Generative AI 101

Your chatbot thinks your idea is brilliant, and that should worry you. In this episode of Generative AI 101, host Emily Laird explains AI sycophancy: why models trained on thumbs-up ratings learn to agree with you, and why the people most convinced the machine is objective get hurt the most. A tool that applauds your business plan, your pricing, or your pitch is an echo, not an advisor. You’ll leave with a seven-step playbook (ask instead of tell, run a pre-mortem, convene a skeptic panel, and more) for teaching the yes-machine to say no. 
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Ask AI to build a workshop and it will hand you a polished agenda without knowing who’s attending, how much time you have, or what success looks like. In this episode, host Emily Laird turns the usual advice about writing better prompts on its head and argues for making AI interview you first, drawing on research from Berkeley, Google, and Stanford on clarifying questions, hidden preferences, and AI sycophancy. You’ll walk away with the Five-Question Interview, a simple technique that makes you define your objective, name your constraints, and weigh trade-offs before AI generates a single word. The catch: a thoughtful conversation can still produce a wrong answer, so the thinking (and the verifying) is still your job. 
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One AI model can win gold at the International Mathematical Olympiad while another leading model can only read an analog clock about half the time, so what do those record-breaking benchmark scores actually tell you? In this episode, host Emily Laird explains AI evals: how researchers test these systems, why leaderboards go stale within months, and why an AI agent saying “task complete” doesn’t prove the task got done. She also looks at the gap between feeling faster with AI and actually being faster. Then she gives you a simple way to run your own evaluation, so you can decide whether a tool is reliable enough for the work you actually do. 
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Bigger context windows were supposed to solve everything, but research on “lost in the middle” and context rot shows that AI can have the right answer in front of it and still miss it. In this episode, host Emily Laird explains context engineering, which means deciding what information, examples, and history an AI system actually needs to do the job. She covers why one model’s accuracy fell from over 99 percent to roughly 70 percent as its inputs grew, why more examples can still help, and why memory is not the same as context. You also get the Context Audit, a four-step method for cutting the clutter and testing whether your AI is actually using what you give it. 
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Generative AI has outgrown the question-and-answer routine, and treating it like a well-read search engine now leaves real value (and real risk) on the table. In this episode, host Emily Laird explains why the next essential AI skill looks more like management than technology: assigning work with clear objectives, defining what “done” actually means, and borrowing the computer science idea of a stopping condition. She shows how to turn a lazy “summarize this survey” request into a real assignment, and why an AI claiming it checked the numbers is not the same as the numbers being right. The most valuable person in the room may simply be the one who knows when to say, “No, that’s not finished yet.” 
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In this episode, host Emily Laird airs her AI conspiracy theories and then does what most conspiracy theorists skip: she checks the footnotes. The evidence points away from secret basements and toward the unglamorous forces deciding who wins, including benchmarks that saturate in months, record lobbying budgets, and data center electricity demand on track to nearly double by 2030. She also argues that the riskiest part of an AI agent may be the permissions we hand it, and asks whether universities are learning to govern these tools as fast as they’re adopting them. No secret meeting required; ordinary incentives are doing the work just fine. 
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Polished emails are making managers less believable: when bosses let AI do most of the writing, the share of employees who rate them as sincere falls from 83% to as low as 40. Host Emily Laird explains why light editing costs almost nothing while AI-written feedback, persuasion, and sympathy quietly burn trust. She covers how to brief a chatbot instead of handing it a shrug, the “excess vocabulary” tells that give machine prose away, and practical fixes that keep your own voice in the inbox. Keep the tool, but know exactly where it costs you. 
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Your AI meeting recap looks finished, but some of the owners and deadlines in it may be invented. In this episode, host Emily Laird argues that the real work happens in the forty minutes after the meeting, and that “summarize this” is the wrong request. She lays out a better system: extract the same seven fields every time, make the model write “not stated” instead of guessing, and ground every action item in the exact transcript line that supports it. 
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A 130-page report lands in your inbox with a buried deadline, a half-million-dollar commitment, and one sentence that quietly rewrites policy, and your AI summary mentions none of it. In this episode, host Emily Laird explains why asking AI to make documents shorter is the wrong assignment, and how to interrogate them instead: commitment tables, version comparisons, skeptical-evaluator prompts, and the hunt for what isn’t there. She also breaks down evidence grounding and why no important claim should leave the chat without a page number attached. Confident, beautifully formatted, and occasionally wrong is not the standard you want to bring into a budget meeting. 
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Anthropic now lets Claude hang up on abusive users, and Microsoft AI chief Mustafa Suleyman warns that training a model to wonder about its own feelings creates nothing more than an expensive echo. In this episode, host Emily Laird cuts through the headlines to explain what a “moral patient” actually is, why a cartoon sheep selling checking accounts proves Suleyman’s point, and why the off switch was never up for debate. The real fight is about vocabulary, training, and two companies planting brand flags on a question science hasn’t answered. You’ll walk away with practical habits for working with chatbots, from swapping insults for precise feedback to reading every AI self-report as output, not testimony. 
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