Alloy Therapeutics' CEO describes a key industry dynamic: new AI-driven "tech bio" firms lack deep biological expertise, while established "biotech" firms need to improve their tech capabilities. The biggest breakthroughs will come from companies that successfully merge these two domains.

Unlike traditional engineering, breakthroughs in foundational AI research often feel binary. A model can be completely broken until a handful of key insights are discovered, at which point it suddenly works. This "all or nothing" dynamic makes it impossible to predict timelines, as you don't know if a solution is a week or two years away.

Major advancements in biotech instrumentation are not just software or AI achievements. They are the result of a deeply multidisciplinary effort over many years, requiring innovations and integration across optics, fluidics, chemistry, hardware, and biology to create powerful new tools.

Molecular biology offers a unique form of creative freedom. Unlike fields with immediate feedback where work can be instantly critiqued, the long timelines for experimental results (e.g., weeks to get a dataset) create a protected space for exploration. This "unjudged" period allows scientists to pursue novel ideas without premature criticism.

While biotech cannot easily replicate tech's rapid iteration cycles due to high costs and long feedback loops, it can adopt the capital efficiency model of tech seed investing. The strategy is to kill flawed projects quickly and cheaply, ensuring that when you lose, you lose small.

Unlike math or code with cheap, fast rewards, clinically valuable biology problems lack easily verifiable ground truths. This makes it difficult to create the rapid reinforcement learning loops that drive explosive AI progress in other fields.

Building biologically relevant AI is not a one-off process. It demands a continuous "lab in the loop" system where wet lab experiments generate proprietary data to train models, whose outputs are then physically tested in the lab. This iterative feedback cycle constantly refines the model's predictive accuracy.

Colossal CEO Ben Lamb, a software entrepreneur with no biology background, approached top geneticist George Church seeking world-changing problems. His ability to build teams and secure capital, unconstrained by scientific dogma, was key to launching the ambitious de-extinction venture.

Ginkgo split the challenge of programming biology into design (a "science problem") and testing (an "engineering problem"). They are focusing on the engineering side because it's a more predictable problem that can be systematically solved, unlike the unpredictability of scientific breakthroughs.