Trading is a short-horizon business of measured edge and clear endpoints, while investing is long-horizon ownership of reinvestment machines. Because returns and careers follow power-law distributions, the index’s mean outperforms the median picker, so broad market beta (e.g., the S&P 500) is the right default for most.
Yet real-world markets are stickier and less efficient than financial ones, so people with genuine proximity should rationally express structured conviction tilts (and even startups as career-level tilts), sized with guardrails, falsifiers, and time checks. The edge lives in months-to-years seams where qualitative signals arrive before machine-readable data.
Trading is not Investing
Trading and investing look similar from the outside, but they’re fundamentally different games. Trading is a business: you spread risk across many tables, measure edge per trade, settle frequently, and let the law of large numbers compound. Long-term prediction is irrelevant because the focus is on buying cheap and selling expensive. Investing is ownership of value-creating machines. The question is not “how off is the market on Q1 earnings?” but “how well will this business grow over time?” The feedback loop is long, messy, and uncertain.
That’s why I think of investing as a life path with room for detours. Putting money in the S&P 500 is like taking the standard road: college to a solid CS / consulting / finance / law / medicine job. It’s well trodden, high Sharpe, and it works because markets, like life, are positive-sum over time. But life is not only the main road. Occasionally your lived experience reveals something others haven’t caught yet, and that’s when a small, deliberate deviation makes sense. The default is market beta; the detours are conviction tilts earned by proximity to the truth.
Finding Your Edge
Most price setters, traders, analysts, quants, trade what they can measure: earnings, guidance, trend, and alternative data. That machinery is powerful but often slow to incorporate qualitative signals you can feel on the ground: heavy product love, cultural pull, talent gravity, or the first murmurs of new buyer cohorts. Sometimes your advantage is simply that you experience something before it’s machine-readable.
If you’re an early, heavy user; if you see top talent flocking to a firm; if you hear institutional friends beginning serious diligence on an asset class, those observations can precede what models and data will eventually confirm. The point is not to worship intuition; it’s to recognize that experience can surface evidence earlier than reports do.
The way to act on that kind of edge is with sizing and structure. Keep most of your capital on the main road in broad, low-fee beta aligned to your risk, think 60–90% depending on your situation. Use a defined “detour budget” of 10–40% for conviction tilts, with each single idea sized modestly, 5-10% or less, so one bad call can’t sink the ship. Express the view with cash equities or options, and use baskets when the insight is thematic rather than ticker-specific.
For example, I personally went long Robinhood because I loved the product and used it heavily; the behavior I saw up close mattered more than a backward-looking snapshot. I went long Palantir because several of my smartest friends chose to join; talent flows raised my base rate for future execution. I went long Ethereum as institutional friends finally began talking about it in earnest, which hinted at a new demand curve. Each position was driven by experience rather than secondhand reports, and each stayed small, under 10%, because my prior on broad market beta remains strong.
Adverse selection is real in markets and in life. When you get a job offer, a fair question is “why didn’t someone better take this first?” Every buyer has a seller. Assume you’re missing something, size so that being wrong barely matters, and make the burden of proof ongoing rather than a one-time ceremony. Markets are typically very efficient, but less efficient than you fear. They’re fastest where signals are machine-readable and slower where signals are qualitative, local, and cultural.
Your advantage lives in those seams, investing from research reports alone is a bit like letting other people tell you how to live. You need strong priors, beta, diversification, and risk management, and you need opinions earned by experience. Most days, follow the well-traveled path. Now and then, take the detour you’ve actually walked. Default to the market. Deviate when your feet, not just your feed, give you the edge.
Power Law Distributions
Now, investment returns, like many outcomes in life, tend to follow asymmetric, power-law distributions. A few big winners drive a lot of the total payoff; the mean is pulled up by rare outliers while the median sits much lower. For investors, this means the index (a cap-weighted claim on the winners) will almost certainly have a mean return higher than the median return of a randomly chosen stock picker, or a randomly chosen path.
The same intuition shows up in careers: in an efficient labor market, the “well-trodden path” concentrates training, mentorship, and matching; on average, those who follow it do better than those who don’t. Defaulting to broad market exposure is, in that sense, an implicit acknowledgment of both market efficiency and labor-market efficiency: let the system’s selection mechanisms work for you.
But “most shouldn’t deviate” does not imply “no one should.” Power laws also mean rare, lumpy opportunities exist, moments when your lived experience, timing, and skills line up to create genuine edge. Seen through the same lens, startups and entrepreneurship are career-level versions of these conviction tilts.
From the outside, founding looks high risk because we judge it against the median outcome. From the inside, when you have unusually crisp conviction, proximity to the customer, unfair access to talent or distribution, or a problem you can’t stop thinking about, the risk is not aligned with the market’s perception. You possess information and fit that are hard to diversify but real: repeated contact with the pain point, early users you can sit next to, a team you’ve shipped with before, or a wedge into a regulated space you understand.
In power-law domains, the distribution of startup outcomes is brutally skewed, which is precisely why the average outsider should prefer stable employment, yet it’s also why the right insider, at the right moment, can rationally step off the path. The other unmentioned distinction is of course that real-world markets are often far less efficient than financial markets.
Market are not Efficient
Physical distribution, switching costs, regulation, capacity constraints, procurement cycles, and messy human habits all create frictions that delay price discovery. A restaurant that nails unit economics can quietly monopolize a neighborhood for years before national competitors notice; an enterprise tool with deep workflows can entrench itself long before adoption shows up in easily-scraped metrics; a regulated wedge can compound behind a moat of licenses and compliance muscle that spreadsheets underweight.
These frictions are precisely what make months-to-years insights durable: customer love, talent gravity, and distribution advantages aren’t instantly arbitraged away the way a mispriced future is. That gap between real-economy stickiness and financial-market speed is where higher-conviction investing can live, if you size it like a professional and keep falsifiers honest.
Which leads to a practical implication: 100% S&P 500 is probably correct for most people most of the time, but if you’re the kind of person who’s constantly hands-on with products, operating in a specialized domain, or surrounded by sharp operators, you shouldn’t immediately assume the market has incorporated the information from your lived reality. On a horizon of months and years, daily user experience, design-partner feedback, channel checks, hiring flows, procurement timelines, can surface edges that cap-weighted indices and quarterly reports will only recognize later.
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