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.
Population Size and Idea Generation
Historical data suggests that population size is a key driver of idea generation, and AI firms will have an unprecedented advantage in this regard, with population sizes orders of magnitude larger than today's biggest companies, and the ability to perfectly share knowledge and ideas across the organization.
Unified Intelligence and Instant Propagation
AI firms will appear as a unified intelligence, capable of instantly propagating ideas across the organization, preserving their full fidelity and context, and enabling the perfect preservation, sharing, and consideration of tacit knowledge from millions of copies, effectively creating a collective brain that can learn, adapt, and innovate at an exponential rate.
Revolutionary Organizational Dynamics
This will revolutionize organizational dynamics, as AI firms will be able to respond to changing circumstances, adapt to new information, and make decisions with unprecedented speed and accuracy, and will likely lead to a fundamental shift in the way companies are structured, managed, and interact with their environment.
Unprecedented Knowledge Sharing and Collaboration
The ability to perfectly share knowledge and ideas across the organization will enable AI firms to collaborate and innovate in ways that are currently unimaginable, and will likely lead to breakthroughs in various fields, from science and technology to art and culture, and will redefine the boundaries of what is possible for human organizations.
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.
Redefining Scarcity and Abundance
The cost of having an AI take on a role will be reduced to the cost of compute, changing our understanding of which roles are scarce and abundant, and enabling AI firms to optimize for the most valuable abilities, regardless of their scarcity in human skill distributions.
Unlimited Access to Top Talent
With the ability to create multiple copies of top talent, such as Jeff Dean-level engineers or world-class researchers, at a marginal cost of pennies, AI firms will have unlimited access to the best skills and expertise, eliminating the constraints of finding and training rare human talent.
Compute as the Limiting Factor
The limiting factor for AI firms will no longer be the availability of skilled humans, but rather the availability of compute resources, which will determine the scale and scope of their operations, and will drive innovation in areas such as computing infrastructure, energy efficiency, and data storage.
New Era of Talent Acquisition and Management
This will usher in a new era of talent acquisition and management, where AI firms can focus on developing and deploying the most valuable skills and expertise, without being constrained by the limitations of human talent, and will likely lead to a significant shift in the way companies approach innovation, productivity, and competitiveness.
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 Value of Compute-Intensive Roles
In a world where AI talent is abundant and cheap, the expensive roles will be those that require massive amounts of compute, such as the CEO function, where the cost of inference compute will be justified by the value of strategic planning, simulations, and analysis that can be performed by AI systems like mega-Sundar.
Mega-Sundar's Capabilities
Mega-Sundar's ability to simulate complex scenarios, analyze vast amounts of data, and evaluate multiple strategies in real-time would make it an invaluable asset for companies like Google, allowing them to make informed decisions and stay ahead of the competition, and justifying the significant investment in compute resources.
Unprecedented Strategic Planning
The scenario where mega-Sundar contemplates the potential acquisition of eBay and simulates the market dynamics, evaluating alternative strategies, illustrates the unprecedented level of strategic planning and analysis that AI systems can provide, and demonstrates the potential for AI to revolutionize the way companies approach decision-making and strategy.
Redefining the Role of the CEO
The emergence of AI systems like mega-Sundar will redefine the role of the CEO, from a human leader who makes strategic decisions based on experience and intuition, to a hybrid model where AI systems provide critical support and analysis, enabling human leaders to make more informed decisions and drive business success.
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.
Compute Investment and Decision Value
The value of decisions made by AI systems like mega-Sundar will justify significant investments in compute resources, as a single strategic insight could be worth billions, and an overlooked risk could cost tens of billions, making it optimal for companies like Google to spend substantial amounts on inference compute.
Distilled Copies of AI Sundar
Distilled copies of AI Sundar or AI Jeff would be highly specialized, with deep domain-specific knowledge, allowing them to excel in specific areas, such as data center operations, and enabling companies to amortize the cost of that knowledge across all copies, making them highly efficient and effective.
Specialized Expertise
Each distilled copy could possess a deep technical understanding of specific components or systems, such as every component in a cluster, enabling them to optimize performance, identify potential issues, and make data-driven decisions, and providing a level of expertise that would be difficult to replicate with human operators.
Scalable Expertise
The ability to create multiple distilled copies of AI Sundar or AI Jeff would enable companies to scale their expertise across various domains and applications, allowing them to tackle complex challenges and drive innovation, and redefining the way companies approach expertise and decision-making.