What happens when someone who’s built startups in Nairobi, invested across emerging markets, and wrestled with US healthcare inefficiencies decides to sit on the other side of the table?

You get a very different kind of venture capitalist.

Emily Durfee is Director of Corporate Venture Capital at Healthworx, the innovation and investment arm of CareFirst BlueCross BlueShield. She backs health tech companies from seed through Series B, but her real edge isn’t capital, it’s pattern recognition across systems that were never designed to work together.

In this episode, Emily unpacks how payer-backed venture actually works, why most founders misunderstand it, and what it really takes to sell into one of the most complex markets on earth. From growing up abroad in South Africa and Israel to investing in healthcare across Sub-Saharan Africa and the US, her worldview is shaped by one recurring question, how do you create value for business without losing sight of humans?

In Today's Episode We Discuss:

00:01 Welcome & Emily’s Role at Healthworx

02:00 Global Upbringing & Early Influences

05:15 Entering Healthcare Through Clover Health

08:30 Healthworx Investment Mandate & Check Sizes

10:45 What Payer-Backed VC Actually Means

14:30 Advantages of Corporate Venture Capital

17:45 Startup Speed vs Healthcare Bureaucracy

20:10 Payer vs Provider Incentives

23:40 Medical Loss Ratio & Profit Caps Explained

26:30 Value-Based Care & Risk Sharing

29:10 Transparency & Data Fragmentation in Healthcare

31:45 How Healthworx Evaluates Startups

34:30 Portfolio Spotlights: Positive Development, SafeRide, Kalina

38:00 Market Headwinds & Why Healthcare Is Contracting

41:00 AI in Healthcare: Hype vs Reality

44:30 The Future of Healthcare Innovation

Along the way, Emily shares hard-earned lessons on navigating bureaucracy without getting crushed by it, building trust across wildly different incentives, and why transparency, not technology, is the real bottleneck in healthcare.

This is a conversation about power, incentives, and patience. And about why the future of healthcare innovation might depend less on breakthrough science, and more on unglamorous systems actually talking to each other.

Pull quotes:

“Just because a new technology exists doesn’t mean healthcare adopts it. We still run on faxes.”

“If you don’t have a payer angle now or in the future, we’re probably not the right partner, not because we don’t like you, but because we can’t help you.”

Emily started her career building in emerging markets. Today, she’s helping founders navigate one of the most entrenched systems in the world. Same skill set, different maze.

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Imagine if your biggest growth bottleneck wasn’t product, talent, or capital, but the sheer chaos of modern go-to-market. Too many tools. Too many channels. Too much guesswork. Now imagine handing that mess to an AI agent that actually knows what it’s doing.

That’s the bet Harsha Vankayalapati is making. Former Microsoft engineer, two-time YC founder, and now co-founder and CEO of AgentWeb, Harsha is building autonomous AI agents that run go-to-market execution for B2B startups, from content and SEO to paid ads and outbound. His contrarian belief, sharpened by hard startup scars, is simple, GTM execution, not ideas, is what kills most companies, and AI is finally good enough to fix it.

In this episode, we unpack what happens when founders stop duct-taping together CRMs, agencies, and automation tools, and instead let agents do the heavy lifting.

In Today's Episode We Discuss:

00:01 – Introduction to Harsha Vankayalapati & AgentWeb

02:00 – Origin Story: From India to Microsoft

04:20 – Getting into Y Combinator & First Startup Pivot

08:50 – Why Go-To-Market Is the Real Bottleneck

11:50 – The Idea Behind AgentWeb

14:00 – Product vs Distribution in B2B

16:10 – Pretotyping & Validating Demand with Paid Ads

18:20 – The Core Pain AgentWeb Solves

21:15 – Why Now: AI, CAC, and the Attention Economy

24:50 – What AgentWeb Has Solved (and What’s Still Hard)

27:50 – Autonomous GTM vs Traditional Marketing Automation

31:40 – Human Taste vs AI Execution

34:20 – Reliability Challenges with AI Agents

38:40 – Benchmarks vs Real-World AI Performance

40:50 – Ideal Customer Profile for AgentWeb

43:30 – Early Traction & Signs of Product-Market Fit

45:10 – Onboarding, Pricing & Managed Service Model

49:50 – The Future of Agentic Go-To-Market

52:50 – Rapid Fire: Strong Opinions & Hard Decisions

56:00 – Long-Term Vision & Closing Thoughts

Harsha’s journey, from building enterprise workflows at Microsoft to watching his own startups stall on distribution, leads to a sharp conclusion, the future isn’t just vibe coding products, it’s vibe growing companies.

Pull quotes:

“Go-to-market isn’t an idea problem, it’s an execution problem.”

“If we don’t fix how products reach people, mediocre products with great marketing will win.”

From newsletters to neural nets, this conversation connects Harsha’s past building systems at massive scale to a future where founders might never need an agency again, just the right agent.

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What if the biggest tech opportunities aren’t the ones that make billions, but the ones that actually work?

Jim Fruchterman has spent three decades proving a quiet, uncomfortable truth, some of the most leverage-rich technology in the world will never be VC-backable, and that’s exactly why it matters.

Jim is a Caltech-trained engineer, serial founder, and MacArthur Fellow who walked away from traditional Silicon Valley success to build something stranger and more ambitious. First at Benetech, and now as the founder of Tech Matters, he’s been building open-source, revenue-generating software for the 90 percent of humanity most tech companies ignore. Crisis helplines, disability access, human rights, mental health infrastructure, all powered by product-first thinking and disciplined business models that just happen to be nonprofit.

In this episode, we unpack what happens when you apply Silicon Valley rigor to markets everyone else calls “too small” or “not scalable.”

In Today's Episode We Discuss:

00:01 — Jim Fruchterman’s Origin Story

01:05 — From Caltech to Silicon Valley Startups

02:10 — Early AI, OCR, and Reading for the Blind

03:00 — When VCs Say No to Social Impact

03:45 — The Accidental Nonprofit Insight

04:45 — Seven Startups and Choosing the Nonprofit Path

06:00 — The Market Failure Between Tech and Profit

07:10 — Applying Silicon Valley Rigor to Social Good

08:20 — Venture-Style Filtering for Nonprofit Ideas

09:30 — Distribution as the Real Bottleneck

10:30 — Introducing Tech Matters

11:15 — Nonprofit Vertical SaaS Explained

12:00 — Crisis Helplines and Cloud Infrastructure

13:30 — Competing with Salesforce in Niche Markets

15:00 — Revenue, Subsidies, and Sustainability

16:30 — Donors as Early Risk Capital

18:00 — When Nonprofits Become For-Profits

19:30 — Selling a Nonprofit and Market Creation

21:00 — Measuring Impact Beyond Vanity Metrics

22:30 — Open Source for Trust and Resilience

24:00 — What Tech Matters Is Building Next

25:30 — Mental Health Infrastructure at Scale

27:00 — AI Hype vs Real Productivity Gains

29:00 — Automating Drudgery, Not Empathy

31:00 — Technology, Ethics, and Design Intent

33:00 — Regulating Tech When It Goes Too Far

35:00 — Optimism About AI and Human Adaptation

37:00 — The Long-Term Role of Tech for Good

39:00 — Legacy and the Future of Social Impact Tech

Jim has founded companies where only five out of seven failed, sold a nonprofit to private equity, beaten Salesforce head-to-head in a vertical SaaS niche no one wanted, and helped define an entirely new playbook for impact-driven technology.

He didn’t reject Silicon Valley logic. He just took it somewhere it was never designed to go.

Pull quotes:

“A two or three million dollar nonprofit that breaks even is a screaming success, not a failure.”

“If your idea doesn’t pencil out, Silicon Valley calls it bad. I call it an opportunity.”

This episode is a reminder that innovation doesn’t disappear when the profit motive breaks down. It just changes shape, and sometimes, it gets more interesting.

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