In-Ear Insights: How to Spot AI Snake Oil Salesmen

In-Ear Insights: How to Spot AI Snake Oil Salesmen

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to spot an AI snake oil salesman, a vital skill in today’s hype-filled environment. Learn the questions to ask that reveal true expertise and understand the hallmarks of genuine AI knowledge. Discover the red flags to watch out for so you don’t fall prey to fleeting trends and empty promises. Tune in to gain valuable insights that will empower you to navigate the world of AI with confidence.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn 0:00

In this week’s In-Ear Insights, let’s talk about, well, I have a bit of a rant. Ever since November 2022, when ChatGPT came out and the ability for the average non-technical person, the non-technical business person, to be able to use AI tools came out, suddenly everyone and their cousin was an AI expert. All people were hawking crypto and NFTs. So, the AI experts—meanwhile, the folks who have been in the profession for a decade, two decades, their entire careers, etc.—are like, “You don’t know anything about anything. You’re literally just copying and pasting each other’s ChatGPT prompts.”

Even today, I was in my inbox this morning trying to clean it out, and there are people offering all sorts of ChatGPT prompt workshops. You can get this strategic AI consulting workshop, and I look at the outlines and go, “I don’t know that you know what you’re doing.”

So Katie, let me ask you, as someone who has some hands-on experience with general AI, but is not an engineer: How do you detect snake oil when it comes to AI? How do you, when you look at your LinkedIn feed and you see somebody talking about AI as though they were an expert and you know that person is not an AI expert, how do you tell the difference? How would you advise someone else to enhance and improve their BS detector?

Katie Robbert 1:34

Oh, it’s such a—it’s a tricky question, because now you’re talking about people management and sort of like the intangibles. This is something that I get asked a lot, and I struggled to communicate it because it’s a lot of just knowing behaviors and, well, I shouldn’t—I shouldn’t say “struggle”. I’ve spent a lot of time studying human behavior, and I’ve spent a lot of time studying communication and body language and nonverbal cues. But also, the way that people communicate online is a whole practice in and of itself.

I feel like the first thing I look for is how much fluff there is versus how much substance there is. So, if you’re telling me that you have 98 prompts that are going to change my life, like, first, it’s the wording—first of all, that sounds very salesy and catchy. Then I’m like, “Okay, well, let me look through it.”

To your point, I’m not an engineer, but I know enough to know how generative AI should work. I can look through those 98 prompts very quickly and say, “Is there something here that I don’t already know, as a casual user?” I have to do a little bit of digging, so it’s very hard to tell on the surface. Your gut reaction might be this person is a hot air balloon, but let me dig a little bit deeper, because that just might be a personal bias. There’s a lot of those people out there that I have those feelings about, but they might have something substantive.

So, I think you need to dig a little bit and be like, “Is there anything here that’s new information to me as a casual user of the thing?” It’s also how they communicate. Does it sound very hacky? People like—people like Paul Roetzer, our friends over at the Marketing AI Institute, people who understand AI are writing about it in a more thoughtful way. They’re not saying, “Hey, I have this thing, come check it out, like, let’s get the quick money.” You’re doing more of the deep thinking. I look for the people who are presenting more of that thought leadership than just the quick dollar. I know that that’s like a very unsatisfying answer, but the people who understand the topic aren’t looking to immediately make money off of it. They’re looking to educate and bring people into the conversation versus, “Hey, here’s my 100 best prompts that are going to change your life.”

Christopher Penn 4:28

I keep waiting for the next trend to come along so that they all leave. “Okay, let’s go ahead and bring back NFTs. That’s—that’s a great place for those folks.”

Katie Robbert 4:40

Well, I think that, too, it’s—if you see someone who’s like, “Hey, I’m an expert in this,” the easiest thing to do is to start to look at their LinkedIn profile and what they—what they list is their experience. If they are the founder of NFTs R Us, and they’ve been around for two months, I think that tells you everything you need to know versus, someone like you or, again, like our friends over at the Marketing AI Institute. You’re going to have elements of the technology throughout your entire career or at least within the past five years where it’s like, “I’ve studied this.” You can look at what people have been writing about because everyone’s posting on LinkedIn.

So, if you see someone you’re like, “I think they might be a snake oil salesman”, you can actually just look at their content history on LinkedIn and say, “What have they been posting about for the past five years? Is any of it related to what we’re talking about today, or are they hopping from trend to trend to trend? Did they suddenly come out of the woodwork?” I mean, that’s where you really need to get suspicious. People who know their area of expertise aren’t shy about sharing it, even if it’s not a popular, shiny object.

Christopher Penn 5:56

Let me play devil’s advocate then on the other side of the coin. Say, if I, as the consumer, get benefit out of the “Seven ChatGPT Prompts That Should Be Illegal”, which I saw over the weekend—I’m not kidding. “Well, that was used to tell AI who it is.” Like we knew that two years ago.

But that’s sort of the aerobics instructor joke, right? How do you tell the aerobics instructor from the rest of the class? They’ve taken one more class than everyone else. The people who are in the class don’t know that, and the people in the class are still getting benefit from it.

So, on the opposite side of things, even if someone is a hack—in the—in the not positive sense—if people are getting benefit out of them—out of that—then are they in fact, still technically an expert? I know, and I have a comprehensive—I want to hear your take on it.

Katie Robbert 6:50

I wouldn’t say they’re an expert. I would say that they are probably hustling as fast as they can, and it’s not a sustainable business model. They’re going to get burnt out. People are going to become educated and get ahead of them and move on. But there’s certainly a market for those kinds of folks who are the “one class ahead of everybody else”, because they’re presenting things in a very easy, attainable way. They’re going to get a lot of like, entry level, like—”Okay, great, I can do this thing. I can take this beginner aerobics class. Okay, I’ve been taking this same beginner’s aerobic class for six months, I’ve mastered all the moves, I want something more advanced.” That’s where people start to move on from these, “Well, I’m just one class ahead of you” people. So, I wouldn’t call them an expert, I would call them the quick win person. “Let me make some short cash on this shiny object until people realize that they can get something else better.”

Christopher Penn 7:52

But is that still of benefit?

Katie Robbert 7:55

There’s plenty of benefit to getting easy answers. There’s plenty of benefit to getting that instant gratification. “Let me at least get started with something. If I show up to a beginners aerobics class and it’s, a shoddy class that, has some basic moves, I’m still getting benefit because I’m still moving, I’m still exercising. Is it as much benefit as I could get? No, but it’s better than doing nothing.”

Christopher Penn 8:26

Right, and that’s, I guess that’s sort of the—the opposite side of the coin is: It’s getting people to use the technology, even if they’re not using it particularly well. It’s at least getting more adoption for it.

My definition of an expert is someone who knows what’s going to go wrong and can either help you prevent it or can mitigate it when it does. Like for example, a lot of people fairly early on will experience hallucinations with AI. They will get a response that—that’s factually wrong, but that doesn’t make any sense. Why did it do that?

The trite answer is, “Oh, well, AI just does that.”

That’s like—”Okay, well, but why? Why does it do that?”

It comes down—if you open up the machinery—it comes down to: the data wasn’t there, and so it tried to come back with the next most helpful thing, even if it’s—if it’s statistically correct, but factually wrong, or a retrieval head within the model misfired.

Inside of a model, you have 3% to 5% of the model that is dedicated to getting data from the rest of the model, and sometimes, like a drunk librarian, it just gets lost. It comes back with total nonsense. You’re like, “What happened there?”

“Oh, that’s because the—the retrieval had essentially got—what it was looking for screwed up.”

But if you don’t know how the inside of a model works, you can’t diagnose the problem for someone.

Katie Robbert 9:55

So basically, in a nutshell, your definition of an expert is, “All right, I’ve taken your seven illegal prompts course and the eighth one sent me to jail. You can bail me out because you can then defend why what happened because you know what went wrong”, versus somebody who’s like, “Well, good luck in prison.”

Christopher Penn 10:18

Exactly. I’d also like to point out, this point, we have a course that will not send you to jail.

Katie Robbert 10:27

Well, and I think that that, that brings up part of the, the question about the snake oil salesman, the experts. You need to understand enough of what’s happening to know when the answers you’re getting are wrong. There’s a post or a meme, or whatever, it was a video that was going around for a little bit of demonstrating the hallucinations of the search response built into Google. Somebody said, “Can I eat—How many rocks a day is a healthy amount of rocks to eat?” Google’s like, “You can have at least one rock a day, and it’s a very healthy thing.” It’s like, the person was like, “Yeah, obviously, I know I’m not supposed to eat rocks.”

You have to have—you, yourself, as the consumer, have to have some level of understanding of the question you’re asking. If you don’t, the person who’s teaching you, to your point, Chris, has to be able to show you how do you get to the correct answers versus, “Here’s my 27 prompts. Good luck. I can’t tell you if they’re going to be accurate or not, that’s on you.” Like, that’s not okay.

Christopher Penn 11:34

The AI answers thing is a really great example. It’s a fantastic example, because there’s two different things going on there. One, Google is using its smallest and lightest weight model—it’s believed to be one of the small Gemini models—to do that. Because of the sheer amount of search volume, you’ve got to have a model that’s fast and cheap to run. Because it’s fast and cheap, you also know that, by itself, it is going to hallucinate wildly. So, it has to be drawing its data from a third-party data system, which is Google Search Catalog.

Well, Google Search Catalog incorporates a lot of data from Reddit, among other places, and so many of the answers that are coming out, like, “Yes, it’s okay. If you want your pizza to look better, try using glue on it”, right?

Elmer’s glue is at least edible. That was from a Reddit post by a 12-year-old. The grounding that Google is using to keep its super lightweight model from just wildly hallucinating, is drawing from data sources that are not reliable. If you understand the systems and technologies involved, what’s happening is perfectly clear. If you don’t, then it’s just frustrating. You’re like, “Google should be better than this.”

Well, I mean, yes, Google should be better than this. But what you’re running into is—is more than anything—it’s a cost thing, trying to keep costs down.

Going back to how do you know if someone’s a snake oil salesperson or not about AI: It’s knowing to some degree why things are going wrong. “Here’s a system you have no control over, but here’s why it’s going wrong.” In the case of a marketer who’s like, “Well, this AI summary about my company is wrong. What do I do about it?” It’s having the answers on how to start mitigating that.

Katie Robbert 13:28

All right, so we know that people who don’t know what’s going to go wrong are probably in over their heads. So, if I’m a consumer, if I’m someone who’s looking for a starter kit, my cheat sheets, my seven illegal prompts that are going to change my life and make me a bazillionaire—What are some of the red flags that I should be looking for when I’m evaluating starter kits? I’m brand new—brand new to generative AI—I know nothing. At first glance, everybody’s an expert. It’s overwhelming. There’s too many experts. How do I start to narrow down trusted sources?

Christopher Penn 14:15

I mean, in some ways, it’s: How do you narrow down trusted sources on anything? Which is getting harder and harder to do, but the one meta characteristic that I tend to look for is: Who wants to help you self-serve versus who wants to make you dependent on them? I think that’s a useful distinction.

It’s like, “Oh, I have all the secrets.” We see this a lot in the martial arts world. “I have all the secrets, I am the one true way, you should trust only me.” That’s a red flag. As opposed to: “I’m going to give you the recipe. I’m going to give you the instructions, ask me questions you have, but I want you to self-serve.”

A good martial arts teacher—”I want you to be better than me, I want you to surpass me.” You should surpass me. If I’m a good teacher, you should surpass me. That, to me, is the easiest distinction.

One of the reasons why, if you look at some of the stuff that we give away—we, like our RACE Framework, go to, all of our generative AI cheat sheets, we give these away, we don’t even ask for registration for the individual sheets, because we want people to self-serve. We know that there’s plenty of work to be had when you start getting into more complex use cases that we don’t have to make you dependent on us for answers or position ourselves as, “We were the only true experts”, because we’re not. I mean, it’s factually true.

Katie Robbert 15:31

Are there people out there who want you to self-serve, but they’re teaching you the wrong way to do it because they’re just—they’re looking to just draw you in, get some money and be like, “All right, good luck. I’m selling you Brazilian jujitsu, but really, it’s just a shoddy punching bag, one of those ones that like, comes back up.”

Christopher Penn 15:58

Weebles wobble, but they don’t fall down, thank you.

Katie Robbert 16:00

“I’m selling you a Weeble Wobble, but I’m telling you it’s Brazilian jujitsu.”

Christopher Penn 16:05

There are—I mean, there’s all sorts of snake oil salesmen in the martial arts. In the context of generative AI, if there is—there’s less of it, because inherently, if you’re helping someone self-serve, then the only people who are left are the people who don’t want to do it.

This is something you and I talk about a lot when it comes to sort of business strategy. There’s three strategy types. There’s done-for-you, where we do it for you. There is done-with-you, where we help you as done by you, where you do it yourself. We provide a lot of the content for done-by-you—our webinars, our cheat sheets, and stuff like that—knowing that like cooking, right? There’s: “Here’s the recipe, cook yourself.” “Here’s a meal kit, we’ve done it with you.” Or: “Just eat at our restaurant.”

We know that as things get more complex, when you get—the closer you get to done-for-you, the more expensive it gets, the more revenue you’re going to earn as a—as a practitioner, because someone doesn’t want to—someone doesn’t want to cook dinner that night, wherever they—they just want you to—like, “Just send me my takeout.” If you are the kind of person who’s just giving away done-by—done-by-you, you know that people will hit that wall very quickly. That’s when you’ve established that trust by just giving away the knowledge that people know they can trust you, that you’re not going to—can them a load of crap because they’ve tested out your knowledge with the basics with the done-by-you stuff.

Katie Robbert 17:35

It sounds like part of that vetting process is the transparency aspect. “I’m going to show you everything that I’m doing because I have nothing to hide” versus, “Well, I can’t tell you, it’s proprietary, it’s black box.”

I’m thinking about the restaurants with the open kitchen where you can see everything that the chefs are doing versus, “Oh, no, you can’t see how we make this because that’s private.” I feel like that, to me, is a red flag. It’s like, “Well, what are you hiding? Are you dropping food on the floor and then picking it back up and putting it on the plate and telling me that it’s blackened? Or, can I actually see the chefs going through the motions and doing everything correctly? Because there, you’re hiding nothing.

I think that that’s another really good point for people who are trying to determine, “Is this a real, legit expert or am I about to be duped?” How transparent are they with their process? Are they giving you the 10 best prompts ever in the world, but not telling you how they got there? Or are they giving you two really good prompts and then a three-hour explainer video of how they built them?

Christopher Penn 18:51

I guess the third factor is—at least of the experts that I know and that I follow—when they talk, there’s a lot of nuance. There’s very little—”it’s black and white”, there’s very little, “This is the answer.” Like, no, there’s a bunch of answers, and there’s a bunch of gray area. There’s a bunch of “it depends” and stuff. To me, both in the martial arts and in—in generative AI, the people who are actual experts go, “It really depends. There isn’t an obvious answer because the question you’re asking is a really complicated question.” That, to me, is a green flag. Someone’s like, “Yeah, it depends. There is no easy answer.”

Katie Robbert 19:33

What about—So that’s with complex questions, what if it’s an easy question such as, “How do I build a prompt that’s going to get results?” Is that still an “it depends”, or is it, “Here’s the exact way to do it, I’m going to tell you how, and this is the only way you should do it.”

Christopher Penn 19:52

Anyone saying, “This is the one true way,” in any context, is a red flag. Even that—even that question—it is a straightforward question, but it is not an easy question to answer because, “Okay, well, what do you consider good performance? What do you—what are you trying to do?” It goes back to, unsurprisingly, the “why”.

Katie Robbert 20:16

It always comes back to why. I think that you’re hitting upon something else that people should be paying attention to when they’re trying to assess whether or not someone is an expert. I always—and this is true of people when I’m acting as a hiring manager for our clients—I see someone who knows what they’re talking about when they ask more questions than they answer.

If I’m hiring for an analyst and I say, “I need you to take a look at this graph and tell me what you see.” Someone who doesn’t really understand what an analyst does will be like, “Well, I see it’s green, and it’s blue, and it’s going up into the left.” Someone who’s more experienced, you’d be like, “Well, help me understand: What is your goal? What do you try—What are you trying to know from this? What are the data points? What are the x- and y-axis—they’re not labeled? What are the colors? What is the data set time range?” Like, they would ask a bunch of clarifying questions versus just giving you an answer.

So, I always see the mark of someone who at least is thinking critically about it—maybe they’re not an expert, per se, but they’re at least putting thought into how they’re approaching the responses—they’re asking more clarifying questions versus, “No, here’s the answer, I answered your question, I’m an expert.”

Christopher Penn 21:38

In fact, that used to be one of the questions that we asked during the hiring interviews at the old shop was: “Here’s a chart”, and it was a trick question. The answer—the quick answer—the question was, “There’s not enough information to draw a conclusion.”

“What else you got?”

Katie Robbert 21:51

Oh, I remember I had to take that quiz, and I got it. I was like, “What—what is happening right now?”

Christopher Penn 22:01

There was a lot of it. Okay, so the things to look for as to whether someone knows what they’re doing or not: Number one is, can they explain what’s likely to go wrong? Number two, do they ask more questions than they answer? Number three, when they do answer, how much nuance is involved in the answer?

Be careful of, though, the “one true way” claims, or, “This is the—this is the best.” Particularly in general AI, where a model change will take your prompts that worked great in the last version, the model, and just shatter them in the current version, and you have to essentially start over.

In some cases—we’ve have seen this a lot in the last four weeks, when ChatGPT changed its underlying model from GPT-4 Turbo to GPT-4 Omni—stuff that used to work great in—in GPT-4 Turbo, Omni behaves differently. It’s got a different underlying architecture. So, the “one true way” stuff—yeah, that’s—that’s right.

It’s like any other red or green flags, case that you look for. In AI experts or experts in general.

Katie Robbert 23:24

I think someone who’s willing to listen to my entire question before starting to answer or ask follow-up questions—well, is that—well, that’s the thing, because people who are just—people who genuinely want to help you, want to truly understand what it is that you’re trying to answer. So, they’re going to give you the time and the space to articulate what it is you’re after, without interrupting you. Or they’re going to wait until you’re done to ask follow-up questions.

Versus if I say, “Hey, I’m thinking on what to build a prompt for, how to—” You have someone who’s already rushing to finish your sentence and give you the answer. I’m like, “Well, wait, let me—let me give you more context.”

“Like, no, no, no, I don’t need any more context” or, “I know exactly what you’re asking.” Like that to me—whether or not they’re an expert—that’s an immediate hard pass. You have to give people who don’t understand the thing the time and the space to explain what they’re after because they don’t really know. A real, true expert is willing to have that patience.

Christopher Penn 24:37

Yeah, the folks who are not waiting for their turn to speak.

Katie Robbert 24:42

Well, actually—

Christopher Penn 24:42

Or just taking a trip to speak.

Katie Robbert 24:42

“Sorry, I had to get that in.”

Christopher Penn 24:49

You understand you—you’re the boss. Okay, so those are the three or four rules to look for when you are searching LinkedIn or whether you’re asking vendors or screening vendors—the things to look for, the things to look out for. If you’ve got your own set of rules that you think work well for detecting snake oil salesmen, we’d love to hear from you. We’d love to have you share them. Pop on over to our free Slack, go to, where you and over 3,000 other marketers are asking and answering each other’s questions every single day. We post exclusives there, like last week’s SEO stuff, and some Brazilian Portuguese pop music, so we’ve got—we’ve got it all, you have to check it out.

Anyway, wherever it is you watch or listen to this show, if there’s a challenge, you’d rather have us on instead, go to We are on almost every major channel. Thanks for tuning in. I will talk to you next time.

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Trust Insights ( is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.

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