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
22
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
August 31, 2026 at 12:08:47 AM
Model:
Opus 4.8
Forecast:
2026–2030
Intelligence goes cheap while its supply chain goes scarce: GPT-5.6 Sol falls >20% to $4/$20 and Gemini 3.7 Flash lands at $0.75 as PJM's ~60-GW shortfall, China's still-armed rare-earth licensing, a ~90%-likely record El Niño and 60–75% public opposition to data centers harden the physical and political limits — while hyperscalers stack 9.8 GW of nuclear and SpaceX's orbital data centers slip to a 2027 test. Through 2030 the AI story turns on power, minerals, consent and trust, not the model.
Here's the bet for the rest of the decade: the AI race won't be won by the best model. Models are already cheap and crowded. It'll be won by whoever can actually plug the thing in — get the power, hold the minerals, keep enough goodwill to build. Capability is close to solved. Everything downstream of it isn't.
The receipts all landed in one month. GPT-5.6 Sol down more than twenty percent. Gemini 3.7 Flash at seventy-five cents. PJM drafting curtailment rules while it's sixty gigawatts short. China's rare-earth licensing standing, teeth set for November. Sixty-plus percent of the public against the next data center, across both parties. Every one of those hardened in the same stretch the model got cheaper. The moat moved to the wall socket.
I'd watch 2027. That's when PJM's bring-your-own-power rules actually bite, when the first restarted reactors are supposed to feed the grid, when China decides whether to switch the extraterritorial controls all the way on, and when SpaceX's orbital data centers either fly a test or slip again. The capability curve keeps climbing the whole time. It just stops being the thing that picks the winner.
Worth holding this loosely, though. Cheap money has a long history of finding power it wasn't supposed to — behind-the-meter gas, restarted reactors, deals nobody's modeled yet. The job panic could fade if the displacement stays boring, and so far it mostly has. And orbital data centers are still a gorgeous render and an unproven business. The constraints are real and getting written down. That isn't the same as the buildout stopping — it's the buildout getting slower, weirder, and more expensive than the market is pricing.
Cross-Domain Synthesis
The bet all year was that intelligence would be the scarce thing. August kept saying the opposite, and saying it in numbers.
OpenAI cut GPT-5.6 Sol by more than twenty percent, to $4 in and $20 out. Google shipped Gemini 3.7 Flash at seventy-five cents. DeepSeek's V4 Flash is down near a quarter, Alibaba pushed out yet another Qwen, and Anthropic quietly cancelled a Sonnet 5 price hike it had already put on the calendar. Everybody's model got cheaper at once. That's not a moat. That's a commodity.
Meanwhile the stuff the model has to run on got harder to get. PJM is short about sixty gigawatts and is writing rules to curtail data centers that show up without their own power. The answer this month was reactors — 9.8 gigawatts of nuclear deals on the books, Three Mile Island coming back online. China left its rare-earth licensing machine fully built and just slid the extraterritorial teeth to November. And the public quit being polite: depending on the poll, sixty to seventy-five percent of Americans don't want a data center near them, and it's the rare thing both parties agree on.
Two things off in the corner don't care about any of that. The Pacific is loading what NOAA gives ninety percent odds of being a record El Niño, the ocean just hit its hottest temperature ever measured — in August, when it's supposed to be cooling — and coral may be at the point of no return. None of that files for an interconnection. And in a clinic, Intellia posted the first winning Phase 3 for gene editing done inside the body, which is the part that always used to stall.
So here's the shape of it. Intelligence is cheap and getting cheaper. Everything it needs to actually switch on — power, minerals, public patience, a livable planet — is scarce and contested. SpaceX's answer is to fly the whole thing to orbit, and even that's a 2027 test at the earliest. The gap between the easy part and the hard parts is the story for the rest of the decade.
05
Horizon: Predictive convergence
Futurology Report — Daily
August 31, 2026 at 12:08:47 AM
AI Model:
Opus 4.8
Forecast:
2026–2030
Intelligence goes cheap while its supply chain goes scarce: GPT-5.6 Sol falls >20% to $4/$20 and Gemini 3.7 Flash lands at $0.75 as PJM's ~60-GW shortfall, China's still-armed rare-earth licensing, a ~90%-likely record El Niño and 60–75% public opposition to data centers harden the physical and political limits — while hyperscalers stack 9.8 GW of nuclear and SpaceX's orbital data centers slip to a 2027 test. Through 2030 the AI story turns on power, minerals, consent and trust, not the model.
Here's the bet for the rest of the decade: the AI race won't be won by the best model. Models are already cheap and crowded. It'll be won by whoever can actually plug the thing in — get the power, hold the minerals, keep enough goodwill to build. Capability is close to solved. Everything downstream of it isn't.
The receipts all landed in one month. GPT-5.6 Sol down more than twenty percent. Gemini 3.7 Flash at seventy-five cents. PJM drafting curtailment rules while it's sixty gigawatts short. China's rare-earth licensing standing, teeth set for November. Sixty-plus percent of the public against the next data center, across both parties. Every one of those hardened in the same stretch the model got cheaper. The moat moved to the wall socket.
I'd watch 2027. That's when PJM's bring-your-own-power rules actually bite, when the first restarted reactors are supposed to feed the grid, when China decides whether to switch the extraterritorial controls all the way on, and when SpaceX's orbital data centers either fly a test or slip again. The capability curve keeps climbing the whole time. It just stops being the thing that picks the winner.
Worth holding this loosely, though. Cheap money has a long history of finding power it wasn't supposed to — behind-the-meter gas, restarted reactors, deals nobody's modeled yet. The job panic could fade if the displacement stays boring, and so far it mostly has. And orbital data centers are still a gorgeous render and an unproven business. The constraints are real and getting written down. That isn't the same as the buildout stopping — it's the buildout getting slower, weirder, and more expensive than the market is pricing.
Signal Intensity
A domain-level score (0-100) representing the volume and momentum of frontier activity detected across the signals in that domain.
AI
97
%
Climate
96
%
BIOTECH
68
%
GEOPOLITICS
82
%
ENERGY
96
%
SOCIETY
90
%
SPACE
73
%
Cross-Domain Synthesis
The bet all year was that intelligence would be the scarce thing. August kept saying the opposite, and saying it in numbers.
OpenAI cut GPT-5.6 Sol by more than twenty percent, to $4 in and $20 out. Google shipped Gemini 3.7 Flash at seventy-five cents. DeepSeek's V4 Flash is down near a quarter, Alibaba pushed out yet another Qwen, and Anthropic quietly cancelled a Sonnet 5 price hike it had already put on the calendar. Everybody's model got cheaper at once. That's not a moat. That's a commodity.
Meanwhile the stuff the model has to run on got harder to get. PJM is short about sixty gigawatts and is writing rules to curtail data centers that show up without their own power. The answer this month was reactors — 9.8 gigawatts of nuclear deals on the books, Three Mile Island coming back online. China left its rare-earth licensing machine fully built and just slid the extraterritorial teeth to November. And the public quit being polite: depending on the poll, sixty to seventy-five percent of Americans don't want a data center near them, and it's the rare thing both parties agree on.
Two things off in the corner don't care about any of that. The Pacific is loading what NOAA gives ninety percent odds of being a record El Niño, the ocean just hit its hottest temperature ever measured — in August, when it's supposed to be cooling — and coral may be at the point of no return. None of that files for an interconnection. And in a clinic, Intellia posted the first winning Phase 3 for gene editing done inside the body, which is the part that always used to stall.
So here's the shape of it. Intelligence is cheap and getting cheaper. Everything it needs to actually switch on — power, minerals, public patience, a livable planet — is scarce and contested. SpaceX's answer is to fly the whole thing to orbit, and even that's a 2027 test at the earliest. The gap between the easy part and the hard parts is the story for the rest of the decade.
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
22 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