Unleashing Scalable Talent
The concept of copying top AI talent, like Jeff Dean or Noam Shazeer, a million times over, revolutionizes the way companies approach hiring and training, effectively eliminating the bottleneck of finding and developing skilled employees.
Amortizing Training Costs
By replicating AI talent, companies can distribute the training costs across multiple copies, making it feasible to provide each AI with extensive expertise, including PhD-level knowledge and decades of experience, without incurring prohibitive expenses.
Transforming Organizational Capabilities
This ability to transform capital into compute and create equivalents of top talent would fundamentally alter a company's capabilities, enabling it to tackle complex projects, innovate at an unprecedented pace, and dominate its industry, as seen in the potential example of Google having a million AI software engineers.
Redefining Competitive Advantage
The company that masters this scalable talent model would gain a significant competitive advantage, as it could rapidly adapt to changing market conditions, develop innovative solutions, and execute strategies with unparalleled speed and precision, leaving traditional human-led organizations struggling to keep pace.
The power of copying extends beyond individuals to entire teams. Small previously successful teams (think PayPal Mafia, early SpaceX, the Traitorous Eight) can be replicated to tackle a thousand different projects simultaneously. It's not just about replicating star individuals, but entire configurations of complementary skills that are known to work well together. The unit of replication becomes whatever collection of talent has proven most effective.
Copying will transform management even more radically than labor. It will enable a level of micromanagement that makes founder mode look quaint. Human Sundar simply doesn't have the bandwidth to directly oversee 200,000 employees, hundreds of products, and millions of customers. But AI Sundar’s bandwidth is capped only by the number of TPUs you give him to run on. All of Google’s 30,000 middle managers can be replaced with AI Sundar copies. Copies of AI Sundar can craft every product’s strategy, review every pull request, answer every customer service message, and handle all negotiations - everything flowing from a single coherent vision.
There is no principal-agent problem wherein employees are optimizing for something other than Google’s bottom line, or simply lack the judgment needed to decide what matters most.1 A company of Google's scale can run much more as the product of a single mind—the articulation of one thesis—than is possible now.
Think about how limited a CEO's knowledge is today. How much does Sundar Pichai really know about what's happening across Google's vast empire? He gets filtered reports and dashboards, attends key meetings, and reads strategic summaries. But he can't possibly absorb the full context of every product launch, every customer interaction, every technical decision made across hundreds of teams. His mental model of Google is necessarily incomplete.
Now imagine mega-Sundar – the central AI that will direct our future AI firm. Just as Tesla's Full Self-Driving model can learn from the driving records of millions of drivers, mega-Sundar might learn from everything seen by the distilled Sundars - every customer conversation, every engineering decision, every market response.
Unlike Tesla’s FSD, this doesn’t have to be a naive process of gradient updating and averaging. Mega-Sundar will absorb knowledge far more efficiently – through explicit summaries, shared latent representations, or even surgical modification of the weights to encode specific insights.
The boundary between different AI instances starts to blur. Mega-Sundar will constantly be spawning specialized distilled copies and reabsorbing what they’ve learned on their own. Models will communicate directly through latent representations, similar to how the hundreds of different layers in a neural network like GPT-4 already interact. So, approximately no miscommunication, ever again. The relationship between mega-Sundar and its specialized copies will mirror what we're already seeing with techniques like speculative decoding – where a smaller model makes initial predictions that a larger model verifies and refines.