O
Olayimika Oyebanji
Guest
Health data is more abundant than ever, yet it remains fragmented across hospitals, wearables, testing companies and apps. At the same time, AI is being applied to highly personal biological information, raising difficult questions about privacy, ownership and how much complexity ordinary people should be expected to navigate.
In this interview with Olayimika Oyebanji, Lisa Loud, CEO of DEPH Labs, discusses the practical limits of AI in healthcare, the challenges of fragmented data, the importance of individual control over biological information, and what meaningful personalization could look like without sacrificing privacy.
Thanks for having me! One key learning is that great technology doesn't automatically translate into adoption. Moving from PayPal into crypto taught me that. In crypto, I saw people who loved crypto building products for other people who were already deeply in tune with the space.
Healthcare can fall into that same trap. Most people don't want to understand the technology underneath their health data, they just want it to improve their lives. The opportunity is taking incredibly complex biology, data and AI and turning it into something simple, useful and trustworthy for ordinary people.
AI is incredibly powerful when you give it the right data, particularly when you're looking for patterns across information that humans couldn't realistically process themselves. But I've spent years working on privacy and confidential computing, and I think we're underestimating that side of the equation.
People are feeding incredibly sensitive information into AI systems without really knowing where it goes. In healthcare, we need better intelligence from the data, but also infrastructure that keeps that data private and controlled.
We've created this strange situation where there's more health data about us than ever, but we don't necessarily control it or benefit from it. Your doctor has some, your wearable has some, a testing company has another piece. I've spent much of my career thinking about how technology removes those kinds of inefficiencies.
With health, bringing that information together could create a much better picture of the individual. But it shouldn't require handing everything over to another centralized company. The individual needs to remain at the center.
Privacy has been a huge part of my work, particularly at Secret Network, and I’ve always believed that privacy and usability shouldn’t be mutually exclusive. There are ways to implement privacy without sacrificing a delightful experience. Blockchain showed that people can own digital assets directly rather than asking a company for permission to access them. I think health data should move in a similar direction. Your DNA is about as personal as data gets. You should decide who can access it, what they're allowed to do with it and when that access ends, without sacrificing useful services.
Over the course of my career I've generally been attracted to technologies that are early but have genuine utility, which is partly what pulled me from PayPal into crypto in the first place. Longevity feels similar right now in that there's some amazing science happening alongside plenty of hype.
I'm less interested in somebody claiming they've found a magic way to stop ageing like Bryan Johnson. What's exciting is getting better at measuring what's actually happening biologically. Epigenetics can potentially give us a more dynamic picture of ageing, and combining that with other health data makes personalization much more meaningful.
I left PayPal partly because I was energized by the prospect of accelerating the adoption of digital assets globally, and later became really interested in privacy and confidential computing. Health brings those threads together.
We now have incredibly valuable biological data and powerful AI, but major questions around privacy, ownership and usability haven't been solved. It gives me the chance to take everything I've learned about consumer products, decentralization and privacy and apply it somewhere very personal.
What interests me is moving away from snapshots. DNA gives us this incredible foundation, but it doesn't tell the entire story of where you are in your health journey, and what levers you can activate to change your direction. Epigenetics can add another layer by showing how biology changes alongside ageing, environment and lifestyle.
Then you have biomarkers, wearables and other signals. Bringing those things together starts creating a much richer picture. The challenge DEPH is tackling is making all that complexity useful without expecting consumers to become scientists just to understand what their own biology is telling them.
For decades, a lot of medical research effectively used male biology as the baseline and then generalized from there. We're getting much better at recognizing why that doesn't always work. Our work around sex-specific epigenetic clocks is a good example.
If men and women age differently biologically, why would we assume exactly the same measurement works equally well for both? I've also spoken before about the value of having different perspectives involved in building technology. Better personalization starts by recognizing that people aren't interchangeable datasets.
The more biological information a platform has, the more personalized its insights can potentially become, but also the more sensitive the data becomes. How are you approaching that trade-off between personalization, privacy and individual control?
This connects directly with the privacy work I've been doing for years. I don't accept that we need to choose between useful AI and privacy. The raw DNA and epigenetic information doesn't need to simply sit exposed on a blockchain.
The model we're working toward gives individuals encrypted data vaults and controls access through permissions. That's important because biological data isn't like a password. You can't change your DNA after a breach. We need to design privacy into the infrastructure from day one, rather than patching it on later.
The end goal should feel surprisingly simple. I've said before that mass adoption of new technology comes when we stop expecting people to understand everything happening underneath it. I think health should work the same way.
You could have this evolving picture built from genetics, epigenetics, biomarkers and other information, with AI helping interpret changes over time. But you remain in control of the underlying data. People shouldn't need to understand blockchain, encryption or machine learning. They should simply get better information about themselves and decide how it's used.
In this interview with Olayimika Oyebanji, Lisa Loud, CEO of DEPH Labs, discusses the practical limits of AI in healthcare, the challenges of fragmented data, the importance of individual control over biological information, and what meaningful personalization could look like without sacrificing privacy.
Hi Lisa, thanks for taking the time to chat today! You’ve worked across Apple, PayPal, BitMEX and ShapeShift. What lessons from those industries do you think are most relevant to the changes now happening in healthcare and health data?
Thanks for having me! One key learning is that great technology doesn't automatically translate into adoption. Moving from PayPal into crypto taught me that. In crypto, I saw people who loved crypto building products for other people who were already deeply in tune with the space.
Healthcare can fall into that same trap. Most people don't want to understand the technology underneath their health data, they just want it to improve their lives. The opportunity is taking incredibly complex biology, data and AI and turning it into something simple, useful and trustworthy for ordinary people.
AI is increasingly being used to interpret highly personal health information. Where do you think AI can genuinely improve healthcare today, and where is the industry getting ahead of itself?
AI is incredibly powerful when you give it the right data, particularly when you're looking for patterns across information that humans couldn't realistically process themselves. But I've spent years working on privacy and confidential computing, and I think we're underestimating that side of the equation.
People are feeding incredibly sensitive information into AI systems without really knowing where it goes. In healthcare, we need better intelligence from the data, but also infrastructure that keeps that data private and controlled.
Health data is still fragmented across hospitals, testing companies, wearables and apps. What are the biggest problems this creates for consumers, researchers and companies trying to build genuinely personalized health products?
We've created this strange situation where there's more health data about us than ever, but we don't necessarily control it or benefit from it. Your doctor has some, your wearable has some, a testing company has another piece. I've spent much of my career thinking about how technology removes those kinds of inefficiencies.
With health, bringing that information together could create a much better picture of the individual. But it shouldn't require handing everything over to another centralized company. The individual needs to remain at the center.
What can healthcare learn from those industries when it comes to data ownership, privacy and giving individuals greater control over their information?
Privacy has been a huge part of my work, particularly at Secret Network, and I’ve always believed that privacy and usability shouldn’t be mutually exclusive. There are ways to implement privacy without sacrificing a delightful experience. Blockchain showed that people can own digital assets directly rather than asking a company for permission to access them. I think health data should move in a similar direction. Your DNA is about as personal as data gets. You should decide who can access it, what they're allowed to do with it and when that access ends, without sacrificing useful services.
Longevity, biological age and personalized health have attracted huge amounts of attention and investment. What developments in this space do you think have real long-term potential, and where do you think the hype is running ahead of the science?
Over the course of my career I've generally been attracted to technologies that are early but have genuine utility, which is partly what pulled me from PayPal into crypto in the first place. Longevity feels similar right now in that there's some amazing science happening alongside plenty of hype.
I'm less interested in somebody claiming they've found a magic way to stop ageing like Bryan Johnson. What's exciting is getting better at measuring what's actually happening biologically. Epigenetics can potentially give us a more dynamic picture of ageing, and combining that with other health data makes personalization much more meaningful.
What initially attracted you to DEPH, and why did you see precision health and biological data as a natural next step after your previous work in consumer technology, fintech and decentralized systems?
I left PayPal partly because I was energized by the prospect of accelerating the adoption of digital assets globally, and later became really interested in privacy and confidential computing. Health brings those threads together.
We now have incredibly valuable biological data and powerful AI, but major questions around privacy, ownership and usability haven't been solved. It gives me the chance to take everything I've learned about consumer products, decentralization and privacy and apply it somewhere very personal.
What can combining different sources such as DNA, epigenetic and other biological data tell us about an individual that conventional health testing cannot?
What interests me is moving away from snapshots. DNA gives us this incredible foundation, but it doesn't tell the entire story of where you are in your health journey, and what levers you can activate to change your direction. Epigenetics can add another layer by showing how biology changes alongside ageing, environment and lifestyle.
Then you have biomarkers, wearables and other signals. Bringing those things together starts creating a much richer picture. The challenge DEPH is tackling is making all that complexity useful without expecting consumers to become scientists just to understand what their own biology is telling them.
Focusing on the biological differences between men and women, particularly around health and ageing, why do you think those differences have historically been underserved by health research and technology?
For decades, a lot of medical research effectively used male biology as the baseline and then generalized from there. We're getting much better at recognizing why that doesn't always work. Our work around sex-specific epigenetic clocks is a good example.
If men and women age differently biologically, why would we assume exactly the same measurement works equally well for both? I've also spoken before about the value of having different perspectives involved in building technology. Better personalization starts by recognizing that people aren't interchangeable datasets.
The more biological information a platform has, the more personalized its insights can potentially become, but also the more sensitive the data becomes. How are you approaching that trade-off between personalization, privacy and individual control?
This connects directly with the privacy work I've been doing for years. I don't accept that we need to choose between useful AI and privacy. The raw DNA and epigenetic information doesn't need to simply sit exposed on a blockchain.
The model we're working toward gives individuals encrypted data vaults and controls access through permissions. That's important because biological data isn't like a password. You can't change your DNA after a breach. We need to design privacy into the infrastructure from day one, rather than patching it on later.
If you achieve what you’re setting out to build, what does the experience look like for an ordinary person? Could we eventually have a continuously evolving health profile that uses our own biology and AI to help us make better decisions throughout our lives?
The end goal should feel surprisingly simple. I've said before that mass adoption of new technology comes when we stop expecting people to understand everything happening underneath it. I think health should work the same way.
You could have this evolving picture built from genetics, epigenetics, biomarkers and other information, with AI helping interpret changes over time. But you remain in control of the underlying data. People shouldn't need to understand blockchain, encryption or machine learning. They should simply get better information about themselves and decide how it's used.