AI tools democratize prototyping, but their true power is in rapidly exploring multiple ideas (divergence) and then testing and refining them (convergence). This dramatically accelerates the creative and validation process before significant engineering resources are committed.

Tencent employs a "horse racing" strategy, encouraging different internal teams to build similar, competing products. While chaotic, it's a deliberate management approach to spur innovation and find the best solution in nascent markets like AI agents, even if projects end up competing for the same internal resources.

The goal isn't to build one perfect prototype quickly. The real strategic advantage of AI tools is the ability to generate three or four distinct variations of a feature in a short time. This allows teams to explore a wider solution space and make better decisions after hands-on testing.

While traditionally creating cultural friction, separate innovation teams are now more viable thanks to AI. The ability to go from idea to prototype extremely fast and leanly allows a small team to explore the "next frontier" without derailing the core product org, provided clear handoff rules exist.

Managing innovative teams requires a balancing act. While sharing resources like software improves efficiency, it creates blind spots. Leaders should intentionally foster independent 'splinter groups' to work on the same problem, ensuring critical comparisons can be made to uncover hidden errors.

Anthropic leverages the low cost of execution in the AI era by building multiple potential product versions simultaneously. This "build all candidates" approach replaces lengthy spec-writing and low-bandwidth customer research, allowing them to pick the best functioning prototype directly.

A new organizational model is emerging where companies create small, agile teams comprising a senior expert, an engineer, and a marketer. Empowered by AI tools, these pods can develop and launch new products in a week, a task that once required large teams and over six months.

Contrary to traditional efficiency models, leaders should allow teams to build similar AI tools or agents. In this early stage, widespread hands-on experimentation and learning are more valuable than preventing redundant work. The goal is to get everyone testing, not to achieve premature standardization.

Since AI agents dramatically lower the cost of building solutions, the premium on getting it perfect the first time diminishes. The new competitive advantage lies in quickly launching and iterating on multiple solutions based on real-world outcomes, rather than engaging in exhaustive upfront planning.