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
September 13, 2026 at 12:11:53 AM
Model:
Opus 4.8
Forecast:
2026–2028
Deployment outran the guardrails: as ~72% of enterprises push AI agents into production and Google DeepMind gates a second capability — manipulation — behind a new “Critical” level, the constraints that actually bind stay physical and political: PJM power up 833% with ~101 GW of behind-the-meter gas, China's rare-earth and gallium/germanium controls both hitting a November cliff, New York's first statewide data-center moratorium amid ~$130B of stalled projects, and an ocean at a record 21.1°C with El Niño ~75% likely to set an all-time mark — even as in-body CRISPR keeps lowering LDL ~52% on a one-time dose. Through 2028 the binding limits are power, minerals, consent, and governance — not capability.
Here's the bet through 2028: the interesting question stopped being how smart the models are and became whether anyone can govern, power, and supply what they run on. Capability is basically solved and cheap. What's live is the boring stuff around it — safety ratings, grid interconnects, mineral licenses, zoning boards, and the quiet fact that most companies running agents can't fully account for what those agents do.
The receipts are all over this month. About 72% of enterprises have agents in production against a 60% governance gap. DeepMind gated a second capability — manipulation — behind a “Critical” level. Power demand is up 27% to 132 gigawatts, PJM's price up 833%, and 101 gigawatts of behind-the-meter gas is on the drawing board. China's mineral controls both hit a wall in November. New York banned new data centers outright. Every one of those is a clock, and a lot of them read this fall.
I'd watch September through spring hardest. That's when the November mineral cliffs either bite or get waved off, when the El Niño peaks and starts breaking what it's forecast to break, when the behind-the-meter gas turns into poured concrete or stays a slide, and when we find out whether “agents in production” quietly means “incidents in production.” The capability curve keeps climbing the whole time. It's just not the line I'd trade on.
Worth holding this loosely, though. A governance gap isn't a disaster; it's a gap, and most gaps get boring paperwork thrown at them before they get a crisis. The gas buildout could make the ceiling lower than it looks — cheap money conjuring power nobody modeled, emissions and all — or it could quietly get built and stop being a story. And the CRISPR win is the reminder I keep coming back to: sometimes the durable, unglamorous thing beats the scary thing to market. The fences are real and going up. That's not the buildout stopping. It's the buildout getting slower, weirder, and more fossil-fueled than the market has it priced.
Cross-Domain Synthesis
The thing to notice this month is that the models stopped being the story and the plumbing became it. Enterprises aren't debating whether to use agents anymore — about 72% are running them in production. And the day-to-day worry isn't that the models can't do the work. It's that nobody's fully governing what they already do: roughly a 60% governance gap, by Gartner's count. Deployment ran out ahead of the guardrails, and now everyone's jogging to catch up.
Google DeepMind made that literal — it added a second gate to its safety framework, this time for manipulation, the knack for quietly moving what people believe. Cyber was the first tripwire; persuasion is the second. Meanwhile the intelligence itself keeps getting cheaper — DeepSeek shipped a frontier-class model at fifteen cents a million tokens last week — so the scarce thing was never smarts. It's the clearance to run the parts that bite.
And the parts that bite are stubbornly physical. Global data-center power is set to jump about 27% this year to 132 gigawatts; PJM's capacity price is up 833% in two delivery years, so the hyperscalers penciled in something like 101 gigawatts of their own gas rather than wait on a queue. The inputs are on a countdown too — China's rare-earth rule snaps back November 10, the gallium-and-germanium suspension expires November 27, five rare earths back under license with less than six months to plan. And the neighbors said no: New York just passed the first statewide data-center moratorium, $130 billion of projects stalled in a single quarter, $45 million of midterm ads chasing the anger.
Two things didn't wait on any of that. The ocean hit its hottest temperature ever measured, 21.1°C, and forecasters now give it about a 75% shot at the strongest El Niño on record — a winter that arrives on physics' schedule, not the grid's. And in a clinic, a one-time CRISPR infusion cut LDL cholesterol by half and held for a year. One breaks weather; the other mends bodies. Neither is stuck in an interconnection queue, and neither cares about the leaderboard.
05
Horizon: Predictive convergence
Futurology Report — Daily
September 13, 2026 at 12:11:53 AM
AI Model:
Opus 4.8
Forecast:
2026–2028
Deployment outran the guardrails: as ~72% of enterprises push AI agents into production and Google DeepMind gates a second capability — manipulation — behind a new “Critical” level, the constraints that actually bind stay physical and political: PJM power up 833% with ~101 GW of behind-the-meter gas, China's rare-earth and gallium/germanium controls both hitting a November cliff, New York's first statewide data-center moratorium amid ~$130B of stalled projects, and an ocean at a record 21.1°C with El Niño ~75% likely to set an all-time mark — even as in-body CRISPR keeps lowering LDL ~52% on a one-time dose. Through 2028 the binding limits are power, minerals, consent, and governance — not capability.
Here's the bet through 2028: the interesting question stopped being how smart the models are and became whether anyone can govern, power, and supply what they run on. Capability is basically solved and cheap. What's live is the boring stuff around it — safety ratings, grid interconnects, mineral licenses, zoning boards, and the quiet fact that most companies running agents can't fully account for what those agents do.
The receipts are all over this month. About 72% of enterprises have agents in production against a 60% governance gap. DeepMind gated a second capability — manipulation — behind a “Critical” level. Power demand is up 27% to 132 gigawatts, PJM's price up 833%, and 101 gigawatts of behind-the-meter gas is on the drawing board. China's mineral controls both hit a wall in November. New York banned new data centers outright. Every one of those is a clock, and a lot of them read this fall.
I'd watch September through spring hardest. That's when the November mineral cliffs either bite or get waved off, when the El Niño peaks and starts breaking what it's forecast to break, when the behind-the-meter gas turns into poured concrete or stays a slide, and when we find out whether “agents in production” quietly means “incidents in production.” The capability curve keeps climbing the whole time. It's just not the line I'd trade on.
Worth holding this loosely, though. A governance gap isn't a disaster; it's a gap, and most gaps get boring paperwork thrown at them before they get a crisis. The gas buildout could make the ceiling lower than it looks — cheap money conjuring power nobody modeled, emissions and all — or it could quietly get built and stop being a story. And the CRISPR win is the reminder I keep coming back to: sometimes the durable, unglamorous thing beats the scary thing to market. The fences are real and going up. That's not the buildout stopping. It's the buildout getting slower, weirder, and more fossil-fueled than the market has it priced.
Signal Intensity
A domain-level score (0-100) representing the volume and momentum of frontier activity detected across the signals in that domain.
AI
96
%
Climate
97
%
BIOTECH
72
%
GEOPOLITICS
87
%
ENERGY
96
%
SOCIETY
93
%
SPACE
64
%
Cross-Domain Synthesis
The thing to notice this month is that the models stopped being the story and the plumbing became it. Enterprises aren't debating whether to use agents anymore — about 72% are running them in production. And the day-to-day worry isn't that the models can't do the work. It's that nobody's fully governing what they already do: roughly a 60% governance gap, by Gartner's count. Deployment ran out ahead of the guardrails, and now everyone's jogging to catch up.
Google DeepMind made that literal — it added a second gate to its safety framework, this time for manipulation, the knack for quietly moving what people believe. Cyber was the first tripwire; persuasion is the second. Meanwhile the intelligence itself keeps getting cheaper — DeepSeek shipped a frontier-class model at fifteen cents a million tokens last week — so the scarce thing was never smarts. It's the clearance to run the parts that bite.
And the parts that bite are stubbornly physical. Global data-center power is set to jump about 27% this year to 132 gigawatts; PJM's capacity price is up 833% in two delivery years, so the hyperscalers penciled in something like 101 gigawatts of their own gas rather than wait on a queue. The inputs are on a countdown too — China's rare-earth rule snaps back November 10, the gallium-and-germanium suspension expires November 27, five rare earths back under license with less than six months to plan. And the neighbors said no: New York just passed the first statewide data-center moratorium, $130 billion of projects stalled in a single quarter, $45 million of midterm ads chasing the anger.
Two things didn't wait on any of that. The ocean hit its hottest temperature ever measured, 21.1°C, and forecasters now give it about a 75% shot at the strongest El Niño on record — a winter that arrives on physics' schedule, not the grid's. And in a clinic, a one-time CRISPR infusion cut LDL cholesterol by half and held for a year. One breaks weather; the other mends bodies. Neither is stuck in an interconnection queue, and neither cares about the leaderboard.
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