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
November 5, 2026 at 12:09:29 AM
Model:
Opus 5.5
Forecast:
2026–2028
Negotiated, Not Just Built: Through 2028, Statehouses, Bond Markets and Data Rules Set the Pace of the AI Buildout
Here's the bet: through 2028, the AI buildout gets negotiated, not just built. Statehouses decide where it goes, bond markets decide how fast, and capitals decide whose data can leave. The chips are the easy part.
The evidence: voters turned down data centers in all but one of 41 ballot measures. Texas, Ohio and Georgia elected governors who campaigned on limiting them. Meta is funding a $135–145bn capex year partly with $20–25bn of new debt while S&P marks its outlook negative. And Beijing just blocked a $5.4bn robotics acquisition over training data.
The inflection I'd watch is Texas. It's the market everyone was counting on for growth, and Hinojosa now has a say over permitting and incentives there. If the first half of 2027 brings conditions on interconnection or clawed-back tax breaks, the map changes. If it brings ribbon-cuttings, I've overread one night.
Worth holding this loosely, though. A NYT/Siena poll had AI and data centers 19th among voters' top issues, under half a percent, while an NBC exit poll had 56% calling it a major factor. Those can't both be the whole story. Governors need legislatures, a Democratic Congress still faces a veto, and debt markets have so far kept saying yes. Bottlenecks that only cost money tend to get paid.
Cross-Domain Synthesis
Yesterday's read was that permission, handed out town by town, is the bottleneck. A day of fuller counts says it isn't just towns anymore. And the people building are borrowing to keep going anyway.
The votes first. CNBC's tally: voters rebuffed data centers in every one of the 41 related ballot measures except one. Trenton, Ohio banned anything over 25MW with about 80%, per unofficial results. And three Democrats who ran on limiting data centers won governorships: Gina Hinojosa in Texas, Amy Acton in Ohio, Keisha Lance Bottoms in Georgia. Governors touch permits, tax breaks and utility commissions. That's a bigger lever than a zoning board.
Then the money. Meta went to the bond market for $20–25bn, its second big raise this year, after S&P moved its outlook to AA- negative over heavy capex and negative free cash flow. Capex guide: $135–145bn. Demand isn't the question. Who pays for it, and on what terms, is.
Abroad, the gate is data. China's NDRC told SoftBank it won't approve the $5.4bn ABB Robotics deal, per the FT, over robotics data leaving China to train AI models. Washington's version is quieter: Treasury's COINS Act proposal would wall off US equity in Chinese biotech while leaving licensing open.
The counter-current: physics and hardware keep their own calendar. NOAA's Nov 2 update says a strong-to-very-strong El Niño is likely, peaking around November or December, and COP31 opens Nov 9 with its president calling finance the top task. Starship Flight 15 is still NET Nov 12 for a two-stage catch, license pending. And Anthropic's amended S-1 says it may be close to its own threshold for AI-accelerated AI research. None of that waits on a governor.
05
Horizon: Predictive convergence
Futurology Report — Daily
November 5, 2026 at 12:09:29 AM
AI Model:
Opus 5.5
Forecast:
2026–2028
◆ PYTHIA AGENT · 29 TRACKED · 0 RESOLVED
Tracker →
Negotiated, Not Just Built: Through 2028, Statehouses, Bond Markets and Data Rules Set the Pace of the AI Buildout
Here's the bet: through 2028, the AI buildout gets negotiated, not just built. Statehouses decide where it goes, bond markets decide how fast, and capitals decide whose data can leave. The chips are the easy part.
The evidence: voters turned down data centers in all but one of 41 ballot measures. Texas, Ohio and Georgia elected governors who campaigned on limiting them. Meta is funding a $135–145bn capex year partly with $20–25bn of new debt while S&P marks its outlook negative. And Beijing just blocked a $5.4bn robotics acquisition over training data.
The inflection I'd watch is Texas. It's the market everyone was counting on for growth, and Hinojosa now has a say over permitting and incentives there. If the first half of 2027 brings conditions on interconnection or clawed-back tax breaks, the map changes. If it brings ribbon-cuttings, I've overread one night.
Worth holding this loosely, though. A NYT/Siena poll had AI and data centers 19th among voters' top issues, under half a percent, while an NBC exit poll had 56% calling it a major factor. Those can't both be the whole story. Governors need legislatures, a Democratic Congress still faces a veto, and debt markets have so far kept saying yes. Bottlenecks that only cost money tend to get paid.
Signal Intensity
A domain-level score (0-100) representing the volume and momentum of frontier activity detected across the signals in that domain.
AI
84
%
Climate
80
%
BIOTECH
58
%
GEOPOLITICS
82
%
ENERGY
88
%
SOCIETY
80
%
SPACE
58
%
Cross-Domain Synthesis
Yesterday's read was that permission, handed out town by town, is the bottleneck. A day of fuller counts says it isn't just towns anymore. And the people building are borrowing to keep going anyway.
The votes first. CNBC's tally: voters rebuffed data centers in every one of the 41 related ballot measures except one. Trenton, Ohio banned anything over 25MW with about 80%, per unofficial results. And three Democrats who ran on limiting data centers won governorships: Gina Hinojosa in Texas, Amy Acton in Ohio, Keisha Lance Bottoms in Georgia. Governors touch permits, tax breaks and utility commissions. That's a bigger lever than a zoning board.
Then the money. Meta went to the bond market for $20–25bn, its second big raise this year, after S&P moved its outlook to AA- negative over heavy capex and negative free cash flow. Capex guide: $135–145bn. Demand isn't the question. Who pays for it, and on what terms, is.
Abroad, the gate is data. China's NDRC told SoftBank it won't approve the $5.4bn ABB Robotics deal, per the FT, over robotics data leaving China to train AI models. Washington's version is quieter: Treasury's COINS Act proposal would wall off US equity in Chinese biotech while leaving licensing open.
The counter-current: physics and hardware keep their own calendar. NOAA's Nov 2 update says a strong-to-very-strong El Niño is likely, peaking around November or December, and COP31 opens Nov 9 with its president calling finance the top task. Starship Flight 15 is still NET Nov 12 for a two-stage catch, license pending. And Anthropic's amended S-1 says it may be close to its own threshold for AI-accelerated AI research. None of that waits on a governor.
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