How AI Agents Will Transform in 2026 (a16z Big Ideas)

1. 0:00 – 0:30

   Introduction: The Future of AI Interfaces

   1. ETErik Torenberg0:00

      Welcome to Big Ideas for 2026. We'll hear from Marc Andrusko on the evolution of AI user interfaces and how the way we interact with intelligent systems is fundamentally changing. Stephanie Zhang discusses what it means to design for agents rather than humans, a shift that's reshaping product development. And Olivia Moore will share her thoughts on the rise of AI voice agents and their growing role in our daily lives. These aren't just predictions. They're insights from the people working directly with the founders and companies building tomorrow's future.

   2. MAMarc Andrusko0:27

      [upbeat music]

2. 0:30 – 1:09

   The Death of the Prompt Box

   1. MAMarc Andrusko0:31

      I'm Marc Andrusko, a partner on our AI apps investing team. My big idea for twenty twenty-six is the death of the prompt box as the primary user interface for AI applications. The next wave of apps will require way less prompting. They'll observe what you're doing and intervene proactively with actions for you to review. The opportunity we're attacking used to be the three hundred to four hundred billion dollars of software spend annually in the world. Now what we're excited about is the thirteen trillion dollars of labor spend that exists in the US alone. It's made the market opportunity or the TAM for software about thirty times bigger. If you start from there, and then you think about, okay,

3. 1:09 – 2:28

   AI as the Ultimate Employee

   1. MAMarc Andrusko1:09

      if all of us want this software to be doing work for us, ideally, it's doing work with at least if not more competency than a human could, right? And so, um, I like to think about like, well, what do the best employees do? What do the best human employees do? And, and I've recently been talking about this graphic that was floating around on Twitter. It's a pyramid of, like, the five types of employees and, and the ones with the most agency and why they're the best. So if you start at the bottom rung of the pyramid, it's, like, people who identify a problem and then come to you and ask for help and ask what to do, and that's, like, the lowest agency employee. But, uh, if you go to the S tier, like the, the most high agency employee you could possibly have, they identify a problem, they do research necessary to diagnose where the problem came from. They look into a number of possible solutions. They implement one of those solutions, and then they keep you in the loop, or they come to you at the very last minute and say, like, "Do you approve of this solution I found?" And that's what I think the future of AI apps will be, and I think that's what everyone wants. That's what we're all working towards, so I feel pretty confident that we're almost there. I think LLMs have continued to get better and faster and cheaper, and I think there's a world in which the user behavior will still necessitate a human in the loop at the very end to sort of approve things, certainly in high-stakes contexts. But I think the models are more than capable of getting to a point where it's suggesting something really smart on your behalf, and you basically just have to click Accept. As

4. 2:28 – 4:09

   Proactive AI in CRM and Workflows

   1. MAMarc Andrusko2:28

      you guys know, I'm pretty obsessed with the notion of an AI-native CRM, and I think this is, like, a perfect example of what these proactive applications could look like. So in today's universe, a salesperson might go open their CRM, explore all the open opportunities they have, look at their calendar for that day, and try to think about, "Okay, what are the actions I can take right now to have the greatest impact on my funnel and my ability to close deals?" With the CRM of tomorrow, your AI agent or your AI CRM should be doing all these things on your behalf in perpetuity, identifying not only, like, the most obvious opportunities that are in your pipeline, but going through your emails from the last two years and harvesting, you know, this was once a warm lead, and you kinda let it die. Like, maybe we should send them this email to, to drum them back up i-into your process, right? So I think there are so many ways in which drafting an email, harvesting your calendar, going through your old, your old call notes, like, the, the opportunities are just endless. The ordinary user will still want that last-mile approval almost a hundred percent of the time. They will want the human part of the human in the loop to be the final decision-maker, and that's great. I think that's, like, the natural way in which this will evolve. I could imagine a world in which the power user is basically taking a lot of extra effort to train whichever AI app it's using to have as much context about their behavior and how they perform their work as humanly possible. These will utilize larger context windows. These will utilize memory that's been baked into a lot of these LLMs and make it such that the power user can really trust the application to do ninety-nine point nine percent of the work or maybe even a hundred, and they'll pride themselves on the number of tasks that get done without a human needing to approve them.

   2. SZStephanie Zhang4:05

      [upbeat music]

5. 4:09 – 5:28

   Designing for Agents, Not Humans

   1. SZStephanie Zhang4:10

      Hi, my name is Stephanie Zhang, and I'm an investing partner on the a16z growth team. My big idea for twenty twenty-six is creating for agents, not for humans. Something I'm super excited about for twenty twenty-six is that people have to start changing the way they create, and this ranges from creating content to designing applications. People are starting to interface with systems like the web or their applications with agents as an intermediary, and what mattered for human consumption won't matter the same way for agent consumption. When I was in high school, I took journalism, and in journalism, we learned the importance of starting with the five W's and H in the lead paragraph for news articles and to start with a hook for features. Why? For human attention. Maybe a human would miss the deeply relevant, insightful statement buried on page five, but an agent won't. For years, we've optimized for predictable human behavior. You wanna be one of the first search results back from Google. You wanna be one of the first items listed on Amazon. And this optimization is not just for the web, but as we design software too. Apps were designed for human eyes and clicks. Designers optimized for good UI and intuitive flows. But as agent usage grows, visual design becomes less central to overall comprehension.

6. 5:28 – 8:48

   Machine Legibility and Content Creation

   1. SZStephanie Zhang5:28

      Before, during incidents, engineers would go into their Grafana dashboards and try to piece together what was going on. Now, AI SREs take in telemetry data, they'll analyze that data, and they'll report back with hypotheses and insights directly into Slack for humans to read. Before, sales teams would have to click through and navigate Salesforce or other CRMs to gather information. Now, agents will take that data and summarize insights for them. We're no longer designing for humans, but for agents. The new optimization isn't visual hierarchy, but machine legibility.And that will change the way we create and the tools that we use to do it. It is a question we don't know the answer to, what agents are looking for, but all we know is that agents do a much better job at, you know, reading all of the text in an article versus maybe a human would just read, you know, the first couple paragraphs. There are a bunch of tools out there that different organizations use to just make sure that they show up when consumers are prompting ChatGPT asking for the best corporate card or the best shoes to buy. And so there's like a bunch of what we call GEO tools out there in the market that people are using. But, um, everybody is asking the question, what AI agents want to see. I love this question, um, when humans may choose to exit the loop entirely. We're already seeing that happen in some cases. Our portfolio company Decagon is answering questions for a lot of their customers already autonomously. But for other cases, security operations or incident resolution, we typically see a little bit more human in the loop where the AI agent takes first stab at trying to figure out what the issue is, running the analysis, and serving to the humans different potential situations. Those tend to be cases of higher liability, more complex analyses, uh, that we see humans staying in the loop, and will probably stay in the loop for much longer until the models and the technology get to incredibly high accuracy. I don't know if agents will be watching Instagram Reels. Um, it's really interesting. At least on the tech side, it is really important to optimize for that machine legibility piece, optimize for insight, optimize for relevance especially, versus in, you know, in the past it was more about hooking people in, capturing attention in flashy ways. What we're seeing already is case of high volume, hyper-personalized content, and maybe you don't create one extremely relevant article, extremely relevant and insightful article, but maybe you're creating extremely high volumes of low quality content, but addressing different things that you may think an agent wants to see, almost like the equivalent of keywords. In the era of agents where cost of creation of content kind of goes to zero and it's really easy to create high volumes of content, that's the potential risk around just high volumes of things to be able to try to capture agent attention.

7. 8:48 – 9:25

   The Rise of AI Voice Agents

   1. OMOlivia Moore8:48

      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, if not deploying them at pretty significant scale.

8. 9:25 – 11:01

   Voice AI in Healthcare, Finance, and Recruiting

   1. OMOlivia Moore9:25

      Healthcare is probably the biggest one here. We're seeing voice agents in nearly every part of the healthcare stack, 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 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 consulting 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 or introducing background noise to make it sound more like a human. When it comes to

9. 11:01 – 12:32

   Challenges and Opportunities in Voice AI

   1. OMOlivia Moore11:01

      BPOs and call centers, I think some of them are gonna see a softer transition and others are gonna maybe see a, a harder cliff when it comes to the threat from, from AI, and specifically voice AI. It's kind of like how people say, "AI isn't gonna 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 gonna use one that's gonna 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, 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, um, 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 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, 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 that right now is so frustrating as a consumer and so frustrating if you're the worker on the other end of the phone.

10. 12:32 – 13:01

    Consumer Voice AI and Wellness

    1. OMOlivia Moore12:32

       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.

11. 13:01 – 13:31

    Building with Voice AI: Tools and Platforms

    1. OMOlivia Moore13:01

       We see voice AI as more of an industry than a market, which in our opinion means there's gonna be winners across and at every layer of the stack. If you're interested in voice AI or, or if you wanna build in voice AI, I would recommend you check out the models. There's lots of amazing platforms like ElevenLabs, 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. [outro music]

Episode duration: 13:33