Mar 23, 2018 · 21m · a16z
Pande & Conde: 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
In this a16z podcast episode, General Partners Vijay Pande and Jorge Conde explore how biology is transitioning from high-risk scientific discovery into a predictable, repeatable engineering discipline. They examine the role of modular biological components, machine learning, platform business models, and exponential scaling in shaping the future of medicine and synthetic biology.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. How this is scored →
The host as informed peer 3.9 Guest teaching 2.3 Guest disagreement 1.1 The host pushing back 1.1
speaking balance: gold is the host, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 6:10 Questioning historic genetic engineering label
Vijay mildly challenges the traditional terminology of 'genetic engineering', pointing out that historical methods were stochastic rather than true engineering design.
Hardest push from the host ▶ 1:42 Testing the Lego analogy fitJorge challenges Vijay's Lego metaphor, asking whether biological components actually fit together with predictable, standardized precision.
Biggest teaching moment ▶ 7:26 Reversing biotech pipeline valuation logicJorge reframes the discussion by educating on how engineering approaches invert traditional drug valuation models, making subsequent assets inherently more valuable than the first.
The host holds their own ▶ 18:35 Explaining the biotech pilot trapJorge demonstrates expert insight into startup business development, explaining how early-stage biotech companies must overcome activation energy to convert pilots into recurring commercial agreements.
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 |
|---|---|---|---|---|---|---|
| Defining Science vs. Engineering in Biology | 3 | 2 | 1 | 1 | Jorge and Vijay introduce the distinction between science and engineering using definitions from children's and science museum books. Jorge gently probes the Lego analogy by asking for concrete examples of modularity working in modern biology. | |
| Applying Engineering Disciplines and Academic Evolution | 4 | 3 | 2 | 2 | Jorge offers a vivid metaphor comparing historical genetic engineering to playing Boggle. Vijay explains how academic departments like bioengineering evolved to enable engineering principles in biology. | |
| Machine Learning and Data-Driven Value Creation | 5 | 3 | 1 | 1 | Jorge demonstrates strong domain knowledge by explaining how engineering models invert traditional biotech pipeline valuations, making later assets more valuable than early ones. Vijay expands on how machine learning uses false positive data. | |
| The Future of Drug Design and Platform Models | 3 | 2 | 1 | 1 | Jorge asks forward-looking questions about the future ratio of dry labs to wet labs in pharmaceutical companies. Vijay details how computational tools and CROs are shifting the role of medicinal chemists toward drug design. | |
| Biological Design and Unconventional Applications | 3 | 2 | 1 | 1 | Jorge notes that biological design allows making novel things possible rather than just making existing things better. Vijay uses the Apollo space program as a paradigm for breaking massive goals into incremental engineering steps. | |
| Business Development, Proof of Concept, and Avoiding Pilot Traps | 6 | 2 | 2 | 2 | Vijay turns the questions back onto Jorge, asking how startups demonstrate proof of concept. Jorge displays deep business expertise outlining commercialization dynamics and how to avoid the trap of dying from pilots. | |
| Compounding Technology and Exponential Growth | 3 | 2 | 0 | 0 | The conversation concludes with complete alignment on how platform technologies compound over time. Vijay uses the fable of rice grains on a chessboard to illustrate exponential growth in technological capability. |
Statements from this episode (15)
Pande: Science-based development is higher risk than engineering
“So, you know, 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 m…”
Pande: More biotech companies will build modular biological components
“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.”
Pande: Standardized parts turn biology from science into true engineering
“Once you actually have the Legos, Then you get to build stuff. Almost like you know, when people build a bridge, they're not researching steel. You know, they're given the girders and the, and all the materials, and then they put a bridge together. So I think …”
Pande: Principles of traditional engineering disciplines carry over to biological systems
“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, and if yo…”
Conde: CRISPR and new tools make genetic engineering a true discipline
“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.”
Conde: Engineering principles make subsequent drug candidates more valuable than earlier ones
“But in your world, in the world you're describing, this engineering world, it's actually the reverse is true. That 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 va…”
Pande: Experimental failures are as valuable as successes in biological engineering
“But if you're in this engineering curve, the false positives are actually as important to learning as the true positives.”
Pande: Pharma companies will transition into data generation and data science companies
“And I think we're seeing this more and more, where pharma companies will start to view themselves more as data generating companies. And data science companies as machine learning gets in,”
Pande: Drug discovery roles will shift from wet-lab chemistry to computational engineering
“I think I think we're already seeing a little bit of that with just the shift to CROs, where there's like not a purely medicinal chemist job, a sort of drug designer job. And medicinal chemists have so much great intuition and experience designing drugs that t…”
Pande: Biotech market shifting to value reproducible platforms over individual assets
“That's a good point. Like, ML is a platform, ah, amongst others, and then there's data generating platforms, there's data analysis platforms, and that, that platform, I think, really could be a really interesting shift, and we're seeing more and more companies…”
Pande: Future infrastructure will rely on engineered biological systems, 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: Biology as a design medium creates new possibilities rather than mere efficiency
“When you have biology as a design medium, it's about making things possible that you didn't even know were possible.”
Pande: Break moonshot bio goals into engineering milestones instead of screening
“If you break it up into little bits, any little bit isn't so bad. And can be engineered. And you sort of do it step by step by step by step. And I think that's, for me, the inspiration for how to take some big crazy thing like going to the moon, that if you di…”