Podcast Host / Narrator (5:02)

Mark gives the interface shift from prompting to execution. The second big idea follows naturally. If agents are the ones navigating software on our behalf, then we have to start building software to be understood by them. Stephanie Zhang calls this machine legible software. In an agent first world, visual hierarchy matters less and structure matters more. The advantage shifts to products, content, and systems that machines can reliably interpret and operate inside.

Stephanie Zhang (5:27)

Here's Stephanie hi, my name is Stephan Dang and I'm an investing partner on the A16Z growth team. My big idea for 2026 is creating for agents, not for humans. Something I'm super excited about for 2026 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 in 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 want to be one of the first search results back from Google. You want to 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. Before or 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 reading all of the text in an article versus maybe a human would just read 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 sheet. And so there's like a bunch of what we call geo tools out there in the market that people are using. But everybody is asking the question what AI agents want to see. I love this question. 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 that we see humans staying in the loop and will probably stay in the loop for much longer until the the models and the technology get to incredibly high accuracy. I don't know if agents will be watching Instagram reels. 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 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 a potential risk around just high volumes of things. To be able to try to capture agent attention.

Podcast Host / Narrator (10:04)

If software becomes machine legible and agents can execute tasks across tools, that the biggest challenge is not cosmetic, it's organizational. That leads to the third big idea. Sarah Wang describes the rise of an agent layer that sits above the traditional system of record and becomes the place where work actually happens. It collapses the distance between intent and execution and changes which software systems control the flow. Here's Sarah.

Sarah Wang (10:28)

I'm Sarah Wang, general partner on a 16Z growth and my big idea for 2026 is that systems of record start to lose their edge. A passive system of record layer stops making sense when agents can independently execute on assigned intent. I expect to see a new dynamic agent layer that actually makes sense for employees to replace legacy systems of record. This is a very exciting development on the long road of inserting intelligence into companies. I don't say that systems of record are losing primacy lightly at all. I used to work at a firm that almost exclusively invested in ERPs and other systems of record because of the stickiness of the data gravity. There was a wave of SaaS 2.0 that was well funded and tried and failed to take on the system of record mostly through a better ui. This is the first time that we've seen a genuine threat to that and that's because the distance between intent and execution is collapsing. And that's creating not a 20 to 50% better experience for the user. But how you get to that magical TEDx. Let's take the concrete example of ITSM IT service management. This has traditionally been the domain of powerhouse company ServiceNow. I chatted with the head of IT recently who told me for the first time in his two decade long career, he believed that IT support was fundamentally gonna change. It will look completely different in five years. So why is that? If you think about the way that the old systems work, how long it takes to do something like request access to new software in the firm, and you contrast that with the ITSM agents that are arriving, they plug into your stack and this type of request becomes nearly instantaneous. Through advancements in LLMs, you can now extract intent. You can classify the request type, you can map it to a known workflow, identify user entities, and the request from the user becomes fulfilled in a way that is efficient and accurate. So we think there's a couple of valuable layers in this new paradigm. Of course there's the foundation model layer we believe that stays valuable, but it's really the emerging agent layer that sits as close as possible to the user and is collecting data on that user, understanding user preferences that we think accrues value in the future. Based on everything that we're seeing in the wild, we believe this is a huge opportunity for new players to come in and win.

Stephanie Zhang (12:48)

Why is that?

Sarah Wang (12:48)

We're in a phase right now where the product is getting better on a weekly, if not daily basis and you need teams that move fast if you're going to collapse in 10 and execution what bridges that is actually having an accurate, reliable solution for your customer. Otherwise they're not going to use it. They're not going to trust the agent that you're building. That's why we're starting to see even agents built on top of classic iconic platforms like datadog lose to some of the new AI SRE companies like a Resolve or a Traversal. We're extremely excited about this opportunity, and 2026 is going to be the year that the dynamic agent layer overtakes the system of record.

Podcast Host / Narrator (13:28)

Taken together, these three big ideas form a single story. First, the interface shifts from chat to action. Second, the design shifts from human first to agent readable. Third, the workflow shifts from systems of record to agent layers that turn intent into outcomes. This is what agentic really means here. AI stops being something you ask and becomes something that does 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@A16Z 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.