Fireside Chat, Interview
a16z Podcast | When (and How) Biology Becomes Engineering
- The biological industry is anticipated to transition from a stochastic "science" approach to a methodical "engineering" model within approximately two years, a shift driven by standardized biological components and the application of principles from mechanical, electrical, and computer sciences.
- Genetic engineering is expected to evolve into a designed discipline utilizing DNA as a "design medium," while machine learning is predicted to emerge as a third pathway for connecting science to engineering, creating an alternative to bespoke development.
- Academic departments are projected to continue accelerating the supply of dual-skilled professionals, a trend that supports a future where pharmaceutical companies operate as data-centric entities and large dry labs with computer-based personnel match the scale of wet labs within ten years.
- New workforce roles such as "drug designers" will emerge to focus on design while outsourcing synthesis to Contract Research Organizations, reflecting a broader move toward outsourcing and specialized engineering functions.
- Valuation models are forecast to reverse, where second drug assets hold more value than first ones due to accumulated learning, and platforms may surpass individual assets in value as they enable the reproducible creation of multiple assets on an engineering curve.
- Large-scale biological goals like curing cancer or increasing longevity by 50 percent are expected to be achieved by decomposing them into smaller, engineered milestones rather than via massive screening.
- The collective ecosystem involving academia, startups, and incumbents is expected to be built collaboratively, with large tech companies like Google and IBM potentially influencing the sector by introducing an engineering mentality.
- Business development will favor companies demonstrating high predictability, allowing them to define project plans with specific deliverables over relatively short periods and facilitating a "land and expand" model if value can be proven at scale.
- Technology is projected to follow a compounding improvement curve, potentially doubling every two years or improving by 30% annually, which serves as the force to achieve currently impossible outcomes.
- A significant risk exists where the envisioned engineering-driven drug discovery model will not materialize or will face a different timeline if machine learning fails to become effective.
- The broader future world is expected to shift away from traditional steel and metal toward engineered biological materials capable of sustainable growth, which is described as the beginning of a trend to fight global warming.
- Companies on the engineering curve are expected to grow and gain traction very predictably rather than relying on sporadic successes, with market acceptance of a platform's generalizability typically occurring once success is demonstrated in three contexts.