Fund/Build/Scale

Conventional wisdom says the hardest part of building a startup is building the product.

Shanea Leven says the harder challenge is figuring out what customers will actually pay for.

Before co-founding Empromptu.ai, Shanea spent more than 15 years building products at companies including Google, eBay, Docker, and Cloudflare.

In this conversation, she explains why sales is every bit as complex as engineering, why customer interviews aren't enough to validate an idea, and why early-stage founders need to spend more time testing demand than perfecting roadmaps.

We discuss the case-study approach she uses to find customers, the controversial belief that the only real product validation is money, and what happened when a LinkedIn post generated a 1,000-person waitlist almost overnight. 

Shanea also shares how she used more than 100 customer calls to shape Empromptu’s direction, why she stopped fundraising when the company took  off, and the go-to-market challenges that still keep her up at night.

If you're a technical founder trying to figure out whether you're building something people truly want, this episode offers a practical framework for separating genuine demand from wishful thinking.

Listen to this episode if you're trying to figure out:

why sales validation should happen before you commit to a roadmap

how to find your first customers using the case study method

what a viral waitlist can teach you about product-market fit

how to distinguish customer feedback from customer demand

why technical founders need to learn sales earlier than they think

which early traction signals are worth trusting — and which aren't

how to validate an idea before spending months building it

RUNTIME 49:06
 

EPISODE BREAKDOWN
(3:16) What Is Empromptu.ai, and Who Is it For?

(6:25) Sales Is Just as Complicated as Engineering

(8:14) The Case Study Method for Finding Early Customers

(11:56) Why Al Is Rewriting the Product Playbook

(13:37)   The Only Real Product Validation Is Money 

(17:10) The S***ty Purple Website That Predicted Impromptu's Viral Launch

(21:38) What 100 Waitlist Calls Taught Shanea About Customer Demand

(26:28) "We evolved the platform."

(34:57) "Ninety-nine percent of VCs are great at one thing."

(36:20) The GTM Problem That Still Keeps Shanea Up at Night 

(39:02) A Process for Selecting Your First Sales/Marketing Hires

(40:59) Why She'd Hire a "Scrappy" Marketer Over a Former Meta Employee "Every Time"

(44:05) The Early Traction Signal She No Longer Trusts

(45:28) A 30-day Experiment Founders Can Run To Validate Their Idea

LINKS
Shanea Leven

Empromptu.ai

Empromptu raises $2M pre-seed to help enterprises build AI apps, 12/9/2025, TechCrunch

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Thanks for listening!

 – Walter

If you're building a startup, there are easier industries to choose than healthcare.

Sales cycles are long, regulations are complex, and earning the trust of providers and patients takes years.

In this episode, Mayo Clinic Platform Accelerate Director Jamie Sundsbak explains how healthtech founders can improve their odds of success.

We discuss what Mayo looks for in founding teams, why the program focuses on product development instead of fundraising, how startups use clinical data and physician feedback to refine AI products, and what healthcare systems are actually buying in today's market.

Jamie also shares lessons from reviewing hundreds of startup applications, the founding team profiles that stand out, why some companies gain traction while others stall, and what founders should know before building in one of the world's most regulated industries.

Listen to this episode if you're trying to figure out:

What Mayo Clinic looks for in healthtech founders

How to use customer feedback to improve AI products

Why some healthcare startups gain traction while others struggle

What health systems are actually buying in the AI boom

How to navigate the long road from MVP to clinical adoption

Watch on YouTube: https://youtu.be/whrR0IVHEOY

RUNTIME 38:35
 

EPISODE BREAKDOWN
(1:39) What Mayo Clinic Looks for in Early-Stage Healthtech Startups

(13:31) How Mayo Uses Clinical Data and Physician Feedback to Improve Products

(23:50) The Art of Creating Provider FOMO

(25:32) The Ideal Healthtech Founding Team

(29:29) Why OpenEvidence Won — and What Hospitals Are Buying Today

(36:03) Healthcare Is Hard. Here's Why Founders Keep Choosing It Anyway

LINKS

Jamie Sundsbak

Mayo Clinic Platform Accelerate

Most U.S. doctors are quietly using this AI tool. Few patients know about it, NBC News, 5/13/2026

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Thanks for listening!

 – Walter

Most startup advice assumes the biggest challenge is finding customers. But what happens when the demand is obvious and the real question is whether the technology can actually work at scale?

In this episode of Fund/Build/Scale, I sit down with Stanford professor and Inception Labs founder and CEO Stefano Ermon to discuss how a breakthrough research paper evolved into a venture-backed AI company serving enterprise customers.

Stefano explains why his team believed they needed a 10x advantage in speed, cost, or quality to compete with industry giants, and how they convinced investors to back an idea that was still largely unproven.

We explore the realities of building a deep-tech startup, including raising capital before product-market fit, assembling a world-class founding team, learning enterprise sales as a first-time CEO, pricing a new category of technology, and competing with companies like Google, OpenAI, and Anthropic.

Along the way, Stefano shares practical advice for technical founders trying to transform research into a business, de-risk ambitious ideas, and create evidence that investors can believe in before the market fully understands what they're building.

In this episode:

Why startups need a 10x advantage to stand out

Turning a research paper into a venture-backed company

Raising capital when the biggest risk is technical, not market demand

Why Inception Labs built before talking to customers

Learning enterprise sales as a first-time founder

The challenge of pricing a fundamentally new technology

What happened when Google announced its own diffusion models

How deep-tech founders can de-risk ideas before fundraising

The hiring lesson Stefano wishes he had learned earlier

📺 Watch this episode on YouTube 

RUNTIME 46:03
EPISODE BREAKDOWN
(0:00) From Research Paper to Startup

(2:11) Why AI Is So Expensive

(4:34) Turning Research into a Company

(7:47) The Bet Behind Inception Labs

(9:01) Raising Money for an Unproven Idea

(12:24) Building the Right Team

(14:27) Dividing Founder Roles

(16:32) Building Before Customers

(20:07) When Customers Started Calling

(21:47) Learning to Sell

(25:04) Pricing a New Category

(26:34) The Wrong First Customers

(31:16) Why Startups Can Beat Giants

(34:04) The 10x Rule for Startup Differentiation

(39:20) Building a Durable AI Company

(41:03) How to De-Risk a Deep Tech Startup

(42:40) The First 90 Days After Funding

(43:54) One Founder Lesson He Learned Too Late

LINKS

Stefano Ermon

Inception Labs

What Are Diffusion Models?, Stanford HAI

Inception raises $50 million to build diffusion models for code and text, TechCrunch, 11/6/2025

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Thanks for listening!

 – Walter.

Most technical founders can explain how their product works.

Far fewer can clearly explain why a customer should care.

In this episode, Cribl CMO Abby Strong shares what she has learned from building go-to-market teams and helping scale one of the fastest-growing companies in data infrastructure.

Drawing on her background in IT operations, product, and marketing, Abby explains why founders often struggle with messaging, how to know when you've found product-market fit, and what it takes to build demand before you have a large team or budget.

We also discuss category creation, customer discovery, the early signals that the market is pulling your product, and why your first marketing hire is probably not a CMO.

Watch this episode on YouTube: https://youtu.be/9TnFwRgy2fw

RUNTIME 45:19
 

EPISODE BREAKDOWN
[3:56] Why Technical Founders Struggle With Marketing

[09:58] When Should You Hire Your First Marketer?

[11:29] Marketing Before You Have A Marketing Team

[13:46] Why Marketing Takes Longer Than Founders Expect

[19:35] Your First Marketing Hire Is Probably Not A CMO

[25:23] Turning Features Into Outcomes

[27:15] Four Questions Every Founder Must Answer

[28:57] How To Understand A Technical Product

[32:44] Building Cribl's GTM Engine

[36:46] When The Market Starts Pulling

[42:09] Build Trust Before You Chase Scale

LINKS

Abby Strong (LinkedIn)

Cribl

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Thanks for listening!

 

 – Walter

What does it actually mean to build an AI-native company?

In this episode of Fund/Build/Scale, I talk with True Ventures Managing Partner Puneet Agarwal and Gather CEO/co-founder Mayank Mehta about how the startup evolved from an AI-powered customer feedback idea into a broader research and content platform for marketing teams.

We get into founder conviction before product-market fit, what investors actually look for when there’s little external signal, how the company reshaped its go-to-market strategy after realizing the original motion wasn’t working, and why Mayank rebuilt major parts of the business around AI workflows in real time.

There’s also a very tactical discussion about customer discovery, early hiring, AI-native operations, and a weekend growth experiment that produced more meetings in two weeks than the previous year of outbound efforts combined.

RUNTIME 51:33
EPISODE BREAKDOWN

03:18

What True Ventures Looks For at Seed

07:00

What Gather Actually Does

11:42

The Five-Slide Seed Pitch

17:21

What They Got Wrong Early

21:06

Rebuilding the Company Around AI

33:53

The Weekend GTM Experiment That Changed the Company

36:56

How Investors Read Founders Who Don’t Have Signal

41:15

Tactical Advice for First-Time Founders

49:18

One Experiment Founders Can Run This Week

LINKS
Puneet Agarwal

Mayank Mehta

Gather

True Ventures

Gather Growth Platform

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📸 Follow Fund/Build/Scale on Instagram: https://www.instagram.com/fundbuildscale/

 

📺 Watch Fund/Build/Scale on YouTube: https://www.youtube.com/channel/UCFFH4cs2B1BKatPGs8SFRJw

 

Thanks for listening!

 – Walter