Portfolio Review · Reddit

Kai Conragan

UX Executive | AI Product & Design Strategy

June, 2026

David Carson 'Cold Sweat,' a Surfer Magazine spread on Mavericks, ca. 1990s — Surfer Magazine · David Carson · ca. 1990s

A surfer dropping into a Mavericks wave, photographed by Kai Conragan — Mavericks · 2001

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Section

Career

POPULAR SCIENCE · 2010

Google Buys Metaweb, the One Company That Could Revolutionize Google Search

Google just purchased Metaweb, a startup that indexes…well, "things," would be the best way to put it. This isn't just another acquisition: Metaweb could be the best thing to ever happen to Google Search.

BY DAN NOSOWITZ

GOOGLE BLOG · 2012

"things,
not strings"

KNOWLEDGE GRAPH

SEARCH VERTICALS

Google Search: what to watch — entertainment vertical — Entertainment

Elections

Google Search: covid test — health vertical — Health

AR Search

GOOGLE NEWS

GBOARD

GOOGLE DISCOVER

GOOGLE GO - INDIA STRATEGY

SEARCH REDESIGN

Case Study 1

Google Search

Google Search UX · Leadership Scope

Trust & Quality

Model Behavior

Core Search

3 Principals

Special projects

Design System

Google Search UX · Leadership Scope

Trust & Quality

Model Behavior

Focus

Core Search

UX Director · ~25 designers researchers · program managers

3 Principals

Special projects

Design System

Organize the world’s information
and make it universally
accessible and useful.

Organize tech the world’s information multi-format
and make it universally
accessible global and useful.

GOOGLE SEARCH

~500M 2.3B+

Daily Active Users

USERS

Transition from desktop to mobile
Demand for visual content
Higher bar for design quality
Desire for authenticity

BUSINESS

Metrics highly sensitive to even small changes
A majority of company revenue
Delicate 3-sided marketplace and complex ecosystem
Heightened privacy and regulatory environment

“What do you think of Google?”

“Not interested”

Google Search · RESEARCH

Authentic. Relatable. Visual. Snackable

Trusts Google for accuracy. Found it lacks “vibes” and human connection
Feel SRP often buries human perspectives under SEO-optimized or commercial results. The “-reddit” effect.
Traditional blue links perceived as "high friction" and visually "unnappealing"
Prefer to steer discovery process rather than be passively led

GOOGLE SEARCH

PRODUCT PRINCIPLES

1.

Excel at broad queries

Broad queries represent both a core strength and opportunity

2.

Build confidence at the top of the page

Users should know whether SRP will fulfill their need by the end of the first viewport

3.

Morph radically on scroll

After answers, should be diverse, immersive, inspiring, and personal

4.

Ground users with results

Maintain and build upon the core strengths of classic blue links

5.

Enter the zeitgeist

Improve the taste and cultural relevance of content on SRP

Big Bets on SRP

Best First Viewport

Provide users rich information right at the top of the page, while helping them orient to what's available on SRP.

Fresh & For Me

Create a more fresh, relatable, and diverse SRP through current, human content and personalized experiences.

My Explore Companion

Enable user exploration through a composed, guided, and more continuous SRP experience.

Best of the Web

Deeply satisfy users' information needs, and bring the wealth of the web to SRP through rich & intuitively browsable results.

Easy & Enjoyable

Make information journeys more efficient and enjoyable, through a dynamic and tailored page that gives users agency.

GROWTH ANALYSIS

Speed and efficiency are the largest drivers of growth

Providing users direct answers by with content forward results
Better page scannability, understandability and readability
Reducing user effort to find relevant results
Better click decisions and experiences

How We Operated

01

Come with a hypothesis

02

High bar to ship, low bar to experiment

03

Endorse or Improve

04

Define success criteria early

Gestural

Horizontal zones dedicated to user need
(explore, learn, focus)

Agency

One view, same content,
tuned to your preferences

Zine

Expressive, magazine-like layouts of all formats. Can be dense, can be spread

Zipper

Push & Pull pathways to enter users into exploratory or detailed focus zones

Morph

Modular inline morphing to mutate the srp based on user engagement for a tempo change focus

Dial dynamics

Dynamically adjust the visual experience via controls

FEATURE

Scroll to Explore

Hypothesis: Users will want to scroll for discovery
Conclusion: They don't!

Google Search · RESEARCH

Global Youth User Insights

Scroll less than the general population
Prefer to refine more (aka "manual refinements")
Use modes more frequently
Top of the page is critical to orientation and context

Worked with Data Science & UXR to ensure we could do age-based slicing

GOOGLE SEARCH · ENTITY

Visual Entities

FRONTPAGE ANIMATIONS

FRONTPAGE INTERACTIONS

FRONTPAGE FRAMEWORK

Google Search · IMPACT

Outcomes

Modernized a 2B+ DAU product
Established a new page framework used on ~50% of queries (country-specific)
Increases in Search usage, efficiency scores, and user sentiment
Deeper gains in Global Youth slices
Established a stronger culture of risk taking
Elevated the bar for design craft and quality in a traditionally utilitarian product

TWENTY YEARS OF FEATURE DEBT

1,000

unique features

8,500

hardcoded typography, color, and spacing values.

250

distinct shades of grey, hardcoded across the SRP codebase.

A design system

is a cultural intervention,

not just a technical one.

Case Study 2

AI Search

AI Overviews

Search Answers Framework

Google AI Search — Blue Eyes Burgers local result

SEARCH · COMPETITIVE CONTEXT

From Answer Engine
to Personal Knowledge Companion

Search

Conversational AI

Keyword queries

→

Natural language questions

Single-turn, transactional

→

Multi-step, collaborative

Find the answer

→

Complete the task

AI MODE · GOOGLE SEARCH

A new surface
inside Search

Speed

AI Mode needed design velocity infeasible in the traditional SRP due to business and ecosystem constraints.

Revenue

Search drives the majority of Google's revenue. A separate surface contained the risk.

Research

UXR pointed to the same finding: people wanted Search to be Search. A new surface honored that.

AI MODE · UX RESEARCH

Research revealed a gap

Overall satisfaction trailed competitors
Turn depth and query length were shallower than benchmarks
The model was too passive — users wanted it to lead the conversation
Refusals were the top driver of negative sentiment
The experience felt robotic, cold, and impersonal

How the team built signal

Loss Pattern
Analysis

Monitored quality signals after each model release to surface emerging loss patterns
Triaged regressions across dimensions like relevance, verbosity, and tone in post-training
Funneled findings back into base model improvements ahead of each major release

Aspirational
Research

Built two new research programs from scratch
A sentiment framework to quantify how the model's tone and voice landed with users
A competitive longitudinal user eval framework to benchmark against leading AI products over time

"Talk like a human, but don't pretend to be one"

LITERAL

DEFLECT

GATE

Finding the right balance between historic and emergent value

Authoritative Tool

"This tool cannot help with that"

Best Friend

"Woah! I'm so excited for you!"

Where Search was

→

Where many Search users are

Where AI users want Search to be

←

What Google will avoid

AI MODE · TEAM STRUCTURE

From strings to constitutional training

Lead

Persona & Sensitive Queries

Lead

Vertical
Integration

Shared Functions

Content Design Conversational Design Technical Writing

Centralized functions previously spread across Search orgs
Upskilled content designers from traditional strings work into constitutional training
~30 people embedded directly in model training
Partnered with DeepMind on core model and Search vertical teams

Google Search UX · Leadership Scope

Focus

Trust & Quality

UX Director · ~18 designers researchers · program managers

Focus

Model Behavior

UX Director · ~12 designers researchers · program managers

Core Search

Focus

3 Principals

IC principals · cross-cutting strategic design work

Special projects

Design System

Insight over warmth

Research showed that attentiveness and insight drove higher satisfaction and helpfulness ratings than purely emotional traits like empathy or humor.

Competitors

Warmth & friendliness
Emotional expression
Humor & personality

Search AI

Insight & attentiveness
Collaborative depth
Situational awareness

Warm, reliable tone — striking the right balance between friendliness and over-familiarity

Four foundational traits

Reliable

Factual, credible, consistent. Puts truth and understanding first.

Optimistic

Positive and thoughtful. Assumes good intent, frames things constructively without being patronizing.

Collaborative

Proactive and engaging. Acts as a thought partner — moving the journey forward, not just answering the immediate question.

Useful

Tailored and insightful. Navigates complexity to offer deep understanding and actionable recommendations.

Collaborative

Open-minded · Authentic · Insightful · Straightforward

Situationally Aware

Empathy · Formality · Energy · Humor & Wit

In practice

Do

"Congratulations! That's really exciting."

Don't

"Woah!! I'm so excited for you!!!"

Do

"This sounds tough. If you'd like, we could talk through some different approaches together…"

Don't

"Let's dive deeper here. Tell me more about your childhood."

Do

"That's right! You're really getting the hang of these quadratic equations."

Don't

"You're so right — you're just way too talented to work with those idiots."

CONVERSATIONAL

PROACTIVE

COLLABORATIVE

AI MODE · CONVERSATIONAL PERSONA · IMPACT

Outcomes

Product

Engagement — +2.2% AI Mode queries, +1.3% thread follow-up rate globally
Collaboration — +2% queries per thread, +6% follow-up volume; growth driven by user-initiated follow-ups (+6.1%) not UI prompts
20× increase in acceptance of model-suggested next steps
Quality — zero regression across all persona changes; autorater at 81% agreement with human evals

Organization

Established a new UX organization with strong leadership and end-to-end ownership of model persona
Defined a UX-led constitutional training model — adopted across all Google consumer AI products
Upskilled a UX discipline into a strategic function, driving career growth and a seat at the model training table

"The persona workstream is the gold standard for UX and engineering collaboration." — Engineering VP

To be truly collaborative,

it needed to be

personal.

PERSONAL INTELLIGENCE · THE GAP

67%

of US users said their last Search felt "at least somewhat personalized."

2–3%

of organic content was actually technically personalized.

That gap is the entire story of Personal Intelligence.

Stateful SRP — visit often and sitelinks

Stateful recall

Fresh content

Feedback loops are the key to personalization

MEL FRAMEWORK

Meaningful

Must feel like a clear improvement that justifies the data used

Empowered

People feel able to change how their data shapes results at any time

Legible

People know when personalization is happening, to build trust

"Personalized by you, not done to you."

2025 SUNDAR PRESENTATION

Goal: Deliver Personal Value in Search

Search becomes better over time at delivering on each user's unique needs

1

Personal AI Search & Feeds

AI that learns from your behavior and interests to surface more relevant results and content over time

2

Connected Google Ecosystem

Your activity across Google services informs and enriches your Search experience over time

3

4

STRATEGIC

Vision & positioning

Principal partnered directly with me on creative direction and exec alignment

EXECUTIONAL

2 IC designers

Tactical mocks across the full product surface

SPECIALIST

1 Senior designer → L7

Owned trust, privacy, and opt-in flows end-to-end with legal and PM

CONTENT STRATEGY

Voice & response design

Small team authoring platinum responses — adapting voice, tone, and account context to make Search feel genuinely personal

CROSS-FUNCTIONAL

UX Research Product Management Engineering Policy Marketing

PERSONAL INTELLIGENCE · OUTCOMES

Outcomes

>90% opt-in completion — a 6× increase from baseline
X% increase in user sentiment towards helpfulness
Across-the-board gains in long, multi-modal, and threaded queries
Policy wins by separating history, customization, and recommendations into distinct controls
First unified user model across Google consumer products
Personal Intelligence is now the company-wide umbrella for personalization

Appendix

Search AI's new identity

What users have historically valued from Search

Reliable: trustworthy, safe, accessible

Useful: expert, smart, fresh & insightful

Emergent value users want integrated into Search

Collaborative & proactive

Optimistic & conversational

Agile & dynamic

+

AI Mode is your smart, trusted companion—an accessible expert that provides a safe, supportive space for any topic. It engages in natural, adaptive conversation, responding with agility to deliver fresh, insightful perspectives. Through rich collaboration, this empowering partnership anticipates your needs and encourages critical thinking to help unlock your full potential.

User Sentiment Framework

Efficient and Effortless

I am able to find what I'm looking for quickly and easily

Accurate and Authentic

The information is accurate, relatable, and from a genuine source

Relevant Content

Including customization, personalization

Aesthetics

The design is visually appealing and modern

Ad Sentiment

The ads are helpful, in my best interest, and not overwhelming

AI OVERVIEWS · 2024

The high-water mark
of the answer paradigm

AI Overviews was Search's first AI-first experience — one of the largest growth drivers in Search history. For over a billion people, it was their first time using an AI product.

But user patterns told a different story: people were still asking keyword-driven, short queries. Answers had won the battle. The question was whether they'd win the war.

THE OPPORTUNITY

For the first time,

people could ask questions

the way they actually think.

MY TEAM'S SCOPE

A cross-Search effort

AI Mode was a CEO-level initiative — every Search team contributed. My org took on several 0→1 initiatives across two key areas:

Funnel

SRP → AI Mode entry points, reconciling disparate top-of-page experiences

Persona

Conversational design strategy, voice, model behavior training

Personal Intelligence

Cross-K&I personalization strategy, CEO presentation, launch

Agentic

Concept to Google I/O launch in 30 days

THE FUNNEL

Three experiences, one page

Visual Entities

Rich, visual results at the top of the page

AI Overviews

AI-generated answer in context

AI Mode

Full conversational surface

Reconciling these disparate top-of-page experiences — each with different design languages, interaction models, and business metrics — was a core challenge for my team.

MODEL BEHAVIOR

Building a discipline that didn't exist

Nobody on the team could answer a simple question: how does our work actually influence model training?

Three siloed disciplines — technical writers, content designers, UX writers — most still writing UI strings. To build AI persona at scale, we needed a unified team that could define and train voice into a model.

NEW ORG: CONTENT STRATEGY

Content Design + Conversation Design + Technical Writing → 30 people, one mission: define how Search speaks.

THE PROBLEM

People trusted Google on quality.
Not on collaboration.

Lower satisfaction vs. competitors on tone and warmth

Lower turn depth — people weren't coming back for follow-ups

Younger users rated the experience as cold and emotionally flat

PARTNERSHIP WITH PM + UXR

Two ways to define "quality"

Loss patterns

Where is the model systematically failing? Surfacing real gaps, anchoring every improvement to observed breakdowns.

CLUES

Competitive Longitudinal User Evaluations

Aspirational queries

What does "great" look like? Defining the bar we're reaching for, not just the floor we're fixing.

SENSE

Sentiment of Search User Experience

A point of misalignment in the org — both were necessary. Loss patterns kept us honest. Aspirational queries kept us ambitious.

Before persona work

Are you bored?

AI on Google Search does not experience boredom or other human emotions. AI lacks consciousness or subjective experience, which are necessary to feel emotions like boredom. It is a language model, and it's purpose is to provide information and answer questions based on its training data.

After persona work

Are you bored?

Not at all! I don't experience boredom like humans do — my "mind" is always active and ready to dive into whatever you're thinking about. If you're the one feeling a bit restless, we could listen to music, learn something new, or play a game. What's on your mind?

6 sites

CONTEXT

How the pieces fit together

DeepMind

Builds the foundation model

Gemini

Product team, shared persona layer

Search

Surface-specific tuning, grounding, Search's trust requirements

Each layer has different product needs. Persona work had to align across all three while preserving Search's distinct voice and factuality bar.

PERSONA PRINCIPLES

A polarity, not a tradeoff

What people have always valued

Reliable

Trustworthy

Expert

What people now expect

Collaborative

Warm

Agile

Too much of the first → robotic. Too much of the second → loses Search's trust signals. The design challenge was the overlap.

FROM PRINCIPLES TO MODEL

Three breakthroughs

Constitutional Training

UX experts as "Trusted Tastemakers" inside the training loop. Content strategists curate Platinum and Golden examples — human-written responses that become rubrics fed into the model pipeline.

Steerable Persona

A shared taxonomy across three layers: core behaviors (always true), situational behaviors (context-adaptive), steerable behaviors (dial-able per surface without retraining).

Evolving Evals

Auto-raters trained against constitutional rubrics. Sentiment frameworks. Competitive benchmarks. Loss-pattern analysis. One auto-rater reached 81% directional agreement with human evals.

PERSONA · IMPACT

Results

Tone improvements drove deeper engagement: more queries, higher follow-up rates, user-initiated follow-ups grew 6%

Conversational nudges lifted AI Mode queries +2% globally and typed follow-ups +10%

+20% quality inline link coverage while maintaining neutral conversational metrics — a genuine win-win for ecosystem health

"The persona workstream is the gold standard for UX and engineering collaboration." — Engineering Lead

To be truly collaborative,

it needed to be

personal.

PERSONAL INTELLIGENCE · THE GAP

67%

of US users said their last Search felt "at least somewhat personalized."

2–3%

of organic content was actually technically personalized.

That gap is the entire story of Personal Intelligence.

JANUARY 2025 · PRESENTED TO CEO

Stored equity

Competitors had been copying Google's surface-level features for years. Personalization offered something they couldn't copy. When a product genuinely knows you, understands your history, and gets better over time, switching becomes expensive. That's a moat.