Gavin Baker - Tech and Consumer Growth Investing

Principles & Lessons:

1. Investing edge arises from structural time arbitrage and simple, falsifiable models of long-range outcomes — not complex near-term forecasting. Gavin describes how short-term estimates (e.g., iPod sales this quarter) are efficient and competitive, whereas long-term insight about the shape of a business (e.g., “Sony sold 400 million Walkmans; Apple might sell more iPods”) offers differentiated, actionable edge. He emphasizes Occam’s Razor as a forecasting tool: “Simple is beautiful. If you can find a simple framework that gives you conviction to look out and hold through volatility… that’s powerful.” His preference for 5–7 year horizons reflects the limit of reliable differential knowledge — beyond that, the signal-to-noise ratio collapses. The insight is that conviction doesn’t require detailed extrapolation — it requires a robust mental model that is both simple and directionally correct.
2. High ROIC companies on the right side of disruption can be priced for multiple compression and still outperform — if valuation is decoupled from thesis. Gavin rejects the common growth-investing pattern of hoping to “get paid twice” via both multiple expansion and fundamental improvement. Instead, he deliberately assumes multiple compression and underwrites returns based on business outcomes alone: “I try to buy these businesses at what I think is a 20–30% free cash flow yield to EV five-to-seven years from now.” This approach forces intellectual discipline — the thesis must work on intrinsic value, not market behavior. It’s also antifragile: valuation surprises to the upside are welcome, but not required.
3. Cumulative knowledge compounds alpha in tech — and the real edge comes from context-rich pattern recognition, not novel data access. Gavin argues that tech investing is unusually amenable to durable knowledge advantages because of its compounding nature: “Tech is a game of cumulative knowledge.” Many investors exited tech post-2000, and others avoid it due to complexity, creating a thinner field. This enables those with 20+ years of engagement to develop second-order pattern fluency — how Moore’s Law affects business models, why semicap shifts matter, how consumer behavior evolves with platforms. His criterion of being in the “top 1% of knowledge” about a company is not about secrets but about depth of structured context.
4. The Metaverse and gaming represent a new platform shift — not just for entertainment, but for value creation and digital social infrastructure. Gavin sees video games as the most likely substrate of the Metaverse: “Video games… are the only form of engagement riding Moore’s Law.” Unlike music or film, which have plateaued in experiential quality, games improve with each processing cycle. He notes that major franchises behave like social networks with high switching costs — Call of Duty, Assassin’s Creed, Fortnite — and already command more time and emotional investment than many “real” social networks. The key insight is that virtual environments are not a genre but a shift in where and how humans spend time — and capital should follow attention.
5. Online scale amplifies power-law effects — and dominant platforms create feedback loops via data, which reinforce competitive advantage in AI. One of Gavin’s clearest structural insights is that scale is not just durable but increasingly self-reinforcing in digital businesses. He notes that “the single most predictive element of AI quality is the quantity of data” used in training, and that each order-of-magnitude increase in data doubles quality. Thus, market leaders improve faster than competitors can catch up — “most users → most data → better models → more users.” The result is that economic returns concentrate. This dynamic reduces the historical tendency toward mean reversion, weakening traditional value investing assumptions.
6. Alternative data has transformed public markets but offers diminishing marginal advantage — unless paired with superior interpretation and structure. Gavin views alternative data as “table stakes” — everyone has access to credit card and email receipt data. But he warns that while such data seems precise, it often comes from small sample sets and introduces false confidence: “A lot of the big moves… are because the alternative data was predicting one thing, and it was wrong.” He values alt data most when used to reconstruct cohorts or build longer-term behavioral insights — not for quarter-to-quarter trading. The broader lesson is epistemic: more data doesn’t equal better understanding unless structured within a falsifiable model.
7. Technology and consumer sectors are alpha-rich because they combine high uncertainty with high leverage to platform shifts — but require sector-specific epistemology. Gavin explains that tech and consumer stand out because they are both “alpha-rich” and mis-modeled by generalists. Consumer preferences are both stable and surprisingly volatile; tech cycles drive nonlinear outcomes. Importantly, he has found that applying the wrong mental models — e.g., analyzing industrials or banks with the same framework — consistently destroys value: “I destroyed alpha very consistently in industrials, energy, healthcare, and financials.” His insight is that investment alpha is as much about domain fit as analytical skill — and that intellectual humility about scope is critical.
8. Most long-term investing mistakes are failures to recognize present trends early — not failures to predict the future. Quoting Matt Cohler, Gavin states: “My job is not to predict the future, it’s simply to notice the present first.” He applies this lens to examples like video games (misunderstood as cyclical in 2012), physical retail (undervalued after Amazon’s acquisition of Whole Foods), and the shift from desktop to mobile to cloud to AI. The epistemological claim is that the future is often already here — embedded in present signals — but investors ignore them due to flawed priors or category error. Great investing doesn’t require seeing what others can’t; it often just requires believing what’s already visible.

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