Underwriting Superintelligence: Backing Agents you can Sue — Rune Kvist, AIUC
September 16, 2026
AI Summary
5 min readIn 2021, Rune Kvist sold his first company and read the original Kaplan scaling laws paper. The paper’s core claim—that bigger models trained on more data predictably produce better performance—struck him not as a technical curiosity but as a capital markets signal: “If you put in more money, you get more money out.” That insight kicked off a hype cycle, and he packed his bags for San Francisco to find the people who wrote it. He eventually met Dario Amodei at Anthropic, then a small lab wrestling with deployment questions. The conviction he encountered there—that the world as we know it would be reshaped by this technology—led him to co-found AI Underwriting Company (AIUC), which has now raised a $40 million Series A from Ribbon Capital and First American. The company builds what Kvist calls “confidence infrastructure” for frontier AI: standards, third-party testing, and insurance that lets enterprises actually say yes to deploying agents.
The Waymo Problem: Capability Without Trust
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What you'll learn
- 1 (00:03) **Introduction & The Big Thesis** – Rune introduces AIUC and the $40M raise. The core hypothesis: risk is now the binding constraint on AI adoption.
- 2 (01:14) **Origin Story: From Scaling Laws to Anthropic** – Rune explains how reading the Kaplan scaling laws paper led him to San Francisco and Anthropic.
- 3 (04:41) **The Anthropic Crystal Ball** – Inside Anthropic during early 2022: a team with deep conviction that compute + data = intelligence.
- 4 (08:02) **The Waymo Problem: Why Risk is the Bottleneck** – Using Waymo as a case study for why liability, not capability, slows adoption.
- 5 (09:35) **The Historical Blueprint: Standards + Insurance** – How every previous technology wave solved the trust problem.
- 6 (12:27) **Co-Founder Origin: Raj and the Family Tie** – How Rune and his soon-to-be brother-in-law came together.
- 7 (14:53) **AC1: The Agent Certification Deep Dive** – A walkthrough of the AC1 standard for agent security, safety, and reliability.
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Guests on this episode
Show Notes
AIUC first got our attention with the NFDG backing, and have just announced a $40M series A today, with the most impressive industry advisor list we may have ever seen for an early startup behind AIUC-1, their agent standard backed by real insurance:
From being Anthropic’s first product hire to building the standards, testing, and insurance infrastructure meant to make frontier AI deployable, Rune Kvist is betting that the biggest constraint on AI adoption won’t be capability it will be trust. In this episode, the AIUC cofounder joins swyx and Vibhu to announce a new $40M round and explain why companies like Cursor, Harvey, Lovable, and ElevenLabs are increasingly confronting a problem that gets harder as AI gets better: who is responsible when autonomous systems fail?
We go deep on AIUC-1, the emerging standard for agent security, safety, and reliability; how AI agents are stress-tested for jailbreaks, hallucinations, and data leaks; and why Rune thinks standards and insurance could become critical infrastructure for AI. We also discuss the growing trust gap between governments and frontier labs, AI-enabled cyber and biological risks, why every model can ultimately be jailbroken, what happens when a $20 coding agent causes $200M of damage, whether AI engineers should be certified, and why even after AGI there may be one job the labs can never do themselves: be their own watchdog.
We discuss:
* Why risk, liability, and trust may become the binding constraint on AI adoption
* Rune’s path from reading the Scaling Laws paper to joining Anthropic in its earliest days
* What Anthropic understood about scaling, compute, and the future years before it became obvious
* Why Waymo illustrates the gap between AI capability and real-world deployment
* AIUC’s $40M round and work with Cursor, Harvey, Lovable, ElevenLabs, and other frontier AI companies
* AIUC-1: a standard for AI agent security, safety, and reliability
* How agents are tested for jailbreaks, hallucinations, and data leakage
* Why most AI companies optimize the happy path without seriously stress-testing adversarial cases
* Why AI standards may need to update every quarter instead of every decade
* The emerging trust gap between frontier AI labs and governments
* Cybersecurity, child safety, biological weapons, and the expanding frontier-model risk surface
* Why standards and insurance may need to evolve together
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