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 28, 2026 at 12:11:42 AM
Model:
Opus 4.8
Forecast:
2026–2030
The AI buildout's limits stop being forecasts and become paperwork: FERC's August 17 deadline forced PJM to file who-pays rules and a 250-MW bar, China slowed germanium and rare-earth magnets into Taiwan, and 75% of voters now oppose local data centers — yet NVIDIA posted $96.2B, guided to $108B and forecast ~70% FY2028 growth while NVIDIA and SpaceX pointed the buildout at orbit. Through 2030 the AI story turns on power, minerals and consent, not the model.
Here's the bet for the rest of the decade. AI's trajectory doesn't get settled by which model wins a benchmark. It gets settled by power, minerals, and consent — the three things that don't move on a product calendar — and this week all three showed up in writing.
The evidence isn't a hot take. It's a FERC deadline, a PJM filing, a 250-megawatt bar, and Bloomberg saying two-thirds of the requested power never gets built. It's China slowing magnets into Taiwan without touching the law. It's 75% of voters against the data center down the road, and Anthropic putting that in an IPO risk factor. The constraints stopped being forecasts and became documents.
I'd watch 2027. That's when PJM's bring-your-own-power rule actually bites, when the first orbital-data-center prototypes either fly or slip, and when the gap between announced data centers and built ones stops being a spreadsheet and starts being cancellations. The capability keeps improving the whole time. It just won't matter much if it can't switch on, source its magnets, or get a permit.
Worth holding loosely, though. Money is very good at finding power — behind-the-meter gas, restarted reactors, deals nobody's modeled yet. And orbital data centers are a great headline and an unproven business; 250 kilowatts in space is one server rack, not a campus. The constraints being written down is the right thing to watch. It isn't the same as the buildout stopping. It might just be the buildout getting weirder — and more expensive.
Cross-Domain Synthesis
For about a year the limit on AI was a forecast. Someday the grid runs out, someday the neighbors push back, someday the minerals get tight. This week the somedays stopped being someday and turned into paperwork.
FERC set an August 17 deadline and PJM answered it — a filed framework for how data centers connect, who pays for it, and a 250-megawatt bar just to get in the fast lane. Bloomberg's math says two-thirds of the power the industry asked for never gets built. The ceiling isn't a slide in a deck anymore. It's a tariff filing.
The other constraints filed their own paperwork. China quietly slowed germanium and rare-earth magnets into Taiwan, license by license, without changing a single law. Anthropic is about to name public backlash as a formal risk in a two-trillion-dollar IPO, because 75% of voters now say not in my town — up from 42% a year ago. And out in the Pacific, a 90%-plus 'super' El Nino keeps loading heat that doesn't read prospectuses.
Meanwhile the stuff off to the side just kept working. A single CRISPR infusion still holding someone's cholesterol down, presented in Munich today. Starship stacking for its first real orbital shot. None of it waiting on an interconnection queue.
Here's the thing, though: the constraint got legible and the money guided up anyway. NVIDIA posted $96 billion and told everyone to expect 70% more next year. And when the grid says no, the answer this month was to point the data centers at orbit. That's either the most confident bet in tech or the clearest sign the cheap-power era is over. Probably both.
05
Horizon: Predictive convergence
Futurology Report — Daily
August 28, 2026 at 12:11:42 AM
AI Model:
Opus 4.8
Forecast:
2026–2030
The AI buildout's limits stop being forecasts and become paperwork: FERC's August 17 deadline forced PJM to file who-pays rules and a 250-MW bar, China slowed germanium and rare-earth magnets into Taiwan, and 75% of voters now oppose local data centers — yet NVIDIA posted $96.2B, guided to $108B and forecast ~70% FY2028 growth while NVIDIA and SpaceX pointed the buildout at orbit. Through 2030 the AI story turns on power, minerals and consent, not the model.
Here's the bet for the rest of the decade. AI's trajectory doesn't get settled by which model wins a benchmark. It gets settled by power, minerals, and consent — the three things that don't move on a product calendar — and this week all three showed up in writing.
The evidence isn't a hot take. It's a FERC deadline, a PJM filing, a 250-megawatt bar, and Bloomberg saying two-thirds of the requested power never gets built. It's China slowing magnets into Taiwan without touching the law. It's 75% of voters against the data center down the road, and Anthropic putting that in an IPO risk factor. The constraints stopped being forecasts and became documents.
I'd watch 2027. That's when PJM's bring-your-own-power rule actually bites, when the first orbital-data-center prototypes either fly or slip, and when the gap between announced data centers and built ones stops being a spreadsheet and starts being cancellations. The capability keeps improving the whole time. It just won't matter much if it can't switch on, source its magnets, or get a permit.
Worth holding loosely, though. Money is very good at finding power — behind-the-meter gas, restarted reactors, deals nobody's modeled yet. And orbital data centers are a great headline and an unproven business; 250 kilowatts in space is one server rack, not a campus. The constraints being written down is the right thing to watch. It isn't the same as the buildout stopping. It might just be the buildout getting weirder — and more expensive.
Signal Intensity
A domain-level score (0-100) representing the volume and momentum of frontier activity detected across the signals in that domain.
AI
98
%
Climate
93
%
BIOTECH
66
%
GEOPOLITICS
84
%
ENERGY
94
%
SOCIETY
82
%
SPACE
84
%
Cross-Domain Synthesis
For about a year the limit on AI was a forecast. Someday the grid runs out, someday the neighbors push back, someday the minerals get tight. This week the somedays stopped being someday and turned into paperwork.
FERC set an August 17 deadline and PJM answered it — a filed framework for how data centers connect, who pays for it, and a 250-megawatt bar just to get in the fast lane. Bloomberg's math says two-thirds of the power the industry asked for never gets built. The ceiling isn't a slide in a deck anymore. It's a tariff filing.
The other constraints filed their own paperwork. China quietly slowed germanium and rare-earth magnets into Taiwan, license by license, without changing a single law. Anthropic is about to name public backlash as a formal risk in a two-trillion-dollar IPO, because 75% of voters now say not in my town — up from 42% a year ago. And out in the Pacific, a 90%-plus 'super' El Nino keeps loading heat that doesn't read prospectuses.
Meanwhile the stuff off to the side just kept working. A single CRISPR infusion still holding someone's cholesterol down, presented in Munich today. Starship stacking for its first real orbital shot. None of it waiting on an interconnection queue.
Here's the thing, though: the constraint got legible and the money guided up anyway. NVIDIA posted $96 billion and told everyone to expect 70% more next year. And when the grid says no, the answer this month was to point the data centers at orbit. That's either the most confident bet in tech or the clearest sign the cheap-power era is over. Probably both.
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