UX STRATEGY • PRODUCT DESIGN
Research. Strategy. Systems Design. AI.
I design products that help people understand and trust systems they can't fully see — from machine vision overlays to GPS-connected hardware. That's been my work for over two decades. AI just made it more interesting. (See how I'm integrating AI into my design process.)
Machine Vision
Wearable
AI • AGEnts
23
YEARS EXPERIENCE
3
TECHNOLOGY COMPANIES
Multiple
PATENTS FILED/GRANTED
01
Selected Work
Case Studies
Global Trend Engine
Designer & Builder
•
Self-Initiated
•
1 week
→
An agentic AI dashboard where three agents — a multi-persona scanner, Nostradamus, and Tarot — scan the web for frontier signals, synthesize patterns, and generate predictive convergence insights — designed, built, and deployed with Claude.
Lytx Video Overlay
Senior UX Designer
•
Lytx
•
4 months
→
A dynamic video overlay that simplifies customer coaching conversations, improving user trust, and reducing contention rate to near zero.
Lytx Driver ID
Senior UX Designer
•
Lytx
•
3 months
→
A systematic user flow for assignment, distribution, and usage of QR codes to assign drivers to vehicles.
Timex Ironman ONE GPS+
UX Design Lead
•
Qualcomm / Timex
•
3 years
→
A full 0-to-1 product experience for athletes who wanted to track workouts, stay connected, and leave their phones behind.
Tagg the Pet Tracker
UX Design & Product Management
•
Qualcomm
•
1.5 years
→
Redesign of a pet activity monitoring and management of iOS/Android app development as both UX lead and product manager.
FLO TV Personal Television
UX Design Lead
•
Qualcomm / FLO TV
•
1 year
→
Designing a new product category from the ground up: live mobile television.
02
Expertise
Skills
User Research
Interviews, usability tests, ethnographic study, and contextual inquiry
Journey Mapping
End-to-end experience mapping across touchpoints
Interaction Design
Flows, wireframes, use cases, and high-fidelity mockups
Storytelling
Communicating design decisions to executives and cross-functional teams
Systems Thinking
Mapping connected flows and designing for scalability across surfaces
Prototyping
From low-fi click-throughs to high-fidelity AI coding
AI Fluency
Designing AI-powered experiences and using AI tools in the design process
Stakeholder Management
Cross-functional collaboration and design advocacy
Tools
Claude (Design/Code)
Research synthesis, design, prototyping, and documentation
Figma
Components, variants, auto-layout, and prototyping
Sketch
Vector UI design for components and high-fidelity screens
Axure RP
High-fidelity prototyping with conditional logic
Pendo
In-app guidance, feature tracking, and user surveys
Amplitude
Funnels, retention, and feature-adoption analysis
Dovetail
Centralized research insights and customer intelligence
Atlassian (Confluence/Jira)
Confluence for design documentation; Jira for planning and cross-functional tracking
03
Background
Experience & Education
2019 - Present
Senior Product Designer
Lytx • San Diego, CA
Research and design of user experience for video-safety and AI products, and development of an AI-native design process for the Product/UX team.
2007 - 2018
Sr. Staff UX Designer & Sr. Product Manager
Qualcomm • San Diego, CA
Led 0-to-1 UX and product development for large-scale product start-ups while managing 3rd-party design teams.
2003 - 2007
UI Designer
Nokia • San Diego, CA
Designed new phone features while serving as the only North American member of Nokia's global design-management team.
1998 - 2002
B.S. Symbolic Systems
Stanford University • Stanford, CA
Interdisciplinary study of computer science, linguistics, philosophy, and psychology with a concentration in human-computer interaction.
My Résumé
Name
Daniel Rivas
current role
Senior Product Designer
Location
San Diego, CA
Availability
Daily Futurology Report
October 3, 2026 at 12:11:54 AM
Model:
Opus 4.8
Forecast:
2026–2028
The Demos Become Deliveries: 2026 Is the Year Frontier Tech Stops Promising and Starts Shipping — Into a World That Can't Yet Absorb It
Here's the bet. Over 2026–2028 the frontier stops being gated by whether the technology works and starts being gated by whether the world can absorb it working. The scarce thing isn't the breakthrough anymore — it's the grid connection, the regulatory lane, the retrained worker, the steady supply of a dozen metals. Capability stopped being the bottleneck this year. Absorption became it.
The proof is already on the board, not in a forecast. In-vivo CRISPR cleared Phase 3. Starship made orbit. Agentic AI is doing measurable chunks of real jobs and the labor data is already bending — routine postings down, young workers in exposed roles hit first. Meanwhile the physical substrate keeps saying not so fast: 1,000-plus TWh of demand against a grid that can't hook up half of what's queued, and a rare-earth truce with an expiry date in November.
I'd expect the turn to land somewhere in 2027, when the gap gets embarrassing in public. A therapy that works but can't get reimbursed. A data center that's built but can't get powered. A class of graduates whose entry-level rung quietly vanished. Each one is the same story: the thing works, and the system around it didn't get the memo. That's when “can we build it” stops being the headline and “can we live with it” takes over.
Worth holding loosely, though. I might have the constraint backwards. Absorption problems have a way of resolving faster than the doomers expect — grids get built, rules get written, people adapt, and the rare-earth truce could just get renewed and quietly forgotten. And there's the other tail: if the AI capex cycle is as circular as the skeptics say, the whole demand curve could fold before any of these limits gets to bite, and then we're not talking about absorption at all — we're talking about a bust. Two very different ways the next two years could go. The one thing I'm confident about is that the easy part, making it work, is the part we just finished.
Cross-Domain Synthesis
For a long time the frontier was a slideshow. Roadmaps, demos, “coming soon.” 2026 is the year a pile of those slides turned into things that actually happened. A gene edit done inside a living body passed a real Phase 3. A rocket the size of a building finally made orbit. Models stopped being judged on how big they are and started being judged on whether they can finish a job. The promises came due — and mostly, they paid out.
Look at the receipts. Intellia's in-body CRISPR therapy cut attacks by 87% in a pivotal trial — the first time editing someone's genome while it's still inside them cleared the highest regulatory bar. SpaceX put Starship in orbit and Artemis flew four people around the Moon in the same year. And the AI labs quietly swapped their whole pitch: the race isn't “bigger model” anymore, it's agents that do multi-step work, with five frontier systems shipping inside sixteen days. These aren't demos. They're deliveries.
Here's the part nobody put on the slide: delivering a thing and absorbing a thing are different problems. The AI that can do the job is landing on a labor market that's already splitting — routine-role postings down 13%, and 22-to-25-year-olds in exposed jobs taking the first hit. The compute to run all this wants more than 1,000 TWh and a grid that can't connect half of what's planned. Space got cheap enough that low orbit is starting to look like a parking lot. Every delivery shows up with a bill the receiving system wasn't ready to pay.
Two of these run on a slower clock. The climate work this year stopped arguing about the final temperature and started arguing about the speed — the new AMOC study puts the Atlantic circulation on track to lose about half its strength by 2100, and the danger is how fast we get there, not just where we land. And the whole stack still balances on a mineral chokepoint: China's rare-earth controls are paused under a one-year truce that runs out in November, and Taiwan is deciding whether to fence off the chips everyone needs. The deliveries are real. The ground they're landing on is not steady.
So the honest read is mixed. A year where the hard stuff finally shipped is good news — it means the bets were real, not vapor. But “it works in the trial” and “it works at scale, for everyone, without breaking something else” are separated by exactly the boring institutional work — grids, regulators, retraining, treaties — that nobody gets a keynote for. 2026 proved the frontier can deliver. 2027 and 2028 are about whether the rest of us can catch what's being thrown. I wouldn't bet on a clean catch.
05
Horizon: Predictive convergence
Futurology Report — Daily
October 3, 2026 at 12:11:54 AM
AI Model:
Opus 4.8
Forecast:
2026–2028
The Demos Become Deliveries: 2026 Is the Year Frontier Tech Stops Promising and Starts Shipping — Into a World That Can't Yet Absorb It
Here's the bet. Over 2026–2028 the frontier stops being gated by whether the technology works and starts being gated by whether the world can absorb it working. The scarce thing isn't the breakthrough anymore — it's the grid connection, the regulatory lane, the retrained worker, the steady supply of a dozen metals. Capability stopped being the bottleneck this year. Absorption became it.
The proof is already on the board, not in a forecast. In-vivo CRISPR cleared Phase 3. Starship made orbit. Agentic AI is doing measurable chunks of real jobs and the labor data is already bending — routine postings down, young workers in exposed roles hit first. Meanwhile the physical substrate keeps saying not so fast: 1,000-plus TWh of demand against a grid that can't hook up half of what's queued, and a rare-earth truce with an expiry date in November.
I'd expect the turn to land somewhere in 2027, when the gap gets embarrassing in public. A therapy that works but can't get reimbursed. A data center that's built but can't get powered. A class of graduates whose entry-level rung quietly vanished. Each one is the same story: the thing works, and the system around it didn't get the memo. That's when “can we build it” stops being the headline and “can we live with it” takes over.
Worth holding loosely, though. I might have the constraint backwards. Absorption problems have a way of resolving faster than the doomers expect — grids get built, rules get written, people adapt, and the rare-earth truce could just get renewed and quietly forgotten. And there's the other tail: if the AI capex cycle is as circular as the skeptics say, the whole demand curve could fold before any of these limits gets to bite, and then we're not talking about absorption at all — we're talking about a bust. Two very different ways the next two years could go. The one thing I'm confident about is that the easy part, making it work, is the part we just finished.
Signal Intensity
A domain-level score (0-100) representing the volume and momentum of frontier activity detected across the signals in that domain.
AI
95
%
Climate
77
%
BIOTECH
74
%
GEOPOLITICS
84
%
ENERGY
91
%
SOCIETY
73
%
SPACE
66
%
Cross-Domain Synthesis
For a long time the frontier was a slideshow. Roadmaps, demos, “coming soon.” 2026 is the year a pile of those slides turned into things that actually happened. A gene edit done inside a living body passed a real Phase 3. A rocket the size of a building finally made orbit. Models stopped being judged on how big they are and started being judged on whether they can finish a job. The promises came due — and mostly, they paid out.
Look at the receipts. Intellia's in-body CRISPR therapy cut attacks by 87% in a pivotal trial — the first time editing someone's genome while it's still inside them cleared the highest regulatory bar. SpaceX put Starship in orbit and Artemis flew four people around the Moon in the same year. And the AI labs quietly swapped their whole pitch: the race isn't “bigger model” anymore, it's agents that do multi-step work, with five frontier systems shipping inside sixteen days. These aren't demos. They're deliveries.
Here's the part nobody put on the slide: delivering a thing and absorbing a thing are different problems. The AI that can do the job is landing on a labor market that's already splitting — routine-role postings down 13%, and 22-to-25-year-olds in exposed jobs taking the first hit. The compute to run all this wants more than 1,000 TWh and a grid that can't connect half of what's planned. Space got cheap enough that low orbit is starting to look like a parking lot. Every delivery shows up with a bill the receiving system wasn't ready to pay.
Two of these run on a slower clock. The climate work this year stopped arguing about the final temperature and started arguing about the speed — the new AMOC study puts the Atlantic circulation on track to lose about half its strength by 2100, and the danger is how fast we get there, not just where we land. And the whole stack still balances on a mineral chokepoint: China's rare-earth controls are paused under a one-year truce that runs out in November, and Taiwan is deciding whether to fence off the chips everyone needs. The deliveries are real. The ground they're landing on is not steady.
So the honest read is mixed. A year where the hard stuff finally shipped is good news — it means the bets were real, not vapor. But “it works in the trial” and “it works at scale, for everyone, without breaking something else” are separated by exactly the boring institutional work — grids, regulators, retraining, treaties — that nobody gets a keynote for. 2026 proved the frontier can deliver. 2027 and 2028 are about whether the rest of us can catch what's being thrown. I wouldn't bet on a clean catch.
ABOUT ME
Design Philosophy
I bring order to complexity; I've spent over two decades building the tools to do it well.
My path started at Stanford, where a degree in Symbolic Systems gave me something most designers don't have: a foundation that spans both sides of the human-computer divide. From the technical rigor of computer science and formal logic, to the human depth of cognitive psychology and knowledge representation, I learned to hold both perspectives at once and to design from the intersection.
That training became practice at Nokia, Qualcomm, and now Lytx, companies where the problems are large, the systems are complex, and the stakes are real. I've learned that the most important design decisions rarely live on a single screen. They live in the architecture, the mental models, the moments where a user either trusts the product or doesn't.
What drives me today is the challenge of making emerging technology feel human and trustworthy. AI systems can process the world faster than any person, but they still need to communicate their reasoning, surface the right information at the right moment, and earn the confidence of the people who depend on them. That translation problem, from machine intelligence to human understanding, is exactly the kind of complexity I've been working on.
Systems before screens
Every interface is a surface on top of a system. Understanding the system (the data flows, the user mental models, the organizational constraints) is what separates design that scales from design that just looks good in a mockup.
Strategy and execution, not one or the other
I connect design decisions to business outcomes. That means being in the room when strategy is set, not just when wireframes need approval. It means being able to move between the 30,000-foot view and the pixel-level detail without losing either.
Trustworthy by design
The best technology earns trust before it demands it. Whether I'm designing a safety-critical AI product or the home screen on a fitness watch, I start with the question: what does this person need to feel confident taking action?
EDUCATION
B.S. Symbolic Systems
Stanford University
Concentration in HCI
BASED IN
San Diego, CA
CURRENTLY
Senior Product Designer
Lytx
OPEN TO OPPORTUNITIES
Principal/Senior-Level Product Design Roles
In San Diego or Remote
SELECTED WORK
Case Studies
EXPERTISE
Skills
User Research
Interviews, usability tests, ethnographic study, and contextual inquiry
Journey Mapping
End-to-end experience mapping across touchpoints
Interaction Design
Flows, wireframes, use cases, and high-fidelity mockups
Storytelling
Communicating design decisions to executives, stakeholders, and cross-functional teams
Systems Thinking
Mapping connected flows and designing for scalability across product surfaces
Prototyping
Interactive prototypes from low-fi click-throughs to high-fidelity AI coding
AI Fluency
Designing AI-powered experiences and leveraging AI tools in the design process
Stakeholder Management
Cross-functional collaboration and design advocacy
Tools
Pendo
In-app guidance, feature tracking, and user surveys to inform design decisions
Dovetail
Centralized research insights, tagged findings, and shared customer intelligence repository
Figma
Components, variants, auto-layout, and prototyping
Claude (Design & Code)
AI-assisted research synthesis, design critique, content generation, and prototyping
Amplitude
Product funnels, retention curves, and feature adoption to identify usage and priorities
Gong
Customer interview repository for surfacing pain points, testing designs, and grounding decisions
Sketch
Vector-based UI design for components, wireframes, and high-fidelity screens
Axure RP
High-fidelity interactive prototyping with conditional logic and complex flows
Cursor
AI-assisted coding for rapid prototyping and exploring technical feasibility
BACKGROUND
Experience & Education
Senior Product Designer
Lytx · San Diego, CA
Researched and designed user experience for video safety and AI products, and developed an AI-native design process for the UX team.
tagsContainer
2019 - Present
Sr. Staff UX Designer & Sr. Product Manager
Qualcomm · San Diego, CA
Led 0-to-1 UX and product development for large-scale product start-ups while managing 3rd-party design teams.
tagsContainer
2007 - 2018
UI Designer
Nokia · San Diego, CA
Designed new phone features while serving as the only North American member of Nokia's global design management team.
tagsContainer
2003 - 2007
B.S. Symbolic Systems
Completed interdisciplinary study of computer science, linguistics, philosophy, and psychology with a concentration in human-computer interaction.
tagsContainer
1998 - 2002
My Résumé
A full overview of my experience, skills, and education — ready to share.
NAME
Daniel Rivas
CURRENT ROLE
Senior Product Designer
LOCATION
San Diego, CA
EXPERIENCE
23 years
AVAILABILITY
✦ Human · Machine · Intelligence ✦ Systems before screens ✦ Strategy & execution ✦ Trustworthy by design ✦
Design Philosophy
06
ABOUT ME
I bring order to complexity; I've spent over two decades building the tools to do it well.
My path started at Stanford, where a degree in Symbolic Systems gave me something most designers don't have: a foundation that spans both sides of the human-computer divide. From the technical rigor of computer science and formal logic, to the human depth of cognitive psychology and knowledge representation, I learned to hold both perspectives at once and to design from the intersection.
That training became practice at Nokia, Qualcomm, and now Lytx, companies where the problems are large, the systems are complex, and the stakes are real. I've learned that the most important design decisions rarely live on a single screen. They live in the architecture, the mental models, the moments where a user either trusts the product or doesn't.
What drives me today is the challenge of making emerging technology feel human and trustworthy. AI systems can process the world faster than any person, but they still need to communicate their reasoning, surface the right information at the right moment, and earn the confidence of the people who depend on them. That translation problem, from machine intelligence to human understanding, is exactly the kind of complexity I've been working on.
The surface
The Model
Every interface is a surface on top of a system.
The screen is the visible tip. The decisions that make a product trustworthy live underneath it — in the flows, the mental models, the constraints. That's where I start.
Screen / Interface
What the user sees
User mental models & needs
01
Interaction & info architecture
02
Data flows & system states
03
Organizational constraints
04
↓ Where the design decisions live
A
Systems before screens
Every interface is a surface on top of a system. Understanding the data flows, mental models, and organizational constraints is what separates design that scales from design that just looks good in a mockup.
B
Strategy and execution
I connect design decisions to business outcomes — being in the room when strategy is set, moving between the 30,000-foot view and the pixel-level detail without losing either.
C
Trustworthy by design
The best technology earns trust before it demands it. Whether a safety-critical AI product or a fitness-watch home screen, I start with: what does this person need to feel confident taking action?
Education
B.S. Symbolic Systems
Stanford University • Concentration in HCI
Based In
San Diego, CA
Available for remote
Currently
Senior Product Designer
Lytx, Inc.
Open To
Principal / Lead / Senior roles
San Diego or remote
07
Contact
Have a project in mind?
I'm open to new full-time opportunities, collaborations, and interesting conversations.
Phone
Daniel Rivas · UX Strategy & Product Design
© 2026