Critical Ignoring: A Conversation With Christopher Mims on the AI Tech Bubble

Today we learn that the best BS detector starts with neuroscience and emotions and that people who admit they were wrong are the best to stay close to. I'm joined by Chris Mims, technology columnist at the Wall Street Journal and author of the book How to AI. Chris has spent more than a decade and 500 plus weekly columns exploring tech and trends. He's one of the very few people I've seen hold himself accountable publicly on predictions. I love it. Big into why disruption is rarer than we think, what AI is actually doing to the way software gets built and how to think clearly when everyone around you is distracting you with hype and nonsense. Please welcome Chris. Welcome to evolving industry, a no BS podcast about business leaders who are successfully weaving technology into their company's DNA to forge a better path forward. If you're looking to actually move the ball forward rather than spinning around in a tornado of buzzwords, you're in the right place. I'm your host George Jacuzziensky. Chris, thanks so much for being here. Yeah, thank you for having me. I was really looking forward to this conversation because I feel like you amongst anyone is probably built up such a fantastic pattern recognition skill and a BS detector. And now more than ever, I think that that's important. How many columns is it that you've written now? I had to count for my 10 year anniversary, but I think it's over 500 because I'm past year 11 now. Wow, that's impressive. So tell me a little bit about what skills or frameworks do you use to do some BS detection to look for what's hype versus real? It's funny. I actually think that where I start is going to sound very woo woo. But it's actually looking inside. I have a background in neuroscience. I had some training in cognitive psychology. I've talked to a lot of researchers over the years in these areas. We're easiest to fool. There's an author named Maria Conocova wrote a really great book about con men, con artists, when we're fooled. I highly recommend it to everyone. She talks a lot about what are the conditions under which we are most easily fooled. I've learned to be careful when I'm excited. I've learned to be careful when everyone else is shouting that this is the latest greatest thing. And you got to get on the hype train or you're going to get left behind. Obviously, we get a lot of that with AI, but we had it not so recently or not so long ago with I was a cryptocurrency. You know, I don't know how far down Bitcoin is now. I really start with how am I going to remain calm, cool and collected when you know, that's hard. I mean, look, I'm a writer. I'm a storyteller. Obviously, I'm passionate about what I do. So I wouldn't be able to do what I do if I wasn't excited about the things that are going on. I think that's true. Most people, once I've gotten past that, there are some formal tools you can use. But again, these are tools that you don't end up being able to use until you can engage, you know, what con men called system two thinking. I mean, I think most people are familiar with this framework. By now, system one is your impulsive thinking. System two is slower, more logical. You're using more of right your forebrain, your neocortex, the thing that really differentiates us from our closest relatives. And that's the point at which, you know, there's all kinds of great frameworks. So when I'm, you know, reading the news, there are things called lateral reading you can use to sort of verify what you're looking at. There's a way to kind of filter out information in advance because of course the amount that you're overwhelmed with information contributes to all these other problems. That's called critical ignoring, you know, and I wrote a column about that. That was kind of I was like, this is the most important skill you need for 2026. So there are a lot of things that you can use, but step one is don't get sucked in by the hype. And you know, the important asterisk on that assertion is all of us are going to get sucked in by somebody's hype at some point. We have to be aware that we're all going to do that. Yeah, totally. Yeah. And not having too much of a ego to think that you can avoid it, right? I know you said it's a woo woo, but it reminds me of because I'm really into mindfulness. It reminds me of that where it's like you have an emotion and the first thing you need to do is look at the emotion and say, why am I having that? Or maybe it's even just a physical sensation. Like, why am I having this physical sensation? And then you could start to explore, you know, what's behind that and how is that going to impact my decision making and all that? On the lateral reading, can you just elaborate on that a little bit? Like what's a good example? Yeah, so lateral reading is a really critical skill now because our information ecosystem is is so polluted. And I don't need to go into why I think most people just kind of nod when I say that. But lateral reading is, you know, you see something, even if it's a reputable source, but especially if it's something that an AI is telling you and you say, OK, what are other sources I can use to verify this that are sort of sufficient. They're not citing the original source. Funny enough, AI can help you with this. Like there are certain prompts that people will use. I mean, you can, you can just use it to test your assumptions, right? You can go in with like, like I will often go in with, you know, researching a topic, start to form an idea in my head about why a particular critical mineral is doing something that days and that day in the markets. And I'm going to test that assumption. So I'm going to be asking me, I like, OK, what's wrong with this hypothesis? What's wrong with this assertion? And because especially things like Gemini, but you know, chat GPT will do this as well because it's scraping Google search index. The sort of dark side of lateral reading is, you know, folks are like, I'm going to do my own research, but that's more of a media literacy credibility issue. You know, we can all kind of get down our own rabbit holes. Yeah. And I saw one of your most recent columns you were talking about how it's shocking the number of educated people are intelligent people that think that AI is actually just thinking like a human behind the scenes. Yeah, they treated as an oracle, which would like knows everything or is kind of infallible when it's really not. Yeah, I've been surprised even, you know, I have computer science background. I weren't software business and I'm finding people even with CS backgrounds are falling into that trap a little bit. Yeah, I mean, I think the challenge with AI, you have to think of it as like the world's like least neurotypical intern in a way. Right. So when you talk about people who are not neurotypical, they have spiky intelligence. So you think about like a person like Elon Musk who clearly has a great deal of intelligence in some areas and not a lot in others. Right. The way that people get tricked is sometimes you'll give an AI a hard problem. Right. Right. Our research problem increasingly they're able to do more kind of analytics. These cannot be able to do math, but now they're actually writing Python code, you know, in the wings so they can do calculations for you and stuff. I mean, even Google's basic search will do this now. It's incredible. So occasionally you'll have this kind of wow moment where you're like, whoa, you'd actually used formal logic to reason through this problem that I gave it. And it gave me the right answer and I checked it and it was correct. But the thing you got to remember is that, you know, it's just as valuable as a human right. You have to test all of its kind of base assumptions and you also have to assume that every once in a while it's going to go completely off the rails in a way that a human never would. So, you know, fundamentally it's software like fundamentally it is a sort of fuzzier way to do what we have always done with software. And as we all know with software it's garbage in garbage out. You know, one assumption, one factor, one data point is wrong. The whole thing is incorrect. So, you know, it can't do more than what a person does. It's not super human in that way. It's only super humanly fast really. Yeah, and we're all human, we're all fallible. We all get it wrong. What I love about you is the accountability that you held for yourself. You published an article that was, I forget the title, but it was essentially all the things that you got wrong. I don't want to rehast the whole thing, but I'd love you to think what motivated you to write that. You know, what did you kind of learn about yourself in writing that? Yeah, I mean, you know, I learned one unsurprising fact, which is that if you're going to be a tech columnist for a decade at the Wall Street Journal, you're going to have often sort of an irrational belief in your own point of view. But, you know, that's true of all of us, right? Like we're all sort of confidently making assertions based on sort of strongly held feelings, which we're rationalizing after the fact that the most insightful thing I think I've ever heard about predictions that people make is that predictions tend to be what we wish would happen rather than sort of a calm calculus about what we think is going to happen. Because there are people who sort of predict for a living, they run hedge funds and stuff, but that's also why you have hedge funds that do great for 10 years and then lose half their value in one year, right? So that sort of fundamental irrationality is something that I learned a great deal about. And then something I learned from befriending and interviewing futurists, so people who literally, you know, predict the future for a living. The first thing any good futurist will tell you is you cannot predict the future. You can do scenario planning where you imagine what's called the cone of possibility and it's like here are all the things that could happen. And then you can start to prepare yourself for all those eventualities or you can wait them by probability and say these are the ones we're going to prioritize. And that is not I think an intuitive or native way for people to think people don't talk about that in every day life. And you know, like if you ask your friend, like, hey, what do you think is going to happen? They're not going to be like, well, 20% this 20% of the time. the complete opposite. Very few people operate that way. It would just be hard to go through life. He would kind of have decision paralysis all the time. That would not be my friend for very long. Yeah, but so there are these formal frameworks that work and big companies use them and they do allow them to sometimes you'll say like oh it's so incredible how these leaders like skating to where the puck is and it's like yes, but what that what that leader has is the talent under them and the resources to prepare for like all of the possibilities or many possibilities and 80% of that effort ends up getting wasted because only 20% of it is relevant to what actually transpires, but that's the way you have to do it. Yeah, that makes a lot of sense. In writing that article, is there one thing that you got wrong that really stuck out to you? You know, I've been too busy obsessing about the more reason things that I've gotten wrong, which might be more relevant to your audience anyway. Yeah, so I'll tell you one of them. One thing that I think I got really wrong in my book, well let's say half wrong because because a lot of the a lot of what I predicted in the book is happening and so that is relevant. But one thing I very confidently predicted in the book is that the main way that people are going to be using an encountering AI day to day is as a feature rather than a product. In other words, AI will be in the background. It is going to be empowering the devices and the services that we use already and that we use every day. So, you know, AI is going to be easy Google doing this a lot, right? It's just going to be embedded in Gmail, doing auto responses for you or automatically populating your calendar from emails and you get things like that. What I underestimated was the degree to which interacting with chatbots was becoming a new default way for people to interact with computers and software to their credit. A lot of the AI companies, you know, especially in Thropic, OpenAI, Google Microsoft trying they'll get there eventually. They're cramming so much functionality that you use to access through conventional software, through tooling, through APIs, they're connecting it with the chatbots and making accessible through this plain language interface in a way that is building a habit in the consumer in people at work, which is analogous to when Google happened, when search engines happened, right? Like that was a profoundly new way to search for and access information and turns out this is not so far removed, right? I mean, talking to a chatbot, it's kind of an adjacency to just typing into a search engine. And so that is becoming a dominant way that people get stuff done and access information. And I really thought that because of the way that early chatbots just required so much expertise and that, you know, people always talked about prompt engineering and everything else. I was like, hmm, not so convinced that this is going to take over because it's going to be easier for people to access AI through more familiar means. But I think I was wrong about that. And I think that people are very rapidly learning the skill of getting a lot of utility. We're all kind of becoming our own prompt engineers. Yeah, and product designers, right? I'm just seeing the skill set of being a product designer, being something that everyone, if you want to really move things forward, you got to be leaning into, right? Right. Because everybody's doing, you know, it's the, it's funny. It's just, it's like Nietzsche or Schopenhauer's eternal recurrence. It's like every 10 years, we get a new way for people to make their own custom one-off software. And we're all going to become our own software developers. Again, it's a thing I'm skeptical of. But right now, I mean, cloud code, you want to go vibe code that thing you've always wanted to build. You can absolutely do it. Yeah, I'll tell you just from our experience is I've spent decades going into companies and they've built this like this one-off custom software internally. And we would always do this test where we'd say, hey guys, is this your secret sauce? Because if it's not, you shouldn't be building this in house. You should be outsourcing this. Use best of breed tools. And I can definitively say that that is turning 180 right now as far as going in because they can, you can build it custom for your company with much fewer resources, right? And just really make it work exactly how you need it to work. So I'm definitely seeing things change there. I'm curious from you, where do you think we're out on the trough of disillusionment with AI? I was thinking about that this morning. I think the challenges there are, because AI is general purpose technology, because it's useful for so many different things. There are many different hype cycles, which are, it's like when you hit a bunch of tuning forks and some of them are resonant and some of those waves are exactly out of phase so they're destroying one another. And so it depends on the industry, it depends on the company, it depends on the individual. One hype cycle right is software. Now in terms of that, like I think we're still approaching the peak because AI is hugely, hugely disruptive to the way that we code and build software, right? Andri Carpathy said the you know, the hot new programming languages English. And I think what that captures is we are moving the programmer up many layers of abstraction away from the code, right? A programmer, a good programmer now in some cases is a planner who is writing an extremely detailed specification, which is being passed off to a sort of boss agent. It's like we're not even talking to agents anymore. We're literally talking to agents who are talking to agents. And we're trying to get that entire hierarchy, which we have built for ourselves to one shot this little software project or big software project based on a sufficiently detailed specification. And then you have other coders who are more like human in the loop blah, blah, blah, blah, blah, that's all just coder talk. When you're talking about using AI and using agentic AI, which again are separate and the minds of a lot of people and in their functionality for everyday tasks, that depends on the industry. So let's take an industry medicine. We're still kind of going up toward the peak because adoption of AI note taking tools is uneven. Some people, frankly, are using the wrong tools. I mean, there's literally two dozen startups in this space. And when I have talked to people who've really thoroughly tested a lot of these, they're like, mostly these are garbage, but this is the one I really like in the law. We're still approaching that peak. So I think where we are hitting a trough of disillusionment, strangely enough, is actually in the financial markets. And that's because even though four-year-old H100 chips from Nvidia for powering all this AI are actually more valuable now than they were a couple of years ago because there's so much demand for inference. Folks are really skeptical about whether or not OpenAI, for example, is going to be able to build as much infrastructure as they have said or how competitive they're going to be with Google and Anthropic. There's a lot of concern about do we have the physical resources to build out the amount of infrastructure that Oracle has promised? That most of Oracle's future unrealized revenues are supposed to come from build out for OpenAI. That's a question mark. Half of Microsoft's future Azure Cloud build out. Again, OpenAI, big question mark. Where do we get all of the electricity for this when gas prices keep rising? And these new data centers are being fueled by fracked natural gas here. When the electrical grid is strained, when we have an administration that is trying to knock out as many renewable energy projects as possible, even though wind plus battery storage in plain states is now cheaper than maintaining existing coal-fired power plants in those states on a percolawatt hour basis. So we're supposed to have this all-in energy strategy, but our energy strategy is our AI strategy. Our energy strategy and the world markets are pretty deranged in terms of that right now. So how does that affect our build out of AI? That's why the magnificent seven is down so much from its peaks right now. There's just a lot of uncertainty, you know, also around dollar devaluation, inflation, you know, and that we're not even talking about tariffs as well. So there's a huge amount of AI has become so big that things that are macroeconomic factors that in the old days you only worried about affecting energy markets and banks. It's like, well, now it's going to affect Microsoft. Yeah, there's so many more questions than there are answers. And I know in my circles, because I'm in software, it does feel like we're coming out of the trough of disillusionment. Like I'm seeing people who are eye-rolling things this time last year are now like really leaning into real ROI of what they're implementing, but they also at the same time have way more questions than answers as far as, you know, how this is all going to play out. And I find it's really interesting in this time of so many questions that you wrote a book about the laws of AI. It's a bold. I like it. I'm curious, like, talk a little bit about what prompted you to write it and what did you learn as you were writing the book? Yeah, I mean, I was inspired by others who had written books about topics that were both fast moving and not so fast moving, where they said, you know, let's get to the root of how this technology, this phenomenon works. And let's extrapolate rules that will be durable that people can return to for years to come. It's important to remember that modern AI, modern generative AI, it's all built on the transformer architecture, right? So if we get a fundamentally, and that's how I like it, a lens or a belt, and that's how everything that represents today's boom is built. And that generative AI and that transformer architecture, that is, of course, distinct from what I call classic AI, but that just means more predictive analytics and different models. If tomorrow, there's a totally radically new architecture that somebody comes up with, and transformers get like thrown in the bin, then my book is irrelevant. But because we are continuing to build on this sort of fundamental technology, which has certain advantages, certain quirks, certain certain characteristics, that gave me the confidence to say, here's how it works across a bunch of different fields, not just large language models. And here's how if we take that all the way up the levels of abstraction to what it means for you, isn't it? So every day individual in your life, here's the things that you need that are going to be tricky about how it operates. Those things have held true. Our engineers working around problems like hallucination, yes. But is hallucination a fundamental characteristic of how these models work? Also yes. So it hasn't been eradicated. So everything that I wrote about in the book about how to be careful about it while using AI is still relevant. So a lot of what I wrote about in the book is at the intersection of management, human nature, big systems, and the adoption of new technologies. And if there's one thing I've learned from more than a decade of writing about tech for the Wall Street Journal, it's that no matter how amazing and new and useful a new technology is, adoption is rate limited by human's ability to learn it, to integrate it into what they do to change all the systems around it. And that's obviously why big companies are slower about this than startups and individuals and entrepreneurs. And when I want to talk to people who are really on the cutting edge, I talk to people who their company is them and they're really early adopters. And then they're just like, I move it, whatever pace I want. One of the laws in there was that I believe it's that experts will benefit from this more than novices. But what's also interesting is AI can kind of teach novices how to learn new skills. How do you balance that and how do you think it's going to play out between experts and novices? So I think that there is a huge opportunity for teaching AI. And I don't mean AI in ed tech because ed tech is a mess. And frankly, I don't want AI anywhere near my grade school age, children like no. I agree. But if you're talking about somebody who is at a tech company, they're being onboarded and they're supposed to quickly get up to speed so they can start making commits and contributing. Two things are happening. One is senior developers are being made more productive. Sometimes only marginally more productive. Let's not get too excited. So a lot of companies have hiring freezes or they just over hired throughout the pandemic so they're laying people off. And eventually we're going to return to a place where people are going to need to hire young coders again. All right. This is just the way these cycles work. How do you onboard that person? So they have enough knowledge that they can be directing the, you know, agent AI that's writing a lot of the code for them. There is I think a huge opportunity for AI's that are sort of gently helping bring up a person's level of knowledge during that onboarding process. And it's eventually going to be true. Not just for software companies can be true for every company. I was talking to a construction company yesterday. They were just kind of outlining all of the things that they do internally just in terms of their sales pipeline. And I was just like, whoa, whoa, whoa. I need to start recording this conversation because there's so much going on here. And then I started thinking to myself like, what a nightmare in a scene of 12 person company. Every time they got an on board, somebody knew who's got to integrate themselves into their process. And I just thought to myself, wow, they need a knowledge base. They need it to be accessible through AI. They need the AI to be integral to their onboarding process. Probably those companies exist. I haven't heard from them yet, but there's a huge opportunity for folks to do that. I think that the opportunity has been missed so far because the ideology in Silicon Valley right now is AI is going to replace all of these humans. And you know, one of my laws of AI is, sorry, it's just not going to happen. Like yes, it will make some people more productive. So you might have leaner companies and we're seeing that with leaner startups. But ultimately, the AI is not intelligent or flexible in the way a human is. And if you want to succeed, you've got to make sure that your AI is augmenting your best people and eventually your best entry level people rather than just trying to replace them. Yeah, I mean, each industrial revolution is kind of how this promise that people are going to get replaced. And there's still last I checked, there's more people doing work today than they'll have been. Yes, that's called the lump of labor fallacy. This idea is only so much work out there. And it's like, no, farmers become web developers. Like this is the way it works. Yeah, yeah. I think you also talk about disruption doesn't happen as often as we think. Can you elaborate on that? A lot of this is just people are excitable, especially people in tech and investors, right? Like the whole, that whole news cycle is just driven by people, investors and CEOs who, I don't blame them. What they're every incentive is to say this is the greatest things in sliced bread. Please give me more money. Fundamentally, you know, most developments are incremental. And big disruption is pretty infrequent. So if I'm going to borrow from another field from evolutionary biology, there was a famous evolutionary biologist named Stephen J. Gould and he wrote about what's called punctuated equilibrium. You look at the history of life on earth or any given species. What you have is you have these like very rapid times of adaptation and, and like species radiation because something happens, right? An ice age happens or whatever. And it's like, okay, adapt or die. But then you have for a long time, relatively little change in the genetics of the body, plan of an animal. The same thing applies to technology. And so people will say, oh my God, you know, like, Claude's new agentic framework. That's the biggest thing since Chatsy BT. And it's like, is it where are we still iterating on what was the true disruption, right? Which was application of transformer architecture to large language models, which was invented. This little thing happened at Google and was exploited by OpenAI. And then you got the Chatsy BT moment. That was a disruption, right? The iPhone was a disruption. The internet was a disruption. In between, it's less turnover than you think. So if you go all the way back to, you know, Christiansons original thesis about disruption, part of his assertion was, these are the times when startups and upstarts can displace big companies. And what we've seen, frankly, in the past 20 years is, you know, the big companies are able to acquire and copy fast enough that they aren't getting disrupted, right? I mean, there was a few years ago when we thought, oh, you know, OpenAI and their back are Microsoft is going to be hugely disruptive of Google. Who's going to be the Google of the AI age? Increasingly, I think it's Google. Bill Gates told you that startups are silly, but the good ideas persevere. What do you see out there, do you think, as silly right now? I think that there are quite a few copycat startups. So Andre Carpathy said on his last appearance on the Dorkish podcast, right now there are more companies than ideas in Silicon Valley. So do we need two dozen medical transcription AI startups? No. You know, do we need 25 different agentech harnesses for folks who are trying to, you know, make agents be less random and behave better? No. I also think that there are certain things in kind of physical AI that are very silly. I mean, here's a big one. I think humanoid robotics are one of the biggest bubbles in not just in tech, but I would say in tech history right now because they require so much capital. When you have Jensen Wong saying, oh, you know, physical intelligence is the next big thing in AI, I agree. I happen to be somebody whose first book was about robotics. And I say with a pretty high degree of confidence that this idea that Optimus is going to send Tesla stock price to the moon or that a lot of these other companies who, let me be clear. I know a lot of their CEOs. I think they're very smart. I think a lot of them have really noble goals. They, you know, one of them told me that his ultimate goal is to deal with America's aging population because he witnessed his own grandfather's decline and he wanted a home companion to just help him live more independently. Great. Very stirring. Are we going to get the kind of AI that's required to make humanoid robots accomplish these kinds of tasks and be truly relevant and cost effective in factories anytime soon? Absolutely not. Are we going to get more robots? Yes. I think it's the biggest bubble in tech right now. Interesting. You may need to talk with Oliver Mitchell. He was on an episode not that long ago with me and he's an investor in the humanoid space and he's really, he did make some compelling arguments for use cases such as welding. You know, it's an industry that, you know, has lost a lot of talent and all that. But me being from Massachusetts, I'm partial to dog robots. I think dog robots are gonna be. humanoid robots. I mean it could be we maybe we're splitting hairs but it could be like human torsos on dog bodies maybe Centaur robots are what's actually gonna be big. Oh I'm looking for to Centaur robots that's good Chris you hold yourself accountable you have a nice balance framework as far as how you look at things I it's really refreshing and I love it I always like to finish these with a fun question which is in your life in your career what's the best advice you've ever received. Oh it was the most basic advice that I got from my college mentor at the very very beginning of my career and she said what you should do in life is whatever's at the intersection of what you're good at and what you enjoy and it was just so practical it wasn't like follow your dreams. Her underlying message was you know be of service and you will always A be employed and B feel a sense of purpose so definitely the best advice I've ever got. I love I think simple advice is the best advice. Chris thanks so much for being here. Yeah thank you so much for having me it's been a pleasure. Thanks for listening to Evolving Industry. For more subscribe and follow us on your favorite podcast platform and pretty please drop us a review we'd really appreciate it. If you're watching or listening on YouTube hit that subscribe button and smash the bell button for notifications. If you know someone who's pushing the limits to evolve their business reach out to the show at Evolving Industry at in cavity dot com reach out to me George Echizen ski on LinkedIn. I love speaking with people getting the hard work done the business environment's always changing and you're either keeping up or going extinct. We'll catch you next time and until then keep evolving.