My presentation on Lean AI-Native companies at a $1B ARR enterprise (and the slides)

Plus what shocked these seasoned business leaders

Last week, I got invited to speak to the 100 top execs at a $1B+ ARR enterprise about Lean AI Native companies.

(If you’re unsure how to apply AI in your business, read this.)

These seasoned leaders, who run a 1000+ person company, were shocked when I shared examples of companies doing $50M ARR with just 30 people.

While they are still debating whether to hire their 37th marketing specialist, lean AI companies are achieving $1M to $5M revenue per employee.

AI startups now reach $5M ARR in 9 months versus 24 months for traditional startups.

ArcAds went from $0 to $7M ARR in one year with 5 people using AI agents.

Stan scaled to $30M ARR with 30 people by building an AI engine that scrapes creators' social media and auto-generates $99-$1,000 courses.

Similarly, my company Super[.]com is at $200M ARR with just 200 employees (a number unheard of for companies at that scale).

You could feel the tension building in the room because they couldn't believe what they were seeing on my slides.

Naturally, there were a lot of questions, and they kept asking about secret tools.

Nobody has any secret tools. Success comes from mapping processes, identifying bottlenecks, and systematically replacing human work with AI.

The CFO wanted to learn how their software compared to other lean AI companies offering the same service, but charging 10x more.

It's about outcome-based pricing versus seat-based pricing.

Their software is a tool that still requires human labor to operate. While Lean AI company’s service delivers the same outcome without the human overhead, which makes all the difference.

Then someone asked how to reach these lean efficiency levels with current resources.

What matters is whether you hire your next new employees or leverage AI to 10x your current team's output.

At Super.com, I had a support team of 4 people. But after stepping onto the board, I just use AI, instead of hiring an entire team.

The marketing leader wanted to know how to drive adoption across resistant teams.

I shared our reward-based approach at Super[.]com: celebrate AI wins in all-hands meetings while making AI usage part of performance reviews.

This resulted in 94% engineer adoption, and 30% of our Cursor sessions coming from non-engineers.

The HR leader then asked if I was advocating for generalists over specialists, even in specialized roles.

Yes, I was, because everything changes so quickly with AI that hiring people with wide operating ranges beats hiring narrow experts who can’t adapt.

These leaders are facing the same disruption that forced every industry to digitize over the past decade.

The only difference now is speed: AI capabilities double every few months, instead of years.

DM if you want me to give a talk at your company about Lean AI transformation.

Here’s the first 10 slides that changed these executives’ perspectives on building companies

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