Sourcerer grewmonthly order volume from $300,000 to $1.5 million while surpassing $2 million in annual recurring revenue.

Peter Cetale is the co-founder and CEO of Sourcerer, an autonomous execution layer for physicalgoods and global trade. Before Sourcerer, he founded and sold Religio, worked as a principal at Human Capital, and operated a medical-products import business that exposed the inefficiencies of international sourcing.

Sourcerer later joined a16z Speedrun and is now processing orders for distributors serving Fortune 500 customers.

Most procurement AI companies are automating administrative work. Peter argues that they are attacking the smaller cost center. A company might spend $10 million on itsprocurement team while purchasing $1 billion in materials, making reductions in the actual cost of goods far more valuable than reductions in headcount.

Sourcerer uses AI agents to find factories, contact suppliers, negotiate prices, conduct background checks, arrange third-party audits, compare freight costs, and monitor delivery risk. The company also aggregates demand across buyers,increasing its negotiating leverage and reducing the incentive for customers to bypass the platform. Peter’s counterintuitive claim is that transaction-based AI businesses operating inside the physical economy may be more defensible thanworkflow software, particularly as frontier model providers absorb basic automation features and compress software margins.

In Today'sEpisode We Discuss:

00:00 – Peter Cetale and Sourcerer’s autonomous trade thesis

01:38 – Selling Religio and learning from an early exit

03:57 – From Human Capital to building Sourcerer

07:24 – Fivefold order growth and surpassing $2 million ARR

10:32 – How autonomous sourcing works

15:11 – Why B2B sourcing marketplaces break

19:54 – Demand aggregation and preventing circumvention

22:32 – Building beyond workflow automation

25:09 – Why AI workflow companies face margin compression

30:22 – Is Sourcerer a brokerage or a technology company?

33:34 – 26% monthly growth and expansion beyond China

36:28 – The long-term vision for autonomous global trade

39:08 – Why Peter changed his mind about B2B SaaS

40:04 – What Peter would invest in today

Peter explains how his first company began as payment processing for churches before customerdiscovery revealed that declining membership and community engagement were the deeper problems. He also details Sourcerer’s land-and-expand strategy: beginwith commoditized, lower-margin products, prove savings and reliability, then move into higher-take-rate categories. In one supplier relationship, Sourcerer went from generating none of the factory’s orders to accounting for more thanhalf of its volume.

Markets have always rewarded superior information and purchasing power; Sourcerer’s bet is that AI can make both continuous, global, and autonomous.

Pull Quotes:

“We're building the fully autonomous supply chain.”

“Do the customer discovery, talk to people, see if it's a problem.”

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Bhaskar Sunkara helped build AppDynamics from a laptop on a couch to a $3.7 billion Cisco acquisition.

Bhaskar Sunkara was employee number one and founding CTO at AppDynamics. Over nearly a decade, he helped turn the company into a category-defining application performance monitoring platform before Cisco acquired it on the eve of its planned IPO. He is now CEO of Bicycle, where he is building AI analytics agents for revenue-critical business data.

This conversation centers on a hard shift in enterprise software: moving from systems that display information to systems that detect problems, explain causes, and take action. Bhaskar argues that dashboard-driven operations are dying because no team can manually monitor every product, region, supplier, conversion path, and technical dependency in real time.

He makes the same critique of chat-based analytics. Letting users ask questions in plain English improves access to data, but it still depends on someone asking the right question at the right time. Bicycle takes a different approach. Its agents monitor business signals such as bookings, checkout conversion, payment approvals, and orders, then work through a decision tree spanning technical, commercial, supplier, inventory, pricing, and external factors.

The discussion also breaks down how Bhaskar and the AppDynamics team created focus before scale. They narrowed the product to Java, sold to operations rather than developers, built specifically for production, and used production proofs of concept to establish trust. That confidence eventually led to a self-service product customers deployed in production without assistance.

Bhaskar also explains why Bicycle chose transactional industries including travel, retail, and payments, where delayed decisions directly translate into lost revenue. He walks through the company’s “DEAL” framework: detect, explain, act, and learn.

In Today’s Episode We Discuss:

00:01 Introducing Bhaskar Sunkara

00:24 From India to Silicon Valley

03:59 Lessons from Building AppDynamics

07:10 Why DevOps Remains a Fuzzy Concept

08:52 Selling to Operations Instead of Developers

10:20 Running Proofs of Concept in Production

12:21 Launching AppDynamics Lite as a Self-Service Product

14:23 Cisco’s $3.7 Billion AppDynamics Acquisition

16:34 Learning Enterprise Scale Inside Cisco

18:05 The Origin of Bicycle

19:42 Turning Business Signals Into Action

21:47 From Business IQ to Proactive Analytics

23:43 Moving from CTO to CEO

25:08 Bicycle’s Product Evolution

28:38 Choosing Travel, Retail, and Payments

31:21 Reducing Resolution Times Across 45,000 Locations

34:49 Why Dashboard-Driven Operations Are Dying

37:01 Why Companies Are Over-Indexing on Chat

40:03 Selling Analytics to Business and Data Teams

42:00 Building the Cursor for Analytics Teams

43:36 How AI Is Changing Product Development

46:23 Prototyping Products With Claude

47:27 Bicycle’s Long-Term Vision

50:01 Rapid-Fire Founder Lessons

53:23 The Most Overrated Startup Metric

Pull Quotes

“Dashboard operations driven by dashboards are kind of dying.”

“You have to get the revenue back on track. You’re losing money.”

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Dark Matter Lab can give Berkeley researchers up to $1 million before incorporation, split between capital, compute, legal, and operating resources.

Emmanuel Vallod is a partner and head of venture research at Hivemind Capital, where he invests across AI, crypto, payments, and frontier technology. He has taught at Berkeley for 14 years, worked at BlackRock, built an AI infrastructure startup, and now uses research as a direct pipeline for identifying technical founders and emerging markets.

This episode examines a funding gap most venture firms avoid: researchers who may have breakthrough technology but are still inside university labs, years away from incorporation, and unable to access enough compute, data, or hardware to test whether their work can become a company.

Emmanuel’s counterintuitive claim is that the next major AI companies will be technology-first. Distribution was once the primary moat, but he argues that durable AI businesses will increasingly begin with technical breakthroughs that make large-scale distribution possible. Most startups built as wrappers around foundation models will not have lasting defensibility unless they control difficult-to-obtain data or operate inside workflows requiring deep expertise.

He also explains why timing in venture is less precise than investors admit. The companies that become unicorns and decacorns rarely look fashionable when they begin. They often appear too early, too strange, or technically impractical. The investor’s job is not simply to identify a large market, but to determine whether the research direction is correct, whether the technical team understands what it does not know, and whether supplying resources now could compress several years of progress.

In Today's Episode We Discuss:

00:01 – Emmanuel Vallod’s Journey from Math to Venture Capital

03:04 – How Teaching at Berkeley Shapes His Investing

05:09 – Building an AI Infrastructure Startup Before the AI Boom

07:53 – The Communication Gap Between Technical Founders and VCs

09:30 – Why Founders Must Tell Investors the Bad News

11:05 – From Failed Startup to Venture Capital

14:26 – Hivemind Capital’s Evolution from Fintech to Frontier Technology

17:09 – The Case for Data Centers in Space

19:16 – The Market Potential of Space-Based Computing

21:41 – Technical Barriers to Space Data Centers

23:36 – Why Timing Is Venture Capital’s Hardest Problem

26:17 – Why Future Unicorns Rarely Look Like the Cool Kids

28:33 – Conviction, Luck, and Investing Too Early

29:32 – The Origin of Dark Matter Lab

31:34 – Funding Researchers Before Company Formation

33:34 – How Compute and Data Bottlenecks Delay Breakthrough Research

36:03 – How Dark Matter Lab Differs from DARPA and Accelerators

39:28 – Evaluating Deep-Tech Companies Before They Exist

41:35 – Identifying the Right Research Direction

Dark Matter Lab is designed around that compression. Researchers can receive funding before forming a company, without Hivemind claiming rights to their intellectual property. Emmanuel describes one team that expected to wait three years for a federal grant and another that needed roughly $500,000 just to create an initial training dataset.

Pull Quotes

“The unicorns or decacorns at a point in time were never the cool kids when they started.”

“Stop being an asshole.”

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Before Atrios existed, Taylor Offer was already making well over six figures a year from warm introductions.

Taylor is the founder and CEO of Atrios, an A16Z-backed relationship intelligence platform built to turn warm introductions into a repeatable go-to-market channel. Before Atrios, he spent 10 years building an e-commerce company that reached tens of millions of dollars in revenue. He started his sales career at LinkedIn, later spent millions on Facebook advertising, and earned well over six figures annually through referral agreements and introductions before productizing the model. Atrios is about a year old, has roughly 10 employees, and raised a little over $4 million.

Taylor’s central argument is blunt: AI has made cold outreach so cheap and abundant that traditional outbound channels are collapsing under their own noise. Companies can still build excellent products and strong sales teams, but many cannot reliably earn the first qualified meeting.

Atrios attempts to solve that problem by activating the trusted people already surrounding a company, including customers, investors, employees, and community members. These “tastemakers” introduce products they genuinely believe will help someone in their network, while Atrios handles qualification, scheduling, and compensation. Taylor believes this model can become a major distribution channel because poor recommendations carry an immediate cost: people who repeatedly send bad introductions destroy their own credibility.

In Today’s Episode We Discuss:

00:01 - Introducing Taylor Offer and Atrios

00:30 - Taylor’s Early Obsession With Distribution

01:45 - Building a College T-Shirt Business

03:03 - From LinkedIn Sales to Influencer Marketing

04:28 - The Justin Bieber Burrito Hoax

05:56 - Selling His Company and Returning to Building

07:20 - The Six-Figure Referral Income That Inspired Atrios

10:04 - Tastemaker Relationship Management vs. Affiliate Marketing

11:05 - Atrios’ Team, Funding, and Early Customers

14:17 - How AI Spam Broke Traditional Outbound Sales

16:36 - How Atrios Qualifies and Prices Sales Meetings

20:30 - Why Taylor Chose A16Z Speedrun

24:29 - Restoring Mutual Value to Sales

26:26 - Why Taylor Is Building Atrios in New York

29:01 - Padel, Basketball, and a $4,500 Rolex

31:18 - Why Warm Introductions Could Become the Next Facebook Ads

33:36 - Protecting Trust in a Paid Introduction Marketplace

35:07 - Hard Work, Broken GTM, and Bad Founder Advice

40:51 - Irrational Ambition, Creativity, and Discipline

43:21 - Meditation, Dream Work, and Looking Inward

46:17 - Building From a Vision of the Future

The episode gets tactical about defining an ICP, writing binary qualification questions, calculating ACV and close rates, and determining what a qualified meeting should cost. Taylor also recounts making 100 cold calls a day at LinkedIn, driving across the country selling fraternity shirts, and awarding a $4,500 Rolex through a pressure-filled free-throw contest.

Atrios is a modern wager on an old commercial truth: trust remains the highest-leverage distribution channel.

Pull Quotes:

“AI has made it so easy to spam at scale.”

“Execution is a measuring stick for how much you believe in your idea.”

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A $300 million Indonesia launch can be reckless when first-year revenue may reach only $5 million.

Frank Lavin, a fellow at the USC Dornsife Center for the Political Future, served in the Reagan White House and later became U.S. ambassador to Singapore. He also worked on international trade policy, banking, advisory work, and helping global brands enter China through e-commerce.

Frank explains why international expansion creates a J-curve of higher costs, slower returns, regulatory complexity, and operational inefficiency. His warning to founders is direct: waiting for the perfect market-entry moment often means arriving after competitors have built distribution, relationships, and brand recognition.

Instead of building a fully integrated foreign operation, companies should identify the smallest legitimate footprint needed to start selling and learning. That could mean using a distributor, outsourcing logistics, forming a joint venture, buying locally, or delaying major capital expenditures until demand is proven.

In Today's Episode We Discuss:

00:01 - Introducing Frank Lavin and His Career Across Government, Trade, and Global Markets

00:55 - From Canton, Ohio to Georgetown’s School of Foreign Service

01:47 - How the Cold War Shaped Frank’s Interest in Foreign Policy

03:41 - Balancing Ideology, Pragmatism, Data, and Market Economics

05:08 - Leadership Lessons From the Reagan White House

07:36 - What State-Level Economic Experiments Reveal About Government

08:23 - How China Combined Private Enterprise With One-Party Rule

11:18 - Why Populism Thrives on Grievance-Based Messaging

14:14 - Aspirational Leadership From Roosevelt, Lincoln, JFK, and Obama

16:24 - What Business Leaders Misunderstand About Government

19:03 - Why Technology Companies Became Political Power Brokers

20:40 - How Singapore Became a Free-Trade Hub for Southeast Asia

23:11 - Why American Consumer Brands Succeed in China While Tech Platforms Struggle

25:24 - When Startups Should Begin International Expansion

27:45 - Why Waiting for the Perfect Market Creates Perfect Competition

29:18 - Entering New Markets With Software and AI Products

30:23 - The Smallest-Footprint Strategy for Managing Geopolitical Risk

32:26 - Selling Into Governments, Defense, and Regulated Industries

34:06 - Humanitarian Missions in Ukraine and the Cost of Russian Aggression

35:47 - Why Ukraine’s Resistance Matters to China and Global Deterrence

37:37 - Putin’s Psychology, Nationalism, and Strategic Miscalculation

41:24 - Reassessing Ronald Reagan’s Presidency and Legacy

44:46 - What Reagan Might Think About America’s Current Foreign Policy

46:44 - How Cold War Success Created Strategic Complacency

49:46 - Preparing America for Its Next 250 Years

Pull Quotes

“Don’t wait for a perfect moment or you’re going to face perfect competition.”

“There already is a conversation taking place about your brand, your product, your company.”

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Arlo Industries is replacing 100-year-old radar assumptions with a passive sensor mesh built to track small drones in real time.

Deo Arlo is the founder and CEO of YC-backed Arlo Industries. Born during violent riots in Indonesia, he later spent six years in Israel, experienced rockets overhead, and spent time in Ukraine studying how aerial defense performs under real battlefield conditions. His background also includes robotics research and patents, where he learned that better software can reduce hardware complexity and eliminate unnecessary points of failure.

The central argument of this episode is that interceptors are not the real bottleneck in counter-drone defense. Deo argues that shooting down drones is already crowded with companies making marginal improvements, while sensing remains neglected. Better awareness, not simply a bigger weapon, determines whether an interceptor can identify, classify, and engage a threat accurately.

In Today’s Episode We Discuss:

00:01 - Introducing Deo Arlo and Arlo Industries

00:28 - Growing Up Around Violence in Indonesia

01:55 - Living Through Rocket Attacks in Israel

02:43 - Why Conflict Should Be Concise and Precise

03:54 - What Traditional Air Defense Systems Still Miss

05:14 - Applying Robotics Principles to Defense Technology

06:13 - Choosing an Unconventional Founder Journey

07:13 - Tracking Small Drones With Greater Accuracy

08:36 - Why Modern Interceptors Still Depend on Old Radar

09:44 - Linear Sensor Costs and Compounding Accuracy

10:46 - What Investors Misunderstand About Drone Defense

12:20 - Expanding Arlo’s Vision From Earth to Space

13:31 - Competing Through Speed, Data, and Infrastructure

14:37 - What Ukraine Teaches About Battlefield Innovation

15:45 - How Arlo’s Passive Sensor Mesh Works

16:46 - Why Bigger Weapons May Not Solve Future Conflict

17:39 - Building Defense Systems Like an Immune System

19:00 - Why Defense Innovation Is Spreading Beyond Ukraine

20:08 - Exploring New Sensing Modalities Beyond Radar

21:58 - The Wrong Lessons Founders Take From Ukraine

23:06 - The Future of Decentralized Air Defense

24:52 - Why Better Awareness Can Matter More Than Firepower

26:15 - Autonomy, Accountability, and Battlefield Black Boxes

28:07 - Whether AI Will Scale Peace or Conflict

30:35 - Blurring the Line Between Civilian and Military Security

32:25 - Why Private Drone Defense Could Become Normal

33:57 - Technology Revolutions and the Recurrence of War

36:47 - Maintaining Moral Clarity in Defense Technology

The conversation includes Arlo’s head-sized, man-portable sensors that automatically connect and calibrate through their own mesh network. Deo explains why products should reach the front line tomorrow rather than wait for perfection while battlefield requirements change. He also shares why founders should gather advice as data, trust their own judgment, and deliberately build the scar tissue that comes from making their own mistakes.

Arlo’s architecture revives an old biological truth: resilient systems survive by distributing awareness and response.

Pull Quotes

“Information is power, and the more you know, the better you can strike, and maybe you can strike less.”

“Power doesn’t corrupt, power amplifies.”

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A multi-turn prompt attack convinced one of the biggest providers’ voice agents to issue a $150 discount code.

Tarush Agarwal is the co-founder and CEO of Cekura.ai, which builds testing and verification infrastructure for voice agents. Before founding Cekura, he studied computer science at IIT Bombay and worked on low-latency quantitative trading systems in London and Chicago, where teams optimized performance at roughly seven to nine nanoseconds. Cekura entered Y Combinator after pivoting from a legal voice-agent product, later raised a $2.5 million seed round, and now works with more than 200 customers while running millions of simulations.

Voice agents can perform well in controlled tests and still fail during real conversations. Interruptions, background noise, mixed languages, transcription errors, emotional callers, and multi-turn manipulation can expose problems that never appear in a text-based evaluation.

Tarush’s most counterintuitive claim is that better models do not automatically produce better voice agents. Around half of Cekura’s customers still use GPT-4.1 because newer reasoning-heavy models can introduce delays that do not work in live calls. Production performance depends on the full system, including latency, transcription, turn detection, interruption handling, speech quality, instruction following, and the infrastructure connecting each component.

In Today’s Episode We Discuss:

00:01 Introducing Tarush Agarwal and Cekura.ai

00:27 From IIT Bombay to quantitative trading

02:45 Founder life versus low-latency engineering

04:26 Building voice agents for personal injury law firms

06:27 Pivoting during the first week of Y Combinator

07:11 Early growth, the $2.5 million seed round, and customer focus

09:49 The current state of voice AI

12:49 The metrics that determine voice-agent quality

15:17 Compliance, healthcare, and high-stakes conversations

18:02 How multi-turn prompt attacks exploit voice agents

19:17 The quiet problem with how companies run evals

22:33 Why testing voice agents through text is insufficient

24:06 Cascading systems versus speech-to-speech models

25:36 Building realistic simulation environments

27:12 What changed in voice AI over two years

29:29 Public benchmarks, latency gains, and accuracy limits

31:14 Cekura’s long-term vision beyond voice

32:16 Moving from founder-led sales to a dedicated GTM team

33:57 The product metric Tarush watches every day

35:37 Why voice AI could become larger than software

Cekura began after Tarush and his co-founders spent three hours after dinner manually calling their own legal voice agent. He explains why healthcare teams must simulate distressed patients, how multi-turn testing exposed the $150 discount exploit, and why his team sometimes shipped a bug fix before the customer reporting it had finished the call.

The episode returns to an old engineering principle: reliability begins when reality is allowed to break the system.

Pull Quotes

“Everyone talks about evals. I don’t think most people know how to do it correctly.”

“You need to build your own evals. You need to own your evals.”

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What happens when the engineer companies call during a technical crisis finally gets to build something that is already working?

Misha Makara is the co-founder and CTO of Rally AI, an AI-powered platform positioning itself as a company’s first PR hire. After more than 15 years helping startups, venture portfolios, and enterprise teams solve technical failures—including work with Gaingels portfolio companies and the SEC-regulated digital securities platform tZERO—Misha is now tackling a different problem: managing demand from more than 200 companies waiting to use Rally without sacrificing product quality.

In Today's Episode We Discuss:

00:01 - Introducing Misha Makara and Rally AI

00:41 - Cybersecurity Roots and Kodak’s Digital Camera Legacy

02:26 - Experimental Design for Founders and Engineers

04:10 - From Wayfair and Dell to Startup Turnarounds

05:57 - Apollo Cameras and Black-and-White Barns

07:31 - Lessons From High-Volume Venture Portfolios

09:35 - Risk Cycles, Late-Stage Liquidity, and Early-Stage Investing

14:46 - Why More Features Fail to Save Startups

17:37 - Founder Coachability and the Ability to Pivot

19:50 - Bundling Risk From Hyperscalers and AI Platforms

22:27 - Why First-Mover Advantage Is Overrated

24:20 - AI Development Speed and the New Product Bottleneck

27:07 - Good Technical Debt vs. Bad Technical Debt

30:21 - Rally AI as a Startup’s First PR Hire

32:33 - Who Rally AI Works Best For

33:47 - Measuring PR Success and Building Media Relationships

35:11 - Rally AI’s Long-Term Vision

36:12 - The Three-Sided PR Marketplace

37:41 - Second-Mover Advantage and Disruptive Technology

41:25 - High-Signal Interviewing and Hiring Engineers

43:20 - When to Hire a Fractional CTO

Misha also shares the interview technique he uses to expose candidates who only know the textbook answer, the turnaround strategy that rescued a government software company, and the lesson his first mentor—one of the engineers behind Kodak’s digital camera—taught him about setting limits on every experiment.

Rally may be using AI to accelerate public relations, but Misha’s core philosophy is much older: define the destination, test deliberately, and know when it is time to take another road.

Pull quotes:

“Try to find a way to do less. Do the things that actually matter.”

“Hiring a fractional CTO to raise money—don’t do that. Bad, bad day.”

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What happens when the person who helped popularize Lean UX looks at AI-powered product teams and sees the same old trap returning—only faster?

Jeff Gothelf is the co-creator of the Lean UX movement, author of Lean UX, Sense and Respond, and Who Does What By How Much?, and co-founder of Sense & Respond Learning. After years helping teams move away from waterfall thinking and deliverables-for-deliverables’ sake, Jeff now trains product leaders to make better decisions through customer evidence, outcomes, and real behavior change.

In this episode, Jeff joins Brian Bell to unpack why AI is making product work faster—but not automatically better. They discuss why synthetic users are not a replacement for real customer conversations, why product managers are not “CEOs of the product,” and why the next great differentiator may be human taste, opinion, and originality in a sea of AI-generated sameness.

In Today's Episode We Discuss:

00:01 — Welcome & Jeff Gothelf Introduction

00:30 — From Failed Musician to Early Web Designer

03:38 — UX vs. Lean UX

05:19 — Waterfall Software and Wasted Design Work

07:31 — Lean UX and Just-Enough Design

08:43 — The AOL Moment That Changed Jeff’s Thinking

10:19 — Internet, Cloud, and Faster Feedback Loops

12:21 — AI’s Impact on UX and Product Teams

13:18 — Why AI Won’t Replace Product Roles

15:15 — Mass Production, Customization, and Human Taste

16:24 — Strong Product Opinions as Differentiation

18:44 — UX as the Competitive Advantage

21:02 — Founder Advice for Building in 2026

23:40 — Problem, Market, and Solution Validation

24:17 — Synthetic Users vs. Real Customer Interviews

28:01 — Finding a Problem Worth Solving

30:00 — Avoiding Bias in Customer Research

32:07 — Taste, Judgment, and AI Slop

34:05 — What the Next Generation Should Work On

37:41 — The Book That Aged the Worst

39:24 — Liberal Arts, Humanity, and Anti-AI Rebellion

41:40 — Using AI as a Harsh Thinking Partner

43:41 — Product Managers Are Not CEOs

44:27 — Disagreements, Qualitative Benefits, and Customer Value

46:02 — Getting Out of the Deliverables Business

48:19 — Customer Conversations as a Practice Muscle

Pull quote:

“There’s literally no excuse not to do this today.”

Another one:

“Producing stuff is not the production of value.”

Jeff’s story starts with rock bands, HTML, and a circus tent—but it ends with a warning for every founder building with AI: the tools may be new, but the hard part is still understanding humans.

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What if the most important financial product in your life isn’t for saving, investing, or spending—but for giving money away?

Adam Nash has spent his career building consumer financial products people actually trust. He was VP of Product at LinkedIn through its IPO, President & CEO of Wealthfront as it helped define modern fintech, VP of Product at Dropbox, and an early angel in companies like Figma, Gusto, Opendoor, and Firebase. Today, he’s co-founder of Daffy, the donor-advised fund for you, which has crossed $1B in charitable assets in under five years.

In this episode, Adam joins Brian to unpack why giving has been one of the most overlooked product categories in finance, and why donor-advised funds shouldn’t just be tools for the ultra-wealthy.

In Today's Episode We Discuss:

00:01 - Introducing Adam Nash and Daffy’s Mission

02:20 - Adam’s Origin Story: Money, Family, and Human-Computer Interaction

05:24 - Fintech Before “Fintech” Had a Name

06:16 - What Wealthfront Taught Adam About Trust, Culture, and CEO Leverage

10:15 - Operator Playbooks from Apple, eBay, LinkedIn, and Beyond

11:27 - LinkedIn vs. eBay: Network Effects, Operational Excellence, and Missed Waves

15:28 - From Wealthfront to Greylock, Dropbox, and the Daffy Idea

18:00 - Donor-Advised Funds Explained and Why Daffy Exists

22:27 - The 401(k), IRA, or Wallet for Charity

25:03 - Daffy’s Business Model: Membership Fees Over AUM

27:11 - Product Innovation in Giving: Transfers, Family Plans, Crypto, APIs, and Private Stock

32:54 - The Donor-Advised Fund Critique: Warehousing Money or Unlocking Giving?

37:57 - Teaching Personal Finance for Engineers at Stanford

41:37 - Adam’s Angel Investing Framework After 160+ Startups

43:54 - Why Seed Investing Takes a Decade

46:12 - Founder-Market Fit, Distribution, and Knowing Why You’re on the Cap Table

48:28 - The Venture Paradox: Saying No Sounds Smart, Saying Yes Makes Returns

50:37 - Figma, Dylan Field, and Founders Who Change Adam’s Mind

One of Adam’s sharpest lessons: great founders don’t just find a market gap. They care about the problem so deeply that they can survive a decade of being early, misunderstood, or underestimated.

From LinkedIn’s network effects to Wealthfront’s trust engine to Daffy’s mission of making people more generous more often, Adam’s career has been a study in building products around human behavior—not just spreadsheets.

Because sometimes the next great fintech company isn’t helping people keep more money. It’s helping them give it away better.

Subscribe on Spotify: https://open.spotify.com/show/6Ga6v0YUsHotLhjap67uu5

Subscribe on Apple Podcasts: https://podcasts.apple.com/us/podcast/ignite-conversations-on-startups-venture-capital-tech/id1709248824

Follow Adam Nash on LinkedIn: https://www.linkedin.com/in/adamnash/

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Visit Our Website: https://www.teamignite.ventures

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