AI Changed How Startups Should Be Structured

Company Structure Redesigned for Speed [0:02]

• Founders who move fastest make an early decision to design their company differently from day one, prioritizing structure that compounds value faster than other elements.
• Traditional hierarchical structures, optimized for control, are insufficient for today's technological environment, where speed and creativity are paramount.
• The core principle is that company form always follows function; if the function is speed and learning, hierarchy becomes friction.

The fix was creating hierarchy, the original org chart. And it worked, but it was optimized for control, not creativity.

AI as an Organizational Inflection Point [0:48]

• AI fundamentally changes how modern companies are organized, shifting the biggest constraint for early startups from coordination to speed.
• A single hire, empowered by AI, can now ship product, run experiments, and automate workflows, tasks that previously required teams and management.
• Founders are encouraged to adopt a new structure and mindset to avoid momentum loss, as many are still hiring based on outdated models from before AI's impact.

What makes AI a true inflection point isn't just technology. It fundamentally changes how we think about modern company organization.

Designing for Leverage and Velocity [1:34]

• From a seed investor's perspective, the fastest teams are designed for leverage, not headcount, with structure being a product decision, not an HR problem.
• The optimal company design focuses on three key elements: shipping speed, creative iteration, and decision velocity.
• Concepts like Amazon's two-pizza teams and Atlassian's pods are precursors, but AI pushes this to the extreme, allowing for smaller, more agile teams and broader roles.

The best companies optimize for three things: Shipping a speed, creative iteration, decision velocity.

Outcome-Oriented Hiring and Evolving Roles [2:42]

• Early hires should not own functions but rather outcomes, as AI can manage playbooks, processes, and repetition.
• Humans are hired for judgment, taste, and critical decisions, leading to changing job titles and roles focused on producing results rather than managing processes.
• The way people work together is crucial; with automated coordination, time spent together should be dedicated purely to innovation.

Your first hires shouldn't own functions. They should own outcomes.

Archetypal Teams and In-Person Collaboration [3:23]

• Early teams are increasingly resembling archetypes or ways of thinking rather than traditional departments or boxes, especially for startups where in-person collaboration fosters speed through shared context and fast feedback loops.
• When it comes to hiring, mindset matters more than full skill in an AI-first company; individuals comfortable with change and possessing strong, loosely held opinions are sought after.
• A refusal to experiment with AI is a red flag, as is blind faith in it; the ideal is informed humility, allowing AI to handle its strengths while humans focus on judgment, creativity, and relationships.

Then there's the hardest filter, people. In an AI first company, mindset matters more than full skill.

Leadership in AI-Native Companies [4:11]

• Leadership must evolve as teams flatten, with vision becoming more critical to prevent speed from devolving into chaos.
• The best AI-native founders delegate early to both people and systems, avoiding the management of every single process and instead designing environments that facilitate movement.
• Founders must learn to trust AI, know what is worth shipping amidst infinite drafts, and create clear interfaces between automated work and human judgment.

They know when to trust AI and when to override it. They know what's worth shipping in a world of infinite drafts.