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