Episode 007 • February 26, 2026 • 49:13

Jay Nathan, Jeff Breunsbach, Jon Olson

Listen on: Spotify | Apple Podcasts | YouTube

Law is a revenue-driving function, not a compliance gatekeeper. Jon opens with a reframe that shapes the entire conversation: without revenue, every other legal concern becomes irrelevant. His team is measured not just on risk mitigation but on how many deals they enable. This orientation changes everything — how they hire, how they engage with sales and CS, and how they approach customer negotiations.

The "ultimate backstage pass" gives CLOs a unique view of the business. Jon borrows a line from Al Pacino's character in The Devil's Advocate: law is the ultimate backstage pass. His team touches 80–90% of Blackbaud's quarterly accomplishments — commercial deals, international expansion, M&A, partnerships. That cross-functional visibility makes the CLO one of the most strategically positioned people in any company.

"Yes, but" beats "no" every time. Jon coaches his team to never just say no. Instead, they surface the risks, quantify them, and propose a creative path forward. His example: when Uber was being conceived, a purely risk-averse lawyer would have killed it — licensing conflicts, independent contractor liability, duty of care. The right legal answer was never to say no; it was to find the structure that worked. The same logic applies to customer contract negotiations.

The Minto Pyramid is a better way to bring legal a problem. Jeff shares the Minto Pyramid — an inverted communication model where you lead with the conclusion (what the customer is trying to accomplish), then support it. Jon has been teaching law students the exact same framework for years: state your position first, then build the argument. Don't bury the lead in 120 pages of analysis and save your recommendation for the last sentence. Executives will question the reasoning — but only after they understand where you're going.

Blackbaud's AI council turned governance into a speed accelerator. When ChatGPT first appeared, Blackbaud's IT team flagged that employees were already using it — including for potentially sensitive content like earnings figures. Rather than locking it down, the company built a structured AI council with a Chief Privacy Officer and Chief Data Officer as co-leads. Use cases flow in, get evaluated, and get shared across teams. The result: clear guardrails that let people move faster, not slower.

AI in law today is about speed; AI in law tomorrow is about intelligence. Today, AI can redline a 100-page contract against a standard playbook in 2–3 minutes instead of hours — a massive efficiency gain. But Jon sees the real payoff coming in the next phase: AI that analyzes a company's full historical contract database, surfaces patterns in where risk actually materialized, recommends negotiation strategies based on past outcomes, and provides real-time updates when a jurisdiction's legal landscape shifts. That's the move from faster to smarter to expert system.

The real AI opportunity is top-line revenue, not just cost savings. Jeff makes a point Jon immediately validates: most companies are capturing AI's efficiency gains on the cost side. The bigger prize is untapped revenue — using AI to surface underpriced customers, enable dynamic pricing, tailor contract terms to individual risk profiles, and find revenue hiding in existing contract data. Jay adds the macro framing: OpenAI and Anthropic are projected to generate $300 billion in revenue by 2030, and that money can't come from the existing $400–500 billion global software spend. New value has to be created.

Jevons Paradox predicts AI will create more work, not less. Jon references the historical pattern: when calculators arrived, accountants didn't disappear — there are more now than ever. The same held for every major productivity technology. The tasks that get automated are often the ones no one liked doing anyway. The people doing those tasks move to higher-value work, productivity expands, and new categories of demand emerge. Jon sees AI as the most exciting development of his career since the dawn of the internet.

Blackbaud – The world's largest provider of technology solutions to nonprofits, headquartered in Charleston, South Carolina, founded in 1981

ChatGPT – Conversational AI product from OpenAI that triggered the current AI wave

Claude – AI assistant from Anthropic; Jeff also built a Claude skill to help his team apply the Minto Pyramid framework

Grok – AI assistant from xAI

OpenAI – AI research and deployment company; Jay cites their projected $300B in combined revenue with Anthropic by 2030

Anthropic – AI safety company and maker of Claude; Dario Amodei referenced in the context of AI's employment impact

Uber / Lyft – Ride-sharing platforms used as a case study in legal innovation and risk-taking

Workday – Enterprise software platform referenced in the discussion of SaaS disruption

The Neuron – AI newsletter Jon reads; he cites a cautionary AI story from it during the episode

Charleston School of Law – Jon's law school partner; Blackbaud has hosted 50+ student interns over the years

Junction – Jeff's company; an API developer tool for lab diagnostics in healthcare

Trump Card – 1990 game show filmed at a Trump-owned Atlantic City casino; Jon's first game show appearance

Jeopardy – Classic trivia game show; Jon appeared in the mid-1990s

Who Wants to Be a Millionaire – Jon's most recent (and most fun) game show appearance; he made it to the hot seat before going out on a tough question