Artificial Intelligence

AI, UX, and Control: Where We Are and Where This Is Going

do our business is what computers are for



There’s a scene in TRON (1982) where Dillinger says, “Doing our business is what computers are for.”

That line stuck.

Because that debate never ended. It just evolved. Back then, it was about control of systems.
Now it’s about control of intelligence.
And the question is the same: Are we using computers… or are they starting to use us?

The Current Landscape: The Three Major Models

Right now, three AI systems are shaping most of what’s happening:

ChatGPT

ChatGPT (OpenAI)

This is the most widely adopted model and the one I use daily.
Strengths:
 • Fast iteration and ideation
 • Strong at structured thinking (flows, systems, breakdowns)
 • Excellent for writing, prototyping language, and reframing problems
 • Flexible across disciplines (UX, code, content, strategy)

Where it fits:
 • Daily workflow acceleration
 • UX thinking partner
 • Content generation and refinement
 • Prompt-driven exploration

Claude image

Claude (Anthropic)

Strengths:
 • Long-form reasoning
 • Pattern recognition across large blocks of text
 • More “human” tone in certain contexts
 • Strong at critique and synthesis

Where it fits:
 • Reviewing strategy
 • Stress-testing ideas
 • Understanding macro trends


It’s also the one that said something that stuck with me:
 • UX job postings dropped over 70% from  their peak.
 • This isn’t personal. It’s structural.

That’s not hype. That’s the reality we’re in.

Google Gemini

Gemini (Google)

Gemini is tightly integrated into Google’s ecosystem.

Strengths:
 • Search + AI hybrid thinking
 • Strong multimodal capabilities (text, image, context)
 • Deep integration with tools people already use

Where it fits:
 • Information retrieval + synthesis
 • Workflow integration at scale
 • Enterprise environments

How I Actually Use AI (Day-to-Day)

This isn’t theoretical. I’m using this stuff constantly.

AI has also made one thing very clear:
The market changed fast. Junior and mid-level work is being automated.
Companies are reducing headcount.
Output expectations are increasing.

That’s not speculation. That’s happening.
So the value isn’t in producing screens anymore. It’s in thinking, connecting systems and making decisions AI can’t make on its own.

Breaking Down Complex Problems

Instead of staring at a blank page, I use AI to:
 • Decompose flows
 • Identify edge cases
 • Explore multiple approaches quickly

This is basically accelerated Double Diamond thinking.

 learn more

Prompting as a Skill

Prompting isn’t just “ask a question.”
It’s setting context, defining constraints and Iterating toward clarity.

I don’t expect perfect answers. I refine.
Example pattern:
 • First pass: broad direction
 • Second pass: tighten constraints
 • Third pass: shape tone, structure, intent
 • That loop replaces hours of solo thinking.

 learn more

UX Writing + Content Systems

AI helps me:
 • Draft UX copy fast
 • Explore tone variations
 • Build consistent messaging systems

But I don’t ship raw output.
I edit for:
 • Human clarity
 • Emotional tone
 • Real-world context

 learn more

Prototyping Thinking, Not Just Screens

AI lets me simulate:
 • Conversations
 • User reactions
 • System responses

That means I’m not just designing interfaces. I’m designing interactions at the language level.

 learn more

What This Means for UX and Product

This is where most people are getting it wrong. AI is not replacing UX.
It’s replacing surface-level execution. What remains valuable:

Framing the Right Problems

AI can generate solutions all day.
It cannot u
nderstand business nuance, balance user needs with revenue, and navigate ambiguity the way humans do.

Human-Centered Judgment

AI doesn’t feel anything. Users do.
Good UX still depends on empathy, timing, context, and emotional resonance. That doesn’t go away.

Systems Thinking

Everything is becoming more connected: AI outputs, Data systems
Interfaces, and Automation layers
The designer who understands how it all fits together wins.

Speed + Direction

AI gives you speed. But speed without direction is noise.
The value is knowing where to go and using AI to get there faster.

The Risk: Letting the Tool Take Over

AI is the most efficient



This is where I come back to TRON. There’s another version of that same idea now:

“Artificial Intelligence is the most efficient way of handling what we do.”

That’s true. Efficiency is not the problem. Control is. Companies will always use technology to reduce cost, increase output,
and scale faster. That’s expected. But if everything becomes generated, automated and optimized for metrics, then we lose
originality, human connection, and meaning. And that’s where things break.

Where I Stand

I don’t see AI as a threat. I see it as a multiplier.
But only if it stays in the right role. AI is the tool.
Humans are the driver.

We are still the ones who define intent, create meaning, and connect ideas to real human experiences.

AI helps us do it:
• Harder
• Better
• Faster
• Stronger

But it doesn’t replace the core of it.

What I Bring Into a Team

I’m not just “keeping up” with AI. I’m actively integrating it into how I work.

That means:
 • Faster iteration cycles
 • Better exploration of ideas
 • Stronger alignment between business and user needs
 • Clearer communication across teams

I’ve spent over a decade designing systems, workflows, and experiences. Now I’m applying that same thinking to:
 • AI-assisted design
 • Prompt-driven workflows
 • Hybrid human + machine systems

TRON ARES red guard

Final Thought

The companies that win in this next phase aren’t the ones that replace people with AI.
They’re the ones that figure out how humans and AI work together without losing what makes the experience human.

That’s the balance. That’s the opportunity.

And that’s exactly where I operate.

All this talk of AI and big tech today

AI Tool List

Looking for other AI tools not listed above? Check out these comprehensive lists.