I've been building and managing a global team for most of my time at MuleSoft. But the last two years — post-ChatGPT — have been genuinely different. The environment changed fast enough that some of the hiring and development intuitions I'd built up stopped working.
Here are seven things I've learned from navigating that.
1. Curiosity about AI is now a hiring signal
Not AI expertise. Curiosity. I want to see evidence that someone is actively experimenting — building things, trying tools, following what's happening. The people who are curious about AI now are the people who will be capable with AI in twelve months. The people who are waiting to be taught are a year behind and falling further back.
2. The skills that AI doesn't replace have become more valuable
Judgment. Nuance. Stakeholder management. The ability to hold complexity without collapsing it into false simplicity. These were always valuable. They are now differentially valuable, because AI has raised the floor on everything else.
If your value proposition is "I am thorough and accurate," AI is eroding that moat fast. If your value proposition is "I exercise sound judgment in ambiguous situations involving human systems," you are becoming more valuable.
3. Output expectations have increased — and that's fair
AI gives capable people genuine leverage. I expect my team to use it. Not because I'm trying to squeeze more out of people, but because the tools exist and not using them would be leaving capacity on the table.
The baseline for what a good professional can produce in a week has shifted. Teams that embrace that shift are ahead. Teams that treat AI tools as optional productivity enhancements are not keeping pace.
4. The evaluation problem is real
When anyone can produce a polished first draft instantly, evaluating the quality of thinking underneath the presentation becomes harder and more important.
I've had to work much harder at finding evaluation methods that actually surface judgment and reasoning rather than surface polish. Work samples that require live thinking, not prepared artifacts. Conversations that probe the reasoning behind conclusions, not just the conclusions themselves.
5. Some roles are changing faster than others
Customer-facing roles are changing faster than technical roles right now. The ability to prepare for every interaction with AI-powered research, to generate personalized insights at scale, to handle a higher volume of interactions with consistent quality — these capabilities are available today and are changing what excellent customer-facing work looks like.
Technical roles will change — are changing — but the gap between AI capability and expert human judgment is still wide in most technical domains.
6. Learning velocity matters more than current skill level
I hire more now on learning velocity than current capability. In a stable environment, someone who is already excellent at the current skillset is the obvious hire. In a rapidly changing environment, someone who learns unusually fast — even if they're a level behind today — will often outperform in twelve months.
The question I ask in interviews has shifted from "what do you know?" to "how do you learn?"
7. Culture around experimentation has become a competitive advantage
Teams that have normalized trying things, failing publicly, and iterating fast are the ones adopting AI capabilities fastest. Teams with cultures that punish failure or value the appearance of expertise over actual learning are the ones that will fall behind.
Building a culture where people feel safe experimenting with AI — including experimenting with things that don't work — is now a direct input to team performance.
The game has changed. The teams that notice and adapt will pull away.
Views are personal.