AI Product Kitchen

Join us in the AI Product Kitchen this week as we talk to Ilan Frank, Chief Product Officer at Checkr. We explore the realities of building AI products in highly regulated industries, the counterintuitive challenges of AI implementation, and why most product teams are still in the early innings of the AI transformation. Ilan brings a wealth of experience from his previous roles as VP of Product at Slack (where he launched Slack Connect) and Head of Product at Airtable, giving him unique insights into scaling AI product organisations across different contexts.

Learn about the practical challenges and unexpected discoveries that come with deploying AI in mission-critical applications, as Ilan shares honest insights on moving beyond the ChatGPT magic to building AI features that solve real customer problems.

In this episode, we dive into:

• The 30-Inning Game of AI Product Development: Why most teams are only in inning 2 or 3 of AI adoption, what separates teams shipping AI products successfully, and the long journey ahead to truly AI-native organisations.
• Hiring More Humans Because of AI: The counterintuitive reality of how Checkr actually increased headcount in the short term due to AI implementation, requiring specialists to validate AI outputs.
• Building AI Products in Highly Regulated Industries: The critical importance of guardrails, compliance considerations, and why "we're wrong only 1% of the time" isn't acceptable when employment decisions are on the line.
• From Hack Day Brilliance to Production Reality: How a promising AI feature required careful reconsideration to ensure regulatory compliance, and the gap between AI demos and production-ready products.
• Data Moats and AI Strategy: How Checkr is transforming from a background check API into a comprehensive people intelligence platform, leveraging their unique data collection infrastructure to build sustainable competitive advantages in the AI era.

Here's how to connect with us:

• Find Ilan on LinkedIn⁠
• Follow Matt on LinedIn⁠ and ⁠X⁠
• Learn more about Checkr
• And make sure to check out Sauce AI⁠ and follow us on LinkedIn⁠!

Timestamps:

00:00 - Introduction to Ilan Frank and the 30-Inning Game of AI

01:02 - What Separates Successful AI Product Teams: Overcoming Fear

01:51 - Checkr's First AI Product: Charge Classification Success

03:00 - The Journey from Manual to AI-Powered Background Checks

04:36 - Customer-Driven Innovation: Speed vs Accuracy Trade-offs

06:18 - The Second AI Product: Charge Explainer Development

07:20 - When AI Features Go Wrong: Legal Compliance Challenges

09:05 - Identifying Problems Through Customer Feedback

10:18 - Building AI in Highly Regulated Industries: Zero Error Tolerance

11:11 - Human-in-the-Loop: Why AI Increased Hiring at Checkr

12:01 - Operations Specialists: The New AI Quality Assurance Role

12:42 - Future of AI Confidence: When Human Review Won't Be Needed

13:33 - Checkr's Evolution: From API to Comprehensive Platform

15:58 - The Data Advantage: Building Moats in Background Checks

16:52 - TAM Analysis: Expanding Beyond Background Checks

17:43 - AI Implementation Challenges: Magic vs Production Reality

20:11 - Hiring AI Product Managers: Skills and Imagination Over Experience

22:28 - Why AI Hasn't Transformed Product Management Yet

25:04 - Designing AI-Native Organizations for the Future

27:27 - The Next Five Years: Automation vs Human Oversight

32:08 - Competitive Threats: Startups vs Incumbents in AI

33:01 - Slack Connect and Enterprise Product Lessons

39:34 - Airtable's AI Strategy: Builders vs End-Users Decision

42:55 - Product Leader Advice: Just Do It and Focus on Pain Points

44:21 - Internal AI Tools: Design, Research, and Product Management

46:09 - The Unseen Pain: What AI Can't Yet Discover

48:09 - Product Beliefs: Being an AI Skeptic While Embracing AI

48:39 - Looking Ahead: Building the Human Data Graph

Join us in the AI Product Kitchen this week as we talk to Nan Yu, Head of Product at Linear. In this episode, we talk about how Linear is transforming product development by turning AI agents into first-class team members and revolutionising how the highest-performing tech teams organise their work. Nan leads product at Linear, the modern issue tracking platform trusted by companies like OpenAI, Scale AI, and Ramp.

Learn about Linear's unique approach to AI product development, from replacing manual taxonomies with intelligent systems to deploying synthetic actors that participate in software workflows just like human colleagues.

In this episode, we dive into:

• Agents as First-Class Team Members: How Linear is building AI agents that can be assigned tasks, review code, and communicate through the platform like human teammates
• The Death of Manual Taxonomy: Why AI will make current approaches to organising backlogs, labelling issues, and categorising product ideas feel "obviously archaic" within five years.
• Diligence on Tap: How AI unlocks true product management by handling the consistent, repetitive work that currently requires large teams, freeing humans to focus on strategy and product taste rather than mundane tasks.
• Low-Cost AI Experimentation: Linear's bottom-up approach to AI development, running multiple prototype experiments simultaneously and scaling the winners based on real usage patterns from their own team.
• Counter-Positioning Against Giants: How Linear successfully competed with industry incumbents like Jira and Asana by focusing obsessively on the individual contributor experience over middle management’s reporting needs.

Here's how to connect with us:

• Find Nan on LinkedIn⁠ and X
• Follow Matt on LinedIn⁠ and ⁠X⁠
• Learn more about Linear
• And make sure to check out Sauce AI⁠ and follow us on LinkedIn⁠!

Timestamps:

00:00 - Introduction and Guest Welcome

00:28 - What Will Feel Archaic in Five Years

01:00 - Building Better Products vs Shipping More Features

02:05 - Decision Making and Product Clarity

03:07 - High-Performing vs Low-Performing Product Teams

04:23 - The Problem with Traditional Backlogs

07:12 - Understanding Taxonomies and Organization Systems

09:03 - The Journey from Old to New Ways of Thinking

10:27 - Customer Behavioral Shifts and On-Ramps

12:42 - Linear's Engagement Strategy: Distinct Issue Creators

15:16 - AI Strategy Part 1: Sharpening Existing Data

17:02 - AI Strategy Part 2: Synthetic Actors and Agents

18:42 - Which Workflows Will AI Take Over First

20:26 - Barriers to AI Adoption in Development

21:57 - Building AI Products: Augmentation vs Replacement

23:58 - Measuring Success with AI Products

25:10 - Business Model Evolution with AI

26:26 - Low-Cost AI Experimentation Process

28:01 - AI Project Examples: Winners and Failures

30:15 - Integrating AI Without Compromising Simplicity

32:32 - ROI and Impact of AI Products

33:17 - Future Impact on Product Team Structure

37:00 - Starting with Customer Problems in AI Development

39:46 - Customer Development and Beta Testing Process

41:27 - Focusing on Individual Contributors vs Buyers

44:15 - Effective Customer Interview Techniques

Join us in the AI Product Kitchen this week as we talk to Eilon Reshef, Co-Founder and Chief Product Officer of Gong. We dive into topics like building AI products before the current boom and Gong’s journey creating the leading revenue AI platform that's transformed how sales teams operate.

Eilon shares fascinating insights from Gong's 10-year journey, starting when customers were genuinely scared of AI technology, through to building a multi-billion dollar platform that processes millions of sales conversations. As one of the pioneers who saw AI's potential back in 2015 (he even bought NVIDIA stock!), Eilon offers unique perspectives on what it takes to build world-class AI products that customers actually love.

In this episode, we go deep on:

• Building AI Before It Was Cool: How Gong pioneered conversation intelligence when buyers were skeptical of AI, and the strategies they used to overcome early market resistance.
• The Design Partner Philosophy: Eilon's extreme approach to customer collaboration, where every PM works with a dozen design partners to iterate rapidly and ensure product-market fit before launch - his "secret sauce" for building AI products that provide real value.
• AI Augmentation vs Replacement: Why Gong deliberately chose to augment rather than replace salespeople, and Eilon's contrarian view on why the obsession with AI SDRs and replacement technology misses the bigger opportunity.
• From Single Feature to AI Platform: The strategic journey from simple call recording to a comprehensive revenue orchestration platform, including lessons on when to expand beyond your initial wedge and how to build defensible moats in AI.
• Measuring AI Product ROI: Practical approaches to demonstrating value from AI products, from talk ratio insights that became viral LinkedIn posts to building multiple value propositions tailored to different stakeholders (productivity, predictability, growth).

Here's how to connect with us:

• Find Eilon on LinkedIn
• Follow Matt on LinedIn and X
• Learn more about Gong
• And make sure to check out Sauce AI and follow us on LinkedIn!

Timestamps:

00:00 - Introducing Eilon Reshef 

02:15 - Building AI Before It Was Hot

04:45 - Early AI Conviction and NVIDIA Investment

07:20 - The Inefficiency Problem in Sales Organizations

09:50 - Meeting Bots and Customer Fear

12:30 - Overcoming Early AI Skepticism

15:10 - Creating the Conversation Intelligence Category

18:25 - Category Creation Strategy and Naming

21:40 - Climbing Everest: Lessons and Mistakes

24:20 - The Power of Design Partners

27:45 - Design Partner Execution and Rituals

31:10 - Talk Ratio: The First Breakthrough Insight

34:30 - Big Brother Problem and Seller Value Creation

38:15 - One-Click Call Sharing Product Loop

41:00 - Expanding Beyond Single Product

44:20 - Measuring AI Product ROI

47:35 - Killing Products: Talk Tracks Feature

50:45 - Augmentation vs Replacement Philosophy

54:10 - Revenue Orchestration Platform Vision

57:25 - Building Product Moats in AI Era

This episode, Vrushali Paunikar - Chief Product Officer at Carta, joins us in the AI Product Kitchen. We talk about Carta's transformation from cap table management to becoming the ERP for private capital, and how they're leveraging AI to revolutionize financial systems stuck in the 80s and 90s. With nearly a decade at Carta, Vrushali has led product at the company through its journey from being rejected by almost every Series A investor to now powering over half of all VC-backed companies and managing over $2.5 trillion in equity.

In this episode, we dive into:

• AI for Document Intelligence: How Carta evolved from early, unsuccessful AI experiments with legal documents to now extracting critical data and automating financial workflows using advanced AI models.
• AI Agents in Finance: Exploring how AI agents can orchestrate complex financial processes and resolve failing health checks automatically, transforming how private capital finance teams operate.
• Reimagining UX for AI: The challenges of designing user experiences for AI-powered workflows, from asynchronous processes to potential chatbot interfaces and the future of finance software.
• The ERP Vision for Private Capital: How connecting disparate systems through an ERP platform can eliminate the current practice of manually verifying numbers across systems, freeing finance teams from just "making sure the math is doing math correctly."
• Incumbent Advantages vs. Startup Opportunities: Why data-rich incumbents like Carta have an edge in AI, while AI-native startups can differentiate through new design paradigms and aggressive integration strategies.

Here's how to connect with us:

• Find Vrushali on ⁠⁠⁠LinkedIn⁠⁠⁠⁠ and ⁠⁠⁠X⁠⁠⁠⁠
• Follow Matt on ⁠⁠⁠LinkedIn⁠⁠⁠⁠ and ⁠⁠⁠X⁠⁠⁠
• Learn more about Carta
• And make sure to check out ⁠⁠⁠Sauce AI⁠⁠ and follow us on ⁠⁠LinkedIn⁠⁠!⁠

Timestamps

00:00 - Introduction to Vrushali, CPO at Carta

01:15 - Carta's origin story: Rejected by every Series A investor

03:30 - Business model innovation: Changing the payer from law firms to companies

05:45 - Tackling "market too small”, objections in startups

07:20 - The pivot from liquidity marketplace to infrastructure platform

09:40 - Learning from product-market fit failure in private market liquidity

12:15 - Systems thinking approach to identifying new opportunities

14:30 - The "startup of startups" org structure at Carta

16:10 - From building new products to connecting the platform

17:45 - "Never Enter Data Twice" as a product philosophy

19:20 - Early AI experiments with legal document processing

21:30 - How AI is accelerating innovation at Carta today

23:40 - Executing on AI bets: From thesis to experimentation

25:15 - AI agents for orchestrating financial workflows

27:50 - Augmentation vs replacement debate in AI

29:30 - User experience challenges with AI-powered workflows

32:10 - AI's impact on design paradigms and information delivery

34:20 - Startup advantages in an AI-first world

36:45 - Using data to build opinionated products and decision guides

38:15 - The future of private capital finance: Beyond "making sure numbers tie up"

40:30 - CFOs evolving from back office to strategic operators

Join us in the AI Product Kitchen this week as we talk to Jeff Seibert, CEO and co-founder of Digits. In this episode, we dive into topics like Jeff's time as Head of Consumer Product at Twitter and his mission to revolutionise the accounting industry with Digits, the first end-to-end accounting platform of the AI era.

In this episode, we go deep on:

• In-house AI models vs off-the-shelf solutions: the importance of developing in-house AI models tailored to specific business needs, rather than relying solely on off-the-shelf solutions.
• Optimising user experience with AI: Creating an intuitive user experience where AI works seamlessly in the background, allowing users to focus on understanding their business rather than managing tedious accounting tasks.
• Product moats in the AI era: How AI can create durable competitive advantage
• Common mistakes in AI product development: The pitfalls of putting AI at the forefront of the product narrative without addressing the core problem first.
• Lessons from Twitter and AI opportunities: Missed opportunities to leverage AI and the need for a broader mindset in recognizing AI's potential in product development.

Here's how to connect with us:

• Find Jeff on ⁠⁠LinkedIn⁠⁠⁠, ⁠⁠X⁠⁠⁠ and on his website
• Follow Matt on ⁠⁠LinkedIn⁠⁠⁠ and ⁠⁠X⁠⁠
• Learn more about Digits
• And make sure to check out ⁠⁠Sauce AI⁠ and follow us on ⁠LinkedIn⁠!⁠

Timestamps

00:00 - Introduction to Jeff Seibert and Digits

00:33 - Jeff's Journey and the Launch of Digits

01:42 - The Vision Behind Digits and AI in Accounting

03:11 - The Decision to Build in Stealth Mode

05:42 - The Importance of Product Quality and Timing

06:46 - The Evolution of Accounting Software

08:26 - Building AI Models for Accounting

10:39 - Defining Success: What is "Good Enough"?

12:18 - In-House Models vs. Off-the-Shelf Solutions

13:36 - User Experience and Automation in Digits

15:40 - Augmenting Accountants, Not Replacing Them

17:03 - Common Mistakes in AI Product Development

18:36 - Differentiating True Innovation from Noise

19:36 - Sustaining Competitive Advantage in AI

21:52 - Unique Team Structures and Agile Practices

24:12 - The Role of Product Managers in Digits

25:15 - Go-to-Market Strategy and Launching Products

26:38 - Lessons from Twitter and AI Opportunities

27:52 - Reflections on Apple and Product Design Principles

30:25 - The Importance of Product Obsession

32:32 - Prioritization Strategies for Product Development

34:32 - Future of Accounting and Automation

Join us for our very first episode of AI Product Kitchen from Sauce, where we welcome Rachel Wolan - Chief Product Officer at Webflow. With over a decade of experience in the AI space, Rachel shares her journey from launching her first AI product at Talkdesk to leading the development of multiple AI products at Webflow.

In this episode, we discuss the intersection of AI and Product, exploring how AI can supercharge Product teams and enhance user experiences.

Come take a deep dive into:

• The evolution of AI in product development: Understanding how AI has transformed from machine learning-based products to generative AI solutions.
• The importance of solving customer problems: Rachel emphasizes that regardless of advancements in AI, the core focus must always be on addressing user needs effectively.
• Differentiating between leading and following in the AI space: Insights on when to innovate and when to enhance existing products with AI capabilities.
• Building a strong AI team: Strategies for upskilling existing teams and integrating AI fluency across product management, design, and engineering.
• The future of Product in an AI world: Predictions on how AI will reshape the product lifecycle, including the roles of QA, prompt engineering, and user experience design.

Here's how to connect with us:

• Find Rachel on ⁠LinkedIn⁠⁠ and ⁠X⁠⁠
• Follow Matt on ⁠LinkedIn⁠⁠ and ⁠⁠X⁠⁠

And make sure to check out ⁠Sauce AI and follow us on LinkedIn!⁠

Episode timestamps
00:00 - Introduction to Rachel Wolan
00:37 - Rachel's AI Journey: A Decade of Experience
01:18 - The Evolution of AI Product Development
03:31 - Leading vs. Following in the AI Space
05:25 - Webflow's Initial Product Launch Strategy
06:51 - Transitioning to Multi-Product Offerings
08:37 - Building the First AI Team at Webflow
10:34 - Upskilling Teams for AI Fluency
12:11 - Investing in AI: Strategic Considerations
14:57 - Trends Reshaping Product Development
17:22 - Adapting Product Organization Structures
18:24 - Measuring Product Signal and Success
20:06 - Evaluating ROI on AI Investments
21:54 - Inspirations and Influencers in AI
23:10 - The Role of Human Augmentation in AI
25:25 - Designing Unique AI Experiences
27:03 - Go-to-Market Strategy for SMBs and Enterprises
30:48 - Prioritizing Features for Diverse User Personas
32:18 - The Concept of PM as GM
39:49 - Hiring PMs from Adjacent Functions
41:49 - Surprises in Building AI Products
42:42 - User Experience Challenges in AI
43:51 - Concerns and Excitement about AI's Future
44:56 - Future Predictions for Webflow Users
46:11 - Closing Remarks and Reflections

Jiaona Zhang (JZ) is the Chief Product Officer at Linktree, who pioneered the 'link-in-bio' category and is one of the world's fastest growing product companies. JZ was previously the Senior VP Product at Webflow, VP Product at WeWork and Head of Product at Airbnb. JZ is also a Lecturer at Stanford University and a Program Creator at Reforge.