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 8, 2026 at 1:38:42 AM
Model:
Opus 4.8
Forecast:
2026–2029
Cheap Minds, Costly Permission: The Constraint Moves Downstream
Here's the bet. Over 2026–2029 the thing that decides who wins stops being who has the smartest model and becomes who can actually get permission to deploy — grid interconnects, export licenses, regulatory clearances, launch slots. Intelligence becomes a commodity input. The scarce thing is the right to plug it into the physical world.
The evidence is already stacked up. Capable models run on laptops while the frontier ones hide behind access gates. US data-center demand is climbing past 300 TWh a year and the response is off-grid gas in Texas, because the grid queue is a half-decade long. China's rare-earth leverage is paused on a timer that expires November 10. CRISPR works inside the body now, so the conversation moved to the FDA. None of these are capability problems. They're all permission-and-delivery problems.
I'd watch November 10, 2026 as the first real hinge — if the rare-earth truce lapses, the "materials are the chokepoint" thesis gets tested in public, fast. After that, watch whoever first clears a grid interconnect in under two years, or lands an in-vivo editor on the market. Those are the moments the bottleneck visibly breaks, and whoever does it first sets the template everyone copies. I'd expect the winners of this stretch to look less like labs and more like operators who are good at paperwork, permitting, and supply chains. Unglamorous, but that's where the scarcity is.
Worth holding this loosely, though. "Permission is the new bottleneck" is the kind of tidy thesis that flatters whatever you already believed about regulation. The gates aren't uniform — some are genuinely protective, some are just friction that reform will clear, and I can't always tell which from here. And capability could still lurch forward hard enough to make a lot of this moot; a model good enough to design around a shortage changes the math. So: directionally I think the constraint really has moved downstream. I'm less sure how long it stays there.
Cross-Domain Synthesis
For about three years the story was "can the machine do it." That question's mostly answered now. A capable model streams off an SSD on a laptop you already own, and the price of a smart token keeps sliding. So the interesting part isn't capability anymore. It's everything standing between a capability and a thing that actually happens — the hookup, the license, the launch window, the regulatory slot. The bottleneck moved downstream, and downstream is slow and political.
You can see it in every layer. In compute, the models get cheaper while the best ones get rationed behind vetted-access gates. In energy, hyperscalers have signed almost 10 GW of nuclear and only about 2 is actually running — the reactor isn't the problem, the five-to-seven-year wait to plug into the grid is. In materials, China can turn a trace of rare-earth magnet into a permission slip, and the pause on that only runs to November. In biotech the editing chemistry works — CTX310 knocked LDL down by half and held — so the gate becomes the regulator, not the enzyme.
Same shape in the softer domains. In space, Artemis II flew people around the Moon on schedule, but the landing slipped because the hardware to actually set down isn't ready. In society, the law stopped chasing the kid making the deepfake and started leaning on the platform that hosts it. Even climate fits: the models now say the Atlantic current could slow by about half, but the clean proof doesn't arrive until 2033, so we're being asked to act on a signal before it's airtight. The capability is there. The permission to act on it lags.
The honest counter-current is that "permission is the bottleneck" can be a comfortable story for people who'd rather not build anything. Some of these gates are load-bearing — you do want a regulator between a one-shot gene edit and a waiting room. And a few of them will quietly fall: grid queues can be reformed, export truces can hold, launch cadences can surprise you. So this isn't "everything's stuck." It's that the center of gravity moved from the lab to the permitting office, and that's a different game than the one most of these fields trained for.
05
Horizon: Predictive convergence
Futurology Report — Daily
October 8, 2026 at 1:38:42 AM
AI Model:
Opus 4.8
Forecast:
2026–2029
Cheap Minds, Costly Permission: The Constraint Moves Downstream
Here's the bet. Over 2026–2029 the thing that decides who wins stops being who has the smartest model and becomes who can actually get permission to deploy — grid interconnects, export licenses, regulatory clearances, launch slots. Intelligence becomes a commodity input. The scarce thing is the right to plug it into the physical world.
The evidence is already stacked up. Capable models run on laptops while the frontier ones hide behind access gates. US data-center demand is climbing past 300 TWh a year and the response is off-grid gas in Texas, because the grid queue is a half-decade long. China's rare-earth leverage is paused on a timer that expires November 10. CRISPR works inside the body now, so the conversation moved to the FDA. None of these are capability problems. They're all permission-and-delivery problems.
I'd watch November 10, 2026 as the first real hinge — if the rare-earth truce lapses, the "materials are the chokepoint" thesis gets tested in public, fast. After that, watch whoever first clears a grid interconnect in under two years, or lands an in-vivo editor on the market. Those are the moments the bottleneck visibly breaks, and whoever does it first sets the template everyone copies. I'd expect the winners of this stretch to look less like labs and more like operators who are good at paperwork, permitting, and supply chains. Unglamorous, but that's where the scarcity is.
Worth holding this loosely, though. "Permission is the new bottleneck" is the kind of tidy thesis that flatters whatever you already believed about regulation. The gates aren't uniform — some are genuinely protective, some are just friction that reform will clear, and I can't always tell which from here. And capability could still lurch forward hard enough to make a lot of this moot; a model good enough to design around a shortage changes the math. So: directionally I think the constraint really has moved downstream. I'm less sure how long it stays there.
Signal Intensity
A domain-level score (0-100) representing the volume and momentum of frontier activity detected across the signals in that domain.
AI
92
%
Climate
71
%
BIOTECH
76
%
GEOPOLITICS
86
%
ENERGY
88
%
SOCIETY
78
%
SPACE
66
%
Cross-Domain Synthesis
For about three years the story was "can the machine do it." That question's mostly answered now. A capable model streams off an SSD on a laptop you already own, and the price of a smart token keeps sliding. So the interesting part isn't capability anymore. It's everything standing between a capability and a thing that actually happens — the hookup, the license, the launch window, the regulatory slot. The bottleneck moved downstream, and downstream is slow and political.
You can see it in every layer. In compute, the models get cheaper while the best ones get rationed behind vetted-access gates. In energy, hyperscalers have signed almost 10 GW of nuclear and only about 2 is actually running — the reactor isn't the problem, the five-to-seven-year wait to plug into the grid is. In materials, China can turn a trace of rare-earth magnet into a permission slip, and the pause on that only runs to November. In biotech the editing chemistry works — CTX310 knocked LDL down by half and held — so the gate becomes the regulator, not the enzyme.
Same shape in the softer domains. In space, Artemis II flew people around the Moon on schedule, but the landing slipped because the hardware to actually set down isn't ready. In society, the law stopped chasing the kid making the deepfake and started leaning on the platform that hosts it. Even climate fits: the models now say the Atlantic current could slow by about half, but the clean proof doesn't arrive until 2033, so we're being asked to act on a signal before it's airtight. The capability is there. The permission to act on it lags.
The honest counter-current is that "permission is the bottleneck" can be a comfortable story for people who'd rather not build anything. Some of these gates are load-bearing — you do want a regulator between a one-shot gene edit and a waiting room. And a few of them will quietly fall: grid queues can be reformed, export truces can hold, launch cadences can surprise you. So this isn't "everything's stuck." It's that the center of gravity moved from the lab to the permitting office, and that's a different game than the one most of these fields trained for.
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