Building a company before all the pieces are in place forces calls on timing, focus, and trust. Concurrence co-founder and CEO Ali Khokhar shares how he has made them while building AI for health systems.

Concurrence (formerly Amigo) captures context at the line of care so health systems can learn from every patient they treat. In this episode, Madrona partner Sabrina Albert talks with Ali about the decisions behind it.

What founders can take from this conversation:
1) How to test whether you're early or wrong.
2) When "go narrow and deep" stops working.
2) How to say no to obvious money. Friends pointed him to revenue cycle management because the ROI is clear. Ali passed and says the opportunity costs are already paying dividends.
3) How to sell to skeptical buyers.
4) How to collect net new context.

Founders building ahead of the market, in a regulated industry, or for cautious buyers will find specific decisions on timing, focus, and trust that they can test against their own company.

Transcript: https://www.madrona.com/is-your-startup-idea-too-early-or-the-wrong-thing

Chapters:
00:00 – Introduction
02:52 – Superintelligent Care vs. Medical Superintelligence
04:59 – How to Tell Whether a Startup Is Early or Wrong
06:27 – Why Go-Narrow Startup Advice Becomes a Small Poisonous Pond in Healthcare
08:26 – When a startup has to say no to revenue to accomplish long term product strategy
09:37 – Earning Trust From Skeptical Buyers
11:39 – Why Decades of Healthcare Data Have Not Made Care Better
14:20 – Example of What Concurrence Does
16:06 – Why Amigo Became Concurrence: Expanding a mission
18:02 – What Healthcare Looks Like in Five Years

Who is responsible when an AI agent does something its user never intended? Janel Thamkul, former deputy general counsel at Anthropic, explains why the answer depends on your contracts, your product design, and the records you keep.

In this episode of Founded & Funded, Madrona General Counsel Joanna Black talks with Janel about the legal decisions every AI founder needs to get right, including:

1) Why AI's constrained supply changes how you incentivize go-to-market teams
2) How a product's UI reveals whether a team understands agentic liability
3) The OpenClaw gym class incident and what it shows about open-ended tasks
4) Why copyright damages can be company bankrupting, and how model providers responded
5) Why granular logging is cheap to build early and brutal to retrofit

Liability for AI agents is still an open question, and founders are making product and contract decisions before the answers arrive. Janel gives concrete choices a team can make before shipping.

Transcript/Summary: https://www.madrona.com/what-founders-should-know-about-ai-legal-risk-before-they-ship

00:00 – Introduction
01:16 – What Founders Building on Frontier AI Models Underestimate: Supply Constraints and Sales Incentives
03:06 – How to Tell If an AI Team Understands Agentic Liability by Looking at the UI
05:38 – The OpenClaw Gym Class Incident: When an AI Agent Exploits a Vulnerability
07:56 – Who Is Liable When an AI Agent Fails? Contracts, Copyright, and Indemnification
10:23 – AI Safeguards That Are Cheap Early and Brutal Later: Granular Logging
11:36 – Why the Ecosystem Isn't Ready for Agentic AI: Principals, Trust, and Identity
13:30 – Why AI Startups Should Not Ship Without Real Terms of Service
15:27 – Don't Be the Slowest Zebra: How Early-Stage AI Companies Should Take on Risk
17:11 – Own Your Most Important Relationship: Why No Third Party Should Sit Between You and Your Customer
19:21 – Final Advice for CEOs: Tokenmaxxing, AI Stack Sovereignty, and the ZEBRA Mnemonic

Recorded live at Seattle Tech Week, this episode of Founded & Funded brings together two people who track the AI capital markets from different vantage points: ⁨Pitchbook⁩ EVP of Research & Market Intelligence Nizar Tarhuni, and Madrona Partner Sabrina Albert.

These two dig into where AI venture capital is actually landing across the model, infrastructure, and application layers, why AI "harnesses" are becoming a winner-takes-most category, and where vertical AI applications may hold an edge that horizontal platforms can't match. They also cover PitchBook's VC Exit Predictor, the new normal for AI seed valuations, and the tranche-financing structures Nizar says are red flags for founders.

And in a live Q&A, they take on a frequently asked question in the AI market: Is this a bubble? Nizar explains why he thinks it's still too early to call, what the data says about the value already being created, and why the next few stages of company growth will tell us much more about which valuations prove durable.

For founders raising capital right now, or operators trying to figure out where durable value is emerging in AI, this conversation offers a data-grounded look at what's happening beneath the headline numbers.

Full Transcript: https://www.madrona.com/is-ai-a-bubble-what-pitchbook-nizar-tarhuni-sees-in-the-data

Chapters:
00:00 — Introduction
04:11 — The Rise of AI Harnesses
06:50 — Vertical vs Horizontal AI Applications
07:51 — Vertical AI Use Cases: Legal, Finance, Healthcare
09:25 — Are Vertical AI Companies Defensible vs OpenAI/Anthropic?
11:32 — Durability, Switching Costs & the Exit Predictor
14:44 — Open Source Models & Cost Management
18:58 — Choosing the Right Model for the Task
20:09 — Funding Environment: Seed to Series B
23:04 — Tranche Financing & Founder Red Flags
26:43 — Audience Q&A: Seattle's Role in AI, Bubble Risk & Services as Software

After leaving a long sales career, David Rubenstein spent a year meeting more than 300 founders across 41 countries and turned what he heard into a framework for how companies sell now. In this episode of Founded & Funded, David sits down with Madrona's Anna Baird and Eric Wong to dig into why AI left buyers with more information and less clarity, why deals stall out of fear rather than disinterest, and how founders can cut through.

The conversation covers selling against build-it-yourself AI objections, the return of in-person selling, and why the founder's own brand is more important to drive pipeline.

This was originally an AMA with Madrona portfolio companies, but we wanted to share David's full sales playbook with founders, GTM leaders, and operators for a grounded look at how buying behavior shifted and where to focus when the old tactics stop paying off.

Full Transcript:

Chapters
(00:00) – Why AI Made Your Buyers Harder to Sell To
(01:30) – How David Met 300 Founders Across 41 Countries in a Year
(03:06) – Why the Old Go-to-Market Math Stopped Working
(04:40) – AI Gave Buyers More Information and Left Them More Confused
(05:27) – The SPRINT Framework for Diagnosing a Stalled Deal
(07:03) – Give the "How" Before the Number So Results Read as Proof
(08:54) – The Question Every Executive Buyer Asks Late in a Deal
(10:16) – 3 Hidden Fears That Kill Deals During Implementation
(12:00) – The Trigger Most Founders Leave Out of Their Customer Profile
(15:24) – Why the First Seven Minutes of a Sales Call Are Wasted
(16:19) – How to Sell Against "I'll Just Build It With Claude Code"
(22:24) – Why AI Sales Prep Gives You the Average, Not the Edge
(25:48) – Why Founder Brand Now Drives Pipeline
(28:50) – Free vs. Paid Pilots and Why Skin in the Game Wins

Joe Beda and Craig McLuckie co-created Kubernetes, the infrastructure standard that became the default for cloud native computing. Now running Stacklok, they're watching enterprises hit the same identity, permissions, and security problems with AI agents that took the container ecosystem years to resolve, and they're building tools to compress that timeline.

In this episode of Founded & Funded, Madrona's Tim Porter sits down with Joe and Craig to talk through what AI adoption actually requires: why MCP is the Docker moment for AI-native applications, how the LLM gateway is becoming a strategic chokepoint for cost, safety, and model flexibility, and why enterprises that don't get the architecture right early will face a familiar trap: vertical integration that looks like productivity and acts like lock-in.

They cover:

• Why the developer workflow is the template for knowledge worker AI adoption, and where the analogy breaks down
• The mainframe vs. open platform question that will define the AI infrastructure era
• Why the knowledge worker transition is harder than it looks — and what has to be built differently before developer-grade AI tooling can scale to the rest of your organization
• The governance gap between human accountability and AI behavior, and what enterprises actually need to build to close it
• Where to start: MCP controls first, LLM gateway second, and why deploying a platform without staying to close the loop consistently fails

Transcript: https://www.madrona.com/the-best-infrastructure-moment-since-cloud

Chapters:

(0:00) – Introduction

(1:04) – Why the Kubernetes Creators Are the Right People to Read This AI Moment

(2:18) – Joe's Lesson from Cloud Native: Ignore Conventional Wisdom, Except When You Shouldn't

(4:16) – Craig on Enterprises and the Chaos of a New Infrastructure Era

(5:32) – Why Joe Rejoined Craig at Stacklok: The Engineer's Case for Getting Your Hands Dirty

(7:05) – Developers as Agent Orchestrators: How the Knowledge Worker Transition Will Follow

(10:10) – MCP Explained: Craig Sees Docker in 2013 When He Looks at the MCP Spec 1

(7:53) – The Mainframe vs. Open Platform Question That Will Define the AI Era

(20:24) – LLM Lock-In Is the Wrong Worry: The Real Risk Is Left of the Model

(25:19) – Where Enterprises Actually Start: Developer Posture First, Knowledge Workers Second

(29:10) – MCP First, LLM Gateway Second: The Concrete Technical Starting Point

(31:19) – How Stacklok Builds Software Now: Agents, Smaller Teams, the Unrecognizable Developer Profile

(38:07) – The Recruiter Who Started Building Agents: What AI Tools Do to Role Boundaries

There's a theorem being tested about how AI reaches general intelligence. Carina Hong's answer: through mathematics.

Carina is the founder of Axiom, and in less than a year of building, her team's AI has scored a perfect 120/120 on the Putnam mathematical competition — a test where more than 50% of brilliant undergraduates score zero. More concretely, Axiom Prover has reached 98.93% on a Lean software verification benchmark that leading alternatives solve at 11–12%.

In this conversation with Matt McIlwain, Carina explains her central thesis: that math and code are the two pillars of the digital world, and that any AI infrastructure missing a formal verification layer is structurally incomplete. She walks through the history of verified AI research at Google, DeepMind, OpenAI, and Meta, and explains why each effort stalled just as commercial pressure mounted. She describes what makes hardware and software verification the natural first commercial market, and what Axiom discovered when they tested their prover against circuits that industry-standard formal checkers could not verify.

For founders and operators trying to understand what's actually changing in AI capability, and for anyone building in adjacent infrastructure spaces, this is a map of where the frontier is and where it's heading.

Transcript: https://www.madrona.com/agi-needs-formal-reasoning-carina-hong-is-building-it-at-axiom

Chapters:
(00:00) – Introduction
(02:01) – How to Define AGI Right Now — and Why There Are Two Competing Definitions
(04:17) – Math Is AGI
(06:12) – Math Data Scarcity: Why a Disadvantaged Domain Accelerates Progress
(08:13) – Formal vs. Informal Math: Why AI Researchers Treat This Like a Religion
(13:44) – Google, OpenAI, DeepMind, and Meta All Abandoned Formal Math Research
(21:20) – The Putnam Story: First AI Perfect Score
(28:13) – Hardware Verification as the Commercial Frontier: What Axiom Found Testing Real Circuits
(31:22) – 98.93% vs. 11–12%: What the Benchmark Gap Reveals About Formal Provers
(34:22) – Math and Code as the Two Pillars of the Digital World
(37:00) – Team Building Around a Shared Dream: Recruiting for Mathematical Superintelligence
(38:03) – What Autonomous Proof Generation Looks Like

Nick LaRovere spent years at Palantir before co-founding Pryzm with friends, including a Lockheed Martin alum. And in this episode of Founded & Funded, Nick shares his inside account of how AI is actually being adopted inside the federal government, and what it takes to sell technology into that market.

Pryzm is an AI-powered intelligence engine for government business development: it aggregates data across CRMs, email, Slack, and public procurement sources to help companies win contracts. Nick's argument is that by the time an opportunity appears on SAM.gov, the deal is already decided. In this episode, Madrona Partner Chris Picardo and Nick cover: why there's still no purpose-built CRM for government buyers, what Pentagon AI adoption actually looked like from the inside (workers hadn't touched an LLM as recently as two years ago), and what forward-deployed engineering at Palantir taught Nick about building close to the customer.

If you're selling into government, building for defense, or trying to understand how AI procurement works inside federal agencies, this is a useful map of how the terrain actually works.

Full Transcript: https://www.madrona.com/palantir-alum-explains-how-ai-is-used-and-bought-by-the-federal-government

(00:00) – Introduction
(01:42 – Why the Defense Budget Growing to $1.5 Trillion Matters for Startups
(04:11) – Colors of Money: How Federal Budgets Actually Work
(06:47) – The Move Toward Colorless, Flexible Defense Spending
(10:51) – What Pryzm Does: AI-Powered Intelligence for Government BD
(12:21) – Why Deals Are Decided Before They Ever Post on SAM.gov
(17:25) – The Government AI Gap: Pentagon Workers Without LLM Access Until Two Years Ago
(21:00) – The Digital Thread Problem: Fragmented CRMs and Millions Wasted on Brittle Tools
(26:11) – What Nick Learned Watching Elon Sleep on the @tesla Factory Floor
(27:00) – Palantir's Forward Deployed Engineer and Why It Produces So Many Founders
(30:33) – Why Hard Problems Make Better Businesses: TAM, Willingness to Pay, and Moat
(32:05) – Hiring Is Harder Than Founders Expect — and More Important

Zapier is doing hundreds of millions in ARR, has 800 employees, and has more AI agents than people. That ratio isn't an accident.

Wade Foster, CEO and co-founder of Zapier, built one of the most capital-efficient software companies in history on less than $1 million in venture funding. When GPT-4 launched in March 2023, he called a company-wide "code red" (a term he'd never used before) and stopped the entire company for a week-long hackathon. What happened next reshaped how Zapier hires, operates, prices its product, and thinks about the future of software.

In this episode of Founded & Funded, Karan Mehandru sits down with Wade to unpack:

1. Why ChatGPT didn't trigger urgency at Zapier, but GPT-4 did — and the specific signal Wade used to make that call
2. How Zapier went from 10% AI tool adoption to 90%+ across the company in a single week
3. The pricing overhaul that simplified Zapier's model around task-based usage and why agents made seat-based pricing structurally broken
4. Why Zapier's head of HR became the Chief People and AI Transformation Officer, and what that reveals about who actually leads change inside organizations
5. The "build first, run always" framework Wade uses for deploying AI agents safely inside enterprise workflows

For founders and operators navigating their own AI transformation, this is a practical, unfiltered look at what it actually takes from a CEO who's in the middle of it.

Full Transcript: https://www.madrona.com/zapier-has-more-ai-agents-than-employees-heres-how-that-happened

Chapters:
(00:00) – Introduction
(01:44) – Zapier Today: Hundreds of Millions ARR, 800 Employees, More Agents Than People
(06:16) – Why ChatGPT Didn't Trigger Urgency — But GPT-4 Six Months Later Did
(07:12) – Code Red: Stopping the Entire Company for a Week-Long AI Hackathon
(08:00) – How Zapier Moved AI Adoption From 10% to 90% of Employees in One Week
(10:11) – Managing the Psychology of Change When Numbers Still Look "Okay"
(13:11) – Why Companies in the Middle Ground Face the Hardest AI Transformation Problem
(15:01) – From Bottoms-Up Adoption to Systematic ROI: The Two-Phase AI Rollout
(17:00) – Why Zapier's Head of HR Became the Chief AI Transformation Officer
(19:54) – Refactoring the Legacy Monolith: Writing Code for Agents Instead of Humans
(21:25) – Zapier's Pricing Overhaul: Why Task-Based Usage Beats Seat Pricing
(23:26) – Why Agents Will Choose What Software to Buy — and What That Does to SaaS
(28:03) – The Build-First, Run-Always Framework for Enterprise Agent Governance
(29:40) – Wade's AI Hiring War Council: A Multi-Agent System He Built That Morning
(34:14) – The Leadership Profile That Thrives When the Job Is Rebuilding, Not Scaling
(40:08) – If You're Starting a Company Today, Distribution Is the Bottleneck
(42:10) – Why AI Deals Are Hard to Renew and the Rise of the Field Delivery Engineer