The Evolution of UX Research: A 20-Year Industry Veteran on AI, Layoffs, and What's Next
What happens when you've been in UX for over two decades and witnessed everything from eye-tracking cameras strapped to foreheads to AI tools that can transcribe and analyze interviews in minutes?
I recently had the privilege of sitting down with Nick Cawthon, a true OG of the UX world and founder of Gauge, to explore this exact question. Nick's perspective cuts through the noise of our current moment—the layoffs, the AI panic, the job market uncertainty—with the wisdom of someone who's seen multiple waves of change wash over our field.
What emerged from our conversation was both sobering and hopeful: a realistic assessment of where we are, and a clear-eyed view of what makes our work irreplaceable.
Let's Get Real About UX History
Nick started with a reality check that made me smile:
"That descriptor [doing UX for 20 years] is a bit of a misnomer. User experience came around maybe 15 years ago with the advent of the iPhone. And anybody that tells you that they've been doing UX for 20 years, present company not excluded, is pulling your chain a little bit."
The real story? What we now call UX research emerged when smartphones put technology in our pockets 24/7, not just during work hours. Before that, it was UI design—subjective, interpretive, with everyone having their own vision of what an interface should be.
The shift happened when companies like Google and Apple realized they needed standardized design systems for their app stores. Suddenly, UI could be handled by "fill in the blanks" and "color within the lines" approaches.
That's when UX research found its purpose: understanding how humans actually experience technology when it's embedded in their daily lives.
Those Primitive Early Methods (Yes, Cameras on Foreheads)
Nick's description of early research methods had me both laughing and cringing:
"Eye tracking was the most bizarre one where you would put a camera attached to the user's forehead. I kid you not. It was like a wearable device that would watch where their eyes would go on the screen."
The goal? Figure out where to place banner ads to distract users and get them to click. It sounds dystopian now, but it was the beginning of trying to quantify human attention and behavior with technology.
The evolution? Moving from asking "where do people look?" to "how can we structure conversations with real humans to understand their actual experiences?"
That shift from the subjective to the objective was what drew Nick to the field—and it's still what makes our work valuable today.
The AI Reality Check We All Need
When we got to the elephant in the room (AI came up 11 minutes into our conversation—"a new record," Nick noted), his perspective was refreshingly balanced.
The good news about AI tools:
They democratize research capabilities
Manual transcription is no longer a barrier
A single practitioner can create professional research repositories
Analysis and synthesis happen faster than ever
The reality check:
"The reason I got into UX research was because I liked the conversations. I enjoyed the human aspect of it. And to think that we're gonna have artificial conversations with agents to replace real conversations with human beings... I don't think you can ever replace that."
Nick's prediction? We won't be replaced—we'll be enhanced and empowered. But only if we embrace these tools strategically.
The Electric Bicycle Analogy That Changes Everything
Nick's analogy for AI tools perfectly captures both the opportunity and the responsibility:
"It feels like riding an electric bicycle the first time you start to use these tools. But there's also the danger of what happens if the bike breaks down and you're 25 miles from home. You better know how to fix the chain, or understand how the brake works or repair the tire."
What this means for researchers:
Use AI to bridge the qual/quant divide
Get comfortable with analytical tools (not all require fluency in R, SPSS, etc.)
Let algorithms give you conversation guides, but stay responsible for the actual conversations
Always be able to explain and adjust your methodology
The key insight: Don't run from the technology, but don't let it replace your fundamental skills either.
Why the Human Connection Is Non-Negotiable
One of the most powerful moments in our conversation came when Nick shared his DocuSign nightmare—getting stuck in a 4-hour loop trying to reach a human for a simple billing issue, ultimately giving up and switching providers.
His takeaway?
"Our advantage as researchers is that we're this humanist representative... There's no technology for now that can really figure out how to get you to reveal yourself."
The jobs AI can't do:
Understanding organizational context for implementing recommendations
Reading between the lines in user conversations
Connecting insights to strategic business decisions
Being the voice of the customer in corporate environments
The DOA Framework: Selling Outcomes, Not Services
Nick's "Dead on Arrival" framework addresses every product team's worst nightmare: building something nobody uses.
The business insight: Stop selling your research capabilities and start selling the outcomes they produce—increased user adoption, satisfaction metrics, reduced development risk.
Why this matters now: As teams move faster with AI-powered development, the risk of building the wrong thing at high speed is greater than ever. Strategy becomes more valuable, not less.
Surviving the Current Market Reality
On the tough job market, Nick was both empathetic and practical:
"With the amount of algorithms on both sides—the applicant as well as the recipient for jobs that may be real or may not be real—that can be an exercise in madness."
His advice:
Focus on human connections over algorithmic job applications
Consider using video in your outreach
Network aggressively at real-world events
Remember that as humanists, we're good at connecting with people
The opportunity: While the numbers game gets harder, our ability to make meaningful human connections becomes more valuable.
What Skills Actually Matter Going Forward
Nick's essential skills for researchers:
Curiosity and flexibility
Eagerness to adopt and play with new tools
Self-assessment of artificial barriers to adoption
Bridging the qual/quant divide using new analytical tools
The warning: Don't be the person who adopted tools in the early 2000s and stayed there. The field keeps evolving, and so must we.
The Optimistic Future (Yes, Really)
Despite everything—the layoffs, the AI disruption, the market uncertainty—Nick ended on a hopeful note:
"What excites me is when that scale starts to counterbalance... We need somebody who knows what they're doing here, because we're just making mistake after mistake of DOA development that didn't go anywhere."
His prediction: The pendulum will swing back. As organizations move fast with new technology and make expensive mistakes, they'll rediscover the value of human-centered research and strategy.
The timeline: We need patience and persistence, but our "glory days" will return.
The Meta-Lesson About Adaptation
What struck me most about Nick's perspective is how he approaches change—not with fear or resistance, but with strategic adaptation. He's witnessed multiple waves of transformation in our field and learned that the core value proposition remains constant: helping organizations build technology that actually works for humans.
The tools change, the speed increases, but the fundamental human need for understanding and connection remains.
That's not just a professional insight—it's a blueprint for navigating uncertainty in any field.
🎧 Listen to the full conversation with Nick Cawthon on Deep Thoughts with Michelle Handy.
🔗 Connect with Nick: LinkedIn | DOA Framework
📚 Nick's recommendation: Just Enough Research by Erica Hall
What aspects of UX research do you think are truly irreplaceable? I'd love to hear your thoughts on how we can best navigate this period of rapid change while preserving what makes our work uniquely human.