AI made every individual stronger and every team more fragmented. Yimao Zhou is building the OS to reverse that

The 23-year-old founder of Emagen AI argues the entire agent industry is optimizing the wrong unit. His answer is an operating system where AI drives the work and calls on humans, not the other way around.

Every week, another AI agent startup launches. They write code, draft emails, generate slides, analyze data. Each one promises to make you more productive. Yimao Zhou thinks they’re all solving the wrong problem.

Zhou is the founder and CEO of Emagen AI, the company behind Cagen, what he calls an “OS Level Agent,” an organizational operating system powered by AI. Backed by MiraclePlus (formerly YC China) and its legendary founder Qi Lu, Zhou is betting that the future of AI isn’t about making individuals faster. It’s about making teams fundamentally different.

We sat down with Zhou to understand what that means, and why he thinks 90% of today’s AI agent companies won’t exist in three years.

You’ve said that AI is actually making teams worse. That’s a pretty contrarian take given that every AI company is promising productivity gains. What do you mean?

Think about what happens when you give every person on a five-person team their own AI assistant. Each person produces more, faster. The product manager generates specs faster. The engineer writes code faster. The designer iterates faster. Sounds great, right?

But here’s what actually happens: the output diverges. Everyone’s moving faster in slightly different directions, and nobody notices until it’s too late. The bottleneck in a team was never “one person works too slowly.” It was always “are these five people building the same thing?” AI tools accelerate the parts that weren’t bottlenecks and make the real constraint, coordination, worse.

60% of knowledge workers’ time goes to what I call coordination costs, syncing progress, writing status updates, relaying information between people, waiting for approvals. And these costs don’t just exist between humans. In the AI era, they multiply: human-to-agent coordination, agent-to-agent coordination, the overhead of keeping everyone and everything on the same page. AI is optimizing the other 40%, the actual doing, and completely ignoring the 60%. That’s not just a missed opportunity. It’s a directional error.

So what should the industry be building instead?

Every major computing shift follows the same path: tools come first, then platforms, then an operating system emerges. PCs had standalone software before Windows. Mobile had individual apps before iOS and Android unified the experience. Cloud had scattered services before AWS became the infrastructure layer.

AI is on the same curve. Right now we’re in the “standalone tools” phase. Hundreds of agents, each doing one thing well, none of them talking to each other. The platform phase is just starting. The OS phase hasn’t happened yet.

That’s what Cagen is. Not another AI tool. The operating system layer for how organizations work with AI.

“OS Level Agent” is a big claim. In concrete terms, what does that actually look like?

Here’s a structural problem nobody’s addressing. Notion built Notion AI. GitHub built Copilot. Salesforce built Einstein. Every SaaS company is embedding AI, but their incentive is to make their own product stickier, not to connect you across tools. Notion AI makes Notion more valuable. It has zero incentive to help you bridge Notion to GitHub to Linear to Slack.

That means cross-tool intelligence is structurally impossible for any incumbent to build. It can only come from an independent layer.

Now, some people will say: “What about MCP? Anthropic’s Model Context Protocol already connects AI agents to multiple tools.” True, and MCP is great. But MCP is a connector protocol. It’s USB, not an operating system. It lets one person’s agent plug into that person’s tools. There’s still no shared organizational context, no persistent team memory, no cross-role orchestration. MCP actually benefits us. The more standardized the plumbing gets, the easier it is to build an OS on top.

Cagen is that OS. But here’s what really separates it from everything else, and this is the part most people miss. Every AI product today, including the ones that call themselves “team AI,” works the same way: capture information, organize it, and wait for a human to query it. The human is still the driver. The AI is a librarian.

Cagen inverts that. Our agents have goals and context. They continuously reason about what needs to happen next based on the team’s objectives, the project state, and organizational context. When they need human judgment, a decision, an approval, creative input, they call on the human. The human is a resource in the system, not the operator of the system.

That’s what makes it OS-level. An operating system doesn’t wait for you to manually manage every process. It runs, it schedules, it handles events. It calls on you when it needs you. That’s how Cagen works for teams, teams of humans and AI agents working together.

The AI market is brutally competitive. Investors will ask: what’s your moat?

After six months of using Cagen, what makes it irreplaceable isn’t any feature we built. It’s what your team built on top of it: decision patterns, communication habits, quality standards, workflow knowledge. All of that is deeply coupled to your specific organization. A competitor can clone every feature of Cagen. They cannot clone six months of your team’s accumulated intelligence.

This is the same reason Salesforce has industry-leading retention. It’s not because the CRM is irreplaceable. It’s because the data, processes, and automations running on it are irreplaceable. The product becomes an organizational asset, not a software subscription.

But here’s the important distinction: that stickiness comes from accumulated value, not artificial lock-in. We’re not trapping anyone. Teams stay because they don’t want to lose what they’ve built.

Individual AI memory is well understood. How is organizational memory different?

Fundamentally different. Individual AI memory scales linearly. I learn something, I benefit. Organizational AI memory has network effects. One person’s learning benefits everyone on the team, and every agent on the team. The compounding rate is n-squared, not n.

That’s why a “Team Agent” isn’t just a multiplayer version of a personal agent. It’s a completely different species. When one team member refines how competitive analysis gets done, that knowledge immediately elevates everyone else’s output, and every agent’s output. When the system learns how your organization defines “good,” what quality looks like, what tone you use, how you structure decisions, it raises the floor for every piece of work across the company, whether it’s done by a human or an agent.

Personal AI makes one person better. An OS Level Agent makes the organization smarter as a unit.

You keep saying “team.” But the trend right now is the opposite: more solo founders, more one-person companies. If teams are shrinking, who needs a team OS?

That’s exactly the right question, and the answer actually makes our case stronger.

There are actually two trends happening simultaneously, and they’re squeezing from both sides.

On one end, organizations are getting larger and more complex. Global teams, cross-timezone coordination, regulatory overhead, multi-vendor supply chains. The coordination burden inside large organizations keeps growing.

On the other end, individuals are getting smaller and more independent. Layoffs are accelerating. The freelancer economy, digital nomads, solo founders, one-person companies, they’re all exploding. But here’s what people miss: a solo founder doesn’t work alone. They hire a freelance designer on Fiverr, a contract developer on Upwork, a fractional CFO, a marketing consultant. The “team” still exists. It’s just not a fixed org chart anymore. It’s fluid, temporary, project-based. And increasingly, it includes AI agents as full team members.

Both ends need the same thing: an orchestration layer. And that need is going to intensify. Work is atomizing. You’ll see more and more granular needs matched with more and more specialized providers, on-demand, globally, in real time. The old model was: hire five full-time employees, put them in an office, manage them. The new model is closer to Uber for work. Assemble the right people and agents for the right task, execute, disband.

But here’s the problem with that model: coordination costs explode. When your “team” is a rotating cast of freelancers, contractors, and AI agents who don’t share context, don’t know each other’s working style, and don’t have shared history, the coordination problem we talked about earlier gets ten times worse.

That’s where Cagen becomes essential. It’s the orchestration layer. It holds the organizational context, the project history, the quality standards, and it dispatches work to the right people and agents at the right time. The solo founder doesn’t need to manage anyone. Cagen manages the constellation.

So “team” doesn’t mean five people in a Slack channel. It means any group of humans and AI agents collaborating toward a goal. The more fluid and atomized work becomes, the more you need an OS to hold it all together.

Who are your first customers? I’d assume tech startups.

Actually, no, and this is counterintuitive. Tech companies already have deeply entrenched toolchains. Slack, Notion, Linear, GitHub. They’re locked in, and the switching cost of adding an OS layer is highest for teams that have already optimized their existing stack.

Our best early customers are organizations with high operational complexity but without deep commitment to any specific tool ecosystem. We’re currently deployed with a boutique hotel in Pittsburgh, for example. A hotel operations team juggles guest communication, maintenance coordination, shift scheduling, vendor management: dozens of handoffs per day across multiple roles. The coordination costs are extreme, but they haven’t built their workflows around a rigid SaaS stack.

That’s the sweet spot: complex enough to need an OS, flexible enough to adopt one. And if it works in hospitality, one of the most operationally dense environments for small teams, it works anywhere.

But hospitality, CPG, logistics: these are all very different industries. How do you scale across all of them without becoming a custom consulting shop?

This is the question everyone asks, and it’s the right one. The traditional answer is: you hire industry experts, do bespoke integrations, and it doesn’t scale. That’s the consulting trap.

Our answer is different. Think about the pipeline from customer acquisition to deployment: understanding a client’s operations, identifying where AI fits, building the right workflows. There’s no inherent reason that entire process has to rely on humans.

The bottleneck today is a mismatch. Non-technical users don’t understand what AI can and can’t do. At the same time, they struggle to articulate their own needs clearly. That’s why every AI integration today requires someone who has both domain expertise and AI expertise, and that combination is extremely rare and expensive.

Cagen’s roadmap is to fuse those two together inside the product. Ideally, a user just describes what their team does day to day, along with their company’s goals. The system then automatically understands, decomposes, and constructs the right workflows. It’s an automated consulting and execution layer. The AI doesn’t just run your workflows; it figures out what your workflows should be.

We’re not there yet. Nobody is. But even at the current stage, the approach gives us a structural advantage. And where full automation isn’t possible today, we can route specific needs into a marketplace: humans acting as builders, similar to Upwork or Fiverr, but orchestrated by the system. That turns bespoke integration from a consulting problem into a platform problem. And platform problems scale.

You were backed by Qi Lu, who decided to invest ten minutes into a thirty-minute pitch. That story’s been told before. What does it actually mean to you now, looking back?

What it means is that he wasn’t investing in a product. He was investing in a judgment.

Qi Lu spent his career at the OS layer: Executive VP at Microsoft, President and COO at Baidu. When he heard me describe the AI agent landscape as “everyone building apps, nobody building the operating system,” he didn’t need a demo. He’d lived through that exact pattern before. He knew what happens when someone identifies the right abstraction layer early.

Most AI pitches are “we do X better than Y.” My pitch was “the entire industry is building at the wrong layer.” He recognized the difference immediately. That’s what the ten minutes were about.

Claude Code surpassed $2.5 billion in annualized revenue by early 2026, contributing to Anthropic’s $44 billion total run rate by mid-year. OpenAI Codex has 5 million weekly users. OpenClaw has over 370,000 GitHub stars, more than the Linux kernel. Whether backed by the most powerful AI labs or the open-source community, the momentum behind AI agents is massive. How do you compete with that?

I don’t. Because we’re not playing the same game.

Look at what those products actually are. Claude Code is a terminal agent that helps one developer mass-produce code. Codex is the same thing inside ChatGPT. OpenClaw is an open-source personal assistant that runs on your laptop. They’re all extraordinary at what they do, and what they do is make one person more productive.

Claude Code even has something called “Agent Teams.” Sounds like team collaboration, right? It’s not. It’s one person orchestrating multiple AI instances. There’s no shared context between team members. No organizational memory. No cross-role coordination. Codex’s “Business plan” is seat management and billing. It doesn’t change how the product works at a team level.

This is exactly my point. The best-funded, most talented AI labs in the world are all converging on the same thing: supercharging individuals. They’re building the most powerful apps the world has ever seen. But nobody is building the OS.

There’s a way to think about this that I find clarifying. The infrastructure for AI-assisted coding, what some people call the “coding harness“, is essentially a solved problem. It’s a continent. Claude Code, Copilot, Cursor, Codex: the land has been claimed. But the infrastructure for AI-assisted working, coordinating teams, managing goals, orchestrating humans and agents together, is still a vast blue ocean. There are a few small islands, but no continent. That’s where we’re building.

When your engineer uses Claude Code and your product manager uses OpenClaw, each person gets faster. But the coordination between them, the context, the decisions, the handoffs, still travels through Slack messages and status meetings and Google Docs that nobody reads. The coordination costs are completely untouched.

That’s the gap. It’s not a feature gap. It’s a layer gap. And it’s not going to be filled by Anthropic or OpenAI, because their business model is selling seats to individuals. An OS for organizations is a fundamentally different product with a fundamentally different architecture.

Last question. Three years from now, what does the AI agent industry look like?

Most of today’s AI agent startups will be dead. Not because they’re bad, but because they’re building at a layer that’s about to get commoditized. When you’re essentially wrapping a prompt around a foundation model and optimizing for one vertical, your moat is prompt engineering. That’s not a moat. That’s a sand castle.

The survivors will be companies that built at a layer the foundation models can’t easily absorb. For vertical agents, that means deep domain-specific data flywheels. For us, it means the OS layer: the orchestration and organizational intelligence that sits above any single model.

But the real disruption isn’t about which companies survive. It’s about what becomes possible. The minimum viable team size for a serious business is about to collapse. Things that required 50 people will require 5 people plus an AI operating system. That doesn’t just change how companies work. It changes which companies can exist. A massive number of business ideas that didn’t pencil out under the old model suddenly become viable.

Three years from now, people won’t ask “what AI tool do you use.” They’ll ask “what OS is your team running on.”

Yimao Zhou is the founder and CEO of Emagen AI, the company behind Cagen. He previously studied medicine at Shanghai Jiao Tong University and cognitive philosophy and philosophy of science. He was the youngest founder in MiraclePlus’s F24 cohort. Learn more at cagen.ai.