Jan 2, 2019 · 22m · a16z
a16z Podcast | When (and How) Biology Becomes Engineering
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
a16z General Partners Vijay Pandey and Jorge Conde discuss the fundamental paradigm shift of biology from high-risk, discovery-driven science to predictable, scalable engineering. They explore how modularity, machine learning, and platform business models enable compounding, exponential progress in healthcare startups.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. The host holds 2.4% of the talking time here. How this is scored →
The host as informed peer 4.3 Guest teaching 2.3 Guest disagreement 1.0 The host pushing back 1.2
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 17:00 Vijay turning the question back on Jorge regarding BD buyers
In a gentle role reversal within a very friendly dialogue, Vijay challenges Jorge to answer his own question regarding how enterprise buyers evaluate platform technologies.
Hardest push from the host ▶ 5:21 Jorge pausing the discussion to challenge Vijay on academic adoptionJorge actively intervenes to pause Vijay mid-thought, pressing him to specifically clarify how academic institutions are adapting to bioengineering.
Biggest teaching moment ▶ 6:56 Vijay explaining machine learning as a third engineering medium in bioVijay provides a structured reframe showing how machine learning replaces bespoke, stochastic discovery with repeatable data processing routines.
The host holds their own ▶ 7:50 Jorge explaining the inversion of drug pipeline valuation under engineering principlesJorge displays strong domain mastery by demonstrating how engineering dynamics flip conventional drug pipeline economics, making subsequent assets more valuable than the first.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | The host as informed peer | Guest teaching | Guest disagreement | The host pushing back | Why |
|---|---|---|---|---|---|---|
| Modular Biology and the Biological 'Legos' Concept | 3 | 2 | 1 | 1 | Jorge sets up the discussion using definitions of science versus engineering from children's books and asks for real-world bio examples. Vijay explains biological 'Legos' using CAR-T and Asimov, maintaining a completely collaborative co-host dynamic. | |
| Interdisciplinary Engineering and the Evolution of Academic Bioengineering | 3 | 2 | 1 | 1 | Jorge asks how traditional engineering disciplines apply to biology and pauses Vijay to query academic department evolution. Vijay outlines how bioengineering departments nucleated new interdisciplinary work in a supportive peer dialogue. | |
| Machine Learning and the Data-Driven Pharma Paradigm | 6 | 3 | 1 | 1 | Jorge articulates a deep insight into biotech pipeline valuation, showing how engineering principles invert the traditional chronological valuation model. Vijay expands on the value of negative data in machine learning, reinforcing Jorge's framing. | |
| Platform Technologies and Novel Biological Design Mediums | 4 | 2 | 1 | 1 | Jorge prompts Vijay with an off-the-cuff question about surprising bioengineering applications. Vijay shares a compelling vision of bioluminescent glowing trees replacing streetlights, maintaining an agreeable and imaginative exchange. | |
| Practical Execution: Applying the Apollo Mission Framework to Bio | 3 | 3 | 1 | 1 | Vijay outlines an execution framework using the Apollo space program as an analogy for breaking moonshots into incremental milestones. Jorge builds on this by linking it back to the modular Lego concept. | |
| Go-To-Market Strategies, Business Models, and Compounding Technology | 7 | 2 | 1 | 2 | Vijay turns the questioning back onto Jorge, asking how enterprise buyers evaluate tech platforms. Jorge leads with detailed business development insights on predictability and land-and-expand strategies, while Vijay concludes with a story on technological compounding. |
Statements from this episode (11)
Pandey: Engineering models carry far less risk than science-based models
“We've seen companies that are built with science risk, and companies, especially tech companies that are engineering companies, you know, how can we take things from the science curve, which is stochastic and high risk, towards something that's more like engin…”
Pandey: Startup Asimov is building standardized modular parts for bioengineering
“Companies like Asimov is developing first the Legos and then putting those Legos, helping you put the Lego pieces together. And I think we're going to see more of that.”
Pandey: Traditional engineering principles carry directly over into biological systems
“But I think mechanical engineering, electrical engineering, material science, computer science, all these disciplines are pushing into biology. And so instead of steel, it's bone or it's muscle. But the same principles actually carry over really nicely.”
Conde: CRISPR and Asimov are turning genetic engineering into true design
“And now I think with the advent of things like CRISPR and things that companies like Asimov are doing where they're actually making DNA a design medium. I think it's, we're actually starting to make genetic engineering be an engineer discipline, but I think th…”
Conde: Engineering principles make later drug programs more valuable than earlier ones
“The second drug is more valuable than the first if you're using engineering principles, because what you learn from example one sort of imbues value to example two and so on and so forth.”
Pandey: Engineering-driven biotech treats false positives as valuable learning data
“If you're in this engineering curve, the false positives are actually as important to learning as the true positives.”
Pandey: Pharma companies will reframe themselves as data science companies
“Pharma companies will start to view themselves more as data generating companies and data science companies.”
Pandey: Biotech platforms are shifting from low-valued tools to asset generators
“Before it used to be that the platform was never really valued very much. It's all about the assets. And now we're getting to the point where people are very curious if platforms can reproducibly create multiple assets.”
Pandey: The future physical world will be engineered biology, not steel
“The future will not be steel and metal. It will be this sort of engineered biological thing that just grows, that has this function.”
Conde: Traditional synthetic biology requires testing 10,000 candidates per success
“I think one of the big things in synthetic biology has really been, you try 10,000 things to get one thing to work.”
Conde: Enterprise 'land and expand' failed in biotech due to bespoke science
“The famous concept of land and expand on the enterprise side. You can actually start to see some of that in biology. The reason why land and expand historically hasn't existed in biotech. Is because the expand part was really hard. Because things were so bespo…”