Podcast Host / Narrator (6:15)

Olivia's point is that once AI can reliably handle sensitive conversations, it starts taking on real operational load, especially in healthcare. But that raises a bigger question. What happens when healthcare becomes something you engage in continuously, even when you're not sick? Julie Yu introduces the Healthy Mouse, a new customer segment built around proactive care, always on monitoring and longitudinal signals. She also explains the hard part, generating evidence and avoiding false alarms when you measure more and more. Here's Julie.

Julie Yu (6:47)

Hi everyone, I'm Julie Yu, a general partner on the A16Z healthcare team. And my big idea for 2026 is healthy mouse. I believe that an entirely new customer and user segment is emerging that we'll call Healthy Mouse. This is in contradict to the status quo, which is that those who interact with the healthcare system frequently are generally sick. And we'll call those Sick Mouse or Sick daos. And those individuals who are healthy are really only interacting with the healthcare system on a very infrequent basis, maybe annually. And we'll call those Healthy yaos, people who might see a doctor maybe once a year at most, but really are not incentivized in any way, shape or form to engage with the healthcare system on a more frequent basis. And what we're now starting to recognize is that higher engagement with the healthcare system can actually be really good, especially amongst healthy individuals or even those who have been diagnosed with some kind of condition, some kind of chronic disease, but need ongoing monitoring and measurement support to keep that chronic disease at bay. What we're seeing is that there's a set of emerging payment models and business models to support this motion of these healthy mouths, so to speak, and that even consumers are starting to exhibit far more proactive, wellness minded behaviors that has resulted in the creation of actually more supply side players to really serve this new need. And so I believe that healthy mouths will take center stage in 2026 as an entirely new customer segment for the healthcare industry. And we're excited to see what kinds of new both B2B and B2C companies emerge from this trend. It can be a combination of traditional insurers and traditional providers repackaging their services and products to fit this need, using AI native capabilities. It can be entirely new AI powered challenger clinics that are really taking on new modalities of care, as we've talked about in the past, to serve directly consumers in this fashion. We also expect to see infrastructure players who are sort of building the utility layer behind the scenes to support the ability for these healthy mouths to engage with these new types of services. In terms of the types of services that we hope to see to help people monitor their health and really stay on top of their health, I think one sort of concept to unpack first is, you know, what is monitoring. And I think there's increasingly a recognition in our space that the standard way that we measure vitals and biological signals is very static and very point in time. If you think about things like blood pressure, for instance, we're generally only taking our blood pressure if we're not diagnosed with hypertension, maybe on an annual basis. And that point in time measure is probably highly inaccurate relative to what's happening on a day to day basis with your body. One of the interesting examples that we've seen emerge in the last several years is cgms, which was for the first time really a means to take what historically had been a very sort of sporadic and static measure of blood glucose levels and all of a sudden make it a continuous longitudinal signal that is much more accurate, captures much more of the nuance of how that measurement changes over time, and therefore can be far more actionable for the individuals who are taking it. So I think that paradigm defines A lot of what we are starting to see and what we expect expect to see play out with respect to vitals. Moving from a very static one time paradigm to one that is much more longitudinal and continuous in nature. We're probably going to see that across a number of different domains, whether it be blood glucose, whether it be things like blood pressure and many other biomarkers that can inform health state. I think in addition to that, wearables is the obvious other category of tools that we see a lot of activity around. And I think there has been a cultural shift in just in terms of the mass market sort of adoption of very basic wearable tools, whether it be Apple watch measuring steps, starting to measure even cardiovascular signals, whether it be things like sleeping measurement devices. You know, you've got the oura rings of the world, you've got whoop. And you know, these have really become, you know, very mainstream consumer wearables that also have a lot of signal that can be useful in the context of health monitoring. So there's always a question that comes up of what do healthy people actually need to know? Like do they need all this signal? Is there a risk of false positives, even false negatives that might come from over measuring these kinds of signals? There's even a term for it called incidentaloma. This notion of if you do measure something, the likelihood that you will find something that deviates from sort of the standard baseline is relatively high. This is a common concept that comes up when people talk about imaging, for instance, you know, kind of using MRIs and other imaging modalities. For healthy populations. The likelihood that you'll find something is, you know, reasonably high. And the challenge is, is that it's not always the case that that finding is actionable, but it could cause a lot of, you know, mental distress and a lot of concern. People might go get a whole bunch of other diagnostics that actually causes costs to increase in our health care system are too expensive for the individual to afford. What that points to is that we just have at this point a, you know, relative lack of evidence in our healthcare system about all the possible interpretations of signals that could be detected by the technologies that we have. In some ways the evidence lags the technological capabilities that exist in our market. And I think that's actually one of the huge opportunities associated with this healthy male concept is how can we create infrastructure to effectively generate that evidence base. As individuals start to adopt these types of technologies on a more sort of mass market basis, we should be running n of 1 real world studies on the data that's being collected, how that actually correlates, correlates to phenotype, how that correlates to disease status. And, you know, use that in a sort of a feedback loop to inform and just create a better data set for how people should be interpreting and acting on the findings that are coming from these types of technologies.

Podcast Host / Narrator (12:29)

Julie shows the infrastructure and behavior shift, people moving toward higher frequency signals and more ongoing engagement, but only if the system earns trust and knows how to interpret what it finds. Now Zuma from Healthcare to Everyday Life Brian Kim argues that the next wave of consumer AI moves beyond productivity towards connectivity. Products that help people feel understood, deepen relationships and create new interaction models, including AI mediating how we show up for each other. Here's Brian.

Brian Kim (12:58)

Hi, I'm Brian Kim. I'm a partner at a16z's AI applications investing team. 2026 marks the year where major consumer AI application products shift from productivity to helping you work to connectivity. Helping you stay connected instead of helping you just do work. AI allows you to see yourself clearly and help build relationships with people you love. AI has been incredibly useful for productivity and I think we'll start seeing AI actually take more mindshare and time from traditional products versus AI productivity tools. There will be folks who use it to really augment and actually get that connection that they feel that they need from others digitally. I think there will be a group of people who really use AI to facilitate their existing relationships in person. We're all social animals and I believe AI has a real place in helping us stay connected with others and help us feel like we're seen by others. Can startups compete with the large incumbent platforms? The incumbents have the platform, they have the network. AI brings a net new user interaction that may be difficult to replicate and may not natively live in the platform that the product. And insofar as there are net new user interaction models, insofar as there is net new creative outlets and atomic units that look different from what's available in current platforms. My strong belief is that startups can absolutely win. Increasingly, we're sharing so much more of our inner life with AI. What I get really excited about is people's willingness to share is deepening with AI. What happens when I'm okay with my AI coming to your AI, my guy talking to your guy and say, look, have you checked in on him? Do you want to talk about abc? I think those would be an opener for net new relationship, net new conversations that we wouldn't have otherwise. And I'M very excited for AI to actually finally help people be seen by others. The mantra in consumer products is look, always try to actually address the core emotion. The core emotion again here is wanting to be seen, wanting to feel connected to others and in order for the first step to happen, I think it's helpful for the AI product to be able to understand who you are. So then the question is what would be the best mechanism for a product to understand you quickly that you narrating your life story? Perhaps it's ingestion of your digital footprint. Perhaps it's ingestion of some of the things that you talked about online or offline. Perhaps it's looking through your photo roll with artificial intelligence or gen AI. We have a net new wave of companies that really help you do work better, think better and get information easier. We have been blessed by an incredible revolution in AI tech today. What I get really excited about is what is the next steps and what can be done. I get very excited to think about the next suite of products that will start addressing and helping people feel like they're being seen by others.

Podcast Host / Narrator (16:05)

These three ideas connect into one story about where AI wins next Voice Agents are taking on real regulated work because they can be measured, tracked and mave reliably compliant. Healthcare is moving towards continuous monitoring and proactive engagement, but it only works if we build the evidence and interpretation layers to match the data. And consumer AI is shifting from help me do more to help me connect built around the core emotion of being seen. In other words, as AI moves closer to human relationships in higher stake decisions, the differentiator is not novelty, it's trust, reliability and whether the system improves real outcomes. Thanks for listening to this episode of the A16Z podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes go to YouTube, Apple Podcasts and Spotify. Follow us on X16Z and subscribe to our substack@a16z.substack.com thanks again for listening and I'll see you in the next episode. As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com disclosures.