Ghost in the Machine

When AI leaders agree that the race needs brakes, should people trust the warning or question the incentives behind it?

Andrew DeGood and Liz Short test the extinction case without treating fear as evidence. They examine a former Anthropic researcher's warning, the industry consensus, open-source competition, recursive self-improvement, and OpenAI's use of thousands of agents on the Navier-Stokes problem. The strongest conclusion is not that catastrophe is impossible. It is that extraordinary claims still need precise facts.

Then the conversation turns from hypothetical risk to present power. Andrew argues that personal privacy is already gone. Liz pushes back: surrendering privacy hurts people with less power first, especially when surveillance tools can be abused by institutions or individuals.

The episode lands on a harder standard for both optimism and skepticism. Ask what a system can do, who controls it, what evidence supports the claim, and who pays when the guardrails fail.

What is Ghost in the Machine?

The AI conversation, without the noise.

Every week, Andrew DeGood and Liz Short sit down for a thirty-minute conversation about artificial intelligence. Andrew comes in as the optimist, a founder building AI products and betting his career on where this technology is headed. Liz brings the harder questions, the ones about what we lose, what we risk, and what we owe the people who didn't sign up for any of this.

They bring in the people actually shaping the field. Researchers, founders, ethicists, skeptics, builders. Real conversations about real implications. No hype cycles. No doom loops. Just two smart people and a guest trying to figure out what this moment actually means.

New episodes stream live every Thursday. Available on every podcast platform after.