AI as Leverage vs. AI as Infrastructure
Don't build your own models; use them to amplify talent. Agent systems are thinking-intensive, not capital-intensive.
There is a misconception that to “win at AI,” you need to build your own Foundation Model. That’s like saying to “win at logistics,” you need to build your own trucks. Unless you are Ford (OpenAI/Google), you shouldn’t build trucks. You should build a shipping company (Applications).
Leverage vs. Infrastructure
- Infrastructure (Foundation Models) is Capital Intensive. It costs billions. It requires rare earth metals and nuclear power plants. It depreciates fast (today’s SOTA is tomorrow’s paperweight).
- Leverage (Agent Systems) is Thinking Intensive. It costs creativity. It costs domain expertise. It costs good prompt engineering. It appreciates with use (as you refine the workflow).
The Catch-Up Strategy
For a country or company playing catch-up, the strategy is clear: Don’t own the best models. Use the existing models better than anyone else.
Use AI to:
- Amplify Human Talent: Make your junior engineers perform like seniors.
- Transform Slow Processes: Turn a 3-week paper approval into a 3-minute digital check.
- Unite Fragmented Systems: Use agents to glue together old databases.
- Speed Up Execution: Move faster than the people who are busy building the model.
The Coworker Economics
AI as a replacement is expensive. When you fire a human, you lose institutional knowledge, culture, and adaptability. AI as a coworker is cheap. You keep the human, but you give them superpowers.
The smart money is on building the “Application Layer” that sits between the raw intelligence and the real-world problem. That’s where the value is trapped. And that’s where you can win.
