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

The 23-year-old founding father of Emagen AI argues the whole agent {industry} is optimising the unsuitable unit. His reply is an working system the place AI drives the work and calls on people, not the opposite manner round.

Each week, one other AI agent startup launches. They write code, draft emails, generate slides, analyse information. Each guarantees to make you extra productive. Yimao Zhou thinks they’re all fixing the unsuitable drawback.

Zhou is the founder and CEO of Emagen AI, the corporate behind Cagen, what he calls an “OS Stage Agent,” an organizational working system powered by AI. Backed by MiraclePlus (previously YC China) and its legendary founder Qi Lu, Zhou is betting that the way forward for AI isn’t about making people sooner. It’s about making groups essentially totally different. Right here’s a Q&A with the founder to know what meaning, and why he thinks 90% of in the present day’s AI agent corporations received’t exist in three years.

You’ve stated that AI is definitely making groups worse. That’s a fairly contrarian take given that each AI firm is promising productiveness features. What do you imply?

Take into consideration what occurs if you give each particular person on a five-person crew their very own AI assistant. Every particular person produces extra, sooner. The product supervisor generates specs sooner. The engineer writes code sooner. The designer iterates sooner. Sounds nice, proper?

However right here’s what really occurs: the output diverges. Everybody’s shifting sooner in barely totally different instructions, and no person notices till it’s too late. The bottleneck in a crew was by no means “one particular person works too slowly.” It was all the time “are these 5 folks constructing the identical factor?” AI instruments speed up the elements that weren’t bottlenecks and make the true constraint — coordination — worse.

60% of data staff’ time goes to what I name coordination prices — syncing progress, writing standing updates, relaying data between folks, ready for approvals. And these prices don’t simply exist between people. Within the AI period, they multiply: human-to-agent coordination, agent-to-agent coordination, the overhead of holding everybody and every part on the identical web page. AI is optimizing the opposite 40%, the precise doing, and fully ignoring the 60%. That’s not only a missed alternative. It’s a directional error.

So what ought to the {industry} be constructing as an alternative?

Each main computing shift follows the identical path: instruments come first, then platforms, then an working system emerges. PCs had standalone software program earlier than Home windows. Cellular had particular person apps earlier than iOS and Android unified the expertise. Cloud had scattered companies earlier than AWS grew to become the infrastructure layer.

AI is on the identical curve. Proper now we’re within the “standalone instruments” section. A whole lot of brokers, every doing one factor nicely, none of them speaking to one another. The platform section is simply beginning. The OS section hasn’t occurred but.

That’s what Cagen is. Not one other AI software. The working system layer for a way organizations work with AI.

“OS Stage Agent” is an enormous declare. In concrete phrases, what does that truly appear like?

Right here’s a structural drawback no person’s addressing. Notion constructed Notion AI. GitHub constructed Copilot. Salesforce constructed Einstein. Each SaaS firm is embedding AI, however their incentive is to make their very own product stickier, to not join you throughout instruments. Notion AI makes Notion extra priceless. It has zero incentive that will help you bridge Notion to GitHub to Linear to Slack.

Meaning cross-tool intelligence is structurally not possible for any incumbent to construct. It could solely come from an unbiased layer.

Now, some folks will say: “What about MCP? Anthropic’s Mannequin Context Protocol already connects AI brokers to a number of instruments.” True, and MCP is nice. However MCP is a connector protocol. It’s USB, not an working system. It lets one particular person’s agent plug into that particular person’s instruments. There’s nonetheless no shared organizational context, no persistent crew reminiscence, no cross-role orchestration. MCP really advantages us. The extra standardized the plumbing will get, the better it’s to construct an OS on prime.

Cagen is that OS. However right here’s what actually separates it from every part else, and that is the half most individuals miss. Each AI product in the present day, together with those that decision themselves “crew AI,” works the identical manner: seize data, set up it, and look forward to a human to question it. The human remains to be the motive force. The AI is a librarian.

Cagen inverts that. Our brokers have objectives and context. They constantly motive about what must occur subsequent based mostly on the crew’s aims, the mission state, and organizational context. Once they want human judgment — a choice, an approval, artistic enter — they name on the human. The human is a useful resource within the system, not the operator of the system.

That’s what makes it OS-level. An working system doesn’t wait so that you can manually handle each course of. It runs, it schedules, it handles occasions. It calls on you when it wants you. That’s how Cagen works for groups — groups of people and AI brokers working collectively.

The AI market is brutally aggressive. Buyers will ask: what’s your moat?

After six months of utilizing Cagen, what makes it irreplaceable isn’t any characteristic we constructed. It’s what your crew constructed on prime of it: resolution patterns, communication habits, high quality requirements, workflow data. All of that’s deeply coupled to your particular organisation. A competitor can clone each characteristic of Cagen. They can not clone six months of your crew’s gathered intelligence.

This is identical motive Salesforce has industry-leading retention. It’s not as a result of the CRM is irreplaceable. It’s as a result of the info, processes, and automations operating on it are irreplaceable. The product turns into an organisational asset, not a software program subscription.

However right here’s the essential distinction: that stickiness comes from gathered worth, not synthetic lock-in. We’re not trapping anybody. Groups keep as a result of they don’t wish to lose what they’ve constructed.

Particular person AI reminiscence is nicely understood. How is organisational reminiscence totally different?

Basically totally different. Particular person AI reminiscence scales linearly. I study one thing, I profit. Organisational AI reminiscence has community results. One particular person’s studying advantages everybody on the crew — and each agent on the crew. The compounding price is n-squared, not n.

That’s why a “Crew Agent” isn’t only a multiplayer model of a private agent. It’s a totally totally different species. When one crew member refines how aggressive evaluation will get finished, that data instantly elevates everybody else’s output — and each agent’s output. When the system learns how your group defines “good,” what high quality seems to be like, what tone you employ, the way you construction choices, it raises the ground for each piece of labor throughout the corporate, whether or not it’s finished by a human or an agent.

Private AI makes one particular person higher. An OS Stage Agent makes the organisation smarter as a unit.

You retain saying “crew.” However the pattern proper now’s the other: extra solo founders, extra one-person corporations. If groups are shrinking, who wants a crew OS?

That’s precisely the precise query, and the reply really makes our case stronger.

There are literally two tendencies taking place concurrently, they usually’re squeezing from either side.

On one finish, organisations are getting bigger and extra complicated. World groups, cross-timezone coordination, regulatory overhead, multi-vendor provide chains. The coordination burden inside massive organisations retains rising.

On the opposite finish, people are getting smaller and extra unbiased. Layoffs are accelerating. The freelancer financial system, digital nomads, solo founders, one-person corporations — they’re all exploding. However right here’s what folks miss: a solo founder doesn’t work alone. They rent a contract designer on Fiverr, a contract developer on Upwork, a fractional CFO, a advertising and marketing advisor. The “crew” nonetheless exists. It’s simply not a set org chart anymore. It’s fluid, non permanent, project-based. And more and more, it contains AI brokers as full crew members.

Each ends want the identical factor: an orchestration layer. And that want goes to accentuate. Work is atomizing. You’ll see increasingly granular wants matched with increasingly specialised suppliers — on-demand, globally, in actual time. The outdated mannequin was: rent 5 full-time workers, put them in an workplace, handle them. The brand new mannequin is nearer to Uber for work. Assemble the precise folks and brokers for the precise job, execute, disband.

However right here’s the issue with that mannequin: coordination prices explode. When your “crew” is a rotating solid of freelancers, contractors, and AI brokers who don’t share context, don’t know one another’s working model, and don’t have shared historical past — the coordination drawback we talked about earlier will get ten instances worse.

That’s the place Cagen turns into important. It’s the orchestration layer. It holds the organisational context, the mission historical past, the standard requirements, and it dispatches work to the precise folks and brokers on the proper time. The solo founder doesn’t have to handle anybody. Cagen manages the constellation.

So “crew” doesn’t imply 5 folks in a Slack channel. It means any group of people and AI brokers collaborating towards a objective. The extra fluid and atomised work turns into, the extra you want an OS to carry all of it collectively.

Who’re your first clients? I’d assume tech startups.

Really, no, and that is counterintuitive. Tech corporations have already got deeply entrenched toolchains. Slack, Notion, Linear, GitHub. They’re locked in, and the switching value of including an OS layer is highest for groups which have already optimised their current stack.

Our greatest early clients are organisations with excessive operational complexity however with out deep dedication to any particular software ecosystem. We’re at present deployed with a boutique resort in Pittsburgh, for instance. A resort operations crew juggles visitor communication, upkeep coordination, shift scheduling, vendor administration: dozens of handoffs per day throughout a number of roles. The coordination prices are excessive, however they haven’t constructed their workflows round a inflexible SaaS stack.

That’s the candy spot: complicated sufficient to wish an OS, versatile sufficient to undertake one. And if it really works in hospitality, some of the operationally dense environments for small groups, it really works wherever.

However hospitality, CPG, logistics: these are all very totally different industries. How do you scale throughout all of them with out turning into a customized consulting store?

That is the query everybody asks, and it’s the precise one. The normal reply is: you rent {industry} specialists, do bespoke integrations, and it doesn’t scale. That’s the consulting entice.

Our reply is totally different. Take into consideration the pipeline from buyer acquisition to deployment: understanding a consumer’s operations, figuring out the place AI matches, constructing the precise workflows. There’s no inherent motive that complete course of has to depend on people.

The bottleneck in the present day is a mismatch. Non-technical customers don’t perceive what AI can and might’t do. On the identical time, they wrestle to articulate their very own wants clearly. That’s why each AI integration in the present day requires somebody who has each area experience and AI experience, and that mixture is extraordinarily uncommon and costly.

Cagen’s roadmap is to fuse these two collectively contained in the product. Ideally, a person simply describes what their crew does daily, together with their firm’s objectives. The system then robotically understands, decomposes, and constructs the precise workflows. It’s an automatic consulting and execution layer. The AI doesn’t simply run your workflows; it figures out what your workflows must be.

We’re not there but. No person is. However even on the present stage, the method provides us a structural benefit. And the place full automation isn’t doable in the present day, we are able to route particular wants right into a market: people appearing as builders, just like Upwork or Fiverr, however orchestrated by the system. That turns bespoke integration from a consulting drawback right into a platform drawback. And platform issues scale.

You had been backed by Qi Lu, who determined to speculate ten minutes right into a thirty-minute pitch. That story’s been instructed earlier than. What does it really imply to you now, wanting again?

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

Qi Lu spent his profession on the OS layer: Government VP at Microsoft, President and COO at Baidu. When he heard me describe the AI agent panorama as “everybody constructing apps, no person constructing the working system,” he didn’t want a demo. He’d lived by way of that precise sample earlier than. He knew what occurs when somebody identifies the precise abstraction layer early.

Most AI pitches are “we do X higher than Y.” My pitch was “the whole {industry} is constructing on the unsuitable layer.” He recognised the distinction instantly. That’s what the ten minutes had been about.

Claude Code surpassed $2.5 billion in annualised income by early 2026, contributing to Anthropic’s $44 billion complete run price by mid-year. OpenAI Codex has 5 million weekly customers. OpenClaw has over 370,000 GitHub stars, greater than the Linux kernel. Whether or not backed by probably the most highly effective AI labs or the open-source neighborhood, the momentum behind AI brokers is very large. How do you compete with that?

I don’t. As a result of we’re not enjoying the identical sport.

Have a look at what these merchandise really are. Claude Code is a terminal agent that helps one developer mass-produce code. Codex is identical factor inside ChatGPT. OpenClaw is an open-source private assistant that runs in your laptop computer. They’re all extraordinary at what they do, and what they do is make one particular person extra productive.

Claude Code even has one thing known as “Agent Groups.” Feels like crew collaboration, proper? It’s not. It’s one particular person orchestrating a number of AI cases. There’s no shared context between crew members. No organisational reminiscence. No cross-role coordination. Codex’s “Marketing strategy” is seat administration and billing. It doesn’t change how the product works at a crew degree.

That is precisely my level. The very best-funded, most gifted AI labs on this planet are all converging on the identical factor: supercharging people. They’re constructing probably the most highly effective apps the world has ever seen. However no person is constructing the OS.

There’s a manner to consider this that I discover clarifying. The infrastructure for AI-assisted coding — what some folks name the “coding harness” — is basically a solved drawback. It’s a continent. Claude Code, Copilot, Cursor, Codex: the land has been claimed. However the infrastructure for AI-assisted working — coordinating groups, managing objectives, orchestrating people and brokers collectively — remains to be an enormous blue ocean. There are a number of small islands, however no continent. That’s the place we’re constructing.

When your engineer makes use of Claude Code and your product supervisor makes use of OpenClaw, every particular person will get sooner. However the coordination between them — the context, the selections, the handoffs — nonetheless travels by way of Slack messages and standing conferences and Google Docs that no person reads. The coordination prices are fully untouched.

That’s the hole. It’s not a characteristic hole. It’s a layer hole. And it’s not going to be crammed by Anthropic or OpenAI, as a result of their enterprise mannequin is promoting seats to people. An OS for organisations is a essentially totally different product with a essentially totally different structure.

Final query. Three years from now, what does the AI agent {industry} appear like?

Most of in the present day’s AI agent startups can be lifeless. Not as a result of they’re dangerous, however as a result of they’re constructing at a layer that’s about to get commoditised. While you’re basically wrapping a immediate round a basis mannequin and optimising for one vertical, your moat is immediate engineering. That’s not a moat. That’s a sand fort.

The survivors can be corporations that constructed at a layer the muse fashions can’t simply take in. For vertical brokers, meaning deep domain-specific information flywheels. For us, it means the OS layer: the orchestration and organisational intelligence that sits above any single mannequin.

However the true disruption isn’t about which corporations survive. It’s about what turns into doable. The minimal viable crew dimension for a critical enterprise is about to break down. Issues that required 50 folks would require 5 folks plus an AI working system. That doesn’t simply change how corporations work. It adjustments which corporations can exist. An enormous variety of enterprise concepts that didn’t pencil out below the outdated mannequin all of a sudden develop into viable.

Three years from now, folks received’t ask “what AI software do you employ.” They’ll ask “what OS is your crew operating on.”

Yimao Zhou is the founder and CEO of Emagen AI, the corporate behind Cagen. He beforehand studied drugs at Shanghai Jiao Tong College and cognitive philosophy and philosophy of science. He was the youngest founder in MiraclePlus’s F24 cohort. Study extra at cagen.ai.