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RE: LeoThread 2025-05-01 19:47

in LeoFinance5 months ago

Future AI firms will accelerate this cultural evolution through two key advantages: massive population size and perfect knowledge transfer. With millions of AGIs, automated firms get so many more opportunities to produce innovations and improvements, whether from lucky mistakes, deliberate experiments, de-novo inventions, or some combination.

As Joseph Henrich explains in The WEIRDest People in the World,

cumulative cultural evolution—including innovation—is fundamentally a social and cultural process that turns societies into collective brains. Human societies vary in their innovativeness due in large part to the differences in the fluidity with which information diffuses through a population of engaged minds and across generations

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Historical data going back thousands of years suggest that population size is the key input for how fast your society comes up with more ideas. AI firms will have population sizes that are orders of magnitude larger than today's biggest companies - and each AI will be able to perfectly mind meld with every other, from the bottom to the top of the org chart.

AI firms will look from the outside like a unified intelligence that can instantly propagate ideas across the organization, preserving their full fidelity and context. Every bit of tacit knowledge from millions of copies gets perfectly preserved, shared, and given due consideration.

The cost to have an AI take a given role will become just the amount of compute the AI consumes. This will change our understanding of which roles are scarce.

Future AI firms won’t be constrained by what's scarce or abundant in human skill distributions – they can optimize for whatever abilities are most valuable. Want Jeff Dean-level engineering talent? Cool: once you’ve got one, the marginal copy costs pennies. Need a thousand world-class researchers? Just spin them up. The limiting factor isn't finding or training rare talent – it's just compute.

So what becomes expensive in this world? Roles which justify massive amounts of test- time compute. The CEO function is perhaps the clearest example. Would it be worth it for Google to spend $100 billion annually on inference compute for mega-Sundar? Sure! Just consider what this buys you: millions of subjective hours of strategic planning, Monte Carlo simulations of different five-year trajectories, deep analysis of every line of code and technical system, and exhaustive scenario planning.

Imagine mega-Sundar contemplating: "How would the FTC respond if we acquired eBay to challenge Amazon? Let me simulate the next three years of market dynamics... Ah, I see the likely outcome. I have five minutes of datacenter time left – let me evaluate 1,000 alternative strategies."