DJ Patil

Auren Hoffman (00:00.952)

Hello, fellow data nerds. My guest today is DJ Patel. DJ was the first US chief data scientist under President Obama. He's currently a general partner at Great Point Ventures. He's on the board of Devoted Health. He co-chairs the task force on Gen.ai at the Pentagon. DJ, welcome to World of DAS.

DJ (00:20.739)

Awesome! Finally I get to be here and hang out with you!

Auren Hoffman (00:23.168)

I am very excited. We've had so many conversations over the years. So now we have one that is tape for eternity. So anyone, any AI can be trained on this going forward. Now you actually coined the term data scientist. And now we've got an interesting juxtaposition in the data science world where AI is kind of transforming that field. What percentage of today's data science work do you think is going to be automated over the next five years?

DJ (00:51.931)

Yeah, so I've been a bunch in there. So I think the first is, you know, the term data scientist, obviously, is kind of, we were trying to figure out what we should call ourselves when we do this weird thing with data. We didn't have a labeling approach. And so I think it's like the interesting thing that I'm constantly surprised by is like, why is the term taken off? And I think it's because it's kind of a catch all phrase for people who play with data.

And that it gives you a lot of space to go do things. Like if you were the BI person, you're not allowed to go build models.

Auren Hoffman (01:25.304)

Yep.

DJ (01:28.263)

to seek.

Auren Hoffman (01:28.268)

It's also, it's just a, it's a better, it's a trending. My major in college was operations research, which today would be called data science, but operations research is just a terrible name for a major.

DJ (01:38.509)

Exactly. the statistics is too narrow, doesn't allow the physicists to be in there or the mathematicians. And so it kind of works better. And so I think because it's like a term that has always been a big tent kind of term to allow people in there, like the data engineers used to be data scientists also, they weren't the data engineers. And so what we're now seeing is a question of like, well, what's this next phase of

Auren Hoffman (01:42.52)

Yep.

DJ (02:07.601)

data look like in terms of roles, responsibilities, like what do you do with these different jobs? And I would expect there's increased specialization. And at the same time, we're starting to see this new disruption that's starting to take place because of the use of these technologies. Like we're starting to see the AIification in there, but there's also a ton more that is like the old school is coming back. Like the ability to design tables and structure data more.

more carefully, more thoughtfully with an eye towards a problem. That's a thing, making sure you kind of have good governance, the kind of the data steward ideas coming back. So a lot of this stuff is kind of.

Auren Hoffman (02:45.624)

doesn't so many, doesn't so much of the traditional data science job is being attacked by AI. Like it seems like you could do so much of it today, way more than most other jobs are being attacked, but maybe I'm wrong.

DJ (02:59.707)

Well, think there's parts of it, yes, parts of it. We're just not seeing it happen. At least from where I sit, we haven't seen it happen. Like the biggest limiter for AI that we see in the enterprise right now is it's still people don't have their data in a good place where they can do something with the data.

Auren Hoffman (03:17.698)

Yeah, got it. So still that data munging part of it has to happen.

DJ (03:20.709)

Yeah. We're still on that, we had that kind of pithy way we said it, it's like data cleaning is 80 % of a data scientist's job. I feel like it's kind of gone up to like 87%, sometimes like 90 plus percent, because it's like, you've got more data in, somebody's kind of like, there's these heterogeneous data types that start showing up. So stuff gets really messy. And then you're like, how do I do something with this? And so you're spending all this time munging that data.

Auren Hoffman (03:29.559)

Yeah.

DJ (03:49.666)

You've been living this, your whole career, doing the exact same thing.

Auren Hoffman (03:53.602)

my God. I've been like only data munging. That's all I do. It's a terrible thing to do, but because you, the fun stuff is all the other stuff you have to do after you munch it.

DJ (03:57.457)

Yeah, and-

DJ (04:02.045)

Totally. And you've seen this a ton also. You interview people and they just don't understand how important that is for having context of doing the job. And then you also see what you've done with everything from rap leaf on. also it's like you're taking advantage of the fact that others are either lazy, incompetent, or it's just technically hard to do. And you kind of go across those three dimensions and you get a lot of defensibility.

get when you are good with this, you know, on the ETML side.

Auren Hoffman (04:31.96)

Though I do think that the AI systems do seem, I mean, least we've been using them quite well to like, actually do a lot of the data munging. Like it takes a lot of the rote work out of it. There's a lot of things where sometimes you need like a low price labor somewhere and AI can, can really do most of that. So maybe could take on 80 % of the low price labor you were doing before. So AI does, is attacking a lot of this data cleaning too. Yeah.

DJ (04:52.029)

Yeah!

DJ (04:55.643)

Yeah, so things that you're sending offshore. Yeah, like the cleaning side, like the stuff that you might be sending to a back office that's typically gone out to Mechanical Turk or offshore to like organizations like in India or the Philippines. This often happens in healthcare. You have these BPO shops, these business process kind of shops that do this. A lot of that data is really ripe for it because it's not like real good data cleaning. It's like, well, let's...

change this from comma delimited to semicolon or put it in the right structure.

Auren Hoffman (05:26.606)

How do you think AI is going to attack different economies? For me, from my standpoint, I don't know that it hurts the China economy because it's so manufacturing based or the Japan economy is so manufacturing based. But if you think of the India economy or the Philippines economy, there's so much of that, it's just back office to the US. It seems like those economies are going to get hurt very, very, very quickly through AI. I don't know if you agree with me or not.

DJ (05:49.948)

Yeah.

Yeah, so I think the areas, you know, it's interesting. We had the opportunity to write the first national strategy on AI under President Obama, and that included autonomy and a whole bunch of other things. And what's interesting is what we got right and what we got wrong. In there, what we got wrong is that we thought there was going to be a lot of job displacement, you know, in the classic self-driving cars, like the robotic. And we realized dexterity is hard. What we, you know, because of transformers and all the other technology,

Auren Hoffman (06:14.626)

Yep, truck driver, right. Yeah.

DJ (06:23.035)

you get the ability to code. the companies like Infosys and others where that coding layer in India typically, where that coding layer is not high quality. It's sort of like just laying down tons of lines of code. That stuff I think is starting to get impacted. I expect it over the next five-ish years to really get impacted. A lot of that stuff in India and the Philippines in the back office, a lot of that's healthcare based. And so that is like, you know,

Auren Hoffman (06:42.806)

Agreed, yeah.

DJ (06:50.757)

robotic process automation was already starting to make inroads on that. A lot of places, because of the way healthcare works, is that you have these artificial barriers that allow you to do something with the data. So as AI comes in, I think that, my belief is that that is going to be where the health systems get margin from. And so they're looking at that and saying, hey, how do I take out that margin? And that margin is directly attributed to dollars.

that are outside the United States. And so that impacts the Philippines and India predominantly. How big of that of a market is it for this like that? I just haven't checked or stayed up on top of

Auren Hoffman (07:27.992)

Yeah, it seems like it's going to impact different groups in different ways, but I would not be long India, Philippines right now, at least unless they make their own pivots or they invest more on kind of go more upstream, which they may be doing as well.

DJ (07:43.803)

Right. Well, we are starting to see more of those people in those BPO shops saying, Hey, why am I not building a company that's just doing this and taking it because they have deep, deep expertise process like it's stunning.

Auren Hoffman (07:52.631)

Yeah.

That's right, in the process, right? So maybe the company does extremely well, but they don't need, instead of having a thousand employees, they have 200 or something.

DJ (08:03.993)

Right. And then like what we're seeing, which is stunning, is how many companies in Israel, out of Israel, out of India, who are healthcare focused, focusing only on US healthcare. And then when I say healthcare, I mean like the back office, the claims engine, all these components. And so they understand healthcare better than many of us in the United States do.

Auren Hoffman (08:20.557)

Yeah.

Auren Hoffman (08:27.18)

Yeah, that's a really good point. My experience has been like that the average software engineer at a top company is super high quality, but the average data scientist at that same company is often like fairly mediocre. Would you disagree with that?

DJ (08:44.187)

I think it's super lumpy all over the place. You can go to some places and you find data scientists who punch way above their weights and you find others where you're just like, it's very frankly weak sauce. I think there's a few things that are going on is that the first is that the places where you see really good data scientists doing stuff, they're not going to hang out if they're not valued. And most of time, even though you have good data engineering, it doesn't mean you value the data scientists or the work that you do.

Auren Hoffman (09:06.488)

Yeah.

Auren Hoffman (09:11.074)

Good point. So at Google, you're just going to value the software engineer more than the data scientist. the data, a data, a great data scientist might leave and you're just left with the good data scientist or something.

DJ (09:15.261)

That's right.

DJ (09:19.945)

That's right. And so what I see a lot of times is that the data scientists are in sufficient demand and they go to good places. And now that what you, especially the data science, so let's kind of split the data science camp into two, like which is traditional, just business intelligence analysis and those that are building models, like that would be typically machine learning algorithms all the way into the AI layer. And so if you get to the business intelligence layer component,

Auren Hoffman (09:41.304)

Yeah.

DJ (09:49.117)

Those people, a lot of times, pick up other jobs. You see a number of data scientists who are really good. But people forget Kevin Wheal, who's head of product for OpenAI, started off as a data scientist at CoolIris, then went to Twitter as a data scientist, and then moved up into product. I would argue the similar thing happened to me, as on the technical side, is data science kind of playing with data, doing stuff, and eventually moved into product roles.

Auren Hoffman (09:52.61)

Yeah, the product manager or there's something like that. Yeah.

Auren Hoffman (10:07.672)

to product. Yeah.

DJ (10:18.053)

And so I think the data scientists often have the ability to take on other roles versus engineering is like you're in the engineering box, like you're technical there.

Auren Hoffman (10:25.87)

Yeah, yeah, yeah, yeah, yeah. And it's a good point because if a company doesn't value engineering, like the great engineers are probably going somewhere else anyway. it's a that's

DJ (10:35.687)

That's right. Well, here's another thing that we see and, Orin, think you fit squarely in this bucket is a lot of the people we see who are data scientists are just multi-dimensional by the nature. Like I think that's why they, get attracted to this, right? You know, could you been a Silicon Valley guy? Could you be a policy guy? Could you be like a dozen of another dozen type of roles? You could, you're equally adept at all of them. The physicists that we saw coming into Wall Street early on.

Auren Hoffman (11:04.449)

Yep.

DJ (11:04.605)

playing with experimental data. They were data scientists too, whatever we were going to call them.

Auren Hoffman (11:08.77)

Yeah, that was the main data science role was that physicist. Yeah.

DJ (11:12.291)

Exactly. They're just like, you're a quant. And so what I think we have now that we see across data science, especially places like Berkeley, where we have an incredible data science major program, is those students are multi-dimensional. Like if you want to do economics and you want to play a ton with data, where's your home? If you want to do anthropology plus data, there's no direct home or social sciences. There's this kind of new

horizontal across the verticals of traditional academia and data science ends up being that layer.

Auren Hoffman (11:47.906)

Yet often that the person who's most successful in those fields, the Raj Chettis of the world, they're also data scientists as well. Like they are, they happen to be an economist. So, but they really have that extra data science skill that's there.

DJ (12:03.005)

Right. think Raj Chetty is a perfect example of like, what do you call him? You call him a guy who can solve problems.

Auren Hoffman (12:07.596)

Right, right. He would call himself an economist, but he's clearly also a data scientist. Like he would not be successful if he didn't have that skill as well.

DJ (12:13.927)

That's Well, here's the thing that we're, I have been seeing for the last almost 10 years is that departments like economics have not given the course load or kept up with the world as data gets messier. And so what you see from the, lot of those students is they want to go, they need the skills and techniques to work with messy data. So where are they getting that?

They're getting it out of the data science shops, out of the AI parts of the computer science departments, other places. And that's the shift I think we're also seeing now with full-blown AI is like, where are you going to get that training? Where is that happening? It's oftentimes not in your home department. It's like there's a little small cadre of people who've really got that. And we haven't figured out how to get that to scale yet.

Auren Hoffman (13:04.93)

Yeah, interesting. Okay, that's really, really interesting. back in the day when I used to interview people for data science related roles, I would care a lot about their proficiency in things like probability and statistics. Does that even matter anymore?

DJ (13:26.107)

Yes. But what I'm testing for is I'm testing for a few things. My default question back at LinkedIn was like, here, we gave you all of LinkedIn data. What do you want to know? What questions have you got? first, I'm looking for creativity, curiosity. So I'm first looking for that. Because I'm looking for the long pole. Because there's a lot of people with the technical skill. So I want that early decision point.

Auren Hoffman (13:37.58)

Yeah. So that's more creativity too.

Yeah.

DJ (13:53.405)

rather than the technical capacity first. Second then what I wanna see is that you're not just doing a cookbook approach to solve problems. Like you're thinking about the different parts of the messiness of data, the other issues. Like, you know, the classic used to be somebody would be like, you give them a bunch of data and they're like, well, I'm just gonna do K-means. And then they're like, okay, then I'm gonna do, you know, SVD. And they're just kind of going through this like recipe book of things. And you're like, did you ever ask like,

Where did this data come from? Did you ask some of the other characteristics? And then the probabilistic and the very technical is, do you have real touch and feel for the nuances of data? Because you can understand probabilities, but there's a difference in being able to appreciate like.

Auren Hoffman (14:23.342)

Yeah, yep.

DJ (14:45.649)

the probabilities may be lying to you or confusing or there may be other intrinsic things. The depth of understanding of probability or statistics is really important for me.

Auren Hoffman (14:57.794)

Yeah, yeah. Okay. Yeah, that's super interesting. What are we like struggling most with data science today?

DJ (15:09.983)

boy, what are we struggling the most? I think we're struggling with a few things. One is, we got to, and I think everyone is gonna struggle with this. It's like, the world's changing. The technology landscape is changing so incredibly fast. What should you do? Like, what should you study? Where should you specialize in? You know, I get asked all the time, I'm sure like you do, it's like, what should I major in? Like, what are those things? And I kind of tell people like, well, I would major in something where you're also

deeply passionate in several other areas. Like you can't just be only computer science or like only data science. think you've got to, yeah, you've got to be like, you know, some, guess the pinker way of saying it is like, you want to be a T. I think you've got to be like an E. You've got to be deep in three areas and then horizontal. And so like, if you're going to be interacting in the physical world, like mechanical engineering or robotics or something and data,

Auren Hoffman (15:44.246)

Yeah. It's the join, the join of the things are so big.

Auren Hoffman (15:57.235)

huh. Yep.

DJ (16:06.865)

That's great. That's like a super dynamic place to be. Like, that's awesome. But you got to have that. The other one is that I would say, is that the dog again? I like your dog.

DJ (16:34.905)

I lost your audio. Nope. Yep, now I can hear you.

Auren Hoffman (16:38.83)

Can you hear me now? Okay. All right. Awesome. All right. Great.

DJ (16:43.387)

You want to that one over?

Auren Hoffman (16:44.576)

Yeah, no, no. yeah, I think, I think we, yeah, that was, I'll add it. I'll add that one. And I really apologize on that one. I guess, okay, let's, let's move to the next question. One thing that I'm surprised about is just the value of data, raw data. I would have thought that was just the rise of data science, the rise of AI that just selling data would be a better business today than it is.

DJ (16:49.509)

Yeah. Yeah, no worries.

Auren Hoffman (17:10.732)

Like the market for raw data is not growing. It's growing a little bit, but it's not nearly growing as exponentially as I would have predicted five, 10 years ago. Why is that the case?

DJ (17:23.226)

Yeah, so I think there's a few things that the first I would point people to is I think you had a really good thread about, you know, walking through the the rap leaf acquisition all the way through right like like what what that look like and the value creation and how companies think about so that's a that's a really important take that I think people don't appreciate it like the business sector. Two is I think when we see the value of the

data ecosystem, there's a few things going on. One is there's a set of data that I think remains very polarized. And I certainly spell in this camp around certain types of data brokers and the information that has been captured, which really aligns with the FTC report that was published almost a decade ago and Congress has yet to act on it. The other form that I think is there is there people have yet to see the unlock of data.

Like one of the versions of the big data collection components that's in there is biobanks. And so we have all this data that's been collected around people, but we haven't yet gotten the pharmaceuticals out of it. So people are asking, hey, what's the value there? In the collection of data, like the type that Safegraph and you've been working on, one of the things that's always been surprising to me is like, we saw incredible value of what could be done with this data during COVID response.

Auren Hoffman (18:45.1)

Yeah, yeah for sure. Yep.

DJ (18:47.261)

data California, a ton of the nation, ton on this front. And we said, look, look at all this is being done, but people haven't been able to appreciate it. I wonder a lot of times, and I think healthcare is a little bit more of the way to look at this, which is it's not just that it's you're selling the data, you're selling a very crisp product that the data sits is underneath of it. And so you don't realize that the secret sauce really is data. So it kind of looks a little bit more like LinkedIn, maybe.

Auren Hoffman (19:17.218)

Yeah, it's an application. You're selling the application is, been a lot easier than selling the data itself.

DJ (19:17.455)

Is it like, the application.

DJ (19:24.091)

That's right. Because people, and to be fair also, there's a lot of data acquisition that you can do. You can buy lots of data and it does not move the needle. It's not that good. Like I had to do this for devoted health in the early days to try to build out our mailing list, physical mailing list to people and figure out, know, like who might be eligible in our markets that could benefit from our service. And I looked at, we did a ton of this looking through the data that we were buying from

vendors and we're like, this is not good at all. we're getting what? It's right.

Auren Hoffman (19:56.206)

Well, marketing data historically is not good because like in marketing data, if you're like a bit better than throwing darts, it's it has some value. So even like gender is like 75 % and maybe 80 % correct in most marketing data.

DJ (20:05.885)

That's right.

DJ (20:12.465)

Right. Well, we thought we were clever because we were like, well, we know this isn't that great. What happens if we bring this data together with other data sets? Could we do something more? you know, it was... Exactly. It turned out not to be as a quality that we wanted.

Auren Hoffman (20:19.598)

Correct. Yeah. Yeah. Yeah. That's the problem. Yeah. Just compound the errors. Yeah. It's like 0.8 times 0.8. It's not, it's not the other way. Right. Right. Yeah.

Auren Hoffman (20:36.642)

Now, you are also involved very much in like the DOD world. What are some non-obvious issues the Department of Defense is grappling with when it comes to AI?

DJ (20:46.693)

Yeah, so there's a whole bunch of things that I think are worth thinking about. the first is how do you use these technologies? Where do you use them? And sometimes people just think, people's heads jump off into just like autonomous weapons systems or, you know, the nuclear, the whole nuclear security footprint is going to go to AI. It's actually that some of the best places, the way to think about this is on the X axis, this is like prime for one of your kind of

Auren Hoffman (21:00.866)

Yeah, yeah, yeah.

DJ (21:14.321)

your kind of social media posts of like the y-axis is impact kind of like think of it as a hand wavy impact slash scale. And then think of the x-axis is consequences. So if you're high on the consequences side, that's where your weapons kind of weapons worlds are. That's where medical like in the medical clinical world, that's like there like surgery, diagnosis, like that's high consequences. High impact.

Auren Hoffman (21:22.978)

Yeah.

Auren Hoffman (21:38.349)

Yeah.

DJ (21:43.395)

Low consequences is like travel systems, paperwork being filled out, things like that, logistics, moving batteries, fuel, those types of things. Perfect, amazing opportunities to do that. You have a lot of people, like over 3 million people who work at the DoD. A lot of them are doing jobs that they don't want to do because they're just like, you you got some enlisted person who's just like recopying a form. It's like a bank.

Auren Hoffman (22:10.754)

Yeah, yeah, totally.

DJ (22:12.551)

So that's the place where it is. You also think about the data sets that the DOD has overall. And what could be unleashed if you bring all that data together, both for the national security apparatus, for research and development purposes, health care? There's an incredible opportunity. Like one of the ones that I think AI has is our fastest path to figuring out how to get synthetic blood developed, which would be amazing, not only in the battlefield, but you know,

People have it in an emergency vehicle, those types of situations. Synthetic blood could be a game changer for the country. Tissue regeneration, these type things, these technologies to get advances in that are material sciences on a path to fusion. That is going to be AI driven. And that, me, is some of the most exciting opportunities. And so the question is, how do do that? When people talk about using AI for planning systems or everything,

This is like the same thing that we've seen in data. Like the reason why you don't see data used in some organizations or in basketball teams or a lot of other places is it's cultural. There's a very big cultural element to this. And if you can't get a notion of trust. So when people talk about trusted systems, a lot of times they're thinking like transparent, fair, fat, all these kinds of things. And what I would encourage people to think about is how do you develop trust with these systems?

Auren Hoffman (23:21.517)

Yeah.

DJ (23:39.549)

Trust includes all of those things, but you have to put this in a commander's hand or somebody who's a decision maker and say, here's the output, do you believe it? Are you willing to take action based on this? And that is a different component of how we build products rather than just the technical ability to build a product.

Auren Hoffman (24:00.546)

The Defense Department writ large is one of the most bureaucratic institutions in the world. And so on one hand, you're the great AI can attack the bureaucracy and make things way more efficient, make things move faster, make decisions get made quicker, et cetera, because it can flatten things out. On the other hand, the hardest thing to attack sometimes is bureaucracy because the bureaucracy will stifle it. They are smart. They don't want to lose their own jobs.

They'll make things really slow. Everyone I know in the Defense Department who's tried to implement whatever it is, some transformer, some other type of large language models, has been stymied, has had 20,000 pieces of red tape thrown at them and not being able to do something. How do we actually make progress in the defense department? are we just like 30 years from now, we're still going to have like

kind of a first rate bureaucracy and a third rate fighting force.

DJ (25:05.169)

So I think that there's a whole ton in this. So the first is my belief is the fighting force that is AI native is the one that wins. You have to be AI native to win and to...

Auren Hoffman (25:18.424)

Correct. Everyone, everyone you talk to in defense department believes that I've never, today. Today I would say a hundred percent of people that I have ever talked to believe that they just, and they all want it to happen. They just, they just, they just all think it won't happen because of the bureaucracy.

DJ (25:24.122)

Not five years ago, five years ago that...

DJ (25:31.207)

So that has taken us.

DJ (25:36.113)

Right. People would never thought like people, the Air Force adamantly used to refuse the idea that there could be autonomous drones in use. you know, that was, that was an active stance. The Navy was there. So I would, I use that as an intentional example that we can create a thinking mind shift. So we've created the thinking mind shift. Now, how do we get the actual work to take place? And that's the other thing. So for example,

Auren Hoffman (25:46.252)

Right. Yeah.

DJ (26:03.589)

One of the programs that we had to run right over, thanks to Ash Carter, was the Hack the Pentagon program, which is allowing the Pentagon to use bug bounty programs. And everyone said, you're going to tell China and all these other countries exactly where the bugs are and where the holes in the security. And you're just like, well, that means they already know where the holes are. Only we're finding out about it. And once we got that in, that everyone sort of that became the de facto way of doing

Auren Hoffman (26:23.414)

Right, right, totally. Right, right.

DJ (26:32.029)

So you have to show people the way to do it. So what we do need is leadership right from the Secretary of Defense all the way through to say, this is the way we have to shift this. Same thing happened when the Navy decided to move to nuclear vehicles, like basically nuclear ships. The other aspect that's not talked about and usually attributed bureaucracy is Congress has to get on board. Every time Congress says, you

Auren Hoffman (26:58.914)

Yeah, my gosh,

DJ (27:01.703)

DoD says, we don't need a new aircraft carrier. We need AI. And Congress says, well, my district produces this part. You get an aircraft carrier. And Rumsfeld, I remember working under President Bush on these problems after 9-11. Rumsfeld was back then saying, we got to transform this stuff to be more agile, to figure these things out. And yet, we're still stuck and mired in this. So the part that I think

Auren Hoffman (27:05.708)

Yeah.

Auren Hoffman (27:09.794)

Exactly. Yeah.

Auren Hoffman (27:30.222)

So what do we do about it? I everyone knows like, you're at this Congress problem. We're spending all this money on aircraft carriers and on tanks that are just going to get easily defeated in the next war. Like we all know all these problems. We've got like these fighter jets that cost a billion dollars each that are just not going to be effective. Like what do we do about it? Like Congress is in the way, the bureaucracy is in the way. Like, is there anything we can do or do we just have to accept defeat?

DJ (27:32.155)

Yeah, so what do we do?

DJ (27:46.311)

Yeah.

DJ (27:53.563)

Yeah. no, no, there's zero chance we can accept defeat. So we should just be first off, think what I tell everyone is it's very easy to be cynical about the DOD. It's very easy to be cynical about the government. Our job is to fight cynicism. Like we have to fight the system because too much is at stake. There's too much at stake. The world is not a safe place. Like when we look around the tax, the way people want to approach things.

That's happening. Other countries want not only our technology, but they want to hit us with the technology so that we're hurt by it. So what are concrete things we can do? So the first is, and I think this is the first tiniest of a step, is the Biden National Security Council issued an executive order just a couple of days ago that says, go faster with AI. National security teams, everyone else, you need to go faster. Two,

is you need programs like the Defense Innovation Unit, things like Doug Beck is leading to go even to be able to get funded. so constituents need to support that. The executive office has to support that to say, have to have DIU, Defense Innovation Unit, to scale at literally 10x. We need that much more to happen because you need new ideas to come in because a lot of the traditional Betway bandits, they're doing what they're supposed to do, which is not helpful.

And then the third thing that is there that we often forget about is we need the right kind of talent to show up. The talent has to show up. that is, of us that have the skills have to go in and take the roles. And not just the roles at the coding layer. We need people who are running major components, who are the AI officers, like the Craig Martels, who used to be at Lyft and at, was it Lyft?

One of the threat sharing companies, was also at LinkedIn and then went in to be the chief AI officer for the DoD and got a bunch of stuff off the ground. You need a deputy secretary who understands this and you need secretaries of defenses who think about this the right way and are going to drive it down from the top. And you need that bottoms up. There's no easy solution when this is 20 % of GD. It's kind of like the same thing as when people are like, why can't we just fix healthcare? You're like, it's so big.

DJ (30:17.285)

You got to attack from all the sides.

Auren Hoffman (30:21.838)

still, I think even if we do all those things, I feel like they all kind of like help on the margins and stuff. Like I'm glad we did D I U X and stuff. I feel like it's helped on the margins and it's good. but like the, the primes don't care. They're still like, they're still as fat as ever. the, all the services companies, Booz Allen, khaki, like all those guys that are out there, like they don't care. Like they're still as fast as like they're, they're all, they're all just like,

And then the DOD itself, it's just like, just have this massive bureaucracy. It's like six layers of paperwork to go do anything. It's so hard to like actually do anything. And I feel like none of those things will, they're all like good ideas. They're all like things we should do for sure, but it doesn't, yeah. But I feel like it doesn't attack like the main problem. And I don't see like the main problem getting any better.

DJ (31:08.187)

It starts.

Auren Hoffman (31:15.948)

Like feel like the main problem still gets worse. All those things help in the margin. But if the main problem gets worse, like it doesn't help. I don't know if you disagree with me.

DJ (31:22.717)

So Oren, part of this is like, you're one of the best conveners on this, people bringing people together. It's people like you and others that have to convene people across.

Auren Hoffman (31:34.882)

Well, honestly, I convened, I'll convene like top generals in DoD and all they do is they all know the problem. They all, but they're like, I have, my hands are tied. Congress has tied it or I can't do anything or it's like, and these are literally like three and four star people. think people have power, but they're like, I have no power. I can't do anything. I'm a three star. a four star, but I literally have no power to go change really anything beyond this like little thing that I'm working on. And then there I can try to do it I can try to grow people and mentor people and all the other things.

DJ (31:45.723)

Right, we need those... I know.

DJ (32:05.383)

So I think there's early indications of positivity, like what we're seeing out of the war colleges with people who are just coming out and they get this, right? So we get that we see that new leadership there. You're absolutely right that there is this paralysis kind of feeling across a lot of people. I look at what CQ Brown is doing, what General Smith is doing, coming down of the Marines who worked with us a ton on a bunch of these things with Ash Carter, and they get it.

And the question that is at hand is how do we create a collective movement for what that is? And that really has to be thought of as major work by Congress. Like there just needs to be a new study of like, do we want the Pentagon to be? And that the problem there is, is that everyone is going to lobby the heck out of it.

And we have to just hold true to that because, part of this, what I hate to see happen is the way it was said to me, which I think is the worst example of this during the Gulf Wars was the country's at war, but the Pentagon isn't. And I think that is a version that we still see today. And the version that I hate to see happen, which is kind of sad is that we have to see an existential crisis moment for us to get our

or act together on the

Auren Hoffman (33:33.998)

Yeah, for sure. If we have an existential crisis, believe we'll figure it out. But ideally, we should be able to figure out without that. Yeah. Yeah. Yeah.

DJ (33:39.418)

We shouldn't wait. We shouldn't wait. That's right. And then we have multiple existential crisis already. I would be very like, there's a reason like I'm an unpaid volunteer to work on these problems at the Pentagon. I would tell everyone I'm doing my part to the max ability that I am legally allowed to by the number of days I'm allowed to spend working on this by under congressional rules, laws. I am maxing out

Auren Hoffman (33:45.838)

Correct. Yeah. They're just slow moving crises. Yeah.

Auren Hoffman (34:08.504)

Right. That's another crazy thing. Like I have, I have friends like you who are, who are volunteering, in national security and they're like, they want to even help more, but they're not allowed to, which makes no sense. So like, don't even want to get paid. Just like, let me help. They're like, no, sorry. You're, we're capping you with the number and you have to like log your hours and all this other stuff. Like it's crazy. Right.

DJ (34:09.03)

all the time I can.

DJ (34:18.941)

That's right.

DJ (34:27.033)

if you look at my paperwork, it's amazing.

Auren Hoffman (34:30.454)

It's so silly. It's like, DJ Padale, like the guy who invented the word data science wants to help out more. And, know, this guy is like crazy expensive, but he, he's willing to help you for free. no, no, sorry. We can't, we can't let them help. It's, it's bureaucracy.

DJ (34:41.831)

Well, we-

DJ (34:46.235)

I mean, this gets into the weeds of something, but there is a real problem we have. you and I lived this. COVID, no one cared about the rules. Right.

Auren Hoffman (34:56.706)

Yeah, that's that was COVID was awesome. Like that, like that, mean, all COVID itself was terrible, but, but the, but that kind of like March through November, let's just say of that, just like intense kind of six months, everyone was working on it. People were, were, like, we're going to break the rules. We're going to, who cares about this? Like transactional authority. Yeah, exactly. Yeah. Yeah. Like, like, let's move fast. Let's break the, like, and things got done.

DJ (35:16.401)

We may not be here tomorrow, but let's just go, right?

That's right. And I think one of the key lessons there, people like you, people like all the people that were written in Michael Lewis's books, all the other stories, all of us, we had deep operating experience. And so we were allowed to come in. Normally, if there's another problem, we're not allowed to come in because of ethics rules or other things. And there's important reasons for the ethics rules, but we need to rethink them.

Auren Hoffman (35:44.813)

Yeah.

DJ (35:50.289)

For example, one of the challenges that the Biden team really has faced, I believe, is they don't have great operators. And why don't they have great operators is fundamentally because the ethics rules prevent operators from being in those jobs because of stock ownership. But if you help build a startup, but there's no liquidity, like there's no public market, you can't get free of it. Or maybe your spouse works.

Auren Hoffman (36:07.544)

Yeah.

DJ (36:16.441)

in an area, or your spouse is doing an incredible amount as a civil servant, and you've lived this too, is that we make it really tough.

Auren Hoffman (36:27.246)

It's so silly. like, imagine if you're like a billionaire and you own like a thousand dollars worth of General Motors stock or something. you might, you probably don't even know it's in your portfolio. Certainly it's not going to affect your decisions or something. Now I can understand if like, if if you, if you own like a billion dollars of General Motors stock and your, and your net worth is 1.1 billion. Yeah. You're going to be making, course, like you should be.

You should be recusing yourself for anything dealing with the automotive industry. That makes sense. But these rules are just so archaic.

DJ (37:03.569)

Well, my soapbox version is I think there's lots of places that I would call theaters, right? Like, you you go through the airport, there's a security theater. There's like a lot of stuff that, there's a bunch of good stuff, but there's a lot of stuff that you're like, this isn't real security. It's just not. There's a bunch around, you know, these things around ethics and there's ethics theater and it's not really addressing the real problem.

Auren Hoffman (37:20.056)

Yeah.

DJ (37:32.125)

There's a bunch of this other stuff. You pick your versions of this. And what I think we would do far better about is just asking, how do we actually solve real problems? And then, as we've learned at Big Data People, taking a probabilistic approach on things is a really healthy, constructive way to actually get maximal forward progress on things.

Auren Hoffman (37:56.888)

Did you read Unit X, the Chris Kirchhoff-Rod Shaw book? OK. Got it right there. OK.

DJ (38:01.117)

I have it right here because Ash Carter is the one that made me figure out how we were going to reboot it. And I was very, very grateful that Raj and Chris decided to jump in the role and do it. And so I owe them a debt of gratitude for taking on that role and driving it.

Auren Hoffman (38:07.778)

Yeah.

Auren Hoffman (38:27.318)

I love the book, I love those two, those guys are great guys, great patriots, but I love the book. But the book is so frustrating. It's like all the red tape, all the ethics things that they threw at, there's so many rules about it, all this ethics stuff that they threw people. It's just like a whole series of terrible anecdotes about how both the Congress and the Defense Department bureaucracy gets in the muck of any type of even minor innovation.

DJ (38:36.421)

Yeah.

DJ (38:56.541)

absolutely. Although I will say this other one that Ash Carter inside the five-sided box, it shows the picture of how it's really brutal to do this, but also some of the tactics and techniques that Ash Carter personally used to make the progress that he did and measured over 30 years, really substantial progress. The thing is, is we can't wait 30 years.

Auren Hoffman (39:07.01)

Yeah.

Auren Hoffman (39:24.771)

Yeah.

DJ (39:24.908)

We can barely wait three years.

Auren Hoffman (39:27.692)

Yeah. mean, he, Carter really did kind of almost like he was a very smart kind of in fighter, very smart about moving things, very smart about getting stuff done. and he had, he had a lot of, imagination and innovation that we just don't see from most people in that role. yeah. Yeah. I mean, but like,

DJ (39:49.789)

You're a physicist!

Auren Hoffman (39:53.652)

Somehow he also had like the strength and the courage to do a lot of stuff. And there's a lot of blowback that could have happened and he could have, there's a lot of people who try and investigate him and try to tarnish him. And there's a lot of. Primes you didn't like him. There's a lot of things that just like, he, we need more people like that in there. And, know, and I, no offense to Lord Austin, I've never met him. seems like a very nice man, but he just doesn't, it just doesn't seem like he's attacking it in the.

way of like, okay, we're in a crisis. We have to do the right thing. he's, he's just more just like managing the, the, the, the, the Pentagon, right? And these are people like from the same party, you would assume there are some, but they're, just very, very different operators.

DJ (40:38.877)

It's, I mean, the DOD, like all federal agencies, and you know, like, we could have this exact same parallel conversation about Health and Human Services, HHS. Why doesn't FDA collaborate more with NIH? Where's the CDC in this? And we saw abject failures from the CDC during COVID. And I think Mandy, who's the current director of CDC, is really working hard to fix some of those things.

Auren Hoffman (40:50.456)

That's right.

DJ (41:09.283)

I would have hoped, and it really pains me to think about the CDC not being the data place that we needed it to be. And we saw what the power of data could be. We saw what you were doing. We saw what COVID tracking project was doing.

Auren Hoffman (41:23.148)

Yeah. Yeah. And 30 years ago, like the CDC, the Atlanta piece of the CDC, that was like amazing. And I don't know if it's 30, 40 years ago, like that was the pinnacle. then somehow it just kind of got, there was some, again, a many of us have been the bureaucracy as well, but just like slowly attacked it. They're able, they're just doing less and less. They still do some good work, but there's so many things that could be doing better.

DJ (41:50.075)

That's right. you know, I'll give you an example. Rob Califf and I worked, who's a current FDA administrator and was FDA administrator when I was working with him and Francis Collins, we were really focused on asking the questions like, why does somebody have to go through phase one trials if something that is effectively the same thing already did? Why should we waste that money and go through all the human trials again?

Auren Hoffman (42:12.514)

Yeah.

DJ (42:18.782)

because we're wasting a lot of time to get there.

Auren Hoffman (42:21.666)

Yeah, because we know it doesn't harm in specific ways. So now it's really just about like, well, solve this particular problem.

DJ (42:25.476)

Exactly.

DJ (42:29.565)

That's right. And then also similarly, like how do you start to get a better level of trust with devices, especially that are AI enabled, all these kinds of questions. so one of the things, so Rob is trying to get that restarted. A lot of that fell apart just because of COVID. I think it's a very bipartisan approach. Everyone is kind of on board with this. The same time, one of the things that, you know, that the reason I got so involved in the creation of Devoted Health,

was we said, who's going to walk the talk of delivering health care? Who's going to figure out new models to do this? Who's actually going to do it? And then our version was, well, we did it on the policy side. Now we need to do it on the entrepreneurial side. And the reason Devoted Health can do so much and is able to deliver care at the price that we can given the margin structure of health care is we are leveraging a ton of data.

We are leveraging not just the data, but we have machine learning, have AI technologies that are used to augment things, mostly the back office, and that's where we get leverage. And that's where we can keep teams smaller, we can keep things cheaper, and we can have better outcomes because we're able to use all of that. But it starts, honestly, with a really good environment and data hygiene and quality with a data as a first class citizen.

Auren Hoffman (43:54.446)

In healthcare, data is so siloed and it's in all these legacy systems and obviously it has to be interoperable. What else do we need to do to make that better?

DJ (44:03.323)

Yeah. So, so healthcare data, many, different types of healthcare data. But the way to think of this was my first big aha with healthcare data in that, that the service of healthcare, not the research side, not pharmaceutical, those are just the service. think hospital data, working with your primary care physician, cetera, insurance payers, that data is not big data. It is very sparse data. It's repetitive data.

Auren Hoffman (44:27.854)

Tiny. Yeah.

DJ (44:31.845)

It's error prone. It's like it's all over the place. And so the infrastructure that we have typically brought to this, which has been consumer internet technology, doesn't work really well. Do you need Kafka moving data around super fast? No, you need more like Airflow plus plus kind of technologies to do this.

Auren Hoffman (44:34.989)

Yeah.

Auren Hoffman (44:46.478)

Yeah.

Auren Hoffman (44:51.032)

Yeah. And ideally it's like somewhat persistent. Like why do you have to answer the same question like 15 times and stuff like that? Like, come on. Or it should just say like, Hey, this is what you answered last time. Like, do want to update it? Like you answered this about your, mother, you know, last time and your grandmother.

DJ (45:06.653)

Right. Well, the fact that the fax machine, you know how hard it is to train someone on a fax machine these days who just graduated from college? They're like, like six generations. Yeah, like, they're like, what's a fax machine? I'm like, you're not supposed to know. It's like trying to teach somebody like what an old school typewriter is, or an eight track or something. And so we have, we,

Auren Hoffman (45:16.058)

yeah, yeah. I have no idea how to use one anymore. yeah. Totally. Yeah.

Auren Hoffman (45:28.342)

Yeah. my God. Yeah. Faxing the prescription. mean, it's just silly.

DJ (45:33.399)

It's ridiculous. And so what we have to do is, and this is where it's so important on policy for these things, is you need technologists in the room. And the reason is because like you have policy, are saying like, we need healthcare interoperability, your data should go from one provider to another, it can't just be locked up. But there's nobody in there who's like, what's the exact API? Like, how do we think about this or these other things? This is actually why Mickey Tripathi, who is the head of the

Auren Hoffman (45:57.261)

Yeah.

DJ (46:01.809)

the national coordinator on this is actually for the first time ever, the national coordinator is not a physician. He's a data guy. He's a technologist, which is like, well, who else would you want? And I can't tell you how much of a fight this was for, this was like one of my big throw downs during the transition, which I'm now somewhat allowed to talk about is saying like,

Auren Hoffman (46:11.31)

That's awesome. Yeah.

Auren Hoffman (46:15.533)

Right.

DJ (46:28.391)

This is the kind of person you need. You don't just want a physician. This is a technical problem. There's enough technologists who understand medicine who can help you here.

Auren Hoffman (46:38.414)

Yeah, I'm with you. it, sometimes we, we, we take this like air of expertise that someone's supposed to have, like they're, they're a good heart surgeon or something, which is a very, very important job. And then we assume like, well, then they could fix the medical, the medical system or something. No, it's like, you're a great heart surgeon. That's what you do. You may not know anything more than anyone else about fixing like the, the IT systems that go through, like it doesn't transfer from one to another.

DJ (47:04.284)

Yeah.

Well, let me address the other side of this, is on the side of health records and using it to discover new drugs, to find new treatment policies. This is what I was really tasked with on the Precision Medicine Initiative was until we were really working on this during the Obama administration, hospital systems thought it was their data, not your data. And so that had to be a major change.

Auren Hoffman (47:29.208)

Right.

DJ (47:35.929)

In there, you have the health system who's saying, well, sorry, it's the EHR company's fault. And the EHR is saying, no, no, it's a hospital's fault. And they're just pointing at each other. And you're like, well, the patient is suffering. What can we do here? Even though we have interoperability today, we have still made it too difficult for large data sets to be brought together for clinical research use. And then when we do bring them together,

Auren Hoffman (47:46.007)

Yeah.

DJ (48:02.607)

Everyone finds a reason why somebody shouldn't have access to the data.

Auren Hoffman (48:06.446)

Yeah, that's right. Right. And today with like, if you use things like homeomorphic encryption or what, you know, you can join datasets and be able to query, ask questions of the datasets without actually seeing the underlying data. So you get rid of, you can have your cake and eat it too. can get rid of the whole privacy issue and be able to get all the benefits of being able to join these like really valuable datasets.

DJ (48:29.553)

Well, you talk to most people who have diseases, are rare diseases, other issues, they're posting all their data online anyway. They're posting it with...

Auren Hoffman (48:39.79)

Correct. Yeah. Because what do you care about your privacy if you're going to die in the next year? You want to get fixed. Yeah.

DJ (48:44.623)

Exactly. And people think that this is just a data perspective. People forget how clinical trials got revamped by a bunch of incredibly courageous women who had stage three, stage four breast cancer, drove a car into the middle of the campus courtyard at Genentech, handcuffed themselves to the car until Genentech said, OK, we're going to listen to your demands about giving us

giving them access to treatments ahead of what they traditionally would. Because they're not going to live until that moment. They know they're going to die. Let's give them a chance now.

Auren Hoffman (49:28.204)

Yeah. What, you know, there's a, a feeling amongst many people I know with the health data world, health tech world, that Epic Systems is the evil empire. How true is that?

DJ (49:41.565)

So here's a challenge is Epic is pervasive. It's like in all these systems. You talk to a lot of physicians, there's a pocket of them, they're like, this is amazing. Look at all the things I can do. There's another pocket, and I fall into this, and I think a lot of other people say, my doctor is spending more time typing than talking to me. And then because of that, you need a scribe and you need all these other systems. And you're like,

Auren Hoffman (50:06.253)

Yeah.

DJ (50:10.631)

Well, who's practicing medicine? And so we, on the data side, have really done in many ways a disservice because we said we could collect all this data, we could do all this stuff with electronic medical record, and yet we have not yielded that result back to the public.

Auren Hoffman (50:28.879)

Even when the data is in Epic, they don't really have easy APIs to get out. Exactly. They're like, no, sorry. It's here. It's ours. It's like, hey, we need a court order to get the data out or something.

DJ (50:33.169)

They won't let you take it out. They won't let you take it out.

DJ (50:39.953)

That's right. Well, and it's not just epic, it's the whole system. when I worked with the Trump administration closely to work on a rule that says hospitals must post their data about pricing online.

Auren Hoffman (50:44.664)

Yeah.

Auren Hoffman (50:53.602)

By the way, I love that rule. Like, why did it take till the Trump administration? I mean, it's so obvious. And people have been talking about that for 30 years before that, right? The Clinton administration talked about it, then the Bush administration, then the Obama administration, finally the Trump administration, finally. I'm like, why did this take so long? Like, it's so obvious we should have done that.

DJ (51:00.977)

FOREVER!

DJ (51:10.779)

Because I can tell you concretely, because every step of the way, eroded their ability to fight back. But it took us first to get people to believe it was their own data. It took Cast Light becoming a company to try this out. And then when this even happened, when the Trump rule went into effect, the Trump administration's rule went into effect on the health care data,

Auren Hoffman (51:29.356)

Yeah.

DJ (51:38.353)

those hospital systems put up the data in file formats that you can't even read it. It's like.

Auren Hoffman (51:42.338)

Where you can't even read it. Yeah. Yeah. They office gated. They did everything possible to not comply.

DJ (51:48.465)

That's right. It's like passive aggressive to the point of like absurdity, right? Until like you have to start enforcing it. And so this is something that most people forget about data technology and making change happen is you have to have an enforcement side. Somebody has to be there to either shame people or other things. And I think the data community kind of collectively came along and shamed the hospital systems into saying this. The big problem.

Auren Hoffman (51:50.86)

Right.

DJ (52:17.123)

still to this day. And one of the things I didn't know about this, I don't think you probably knew about this, but like when we were building our companies and doing stuff in Silicon Valley, the government was saying, we are going to make a move to electronic health records. We need companies to show up. And guess what? None of us showed up. No one in Silicon Valley showed up. Who showed up? Epic's like, well, we're the building layer. I guess we'll do that.

Auren Hoffman (52:39.288)

Yeah.

Auren Hoffman (52:45.358)

Yeah, yeah, Booz Allen, I'm sure is in there. Yeah, yeah, yeah.

DJ (52:48.381)

Exactly. so we're like grumpy with them. like, well, why doesn't have Silicon Valley tech behind it? Well, we didn't show up. We didn't compete.

Auren Hoffman (52:58.114)

Well, my guess is, I don't know the story, but my guess is like these things are rigged from the beginning to make it hard to compete because it's like, you have to have these like 572 requirements, most of which are not needed. And so the government is very good at like specifically requesting software and they're really good at requesting features that will A make things be at least two or three times more expensive and B

Those features will actually degrade things two to three times as well. So they request things all the time that add costs and that make the product worse. And then it's like, well, then like, well, why would you want to build a bad product? If you're, if you're a great tech company, like I don't want to build, I don't want to build a piece of crap. I want to build something beautiful and something that works.

DJ (53:38.63)

Thank you.

DJ (53:43.259)

Right. There's another problem, we call is that a startup is like crossing this desert. The desert is short, the desert is long until you get your first customer. Government makes it really long and then longer and then longer and then longer. And you're like, there's no basis. And that's kind of like why we started the Defense Innovation Unit. And I can't tell you the number of times I jumped in to kind of say like these requirements make no sense or are unnecessary.

Auren Hoffman (53:56.79)

Yeah, totally. Yeah.

Auren Hoffman (54:03.736)

Yeah.

DJ (54:10.875)

The problem also in this is, to be fair, and this is like where this is one of my hopes around AI that we're starting to see is people to kind of go in and say, where is this stuff screwed up? Where is friction in this collection of, because like the number of times general counsel would say, well, it turns out there was this one small law that was passed over here. And you're just like, that's what's going to break everything. This one, this one tiny thing is going to stop the entire

Auren Hoffman (54:23.64)

Yeah.

Auren Hoffman (54:36.263)

Totally.

DJ (54:41.105)

fixing of the healthcare system, you're like, that's insane. And then you have to go to Congress with this one small thing.

DJ (54:51.665)

Like we need a big refactoring. I think that this is one of, oddly enough, Macron is the one who just said this in France, is that we have to ask ourselves really what outcomes we want. And then have we the right protections, the right barriers on it? And in many cases, we have areas where we have too much regulation. And then we have other areas where the regulations do not take into account today's world. And this is like,

just for people to know where I stand on this, this is where I think SB 1047 did not make sense, which is not major. So there were 18 laws that were signed by Governor Newsom here in California around AI. And those 18 were very common sense. You can't use AI to create child porn.

Auren Hoffman (55:24.642)

What is that? I've never heard of that. What's SB 1047?

Auren Hoffman (55:34.648)

Okay.

DJ (55:41.435)

You can't use it to create revenge porn. can't, you have to disclose it if you're using this in clinical settings. You can't use it for hurting people with denial of claim. Like you're like, duh. You're like, that's just stuff shouldn't happen anyway. There's one that is, those are typically at the application layer. There's one at the modeling layer, which is, saying, you know, models above a certain size must do certain things. There's like a whole bunch of other requirements. And you're like, well, that's a yesterday.

Auren Hoffman (55:50.05)

Yeah, seem all very reasonable. Yeah.

Auren Hoffman (56:04.629)

right, right. Okay, yeah.

DJ (56:10.095)

approach the problem. And two is it really hurts the opportunity to leverage open source. And then three is like, where's the enforcement? And four is it's going to cost a ton to stand up a new government organization to look at this stuff. And so and then it's going to hurt our ability to actually use these systems for good purposes.

Auren Hoffman (56:10.21)

Yeah, right.

Auren Hoffman (56:24.94)

Yeah, yeah, yeah.

DJ (56:32.401)

So then it means that we lose this opportunity to actually have this technology, because the laws aren't going to get changed for 30 years. So my view of that is, we'll kick it back down to a collection of people from broad-based around the community. Let's figure out what the common sense approach for this is, and then go.

Auren Hoffman (56:53.326)

So there are so many data founders and CEOs that are listening to this. What is the one data set you wish you could get your hands on that they could create for you?

DJ (57:02.493)

Yeah, so my greatest one that I want to see is the precision medicine data set, like a giant data set of healthcare data, clinical data, payment data, environmental condition data, because I want to ask questions just like we do with an LLM, where we say something like, hey, process this or show me like

find me interesting recipes or build a recipe for me for, you know, a group of 10 people, two of which don't eat fish, three of which are vegan, you know, I can do stuff like that. I want to look at that data set and I want to say, show me all the common characteristics of people who have Lyme disease. Show me all the people who have this rare disease that people like to say is one of N and it turns out there's 30 of them.

Auren Hoffman (57:34.744)

Yup.

DJ (57:56.709)

and what we might have an off label use for that drug. Find me all the places where healthcare costs make no sense because they're outlier costs and we're getting paying for it, but it's fraud or it's abuse of use of systems and it doesn't make sense. I want to reevaluate why we give certain therapies or treatments and that we look at them and we say, is that really makes sense or are we over treating people? Or maybe we're under treating in the case of maternal health like with

Black mothers, example, people of color where the mortality rates are too high for a society, a first world society like the United States.

Auren Hoffman (58:35.918)

Now, a few personal questions. When is that? Okay. Like if anyone listened to you, you, you're obviously a smart person, you're a PhD from math, et cetera, but you, you know, you, think, and I think you've told me before in high school, you didn't have like the best grades. You started out at a junior or kind of community college, like, kind of like, so we're a little bit more of a late bloomer. How do we, there's my guess is there's millions of people that are late bloomers.

maybe tens of millions of people that are late bloomers didn't have like the most amazing grades in high school or something. How do we tap into that potential?

DJ (59:10.363)

Yeah, so it's true. So I got super lucky, you know, despite growing up in Cupertino, the hotbed of technology, right, in the early 80s. I still was, you know, the school system just didn't work for me. That style of education just didn't work for me. I needed something else. I would have loved it if I had Wikipedia and Khan Academy and these other things. That assumes I would have had broadband, the laptop and

Auren Hoffman (59:38.733)

Yep.

DJ (59:39.229)

you know, access to tech. But I think one of the things that's there is I got so lucky because I had an institution that was effectively an alternative safety net. It's like you didn't go, you didn't get into regular college, fine, come hang out here for two years and grow up. And I can't emphasize enough how lucky I was because I had lecturers and teachers who were more interested in teaching than research were going to spend time. This is at De Anza College.

Auren Hoffman (01:00:05.762)

This is when you're at De Anza College. Yeah, yeah.

DJ (01:00:09.309)

And they were very invested in helping me. Anyone can go to and this is it's a deal. It's like unbelievable economic value. And so it's why I'm such an advocate for two years of college education should be free for everybody, because I know only for retraining, but for all the late bloomers that are out there, I met women at study groups who were.

Auren Hoffman (01:00:13.184)

anyone can go to, right? Literally anyone can go sign up. Anyone can go. Yeah. Yeah.

Auren Hoffman (01:00:33.23)

But it's essentially free. mean, it's cheap, right? I mean, it's so cheap. Even today, even today, it's super cheap, right? Go to, yeah, if you want to go there.

DJ (01:00:36.581)

It's cheap. It's cheap and it gives you a shot. Super cheap. And you get to mix with people. You're not just dealing with people in the ivory tower. You're dealing with people who, you know, they're juggling like single mother, single father. They have another job and they're studying for this. You know, real, real hard trade-offs. And those people taught me work ethic in a very different way. So when I got ready and I literally went into a

Auren Hoffman (01:01:02.349)

Yeah.

DJ (01:01:04.893)

I finally moved to University of California, San Diego, I was like very focused. I was very determined. But I don't think I would have found my love for mathematics without that, or definitely not my love for data without those professors and those teachers who were there. It's also one of the reasons I think liberal arts is so important. I think too often we kind of just ignore the liberal arts, know, or in Europe. I'm always envious of your ability to read both breadth and depth.

But like that ability to think critically and learn those things, I think the liberal arts schools are phenomenal for this and helping foster what you want to be.

Auren Hoffman (01:01:41.774)

I'm somewhat skeptical of that, is like, mean, I think most people I know who are like really into the liberal arts stuff today, it's it's almost like, it's almost like school was not good for them on that. I don't know if school is always the best place for the liberal arts. And a lot of people develop the passion almost in spite of school. And many people I know who like were like, you know,

history majors or polyscience majors, something like this, they don't even read anymore. Like 20 years later, they don't even, they're not even reading those types of things. So it's, it's, I'm not sure that it actually fosters it. feel like it can almost like take out the passion.

DJ (01:02:22.425)

Interesting. So for me, my minors are in theater and psych. It's like what we call program of concentration. They're effectively minors. those things taught me so much more about the world. And maybe it's because also I was coming from a technical field. I was like, this is how I got to see what the world was like versus staring at this math proof for four hours and being like, how does this work? But I guess the thing that I think about these days is

Auren Hoffman (01:02:37.282)

Yeah, maybe.

DJ (01:02:52.379)

We're so good at customization of apps and technology and ads, yet we're so poor at being able to customize our curriculum for students to figure out what works for

Auren Hoffman (01:03:03.756)

That's for sure. that's for sure. And teaching generally not that good. I mean, you mentioned I was an engineer at UC Berkeley. Most of my classes, at least in my early years, were taught by very well-known professors. Most like many of them had Nobel prizes and they were on average terrible. They were not Richard Feynman, know, who's maybe the one Nobel prize winner who's like amazing at teaching.

and, they were, they, they, they, couldn't even comprehend them. couldn't understand them. They were probably great for post-docs, but not great for an 18 year old. And then one year I had, I took a summer class and I had a professor, an engineering professor from San Jose state. And he was the best teacher I had had in two years. And that's cause his profession was teaching. He knew how to teach. right.

DJ (01:03:55.931)

Mm-hmm.

DJ (01:04:01.403)

And he's at San Jose State because he cares about that.

Auren Hoffman (01:04:03.978)

He cares about teaching. He was amazing and he brought it to life and he really like helped us all understand the material. All of us were so much better at the end of that class than we were at any other, like the step function than any other class that we took.

DJ (01:04:18.397)

That's right. And that's the same thing for me at the junior college level. And you know, like I had it also in versions at UC San Diego. You know, I had this professor who basically is a late undergrad class, so it's upper level, but he basically treated us like grad students. And he like handed us our ass daily, as like crushed us, crushed us. But his kind of approach was like, like, this is what it means to be a

Auren Hoffman (01:04:36.227)

Yeah.

DJ (01:04:48.177)

really understand things. like, you know, we're so lucky when we get to be with an educator that is like that. You know, one message I would have for people out there, that in the corporate world also is I think, Orin, I know you have been this kind of person, is that we often get so myopic in our own world. Like we're so narrow in our own, like our own, like the amount of things our hand, we forget to lend a hand to somebody else.

Auren Hoffman (01:05:15.118)

That's true. And we sometimes we forget how many people lend a hand to us. Right?

DJ (01:05:15.285)

We forget the ability like somebody else's. Exactly. I got lucky to meet you early on when I moved out here in Silicon Valley. You massively informed a lot of my thinking. Right. But it was like we were you could always pick up the phone call. You could always like there was never like what are you going to do for me? There was never a

Auren Hoffman (01:05:28.062)

I got lucky to meet you. mean, we got lucky to meet each other at that point. We were peers, right? So yeah.

DJ (01:05:42.663)

There was always like a community vibe. you know, that's why I've always tried to say data science is a team sport. However you want to define data science, I think it's a team sport. And so if you're suffering in silence, you're doing it wrong. You need to raise your hand and let people know that you need help. If you're out there and you have the capacity and the ability to help somebody, you should be helping somebody else. You'll be that...

Be that teacher like I had or you had at San Jose State. Be that person because when you do that, you're gonna get so much more out of it. also, that's how we create systemic change.

Auren Hoffman (01:06:23.362)

This is great. Last question we ask all of our guests. What conventional wisdom or advice do you think is generally bad advice?

DJ (01:06:32.785)

Follow your passion. Because I think it's just like, it's so easy just to say follow your passion without doing the hard work. I think it's really about like, not just passion, it's about finding experiences, figuring out that and knowing that your interests are constantly in flux and changing and you should be having so many ideas and working with so many different people that is out there. Find what it, I find like the one is people always say, what do you like, what you don't like?

Find what gives you energy. Figure out what takes away your energy. Get rid of the things that take away your energy and focus on the things that give you energy and inspire you. And that's a better way to find.

Auren Hoffman (01:07:12.502)

It's interesting. So I've asked this question now to over a hundred people on this podcast, you know, the conventional wisdom or advice that's generally bad advice. And I, I'd say the number one thing people say is, is, is that that's bad is follow your passion. So you are, you're, you're in that, in it. And I would say, no. And I think it's like, it's probably, it's probably, it's probably a third of the people have said that exact thing. that it must either.

DJ (01:07:28.335)

I did not do that just because I listened to the podcast, but it is true. Now that you say it, it isn't friend.

Auren Hoffman (01:07:38.53)

Now that might be the conventional wisdom already that it's bad. Like it's, already moved to the other side. I'm not sure. Cause like everyone now thinks it's bad. Yeah. I want someone to be like, follow your passion is the right thing to do. Right. Yeah. This point. Yeah. Yeah. Yeah. Yeah. It's like, it's now committed now. Now it's clearly conventional wisdom at this point. Yeah. This has been amazing. Thank you, DJ Patel for joining us at world of dance. I follow you at D Patel.

DJ (01:07:43.685)

That's already good. You have to have a... You have to have fun with your...

You have to have one of the classic Orrin Hoffman graphs that show this. You need...

Auren Hoffman (01:08:08.426)

On X, I definitely encourage our listeners to engage you there. This has actually been a ton of fun. We're old friends. So I really enjoyed this conversation.

DJ (01:08:16.687)

Always, brother.