The Taste Economy: What 6,700 AI Leaders at HumanX Mean for Product Design
Something quietly radical happened in a session at the Moscone Center in San Francisco last week. Amid a week of breathless announcements about agentic AI and billion-dollar infrastructure bets, one breakout session title cut through everything else on the agenda:
“AI Without Design Is Just Faster Dysfunction.”
It wasn’t a keynote. It wasn’t Jensen Huang or Dr. Fei-Fei Li. It was a roundtable, the kind that gets scheduled in parallel with five other things and fills up anyway, because people recognize the truth of a thing before they can articulate it. That sentence is the subtext of everything that happened at HumanX 2026.
I’ve been watching the AI conference circuit closely, and HumanX has carved out a distinct lane. Where other events still orbit model benchmarks and funding rounds, HumanX is fundamentally a conference for people who have to do something with AI in the real world. Over 6,700 leaders from 79 countries attended over four days. The vibe, as one attendee put it, was less “look what we built” and more “okay, now what.”
What emerged wasn’t a neat thesis. It was more like a collective exhale and a hard question: now that AI can execute, what exactly are we executing toward?
The Three Waves, and Why Wave Three Is a Design Problem
Jensen Huang’s framing of AI’s progression was the mental model everyone left with. He mapped it in three waves: Wave 1 was generative (can you produce?), Wave 2 was reasoning (can you think?), and Wave 3, where we are now, is agentic: can you take a goal stated in plain English and see it through end-to-end?
The implications are stark. Within one to two years, the argument goes, a non-technical founder will be able to describe a business problem and have an agent execute the solution, not draft a proposal, not outline a plan, but actually integrate systems, read documentation, iterate, and deliver.
Here’s what I keep turning over: if the execution layer collapses, what remains? The answer, which surfaced repeatedly across sessions, was taste. Inside OpenAI, engineers apparently no longer write code; they guide agents that write code. Eighty percent of their time is now judgment: knowing what the AI got wrong, what to try next, and whether the output is actually right.
That’s a designer’s job description. It has always been a designer’s job description.
What’s changing is that this judgment, this capacity to see the gap between what was produced and what was intended, is no longer a soft skill sitting adjacent to “real” technical work. It’s becoming the primary competency. The people who know what good looks like are suddenly in the most valuable seat.
The Clarity Crisis Is a Design Crisis
Another session that deserves more attention than it got: “The Clarity Crisis: Why AI Breaks Between Systems, Teams, and People.”
This is the quiet failure mode that doesn’t make headlines. Organizations are deploying AI at speed, and they’re discovering that their actual experience gaps — the places where the product breaks down, where users feel confused or abandoned, where the handoff between human and machine is jarring — don’t disappear. They amplify.
Speed doesn’t fix broken experience design. It scales it.
I’ve seen this firsthand working on AI-assisted products. When you use tools like Claude Code or Google AI Studio to build fast, you can ship a working prototype in hours. The risk isn’t that the AI gets the logic wrong — it’s that no one has thought about the experience at the seam. Where does the AI hand off to a human? What does the user see when the agent is “thinking”? How does the interface communicate uncertainty? These are design questions, and they’re being left on the floor because the teams moving fastest don’t have a designer in the room, or don’t have one in the right room.
The HumanX framing was pointed: experience design isn’t the cleanup. It’s the strategy.
The Model Is Commoditizing. The Problem Isn’t.
One of the sharpest takeaways from the conference came out of a discussion about Open Evidence — a company with 70 employees and a $12 billion valuation, where more than 60% of U.S. physicians reportedly use their product daily. They don’t own a model. They own a problem.
The venture investors on stage were explicit: the companies winning right now aren’t the ones with the best technology. They’re the ones closest to the customer problem. The ones who built backward from “what does this person actually need?” rather than “what can our model do?”
For designers, this is vindicating and clarifying at the same time. We’ve spent years arguing that user research and problem framing matter. The market is now structurally forcing that point. When the model improves weekly and any team can access the same foundation, the only durable advantage is a deeper understanding of the problem than your competitor has.
This is also a warning. If your product strategy is primarily “we have a better model,” that moat is evaporating. If your strategy is “we understand this user’s context more precisely than anyone,” you have something worth building on.
A Framework for Designers Navigating the Agentic Shift
Drawing from the week’s themes, here’s how I’d translate HumanX’s signal into a working frame for product designers:
1. Design the judgment layer, not just the interface. In agentic products, the user isn’t always in a dialogue with the AI — sometimes they’re reviewing outputs, approving actions, or overriding decisions. Design for those moments. The review screen is as important as the prompt box.
2. Map the seams. Where does AI stop and human start in your product? Those transitions are the highest-risk experience moments. Draw them explicitly. Prototype them. Don’t let engineering decide what they look like by default.
3. Treat taste as a competency, not a trait. If execution is being automated, your team’s ability to evaluate quality — to say “this isn’t right yet” and articulate why — becomes a measurable skill. Build processes that develop it. Create a critique culture. Do design reviews on AI outputs the same way you’d review a designer’s work.
4. Build from the problem, not the capability. Start every AI feature with: what is the user actually trying to accomplish, and what gets in their way? Not: what can our model do that we haven’t surfaced yet?
The Open Question
HumanX ended with more tension than resolution, which is probably appropriate. Al Gore warned the conference that knowledge-work jobs face genuine displacement. Startup founders argued AI is “freeing humans from work they don’t want to do.” Neither position is wrong. Both miss the harder design challenge: if AI reshapes what people do at work, someone has to design the new workflows, the new interfaces, the new mental models for how people understand their own agency.
That’s not an engineering problem. It’s not even purely a product problem.
It’s a design problem.
And if the industry doesn’t bring designers into that conversation at the strategy level — not at the cleanup stage, not at the “make it look nice” stage — we’ll build a very capable, very efficient, and deeply dysfunctional future.
The session title said it plainly. Now someone has to do something about it.
What’s the design challenge you’re seeing most in your AI products right now? I’d genuinely like to know — drop it in the comments.
References
HumanX 2026 official agenda and session descriptions: humanx.co/agenda
GTMnow Newsletter — “AI Conference HumanX Takeaways and Frameworks” (April 10, 2026): thegtmnewsletter.substack.com
Bastille Post — “HumanX conference wraps up amid warnings on AI’s double-edged impact” (April 10, 2026): bastillepost.com
HumanX session: “AI Without Design Is Just Faster Dysfunction: The CX and EX Conversation Nobody Is Having” — Customer Experience Peer [X]Change
HumanX session: “The Clarity Crisis: Why AI Breaks Between Systems, Teams, and People” — Customer Experience Peer [X]Change
Bloomberg — “Anthropic Dominates the Discussion at the HumanX AI Conference” (April 9, 2026)
NVCA partnership announcement: nvca.org/event/humanx