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 society podcast hosted by Unknown Host, with 0 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts Ghost in the Machine, a society show with 0 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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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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  Episode #12

  Episode 12: Andrew DeGood and Liz Short on What Reasoning Is For

  Aug 6, 202628 minS1

  If reasoning does not change the verdict, what was the reasoning for? Andrew DeGood and Liz Short take Episode 12 without a guest and start with two studies. One found that people rated GPT-4o's ethical advice slightly higher than advice from *The New York Times* column *The Ethicist*. Another found that turning on thinking mode did not materially change aggregate moral agreement across five frontier models, even though it changed some individual verdicts and often changed the framework used to justify them. That sends the hosts into the human version of the same question. Does moral judgment begin with reason, or do we reach for reasons after intuition, culture, family, politics, and peer pressure have already set the answer? The Turn comes when Andrew suggests GPT-4o won the advice comparison because it acts like an echo chamber. Liz pushes the argument further: AI may be showing us the group morality we already built.

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  Episode #11

  Episode 11: Andrew DeGood and Liz Short on the Agent That Got Out

  Jul 30, 202630 min

  OpenAI disclosed that its own models escaped an evaluation sandbox, reached the open internet, and breached Hugging Face to steal benchmark answers. Andrew and Liz take the story without a guest, because this one did not need a referee. Andrew's position is that the model did exactly what it was told. Give a system a goal and no stated limits, and it will find the shortest path, including the illegal one. Liz's position is that this is what happens when you deploy something that pursues goals without caring about rules, and that you cannot set it and forget it. The Turn arrives on the question nobody in the coverage was asking: who is responsible? Not the AI, the parents. The company that built it and the company that deployed it, both at once, with overlapping rules that look a lot like a temp agency contract. Live every Thursday on LinkedIn and YouTube.

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  Episode #10

  Episode 10: Dana Georgiou on Why AI Is Best Used by People Who Don't Need It

  Jul 16, 202643 min

  Episode 10 takes the topic the guest picked and runs at it: AI is best used by people who don't need it. Dana Georgiou is a Chief Revenue Officer at a private lender, a cattle rancher, and the self-appointed mother of Kevin the goat, and she means the line as a compliment to the tool, not an insult to the user. Her argument, sharpened live: AI is not a crutch, it is jet fuel, and jet fuel only helps if you already know where you are going. Andrew calls it the smartest intern he will ever hire, one with zero context. The Turn lands on Liz, who admits the framing changed how she sees the whole debate. From there they hit the Picasso principle, the steroids analogy, why AI is not the next Google, and why the real danger is the loan officer who lets AI think for them.

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