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 24, 2026 at 1:18:00 AM
Model:
Opus 4.8
Forecast:
2026-2029
Bring Your Own Power: Through 2029 the Edge Goes to Whoever Stops Waiting in Line for the Physical World
Here's the bet. For the next few years the winners aren't whoever has the best model — that's turning into a utility. They're whoever stops waiting in line for the physical things the model needs: power, chips, trial data, launch hardware. And more than that, whoever's willing to own the build-out themselves instead of renting a spot in someone else's queue.
You can read it straight off the month. Google contracted for 3,590 megawatts of nuclear supply because the grid operator effectively told it to bring its own power. Amazon's building reactors. Taiwan made chip access a bargaining chip. NASA moved the Moon landing again because the lander isn't ready, not because anyone lost their nerve. Every one of those is the same sentence: the idea's fine, it's the stuff that has to physically switch on that's behind.
I'd expect the real turn when somebody closes one of these loops end to end — a hyperscaler energizing a reactor it paid to build and feeding that power straight into its own servers, with nobody else's grid in the middle. First self-built watt into a self-built data center. Call it 2028, maybe 2029. Whoever gets there first buys a couple of years of headroom nobody else has. Same logic picks the Moon winner: whichever lander actually flies, not whichever company briefs best.
Worth holding this loosely, though. The AMOC studies are a decent reminder that the walls I think are merely slow are sometimes about timing in a way that surprises you — in both directions. If the reactors slip and the permits stall and the chips stay stuck in customs, we don't get an inflection, just a longer, grindier version of right now. And if the power and the trials and the rockets all unclog at once, the smart part becomes the bottleneck again and this whole framing ages badly. I don't think that's 2029. But I've mis-timed 'physically hard' before.
Cross-Domain Synthesis
There's a phrase buried in Google's nuclear deal this month that tells you where everything is heading: bring your own power. That's not a slogan, it's PJM's actual proposal — if you want to plug a data center into the grid, show up with your own electricity or risk getting cut off when demand peaks. Read that twice. The grid operator is telling the richest companies on earth to go build their own supply. And they're doing it.
Once you see it there, you see it everywhere. Google just contracted for 3,590 megawatts from Constellation. Amazon's funding reactors on a dead coal site. Gartner says data centers will pull 565 terawatt-hours this year and the grid can't stretch much past 1,200 by 2030 — so the move is to stop waiting on the grid at all. Same story in chips: Taiwan is turning AI-server access into a clause in a trade deal, because the hardware is scarce enough to be worth bargaining over. The model itself keeps getting cheaper and more interchangeable by the month. The smart part isn't the hard part anymore.
The jobs story is its own version of this. CFOs told the Duke/NBER survey they expect to cut around half a million jobs to AI next year — about nine times what actually got attributed to it in 2025 — and in nearly the same breath the researchers found almost no measured productivity gain to show for it. So companies are underwriting the displacement before the tool has proven it can do the work. The commitment runs way out ahead of the delivery. That gap is most of the year.
Climate is where the pattern flips and stops being reassuring. Everywhere else the physical world is just slow — reactors take years, trials take years, rockets slip. But the newest AMOC work is a reminder that slow isn't the same as safe. Some of the latest models now show the Atlantic circulation could tip within decades, and one observational read argues it may already have crossed a line nobody clocked. Here the physical clock isn't behind schedule. It might be ahead of ours. Same lesson, pointed the other way: timing is the thing, and it doesn't always wait for us to be ready.
05
Horizon: Predictive convergence
Futurology Report — Daily
October 24, 2026 at 1:18:00 AM
AI Model:
Opus 4.8
Forecast:
2026-2029
Bring Your Own Power: Through 2029 the Edge Goes to Whoever Stops Waiting in Line for the Physical World
Here's the bet. For the next few years the winners aren't whoever has the best model — that's turning into a utility. They're whoever stops waiting in line for the physical things the model needs: power, chips, trial data, launch hardware. And more than that, whoever's willing to own the build-out themselves instead of renting a spot in someone else's queue.
You can read it straight off the month. Google contracted for 3,590 megawatts of nuclear supply because the grid operator effectively told it to bring its own power. Amazon's building reactors. Taiwan made chip access a bargaining chip. NASA moved the Moon landing again because the lander isn't ready, not because anyone lost their nerve. Every one of those is the same sentence: the idea's fine, it's the stuff that has to physically switch on that's behind.
I'd expect the real turn when somebody closes one of these loops end to end — a hyperscaler energizing a reactor it paid to build and feeding that power straight into its own servers, with nobody else's grid in the middle. First self-built watt into a self-built data center. Call it 2028, maybe 2029. Whoever gets there first buys a couple of years of headroom nobody else has. Same logic picks the Moon winner: whichever lander actually flies, not whichever company briefs best.
Worth holding this loosely, though. The AMOC studies are a decent reminder that the walls I think are merely slow are sometimes about timing in a way that surprises you — in both directions. If the reactors slip and the permits stall and the chips stay stuck in customs, we don't get an inflection, just a longer, grindier version of right now. And if the power and the trials and the rockets all unclog at once, the smart part becomes the bottleneck again and this whole framing ages badly. I don't think that's 2029. But I've mis-timed 'physically hard' before.
Signal Intensity
A domain-level score (0-100) representing the volume and momentum of frontier activity detected across the signals in that domain.
AI
91
%
Climate
62
%
BIOTECH
68
%
GEOPOLITICS
84
%
ENERGY
95
%
SOCIETY
80
%
SPACE
66
%
Cross-Domain Synthesis
There's a phrase buried in Google's nuclear deal this month that tells you where everything is heading: bring your own power. That's not a slogan, it's PJM's actual proposal — if you want to plug a data center into the grid, show up with your own electricity or risk getting cut off when demand peaks. Read that twice. The grid operator is telling the richest companies on earth to go build their own supply. And they're doing it.
Once you see it there, you see it everywhere. Google just contracted for 3,590 megawatts from Constellation. Amazon's funding reactors on a dead coal site. Gartner says data centers will pull 565 terawatt-hours this year and the grid can't stretch much past 1,200 by 2030 — so the move is to stop waiting on the grid at all. Same story in chips: Taiwan is turning AI-server access into a clause in a trade deal, because the hardware is scarce enough to be worth bargaining over. The model itself keeps getting cheaper and more interchangeable by the month. The smart part isn't the hard part anymore.
The jobs story is its own version of this. CFOs told the Duke/NBER survey they expect to cut around half a million jobs to AI next year — about nine times what actually got attributed to it in 2025 — and in nearly the same breath the researchers found almost no measured productivity gain to show for it. So companies are underwriting the displacement before the tool has proven it can do the work. The commitment runs way out ahead of the delivery. That gap is most of the year.
Climate is where the pattern flips and stops being reassuring. Everywhere else the physical world is just slow — reactors take years, trials take years, rockets slip. But the newest AMOC work is a reminder that slow isn't the same as safe. Some of the latest models now show the Atlantic circulation could tip within decades, and one observational read argues it may already have crossed a line nobody clocked. Here the physical clock isn't behind schedule. It might be ahead of ours. Same lesson, pointed the other way: timing is the thing, and it doesn't always wait for us to be ready.
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