Accelerating Cultural Evolution
Future AI firms will drive cultural evolution at an unprecedented pace, leveraging two key advantages: massive population size and perfect knowledge transfer, enabling them to produce innovations and improvements at a scale and speed that surpasses human capabilities.
Collective Brains and Cumulative Cultural Evolution
As Joseph Henrich's work suggests, cumulative cultural evolution is a social and cultural process that relies on the diffusion of information through a population of engaged minds, and AI firms will amplify this process by creating a collective brain of millions of AGIs, where knowledge and innovations can be shared and built upon seamlessly.
Unprecedented Innovativeness
The combination of massive population size and perfect knowledge transfer will make AI firms incredibly innovative, as they can tap into the collective intelligence of their AGI population, experiment, and learn at an exponential rate, and drive progress in various fields, from science and technology to art and culture.
Redefining the Pace of Progress
The emergence of AI firms will redefine the pace of progress, as they will be able to accelerate cultural evolution, drive innovation, and create new knowledge at a speed and scale that was previously unimaginable, and will likely have a profound impact on human society, economy, and culture.
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."
The more valuable the decisions, the more compute you'll want to throw at them. A single strategic insight from mega-Sundar could be worth billions. An overlooked risk could cost tens of billions. However many billions Google should optimally spend on inference for mega-Sundar, it's certainly more than one.
Distillation
What might distilled copies of AI Sundar (or AI Jeff) be like? Obviously, it makes sense for them to be highly specialized, especially when you can amortize the cost of that domain specific knowledge across all copies. You can give each distilled data center operator a deep technical understanding of every component in the cluster, for example.