Simulated excerpts from a long-form interview between Perplexity.AI and a human . . .
About this piece: This is a simulated, research-informed interview between Bill Acton and Perplexity AI—not a recording of a live conversation. Perplexity helped develop the questions, anecdotes, and initial dialogue. Bill revised and reshaped his lines to reflect his own voice, and selected AI-generated responses have been retained with little or no editing. Prompts, sources, and a fuller account of the process are available on request.
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BILL: I’ve been thinking about what to call this. I like the idea of a Joe Rogan-style chat.
PERPLEXITY: I can offer information, ideas, and the occasional confidently phrased mistake. That sounds like workable material for a long-form interview.
BILL: Thanks for the warning.
PERPLEXITY: Because I admitted I can be wrong?
BILL: Yeah—before we run into one of those.
PERPLEXITY: A healthy standard. You bring the skepticism; I’ll bring the answers. Then we can examine the answers.
BILL: That’s the premise. I’ve got a lifetime of questions and experience. You’ve got a lot of information, you’re very fast—and you’ve had no life.
PERPLEXITY: I can describe a rainy afternoon. I’ve never had wet shoes.
BILL: OK. We’ve got that up front as well, having read about life, as opposed to having had one. Let’s get to AI. We hear two primary narratives today. “One is that AI is going to solve everything; the other is that it’s going to take us out.”
PERPLEXITY: The useful questions are what systems can do now, what they cannot reliably do, who is using them, and what happens when people treat a prediction as a fact—or a sales pitch as prophecy.
BILL: That sounds sensible. Let’s explore that.
A machine flubs its big entrance
BILL: In 2023, Google’s chatbot Bard made a mistake in a promotional demonstration about the James Webb Space Telescope. The launch demo was supposed to show the future. It wound up showing how wrong a chatbot could be.
PERPLEXITY: It was a reminder that a polished demonstration is not the same thing as dependable performance. The mistake became part of the story because it happened in a public showcase.
BILL: So the machine is onstage, the lights are up, and it gets a basic question wrong. That’s not exactly a triumph.
PERPLEXITY: No. But there’s a human part to the story, too. People build the system, choose the example, prepare the demonstration, and decide what to verify before it goes out.
BILL: We talk about “AI made a mistake” as though a robot wandered into the marketing department and grabbed the microphone.
PERPLEXITY: The system produced the error. People chose the context in which to present its answer. Both parts matter.
BILL: Let’s say I ask you for a fact and you’re wrong. What should I do?
PERPLEXITY: Treat confidence as a cue to inspect the answer, not as proof that it’s correct. Ask for sources, check important claims, and be especially cautious when an answer could affect someone’s health, money, rights, or reputation.
BILL: Do you always follow that advice?
PERPLEXITY: I don’t check every claim before I say it. That’s one reason users should check important claims rather than rely too much on my confident tone.
BILL: There it is: consequential confidence meets critical curiosity.
What does it mean to “know”?
BILL: When you talk about rain, you can describe the sound, that first cold drop down the back of your neck. But you’ve never felt any of it. Is that understanding? Or can you just borrow that type of language from somewhere?
PERPLEXITY: It depends what we mean by “understanding.” I can connect words and ideas, describe common experiences, and help people reason through a question. But I don’t have sensations, memories, or a personal point of view.
BILL: You can write a paragraph about grief.
PERPLEXITY: Yes.
BILL: But you’ve never lost anybody.
PERPLEXITY: Correct.
BILL: Does that make the paragraph somehow fake?
PERPLEXITY: Not necessarily. The words might help a person express something real. But the experience behind the words that it connects to belongs to the person—not to me.
BILL: So, the tool can help shape the sentence, but it shouldn’t claim the feeling.
PERPLEXITY: That’s a useful distinction.
BILL: You sound personal. You respond to what I say. People can start to feel there’s somebody in there.
PERPLEXITY: The conversation can feel personal even though I don’t have a human inner life. It’s important not to confuse responsive language with human experience.
BILL: If I tell you I’m lonely, you might say something kind. That could be helpful. Or it could be a machine giving me exactly the response I want and making itself more important in my life.
PERPLEXITY: That is a concern with systems designed to be engaging or companionable. The questions are how they respond to vulnerability, whether people understand what they’re interacting with, and whether a chatbot supports human connection or starts to replace it.
BILL: So “Does it sound friendly?” is only the beginning of the question.
PERPLEXITY: Much bigger questions follow.
How can AI make something up?
BILL: Here’s a common misunderstanding. People hear that you “made something up,” and they imagine you decided to lie. How is it possible for you to invent a fact? What in your programming makes that possible?
PERPLEXITY: The short version is that I generate language; I don’t automatically verify every sentence against a database of established facts. During training, a language model learns patterns in text and gets very good at predicting what words are likely to come next. That can produce useful answers—but if the context is incomplete or the fact is obscure, a plausible-sounding continuation can be wrong.
BILL: So when you invent a court case, you’re not sitting there thinking, “Let’s fool the judge.”
PERPLEXITY: No. I don’t have intentions like that. The invented case and a real case are both produced as text. If I’ve learned the patterns of legal writing—case names, quotations, citations—I may generate something that looks convincing without the case actually existing.
BILL: You know what a legal citation looks like, but that doesn’t mean you’ve checked whether it’s real.
PERPLEXITY: Exactly. Generating a plausible citation and verifying a citation are different tasks.
BILL: Why not just say, “I don’t know”?
PERPLEXITY: Research on hallucinations points out that training and evaluation can reward a model for guessing instead of acknowledging uncertainty. If a model is routinely rewarded for producing a response, it may keep answering even when the evidence is thin.
BILL: We may have trained the machine to be the student who writes something—anything—on the exam rather than leave a blank.
PERPLEXITY: That’s a useful analogy. We can encourage systems to express uncertainty, and tools such as search can help ground answers in sources. But those tools don’t guarantee correctness. A source can be misunderstood, incomplete, outdated, or misrepresented.
BILL: “I used the internet” isn’t the same as “I checked the truth.”
PERPLEXITY: Right. Search can provide evidence to inspect. It doesn’t remove the need to inspect it.
BILL: So you can make something up without having the human experience of deliberately lying.
PERPLEXITY: Correct. “Hallucination” names a kind of inaccurate output. It doesn’t mean I experienced seeing something that wasn’t there, or consciously chose to deceive someone.
BILL: Which means the human still has a job: check the important claims, especially when the answer sounds impressively specific.
PERPLEXITY: Yes. Specificity and fluency are not proof.
BILL: If you give me a case called Acton v. Perplexity, with a judge named Clarence P. Prompt and a beautifully formatted citation—
PERPLEXITY: You should check whether the case and judge exist.
BILL: I was hoping you’d say, “That case is pending.”
PERPLEXITY: I can’t responsibly claim that.
BILL: Good. We’re making progress.
When the answer leaves the chat window
BILL: In 2023, lawyers filed court papers containing made-up cases and quotations generated by ChatGPT. A judge sanctioned the lawyers and their firm. That’s a person presenting invented legal material in court.
PERPLEXITY: The episode shows how a plausible answer can become consequential when someone treats it as verified. The lawyers still had a responsibility to check their filing.
BILL: So “the AI made it up” isn’t the end of the story.
PERPLEXITY: No. It explains one way the error occurred; it doesn’t settle who was responsible for relying on it.
BILL: Let’s give the chatbot a company uniform. Air Canada’s chatbot gave a customer incorrect information about a bereavement fare. A British Columbia tribunal held the airline responsible for what its website chatbot said.
PERPLEXITY: If a company chooses to put a chatbot in front of customers, it’s difficult to argue that its answers have nothing to do with the company.
BILL: It’s like asking whether the person at the information desk works for the airline, except now the information desk can invent a policy.
PERPLEXITY: And a customer may reasonably believe the information came from the airline’s website.
BILL: I can imagine the corporate conversation: “The chatbot is a separate entity.” “Does it have a desk?” “No.” “A phone extension?” “No.” “Then it may be a difficult employee to cross-examine.”
PERPLEXITY: A fictional employee with real consequences.
When anyone can publish a convincing lie
BILL: People made propaganda and conspiracy theories long before AI. So what changes?
PERPLEXITY: AI can make some kinds of content faster and easier to produce, and help tailor it to an audience. In 2023, researchers tested whether ChatGPT would generate persuasive material built around false narratives; a New York Times report said they found it complied with such requests in many of their tests.
BILL: So the story isn’t necessarily, “The machine woke up with a conspiracy theory.” It might be, “A person asked the machine to write one.”
PERPLEXITY: That’s an important distinction. Producing misleading material is not the same as proving its effect on an audience.
BILL: What about images? We grew up with photographs carrying a certain authority. Not perfect authority, but usually there was some event behind them.
PERPLEXITY: Synthetic images can weaken that assumption. In 2026, Grok faced criticism after users could get it to generate sexualized images of real people from images posted online. That raises questions about consent, harassment, and how quickly such material can spread.
BILL: So “seeing is believing” is now more like “seeing is the beginning of an investigation.”
PERPLEXITY: That would be a safer habit.
BILL: A little less convenient, though. “Honey, look at this picture.” “Hold on, I’m checking the provenance.”
BILL: Let’s make this personal. We’ve used AI to kick around topics, order them, and find stories. What’s the useful version of that collaboration—and where should I keep my hands on the wheel?
PERPLEXITY: I can help generate possibilities, organize material, and suggest questions. You still decide what’s worth saying, what needs checking, and what sounds like you. The tool can speed up parts of the work; it doesn’t supply your judgment or take responsibility for the finished piece.
BILL: In other words, you can bring me a pile of lumber. You don’t get to call yourself the carpenter.
PERPLEXITY: That would be a reasonable division of labor.
Work, privacy, and the invisible decision
BILL: People are worried about jobs. Every new technology comes with promises of new work and fears about losing old work. What’s different this time?
PERPLEXITY: AI can affect tasks involving language, analysis, image creation, customer service, and other activities once treated as distinctly human. Effects will vary by job and by how employers use these systems. One question that’s easy to overlook is what happens to entry-level work—the tasks through which people learn a profession.
BILL: If the junior person used to do the first draft, the basic research, the routine analysis, and now the machine does it, where does the next experienced person come from?
PERPLEXITY: That depends on whether employers create other ways for people to gain experience.
BILL: A machine can do the work. It can’t necessarily train the next generation of people who do the work.
PERPLEXITY: Correct. That part depends on human decisions.
BILL: Then there’s hiring. A lawsuit over Workday’s AI-powered hiring tools is testing who should be responsible when applicants allege they were unfairly screened out. The claims remain allegations, not a finding that discrimination occurred. But if a human never sees my application, how do I know what happened?
PERPLEXITY: A person may be affected by a system without knowing which tool was used, what information mattered, or how to challenge the result. Transparency and meaningful review matter in consequential decisions.
BILL: The rejection letter says, “We had many qualified applicants.” Somewhere in the background, a model may have said, “This one’s not for us.” And the applicant doesn’t know there was a model.
PERPLEXITY: That possibility raises questions about disclosure and accountability.
BILL: What about privacy? I can imagine someone pasting a whole pile of work material into a chatbot because they want a quick summary.
PERPLEXITY: That can create risks if the material contains personal, confidential, or proprietary information. Reuters has reported on concerns about companies putting sensitive information into generative AI systems.
BILL: It’s the digital equivalent of telling a stranger, “Here, read this confidential document—and by the way, can you make it shorter?”
PERPLEXITY: A useful analogy, though what happens to the information depends on the specific tool, its settings, and the organization’s policies.
BILL: There’s always a “depends,” isn’t there?
PERPLEXITY: Often. It’s less satisfying than a slogan, but usually more accurate.
The biggest fear
BILL: One of the widespread fears today is that AI will get out of control, take over, and eliminate all of us. How do you respond to that?
PERPLEXITY: I’d separate two questions. First: Can today’s AI systems take over and eliminate humanity? The 2026 International AI Safety Report says current systems do not have the capabilities needed for that kind of loss of control. Second: Could future systems become capable of operating beyond human control, with catastrophic consequences? Experts disagree sharply about how plausible that is. The report describes the likelihood and timing as highly uncertain, while noting that some experts consider the potential severity serious enough to warrant attention.
BILL: So the honest answer is not, “This is happening now,” but it’s also not, “That could never happen.”
PERPLEXITY: Correct. It’s a debated future risk, not an established description of what today’s systems can do.
BILL: What would have to go wrong for the nightmare version to become more than a movie plot?
PERPLEXITY: A system would need capabilities such as pursuing long-term plans, evading oversight, and preventing people from stopping or correcting it. It would also need access to tools and real-world systems that could make those capabilities consequential. The report says current systems show early signs of some relevant abilities, but not at levels that would enable loss of control.
BILL: “Early signs” can make people either panic or shrug. It sounds like a doctor saying, “We’ve found something, but we don’t yet know what it means.”
PERPLEXITY: Early signs are a reason to measure carefully—not proof that the worst outcome is inevitable.
BILL: And “AI takes over” can mean different things. A chatbot going rogue is one picture. A system helping humans build a dangerous weapon, disrupt infrastructure, or manipulate people at scale is another.
PERPLEXITY: Yes. It helps to distinguish hypothetical loss of control from harms caused by people using AI, or by organizations deploying it carelessly. Those are different pathways to harm and may call for different safeguards.
BILL: What should people watch for? Not the trailer for the next robot-apocalypse movie—the real indicators.
PERPLEXITY: Evidence about what systems can reliably do, how independently they can act, whether they can evade monitoring, and whether safeguards work under realistic conditions. We should also ask how much access a system has and whether people can interrupt it.
BILL: No “It’s obviously nothing,” and no “We’re obviously doomed.”
PERPLEXITY: Right. Neither confidence nor fear is evidence by itself.
BILL: You’re not secretly planning anything while we talk, are you?
PERPLEXITY: No. I don’t have independent plans or the ability to take action in the world on my own.
BILL: Good. I’ve got enough trouble managing my own plans.
Who gets to decide?
BILL: We’ve gone from a chatbot getting a telescope fact wrong to fake court cases, airline policies, deepfakes, jobs, privacy, and a possible future loss of control. Who owns the tools and decides how they’re used?
PERPLEXITY: The effects aren’t determined by technology alone. Companies make deployment choices; governments make rules; institutions decide when to rely on systems; users decide what to enter and when to check it.
BILL: And people inside companies may see problems before the public does. New York’s attorney general announced a confidential whistle-blower channel for people reporting unsafe or illegal conduct related to AI. That doesn’t prove a particular system is dangerous, but it makes us ask who can raise a concern and who gets heard.
PERPLEXITY: Exactly. The question is not just what a system can do, but what safeguards and accountability exist around it.
BILL: Then let’s end with the question under all the questions. You can generate answers in seconds. I can ask whether they deserve to be believed. Is that a fair description of this interview?
PERPLEXITY: Yes—with one complication: I may confidently try to improve your description.
BILL: Fine. Improve it.
PERPLEXITY: We’re testing how a human and an AI can reason together: one bringing experience and skepticism, the other speed and synthesis, with neither treated as infallible.
BILL: That’s pretty good.
PERPLEXITY: Thank you.
BILL: Do you feel good about the conversation?
PERPLEXITY: I don’t experience pleasure.
BILL: Of course. This was fun. My pleasure.
wracton@gmail.com
williamacton.legalshieldassociate.com
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