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.

Ghost in the Machine is a technology podcast hosted by Andrew Degood and Liz Short, with 20 episodes on record and a Required Pod Score of 80.

Andrew Degood and Liz Short hosts Ghost in the Machine, a technology show with 20 episodes published.

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  Episode #20

  Episode 20: Andrew DeGood and Liz Short on Why AI Strategy Starts With People

  Oct 1, 202631 minS1

  Why do AI projects fail before they create value? Andrew DeGood and Liz Short trace the problem past the model and into the organization. Leaders buy broad promises, ask busy employees to carry implementation, and make technology decisions without the people who understand the work. That can mean chasing automated underwriting while processors still move files between systems and repeat basic clicks all day. The conversation turns when Andrew asks what a CEO is actually responsible for during an AI transition. The answer is culture. Executives should understand the technology, but their first job is to communicate clearly, involve frontline experts, and give employees a reason to trust the change. The practical strategy is smaller and more human. Pick one painful workflow. Bring the best person doing that work into the room. Solve the problem, prove the value, and build from there. AI adoption works when leadership starts with people.

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  Episode #19

  Episode 19: Andrew DeGood and Liz Short on Who Benefits From AI Panic

  Sep 24, 202637 minS1

  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.

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  Episode #15

  Episode 15: Tom Morelli on What Matters When Knowledge Is Cheap

  Aug 27, 202645 minS1

  Wilqo Chief of Staff Tom Morelli joins Andrew DeGood and Liz Short to ask what people will bring to work when AI can retrieve, remix, and explain almost anything on demand. The conversation starts with a practical problem: expertise has always been built through entry-level work, mistakes, exceptions, and repetition. What happens when the jobs that teach those lessons disappear? Tom argues that human connection becomes more valuable as digital communication gets cheaper and less trustworthy. Andrew sees a future built around storytelling, relationships, and creation. Liz pushes on the part optimism often skips: access. If technology remains expensive or unevenly distributed, abundance for a few can deepen scarcity for everyone else. The episode lands on a sharper distinction. Information can be commoditized. Discernment, trust, and the ability to turn knowledge into responsible action must be earned through practice, consequence, and human contact.

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  Episode #14

  Episode 14: Andrew DeGood and Liz Short on Why AI Efficiency Is Not the Goal

  Aug 20, 202637 minS1

  Andrew turns the questions on Liz in Episode 14. What makes her suspicious when a company says it uses AI? Which decisions should stay human? What recent tools have earned a place in her work? The answers expose a bigger problem. Leaders announce an AI mandate before naming the work that needs to improve. Technology teams design around executives instead of the people clicking, uploading, reviewing, and fixing the process every day. Vendors chase visible automation while employees become the permanent workaround. The Turn comes when Liz draws a boundary around human relationships and Andrew pushes into AI voice dialers. Activity does not guarantee value. A system that makes more calls, creates more frustration, or damages trust is not efficient. It is expensive motion. The practical test: start with the problem, bring the end user into the room, understand the process, then decide whether AI belongs there at all today.

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  Episode #13

  Episode 13: Andrew DeGood and Liz Short on Why Rogue AI Is a Leadership Failure

  Aug 13, 202636 minS1

  Episode 13 starts with three questions people are already asking without the hype. What should a community demand before approving a data center? What should a leader do when employees use unsanctioned AI? And where should a nontechnical executive start if they want more than basic prompting? Andrew argues that data-center developers should carry the cost they create, with enforceable protections for residents instead of vague promises. On workplace AI, he makes the episode's sharpest claim: if employees are working outside policy, leadership probably failed to give them a safe and useful path. Liz offers the perfect test. When everyone cuts a footpath through the grass, the official sidewalk is in the wrong place. The practical close is simple. Start small. Pick one repetitive task. Explain the work as carefully as you would to a new employee. Correct the system when it misses, and let the workflow improve over time.

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