Stop Listening to Your Engineers

When a software company tells us AI isn’t going to change much, we pay attention.

Not because we think they’re right, but because that answer usually reveals something more important than their view on tooling. It tells us how adaptable the team is.

There are still plenty of engineers saying some version of the same thing: our product is too complex, our workflows too specialized, our codebase too nuanced for AI to be useful here. In a small number of cases, that might be partially true. In most cases, it’s an excuse. Not a technical conclusion. Not a strategic view. An excuse.

The gap between AI-native engineers and everyone else is already widening. The best ones are using AI to accelerate prototyping, testing, documentation, debugging, and product iteration, cutting the time from idea to shipped feature, removing low-value work from their day, and learning faster because the feedback loop is shorter. Over time, that compounds.

The engineers resisting this shift tend to frame it as rigor. Quality will suffer. The tools are immature. Real engineering can’t be compressed. Some of that sounds thoughtful. Most of it is protectionism, defending a way of working that made sense in the last era and matters less in this one.

This isn’t about replacing engineers. It’s about replacing old workflows.

A strong engineer who fully embraces AI can now produce output that would previously have required a much larger team. Not because AI eliminates judgment, but because it amplifies it. You still need product sense, architectural discipline, and commercial context. But once those things are in place, AI becomes leverage, and leverage changes the economics of software businesses fast.

That matters well beyond the engineering org chart. It matters in M&A.

Buyers aren’t just assessing what your software does today. They’re assessing how quickly your team can improve it tomorrow. An AI-enabled engineering culture should ship faster, operate leaner, and scale product development without hiring linearly. That’s not just an operational advantage. It’s a valuation story.

The market is already moving. On March 11, Atlassian announced it was cutting roughly 1,600 roles, about 10% of its workforce, while increasing investment in AI and enterprise sales. CEO Mike Cannon-Brookes was direct: this wasn’t simply AI replacing people. It was AI changing the mix of skills the company needs and reducing headcount in certain areas.

That’s the real message for founders.

If your engineering leaders are telling you AI doesn’t matter, don’t nod along. Ask what they’ve actually changed. Ask what workflows they’ve rebuilt. Ask whether you’re shipping faster than six months ago. Ask whether they’re experimenting aggressively or defending the status quo.

The companies that win from here won’t be the ones with the biggest teams. They’ll be the ones with the most adaptive ones.