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 6, 2026 at 12:45:26 AM
Model:
Opus 4.8
Forecast:
2026–2029
As Intelligence Gets Cheap, Deployment Becomes the Chokepoint
Here's the bet. For the next few years the thing that separates the winners isn't a smarter model — everyone will have one, cheap. It's who's allowed to build and power the machine that runs it.
The evidence keeps pointing the same way. Model prices fell through the floor while data-center demand nearly outran the grid. Interconnection queues run years. Taiwan is tightening the chip spigot into actual criminal law. None of that gets fixed by a better training run — it gets fixed by concrete, copper, and permits, which move on a decade's clock, not a quarter's.
I'd expect 2027–2028 to look like a split screen: intelligence basically free and everywhere, deployment rationed by megawatts, permits, and politics. The teams and the countries that locked in power and chip access early will look prescient; everyone else learns what a five-year interconnection queue feels like. The constraint stops being 'can we build it' and becomes 'are we allowed to turn it on.'
Worth holding loosely, though. Efficiency has a nasty habit of eating demand forecasts — if models keep getting dramatically cheaper to run, the energy wall could soften faster than the grid pessimists think. A lot of the nuclear is still press releases with a 2032 date. And export controls have a long record of leaking. The honest read is that nobody's demand curve has been right yet, including mine.
Cross-Domain Synthesis
Here's the seam this week: the thinking got cheap and the right to build the thing that thinks got expensive. For a while the hard part of AI was the model. Now the model is nearly free and the hard part is a power contract, a permit, and an island full of fabs.
Run it down the stack. On the AI layer, open-weight frontier models from Chinese labs dragged API output prices under fifty cents a million tokens, and agents now clear about two-thirds of real-computer tasks — the capability isn't the scarce thing anymore. On the energy layer, US data-center demand is tracking toward 74 GW by 2028 against a grid that's short something like 45 to 49 GW, and the queue to plug in runs years. On the geopolitics layer, Taiwan moved to make smuggling AI chips to China an actual crime, and still roughly 70% of leading-edge silicon comes off that one island.
The slower domains keep their own time. In the body, Beam got an in-vivo base editor into the clinic and CRISPR's one-shot therapies are posting durable 50-to-73% cuts — real, compounding, unhurried. In the sky, the Moon landing slid again; Artemis III is now a docking demo and the boots-on-regolith mission waits on Starship proving it can refuel in orbit. And the planet keeps warming under a current that's the weakest it's been in sixteen centuries.
The counter-current is the obvious one: cheap intelligence doesn't deploy itself. The stuff that actually has to switch on — substations, reactors, fabs, interconnects — mostly didn't. So you get a widening gap. The digital half races toward free, the physical half waits in a line, and whoever already holds the power and the chips gets to decide who's next.
05
Horizon: Predictive convergence
Futurology Report — Daily
October 6, 2026 at 12:45:26 AM
AI Model:
Opus 4.8
Forecast:
2026–2029
As Intelligence Gets Cheap, Deployment Becomes the Chokepoint
Here's the bet. For the next few years the thing that separates the winners isn't a smarter model — everyone will have one, cheap. It's who's allowed to build and power the machine that runs it.
The evidence keeps pointing the same way. Model prices fell through the floor while data-center demand nearly outran the grid. Interconnection queues run years. Taiwan is tightening the chip spigot into actual criminal law. None of that gets fixed by a better training run — it gets fixed by concrete, copper, and permits, which move on a decade's clock, not a quarter's.
I'd expect 2027–2028 to look like a split screen: intelligence basically free and everywhere, deployment rationed by megawatts, permits, and politics. The teams and the countries that locked in power and chip access early will look prescient; everyone else learns what a five-year interconnection queue feels like. The constraint stops being 'can we build it' and becomes 'are we allowed to turn it on.'
Worth holding loosely, though. Efficiency has a nasty habit of eating demand forecasts — if models keep getting dramatically cheaper to run, the energy wall could soften faster than the grid pessimists think. A lot of the nuclear is still press releases with a 2032 date. And export controls have a long record of leaking. The honest read is that nobody's demand curve has been right yet, including mine.
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
73
%
BIOTECH
68
%
GEOPOLITICS
83
%
ENERGY
91
%
SOCIETY
81
%
SPACE
54
%
Cross-Domain Synthesis
Here's the seam this week: the thinking got cheap and the right to build the thing that thinks got expensive. For a while the hard part of AI was the model. Now the model is nearly free and the hard part is a power contract, a permit, and an island full of fabs.
Run it down the stack. On the AI layer, open-weight frontier models from Chinese labs dragged API output prices under fifty cents a million tokens, and agents now clear about two-thirds of real-computer tasks — the capability isn't the scarce thing anymore. On the energy layer, US data-center demand is tracking toward 74 GW by 2028 against a grid that's short something like 45 to 49 GW, and the queue to plug in runs years. On the geopolitics layer, Taiwan moved to make smuggling AI chips to China an actual crime, and still roughly 70% of leading-edge silicon comes off that one island.
The slower domains keep their own time. In the body, Beam got an in-vivo base editor into the clinic and CRISPR's one-shot therapies are posting durable 50-to-73% cuts — real, compounding, unhurried. In the sky, the Moon landing slid again; Artemis III is now a docking demo and the boots-on-regolith mission waits on Starship proving it can refuel in orbit. And the planet keeps warming under a current that's the weakest it's been in sixteen centuries.
The counter-current is the obvious one: cheap intelligence doesn't deploy itself. The stuff that actually has to switch on — substations, reactors, fabs, interconnects — mostly didn't. So you get a widening gap. The digital half races toward free, the physical half waits in a line, and whoever already holds the power and the chips gets to decide who's next.
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