What We Heard at HumanX 2026: the Production Problem is Getting Harder

Last week's HumanX marked our first conference since AWS re:Invent in December, and people’s framework for how they think about AI has already changed.

AI has made it dramatically faster to write and ship code, but it has not made it any easier to run what you've shipped. For a lot of teams, it's made it harder. More code shipping faster equates to more services, more dependencies, more alerts, more incidents, more people getting paged at 2:00AM. You get the picture.

The operational pain is getting worse, and it's getting worse because of AI, not in spite of it.

From Hype to "So What?"

AI, AI, AI.

Every booth had it, every pitch referenced it. But the mood on the floor wasn't translating to excitement. It was closer to impatience or anxiety. The engineering leaders and executives walking the aisles weren't asking "what can AI do?" They were asking: where is this actually working? Is it in production, working at scale, on real systems where things break?

We're past the phase where a slick AI demo earns you credibility. The bar now is whether your product holds up in extreme scenarios, when the AI is most valuable. Most of what's being built and sold today doesn't clear that bar.

Our booth had the highest badge scan count of any exhibitor at HumanX. People weren't stopping by out of curiosity. They came because they're feeling the weight of running production and they wanted to talk about it. VPs of Engineering, SREs, platform leads, CTOs. They showed up asking about on-call burden, alert fatigue, incident investigation, and why their AI coding investments weren't making their teams feel any less stretched.

Where the Market Is

Very few companies have an AI strategy for production. All the budget and attention has gone to AI for code generation. Copilots, coding agents, automated PR reviews. That's where the hype pointed, deservedly so, and that's where the money went. But the other side of the house, the infrastructure and operations that actually keep things running, has been left behind. Same dashboards, same runbooks, same 3AM pages, even as the systems underneath have gotten dramatically more complex.

And when teams do try to close that gap, they almost always start by attempting to build it themselves. We heard some version of this from dozens of people at our booth. They spin up an internal AI project for production, early results look promising, they put more engineers on it, and then the edge cases start compounding. Every environment is different. The prototype that worked on three services falls apart at fifty. Models change underneath you.

That's the gap we're building into at Resolve AI. Not AI that observes your operating environment from the outside, but AI that actually works inside it. Investigating alerts, connecting signals, and getting to root cause before a human has to.

The next wave of AI investment isn't going to be about writing code. It's going to be about running it.

If your team is feeling that production burden, see what AI for prod could look like for you.