The physical AI industry is no longer in the fundamental research stage. It has entered a crucial "advanced engineering" phase between R&D and mass production. The focus is now on solving the subcomponent and reliability problems required to productionize existing technologies.

A major bottleneck in AI progress is the gap between research and production. Researchers produce powerful models but often lack software engineering discipline. This results in code that is not portable, extensible, or robust, hindering the transition from a novel idea to a scalable, reliable product.

Advised by Dr. Bob Langer, Vivtex's founders understood that academic tech often fails due to insufficient validation. The spin-out was triggered not by initial exciting results, but after years of rigorous validation proving the platform's commercial application in large animal models, a crucial de-risking step.

A persistent gap exists where academic innovators develop brilliant science but fail to articulate how it becomes a product. Investors can't fund technology 'thrown over the transom'; they need to see a clear Target Product Profile (TPP) and a path to a return on investment, even at the earliest stages.

Drone company Pika stresses that going from an initial working prototype to a commercially viable product that customers can rely on for daily, intensive operations constitutes 99% of the development effort.

A flashy AI demo can be created quickly, showcasing best-case performance. A real product, however, must be robust and reliable even on its worst day. The unglamorous engineering effort to bridge this gap between a demo and a production-ready product is immense and often underestimated by stakeholders.

Moving technology from academia to a startup requires a crucial mindset shift. The academic goal of publishing data must be replaced by the industry requirement of extensive validation. For Vivtex, this single piece of advice added years of work but was essential for creating a commercially viable platform.

Resvita Bio's CEO notes that in academia, scientists conduct numerous experiments to prove a single point for publication. In a startup, the focus shifts to building momentum. Once a concept is proven, the team must immediately move to the next challenge rather than over-verifying with redundant experiments.

Moving from a science-focused research phase to building physical technology demonstrators is critical. The sooner a deep tech company does this, the faster it uncovers new real-world challenges, creates tangible proof for investors and customers, and fosters a culture of building, not just researching.