Business users love Lovable. Engineers tend to panic. A real-world case study of how to wrap an AI builder in guardrails so non-technical teams can move fast without quietly rewriting the systems that give your product its edge.
I used to teach people to code. And looking back, I was teaching students to write it by hand while the tools that write it for them were getting better every single month. So what should a coding classroom actually look like now?
Most AI-powered customer support is optimised for deflection, not resolution. The problem isn’t bad agents, it’s architecture: no shared context, no real permissions, no escalation path that works.
AI removes bottlenecks until it reaches the one that doesn’t move: human cognition. The faster AI makes your system, the more your team’s mental capacity becomes the constraint. You can’t add more of it.
When AI closes the execution gap, taste becomes the differentiator. Curation, judgement, and the willingness to say “not this” compound over time in ways that models can’t replicate.
In 2010, every business convinced itself it needed a mobile app. Fast forward to 2025, and the script is identical, just with AI replacing mobile as the technology everyone insists they can't afford to be without.
AI-generated content has made polished writing look suspicious. The deeper cost is the thinking you skip when you outsource the words that define your position.
Half of today's AI best practices are coping mechanisms for temporary scarcity, not timeless engineering insights. Geoffrey Dhuyvetters traces the arc from SMS bundles to token limits, and argues the price curve only goes one direction.
After auditing 180+ SaaS companies, the same patterns keep showing up: a CTO who does everything, documentation nobody updates, a backlog from 2019. Here's what the bingo card looks like, and what AI is changing about it.