Jamie Hurst's Blog - Beyond the Hype: Lessons from Pragmatic Summit

Beyond the Hype: Lessons from Pragmatic Summit

Posted on Monday February 16, 2026

There's something about being in a room with people like Gergely Orosz, Laura Tacho, Martin Fowler, Kent Beck, and Steve Huynh that you simply can't replicate through blog posts and Twitter threads. The summit struck a nice balance: small enough that you could actually have meaningful conversations during breaks, large enough that there was a constant stream of sessions to attend and ideas to absorb. I wasn't there to speak or present; I came as someone who's spent the last year deep in the world of GenAI tooling in developer experience at Booking.com, wanting to see how our experiences and challenges compared to what's happening across the wider industry.

What struck me most wasn't any single quote or revelation, but rather the overall tone of the conversations happening. While the rest of the tech world seems to oscillate wildly between "AI will replace all developers by next Tuesday" and "this is all just hype that will blow over," the people in that room were having much more nuanced discussions. They were talking about real implementations, real challenges, and real results. The hype machine is still running at full speed outside these walls, but inside? People were focused on actually making this technology useful. I guess the hint was in the title: "Pragmatism".

The Hype Machine is Still Running

I wrote about the dangers of hype last year, and if anything the noise has only gotten louder since. If you've been following the tech press over the last year, you'd be forgiven for thinking that by the end of 2026 all developers will be an extinct species. The headlines keep coming: "AI will write all code by year's end", "Junior developers obsolete", "Software engineering jobs at risk", etc. It's the same cycle we've seen before with every transformative technology, but this time it feels more insistent, and frankly, more exhausting.

The disconnect between what's being promised and expected in boardrooms and what's actually happening on the ground is vast. There is a consensus that the big investments are still targeting anything with "AI" in the pitch deck. Companies are under immense pressure to demonstrate their AI strategy, often before they've figured out if they actually need one. I've lost count of how many product announcements I've seen where "AI-powered" has been slapped onto features that would work just as well—or better—without it. AI is a solution to a swathe of problems, so we're told.

What struck me at the summit was how little time people spent talking about this narrative. Not because they were ignoring it, but because they'd already moved past it. When you're actually building and using these tools day-to-day, the gap between the hype and the reality becomes impossible to ignore. Yes, LLMs can generate code. Yes, they can be remarkably helpful. No, they're not going to replace the need for engineers who understand systems, make architectural decisions, and know when the generated code is leading you down a terrible path.

The hype persists for predictable reasons: investor pressure to justify valuations, competitive positioning (nobody wants to be seen as falling behind), and genuine excitement about the technology's potential. The reality, as ever, is that meaningful change happens gradually, messily, and with a lot of trial and error along the way.

Seeing Through the Fog

Once you get past the noise, there's actually something substantial happening. The statistics shared at the summit were surprisingly consistent: most companies are seeing around a 10% productivity increase from developers using AI tools day-to-day. That's not the revolutionary 10x improvement that the hype promised, but it's also not nothing. More importantly, the companies seeing real benefits aren't the ones treating GenAI as magic: they're the ones building deliberate platforms and workflows around it.

Uber's approach particularly stood out. Rather than just giving developers access to ChatGPT or GitHub Copilot and hoping for the best, they're investing in tooling that makes it easy to use AI while being explicit about the trade-offs. Code generation platforms that integrate with their review processes, systems that help developers understand what they're accepting when they use AI-generated code. It's not about replacing developer judgement; it's about augmenting it with better tools and clearer guardrails.

Hearing these examples was genuinely heartening, because it confirmed we're on the same journey at Booking.com. We've been experimenting with similar platforms over the last year, and seeing other mature engineering organisations grappling with the same challenges, and arriving at similar solutions, was a fantastic reinforcement. It's easy to second-guess yourself when you're deep in the work, wondering if you're overthinking things or moving too...