Zero-to-One Team Building
For brand-new categories, hire generalists, adjacent-domain experts, and AI-native new grads.
- Difficulty
- Moderate
- Time to result
- ~months to results
- Steps
- 4
- Confidence
- 90%
When you're building something that has never existed, you can't staff it with people who've done exactly this before — because no one has. Kalinowski's hiring recipe for zero-to-one work blends strong generalists who transfer skills across fields, a mix of build-the-new and scale-the-known experience, adjacent domains (like autonomous vehicles for robotics), and genuinely AI-native young engineers who can teach the rest of the team. Mission alignment unifies the mix, and gut-feel for 'spark' is the final filter.
Origin
Caitlin Kalinowski, on hiring for OpenAI's robotics program and prior 0-to-1 hardware teams.
Core principles
- 01For a truly new category, exact prior experience doesn't exist — stop requiring it
- 02Generalists who adapt lessons across fields outperform narrow specialists in new domains
- 03You need both zero-to-one builders and people who've scaled other things
- 04Only genuinely AI-native engineers (often ~20-21) can teach a team AI-first workflows
- 05Mission alignment unifies teams drawn from very different worlds
How to run it
- 1
Recruit strong generalists who transfer across fields
Prioritize hybrid people who can adapt what they learned in one field to a new one. In new categories the 'exact same thing' simply doesn't exist, so adaptability beats a narrow track record.
Pro tip Mine adjacent domains: for robotics, autonomous-vehicle talent brings the sensing stack, safety trade-offs, and hardcore engineering.
- 2
Balance zero-to-one builders with proven scalers
Hire some people with experience building the new thing and some with experience scaling other things. You need both capabilities on the same team, even if the team is smaller than teams used to be.
- 3
Bring in genuinely AI-native new grads to teach the team
Only engineers who grew up using AI so natively it's baked into their process — often around 20-21 — can show senior people how to work AI-first. They approach problems completely differently and move faster. Hire them not just to execute but to teach.
Pro tip Pair AI-native juniors with senior engineers deliberately — the goal is knowledge transfer in both directions, and you must have both seniority levels.
Watch out Assuming AI erases junior roles is wrong; without junior + senior mix you stop building new technologists and lose the AI-native way of thinking.
- 4
Filter on mission alignment, then gut-feel spark
Require alignment to the mission — it unifies people coming from AI-research and hardware worlds who otherwise miscommunicate. Once every hard criterion is checked, use gut feel for the 'spark': genuine motivation, hunger to learn, willingness to update their view, and a drive to win.
In the wild
Building a robotics program for a robot that could move through the world at the scale of millions — something no one had done — Kalinowski looked to autonomous-vehicle talent for the sensing stack and safety trade-offs, combined hardcore roboticists, and added AI-native young engineers.
→ She reports attracting some of the top robotics talent in the world to a from-scratch program.
Common mistakes
Requiring exact prior experience for a novel category
Insisting on people who've built 'this exact thing' is impossible when the thing has never existed, and screens out the generalists who can actually adapt to it.
Assuming AI has erased the need for junior hires
You must have both senior and junior engineers; without junior AI-native talent you can't learn AI-first workflows or build the next generation of technologists.
Is it for you?
Best for
Founders and hiring managers staffing a from-scratch team in an emerging, undefined technical category
Not ideal for
Filling a well-defined role in a mature domain where deep, exact specialist experience is directly available and preferable
From the transcript
“some people who have experience building the thing that you're building that's new”
“the only AI native people essentially who use AI so natively that it's like baked into their engineering process are 20 years old”
“I rely a lot, Lenny, on my gut feel for people, assuming everything else that I'm looking for has been checked.”
From the episode
Why we’re at the beginning of the AI hardware boom
Caitlin Kalinowski (ex–OpenAI, Meta, Apple)