AI excels at probabilistic thinking and pattern matching (optimization), while humans excel at possibility thinking and innovation. The most powerful approach, the "centaur model," uses AI to handle optimization, freeing human cognition for imaginative tasks that create the future.

Even a specialized task like coding involves a wide range of human-like interaction: brainstorming, searching, and more. This "AGI-completeness" means a powerful general model with a good "bedside manner" can outperform a narrowly specialized one, complicating the strategy for vertical AI apps.

The fear of 'superhuman' AI is based on a flawed premise. Our definition of measurable intelligence—tallying numbers, memorizing lists—was created for the industrial workforce. AI is simply automating these now-outdated tasks, suggesting we need to recalibrate our measurement of human intelligence itself.

AI intelligence shouldn't be measured with a single metric like IQ. AIs exhibit "jagged intelligence," being superhuman in specific domains (e.g., mastering 200 languages) while simultaneously lacking basic capabilities like long-term planning, making them fundamentally unlike human minds.

Framing AGI as reaching human-level intelligence is a limiting concept. Unconstrained by biology, AI will rapidly surpass the best human experts in every field. The focus should be on harnessing this superhuman capability, not just achieving parity.

Current AI models resemble a student who grinds 10,000 hours on a narrow task. They achieve superhuman performance on benchmarks but lack the broad, adaptable intelligence of someone with less specific training but better general reasoning. This explains the gap between eval scores and real-world utility.

The true paradigm shift with technologies like ChatGPT was the explosion in *generality*. AI moved from narrow, purpose-built tools (like a Go-playing machine) to systems that could perform a wide range of cognitive tasks. This generality, rather than just improved performance, is the key driver of its broad economic implications.

Human intelligence is fundamentally shaped by tight constraints: limited lifespan, brain size, and slow communication. AI systems are free from these limits—they can train on millennia of data and scale compute as needed. This core difference ensures AI will evolve into a form of intelligence that is powerful but alien to our own.

The next leap in AI will come from integrating general-purpose reasoning models with specialized models for domains like biology or robotics. This fusion, creating a "single unified intelligence" across modalities, is the base case for achieving superintelligence.