In a recent conversation with Sequoia partner Brian Halligan (former HubSpot CEO), Notion CEO Ivan Zhao introduced a new concept: Jazz Mode. He argues that after Manager Mode and Founder Mode, organizations in the AI era should operate like a jazz band: everyone has room to improvise, yet the whole still comes together in collaboration.

This article doesn’t intend to recap everything Ivan said. Instead, it tries to extract a few structural changes that are easy to overlook from his remarks, and what those changes mean for teams building products with AI.

From Building Bridges to Brewing Beer: Why AI Rewrites the Underlying Logic of Product Development

Ivan used a metaphor to distinguish traditional software development from AI product development: traditional software is like building a bridge — designers draw the blueprint and engineers build to it; AI products are more like brewing beer — you can only experiment, observe, and adjust, without precisely controlling the final result.

The value of this metaphor isn’t rhetorical; it exposes a structural problem that has been underestimated in AI product development: traditional software development is requirement-driven; AI product development is technology-driven.

In the traditional model, product managers define requirements, designers produce solutions, and engineers implement them. The whole process starts from customer needs, with technology as the means of execution. But AI products are different. The boundaries of model capability determine what a product can do, and those boundaries change every week. If you plan your product roadmap strictly according to customer requirements, three months later what you’ve built may already be obsolete.

Ivan said their internal development model has shifted from “customer-driven” to “technology-driven experimentation.” The traditional boundaries between PM, designer, and engineer have been thoroughly blurred — even product designers are among the team members who consume the most LLM tokens.

This isn’t just Notion’s practice. As LLM capabilities iterate quickly, more and more AI product teams face the same dilemma: you can’t fully plan your product at the start of the year, because three months later the model’s capabilities have already changed. An AI product roadmap is, in essence, a collection of assumptions that keep being overturned.

For teams building AI products, this means two things. First, you need to accept that “plans can’t keep up with change” isn’t a management problem but a consequence of the product form. Second, you need to let the people with the best judgment on your team (not just engineers) work directly with the model, because instinctive judgment about model capability is becoming a more valuable product asset than a requirements document.

Hierarchy Won’t Disappear, But Its Function Is Being Redefined

Ivan was explicit in the conversation that he doesn’t believe in hierarchical-free organizations. His reasoning is simple: hierarchy is human nature — even chimpanzee societies have natural hierarchies, and you can’t eliminate it by forcibly flattening the org.

But he also pointed out that language models are becoming the new infrastructure of organizations. Information transfer, state synchronization, and decision coordination — work that once required many middle managers — can now be partly handled by AI. Future organizations will be flatter, but hierarchy won’t disappear.

Hidden beneath this is a deeper change: the rationale for hierarchy is shifting from “information transfer” to “allocation of judgment.”

In traditional organizations, one core function of hierarchy is information filtering and passing. A CEO can’t know every detail, so you need VPs; VPs need Directors; Directors need Managers. Each layer compresses information and reports upward. But AI tools (including Notion’s own products) are making information transfer more efficient, even automated. When information transfer is no longer the bottleneck, hierarchy only retains two values: decision judgment and resource allocation.

This means the job of future middle managers will fundamentally change. They will no longer be transfer stations for information, but carriers of judgment in specific domains. The value of a middle manager will no longer depend on how many people they manage, but on how broadly they can make high-quality decisions.

For organizational designers, this is a very practical question: if your middle managers mostly do meetings, reporting, and passing information, their roles are being eroded by AI; if your middle managers are the judgment centers of a domain, their value is actually rising.

The Talent Formula Has Changed: Capability or Taste — Which Is Scarcer?

Ivan proposed a talent formula he currently endorses most: Talent = Capability × Taste × Agency.

In the past, Capability was the most important variable. An engineer’s technical skill determined their output. But in the AI era, capability is being rapidly commoditized. Tools like Claude Code, Cursor, and Copilot are making “writing code that runs” increasingly easy.

When capability becomes cheap, Taste and Agency become scarce resources. Taste is what you think is good; Agency is whether you proactively push things forward.

This isn’t empty philosophy. Notion has adjusted its hiring strategy along these lines: it no longer focuses mainly on work history, big-company background, or resume length, but on curiosity, optimism, energy, and proactivity. Their first-round interviews no longer review resumes; they simply ask candidates to “build something,” looking at the work before the background.

Even more noteworthy is their “barbell model” engineering organization: at one end, very young engineers responsible for fast trial-and-error and efficient execution; at the other, a tiny number of super-senior architects responsible for judgment, Taste, and architecture. One senior architect guides 2–3 junior engineers, and with the help of AI Agents, each junior engineer can produce as efficiently as managing a mini-team.

The underlying logic of this model is: AI amplifies individual execution, but not judgment. A tasteful senior engineer, plus AI tools and a few junior executors, can produce what a small team used to. But if the Taste is wrong, no amount of execution is doing anything but accelerating in the wrong direction.

For team managers, this means you need to reassess who in your team is the “Taste center,” and ensure those people’s judgment directly shapes product direction rather than being diluted by layers of reporting.

Founders Are an Organization’s Decalcifying Agent: Why Notion Took In 60 Founders

Notion has 50–60 former startup founders internally — an unusual number. Ivan said founders are an organization’s “decalcifying agent.”

His logic is clear: a 1,000-person company will naturally tend toward bureaucracy; that’s a physical law. But if a steady stream of founders keeps mixing in, they continuously launch new projects, challenge old processes, propose new ideas, and push organizational change — effectively injecting new vitality into the organization.

This observation reveals a problem often neglected in organizational theory: bureaucratization isn’t a management failure, but a natural result of organizational scale. Any sufficiently large organization will produce processes, norms, and hierarchy. These things ensure efficiency early on, but once they accumulate past a certain point, they become obstacles to innovation.

The value of founders is that they naturally distrust process. They’re used to “doing first, then talking,” used to breaking existing frames and rethinking problems. Scattered across a large organization, they act like so many “anti-entropy nodes,” constantly resisting the force that drives organizations toward rigidity.

But there’s a prerequisite: the organization must give these people enough autonomy. If founders are assimilated by existing processes after joining a large company, the “decalcifying agent” fails. Ivan said Notion’s approach is to keep these people highly autonomous, consistent with the spirit of Jazz Mode.

Enterprise Sales Is the Last Fortress AI Can’t Take

One detail in the conversation is easy to overlook: Ivan said they had previously made mistakes in the sales area — they tried to reinvent sales and failed. Later they found that many enterprise customers simply want to talk to a real person.

This observation forms an interesting contrast with the current narrative of many AI companies. Over the past two years, a lot of AI startups have tried to replace the sales process with AI, from automated outbound calling to intelligent customer service to AI SDRs. But Ivan’s experience suggests that, at least in the enterprise market, trust relationships between people remain the key to closing deals.

The logic behind this: enterprise procurement decisions are costly, and the consequences of error are severe. In such scenarios, customers need more than just information and efficiency; they need “someone who can be found when something goes wrong.” AI can handle information, but it’s hard for it to provide that kind of psychological security.

For AI entrepreneurs, this points to a pragmatic direction: AI’s best position in the enterprise market may not be replacing sales, but empowering it. Let salespeople use AI tools to prepare materials, analyze customers, and follow up leads more efficiently, but the final customer relationship is still maintained by humans.

Planning Cycles Are Shrinking Dramatically

Ivan said something very direct: financial planning can be done quarterly, but product planning may need to change weekly, because model capabilities change too fast.

That’s not an exaggeration. Over the past two years, the capability curve of LLMs has been nearly exponential. Tasks considered impossible when GPT-4 was released in early 2023 had been matched or surpassed by multiple models by the end of 2024. If your product planning is built on the assumption of “GPT-4-level capability,” that assumption needs updating every few months.

Ivan also stressed that a CEO must experience AI firsthand — not just watch videos, talks, or summaries. He used two phrases: Feel the AI, Feel the AGI. Only by using it yourself and building with it can you know which opportunities really exist.

The practical meaning of this advice: product decisions in the AI era increasingly rely on intuitive judgment of model capability, and that intuition can only come from hands-on use. You can’t gain an accurate sense of model capability by reading secondhand information, just as you can’t learn to swim by reading a swimming tutorial.

A Company Eventually Grows Into the Image of Its Founder

At the end of the conversation, Ivan touched on a view: many CEOs like to imitate others — Jobs, Musk, or Brian Armstrong — but a company is ultimately a projection of its founder. If you’re a craftsman, the company becomes a craftsman culture; if you’re a salesperson, it becomes a sales culture; if you’re a jazz musician, the company also eventually becomes a jazz band.

He was very candid about the quasi-religious/“cultish adoration” perception of the Notion community, even saying he “liked” it. He believes a company is, in a sense, a religion, projecting a worldview and value system onto the real world through commerce and products.

The deeper meaning of this view: organizational culture isn’t designed; it’s an amplification of the founder’s personality. You can draw a perfect org chart, but what ultimately determines organizational behavior is what the founder believes, values, and how they make decisions.

Jazz Mode suits Notion not just because it’s a good management concept, but because it matches Ivan’s own personality. He doesn’t like pure delegation, pure process, or pure management, so he needs an organization that allows improvisation. If a founder is a natural controller by nature, Jazz Mode will most likely fail in their company.

For entrepreneurs, this may be the question most worth thinking about: does the organizational model you’re trying to build match your own personality? If it doesn’t, even the best concept is nothing but a castle in the air.

Original link: Notion CEO on the New Paradigm for AI-Era Organizational Management: Jazz Mode