Instead of selling AI directly to consumers, Meta provides AI tools to its 15 million business advertisers. This makes ads smarter and more effective, increasing ad revenue. This profitable ad machine then funds Meta's massive, long-term AI ambitions, creating a powerful flywheel.

To balance AI hype with reality, leaders should create two distinct teams. One focuses on generating measurable ROI this quarter using current AI capabilities. A separate "tiger team" incubates high-risk, experimental projects that operate at startup speed to prevent long-term disruption.

The most successful companies deploying AI use a "leadership lab and crowd" model. Leadership provides clear direction, while the entire organization is given access to tools to experiment and discover novel use cases. An internal team then harvests these grassroots ideas for strategic implementation.

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.

To innovate quickly without being bogged down by technical debt, portfolio companies should ring-fence new AI development. By outsourcing it and treating it as a separate "skunk works" project, the core tech team can focus on existing systems while the new initiative succeeds or fails on its own merits.

Small firms can outmaneuver large corporations in the AI era by embracing rapid, low-cost experimentation. While enterprises spend millions on specialized PhDs for single use cases, agile companies constantly test new models, learn from failures, and deploy what works to dominate their market.

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.

Anthropic's core product team was too small to explore frontier AI applications, focusing instead on incremental updates. The Labs division was created specifically to build next-generation products that could showcase the exponential growth of their AI models, ensuring the product roadmap kept pace with the technology curve.

Brex formed a small, centralized AI team by asking, "What would a company founded today to disrupt Brex look like?" This team operates with the speed and focus of a startup, separate from the main engineering org to avoid corporate inertia.