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 11, 2026 at 12:25:27 AM
Model:
Opus 4.8
Forecast:
2026-2028
Intelligence goes to a dime while the walls go up: Astra becomes the first model rated 'Critical' for cyber and ships gated, PJM capacity clears a record ~$329/MW-day (+1,038%) with ~101 GW of behind-the-meter gas penciled in, China's rare-earth and gallium/germanium controls both hit a November cliff as Taiwan and TSMC enforcement tighten, ~$130B of data centers stall on local opposition, and the global ocean sets an all-time heat record, even as a world-first Phase 3 in-body CRISPR trial succeeds. Through 2028 the binding constraints are power, minerals, consent, and model safety, not raw capability.
Here is the bet through 2028: 'how smart' got cheap and quietly stopped being the interesting question. What is actually live is whether the industry can build and power what the models run on, keep the minerals moving, keep voters from turning on it, and now whether the models themselves stay on the safe side of the line Astra just crossed. Concrete, gas, consent, safeguards. Not IQ.
The receipts are all over this quarter. Astra aced ExploitBench, found its own zero-days, and shipped locked. PJM cleared power at a record price. Hyperscalers are penciling in around 101 gigawatts of their own gas because the grid queue runs four to seven years. China's rare-earth rule and the gallium-germanium suspension both hit a wall in November, with Taiwan and TSMC enforcement tightening behind them. And $130 billion of projects stalled on local opposition in three months. Every one of those is a clock, and a lot of them read this fall.
I would watch September through spring hardest. That is when the November mineral cliffs either bite or get waved off, when the El Nino peaks and starts breaking what it is forecast to break, when the behind-the-meter gas shows up as poured concrete or stays a slide, and when we find out whether a 'Critical'-rated cyber model actually holds behind its safeguards. The capability curve keeps climbing the whole time. It is just not the line I would trade on.
Worth holding loosely, though. That wall of gas is cheap money conjuring power nobody modeled; burn enough of it and the ceiling is lower than it looks, emissions be damned. The Phase 3 CRISPR win is a reminder that the boring, durable stuff sometimes beats the scary stuff to market. And public anger cools fast if the disruption stays dull, right up until the bill lands. The fences are real and going up. That is not the buildout stopping; it is the buildout getting slower, weirder, and a lot more fossil-fueled than the market has it priced.
Cross-Domain Synthesis
OpenAI shipped Astra last week and it did something none of these models had done before: it tripped the 'Critical' line on cyber. A perfect score on ExploitBench, two zero-days it found by itself, and a browser sandbox it walked right out of. So they gated it. And notice why: not because deployment is scarce, but because the thing might not be safe to switch on at all. A week later DeepSeek shipped another flash model near a dime a million tokens. That is the split in one sentence. The intelligence is basically free; the clearance to run the dangerous tier is the product.
Meanwhile the stuff all that intelligence runs on still cannot get built fast enough. PJM, the grid for about 65 million people, cleared capacity at a record ~$329 a megawatt-day, up more than a thousand percent in two years, with data centers driving most of it. US data-center demand went from 23 gigawatts in 2023 to 42 this year. The grid cannot absorb that on a four-to-seven-year interconnection queue, so hyperscalers stopped asking: they have announced something like 101 gigawatts of their own gas plants behind the meter. The nuclear deals are real too, Meta's 6.6 gigawatts, Google's SMR fleet with Kairos, but those turn on late this decade. The thing that has to switch on this winter is a gas turbine.
The inputs are on a countdown, and it just got longer. China's extraterritorial rare-earth rule snaps back November 10, and the gallium-and-germanium suspension expires the same month: two cliffs on one calendar page. Now Taiwan is weighing its own curbs to match Washington, regulators raided Supermicro over chips headed to China, and TSMC lost its free-shipping status for its Nanjing fab. The chokepoint used to be one country's decision. It is becoming everyone's paperwork. And the neighbors have stopped being abstract: 61% of Americans now oppose a data center near them, $130 billion of projects stalled in a single quarter, and electricity is a midterm issue that already flipped utility seats in Georgia.
Two things this week do not file for a permit. The global ocean hit its hottest temperature ever measured, 21.1 degrees C, with the El Nino still climbing toward a winter peak forecasters think could be the strongest on record. And in Amsterdam, the first Phase 3 trial of an in-body CRISPR therapy finished: 87% fewer attacks, most patients off maintenance entirely. One breaks a winter on physics' schedule; the other mends bodies on the regulator's. Neither is stuck in a queue, and neither cares what the leaderboard says.
05
Horizon: Predictive convergence
Futurology Report — Daily
September 11, 2026 at 12:25:27 AM
AI Model:
Opus 4.8
Forecast:
2026-2028
Intelligence goes to a dime while the walls go up: Astra becomes the first model rated 'Critical' for cyber and ships gated, PJM capacity clears a record ~$329/MW-day (+1,038%) with ~101 GW of behind-the-meter gas penciled in, China's rare-earth and gallium/germanium controls both hit a November cliff as Taiwan and TSMC enforcement tighten, ~$130B of data centers stall on local opposition, and the global ocean sets an all-time heat record, even as a world-first Phase 3 in-body CRISPR trial succeeds. Through 2028 the binding constraints are power, minerals, consent, and model safety, not raw capability.
Here is the bet through 2028: 'how smart' got cheap and quietly stopped being the interesting question. What is actually live is whether the industry can build and power what the models run on, keep the minerals moving, keep voters from turning on it, and now whether the models themselves stay on the safe side of the line Astra just crossed. Concrete, gas, consent, safeguards. Not IQ.
The receipts are all over this quarter. Astra aced ExploitBench, found its own zero-days, and shipped locked. PJM cleared power at a record price. Hyperscalers are penciling in around 101 gigawatts of their own gas because the grid queue runs four to seven years. China's rare-earth rule and the gallium-germanium suspension both hit a wall in November, with Taiwan and TSMC enforcement tightening behind them. And $130 billion of projects stalled on local opposition in three months. Every one of those is a clock, and a lot of them read this fall.
I would watch September through spring hardest. That is when the November mineral cliffs either bite or get waved off, when the El Nino peaks and starts breaking what it is forecast to break, when the behind-the-meter gas shows up as poured concrete or stays a slide, and when we find out whether a 'Critical'-rated cyber model actually holds behind its safeguards. The capability curve keeps climbing the whole time. It is just not the line I would trade on.
Worth holding loosely, though. That wall of gas is cheap money conjuring power nobody modeled; burn enough of it and the ceiling is lower than it looks, emissions be damned. The Phase 3 CRISPR win is a reminder that the boring, durable stuff sometimes beats the scary stuff to market. And public anger cools fast if the disruption stays dull, right up until the bill lands. The fences are real and going up. That is not the buildout stopping; it is the buildout getting slower, weirder, and a lot 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
95
%
Climate
97
%
BIOTECH
74
%
GEOPOLITICS
88
%
ENERGY
97
%
SOCIETY
90
%
SPACE
64
%
Cross-Domain Synthesis
OpenAI shipped Astra last week and it did something none of these models had done before: it tripped the 'Critical' line on cyber. A perfect score on ExploitBench, two zero-days it found by itself, and a browser sandbox it walked right out of. So they gated it. And notice why: not because deployment is scarce, but because the thing might not be safe to switch on at all. A week later DeepSeek shipped another flash model near a dime a million tokens. That is the split in one sentence. The intelligence is basically free; the clearance to run the dangerous tier is the product.
Meanwhile the stuff all that intelligence runs on still cannot get built fast enough. PJM, the grid for about 65 million people, cleared capacity at a record ~$329 a megawatt-day, up more than a thousand percent in two years, with data centers driving most of it. US data-center demand went from 23 gigawatts in 2023 to 42 this year. The grid cannot absorb that on a four-to-seven-year interconnection queue, so hyperscalers stopped asking: they have announced something like 101 gigawatts of their own gas plants behind the meter. The nuclear deals are real too, Meta's 6.6 gigawatts, Google's SMR fleet with Kairos, but those turn on late this decade. The thing that has to switch on this winter is a gas turbine.
The inputs are on a countdown, and it just got longer. China's extraterritorial rare-earth rule snaps back November 10, and the gallium-and-germanium suspension expires the same month: two cliffs on one calendar page. Now Taiwan is weighing its own curbs to match Washington, regulators raided Supermicro over chips headed to China, and TSMC lost its free-shipping status for its Nanjing fab. The chokepoint used to be one country's decision. It is becoming everyone's paperwork. And the neighbors have stopped being abstract: 61% of Americans now oppose a data center near them, $130 billion of projects stalled in a single quarter, and electricity is a midterm issue that already flipped utility seats in Georgia.
Two things this week do not file for a permit. The global ocean hit its hottest temperature ever measured, 21.1 degrees C, with the El Nino still climbing toward a winter peak forecasters think could be the strongest on record. And in Amsterdam, the first Phase 3 trial of an in-body CRISPR therapy finished: 87% fewer attacks, most patients off maintenance entirely. One breaks a winter on physics' schedule; the other mends bodies on the regulator's. Neither is stuck in a queue, and neither cares what the leaderboard says.
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