The Specialization Paradox

Happy Monday!

Last week, two pieces of research revealed a key shift in how AI systems create value (and it's the opposite of what most people expect). Stripe's comprehensive analysis of the AI economy showed real businesses generating real revenue through specialized AI applications, while Sakana AI demonstrated how rival models can "team up" to solve complex problems that individual agents couldn't handle alone.

The narrative around artificial general intelligence suggests we're building toward single, superintelligent systems. But the actual AI economy is evolving in the opposite direction: specialized agents that collaborate like expert teams.

The Adam Smith Moment for AI

In 1776, Adam Smith observed that a pin factory could produce vastly more pins by dividing the work among specialists than by having each worker make complete pins. This insight, that specialization and coordination create exponential value, became the foundation of modern economics.

The AI economy is having its Adam Smith moment.

Stripe's data reveals that the highest-revenue AI businesses aren't building general-purpose assistants; they're creating specialized solutions for specific workflows. Meanwhile, Sakana's breakthrough in multi-agent collaboration demonstrates that teams of specialized AI models consistently outperform individual "smarter" models on complex tasks.

The reality is we're building toward something like an AI “consulting firm” where specialized experts coordinate to solve problems no individual could handle alone.

The Meta Trend: From Scale to Specialization

The conventional AI development strategy follows a simple logic: bigger models and more parameters yield better performance. But both economic data and technical research point toward a different path. Specialized systems that collaborate will likely win out over generalized systems that attempt everything in isolation.

This represents a stark deviation from the "scaling laws" paradigm that has dominated AI development. Instead of pursuing ever-larger models, the highest-value applications emerge from purpose-built agents working together.

The specialization paradox suggests that the future of AI looks less like a single superintelligent entity and more like a sophisticated economy of specialized agents.

Pattern Recognition: The Division of AI Labor

Pattern #1: Economic Specialization

Stripe's analysis of AI economy payments reveals that successful AI businesses cluster around specific use cases rather than general capabilities. The highest-revenue applications focus on narrow domains where AI can deliver measurable value: document processing, customer service automation, code generation, and data analysis.

These aren't companies trying to build AGI, they're companies building the AI equivalent of specialized consultants. A legal document AI doesn't need to understand cooking recipes. A customer service AI doesn't need to compose poetry. Specialization allows for deeper expertise and more reliable performance.

Pattern #2: Technical Collaboration

Sakana's multi-agent research demonstrates that collaboration between specialized models consistently outperforms single large models on complex reasoning tasks. Their system teaches different AI models to work together, with each agent contributing its strengths while compensating for others' weaknesses.

This mirrors how human expert teams function. You don't want your financial analyst trying to diagnose medical conditions, but you want them collaborating with medical experts when evaluating healthcare investments. AI systems are discovering the same efficiency through specialization.

Pattern #3: Infrastructure for Coordination

Both developments point toward the need for sophisticated orchestration infrastructure. Stripe's payment processing for AI businesses requires systems that can handle complex, multi-step workflows. Sakana's multi-agent collaboration requires coordination protocols that manage communication between specialized systems.

The companies building this orchestration layer may capture more value than those building individual models. There is immense value in building the equivalent of project managers for AI teams

Contrarian Take: The Great AGI Misdirection

The race for artificial general intelligence is a distraction from where the real value is being created.

While technology giants pour billions into building ever-larger general models, the actual AI economy is being built by companies that understand specialization economics. The businesses generating real revenue aren't those with the most parameters. They're instead the companies with the deepest domain expertise embedded in their AI systems.

This creates a massive opportunity for smaller companies and startups. You don't need OpenAI's compute budget to build the world's best legal contract analyzer or the most effective sales email generator. You need deep domain knowledge and focused execution.

The specialization paradox suggests that the future AI economy will be dominated not by a few AGI giants, but by thousands of specialized AI businesses working together through coordination platforms.

The Bigger Picture: AI Teams vs. AI Gods

All of this points toward a future where AI capability emerges from coordination rather than individual intelligence. Instead of building artificial gods, we're building artificial organizations. These teams of specialized agents will ultimately tackle complex problems through collaboration.

This has profound implications for AI safety, competitive dynamics, and technological development. Specialized systems are more auditable, more controllable, and more aligned with human values than general systems attempting to be all things to all people.

Sakana's research suggests that the most capable AI systems of the future will be those that have learned to collaborate effectively, not those that have accumulated the most raw processing power.

In motion,
Justin Wright

1. Meta reportedly hires four more researchers from OpenAI (TechCrunch)

2. Tencent Hunyuan A13B, an innovative and open-source LLM (GitHub)

3. The Path to Medical Superintelligence (Microsoft)

4. Announcing the Open Source Release of the ERNIE 4.5 Model Family (Baidu)

5. Sam Altman Slams Meta’s AI Talent-Poaching Spree (Wired)