Pod Insights a16z Podcast Big Ideas 2026: Voice Agents and High-Stakes Trust About this episode Voice is becoming one of the fastest paths for AI to do real work, especially in regulated environments where accuracy and compliance matter. In this episode, we look at voice agents replacing and augmenting phone-based workflows, what trust and measurement look like when AI runs sensitive interactions, and how healthcare and consumer products shift toward continuous monitoring and deeper connection. The throughline is simple: as AI enters higher-stakes moments, the winners will be the systems people can trust and actually rely on.
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 It's kind of like how people say AI isn't going to take your job, a human using AI will.
0:05 Speaker 01 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.
0:20 Speaker 02 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?
0:31 Speaker 01 Can startups compete with the large incumbent platforms?
0:37 Erik Torenberg? Understanding where technology is headed requires looking around corners. Our 2026 Big Ideas capture the areas we believe founders will be building toward in the new year ahead. This episode is built around three big ideas where AI is moving next when the work is real and the stakes are high. First, voice agents are becoming practical, deployable systems, not demos. Second, in healthcare, AI pushes us from occasional checkups towards continuous monitoring, which forces new standards of evidence and interpretation. And third, as productivity becomes table stakes, consumer AI shifts towards connection, identity, and helping people feel seen.
1:15 Erik Torenberg? We'll start with the most concrete change you can already see in the market: voice agents moving from novelty to deployment. Olivia Moore breaks down where enterprises are buying voice AI today, why it's showing up first in regulator workflows, and how reliability, compliance, and tracking turned voice into a true AI employee category. Here's Olivia.
1:36 Speaker 00 I'm Olivia Moore, and I'm a partner on our AI Applications Investing Team. My big idea for 2026 is that AI voice agents will start to take up space. In 2025, we saw voice agents break out from something that seemed more like science fiction into something that real enterprises are buying and deploying at scale. I'm excited to see voice agent platforms expand, working across platforms and modalities to handle full tasks and bring us closer to the true AI employee vision. So we've seen nearly every vertical have enterprise customers that are testing voice agents. Agents, if not deploying them at pretty significant scale. Healthcare is probably the biggest one here. We're seeing voice agents in nearly every part of the healthcare stack,
2:19 Speaker 00 calls to insurers, pharmacies, suppliers, but also in perhaps more surprisingly, patient-facing calls. It could be things like scheduling and reminders that are kind of table stakes, but also even more sensitive calls, like post-surgery follow-up calls or even intake calls for psychiatry are being handled by voice AI. I think honestly, a big driver here is just the high turnover and the difficulty in staffing in healthcare right now, which makes voice agents that can perform with some reliability a pretty good solution. Another category that's like that is banking and financial services. You would think there's so much compliance and regulation that voice AI can't operate there yet, but it turns out this is an area where voice AI actually outperforms
3:02 Speaker 00 because humans are actually very good at violating compliance and regulations. And voice AI can get it every time. And importantly, you can track how voice AI is performing over time. Lastly, I would say another area where voice has taken off is recruiting. This is everything from retail frontline jobs to entry-level engineering roles, to even mid-level consultant roles. With voice AI, you can create an experience for candidates where they can interview instantly at whatever time works for them, and then they're sent through the rest of the human recruiting process. We've seen big improvements on accuracy and latency this year as the underlying models get better and better. Actually, in some cases, I've heard of voice agent companies slowing down their agent
3:44 Speaker 00 or introducing background noise to make it sound more like a human. When it comes to BPOs and call centers, I think some of them are going to see a softer transition and others are going to maybe see a harder cliff when it comes to the threat from AI, and specifically voice AI. It's kind of like how people say AI isn't going to take your job, a human using AI will. What we're seeing is a lot of end customers may still want to just buy the solution, not buy technology that they have to implement. So they might still use a call center or a BPO in the kind of near to medium term. But they're probably going to use one that's going to offer a cheaper price or be able to do more volume because they're utilizing AI. Interestingly, there's a couple geographies where humans are still actually cheaper on a permanent basis than kind of best-in-class voice AI.
4:29 Speaker 00 And so it'll be interesting to see as the models get better, if costs come down there. And then call centers in those markets might face a little bit more of a threat than they do now. AI is actually remarkably good at multilingual conversations and heavy accents. Oftentimes, I'll be on a meeting and there'll be maybe a word or a phrase I don't catch. And I'll check like my granola transcripts and it has it down perfectly. So I think that's a good example of what most ASR or speech-to-text providers can do now. There's a couple of use cases that I'm hoping we see a lot more of next year. Anything government. So we were investors in Prepared 911. If you can run 911 calls, and they were the non-emergency calls, but if you can run that with voice AI, you should be able to run DMV calls and anything else government-related
5:13 Speaker 00 that right now is so frustrating as a consumer and so frustrating if you're the worker on the other end of the phone. I'm also really intrigued to see more in consumer voice AI. It's mostly been B2B so far, just because it's so obvious to replace or supplement a human on the phone with much lower-cost AI. One category in consumer voice that I'm excited about is around kind of health and wellness more broadly. We're already seeing voice companions take off in assisted living facilities and nursing homes, both as a companion for, again, the residents, but also they can kind of track different measures of wellness over time. We see voice AI as more of an industry than a market, which in our opinion means there's going to be winners cross and at every layer of the stack.
5:56 Speaker 00 If you're interested in voice AI or if you want to build in voice AI, I would recommend you check out the models. There's lots of amazing platforms like 11 Labs, where you can test both creating your own voice and creating your own voice agent. And you get a really good sense of what's possible and what's to come.
6:16 Erik Torenberg? 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.
6:47 Speaker 02 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 contradiction 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 Dows. 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 Yows, 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
7:31 Speaker 02 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 Mouse, 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 Mouse 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.
8:16 Speaker 02 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
8:59 Speaker 02 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 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.
9:44 Speaker 02 So I think that paradigm defines a lot of what we are starting to see and what we 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.
10:09 Speaker 02 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've got the Oura Rings of the world. You've got Whoop. And these have really become 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?
10:54 Speaker 02 There's even a term for it called incidentaluma, this notion of if you do measure something, the likelihood that you will find something that deviates from the standard baseline is relatively high. This is a common concept that comes up when people talk about imaging, for instance, kind of using MRIs and other imaging modalities for healthy populations. The likelihood that you'll find something is reasonably high. And the challenge is that it's not always the case that that finding is actionable, but it could cause a lot of 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 healthcare system or too expensive for the individual to afford. What that points to is that we just have, at this point, a relative lack of evidence
11:39 Speaker 02 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 mass-market basis? We should be running N of 1 real-world studies on the data that's being collected, how that actually correlates to phenotype, how that correlates to disease status, and use that in a feedback loop to inform and just create a better dataset for how people should be interpreting
12:23 Speaker 02 and acting on the findings that are coming from these types of technologies.
12:29 Erik Torenberg? 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.
12:58 Speaker 01 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, 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
13:43 Speaker 01 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 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 platforms of 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.
14:24 Speaker 01 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.
15:10 Speaker 01 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 today. What I get really excited about is what is the next steps? And what can be done?
15:55 Speaker 01 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.
16:05 Erik Torenberg? 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 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 and higher-stake decisions, the differentiator is not novelty. It's trust, reliability, and whether the system improves real outcomes.
16:43 Erik Torenberg? 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 X at A16Z and subscribe to our Substack at 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.
17:27 Erik Torenberg? For more details, including a link to our investments, please see a16z.com/disclosures.
Previous Big Ideas 2026: The Enterprise Orchestration Layer Next Big Ideas 2026: Physical AI and the Industrial Stack Page updated September 30, 2026 · transcript and summary are machine-generated
