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Stop Disease 5 Years Early: AI + DNA Playbook With 23andMe's Founder Anne Wojcicki

📌 If you're building with agents — visit outshift.com to see how real companies build with multi-agent systems: how to connect agents across different vendors, let them share what they've learned instead of starting from zero, and improve your own agent infrastructure with no vendor lock-in: https://outshift.cisco.com/?utm_campaign=fy26q3_agntcy_ww_paid-media_ioa-svg-outshift_podcast&utm_channel=podcast&utm_source=podcast Learn more or join us at AGNTCY.org (https://agntcy.org/) ↓ In this episode of the Silicon Valley Girl Podcast, Marina Mogilko sits down with Anne Wojcicki, co-founder and CEO of 23andMe — the genetic testing company that has helped more than 14 million people understand their ancestry and health risks, and built one of the largest human genetic datasets in the world. After 23andMe went through bankruptcy and Anne Wojcicki lost control of the company she started in 2006, she fought to win it back at a court-approved auction and relaunched it as a nonprofit (TTAM Research Institute) — so no single pharma company would ever own your DNA. In this conversation she breaks down what AI + your DNA can actually do for your health right now: predicting disease years before you feel it, the genetic risk factors everyone should test for, why the healthcare system is built to treat disease instead of preventing it, and the small daily habits that move the needle most. She also opens up about the hardest year of her life — losing her sister, former YouTube CEO Susan Wojcicki, to lung cancer, going through bankruptcy, and why she refused to walk away. Plus: raising kids as a founder, having a baby at 45, co-parenting with her ex-husband Sergey Brin, and the single smartest thing you can do for your body in 2026. *Timestamps:* 00:00 — Intro 00:55 — What AI + DNA can do today that a doctor couldn't 5 years ago 01:27 — Why the system pays to treat disease, not prevent it 02:00 — The cholesterol gene no diet can fix (Marina has it) 04:36 — Genetics vs. environment: how much can you actually control? 06:11 — "I'm 36 and don't want statins" — is there another way? 07:24 — The best health prompts to put into your AI right now 08:42 — Ad 09:52 — From 14 million to 100 million: training AI on human DNA 12:07 — What happens when we can predict cancer before it starts 13:16 — Connecting your wearable data to your genes 14:34 — The environmental factors quietly raising your risk 16:22 — Why small daily movement beats everything 17:22 — The one food society agrees is bad for you 18:20 — What changed for Anne after losing her sister Susan 22:52 — Why the next five years could be transformative 24:14 — Who should get genetic testing — and why no one should be surprised 26:39 — Baby KJ, CRISPR, and editing disease out of your genes 28:08 — A reality check on biotech, drug discovery, and the FDA 30:00 — The top things to do for your health after watching this 31:57 — Full-body MRIs, chest CTs, and catching things early 34:23 — Founder mindset: playing the 10+ year long game 38:00 — Rebuilding a town: community, kids, and downtown Los Altos 41:08 — "Did you ever think about giving up?" 43:59 — Her biggest lesson from the hardest year 46:56 — Having a baby at 45 and doing it her way 51:28 — Hard rules as a working mom 54:01 — Two ambitious founders, one marriage (on Sergey) 57:43 — The smartest thing you can do for your body in 2026 🧬 Anne Wojcicki: Instagram: https://www.instagram.com/annewoj23/ X: https://x.com/annewoj23 LinkedIn: https://www.linkedin.com/in/anne-wojcicki 23andMe: https://www.23andme.com *Links:* 📩 Follow my Newsletter (Future Proof): https://siliconvalleygirl.beehiiv.com/subscribe?utm_source=youtube&utm_medium=video&utm_campaign=futureproof-sub&utm_content=Anne-Wojcicki 🔗 Instagram: https://www.instagram.com/siliconvalleygirl/ 𝕏 : https://x.com/siliconvalleymm 📌 My Companies & Products: https://Marinamogilko.co #23andme #podcast #annewojcicki

Anne WojcickiguestMarina Mogilkohost
Jul 28, 20261h 1mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 2:07

    Why prevention (not treatment) is the real healthcare business problem

    1. AW

      Every single person can benefit from genetic information, and use that to modify and prevent disease.

    2. MM

      This is Anne. She built 23andMe, a genetic testing company that provides ancestry breakdowns and health predisposition insights. More than 14 million people have used its DNA tests, helping build one of the world's largest genetic datasets. Then, it all collapsed. Her board resigned, and she lost her company. 2024 was a brutal year for you, with bankruptcy and having to buy out your company. Did you ever have thoughts of giving up?

    3. AW

      I thought about it a lot, because everything is pointing in the direction that things are not going well.

    4. MM

      But after winning a court-approved auction, she bought the company back and turned it into a nonprofit. Now she's working to combine DNA datasets with AI to help people catch disease before they feel it. What can AI plus DNA do now what a doctor couldn't do five years ago?

    5. AW

      You can pull in your medical record, link all this then with your genetics, and understand where you could potentially intervene today and change the outcome.

    6. MM

      From all the data that you're seeing, what do you think are the diseases that have the best chance of being cured or treated in a different way in the next five years?

    7. AW

      I think a couple things. One ...

    8. MM

      My audience really loves using AI for everything. What can AI plus DNA do now what a doctor couldn't do five years ago?

    9. AW

      I love that, and, um, I feel like we're finally getting to the point where everything that we set out to do back in 2006 is, can, can start to happen, and that really revolves around preventing disease. And I think the fundamental issue in the healthcare system is that when you go to the doctor today, they're compensated to treat disease.

    10. MM

      Mm-hmm.

    11. AW

      Like, almost no aspect of healthcare rewards you and makes money on disease prevention. And so it's really gonna fall in the hands of you, the consumer. Like, you, the individual, are gonna have to leverage all these different, you know, ways, you know, all these different means that are now in your, in your power to start to understand what your health risks are, and then to help start to manage that more and more.

    12. MM

      Mm-hmm.

  2. 2:07 – 2:57

    Genetics as the “base layer”: cholesterol, hereditary cancer, and actionable screening

    1. AW

      And so 23andMe started with this idea is that the most fundamental layer of your health is your genetics. So for example, if you wanna be healthy, and you're worried about your, all your cholesterol numbers, and let's say you, um, you just have very high numbers, and you have a genetic risk factor called familial hypercholesterolemia, and-

    2. MM

      That's me. [laughs]

    3. AW

      Is it really?

    4. MM

      Yes. [laughs]

    5. AW

      Okay, so you're a prime example of this. Like, no matter what you do with your diet-

    6. MM

      Mm

    7. AW

      ... you need to take a sta-

    8. MM

      Doesn't affect anything

    9. AW

      ... you have to take a statin.

    10. MM

      Yeah.

    11. AW

      And so it's that type of thing is like you, you find out that you have something, you have high cholesterol numbers, but, but there's a limit to how much the environment can be modified to impact that.

    12. MM

      Yeah.

    13. AW

      So you have to go on a therapy. And there's aspects of, for instance, in cancer, like if you have hereditary colon cancer, hereditary breast cancer, you need more proactive screening.

    14. MM

      Mm-hmm.

  3. 2:57 – 4:36

    AI + multimodal health data: linking DNA, wearables, environment, and medical records

    1. AW

      You know, environment definitely makes a difference there, but you have to be proactive with the medical world. And so for me, your genetics was always this core layer where you have to understand genetically, do you have a significant risk factor? Where are you on the spectrum for heart disease, for cancer risk, for, you know, other things that could be passed down to your children? Like, understanding all of that is really step one. What's amazing today is that you can pull in all this other information, your wearable information, your zip code has your air quality and your water quality.

    2. MM

      Mm-hmm.

    3. AW

      Um, you can pull in your medical record, and you can link all this then with your genetics, and really understand, are there, are there signs, are there things happening-

    4. MM

      Yeah

    5. AW

      ... where you could potentially intervene today, either with the medical world or with your lifestyle, and change the outcome? So for instance, even, like, with my mom, types of things I look at, like we're genetically higher risk for Type 2 diabetes. People in the family-

    6. MM

      Mm

    7. AW

      ... had Type 2 diabetes. My mom knew that she was pre-diabetic. You know, I put on a CGM on her.

    8. MM

      Yeah.

    9. AW

      And suddenly she's like, "Oh my God," like, "I can now change my behaviors."

    10. MM

      Yeah.

    11. AW

      So it was a combination of her genetics, her medical records, her blood values, and then using a new tool, like a CGM, or again, you know, we're gonna be able to use Oura Rings, other things as well, to then actually change your behavior. So that's gonna be the magic of AI, is combining all of that together, and your genetics is gonna be what powers the customization of all this other information coming in, and knowing where do you need to be proactive with your lifestyle, and where do you need to be proactive medically?

  4. 4:36 – 6:11

    How much is genetics vs environment? Monogenic variants vs polygenic risk scores

    1. MM

      How do you know how much genetics are actually contributing? 'Cause if you look at cancer stats, it says on average, it's 70 to 90% environmental.

    2. AW

      Mm-hmm.

    3. MM

      For coronary diseases, same.

    4. AW

      Yeah.

    5. MM

      79%. How do you know that for you specifically, it's mostly genetics or it's mostly environment, so you can control and take action?

    6. AW

      There's two types of reports, what we call monogenic risk, and that's where you can get, like, familiar hypercholesterolemia. One variant that says you are much higher risk based on this one variant. And so in some ways, those general numbers are not meaningful to you as an individual. They're meaningful to a population-

    7. MM

      Mm-hmm

    8. AW

      ... but not meaningful to you. So you have one monogenic variant, and it puts you really high risk for heart disease, or sudden cardiac death, or high cholesterol. It, that impacts you. That range is no-

    9. MM

      Mm-hmm

    10. AW

      ... longer relevant. You have another area called polygenic risk scores, and polygenic risk is looking at hundreds or thousands of genetic variants that add up into a score, and it puts you almost on, like, a bell curve.

    11. MM

      Mm.

    12. AW

      Like, where are you on that curve? Like, I can look at my Type 2 diabetes risk, and I'm, like, fit, I'm healthy, I work out a lot, but genetically, I'm still higher risk. And so for that reason, that's part of why I can say, "Okay, I'm higher risk," but there's actually been a lot of data that shows if you manage your environment, if I manage what am I, I eat, I m- um, regularly get my hemoglobin A1C test, I use, you know, a, a continuous glucose monitor, I stay fit and healthy, I can reduce that risk. So I have a genetic risk factor, but I can reduce it now.

  5. 6:11 – 7:24

    Personalized medicine and pharmacogenetics: choosing the right therapy (e.g., statins)

    1. MM

      When it comes to, uh, c- Cholesterol since we touched it

    2. AW

      Mm-hmm

    3. MM

      ... and it's a huge problem for me, 'cause I'm 36 and I want to go on statins.

    4. AW

      Mm-hmm.

    5. MM

      And every doc- well, almost every doctor is like, 10% of doctors are like, "We can wait-

    6. AW

      Yeah, yeah

    7. MM

      ... till you have, like, a third kid, 'cause it affects pregnancies and we haven't studied stat- statins in pregnancies, et cetera." But is there any study that shows where... 'Cause you mentioned different people react differently to different drugs.

    8. AW

      Correct, yeah.

    9. MM

      And ideally I'd love to do some testing before-

    10. AW

      Yeah, yeah

    11. MM

      ... we put in statins. Is there any research around that, that shows that some people don't have to take statins, or wouldn't react to that, or-

    12. AW

      I think this, this is-

    13. MM

      ... is me hoping? [laughs]

    14. AW

      This is where the fu- this is the future of personalized medicine. You should have a physician who has a very comprehensive genetics background, and I think that's where you're gonna be able to have AI training to say, based on all the literature with genetics, let's no longer look at you just as an average.

    15. MM

      Yeah

    16. AW

      ... as like a one-size-fits-all of, like, here's what we do on a population level with, you know, with, you know, treating high cholesterol, that we should actually now understand specifically based on your genetics, based also on pharmacogenetics, because there's interactions of your genetics and statins.

    17. MM

      Mm-hmm.

    18. AW

      So what's the right one for you, and what's the best way for us to reduce that?

  6. 7:24 – 9:43

    Turning today’s AI into a “health assistant”: practical prompting and ApoE/Alzheimer’s

    1. MM

      What are the best prompts that people can put into their AI today-

    2. AW

      Mm-hmm

    3. MM

      ... uh, to make this kind of personalized health assistant?

    4. AW

      That is a great question. You know, I think it's still pretty early days. Um, I have to say the areas I've been coaching people the most is around, um, blood values, cancer information, anybody who gets a genetic test in cancer, or even just basic things of, like, helping understand, um, lifestyle information with a hereditary factor like ApoE. Based on your age, uh, based on, you know, all your metrics of, you know, your weight, how you are living your life, like, you know, asking it for prompts. You know, ask me 10 questions about my lifestyle.

    5. MM

      Hmm.

    6. AW

      And, um, and including it for something like ApoE, what are the things that you can do that can make a difference?

    7. MM

      ApoE, can you-

    8. AW

      ApoE is about, um, is the genetic risk factor for Alzheimer's.

    9. MM

      Mm-hmm, mm-hmm.

    10. AW

      And so that's one area where we get a lot of questions from our customers about, like, "Well, what can I do?"

    11. MM

      Mm-hmm.

    12. AW

      "What can I do for that?" And that's where when you go to a traditional doctor, it's going to be worse at finding out, okay, here's your lifestyle information, what are the things you can do? But there is a lot of research out there that's happening, and more and more you're finding there's all these s- you know, groups coming together to run their own studies about what is it that we can do, what's having an impact?

    13. MM

      The last couple months I've been telling you about two things Outshift by Cisco is building: the Internet of Agents, so agents from different vendors can connect and collaborate; and the Internet of Cognition, so agents can share what they've learned instead of starting from zero contacts every time. Both run on the same idea: open and interoperable, no vendor lock-in, no silos blocking agents from working together, and none of this is just Outshift's idea in a lab. It's open source code with a community of engineers actually contributing to it. And now there's proof it's not just theory. Teams at ServiceNow, Webex, Splunk, and Swisscom are already running multi-agent systems on this. Outshift by Cisco just put up a use cases hub at outshift.com. Real companies, what they build, what changed. If you're trying to improve your agent infrastructure, Outshift is your source for expertise and real examples. That's outshift.com. Now back to the video. And now

  7. 9:43 – 12:07

    Why 23andMe wants 100M people: training AI on DNA (and why more data matters)

    1. MM

      you have 14 million people participating-

    2. AW

      Mm-hmm

    3. MM

      ... in your research, and you said your goal is to get 100 million people.

    4. AW

      Mm-hmm.

    5. MM

      What does this unlock?

    6. AW

      We see the potential with AI right now, and your listeners definitely are early adopters here. So AI came because all the big tech companies had data. Like, they had tons and tons of data. They could, um, invest in training models. Like, it came because there was a world of billions of people who contributed data. So in many ways you need to do something similar with respect to healthcare information. And 23andMe, I think as impressive as it is that we have 14 million and it's the largest genetic data set that's out there, it's only 14 million. And your DNA is fascinating to me because it's a digital code. Every form of life has an A, C, G, and T, and so you just have different combinations, different variations, different, you know, ways it's organized. And you could be you, you could be a frog, you could be a banana. I find it amazing-

    7. MM

      Mm-hmm

    8. AW

      ... that you get this kind of genetic, like, this variability in the world. So what you need to really understand the code of life is more data, and you're gonna need to train models not on words, but you're gonna need to train models on DNA. And so that's where I look at is, like, we're at 14 million today. I point to 100 million as sort of the next milestone. But the reality is you're gonna need data sets that are massive to really-

    9. MM

      So you need more than that? It doesn't, 14 million doesn't really represent a significant group of people to-

    10. AW

      Well, it does

    11. MM

      ... make conclusions?

    12. AW

      So what we found, I mean, what we found is the publication that we had at NeurIPS actually showed that at over a million people, you start to really get the benefits of training-

    13. MM

      Mm

    14. AW

      ... models with AI, and so you get non-linear improvements with risk prediction. That, for us, sort of valid- validated that more and more data... 'Cause we get this question from people all the time, like, "Why do you need more data?"

    15. MM

      Yeah.

    16. AW

      "You have enough data." It's funny 'cause we get that a lot from the biotech world.

    17. MM

      Mm-hmm.

    18. AW

      It's like, "Ah, that's enough data. Why do you need any more?"

    19. MM

      Yeah. For me it also looks like in, like, 14 million, maybe that's-

    20. AW

      This is where you see differences-

    21. MM

      Mm-hmm

    22. AW

      ... and that's what's been interesting with our background, is like the tech world is, recognizes you always want more data.

    23. MM

      Mm-hmm.

    24. AW

      And the biotech world sometimes is like, "Ah, it's enough." Like, you don't necessarily need more. And I think now you're having this convergence where people recognize, like, to really be able to take advantage of AI, you need to build these extraordinary data sets based on human data.

  8. 12:07 – 13:17

    What big datasets can do first: earlier risk prediction and understanding cancer biology

    1. MM

      What do you think it's gonna unlock? So like, for example, we can't really treat cancer now on genetic level. Do you think at 100 million we're gonna see some paths?

    2. AW

      Well, what I'm most focused on, if I think about, like, what, what do I want? And again, this is like, 23andMe is very focused on sort of like a single area, which is focused on prediction, and so disease prediction. Because we all know it's tough. Like, if I look at my sister, Susan, it was tough having a late stage cancer diagnosis. What you really wanna know is are you at risk, and then find it at those earliest stages. And that is what I think is exciting, is being able to identify something really early, and then also understand the basic biology, why are you getting something?

    3. MM

      Yeah.

    4. AW

      Like, we don't understand. Like, we know that bodies are constantly getting, you know, you're always under assault.

    5. MM

      Yeah.

    6. AW

      So you always have, like, little micro cancers, and so why is it that sometimes they take off and sometimes they don't?

    7. MM

      Yeah, it will be fascinating when you have this DNA, even now with 14 million, when people start adding their WHOOP information or their lifestyle, and then connecting and seeing correlation between-

    8. AW

      Yeah

    9. MM

      ... lifestyle and genes, um, that's the most important.

    10. AW

      I mean, that's what we don't really know.

    11. MM

      Yeah.

  9. 13:17 – 18:20

    Environment is under-measured: trials, biomarkers, pesticides, air quality, and water

    1. AW

      Like lifestyle and, and people have to recognize the, the challenge that you have with AI and health is that you have to run experiments.

    2. MM

      Mm-hmm.

    3. AW

      You have an intervention, and maybe it's that we'll eliminate microplastics from water, or you have a new drug that you think is going to, um, prevent heart attacks. But then you have to run the clinical trial to see, do you get that outcome?

    4. MM

      Mm-hmm.

    5. AW

      You have to wait for people to get heart attacks. You have to wait for people to not develop cancer.

    6. MM

      Yeah, is it really working?

    7. AW

      So it's just hard to run models in the same type of way, and so that's why we're always trying to come up with, like, you know, other types of biology experiments to, you know, mimic what we think is potentially-

    8. MM

      Yeah

    9. AW

      ... you know, a biomarker for it.

    10. MM

      Well, and the more data, the better. What was-

    11. AW

      The more data, the better. This is where we're just getting into those early days of, you know, on AI and, um, all the environmental information. You know, you see this very clearly, things like, you know, people who live near a golf course and people who live near farms, the pesticides.

    12. MM

      Yeah.

    13. AW

      So you can see from a population level, you have increases in areas like, or diseases like Parkinson's. We barely understand the impact of all this environmental damage. I think we're just scratching the surface of what all the environmental impacts are, and it hasn't been that well-studied. It's hard. It's a hard area to study. Yeah.

    14. MM

      What are you seeing from your data? Are there, like, top three environmental factors that could affect your potential risk?

    15. AW

      I mean, pesticides is one of the biggest.

    16. MM

      So don't eat non-organic? Is that something-

    17. AW

      I think that's-

    18. MM

      ... that's practical? [laughs]

    19. AW

      I think it's, um... I mean, I actually don't... I mean, in some ways the, the debate around organic and non-organic, um, is controversial, and I haven't, I ha- again, I'm not an expert in that area. Um, I mean, I definitely would say, like, don't-

    20. MM

      Don't live near a farm

    21. AW

      ... living, living near... And again, I think that's where there's all the occupational hazard issues.

    22. MM

      Mm-hmm.

    23. AW

      You know, people who are, who don't have a choice, who are spraying pesticides.

    24. MM

      Yeah.

    25. AW

      Um, but you know, it's very clear that pesticides, um, have health impacts.

    26. MM

      Mm-hmm.

    27. AW

      Um, and so you look at all of that data. I think air quality is huge.

    28. MM

      Mm-hmm.

    29. AW

      People having-

    30. MM

      Mm

  10. 18:20 – 24:14

    After Susan: meaning, stress, and the limits of ever knowing ‘the cause’

    1. MM

      After what happened with Susan and, uh, with her diagnosis, have you changed anything in your lifestyle and your kids' lifestyle?

    2. AW

      I mean, like, I think that the number one thing that happened after Susan was diagnosed, um, and I think a lot of her friends said this, is they sort of reevaluated what's most important to me. I've been reading this book recently, um, by Eddie Jager, um, called The Choice.

    3. MM

      Mm-hmm.

    4. AW

      A Holocaust survivor who actually lived to 98. I think there's a big question for people in life about are you doing what's happy, what makes you happy-

    5. MM

      Mm-hmm

    6. AW

      ... and that you personally find rewarding and meaningful. What I found impacted us, me the most after Susan, was less on lifestyle of like, okay, am I exercising enough? Am I getting enough checkups? But more, are you living the life that you actually want? I think there's a number of her friends who changed jobs, made pretty dramatic decisions of, like, I, like, I wasn't, and you have one life and you have, um... There's a number of things that you can control, but ultimately, you also just have, like, you, you always have lightning, and you have no idea what's gonna strike. You should enjoy every day, and it's kind of the things that Susan said. It's like being, um, meaningful. And again, that's why I do really love this book, The Choice, of, you know, this ability to choose every single day-

    7. MM

      Yeah

    8. AW

      ... um, to be happy and to appreciate the things around you, and I think that was overwhelmingly the number one thing. And I think it's an interesting component is, like, we live right now in a time period where it's like everything's moving so fast. Like, even this-

    9. MM

      Yeah

    10. AW

      ... morning, I was doing, like, a new Claude training and, and it was different than, like, two weeks ago. [laughs]

    11. MM

      Oh, yeah.

    12. AW

      It's different.

    13. MM

      It's different every day. [laughs]

    14. AW

      It's different every day, and I ha- it's like things are moving so quickly, and it is exciting. It's an exciting time because we're rebuilding the entire world, but it's also one of those things to, for people to check in and say, like, are you doing something that brings you true meaning?

    15. MM

      Yeah.

    16. AW

      And, you know, it's one of the things that was really clear with Susan is, you know, when she died and she was saying, like, you know, if you're all left behind, like, do things that are meaningful with your life.

    17. MM

      Mm-hmm.

    18. AW

      And I think that's something that, um, that resonates the most with me.

    19. MM

      100%, but were you able to identify any causes to what, to why that happened to her, or it's still-

    20. AW

      No, I mean, we all think about... I w- I will never know, and I think-

    21. MM

      You think so, even-

    22. AW

      No, you'll never know

    23. MM

      ... with AI advances?

    24. AW

      I think that there's i- there's always gonna be a combination of factors. So clearly, uh, we're, we're running right now the 23andMe Lung Cancer Study. I'm super interested to understand genetics.

    25. MM

      Mm-hmm.

    26. AW

      Like, do we have an... Like, there's no polygenic risk score for lung cancer right now. Like, we don't understand why so many women who were never smokers are getting lung cancer. So is there a risk factor? Like, I would love to understand that, and is there some kind of susceptibility? Like, are some people more susceptible-

    27. MM

      Yeah

    28. AW

      ... to pollution? People have reached out to me now who have lung cancer who were triathletes and runners. Is there something, then, about-

    29. MM

      Oh

    30. AW

      ... increased exposure to air quality? Like-

  11. 24:14 – 26:39

    Diseases most likely to improve soon: hereditary cancer, cardiovascular risk, and community screening

    1. MM

      From all the data that you're seeing, what do you think are the diseases that have the best chance of being- ... cured or treated in a different way in the next five years?

    2. AW

      I think a couple things. One, I'm Jewish. I find it absolutely the crazy that the Jewish community does not own the responsibility for the next generation to all have genetic testing, and the Jewish community has the higher rates of hereditary conditions and conditions like BRCA variant for-

    3. MM

      Mm-hmm

    4. AW

      ... breast cancer, ovarian cancer, prostate cancer. Like, why doesn't everyone know? Like, why is anyone dying of a BRCA variant, especially when there's things that you can do? If you know you have it, you can have a prophylactic mastectomy. You can have, you know, you can have kids earlier, take out your ovaries. Like, you can have more aggressive screening. Like, there's things you can do. So I look at that's one area where there just should be, this is a community that should step up and have proactive testing, and no one in the next generation should be surprised. Cardiovascular, there's so many genetic variants that if you know ahead of time, you can absolutely manage them.

    5. MM

      Mm-hmm.

    6. AW

      Like you with f- familiar hypercholesterolemia. I've met so many people in this journey who are like, "Oh, I have this, like, really high cholesterol," and I'm like, "I'm eating kale every day." And I'm like, "You know, you might, [laughs] you, you might want-"

    7. MM

      It's not enough

    8. AW

      ... "the genetic test."

    9. MM

      Yeah.

    10. AW

      And then you just, there's aspects that you can change and aspects where you can prevent.

    11. MM

      Mm-hmm.

    12. AW

      Like, you can medicate and you can prevent. I think in cancer, hereditary cancer is one of those areas, like we know there's a number of genetic variants that can put you at high risk. Genetic testing now is so inexpensive, and those people should, should never be surprised, and that's where I look at. Like, cancer, heart disease, the Jewish community.

    13. MM

      Mm-hmm.

    14. AW

      You have all these other areas, the African American community as well, with, um, sickle cell, um, a condition called TTR amyloidosis. There's chronic kidney disease. Every single person can benefit fr- from genetic information-

    15. MM

      Totally

    16. AW

      ... and use that to modify and prevent disease. But I do look at, like, the Jewish population, um, African American population, frankly, um, you know, cardiovascular disease and cancer.

    17. MM

      Mm-hmm.

    18. AW

      I think you should be able to have a significant, like, you know, remove the, the, the very regrettable deaths that happen because people had a risk factor and they didn't know.

  12. 26:39 – 30:00

    Gene editing (CRISPR) and ‘Baby KJ’: promise vs realistic timelines in healthcare

    1. MM

      Totally. And we, we already saw a kid being treated with them modifying, uh-

    2. AW

      Oh, yeah, yeah

    3. MM

      ... in how, how the kid processes protein.

    4. AW

      Yeah, yeah. Yeah.

    5. MM

      Modifying their gene.

    6. AW

      You mean Bob, Baby KJ?

    7. MM

      Yeah, yeah.

    8. AW

      Yeah. Baby KJ's amazing.

    9. MM

      How far are we from doing this to common diseases? 'Cause I think what I've heard is that it's mostly, like, those genetic diseases you're born with, so you can modify a gene. Can we in five years just, if you're predisposed to breast cancer, just modify the gene?

    10. AW

      This is outside of my area, um, but if you look at Jennifer Doudna and people in the CRISPR field, and there's a number of experts in this, and, um, I sat at a conference recently with one of them, and he was just, like, you know, so passionate. Like, every disease is genetic, and we should be able to... Like, we're at the beginning of a CRISPR era-

    11. MM

      Mm-hmm

    12. AW

      ... where you should be able to figure out, like, cystic fibrosis or you with familiar hypercholesterolemia or BRCA variant, like, th- there should be a world where that's coming, where you can fix that.

    13. MM

      Yeah.

    14. AW

      We're not there yet.

    15. MM

      Okay.

    16. AW

      But Baby KJ is a pretty amazing-

    17. MM

      Yeah

    18. AW

      ... story.

    19. MM

      It's the first-

    20. AW

      It's the first, it's the first of the future

    21. MM

      ... sign of glimmer.

    22. AW

      I remember when there's the first IVF children, and it was, like, like it was so extraordinary, and now it's so common.

    23. MM

      Yeah.

    24. AW

      So I think we're, the next 20 years will be, you know, a blowou- e- extraordinary in that way.

    25. MM

      Hopefully sooner as well. I was just talking to an economics professor from Stanford yesterday, and he said in the next two or three years we're gonna see in something dramatic-

    26. AW

      Yeah

    27. MM

      ... that's gonna happen because of AI that's gonna change our own lives.

    28. AW

      I'm always more hesitant in healthcare.

    29. MM

      Mm.

    30. AW

      Like, I'm, I'm a little bit-

  13. 30:00 – 34:52

    A practical prevention stack: exome + blood labs, scans, wearables, and disciplined follow-through

    1. MM

      What do you think are the top three things that everyone should do after listening to this conversation in regards to their health? Because when I was at Susan's, uh, foundation launch, I was like, "Okay, I need to do this Prenuvo scan."

    2. AW

      Yeah, yeah.

    3. MM

      I have done my 23andMe, then I'm gonna connect all of that with AI-

    4. AW

      Yeah

    5. MM

      ... and see what it tells me.

    6. AW

      Look, I think-

    7. MM

      Do you think it's decent, decent plan?

    8. AW

      I think that the most important thing, um, 23andMe has, has three different products. We have a product called Total Health. Total Health is your exome, so it's a deeper level of sequencing, and it's blood.

    9. MM

      Mm.

    10. AW

      Everyone from a medical perspective should go and get that. It's the gold standard what people wanna be able to go and get.

    11. MM

      So it's better than spitting?

    12. AW

      It's two products. So this, the DNA is still spitting.

    13. MM

      Uh-huh. Yeah, okay.

    14. AW

      But at some point you wanna get your blood.

    15. MM

      Mm-hmm.

    16. AW

      You wanna be able to understand your blood as well. And so Prenuvo is super interesting, and I'm an investor in it. Um, I do it every year. There's still, like, a world of people all assessing, like, exactly what do you do with this information. And I recognize there's people who are anxious, um, because you'll find all kinds of things. And so it's, it's for people who want to embrace, like, I, I can handle it if you go and you tell me you have, like, a strange lump in your uterus. You're not gonna freak out.

    17. MM

      Mm-hmm.

    18. AW

      And that you're okay to follow up, because you'll find a lot of things, and some of them... Like, I know lots of people who found brain tumors and heart issues and aneurysms. Like, the stories are astounding, but you also have stories of people saying, "Oh, they found something and it was nothing and I had this whole workup." That's where Prenuvo I think is doing a lot of work to understand exactly how you wanna do an MRI. I do annual chest CTs. So a low dose chest CT is minimal radiation. I'm in a category that's genetically higher risk now because Susan had it.

    19. MM

      Mm-hmm.

    20. AW

      And I do that, and there's amazing AI work here where... And this is part of what the Susan Wojcicki Foundation was also part of in helping to support, is can you use your, um, my chest CT to give me a five-year prediction.

    21. MM

      Mm.

    22. AW

      So when do I-

    23. MM

      Yeah, because it's able to identify-

    24. AW

      Right

    25. MM

      ... the early stages.

    26. AW

      So when do I wanna get, um, when do I need to get another scan?

    27. MM

      Mm.

    28. AW

      Similar to a colonoscopy when you go and they say, like, "Do you need to come back in two years? You need to come back in 10 years."

    29. MM

      Mm-hmm.

    30. AW

      So it's gonna be a similar kind of experience there. The other thing that's important for people to recognize is, like, health can be really stressful and triggering for some people.

  14. 34:52 – 41:10

    Founder mindset and long-term bets: building 23andMe, a community, and downtown Los Altos

    1. MM

      I want to focus in this conversation as well-

    2. AW

      Mm-hmm

    3. MM

      ... on your mindset as a founder, 'cause a lot of your bets, I think all of your bets, 'cause you're doing 23andMe, I wanna talk about downtown Los Altos-

    4. AW

      Mm-hmm

    5. MM

      ... you're doing Susan's foundation. They're all long, long-term bets. How does your mind function in the world where every day there's something shiny, something fancy, and all of your bets are, like-

    6. AW

      Mm-hmm

    7. MM

      ... 10 plus years?

    8. AW

      You know, it's interesting 'cause when, when I was driving up here, um, and I was talking to- Um, some of the bankers about the SpaceX I- IPO. I knew Elon because of Sergey. And I remember in, like, the very earliest days when he was starting Tesla, and he would stay with us. And he would... He was so consistent talking about his vision of, like, Tesla and the space world and the potential. I have to say, like, it's complete kudos of, like, when you think of, like, long-term vision, the world... And I look at all around us, so, like, everything that's happened today. To make significant changes in the world, you have to have a long-term vision, and you have to be c- so consistent and maniacal about doing that, and you have to also love what you do.

    9. MM

      Mm-hmm.

    10. AW

      And that's where, like, I look at... Again, I talk ad nauseam about, like, how much I love genetics and... But I do, again, it, it just occurred to me thinking this morning, is like, people who love what they do, you have a long-term vision and you're consistent with it, and it never feels like work.

    11. MM

      Hmm.

    12. AW

      And, and every single day builds on the previous days. And so I look at 23andMe, every single day I walk into the office and I'm fascinated by the research that we can do. Like, what motivates me every day is this idea of, um, one, I'm helping people. Like, I always wanted to be a doctor, but I feel like now I, like, I help more people because I help connect on identity, I help connect on, um, family connections, like reuniting all these people, and we save lives. Like, I give people-

    13. MM

      Yeah

    14. AW

      ... valuable information about themselves. Um, and then the research is, like... I mean, for me, it's, again, my dad who loved neutrinos, like, and he loved space and all that. Like, I was like, wait, you like looking up in the sky, and I like looking inner. I, like, find that there's an entire universe in every cell. We understand nothing-

    15. MM

      Mm-hmm

    16. AW

      ... about it. Like, even I was with somebody talking about, like, the beauty of the mitochondria and, like, that you have this, like, power engine and how it works. Like, we understand so little.

    17. MM

      Yeah.

    18. AW

      And that's what I love. Like, every day it's like little tiny bricks coming together to build this foundation of knowledge, and it's so interesting. And again, that's what, like, I always advise kids. Like, whatever you do, like, make it so every day you have, like, that spark of joy where you're like, ooh, that was so cool. What was fun is, like, when we first moved into Los Altos, it was a pretty sleepy town, and there wasn't much... Like, there wasn't much going on at night. There wasn't much, um, for the kids to do. And, you know, I have to say, it's like, I, it's been such a pleasure because, like, you can buy buildings. I've bought buildings and, um, and then I could bring in, you know, really extraordinary owners who love what they do. So, like, Linden Tree.

    19. MM

      Oh, yeah.

    20. AW

      It's an amazing-

    21. MM

      The bookstore

    22. AW

      ... bookstore. You know, bookstores... And, like, this is where I was like, I have a pleasure. I can, like, help get a building, I can renovate, can make it nice, and then, you know, create an environment and a, uh, an economic environment where it can support a children's bookstore.

    23. MM

      Mm-hmm.

    24. AW

      Um, we started State Street Market so that-

    25. MM

      Oh, the best

    26. AW

      ... we could have-

    27. MM

      The best. Thank you so much. [laughs]

    28. AW

      You're welcome. So, so my whole idea there was like, I wanna go somewhere where I can bring the whole family and everyone's happy. And in the earliest days, I remember I would go out, we had 17 people in the family. And, like, one, you call a resura- a restaurant and you're like, "I need a reservation for 17."

    29. MM

      [laughs]

    30. AW

      And everyone's like, "No."

  15. 41:10 – 46:55

    Bankruptcy, buying back 23andMe, and why a nonprofit structure protects genetic data neutrality

    1. MM

      And all the things that you were doing, so 2024 was a brutal year for you with br- bankruptcy and having to buy out your company. Did you ever have thoughts of giving up? Like, this is not something I wanna do. I just wanna live a happy life.

    2. AW

      I thought about it a lot. Like, do I... Because it became... You know, there's moments where I'm like, is this a sign from God that, like, I should give up? Because, like- Everything is pointing in the direction that things are not going well. You know, there's... I called a friend who, um, had also started a company and given it up and had walked away, and he, um, he was like, "Look, I think about it every single day of my life." He's like, "I just always regret it." He's like, "I, I just-

    3. MM

      Mm

    4. AW

      ... I wish I hadn't." And in some ways it's, I, I felt like I had a responsibility. Like, one, I love 23andMe, so there was a selfish component of like, I just love it. The second thing is like, I felt a real moral responsibility to my customers, all 14 million, and my employees. And the responsibility that I felt was that I promised them that 23andMe was going to be a company, a mission-driven company focused on helping people access, understand, and benefit from the human genome. I really believe strongly that you want the company that owns that genetic information should be a neutral party, meaning that they should be supporting research all around the world, but it should not be owned by a single pharma company.

    5. MM

      Mm-hmm.

    6. AW

      It should not be owned by a single entity. And I thought about, like, my sister, like my deceased sister, her genetic information's in 23andMe. I don't want that owned by a single company. I want every single researcher in the world who's doing lung cancer work to have access-

    7. MM

      Mm

    8. AW

      ... to be able to make a difference. And, like, this is a community that came together to make a difference. I felt this huge moral responsibility that I should do whatever I could to make sure that we were true to that mission. And I feel really lucky. I feel really, really lucky that I got it. And, um, I'm not a person that was like, oh, I really wanted to just, like, hang out and, like, sit at the pool all day. I would love slightly more work-life balance.

    9. MM

      [laughs]

    10. AW

      Um, and I, I would... Like, I am trying to take, like, have... Again, now I have an amazing, I have 175 people at the company. I have an amazing management team. So I have more of that time to do things like this and to, um, you know, volunteer at my kids' school, and things like that. But, you know, it's kind of all the things that we talked about at the very beginning, is that you wanna do something in your life every single day that brings meaning.

    11. MM

      Yeah.

    12. AW

      And if I thought of, again, anything that I learned from Susan was, like, contributing on a daily basis and feeling like you're having an impact on the world is the most important thing. And it was a really, really hard year, and there's definitely things I would've changed. Um, but I love what I do.

    13. MM

      What's your key learning from that year?

    14. AW

      There's a dark side, and then there's, like, the positive side. I think the dark side was I was really surprised that, um, how many people backed away from helping me. I was really surprised in bankruptcy at, you know, the friends who showed up.

    15. MM

      Mm.

    16. AW

      And so I think it kind of came to me as a bit as like, you know, you do live... We're in Silicon Valley. It's a very competitive, aggressive environment. And I have always, like, I operate in a way where I feel like I try to always be honorable, and I try to be genuine and, um, and ethical. I think that was, like, one of the hardest things for me, is like, wow, like I'm, I m- I might be, I might be playing in the wrong community-

    17. MM

      Mm

    18. AW

      ... and, um, in a community of people that are not necessarily there to support. I think on the positive side, the thing that impressed me the most was, you know, my customers and my employees who rose to the challenge. I couldn't do it without them, and they were there. They stuck with it. Um, and they also, like, they embody the mission, and they care, and they feel this responsibility and that passion. And there's really good people in the world. You have to just make sure that you spend the time with them and align yourself. Like, I feel really, really good every single day, that I feel like I'm doing things that make a difference. Like, I'm doing things that make a difference with 23andMe. I'm totally unfettered on making decisions that are really about best interest of humanity. I love what I do with Los Altos. I love what I do with Susan Wojcicki Foundation. And I work with extraordinary people. Having a community that supports you, and, um, supports the mission, and you can trust them is, um, a really special thing. So, you know, I think about, like, community as a theme that I have throughout my life. Like, I have-

    19. MM

      Mm

    20. AW

      ... my family I'm really s- close to. I've tried really hard to build a community in Los Altos. I have a very strong 23andMe community, and I try to have a really strong, like, 23andMe experience and ecosystem. Be a brand that fundamentally is always gonna be trusted and have the back of customers.

    21. MM

      I really like, uh, what you said about being true to yourself. It's tempting to be dishonest to chase success-

    22. AW

      Right

    23. MM

      ... especially in Silicon Valley-

    24. AW

      Right

    25. MM

      ... when everyone's moving so fast, some people are cheating. Uh, I love that. Whenever I think about projects, I think about, how can I do them long term and be true to myself?

    26. AW

      Yeah. My parents and, like, my grandparents and everyone, like, I think also people, again, you're an immigrant, like I'm, my, my parents, my dad, um, you know, escaped communism, and his father was imprisoned by Stalin. And I just think about, like, the really important decisions that people made that really impact society, and I want to be somebody who's, like, always operating with that kind of, you know, ethical compass.

  16. 46:55 – 57:33

    Family decisions, parenting at 45, and making work more family-friendly

    1. MM

      Yeah. Talk to me about your kids.

    2. AW

      Yeah. They're cute.

    3. MM

      You made-

    4. AW

      [laughs]

    5. MM

      They are. And you made such an inspired decision. Like, I've never thought about that.

    6. AW

      Mm-hmm.

    7. MM

      When I realized you had a baby at 40...

    8. AW

      45.

    9. MM

      45.

    10. AW

      Mm-hmm.

    11. MM

      So I, I told you I really want a third kid, but I don't feel like I'm ready. [laughs] Like-

    12. AW

      Yeah, yeah, it's a lot

    13. MM

      ... these two are a lot. Talk to me about that decision. Did you have any concerns about having a kid at that age?

    14. AW

      Being divorced was really, really hard for me. There was the aspects of, like, the loss of a family. Um, but there was also the loss of, like, oh, my God, I, like, I wanna have more kids. I felt really determined that I was not gonna let, you know, a marital status impact- ... my decision to have kids. And at the same time, I think I always joked with people that the only thing worse than one custody agreement is two custody agreements. And so I was-

    15. MM

      [laughs]

    16. AW

      I was very, I was very gun shy. I was like, "Okay, am I actually gonna reproduce with, like, another consenting individual?"

    17. MM

      [laughs]

    18. AW

      I ran into my, my OB one day at, at Pete's and I was like, "Oh, I wanna have another child." And she was like, "You do?" [laughs]

    19. MM

      Classic. [laughs]

    20. AW

      And, and she was... And I was like, "Oh, shit. You know, you're right. Like, I, I should get on this." And, you know, they kind of torture you going through a donor, a sperm donor process where you have to, like, like, do all these therapy meetings.

    21. MM

      Did, did you pick a scientist or something, at least someone with a PhD? [laughs]

    22. AW

      Yeah. No, I wanted, I wanted to find somebody who was gonna... characteristics that would be, that would match in well. And, um, and I, I... listen, I... like, it was, it was one of those things. Like, I was really, um... I went into it... I, I w- we- again, you have to go through all this therapy, and everyone told me, "You're never gonna get pregnant. You're never gonna get pregnant. You're never gonna get pregnant." And so you had to do all these therapy sessions doing it. I was like, "I get it, you guys. Like, I'm old and my numbers are bad. Um, but I'm just gonna try it." Because if you don't try-

    23. MM

      You never know

    24. AW

      ... you never know.

    25. MM

      Yeah.

    26. AW

      But I didn't even do a pregnancy test 'cause I was like, "Ah, there's no reason." Like, I'm just-

    27. MM

      First attempt, it worked?

    28. AW

      No. The first... I actually got pregnant the first time and then I miscarried.

    29. MM

      Mm.

    30. AW

      But I was like, "Holy cow, like, the machine works." Um, and, and then a couple months later I got pregnant, and I just... Every- and I, I was almost in denial. Like, even at eight months I was still, like, riding my bike around and I was like, "Ah," like, "I think I'm pregnant." And-

  17. 57:33 – 59:12

    The 2026 health sentence: get genetic testing, exercise daily, and cut sugary soda

    1. MM

      All right. One last question.

    2. AW

      Mm-hmm.

    3. MM

      Can you finish this sentence for everyone who's watching? The smartest thing you can do for your body in 2026 is...

    4. AW

      One, everyone should get genetic testing. Hands down. Like everyone should know what their genetic risk is. Second, exercise every day. Just like right now.

    5. MM

      Every day?

    6. AW

      Like do 100... Like, but small things.

    7. MM

      Mm-hmm.

    8. AW

      And I think people shouldn't get overwhelmed. Do 50 squats a day broken up into five segments. Like do 10... We're gonna do, we're gonna do squats after this, like 'cause we've been sitting-

    9. MM

      [laughs] Yeah

    10. AW

      ... for an hour. Like just something small. The way to build the habit is like you just do something small every day.

    11. MM

      Mm-hmm.

    12. AW

      And then I'd say like cut out soda.

    13. MM

      Oh, even probiotic? What, what do you have with you? [laughs]

    14. AW

      I, I have-

    15. MM

      What, what kind of soda do you have with you? [laughs]

    16. AW

      I think that that was just sparkling water. [laughs]

    17. MM

      Okay. [laughs]

    18. AW

      I have... I- in general like-

    19. MM

      Yeah

    20. AW

      ... I, I always think about like understanding what your core risks are, and then exercise makes a huge difference in your life.

    21. MM

      Yeah.

    22. AW

      And so like the more you can just integrate it into your life, like take the stairs instead of the elevator, you know, do some jumping jacks. Like if your kids are playing, you play.

    23. MM

      Yeah.

    24. AW

      Move around. Get a walking, like a standing desk, things like that. Like it makes a huge difference. Like walk the dog.

    25. MM

      Mm-hmm.

    26. AW

      Um, but like all of that makes a big difference. And then on food, like my kids are really good. We have a garden, but I try to make them like have a salad, have cucumbers. Like my kids are very good at eating cucumbers with salt. But like just something... Like I avoid sugar. Like I, and the, as soon as I cut it out, it's like I never want it again.

    27. MM

      Mm-hmm.

    28. AW

      So like I just never, um, have like soda, like sugar wa- sugar water is just like one of the worst-

    29. MM

      Yeah

    30. AW

      ... things for you. I always have my vegetables.

  18. 59:12 – 1:01:13

    Why 23andMe was early—and why the AI moment finally fits the original big-data thesis

    1. MM

      It looks like what you are doing has come together-

    2. AW

      Yeah

    3. MM

      ... in this day and age with AI.

    4. AW

      Yeah.

    5. MM

      It looks like you've been preparing for this. [laughs]

    6. AW

      I mean, I think that the... I, I mean, going back to sort of where we started, I, I do feel like- You know, 23andMe in many ways has just been very early. We set out, like I look at my deck, my fundraising deck from 2013, it was like, you know, we're not a genetic testing company. Like we're not just, you know, an ancestry company. Like we're a big data company. And that goal has always been like fundamentally, if you can understand the human genome, we can all benefit.

    7. MM

      Mm-hmm.

    8. AW

      And so that's why the, the mission has always been, you know, help people access, understand, and benefit from it. We're set up, like everything about how we set up our research infrastructure and how we consent people, and we give people choice and transparency, and we've created this community. It's like 14 million people, 90% have opted into research. We collect tons of information on, we ask our customers questions like, you know, "Do you have dogs?" Or, "Do you have allergies?" And we can do extraordinary research. Like people have no idea how many insights we have across everything from like fun phenotypes like, you know, handedness. Are you left-handed, right-handed? Eye color, um, medication response, you know, pet allergies, cancer risk, like all of it. It's, it's extraordinary. And suddenly now with AI, it needs datasets.

    9. MM

      Yeah.

    10. AW

      And you know, the number one thing for us blocking, you know, 23andMe, again we have the NeuraPS paper on doing risk prediction, like what we need next is just like we have capital, we're capital constrained, and I'm resource constrained. But those are all things that we'll fix in time. And so, but the world is suddenly primed. Like the technology's there, we have the data-

    11. MM

      Yeah

    12. AW

      ... that's there, and I have customers who are hungry to say like, "Let's go and we wanna be healthier. We wanna prevent disease." And like the world is 100% moved into that, that space now.

    13. MM

      And that's amazing that you never gave up.

    14. AW

      Yeah. We're stubborn. Yeah.

    15. MM

      Thank you so much.

    16. AW

      Yeah. Thank you so much. So good to see you.

    17. MM

      So inspiring. Thank you

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