We’re Greg & Coco: engineers turned founders, with roots in the US tech industry. Greg was an early AI tech lead at Stripe and Coco was previously a cofounder of GitStart (YC S19). We’re spending Aug 19–22 in NYC and Aug 23–Sep 2 in SF on a field trip: we want to collide our intuitions about AI and hiring with what people are actually doing and experiencing already.
The ask is simple: breakfast or lunch, about 45 minutes of good questions. In return, we’re compiling everything we hear into a short, anonymized report on how companies are hiring for AI skills and hiring with AI. Everyone who meets with us gets it: you’ll see how your approach compares to others’.
We’re looking to meet founders, engineering leaders, and heads of talent at companies actively hiring for and with AI.
We’re exploring three guesses
- What you hire for is changing: AI-native professions and AI-specific skills are emerging.
- How hiring gets done is changing: The day-to-day is shifting from a human-owned and human-driven activity to a human–AI collaboration. The grunt work nobody loves but that requires patience will be the first to go.
- What happens when parts of hiring suddenly collapse in cost: AI can apply patient, individualized attention at scale, making previously uneconomical processes viable.
Hiring AI engineers
There’s a new job description being written by the tech industry: AI engineer. Our own stab at it: a software engineer building AI-native products where product quality depends on how well they construct context for models, steer and evaluate their behavior, and integrate model intelligence into reliable systems.
This looks very different at Cursor, building an AI-native code editor, and Sierra, building customer support agents. But both require this emerging flavor of engineering.
The craft is being invented in real time and spreading fast across the industry. We’re keenly interested in how companies find software engineers with nascent AI engineering skills, how they define and assess those skills, what signals they trust, and what they’ve learned the hard way.
Hiring AI-native employees
Existing jobs are being rewired to be AI-native: using AI effectively is becoming the baseline expectation. This new flavor of existing jobs requires awareness of AI tools, knowing how to connect them, and most importantly: a clear understanding of which parts of the job belong to humans and what can, or should, be offloaded to AI agents.
We’re interested in how people hire for this. Do you simply ask, “show me your agent setup” and look at past results? Do you run work trials? Or do you go upstream and look for signals that someone who isn’t AI-native yet can quickly become so?
The art of hiring in the AI era
The flood of AI-generated applications is the most visible symptom of this shift: the numbers game got automated on the candidate side first, and existing human screening is breaking. How is it changing your funnel, and what have you already changed in response? For example, do you fight fire with fire and apply AI aggressively to weed out AI noise?
We also want to put a magnifying glass on concrete examples of AI being used in the hiring process itself. It could be a Codex or Cowork skill set covering some part of the hiring process like screening. Maybe it’s something else entirely.
We’d also love to understand the most painful part of hiring right now that you’d love to shift to AI but don’t know how.
And we want to open up fun discussions like: what would you do if you had an unlimited supply of 7-out-of-10 recruiters at your disposal? Would you run reference checks much earlier in the process? Would you go much deeper into people’s interests than a CV ever had space for? Would you give thoughtful feedback to every candidate, once the economics allow it?
We’re also deeply interested in negative results: what did you try with AI in hiring that didn’t work?