Five great reads (4/24/2025)
Five sharp takes: GTM hustle, AI-era quality, platform power, metric myths, and OKRs that actually guide work.
Engineering Success in a Technical Startup (Gil Dibner)
Why deeply technical founders stall at $10 M ARR: most are “sleepwalkers” who underestimate GTM. Dibner maps the journey from sleepwalker → humble learner → all-rounder and says the unlock is a top-1 % sales-DNA transfusion. Treat GTM as just another engineering problem—then iterate, hire, and measure until it hums. (link)
The AI Quality Coup (Julie Zhuo)
AI is now table-stakes; when anyone can spin up a “Ghibli-fied” selfie, quality shifts to what’s still rare—story, craft, proprietary feedback loops, and first-mover use cases. Zhuo argues that teams who treat AI like oxygen—but chase freshness, not imitation—will keep the edge. A reminder that in 2025, differentiation starts after the model. (link)
Demystifying Platform Product Management (Ibrahim Bashir & Waqas Sheikh)
Platform PMs rarely get the spotlight, yet every technical product runs on the plumbing they design. Bashir and Sheikh unpack the role—capabilities, experience, extensibility, economics—and explain why platform PMs must balance portfolio trade-offs, speak both code and commerce, and mediate org-wide priorities. In an AI-everywhere world, their behind-the-scenes craft is as critical as any outward-facing roadmap. (link)
Your Product Team Doesn’t Need a “North Star Metric” (Ravi Mehta)
I’ve been on a strategy kick lately, so Mehta’s take felt like validation: reducing strategy to one KPI is a trap. He proposes a North Star Strategy—a succinct narrative of the outcome you’re chasing, backed by a few context-specific metrics. From Apple’s iPhone leap to Tinder’s “good-match” dilemma, he shows vision > vanity numbers. (link)
Make OKRs Drive Decisions, Not Spreadsheets (Tim Herbig)
If your team breathes a sigh of relief after quarterly OKR theater, you’ve got “Alibi Progress.” Herbig reframes OKRs as a product: a decision tool that links daily work to strategy in real time. That means teams author their own OKRs, favor leading metrics that surface risk fast, and check progress continually, not just at kickoff and post-mortem. When OKRs stop guiding prioritization, they’re noise. (link)