Startup Team Building

How to Hire AI Engineers when EVERYONE is cheating with AI — Beth Glenfield, DevDay

Jul 22, 2025 · 6:45

Beth Glenfield, from DevDay, argues that AI has broken technical hiring because candidates cheat with AI tools, making LeetCode puzzles obsolete, and small companies are crushed in the talent war with big tech. She cites the AI cheating service CLUI, which raised $5.3M and is heading toward $1M ARR, and notes 93% LeetCode wizard success rates for Google/Meta interviews, with 1 in 3 interviews now using AI assistants. Instead of puzzles, she proposes real-world workplace simulations where candidates collaborate with AI agents (perfectionist, pragmatist, etc.) to measure skills like handling ambiguity, mentoring, and business judgment. She highlights that big tech can brute-force hiring (100 candidates for 5 hires), but startups cannot afford bad hires costing $20k-$60k. Referencing Mark Zuckerberg's prediction that AI will handle mid-level engineering and Marc Benioff's announcement that Salesforce won't hire software engineers due to a 30% productivity boost from AI, Glenfield argues engineering jobs now require creativity, collaboration, and working with AI rather than being replaced by it.

Rethinking Team Building: how a 30-person Startup serves 50 Million Users — Grant Lee, Gamma

Jul 15, 2025 · 18:06

Grant Lee, CEO of Gamma, explains how his 30-person team serves 50 million users by ditching blitzscaling for lean teams of generalists and player coaches. He argues that hiring generalists—like his head of design who codes, researches UX, and mentors—enables rapid adaptation. Player coaches, such as engineering leads who still write code, make fast technical trade-offs without top-down mandates. Scaling with brand and culture, Gamma invests in a living culture deck and three weekly all-hands meetings to maintain tribal knowledge. In Q&A, Lee advises doing the job yourself before hiring for non-engineering roles, probing for high agency by asking candidates to drill into problem layers, and using work trials (five successes, high failure rate without) to avoid mismatches. He also wishes they had prioritized infrastructure for experimentation earlier given AI's speed.