Dwarkesh Patel's essay imagines companies whose ability to expand depends on copying trained AI workers and teams. Under its assumption of broadly human-capable AI, a company could spread the cost of developing expertise across many copies and replicate a successful team across projects. That would change a familiar constraint on growth: recruiting and training each additional person. Patel explicitly presents the January 2025 essay as speculative. Its useful argument is that digital replication could change the organization of work even beyond improvements in individual intelligence; whether expertise and coordination would survive such replication remains part of the scenario.
Key takeaways · AI-distilled
In an essay the authors rate at about 25% confidence, copying is the first lever: training cost is amortized across thousands of instances, justifying deeper expertise per AI and letting small successful teams be replicated across a thousand projects.
The essay argues copying changes management more than labor: copies of an AI CEO could set every product's strategy, review every pull request and run negotiations from one vision, with no principal-AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → problem.
Under 'merge', a central model would absorb what its distillationTraining a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.Full definition → copies learn through explicit summaries, shared latent representations or direct weight edits, with instances communicating through latent representations.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
distillation — Training a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.
