Key Takeaways

  • ElevenLabs reached a valuation of $11 billion and crossed $450 million in annual recurring revenue with a team of 470 people.
  • Co-founders Mati Staniszewski and Piotr Dabkowski maintain over 15 direct reports each to eliminate middle-management layers and keep project groups under 10 people.
  • Instead of siloing engineers in product divisions, ElevenLabs assigns dedicated technical leads inside recruiting, sales, and operations to automate routine work.
  • Staniszewski models this decentralized structure on Ukraine's wartime DIIA app deployment, which embedded technical resources into every government ministry.
  • The operating model relies directly on Staniszewski's AI-Native Organizational Design Framework.

The Staniszewski's AI-Native Organizational Design Framework

1. Flat Hierarchies and Broad Span of Control

Maintain sub-10-person project teams and an unusually wide span of control where executive founders and department heads each maintain 15+ direct reports, eliminating traditional middle-management overhead.

2. Embedded Technical Leads in Non-Technical Teams

Assign dedicated technical leads directly inside non-engineering departments (operations, talent acquisition, go-to-market) to build internal automation amplifiers, scrape pipeline data, and up-level team workflows.

3. End-to-End Workflow LLM-ification

Systematically expose organizational data to LLMs and agents to automate manual, repetitive steps, such as automatically populating customized customer pitch decks with verified numbers or deploying culture exploration interview voice agents.

4. High-Agency Hiring Filter

Screen candidates primarily for high personal agency, first-principles problem-solving, and ownership over rigid seniority brackets, enabling individuals to use AI tools as force multipliers across their craft.

When This Works (and When It Doesn't)

This framework fits software businesses scaling quickly through AI tooling. When individual contributors have direct access to automated systems and code generation, managers spend less time coordinating and more time shipping. As Staniszewski points out, “Both me and my co-founder will have over 15 direct reports each that we'll work with.” Keeping teams small prevents the communication slowdown that kills early momentum.

It breaks down in regulated or safety-critical fields where compliance checkpoints require strict separation of duties. If you build medical devices or banking infrastructure, assigning an engineer to rewrite internal ops scripts without centralized review creates security and audit risks. It also fails when hiring junior staff who need structured apprenticeship rather than broad autonomy.

Collison summarized the talent filter clearly: “My biggest takeaway from all this has been that around agency, where I feel like high-agency people are the winners of the advances in AI, and within organizations, low-agency people will lose out.” When you remove middle management, workers who wait for task assignments stall the entire group.

Staniszewski points to Ukraine's wartime digitization as proof: “Every ministry had technical resources working on creating that agentic version of their work. Then it was a central digital transformation team that would assemble this all together to deliver that through the central citizen support, which I thought was brilliant.”

What to Do With This

Take your current organizational chart and identify your largest non-technical team, such as recruitment or customer onboarding. Move one full-stack software engineer out of core product development and embed them directly inside that unit for the next four weeks.

Give that engineer a single goal: audit the team's top three repetitive manual tasks, connect internal databases to an LLM endpoint, and eliminate 50 percent of the team's manual data entry. Track whether the non-technical team begins running their own automation experiments once they have a technical lead sitting beside them.