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The a16z Show

a16z Podcast: Move Fast But Don't Break Things (When It Comes to Computational Biology)

The a16z Show

a16z

Disruption, Culture, Technology, Software Eating The World, Innovation, Science, Entrepreneurship, Business

4.21.2K Ratings

🗓️ 14 June 2016

⏱️ 29 minutes

🧾️ Download transcript

Summary

The mindset of "move fast and break things", while great for code, isn't exactly great for the human body. So adding computation to biology -- especially in the slow-moving pharmaceutical industry, where drug approval can take years -- brings with it...

Transcript

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0:00.0

The content here is for informational purposes only, should not be taken as legal business,

0:05.3

tax, or investment advice, or be used to evaluate any investment or security and is not

0:10.0

directed at any investors or potential investors in any A16Z fund. For more details,

0:15.0

please see A16Z.com slash disclosures.

0:18.9

Welcome to the A16Z podcast.

0:26.4

I'm Michael Copeland, and we are here in the room with three people to talk about computational biology and how compute meets biology and generally speaking how health care can get better

0:32.6

through means of technology.

0:34.5

And to help us do that, we have A16Z's Vijay Ponday.

0:37.9

We have Jeff Kindler, who is the former CEO of a little company you might have heard of Pfizer,

0:42.4

and who's now an advisor and an investor in the biotech space. And finally, Andrew Radin,

0:47.5

who's the CEO of Tuzar, which is a portfolio company of ours, which is in the computational

0:51.9

biology space, not surprisingly. So welcome, guys.

0:54.7

It's great to be here. We have seen technology and software in particular, you know, from our vantage

1:00.2

point, seep into all kinds of industries, right? So we're seeing it in finance. We're seeing it

1:05.6

in driverless cars. And what there's been this promise in the healthcare space that technology will

1:11.9

come in and revamp all these kind of very expensive, very time-consuming processes to help us

1:20.7

get to better health in the end, better therapeutics, better, you know, testing for that matter.

1:25.2

Maybe, Jeff, let's start with you. Why is this problem so hard and

1:30.1

how you've been in this space for a long time and kind of seen it from all sides? Where are we

1:35.7

headed now and, you know, how do we get there? Sure. Well, first, if I could just go back and

1:40.6

give a little analogy to another industry that I think is appropriate. So if you think of

1:44.7

Hollywood, the movie business, in the 1930s and 40s, the studios had everybody as an employee of

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