Hunting, Automation and Leverage
What Hunting can Teach Us for Data-Driven Business and Investing Today
I had a fascinating conversation with Bogumil Baranowski on Talking Billions:
Automation, Hunting, and the Leverage You’re Missing
For most of human history, survival meant hunting. But hunting wasn’t just about bravery and brute force; it was about automation, pattern recognition, and portfolio strategy. Humans thrived because they trapped rabbits, stalked deer, and scavenged tiger, lion, or wolf and wild dog kills. They spread risk by automating small, reliable wins while redeploying their capabilities towards a few big asymmetric bets, and of course, the occasional opportunistic plays.
Success rates made the logic clear. Small prey and traps had 40–70% success rates. Large prey hunts only about 2–10%. Traps, an early form of automation, accounted for about 30–60% of meat consumed, depending on region, season, etc. And scavenging fresh carcasses from predators had success rates north of 80%. There is nothing simpler than letting someone else do the hard work and then taking the prize away from them, isn’t it?
This portfolio approach is probably why humans survived. Not because they always landed the mammoth hunt, but because they automated what they could, redeployed resources to take big swings for asymmetric returns, and then took advantage of any unplanned opportunity when it appeared.
This is not just a prehistoric relic; it’s a blueprint for how leaders should think about building and scaling businesses in the data-driven paradigm. And it connects directly to the idea of leverage.
Hunting as a Portfolio
Note: below are rough approximations based on a range or research studies and meta-studies where factors like region, climate, season, etc play huge roles and result in large variations.
Anthropological data from contemporary hunter-gatherer communities give us broadly the following numbers:
Small prey & traps success rates: 40–70%.
Large prey success rates: 2–10%.
Scavenging large kills: 80%+.
Traps (automation): 30–60% of total hunting output.
The portfolio logic was clear. Traps and small prey delivered reliable throughput that made sure people survived. Large prey justified the effort despite low odds because of the asymmetric payoff in case of success. Scavenging was pure opportunism: high success, high reward, low risk and low effort, but impossible to plan or depend on.
This portfolio approach is also a good mental model for business & investing.
Note of caution: While there are significant variations by region, season and other conditions, analyzing the data leads to interesting patterns, that I have summarised in the numbers above. These numbers are by no means precise, but they indicate overall patterns that are helpful. For more details see the references below.
Before going deeper, let’s cut straight to the core. If you run a business or manage capital, here’s the blunt reality:
The Hunting Portfolio Checklist
If you’re not setting traps, you’re wasting energy.
Where are you automating steady returns?
If you can’t point to them, you’re missing out on steady returns that allow you to redeploy attention and capabilities towards asymmetric returns.
If you’re not chasing mammoths, you’re missing the upside.
What asymmetric bets are in your portfolio?
If none, expect flat returns and steady decline over time.
If you’re not scavenging, you’re missing opportunistic wins.
Where are you pouncing on narrow opportunistic plays or market gaps?
If you can’t name one, you’re not looking widely enough.
If you don’t know your leverage, you’ll never win big.
What are your unique unfair advantages that allow you to counterposition in your market?
If competitors could copy you tomorrow, you don’t have leverage.
If you can’t see your inflection points, you’ll never compound.
Where is your relative superiority?
If you can’t map it, you’ll waste resources in execution.
Leverage is How You Compound Returns
Leverage means maximum impact with minimum input. Hunters used traps to multiply their chances. Businesses can do the same with assets, data, and customer demand.
But most organizations don’t. Instead, they fall into what Matt Lerner calls the “7 problem.” Leaders don’t choose the asymmetric “10” plays, or even the safe “5s.” They pick consensus “7s”—politically comfortable, low-risk, low-reward decisions. Over time, this satisficing guarantees mediocrity over time.
Evolution wired us for this. Probability matching—the tendency to split choices in proportion to perceived odds—shows up in everything from how goldfish forage to how World War II bomber crews alternated between parachutes and flak jackets, even when statistics showed flak jackets were the rational choice. Picking “7” feels safe. But it ignores power laws.
A “10” may look risky, but if it carries a 1000× outcome, its expected return dwarfs the safe 7. By avoiding tens, leaders compound opportunity costs, guaranteeing decline.
Power Laws and Customer Demand
The biggest leverage in business isn’t in supply anymore. It’s in customer demand. Demand doesn’t distribute evenly; instead, it clusters in power laws. Some products, channels, and customer groups deliver outsized returns.
Look at Walmart. Once seen as a liability, its physical stores became data assets, generating granular behavioral insights that Amazon couldn’t match. Airlines turned loyalty programs into banks by monetizing customer data as financial products. Bajaj Finance used in-store presence at appliance shops to turn physical retail into a high-growth lending engine.
These companies found leverage in their “unfair non-digital advantages”—assets competitors couldn’t copy without massive self-cannibalization. That’s counter-positioning. Netflix killed Blockbuster not because streaming was obvious, but because Blockbuster couldn’t pursue it without destroying its late-fee and retail model. HUK24 did the same to German insurers by going online-only, while incumbents were trapped by broker networks.
Leverage isn’t about copying a recipe. It’s about identifying your unique asymmetries—assets, data, or positions that scale disproportionately—and then doubling down on them and compounding them.
Focus on Leverage, Starve the Rest
The LAW framework makes the trade-offs clear:
Leverage: growing payoffs that can compound over time (e.g., proprietary data-driven products that focus on demand).
Average: constant or declining payoffs over time (e.g., workflow optimisation).
Wastage: one-time payoffs (e.g., analysis or committee reports).
Most companies waste resources optimizing overhead, which, from an investor’s perspective, is just Wastage. Hunters didn’t do this—they didn’t write memos about failed hunts. They set more traps.
Leaders should do the same: starve Wastage, automate Average tasks, and concentrate resources and redeploy them on Leverage.
Relative Superiority and Execution
Admiral William McRaven, architect of the bin Laden raid, coined the term “relative superiority”, the moment when a smaller force gains decisive advantage over a larger one. In business terms, this is when a leverage play crosses its inflection point. The execution risk drops, and the compounding begins.
Hunters hit relative superiority when a trap is set or when a predator has just completed their kill. Businesses hit it when a leveraged asset—customer data, distribution advantage, network effect—kicks into compounding mode.
The Modern Hunting Portfolio
For leaders and investors, the hunting portfolio translates into:
Automate the high-volume routine. Build traps that deliver steady throughput.
Take asymmetric swings. Allocate resources to a few bold “10”s with power-law potential.
Be opportunistic. Move fast when competitors falter or markets dislocate.
Exploit leverage. Identify your unfair advantages and counter-position them.
Avoid “7”s. Comfortable consensus decisions are the fastest route to decline.
Our ancestors survived because they mastered leverage. The leaders who thrive today will be the ones who do the same.
The Hunting Leverage Checklist (Self-Audit Edition)
When evaluating strategy, investments, or execution, use this as a hard-nosed audit:
Where are my traps?
What processes have I automated into steady returns?
Am I still wasting resources on manual overhead?
Where are my mammoths?
Which high-risk, high-reward bets have asymmetric potential?
Do they justify the low hit rate with high expected return?
Where is the fresh hunt?
What short-term market dislocations or competitor mistakes can I exploit right now?
Do I have the capability to move faster than others?
Where is my leverage?
What unique unfair assets (data, distribution, trust, reputation, non-digital advantages) can I compound?
Am I counter-positioning against incumbents who can’t copy without self-harm?
Am I picking “7”s?
Which decisions have been watered down to consensus-safe mediocrity?
What bold tens am I avoiding, and what’s the real opportunity cost?
Where are my relative superiority points?
What milestones mark the inflection where compounding begins?
Am I allocating resources to get there first?
Humans didn’t survive by being the fastest or strongest predator. They survived by combining automation, asymmetric bets, and opportunism—and by exploiting leverage wherever they could find it.
Business leaders and investors who do the same will thrive. Those who keep picking consensus groupthink 7s will decline.
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References:
Hill, K., & Hawkes, K. (1983). “Neotropical Hunting Among the Ache of Eastern Paraguay.” In R. B. Hames & W. T. Vickers (Eds.), Adaptive Responses of Native Amazonians (pp. 139–188). Academic Press
Hawkes, K., O’Connell, J. F., & Blurton Jones, N. G. (1991). “Hunting Income Patterns Among the Hadza: Big Game, Common Goods, Foraging Goals and the Evolution of the Human Diet.” Philosophical Transactions of the Royal Society B: Biological Sciences, 334(1270), 243–251.
Lee, R. B. (1979). The !Kung San: Men, Women, and Work in a Foraging Society. Cambridge University Press.
Smith, E. A. (1991). “Inujjuamiut Foraging Strategies: Evolutionary Ecology of an Arctic Hunting Economy.” Aldine de Gruyter.
Gurven, M., & Kaplan, H. (2006). “Determinants of Time Allocation Across the Lifespan: A Theoretical Model and an Application to the Tsimane.” Human Nature, 17(1), 1–49.
Hames, R. (1979). “A Comparison of the Efficiencies of the Shotgun and Bow in Neotropical Forest Hunting.” Human Ecology, 7(3), 219–252.
Greaves, R. D. (1997). “Hunting and Multifunctional Use of Bows and Arrows in Pumé Foragers.” Human Ecology, 25(4), 405–415.
Blurton Jones, N. G., Hawkes, K., & O’Connell, J. F. (1999). “Some Current Ideas about the Evolution of the Human Life History.” In P. C. Lee (Ed.), Comparative Primate Socioecology (pp. 140–166). Cambridge University Press.