Shift focus from the physical object to the process it enables. Whether for surgery, labs, or logistics, successful product development requires deeply understanding and improving the underlying workflow. The specific technology is secondary to a system design that correctly supports the process.

Instead of forcing a microbe to create a foreign product through extensive engineering, first identify what it is predisposed to make. Then, apply minimal genetic "nudges" to optimize existing pathways. This "downhill" approach creates a much more efficient and viable R&D process.

The 'AskNature' website catalogues nature's solutions to complex problems, providing a free R&D resource for product innovation. Entrepreneurs can leverage millions of years of evolution for design inspiration (e.g., water-resistant feathers) and create powerful, built-in marketing narratives for their products.

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.

Mercedes-Benz's Bionic concept car, based on a flawed understanding of the boxfish, was never commercialized. However, the biomimetic story generated immense positive PR, associating the brand with innovation and nature. This proves the narrative itself can be the primary, and highly successful, product.

The act of working through a project over time is where the best ideas are discovered. Shortcutting this tedious process with AI might produce a result faster, but it will likely be far worse because it skips the essential journey of discovery and transformation.

The honeycomb panel wasn't simply copied from bees. Its creation involved centuries of human geometry, Darwin's research, and engineering. Now, modern engineers are re-examining bees' imperfect honeycombs to find new efficiencies our idealized models missed, creating a complex feedback loop between nature and technology.

While biology (birds) provides initial inspiration for flight, progress eventually requires engineering machine-specific solutions (jet engines). Similarly, AI learned foundational principles from human cognition, but its recent breakthroughs come from non-biological methods like massive scaling. The focus should be on universal "laws of thought," not just mimicking biological hardware.

Resvita Bio's approach isn't about creating proteins from scratch. Instead, they use machine learning to 'read the book of life comprehensively,' analyzing how different organisms have evolved to solve the same biological problem. This allows them to synthesize nature's best solutions into an ideal therapeutic protein.