evaluate those candidates after a screen. If you think otherwise, you'd be implying that recruiters have an ability to predict and sell you candidates that can fool you in a screening call but not in further stages of assessments. This is incompatible with how insightful most people think recruiters are.

In fact, if the model is one where the recruiters face high probabilities of zero payoff and low probabilities of very high payoffs, there is an incentive to put much less effort into qualifying each candidate and instead focus on finding more candidates, and this is precisely what you see.

Again, this works for everyone but people who foot the bill - founders. Recruiters will externalize the cost of finding too many, crude candidates onto your employees in terms of time and opportunity costs. Your employees will externalize the cost of their time to you because that's how salaries work.

Addressing Skepticism

I expect skepticism to be the typical first response. People come up with all sorts of reasons why this is bad but most of it is premised on assuming they will be incompetent in their recruiting process and thus end up overpaying us. "But what if I spend $5K and hire no one?" That $5K is a signal that you need to re-evaluate the specs, comp etc or that you weren’t screening hard enough at the free screening call stage. Conversely, if you're good at recruiting, you might spend just $3K instead of $30K, making this model even more advantageous for companies with strategic hiring practices.

The misunderstanding comes from interpreting an increase in price as a negative signal for investing. After all, the asset you wanted to buy just got more expensive, reducing your expected returns. So it seems odd that VCs update positively not negatively on increased demand (which is equivalent to a price increase).

In public markets, a price change is neither negative nor positive (a la Scott Sumner - Never Reason from a Price Change). The new price just reflects the most updated supply-demand equilibrium. In more liquid markets, the price change itself shouldn't be a catalyst for action, unless you have a very good reason to believe that this particular change reflects some mispricing/irrationality on behalf of other investors. In fact, if you're a market maker who doesn't necessarily follow any particular security, an increase in price, all else equal, would make you adjust your bid/ask upwards.

However, in VC markets:

(1) The amount of relative signal to be found in the the price change (via actions of other market participants), is much higher since there is not much else to go by (relatively speaking) and the market participants are all institutional and visible to you.

(2) Unlike market makers, VCs aren't making a two sided market so they can't totally ignore the price increase but given the importance of avoiding false negatives in VC, the downward update on price is negligible. In other words, it makes sense to be price insensitive on any given deal.

(3) The company's odds of success are it self substantially improved by having multiple backers - since more capital gives a company longevity via more shots on goal etc.

Now, one could question how rational it is to update this way from a mimetic process that might be on much shakier epistemic grounds than it appears. If literally everyone reasons this way, you'd be acting on a worthless signal. It is also true that there are perverse incentives to fail with the herd, as being wrong alongside everyone else is less damaging institutionally than being wrong alone.

That said, perhaps the meta game in VC is having a pulse on the levels of herdiness in the market to figure out when it makes sense to act more or less 'herdy' yourself?

Take my confusion about diversification for example which was really a gnawing sense that I didn’t really understand why people cared about risk-adjusted return/sharpe ratios:

"Diversification is good."

"Why?"

"Because it reduces portfolio volatility."

"But won't it also reduce expected return? After all, you're not putting everything into your single highest-return idea."

"Yes, diversification might lower your expected return somewhat, but typically it reduces volatility enough to compensate—meaning your risk-adjusted returns (like your Sharpe ratio) are actually higher."

"But why should we even care about volatility? If I'm investing for the long run, shouldn't I just pick whatever maximizes my expected return, regardless of fluctuations along the way?"

The answer:

Diversification works because it reduces the likelihood of experiencing severe, simultaneous drawdowns across your portfolio. If your investments move up and down together, you concentrate risk: the 'bad scenarios' in one asset become the bad scenarios in every asset. While this symmetry of large upsides and downsides doesn't necessarily affect your expected return (the average outcome across all possible scenarios), it does create scenarios with extreme outcomes—both good and bad.

The theoretical 'long-term'—in which volatility averages out—is largely an abstraction. In reality, no one truly invests in an infinitely long-term scenario; we invest with tangible life constraints and finite time horizons. Obligations like mortgage payments or living expenses don't disappear just because markets are down. Consequently, large simultaneous losses carry disproportionately severe real-world consequences, significantly increasing your risk of financial distress or ruin.

Meta-lesson

It could have been that I was too dense to have a clean picture in my head or that the lecturer was doing a bad job. But take a look at Investopedia's comprehensive piece on diversification. They cover correlation coefficients, standard deviations, and portfolio weightings in extensive detail. Yet, surprisingly, nowhere in this thorough explanation did they tie this back to why this matters to investors.

Needless to say, the people who wrote this probably know this deep in their bones, much more than I do. It’s likely because people who write finance books and teach seminars think it's too obvious to state. It's a breakdown in theory of mind. The second reason is that I was too lazy to think about it for more than a second, since I had no incentive to. If I could pass the CFA examination and get a job at JP Morgan without understanding diversification properly, that should worry everyone a little bit.

This phenomenon is everywhere, not just in finance. (in fact, finance has much better epistemic norms than other industries) In business environments, surface-level understanding often goes unchallenged - partly because that’s all you need to do your job nine out of ten times. Moreover, it selects for generalists and pragmatists that are in a rush to get things done, so it’s unlikely that groups will be particularly good about policing each other on this dimension. In fact, if you don’t understand something well, the preferred instinct is to be vague and high level, not press your colleagues on mechanics, lest you reveal your ignorance. Again, in most cases, someone with superficial knowledge navigates just fine through meetings and decisions. But then the status quo breaks down in some interesting way — and everyone who’s not a nerd is caught with their pants down.

Spending time around academics and the process of writing have been antidotes to this for me - helping rewire my brain after spending my early career around people who just didn’t care that much about what was true. (In fact, as if it to prove my point, caught an error in my initial version of this post and pointed out that I’m confusing expected returns — which is a geometric mean — for an arithmetic average of annual returns. Damning for my my prospects of truly grasping diversification but proves my meta point about being forced to write and unpack your conceptual model). This is why I'm a fan of the written memo culture at Amazon . You can conceal a lot with decks and presentations, but in my company, I want to reserve the right to be annoying and ask as many “Why’s” as I’d like and force simple explanations in english, not formulae or slides.

If you’re hiring someone who will exercise significant agency and have significant leverage within your organization, it’s worth probing the foundations of their model of what it is that their job is trying to optimize and why - because it’s pretty easy for smart people to reference common knowledge and talk about trends and market dynamics with lucidity, while still missing the core intuition from which everything flows. More importantly, whether they are right or wrong, you’ll know if your inquiry is met with intellectual curiosity and course-correction or defensive evasion and doubling down on vague generalities.

For example, if I were an early stage VC fund hiring an associate, I’d consider this for an interview question:

Interviewer: “You know what power laws in VC are?"

Candidate: "Yes, they're very important - one massive winner can make up for all the losers, so really, focus on the winners."

Interviewer: “Okay, but since everyone is doing this model, why shouldn’t we try a different model - one where we pick ten moderate winners instead of one big winner?”

The answer is stupidly simple if you understand the fundamental intuition behind VC investing and impossible to answer if you don’t. In fact, even LLMs will give you only the superficial answer until you press them.

Legibility exists on both sides of the market. There's supply-side legibility (how clearly defined and understandable someone's skills and abilities are) and demand-side legibility (how well-specified and comprehensible the job requirements are). While legibility can facilitate liquidity by reducing transaction costs, it's neither necessary nor sufficient for high liquidity. A skill or role could be perfectly legible but highly illiquid (like specialized academic research positions), or relatively illegible but liquid (like early employees at successful startups whose skills are hard to define but who find themselves in high demand). Here's a breakdown of the steps to convert skills into jobs/cash:

1. Find people who will pay you for the job you want.
   
   

2. Communicate and specify what you do and how you create value
   
   

3. Prove to them that you can reliably create value.

Legibility helps with all these steps - it makes it easier to find relevant opportunities or candidates (step 1), communicate value propositions clearly (step 2), and demonstrate/verify capabilities (step 3). However, high legibility alone doesn't guarantee liquidity - other factors can override the impact it has on liqudity.

Consider highly specialized fields like AI research or precision engineering. These skills might be perfectly legible (clear and well-defined), but their liquidity is limited because few employers can properly evaluate and utilize them. This highlights an important facet of liquidity that I should have mentioned in my first piece on the topic. In financial markets, some assets are fundamentally illiquid at almost any price point, while others become significantly more liquid if you're willing to accept a price discount. The same principle applies to careers: liquidity needs to be discussed in relation to your acceptable ranges for salary, location, role scope, and other key variables.

For instance, a senior machine learning engineer might find their skills highly illiquid if they insist on a $500K salary, remote work, and a specific technical domain. The same skillset becomes much more liquid if they're willing to consider a broader salary range or more varied applications of their expertise. Generally speaking, the pickiest person in the world, no matter how legible his qualifications and experience, is basically as good as fully illiquid because the search space collapses as you add too many constraints across multiple dimensions or as your constraints in any one dimension stretch far out into the tails of the distribution.

Two types of costs are associated with illiquidity:

1. Search costs/time (How many buyers in the market × how easy is it to identify and find these buyers)

2. Evaluation costs/time (What needs to be evaluated × the confidence required to sign contract)

Where evaluation costs get high:

• When many dimensions need strong evidence (perhaps due to the role being high paid/high leverage)

• When dimensions are hard to simulate/test

• When past evidence isn't directly transferable

• When dimensions interact in complex ways

These insights about liquidity and legibility have implications for both job seekers and employers. In markets where skills are highly legible but opportunities are few and far between (like specialized technical roles), the main challenge is finding and connecting with the right buyers. The solution might involve better ways to discover rare but valuable matches. Conversely, in markets where opportunities and skills are not super rate but less legible (like early-stage startup roles), the challenge is finding better ways to evaluate and validate both talent and organizations. These low liquidity quadrants is where Clout expects to add most value as a platform.

Understanding which type of friction you're dealing with - search costs or evaluation costs - matters for both sides of the market. Job seekers need to recognize whether their main challenge is finding buyers for well-understood skills or better articulating less conventional capabilities. Employers need to know whether they're struggling to discover rare talent, evaluate non-traditional candidates or articulate their value proposition/requirements.

• Optima & Outliers Drinks - New York: Quick reminder for those attending our first community event this week - looking forward to seeing you all on Wednesday at Fractal Tech in Wlliamsburg.

• Clout is helping a leading fintech platform (with 200B+ in client assets) hire a data scientist. This hybrid role based in NYC offers $140-170k base salary + bonus + equity. Strong engineering/coding skills are a must - specifically Python for building analytic tools (like sales dashboards and ROI calculators) and SQL/Tableau for analytics. If you can vouch for someone, fill out this referral form, and you'll earn $10,000 if they get hired. If they're not actively looking, you can specify that - we'll reach out without presuming interest on their behalf.

• If you're interested in receiving similar opportunities to help surface talent within your network, you can leave your information here.

What I've Been Reading and Thinking About

• with an intellectually honest take on whether you should work as a VC Associate

  • While his newsletter has 10x the reach and some of you may have discovered my newsletter through him, for those who haven't explored his writing yet, I've been enjoying going through the repository of content at Derisible

  • One meta thought that popped into my head while reading this piece - and a topic I've been meaning to write about - Always think through what people are paying you for, given your skills and comparative advantage. This can help close the gap between your instinctive reaction when you hear "investment banking" and the likely phenomenology associated with being a junior person in that job. From my time in VC, the subjective experience of being a junior person in the team can actually be quite good, especially because what you're being paid for is to capture deal flow. And if you're extraverted and intellectually curious, work doesn't really feel like work. (this is less true of later stage funds) But Yoni goes through all the factors that can potentially override this consideration of “it’s a cool job”.

• Face Time Culture: There's an important distinction between reasonable return-to-office policies and what I'll call "face time culture" - the phenomenon in certain finance and consulting firms where appreciation scales almost linearly with hours spent in the office. This practice sends an unintended (or not) message: "We can't effectively judge your work output, so we'll use time spent in office as a proxy.” It’s worth asking what this selects for and if that trade-off is worth it. The type of conscientiousness that is most conducive to thriving in this environment probably only weakly correlates with agency and effectiveness, and probably negatively with creativity. The answer: junior people in client service roles are probably mostly being paid to be available whenever senior folks need anything. And if that need seems to be more important to your boss than how effective or agentic you can be, it’s worth asking: Do I want to be paid for that?

• Senior folks should also consider if they have priced in the second order effects on the internal talent pipeline within the company. Is it worth having slavish analysts but a mediocre internal pool from which to promote?

First, your employee must have a near-linear preference for money—they need to consistently prefer more money to less, even at higher amounts. If someone is spiritually enlightened and sees little difference between making $500,000 and $5 million, you have a problem. There's a hard ceiling on how much you can motivate them through financial incentives.

Second, they must be competent in a way that translates motivation into value creation. Raw desire for money isn't enough—they need the skills to convert that motivation into better investment decisions. A portfolio manager who desperately wants to make money but lacks investment skill will likely destroy value in their attempts to create it.

Third, their actions must have a direct, measurable impact on value creation. When a trader makes a decision, the P&L impact is immediate and clear. This creates a tight feedback loop between decisions and outcomes, allowing precise measurement of value added.

Finally, you must be able to capture enough of the value created to sustain the incentive system. If your manager creates $10 million in value but must be paid $11 million to do so, the system breaks down. You need sufficient margin between value creation and compensation to make the arrangement worthwhile.

When all four conditions are met, you can create near-perfect incentive alignment by giving them a percentage of profits. They make more money when you make more money. (Though even here, the alignment isn't quite perfect—since the manager's downside is limited to their job while your downside is your capital, they might be incentivized to take larger risks than you'd prefer.) This elegance is rare in—most jobs break down on one or more of these conditions.

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Consider how different this is from most corporate roles, where the link between individual decisions and value creation becomes increasingly diffuse. A trader's decision has immediate, measurable impact. But in most jobs, value creation requires complex coordination across teams and time. Even obvious value creators like salespeople operate in environments where their success depends on product quality, market conditions, and support teams. The further you get from direct market activities, the harder it becomes to maintain that clear line between individual decisions and outcomes.

Incentives drive culture. The trader who obsesses over their P&L and the quant who treats markets like a complex puzzle are both drawn to an environment where success is objective and atttributable. They’re here because it’s one of the few parts of corporate America that will keep them intellectually engaged and not bog them down with soul crushing meetings and manager speak, where you’re comfortably abstracted away from the value creation process through layers of management and hierarchy.

You should want your employees to want what you can actually deliver. In trading, pure financial incentives work because firms can offer high compensation, money drives better decisions, and they can pay more for better performance. But if you're running a non-profit or an early-stage startup, you need employees motivated by what you can uniquely provide.

But this reveals a subtle problem in organizational design. A company's unique value proposition might be an excellent static motivator—getting people through the door—without being a strong dynamic motivator that drives continuous performance improvement. Consider a climate tech startup. The mission of fighting climate change might attract passionate employees willing to accept lower compensation. While this solves the immediate hiring problem, it doesn't necessarily create the ongoing performance incentives you need.

What you need are dynamic motivators—things that create virtuous cycles of performance and reward. A dynamic motivator has this key feature: when employees deliver more of what you want, you can give them more of what motivates them, which in turn drives them to deliver even more value. Money works perfectly this way in portfolio management. Professional growth can work similarly—high performers can be given more challenging projects and responsibilities, feeding their desire to learn and grow. But static motivators like work-life balance or mission alignment, while valuable for attraction, can't easily scale with performance. In fact, selecting heavily for these static motivators might mean hiring people who are inherently less responsive to the dynamic motivators you have available.

This is what great company cultures do when they work well—they create a system of incentives and norms that both attracts the right people and keeps them motivated to perform. But not all cultures achieve this alignment.