00:38:39 · Mark Jacobstein, S3
[00:38:39] S1: Thanks. All right. Who's next? I see a hand. [00:38:45] S3: We have a similar background, E major, undergrad, masters and PhD in information theory. So during my PhD, got heavily influenced by John Wheeler's It From Bit thesis. And now comes the question of what part of biology is conquerable through the instrumentation of or the project of computation itself. What part of biology can be conquered and what answers can be answered using the abstractions afforded by the philosophy of computation, right? So there, I'm curious from your vantage point, do you see that this compute maximalist or compute reductionist attitude can take us further? Because I'm trying to understand, like if isomorphic lapse, which now claims that if biology and hence disease is mod level and computable and you can help climb on GPUs, then where is the residue from a company such as yours coming from? What is it that you can solve using your data assets and your compounding flywheels that a computation reductionist research lab like Isomorphic cannot?
00:39:54 · Helmy Eltoukhy
[00:39:54] S0: Yes, I don't know much. I mean, I don't want to speak sort of out of turn in terms of what they're doing, what they're not doing. Obviously, a lot of smart people there, a lot of funding and so on, but just from seeing other sort of what exists right now in terms of the world of biology, there aren't amazing data. I mean, are some data sets. Some of it was, I'll give you an example. We detect things down at one in a thousand, some of the newer tests, one in ten thousand, at lower levels. Imagine you said that I tested a million people and I detected at levels of one in ten or one in one hundred. So the claims all sound the same. I have a million blood samples and so on, but you may not be seeing everything that's there because you're not actually seeing what's unique, like the unique biology that's being captured by some of those rare variants. It's the same thing. Like a lot of the data that's out there that's RNA is they're doing like 10,000 transcripts per cell. But it turns out like, you know, most of that is capturing housekeeping things and so on. It's not actually capturing the low level sort of transcripts and so on that actually differentiate that. So, the devil's in the details in terms of like, and I think we've seen this, there's the Gartner hype curve, right, of peak of inflated expectations and science because we create a new tool, it allows us to see something that we haven't seen before, and we make the assumption that we're seeing 100% of what's there with that tool, when in reality we're still under sampling 1000X from what's actually there. Yeah, we improved the 10X, but unfortunately there's 10000X sort of necessary. And so the question is there'll be advancements there, but it's unlikely to be like solving all of human disease either because we're still heavily under sampling. I think our sort of unique advantage is that we're sequencing more and more data. Like, we have a data collection engine. Next year we'll be testing probably a million sort of individuals across different disease types. We're getting all that data. We're improving the payload of the tests, so sequencing goes from 3,000,000,000 to $1,000 to $100 to $10 to $1 Our tests will be collecting 10x, 100x, 1000x more data per test. And so we have sort of all of that working in our advantage.
00:42:31 · Helmy Eltoukhy, Mark Jacobstein, Ross
So it's just a matter of time before we collect enough data to make some of those insights. But that's not to say that there won't be like a lot of advances made. It's just probably not 100% advances, but there'll be some advances in certain areas. [00:42:50] S1: Fantastic. We have time for, we got time for one more? We're sort of at five. Let's do one more. Who's got the last question here? All right. I'll take [00:43:00] S4: it. Go, Ross. So I heard what the next steps are for Guardant in terms of leveraging new technologies, but if you were exiting your PhD now and you had access to looking at what the new technologies are, what the new mountains are, and finding that valley between them, You know, with a blue sky, what would be the next direction for diagnostics? [00:43:21] S0: Yeah, I don't know. It's kind of hard. I think so much about what we're doing at Guardant. I don't know all the stuff that's being done in academia, but I would say that, I think I said this before, is that almost every great advancement in medicine has come from seeing something that was once invisible. So it's basically like going and let's take, you know, there are new tools. There are new sort of spatial biology tools. There are new like analysis tools that are 10x ing, 100x ing what was possible a year ago. So it would probably be at the intersection of one of those tools, sort of AI, obviously, and taking that and applying it in sort of a cool way to a new application. [00:44:08] S1: Fantastic. Helmy, thank you so much. Please join me in applause for Helmy.