𝐖𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐟𝐢𝐫𝐞 𝐬𝐨𝐦𝐞𝐨𝐧𝐞 𝐟𝐨𝐫 𝐫𝐞𝐟𝐮𝐬𝐢𝐧𝐠 𝐭𝐨 𝐮𝐬𝐞 𝐀𝐈? 𝐂𝐨𝐢𝐧𝐛𝐚𝐬𝐞 𝐣𝐮𝐬𝐭 𝐝𝐢𝐝.
I've been experimenting with building small apps on Lovable for months now. The promise is compelling - rapid prototyping, visual development, ship faster. But despite huge progress in the platform, the dev process often stalls in frustrating ways. Too much time just getting things to work.
Last week, fed up with yet another afternoon lost to tooling friction, I tried Claude Code. The difference was immediate and stark. In under an hour I built a complete app that parses financial statements, analyzes portfolio allocation, and prepares a formatted tax report.
But here's what really struck me: Claude didn't just write code snippets I had to stitch together. It built a working interface, handled edge cases, and structured the project properly - all with minimal instructions from me. The speed wasn't just faster; the accuracy was shocking. No mysterious bugs, no configuration hell, just working software.
The experience left me thinking about productivity multipliers and competitive advantages. Then I read this story about Coinbase, and everything clicked.
CEO Brian Armstrong personally told engineers across the organization:
AI development tools (GitHub Copilot, Cursor, and similar) are now standard equipment, not optional experiments.
Everyone has exactly one week to onboard and demonstrate basic proficiency.
No valid technical or business reason for non-adoption = goodbye.
Harsh? Absolutely. Clear? Even more so.
This isn't about being an AI evangelist or drinking Silicon Valley Kool-Aid. At Coinbase, AI assistance isn't a nice-to-have or a pilot program - it's the baseline expectation for how engineers work in 2025.
The rollout is systematic:
Monthly team demos showcasing real velocity improvements with AI tooling.
Clear, measurable metrics: today roughly 33% of code commits show AI assistance; the target is 50% by quarter's end.
Explicit guardrails: critical financial systems handling real money can't be "vibe-coded" - mandatory human review, testing protocols, and additional oversight for AI-generated code in sensitive areas.
What makes this story fascinating isn't just the ultimatum - it's the systematic approach to organizational change.
What Armstrong is saying "here are the tools, here's the timeline, here's how we measure success, and here are the safety rails. Now adapt or find somewhere else that moves slower."
As someone who's spent years thinking about team dynamics and competitive advantages, the leadership takeaways are impossible to ignore:
1. Minimum viable curiosity: You don't have to believe AI, but you must demonstrate baseline curiosity about tools that measurably improve team velocity. Philosophical objections don't trump measurable productivity gains.
2. Pace becomes public: Vague commitments to "explore AI" mean nothing. Measurable goals work - AI-assisted code percentage, time from feature idea to pull request, repositories of best practices that teams actually use. What gets measured gets managed.
3. Safety by default, not by accident: This isn't about moving fast and breaking things. It's about moving fast with systematic safeguards - mandatory code reviews, testing checklists, restricted AI use in critical paths without additional human oversight.
The deeper insight here is about competitive dynamics in 2025. Companies building software products are essentially in a race - who can validate ideas faster, ship features quicker, iterate more rapidly based on user feedback.
My conclusion: learning speed has become a hard skill, just as critical as understanding algorithms or system design. The developers who thrive won't necessarily be those who write the most elegant code from scratch - they'll be those who can most effectively collaborate with AI tools to ship working solutions faster.
Not experimenting with these tools is also a choice. It's just a choice that's increasingly incompatible with companies competing on velocity.