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.

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What does it take to spot a generational founder before there’s a product, revenue, or even a fully formed company?

Jeffrey Becker has built his career around that question. As General Partner at Antler, he co-leads the firm’s US Fund from New York, backing founders at inception through Antler’s day-zero, residency-based pre-seed model. With 27 offices globally, roughly 1,900 portfolio companies, and standout names like Lovable, Airalo, Micro1, and Pixverse, Antler is making a bold bet: the best time to understand a founder is before the startup noise begins.

Before Antler, Jeff spent nine years at LinkedIn during its hypergrowth era, holding nine roles across sales and leadership as the company scaled from post-IPO momentum into one of the defining platforms of the modern internet. That experience shaped how he thinks about culture, focus, communication, and what separates high-performing teams from average ones.

In Today's Episode We Discuss:

00:01 - Introducing Jeffrey Becker and Antler’s Day-Zero Model

00:57 - Jeff’s Origin Story: Competition, Sales, LinkedIn, and Angel Investing

03:48 - Backing Maniacs at Inception

04:33 - Lessons from LinkedIn’s Hypergrowth Era

05:58 - Why Jeff Tells People Not to Become VCs

08:22 - How Antler Works Before a Company Exists

10:54 - Why Antler Increased Its Check Size to $600K

12:49 - How Antler Differs from YC and Traditional Accelerators

14:52 - Antler’s Global Founder Funnel and Selection Process

16:05 - How Founders Can Stand Out in an AI-Generated Pitch World

18:43 - Why “I Want to Build a Billion-Dollar Company” Can Be a Red Flag

21:57 - The Magic of Founders Doing Their Life’s Work

23:00 - Risk, Diversification, and the Math of Inception Investing

24:30 - Why More Early-Stage Bets Can Improve Venture Outcomes

26:40 - Using SPVs and Follow-On Capital to Double Down on Winners

29:03 - The LP Retreat and the Future of Emerging Managers

30:56 - How AI Is Collapsing the Cost of Building Startups

32:44 - Agentic Company Builders and the Limits of AI-Generated Startups

34:24 - Jeff’s Content Engine: Substack, Podcasts, and AI Workflows

36:35 - The Hidden Risk of Overfunding and High Valuations

38:42 - Boards, Governance, and Staying Aligned with Founders

40:31 - Antler Founders Who Redefined What a Maniac Looks Like

43:12 - Why Meeting Great Founders Keeps VCs in the Game

45:06 - The Sharpest Writing in Venture Today

46:26 - The Best Advice Jeff Lives By: Be Different to Be Better

47:48 - A Cold Intro That Turned Into a Standout Founder Bet

50:15 - The Most Overrated Metric in Pre-Seed Venture

53:14 - Why Jeff Changed His Mind on Valuation Discipline

54:18 - Breaking Rules to Avoid Missing Generational Founders

Pull quotes:

“Don’t do VC unless I can’t talk you out of it.”

“To be better than average, you have to be different.”

Jeff’s story started with competition—as a younger brother, athlete, sales leader, founder, and angel investor. Today, that same instinct shows up in how he evaluates founders: not by who looks polished on paper, but by who is wired to keep going when the game gets brutal.

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Jonathan David Nelson has lived a stranger founder journey than most: missionary kid in Latin America, trauma nurse, software engineer, founder community builder, and now capital markets contrarian. After building Hackers and Founders from a bar meetup into a global startup community, Jonathan now runs HF Capital—an AI-native investment bank focused on IPOs, secondaries, and M&A.

In this episode, Jonathan breaks down why he believes the U.S. public markets are failing most companies below decacorn scale, why the London Stock Exchange may be a better path for growth-stage startups than another brutal private round, and how AI could rebuild the infrastructure behind investment banking.

In Today's Episode We Discuss:

00:01 - Introduction to Jonathan David Nelson and HF Capital

01:23 - From Missionary Kid in Latin America to Trauma Nurse

03:10 - How Hackers and Founders Started as a Bar Meetup

04:26 - Why Fundraising Is a Brute Force Algorithm

05:37 - Understanding Capital Flow Like Blood Flow

08:15 - Advising the SEC and the Limits of Crowdfunding

09:40 - Why Startup Exits Remain the Broken Piece

10:47 - Why U.S. Public Markets Fail Smaller Companies

13:02 - The Origin of HF Capital and Tokenized Stock

15:26 - Discovering the London Stock Exchange Alternative

17:05 - Lower IPO Costs, Sponsor Banks, and Less Litigation

19:23 - Why Founders Still Default to U.S. Markets

21:19 - The “50 and 50” Growth-Stage Startup Profile

24:28 - When an IPO May Not Be the Right Move

26:34 - SPACs Explained and Why They Often Collapse

30:32 - Private Rounds vs. IPOs for Growth-Stage Companies

31:37 - Liquidation Preferences and Founder Dilution

35:24 - Why Boards Resist Alternative IPO Paths

36:23 - Capital Markets as a “Capital API”

38:02 - Building an AI-Native Investment Bank

40:14 - Why HF Capital Is Becoming the Bank, Not Just Selling Software

41:11 - The Coming Explosion of Smaller AI-Native Startups

42:26 - Secondaries, Latin America, and Undervalued Growth Companies

44:39 - What Startup Secondaries Actually Are

45:30 - Anthropic Hype, SPVs, and Risky Secondary Deals

47:13 - Custody, Forward Contracts, and Secondary Market Due Diligence

Jonathan is blunt, funny, and allergic to sacred cows. His view is simple: venture, IPOs, secondaries, and capital formation are not laws of nature. They are systems. And broken systems can be hacked.

Pull quote: “The system is broken, must fix. The ecosystem is sick, must heal.”

Pull quote: “I think of capital markets, stock markets as a capital API.”

From wiping asses and saving lives in the ER to reengineering how founders access liquidity, Jonathan’s story is a reminder that sometimes the best person to fix finance is the outsider who never agreed to pretend it made sense.

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What does a founder learn after selling four companies, burning one to the ground, and spending years building AI agents before “AI agents” became the phrase of the moment?

Dennis Mortensen has the scars to answer that. A Danish-born, New York-based serial founder, Dennis has built and exited companies across analytics, media optimization, and AI, including X.ai, the AI scheduling assistant that raised $44 million from FirstMark and others before being acquired by Bizzabo in 2021. Today, he is building LaunchBrightly, a company automating product screenshots for help centers—a problem that sounds painfully unsexy until you realize every software company has it, every product team pays for it, and every outdated screenshot quietly creates support debt.

In this episode, Dennis joins Brian Bell for a wide-ranging masterclass on founder judgment, painful pivots, and the unglamorous infrastructure problems that make or break software companies.

In Today's Episode We Discuss:

00:01 – Meet Dennis Mortensen

01:25 – From IBM Dreams to Serial Founder

03:51 – Selling His First Company During the Dot-Com Era

05:00 – Building in Budapest and Moving to New Yor

07:08 – Why European Founders Look West

09:25 – The “Expensive MBA” Startup Failure

11:52 – Why Dramatic Pivots Are Overrated

13:51 – The Marketplace Mistake That Killed the Business

16:27 – When the Market Is Telling You You’re Wrong

18:23 – The Twitter Pivot and Founder Mythology

20:46 – Why Business Model Flexibility Matters

22:35 – Founder Bias, Persistence, and Not Dying

24:30 – Shutting Down and Moving On

26:34 – Building IndexTools and Real-Time Analytics

31:34 – Why Founders Should Take M&A Calls

35:05 – How Optionality Creates Future Exits

36:45 – From Yahoo to Visual Revenue

40:01 – The “List of Hate” Startup Ideation Process

44:57 – Why Founder Focus Beats Angel Investing

48:43 – Building Visual Revenue for Digital Publishers

53:44 – Selling Visual Revenue to Outbrain

54:58 – The Pain Behind X.ai

55:26 – Market Challenge vs. Science Challenge

56:59 – Why Scheduling Was a Worthy AI Problem

01:00:40 – Testing X.ai with Human Assistants First

01:02:31 – Wizard-of-Oz Testing and Scheduling Complexity

01:05:34 – Building AI Before Modern LLMs

01:06:07 – 47 Intents and 32 Million Labeled Data Points

01:10:13 – Lessons from the X.ai Journey

01:11:14 – Why Winning the Turing Test Was the Wrong Goal

01:14:55 – When Customers Stop Being Sold and Start Buying

01:17:04 – Introducing LaunchBrightly

01:17:43 – Building for the Love of the Sport

01:19:20 – Why LaunchBrightly Exists

Pull quotes:

“If you can just figure out a way to just not die, that’s probably the best way you can somehow win.”

“I have a little list of hate on my phone.”

“It’s okay to be a machine doing machine things in a machine-like way.”

Dennis’s story is not the sanitized founder mythology of perfect timing and clean wins. It is a sharper, more useful version: build, sell, fail, learn, repeat—and keep choosing problems painful enough that someone already has a human doing the work.

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What if the hardest part of raising venture capital isn’t building the company — but understanding the game being played across the table?

Charlie O’Donnell has spent more than two decades inside the venture machine: first on the LP side at the General Motors Pension Fund, then as the first analyst at Union Square Ventures, then helping First Round Capital build its New York presence, where he sourced early deals like GroupMe and SinglePlatform. In 2012, he founded Brooklyn Bridge Ventures, the first VC fund based in Brooklyn, writing first checks into more than 100 companies and becoming one of New York’s most accessible early-stage investors.

Now, after stepping away from active fund investing, Charlie is focused on helping founders understand what investors often won’t say out loud. His new book, Founder Unfriendly: What Investors Won’t Tell You About Getting Funded, pulls back the curtain on why good companies get passed on, why mediocre companies still get funded, and why fundraising is less about “being impressive” than proving fund-returning potential.

In Today's Episode We Discuss:

03:55 — Surviving the Dot-Com Crash and Negative Returns

06:29 — What LPs Don’t See About Venture Capital

09:39 — Why VCs Are Still Middlemen in the Startup Ecosystem

11:33 — Lessons from Being the First Analyst at Union Square Ventures

14:02 — Building a Network Without Money or an Ivy League Background

17:10 — Creating Access Through Community and Events

19:57 — Joining First Round Capital After a Failed Startup

20:31 — Pitching During the 2008 Financial Crisis

21:01 — Helping Spark the Foursquare Funding Race

22:28 — Why New York Needed a Different VC Playbook

24:26 — GroupMe, SinglePlatform, and Early Wins at First Round

25:33 — Price Sensitivity vs. Price Takers in Early-Stage VC

28:05 — Why One Lucky Deal Is Not an Investment Strategy

32:15 — Leaving First Round to Launch Brooklyn Bridge Ventures

34:21 — Why Charlie Walked Away From Active Fund Investing

37:09 — Writing Founder Unfriendly for the 99% of Founders

39:20 — Why Good Businesses Still Get Rejected by VCs

41:00 — Pitching Potential Instead of Conservative Promises

45:35 — Why Fundraising Is a Potential Conversation

46:10 — What Founders Can Learn From Parenting a Small Child

47:30 — Why Every Slide Needs to Scream Fund-Returning Outcome

48:30 — Team, Market, and Traction as the Core Pitch Narrative

50:48 — How Founders Can Redirect Bad Investor Questions

53:28 — Controlling the VC Meeting Without Being Obnoxious

55:47 — Why Founders Should Read Founder Unfriendly

56:41 — The One Deal Charlie Wishes Hadn’t Fallen Through

Charlie’s advice is blunt: venture capital is not a validation system. It is a financial product with its own incentives, blind spots, and pattern-matching problems. Founders who understand that can stop treating rejection as a judgment on their worth — and start pitching the upside investors are actually paid to chase.

As Charlie puts it: “This is not a promise conversation. This is a potential conversation.”

And that may be the real founder lesson: the best pitch is not the safest version of the truth. It is the clearest version of the possible.

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