An ungoverned AI is like a chaotic, unpredictable forest. To achieve consistent business value, AI must be 'farmed'—a process of applying governance, organization, and boundaries to cultivate predictable results. This regulated approach is key to harnessing AI for reliable revenue generation.

The rapid pace of AI advancement requires designing systems with the assumption of frequent, fundamental change. This means avoiding attachment to current workflows, even recently successful ones, and being culturally ready to reimagine everything from first principles on a regular basis.

As AI accelerates engineering output, the traditional feature-by-feature design process becomes a bottleneck. Designers must now prioritize systems thinking: creating robust, reusable components. This "building blocks" approach enables speed and consistency, with the mantra sometimes being "do less; don't design if you don't have to."

Unlike traditional software where problems are solved by debugging code, improving AI systems is an organic process. Getting from an 80% effective prototype to a 99% production-ready system requires a new development loop focused on collecting user feedback and signals to retrain the model.

In a rapidly changing AI landscape, don't wait to build. Instead, use this litmus test: if a more intelligent future model would make your project better, build it. If a smarter model would render your project obsolete (e.g., a complex rules-based automation), your approach is too fragile and should be rethought.

Industrial monocropping depletes topsoil and requires pesticides. AI-powered humanoid robots could manage complex, multi-species "food forests" (like the Aztec Milpa system), creating a regenerative, resilient, and pesticide-free food supply.

The goal of AI development shouldn't be to perfectly replicate human cognition, a complex and perhaps unfalsifiable target. Instead, a more pragmatic approach is to draw high-level inspiration from nature to build novel forms of intelligence designed specifically to understand and serve human needs.

The biggest gains from AI come not from automating steps in an existing process, but from starting with the desired outcome and co-creating a new workflow with AI. This "first principles" approach leverages AI's capabilities far more effectively than piecemeal automation.

A more advanced use of AI involves working backward from an ultimate goal. By having AI interview you about your objectives and context, you can uncover opportunities to fundamentally change or eliminate workflows, rather than just making inefficient processes faster. This shifts the focus from productivity to innovation.