The Shifting Technical Landscape
Over the past 24 hours, I’ve been mulling over some exciting insights on AI tools and their impact on our design work. Nick Potkalitsky’s recent deep dive reminds us that while large language models (LLMs) are undeniably brilliant, they’re still in an “intermediate stage.” It’s an interesting moment—one where AI is both a marvel and a bit of a head-scratcher.
Nick’s piece, “Understanding AI in 2026: Beyond the LLM Paradigm” (read more), outlines how today’s models are just scratching the surface. Meanwhile, Karpathy’s retrospective on 2025 (full review) adds that our current models, despite their occasional brilliance, are often like “summoned ghosts”—genius in spots, yet frustratingly unreliable in others. It’s a heady reminder that in our rapidly evolving tech environment, constant learning isn’t optional.
This signals a call for us designers to not only harness these tools but also to keep up with the pace of change, embracing both the possibilities and the challenges.
The Code and Speed Revolution
There’s been a lively buzz around how fast AI-powered tools are maturing, especially in generating code. Simon Willison recently argued that by 2026, dismissing LLM-generated code as “garbage” will be a credibility killer. The progression from dismissiveness in 2023 to a more appreciative approach today suggests that our tools are now truly writing code worth having a nod to.
Experts like Sebastian Raschka highlight that the key now is enhancing inference—improving how these models function in real-time—rather than just ramping up training volumes. This shift means that as designers, we also need to rethink our prototyping and testing cycles, ensuring our designs are robust even when the underlying code is evolving at breakneck speed.
The pace of change is exhilarating, and it offers a clear signal: our approaches and expectations for traditionally “developer-centric” tasks in design are about to get a fascinating makeover.
Designing for a Future of AI-Enhanced Interaction
On the design front, the conversation is shifting towards creating interfaces that truly bridge the gap between human intent and machine capability. Anne Cantera’s recent musings on conversational AI (find out more) resonate deeply with anyone who’s had to make sense of an overly complex system. As these systems grow more potent, our challenge isn’t just building them—it’s ensuring they feel natural and intuitive.
Dan Saffer’s thoughtful reminders about the pitfalls of AI projects (check out “The Four Horsemen of the AIPocalypse”) further steer our attention to the human side of the equation. Conversations, usability, and the often overlooked nuances of interaction design are key. As we face compressed timelines and multiple breakthrough fronts, keeping the human experience central is more crucial than ever.
For those of us in UX and product design, it’s about blending technical acumen with a genuine sensitivity for how users engage with AI-powered tools. Let’s keep experimenting, learning, and (occasionally) laughing at the madness of it all.