Learn more about the host Laurence Gill at: www.laurencegill.com

For twenty five years, software pricing has worked like flat-rate insurance: pay a fixed premium for the capacity you own, not for the value it delivers. AI agents just broke that model, and the fallout is bigger than a line item on your next renewal.

In this episode, Laurence Gill breaks down why AI agents are killing per-seat software pricing, and why what’s replacing it is really a risk-transfer mechanism in disguise. Drawing on principal-agent economics, a real vendor case where efficiency gains cost a company most of its recurring revenue, and two decades of federal IT audit experience, Laurence lays out exactly what leaders need to understand before signing their next AI vendor contract, including the four questions that determine who actually holds the risk when the system fails.

If you’re evaluating, renewing, or negotiating any AI agent contract this quarter, this episode gives you a framework for asking better questions than “how many seats do we need.

Learn more about the host Laurence Gill at www.laurencegill.com.

In Episode 12, Laurence Gill takes that number apart. A Stanford research team called BetterBench built a 46-point audit covering benchmark design, reproducibility, and documentation, then scored 24 widely-cited tests against it. MMLU came in at 5.5. GPQA, a far less publicized test, scored double that. The reasons are specific: ambiguous question phrasing that swings scores when a comma moves, a reproducibility gap across most published benchmarks, and a quiet contamination problem where models may have already seen the answer key buried somewhere in their training data.

Laurence walks through how these tests actually work, why Goodhart’s Law explains the industry’s race to game them, and how newer benchmarks like GPQA and ARC-AGI are trying to close the gap. It closes with five questions to run through before any benchmark score is allowed to inform a real decision and one open question about what happens when AI starts writing the tests that grade other AI.

Over 90 percent of U.S. employers now use AI to screen job applicants. And over 60 percent of the Fortune 100 runs that screening through the same vendor model. A 2026 study from Stanford, Chapman, and Northeastern Universities — the largest independent research ever conducted on deployed hiring algorithms, reveals what that concentration is actually doing to real people at scale.

Researchers analyzed 3.4 million applicants submitting 4 million applications across 156 employers and 11 market sectors. What they found is not a conventional bias problem. It's an architectural one. More than a quarter of all applications submitted by Black applicants landed in positions where the algorithm was actively producing adverse impact. 29,000 additional Asian applications would have moved forward in a fair system. And to statistically guarantee one interview, candidates in an algorithmic monoculture now need to submit 25 applications, two and a half times the number required in a human-driven system.

This episode breaks down how algorithmic monoculture works, why prior vendor studies missed the discrimination, what the disaggregated data reveals, and what a governance framework capable of addressing it actually looks like. Essential listening for every leader whose organization relies on AI in hiring or whose team members are navigating this market right now.