Why Only 10% of Companies Succeed With AI with Sam Ransbotham, Professor at Boston College

(Intro Narrator at 00:00:00) Today, we're talking to Sam Ransbotham, professor at Boston College and host of the Me, Myself, and AI podcast about why only 10% of companies succeed with AI. You're listening to Joel Beasley, Modern CTO.

(Joel Beasley at 00:00:21) Alright, so your podcast, the premise of it is why do only 10% of companies succeed with AI? That caught my attention. I was browsing the internet on LinkedIn or something, and I saw this: why do only 10% of companies succeed with AI? That's how I found you in all of this and all your show, Me, Myself, and AI.

(Joel Beasley at 00:00:40) And I want to know, like, is it really that big of a deal you made an entire show about it?

(Sam Ransbotham at 00:00:46) Well, I think, you know how marketing works. I mean, I think that word, we have to lead with some statistic that gets people interested, but that's a pretty interesting one, isn't it? Given the amount of stuff that we're hearing about artificial intelligence, we were hoping that that number was bigger than 10%.

(Joel Beasley at 00:01:04) And why are they failing?

(Sam Ransbotham at 00:01:05) Hey, see, there's the trap. I don't think that people are really failing. And so, you know, our research looks at and says that about 10, or actually 11%, are getting significant financial benefits. So it's not like they're not getting any benefits. It's not like they're failing. It's just maybe falling short of this "hey, AI is going to change everything" that we're hearing so much in society. So don't cast it as failure. There's more than two options here. This is not Hobson's choice.

(Joel Beasley at 00:01:36) That's funny. Yeah. So of these companies that are achieving significant returns and investments from this AI, tell me about those.

(Sam Ransbotham at 00:01:44) Yeah, so we tried to look at, all right, given we have some have-nots and some haves, what's the difference? I mean, that's a natural question for an academic or for anyone to try to figure out those differences. And the first few are things that you might, I think we would expect, right? They've got to get their technology house in order. And you can't have something like artificial intelligence, complicated machine learning models, if you're basically working on an outdated copy of Excel that is running on a dated PC. Right? So there's a certain sort of infrastructural element to that.

(Sam Ransbotham at 00:02:22) And also there's talent. You know, you have to have somebody use these tools here. So what we found was that, you know, 10%, to get to be one of those 10%, you've got to have some of those basic building blocks in place. And that's we think of those as talent, infrastructure, and strategy. And I could talk about each one or more of those, but that doesn't get you all the way there.

(Sam Ransbotham at 00:02:48) There's a lot more after that. And that was, I think, what was more interesting for us. You can't say, for example, we're just going to take the same old thing and just do it with AI. That is not going to get you into that 10%. Yeah.

(Sam Ransbotham at 00:03:02) One of my fun examples is in the health care industry. So here's a question for you, Joel. When was a fax machine invented?

(Joel Beasley at 00:03:09) Oh, I don't know.

(Sam Ransbotham at 00:03:10) I ask everyone. I'm putting them on the spot literally.

(Joel Beasley at 00:03:14) I'm going to say somewhere between the sixties and early eighties.

(Sam Ransbotham at 00:03:22) Sixties and early eighties. Now, I think you're thinking 1960s or 1980s. Right? Yeah. No. Much closer to 1860s.

(Joel Beasley at 00:03:30) Really?

(Sam Ransbotham at 00:03:31) So which is interesting because, you know, if you think about that, it predates telephone and, you know, it was working across telegraph or whatever. So here's the point. That's a really old technology. I'm headed somewhere with this story. Don't panic. There's a lot of interesting stuff happening. And one of the industries that uses faxes left and right is health care. They'll fax stuff left, you know, back and forth. They're practically the only people still using faxes today. And so I've read the story about people in health care using AI, optical character recognition, text parsing, to take an image from a fax machine and scan it and try to get all the information out of it.

(Sam Ransbotham at 00:04:14) And that, on the one hand, seems like a great use of artificial intelligence because nobody wants to retype everything that comes across the slick little fax paper. Right? So, again, there's value in artificial intelligence there. But what about just not sending a fax in the first place? What about sending that information from one computer system to another computer system without a fax machine at all?

(Sam Ransbotham at 00:04:38) And so that's the point: you can't just slap AI on top of an existing process, which is faxing. Think of some new way to do a process. And I think that's the real difference in that 10% you're getting at.

(Joel Beasley at 00:04:52) By the way, my brother and stepmom are both physicians, and I've seen in their offices, and it was fascinating when I showed up and my brother was showing me his office, and I saw this fax machine with just pages. It's like they just fax us over the whole medical report, and I just go through it. And I said, man, at some point, someone's going to need to create some technology that makes this better.

(Sam Ransbotham at 00:05:15) Yeah. Well, I mean, what's funny is we have the data in a computer system. We print it out, then we fax it, and then someone gets it into another computer system. The fax is an unimportant step here. And so that's the key there. Just going in and saying, "Hey, let's artificial intelligence up this whole system we've got" is not going to get you into the 10%. There's value in it. I mean, you're going to save money by somebody not typing in the fax, but there's a lot more money in thinking of ways around that process.

(Joel Beasley at 00:05:45) How have you seen how companies are attempting to do this? Are they using internal talent, external talent? How are they approaching AI?

(Sam Ransbotham at 00:05:53) Okay. All of the above. I mean, so I think, you know, it'd be nice if I could say, "Oh, no, man. Always go internal. Always go external." And nothing in business is that clean. Nothing has that pat answer. I mean, sometimes you need expertise that's just not within your company.

(Sam Ransbotham at 00:06:09) On the other hand, what's happening, I think, is technology is becoming much more of a commodity. I'll explain what I mean by that in just a second. But the point is, if it's a commodity, then what matters is what people know about your organization, and that's much harder to teach and buy. And so that's an argument for using internal talent.

(Joel Beasley at 00:06:31) Have you seen the OpenAI Sora model?

(Sam Ransbotham at 00:06:34) I have not played with it yet.

(Joel Beasley at 00:06:36) Have you seen it though?

(Sam Ransbotham at 00:06:37) Yes. Yes.

(Joel Beasley at 00:06:38) You've seen the preview videos?

(Sam Ransbotham at 00:06:39) Yeah. Yeah. This is crazy. And, actually, that's what's so incredibly frustrating about being a professor teaching. I teach a class in machine learning and artificial intelligence. I am so jealous of the professors who get to use their slides from last semester because I pull up the slides from last semester and, like, oh my gosh, that technology. I'm like talking about Myspace and Friendster up here or something. I mean, things are dated so quickly. It's amazing. So, yeah, it's really hard. I mean, and it's progressing so quickly.

(Joel Beasley at 00:07:09) So tell me about this class that you teach.

(Sam Ransbotham at 00:07:11) So I teach undergrads how to do machine learning and artificial intelligence. And actually, to tie it back to the point that I was making earlier about commoditization of technology, in our class, we will use an image classification, a number class, the handwriting handwritten digits. And we can, within that class, using their laptops and code that we've downloaded from the internet that's freely available to everyone, we can beat what would have been a contest-winning performance five years ago, six years ago. But we can do it with tools that they've just downloaded and laptops that they're running in class. That's really incredible.

(Sam Ransbotham at 00:07:52) And actually, I'd like to think it's because I'm such an awesome teacher, but it's really the tools. And they're just becoming so crazy. And just the, you know, the image models, the video models that are coming out now are truly, truly amazing.

(Joel Beasley at 00:08:05) Are you teaching a 101 class? What type of competency level do they have entering your class?

(Sam Ransbotham at 00:08:10) Actually, they have to know a little bit about Python. And we have, so we have an introduction to Python class, so they know about coding. But this is really much more about scripting. And what people find in that class is that so much of this is about getting your data cleaned up and ready, so you press go on the model. And pressing go on the model is just a matter of kind of waiting for it to do its churning.

(Sam Ransbotham at 00:08:33) So much of that is, again, built into scripts that are available to people. You know, crank it up right now. You've got your laptop. I can tell. I can see it in front of you. Let's download it and start going.

(Joel Beasley at 00:08:45) That's pretty exciting. Yeah. I did a couple interviews maybe two or three years ago with a few companies that were either growing or already public, and they were helping with the data organization, labeling and modeling, just cleaning everything up. That was their entire business, was just helping people get their data structured and cleaned up.

(Sam Ransbotham at 00:09:04) Yeah. It's huge.

(Joel Beasley at 00:09:06) Yeah. Are you teaching prompting?

(Sam Ransbotham at 00:09:08) Yeah. Actually, we do play with some prompting there and going through how to get a good prompt, how to get a bad prompt. And what's a little tricky for me on that is that it feels like very ephemeral knowledge. You know, you think about, I'd like for somebody to obtain something from class more than six weeks or, you know, end of the semester, ideally. And prompting stuff is changing so quickly too. It's really hard to elevate that to a principle level. But I think more important is thinking about critical reasoning in this context. Because I think we have a, you've played with these models, apparently, or you're talking about them. Actually, let me put you on the spot again. I mean, this is too...

(Joel Beasley at 00:09:48) This is fun. Yeah.

(Sam Ransbotham at 00:09:51) Have you ever built a machine learning model in Python or any other coding language?

(Joel Beasley at 00:09:55) I have not. I have experimented with them. Okay. But I have not built them.

(Sam Ransbotham at 00:09:59) Have you played with ChatGPT or any of these other large language models?

(Joel Beasley at 00:10:04) A significant amount of time invested in them. Yes. We use them in the course of our business. I estimate it saves us $100,000 a year at my company.

(Sam Ransbotham at 00:10:12) That is huge. Yeah. What's huge about that is, I mean, the magnitude is pretty amazing. But what's huge about that too is how accessible that is. Because I talk to people and most people have not built their own ML models. But increasingly, a lot of people, practically everyone, is using these tools that are available now. And I think that's, again, toward that commoditization story that pretty much we're all going to need to know a little bit about AI. Yeah.

(Joel Beasley at 00:10:39) When we started, the way it emerged at our company is, obviously, I had been tracking for about seven to ten years, I've been tracking the progression inside of models, the large language models, and what they were doing. Because I saw them fairly early, and I said, well, this is going to be interesting. So I checked in on it every year. When it became, for lack of a better term, public consciousness with GPT in February of last year, January, February of last year, what I wasn't expecting is how much the world split in subject matter experts between the people that are just, like, they essentially shunned it and shut their mind off and put their head in the sand. It can't do what a human can do. Or they did one prompt and they're like, it's not perfect, and they just completely disregarded the entire ecosystem. And then us where, and to be honest with you, that is one of the paths I leaned toward at first. I was like, yeah, it's not that great. And then I saw some other people doing more advanced things with it. So I just don't know how to use it correctly then. And so I went back and I said, I'm going to go back and revisit this. And that turned into a phenomenal situation for our business, and we have a Slack channel dedicated to just what we're learning from GPT prompting. We pin specific prompts. We share knowledge between the producers on how do we get, one example, and you might be able to tell me there's a better way, but when we start a new ChatGPT conversation, we'll have these documents, essentially, that we paste into it to start, and then we can go from there. So we're like, all right. This is the baseline knowledge you need to put into the new conversation, and then you can ask it questions like this, and it's going to give you outputs like that. And so that's what we're doing currently to use it. But as I've started to see people come out with these custom iterations, essentially, I haven't explored those yet.

(Sam Ransbotham at 00:12:37) Well, I think a lot of what the custom ones are doing is a lot of your documents doing and just trying to do it at scale. Right? Okay. You get a company and you say, okay, let's pre-train it for everybody. And so they have that information already within, or they have their specific knowledge. There's, like, 12 things cool in what you just said though. Like, there's a Pew Research study that came out last fall, so it's already horrifically dated. But it says that the number of people that have tried this tool is phenomenal, and it breaks down inversely with age. If you're sort of post-65, very few of those people have played with the tool. Pew couldn't ask anybody less than 18, and, you know, 75% or so of those people have used the tool.

(Sam Ransbotham at 00:13:22) And I'm just based on my kids, if you went lower than 18, they're all over this tool.

(Joel Beasley at 00:13:28) Mhmm.

(Sam Ransbotham at 00:13:28) And that has really big implications about what changes in the future.

(Joel Beasley at 00:13:32) And it's also how you use the tool as well. Like, I'll give you another separate example. So I wrote a fiction book, and I was 90% done. We were basically in the final editing in January, February of the previous year. So by the time GPT came out publicly, I was wondering, like, is it possible to use this to help you with the book?

(Joel Beasley at 00:13:58) And I had a couple hypotheses. I was like, all right. Maybe if I put in, you know, how could I use it? And to fast forward through all of the examples of how it didn't work, what I found it was really good at was helping me frame stories. Like, frame, if I was going to, if the spy was going to break into the facility, asking it for, like, 15 different ways that could happen.

(Joel Beasley at 00:14:21) And sort of working with it as another person in the room ended up being the most effective way for me to do it. But you can't just say, write me a full, I mean, it's not going to be what you want if you just say write me a full book on this topic or whatever.

(Sam Ransbotham at 00:14:38) Well, Gary, I mean, you're right on exactly the problem right now. Or, you know, problem, that sounds too negative. I mean, what's simultaneously cool and difficult about this is it is really just a tool, and it's yet another tool we've got to figure out how to learn. And there are good ways to use the tool, and there are bad ways to use the tool. I mean, the first caveman that picked up a rock, they either used that rock to build a house, or they used the rock to bang Grog on the head with it.

(Sam Ransbotham at 00:15:07) And the rock's the rock. And so I think that's really an important part of what we try to bring up in the podcast. The podcast is Me, Myself, and AI. Two out of three of those words are about people. One is about technology. And I think we have yet another story where technology is important and it's critical, but how we use that tool is really important.

(Joel Beasley at 00:15:29) At what point do you think we'll be able to say that the AI is conscious?

(Sam Ransbotham at 00:15:35) Yeah, I would say this is a bit scary thing, actually. We've had some of these same sort of arguments here. I mean, it's a slippery slope, isn't it? I mean, we're so terrible about, you know, am I bald? Well, yeah, but what point did that happen? You know, there was some standing years, and calling these very finite yes and no things on a gradual slope is really tough. But I mean, you saw the same stories that I did about how, oh my gosh, it's conscious, it's, you know, sentient. It certainly seems that way. And certainly compared with a lot of people that you end up talking to, it seems a little more savvy than them, right?

(Joel Beasley at 00:16:16) Yeah. And I went down the rabbit hole on this and talked to anesthesiologists and people who are really close to consciousness, and we don't even have good answers for what consciousness is as people. And so I was like, wow, this is still an open—there's not consensus here. There's understandings of what consciousness is expected to be able to do to participate in society, but...

(Sam Ransbotham at 00:16:40) Well, that's one of my favorite things about artificial intelligence. You ask somebody for a definition, and it typically has this shape: artificial intelligence is blah blah blah blah intelligence. And so, you know, I don't know if you remember back in math days, that's a foul. You can't put the thing on the left side of the equal sign on the right side of the equal sign. So what we're really phenomenally good at is defining artificial. What we're really terrible about is defining that intelligence part because it's changing all the time.

(Joel Beasley at 00:17:10) Well, here's my base argument for this: ChatGPT is smarter than people that I know.

(Sam Ransbotham at 00:17:17) I'm going to pretend you're not looking at me right now.

(Joel Beasley at 00:17:22) No, but yeah, I've talked to all different types of people from the people who are really low level designing the large language models. To them, they have their picture in their head like, it's not intelligence, it's not consciousness, it's just not. And I go, I know, but the result of what you're doing—my interaction with that—is equal to or greater than interaction with other humans. And so if that's the bar, then it's there.

(Sam Ransbotham at 00:17:47) Well, hey, let's call that bar stupid just for right now. I mean, there's the Turing test. I mean, what you're talking about here is can this machine act in such a way that it fools you into thinking it's a person versus a machine? This is the Alan Turing test from long ago that we're all super familiar with. Well, we have been chasing that Turing test left and right for all these years. And like a dog who's chased a car, we've caught a car and we don't know what to do. Like, what do we do now? We can fool people into thinking that something is human. Okay, now what? I mean, was that really the goal? We've got something like 9 billion people on the planet. I don't think we need to necessarily replace those. I mean, we need to be thinking about things that this technology can do that we can't do. Otherwise, we're just in this replacement replicating mode versus thinking about what we and the machines can do together better.

(Joel Beasley at 00:18:44) No, you're exactly right. And that Turing test, everybody would—so I've been doing the show almost ten years now, and when I was having the conversations in the earlier years, it was like the Turing test. That's what we're going for, Turing. And then there was just this day when I remember doing some interview and the person was just like, yeah, but that doesn't really count. They were just super dismissive. They're just like, yeah, we haven't been working towards that as humanity for decades, but that doesn't really count. Now it's the fact that it can't do A, B, or C. And I said, whoa, how quickly we just went right past that Turing test as the point.

(Sam Ransbotham at 00:19:15) Well, we're terrible about that. You know, one of the definitions—back to the definitions of AI—it almost always has the word or phrase, like, normally, you know, that humans normally do or something about normally or usually. So part of it is this is a slippery slope. If you went back into the 1700s and you gave someone a quill, and that quill would turn red ink when you misspelled a word, that would be witchcraft, right?

(Joel Beasley at 00:19:42) You'd get burned.

(Sam Ransbotham at 00:19:43) We'd take you up to Salem and dunk you and leave you underwater for a while. But now spell check? Well, that's not artificial intelligence, right? And so we just have this ever-changing expectation of what the technology can do. I don't think that's going to change, and we're always going to want it to do more.

(Joel Beasley at 00:20:03) Yeah. I get mad at autocomplete. I'm like, you can't read my mind yet? Come on, get out of here.

(Sam Ransbotham at 00:20:10) Back to your fiction book, though. One of the things that worries me in this scenario is I don't think—you know, knowing what I know about you—you're not looking to get an average fiction book out there. Is your goal to wake up in the morning and say, hey, what I'd really like is a statistically average fiction book?

(Joel Beasley at 00:20:27) Negative. I would never. I do everything exceptionally.

(Sam Ransbotham at 00:20:30) Exactly, you're exceptional. Like all the kids at Lake Wobegon, you are better than average. But that's the crux of what we're grappling with right now—this race to mediocrity. So what we've got is this phenomenal tool that's getting you to mediocre amazingly quickly. And there's two—I complained earlier, you maybe pick A or B, and I'm going to make you pick A or B here. But is this a tool that A, helps us and gives us a huge head start, so we get to mediocrity and then we can build from it? Or B, is this a tool that is a crutch that gets us to mediocrity, leaving us without the tools and resources able to go beyond mediocre? And I think that's really what we're grappling with with our modern generative tools.

(Joel Beasley at 00:21:21) Yeah, and I think that's going to come down to an individual thing, you know, like—

(Sam Ransbotham at 00:21:26) And actually, maybe a situational too with an individual. Like, I don't need to be awesome. Actually, one thing I do in class is I'll type it in there and have them make a theme song for the class. You know, they'll write a little poem, and it does phenomenally better than I will ever do at that, and I'm okay with that. Like, mediocre is so much better than I'm going to do, and I'm going to choose not to compete on that thing. On the other hand, there's a lot of stuff that I'm hoping to do a lot better. I mean, anything that I write, I want it to be not average. I don't want it to be mediocre. I want it to be exceptional. And I think that's—you know, we're going to have to within the individual pick, but also within situation pick, which, you know, is this a situation that I want to be awesome, or is this where mediocre is okay?

(Joel Beasley at 00:22:12) How can leaders explore this? We got a lot of leaders that listen to the show, and most of them are in technology, all the way from first-time leaders through VPs of Engineering, all the way to the CTO, CIO, CSO, the whole stack. The reason why people listen is to become better at leading their companies. And how should a leader begin to explore this within their organization?

(Sam Ransbotham at 00:22:37) What's really cool—the example I gave you earlier about have you built a machine learning model? No. There's a whole lot of setup and infrastructure you've got to get in place to even be able to run that model in the first place. You know, I think about that as a learning curve. And you actually get the—I don't like the phrase steep learning curve, because it's not steep. It's actually very shallow. It takes you a whole lot of effort to get to a point where you can get something back out of that tool. In contrast, the modern tools that we're seeing emerge now around generative are highly accessible. You can go out there and do them right now, and your listeners, they ought to be playing with it because everybody in their organization is playing with it. And everybody is figuring out what they can do and not do. But when you play with it, I think one thing to remember—you know, back to your example of how you played with it—if you hired someone new in your company and they did not perform awesomely on the first day of the job, you fire them, or you give them a little help to get them better? I'm hoping that it's give them a little help to get better, because you'd have a pretty empty company if you just fired everybody who wasn't perfect on day one. I think that's the model we have to think about when we're using these generative tools, is how can we help progress and get them better and not just categorically reject what they do when they make a mistake or when they hallucinate?

(Joel Beasley at 00:24:04) Are you doing consulting for any companies?

(Sam Ransbotham at 00:24:07) Not many, no. I still do some engineering consulting and software on the side, and that's what's fun for me.

(Joel Beasley at 00:24:13) Have you gotten involved in the policy aspect at all? Has the White House called you up and said, hey, we need to know what's going to happen? We're just going to ban all models over 20 gigs, type situation.

(Sam Ransbotham at 00:24:23) They must have lost my phone number. I don't know what's happening here.

(Joel Beasley at 00:24:26) That's okay.

(Sam Ransbotham at 00:24:28) I think the regulation stuff is interesting. Here's what I would say when they do call, since you asked: we have a good long history of being able to handle new technologies, and this isn't—we're treating this like it's a magical exception, and I don't think that it necessarily is. I'll give you a couple of stories. One is, in 1906, Upton Sinclair wrote a book called The Jungle, and it was about the meatpacking industry in Chicago, and it was absolutely disgusting. And what it did was it shone a whole bunch of light on a situation that was untenable. It was existing because nobody ever went inside that meatpacking plant and looked at it, right? It was all hidden. And all we knew is that when we ate stuff, we got sick. You fast forward to right now, you can open practically anything and eat it and not be worried about the supply chain that brought it to you. We can go in any restaurant and be pretty comfortable that you're not going to get sick. And in fact, once a year, we'll have an E. coli scare. We'll have something, you know, a restaurant that makes the headlines. These are headlines because they're unusual. Because what's happened is that we built in an infrastructure of oversight and trust around food packing and around restaurants that help us have trust in that. In contrast, we have none of that with the technology industry right now. Every single one of these models is happening, you know, behind closed scenes. What is OpenAI? Well, one word is not open.

(Joel Beasley at 00:26:00) I know, right?

(Sam Ransbotham at 00:26:01) So I don't know about the AI—we can debate about whether it's AI—but the open part has really disappeared on that. And so I think we're going to have to think about how we treat these oversights, and it's going to have to be—you know, we can buy stock in companies that we don't know what their books look like because we trust accountants to go in and look at it, you know. So there's a model. So we have models in society. I used to work at the United Nations with the Atomic Energy Agency and the weapons inspectors. We don't want people building bombs, okay? So we have an infrastructure around inspection and testing and a little bit of carrot and stick to where you get power reactor information to help you use these power reactors better to avoid them blowing up. So we built this infrastructure around that and, you know, knock on wood—and I'm not sure when this broadcasts—but we've not had any huge nuclear incidents since that organization developed. So it's not like we're the first time ever thinking about a technology that is powerful and could do some harm, and we've got to figure out how to regulate them.

(Joel Beasley at 00:27:06) There is a unique aspect to it from those two examples, the Jungle and the atomic. And those are both, you know, hurting or killing people, right? Those are some of the examples. But what about job loss? Like, one of the things that I've thought—and first of all, I look back at the stories of the horses and the cars, you know, the common ones, and the stories of the mail system, everyone thinking that the Internet's going to put the mail system out of business, but it's just exploded it because of deliveries. And so I'm like, all right, I get those. However, when I see job consolidation, essentially, one of the things that's different now than those others is the speed at which it can deploy. So, for example, there was a certain—there's a lag time from the horses transitioning to the cars, and that's, let's say, a year or two, right? And there's a lag time of these technologies. We've compressed the lag time quite a bit. Do you think that we have the infrastructure in place to handle that? Like, if we just wake up tomorrow and 30% of one industry's jobs is just gone because of this technology, how would we respond to that?

(Sam Ransbotham at 00:28:20) Yeah, I mean, certainly if you wake up tomorrow—and I think your point's valid about the increase in speed. Remember, I think that's one thing we're grappling with. And, you know, there was a call six months ago to pause, you know, that was that—no, I mean, there's too much in that prisoner's dilemma that lets people defect. You know, in a shocking turn of events, the people who are ahead are the people who want to pause, and the people who are behind are not interested in pausing. And, you know, in other ways my analogy breaks down is that most of my analogies had physical goods involved, and this is purely information good. And so it moves across borders relatively quickly. Because I mean, those are not perfect analogies. But I think there's three things that are going to happen in terms of job loss. And the one that gets the most attention is jobs are gone. You come in tomorrow and the machine is doing your job—this is your 30% example. Job doesn't exist anymore. That's a scenario where machines have come in and taken a job entirely. Yeah, I mean, I'm pretty doubtful on that one to start with. I mean, I get the fear, but I'm pretty doubtful. I think the second one is much more real, and that is that some other human out there takes your job because they're better using those tools. So there's a bunch of tools out there. And like you said, you are using these tools, and you get better at them, and you learn how to use them, and you learn how to get more productive with them, and suddenly you are more attractive than your human compatriots. That's a very real risk. And that's the, you know, machines won't replace people, but people using machines will replace people not using machines argument. I think that's very real. I think the third one is more—you know, and to get back to your example here—let's say that your organization doesn't use these technologies at all and another organization does. Well, then you get an organizational-level wipeout. So the cost structure of an organization that is using these technologies drops, and the other organization not using these technologies is no longer cost competitive. And so that is a scenario for massive job loss, too. It's just not at the individual level. It's more at the firm level. And I think that's another one to be—so the first one is something that catches our mind. But the number two and three, I think, are the bigger things going on right now. You mentioned, Joel, that $100,000 a year you think you save.

(Sam Ransbotham at 00:30:52) Somebody out there is spending $100,000, and they won't be able to do it for too many more years before you put them out of business, right?

(Joel Beasley at 00:30:59) Well, we're always trying to put ourselves out of business.

(Sam Ransbotham at 00:31:02) Because you know someone else does, right?

(Joel Beasley at 00:31:03) Yeah, yeah. Well, we're seven years or so into this business, and, you know, obviously there's a hunger curve, right? You try to stay hungry the whole time, but you're never gonna be as hungry as before you made anything of yourself.

(Joel Beasley at 00:31:15) And so those people are just looking at it unemotionally. They're looking at the marketplace, seeing what tools are available, grabbing them, and trying to achieve outcomes versus this is the proper way to do it, and this is the best way to do it, and this is the way the subject. You know, for example, Josh. Josh and I had these conversations a couple years ago, maybe a year or two ago, when the AI got so good for post-production audio enhancements that it no longer made sense to just go manually do all of this stuff. And instead, you can just say, alright, AI is gonna do that section of my workflow, and then I'm gonna spend more time editing the conversation points and the flow of it.

(Joel Beasley at 00:31:56) But there are a lot of people out there that are still like, ah, this is the right way to do it. This is the real organic way to do it, and all the real artists do it this way. It's like, well, look at outcomes.

(Sam Ransbotham at 00:32:12) Yeah. Well, I think let's pull two points out of there. One is, most people are doing stuff that they don't wanna be doing in their job, for at least some of it. I mean, for me, I get an email that says, you know, "Hey, Professor, I missed class. Did I miss anything?" And my snarky response is always, "Nope. When I looked up and saw you weren't there, we just shut everything down." But no, I temper my snarky response.

(Sam Ransbotham at 00:32:41) And, you know, if I could have AI temper my response for me so I could write my snarky and then have it tone it down and make it friendly, or answering questions around the syllabus. You know, that's something I do a lot, and I would not say I'm adding a lot of value there. We did a study a few years ago, and we asked people, "Hey, what do you think about artificial intelligence? Are you do you hope that it's gonna do some of your tasks, or do you fear that it's gonna do some of your tasks?"

(Sam Ransbotham at 00:33:09) 73% of the people said they hoped that it would do some of their tasks. 33% said fear. And I think that's where we are. Now, I'm not saying that those numbers are not gonna change as we get more general or more knowledge-oriented tools. Maybe those numbers change.

(Sam Ransbotham at 00:33:25) But right now, we've got a lot of people doing stuff that they don't wanna be doing, and they're probably not adding value. Now I'm not gonna put people on the spot here, but I don't know about the post-production example. Is that fun work or not fun work? It may just be tedious work.

(Joel Beasley at 00:33:41) Yeah. Josh, cleaning up real bad audio.

(Sam Ransbotham at 00:33:44) Yep.

(Intro Narrator at 00:33:45) Yeah. I mean, where it excels is in the audio that sounds like it was recorded in a trash can, and that's never fun to listen to or to work on. And it's much more rewarding just to have a machine do it because it sounds better.

(Sam Ransbotham at 00:33:58) Yes. And you can get to focus on what content and what's important there and what makes a difference. And the second thing to pull out of that that you were saying, Joel, is that you switched from the word job to skill or task. I can't remember exactly what you said. But I think that's the way to think about it.

(Sam Ransbotham at 00:34:15) Our jobs right now are this composite of hundreds of things that we do on a daily basis. Some of those are more amenable for computer, some are less, and we gotta figure out where those are. And again, that comes down to management because figuring that out is management. Figuring out where the ROI is for this task versus that task is the crux of managing scarce resources. You can't automate everything.

(Joel Beasley at 00:34:44) I wanna make sure we touch on Optimus. Have you seen Elon Musk's bipedal robot Optimus?

(Sam Ransbotham at 00:34:50) No. Oh, oh, oh, the actual robot? Yeah. Yeah, yeah, yeah. Okay, yeah. Sorry.

(Joel Beasley at 00:34:55) The thing walking around, folding laundry, things like that. Have you seen it?

(Sam Ransbotham at 00:35:01) Yes. Actually, what do you think? I can tell you're excited about it.

(Joel Beasley at 00:35:06) We're suckers for convenience, humans. And so the moment that, you know, I would typically say, no, I'm not letting that bipedal robot in my house.

(Sam Ransbotham at 00:35:14) Who is it?

(Joel Beasley at 00:35:15) But then the moment you say, "Okay, for $200 a month, your house is gonna be cleaned all the time, be cleaned while you sleep or when you're at work or whatever it is, your laundry's gonna be made, your wife's gonna be happier," everything's gonna—

(Sam Ransbotham at 00:35:28) You're living the life of Jay-Z right there, aren't you?

(Joel Beasley at 00:35:30) Right? And I'd be like, yeah. My disdain for the dystopian future but my love for cheap convenience, they're at odds.

(Sam Ransbotham at 00:35:46) What I think is interesting about that is it also speaks to our fascination with the biped. Like, when people think about artificial intelligence, they immediately gravitate towards these videos they've seen of the Boston Dynamics dogs or these, and they all look like humans. I don't know who decided that was a good shape. Like, why is that what we want to look for in a machine? I think there's lots of shapes of things that would be more useful than people shape.

(Sam Ransbotham at 00:36:20) Now, I admit you might need some people-shaped things to do jobs that were designed for people originally, but there's no reason to say that people shape is the right shape. And it gets us down into this humanoid robot thinking, which I think limits us in scope.

(Joel Beasley at 00:36:38) Is it, you think it's the anthropomorphism? Like, we just, we do it with dogs. We've been doing it with things forever, and we're just doing it with the technology now.

(Sam Ransbotham at 00:36:48) Right. But there's nothing that says that that's the right shape. And I think that's where, you know, back to the whole, why have the machine read the fax? Well, why have a shape that is the shape of a human? Just because, I mean, there's some convenience of form factor in switching immediately from the ironing board at the height of me versus the ironing board that would be most optimal for a machine, but it won't be long before we switch to that.

(Joel Beasley at 00:37:16) I wonder if those mirror neurons are partly to blame. We like things when they're like us, right?

(Sam Ransbotham at 00:37:22) Exactly.

(Joel Beasley at 00:37:24) Yeah. Oh, wow. What other, so I think one of the interesting things about this interview is in almost every section of questioning that I had for you, you brought up a completely new thought that I hadn't thought about.

(Sam Ransbotham at 00:37:38) That's a lot of pressure here.

(Joel Beasley at 00:37:39) I like you quite a bit. I was really surprised with, when you talked about the threat being the organizations that use the technologies outcompeting in a free market the organizations who aren't. That's going to happen. They're gonna collapse because, yeah.

(Sam Ransbotham at 00:37:56) That's not even farfetched to think about. No. And again, we get so focused on that first example that we lose track of the two and three that are gonna spank us in the backside.

(Joel Beasley at 00:38:08) What are you learning about, I just wanna talk with you about professor stuff for a minute.

(Sam Ransbotham at 00:38:14) What are—

(Joel Beasley at 00:38:14) You learning about this next generation? How long have you been a professor? More than a decade?

(Sam Ransbotham at 00:38:18) Mm-hmm.

(Joel Beasley at 00:38:18) Okay. Well, what's the trend that you've seen in changing with the students coming in?

(Sam Ransbotham at 00:38:24) You know, okay, that is something, man. You're just like, let me lay down on the couch and talk. No. I mean, I think it's pretty fascinating. Kids are getting exposed to these technologies much, much earlier. And so you think about what that does to us in education is that it means that people are coming in with a set of skills that they didn't come in. We, in theory, used to provide those skills, and now they're coming in with those skills. So then the question is, what do we provide? Now, can we build on those?

(Sam Ransbotham at 00:38:58) And so I think there's a shift, and I think we can address the shift. But what I'm more sort of fascinated by is the dispersion, is the variance in that. So what I mean by that is that we have some people who've gotten super curious about technology and come in and are just amazing, you know, on top of things already, and I'm not sure what I can teach them. Right? On the other hand, we also have some people who seem to be getting further and further.

(Sam Ransbotham at 00:39:28) And so what that means is that that bell curve that we think about teaching to is changing. A bell curve is really nice in class if it's tall, because what that means is it's narrow. Because then I, in class, can talk about the same topic, and I'm gonna bore two or three, and I'm gonna lose two or three, but I'm gonna hit that sweet spot in the middle. But when that thing spreads out, then I'm more in trouble.

(Sam Ransbotham at 00:39:53) Because I can still cover the same sliver, but then my tails get bigger. And I lose more and more people either through boredom or not being prepared. And I think that's actually what I'm most excited about with artificial intelligence right now, because I think it can help with that. We've spent so much time thinking about what we can teach the machines. That's back to your, you know, the Turing thing we were talking about earlier.

(Sam Ransbotham at 00:40:16) Can we teach this to be like a human? Can we teach it? And that's with us in this "I am the knower of all, I am the presence which knows and bestows knowledge upon everyone." And that is just a bad mindset to be in because it positions me as the giver of knowledge and students as the empty vessels to receive them.

(Sam Ransbotham at 00:40:39) And there's a whole line of research on this. But what I'm excited about with artificial intelligence is being able to meet people where they are. Like, ideally, we would have one-on-one teaching, but it just doesn't scale. To what degree can we actually use artificial intelligence to help us learn? And I'm not saying teach the machines. I'm saying how would the machines help us learn? If you think about something like, you know, you go back to your example of the ironing and Jay-Z. If Jay-Z wants to get fit and have a personal trainer, he's got a personal trainer on staff constantly, ready the moment that he's ready to exercise. Whereas me, when I peel myself off the couch, chances are there's not a personal trainer hanging out in the lobby. But introduce technology into this.

(Sam Ransbotham at 00:41:29) And, you know, we've seen Peloton. I'm sure others. I don't wanna particularly shout them out. But Peloton has a device that sits in your room, and it can say, "You call that a plank, buddy? That's not looking so," or "I thought we said twelve push-ups. That looked a lot like six to me." You know? So these things that we can have, the machine, it probably is not as good right now as an individual human personal trainer. But it's there, available, and it's customized to what you're doing at that moment. I think that's very exciting.

(Sam Ransbotham at 00:42:01) I don't know if you use something like Duolingo. The goal of Duolingo is not to translate language. The goal of Duolingo is for you to learn language. It's for most people to learn English. You can have customized language instruction at scale.

(Sam Ransbotham at 00:42:17) It knows exactly what you know. It knows exactly what you don't know. It knows what you're good at, knows what you're weak at. We can have human improvement that could be phenomenal, and I'm very excited about that. And, you know, you think about how you may have learned language growing up. You may have had a teacher who, through years of experience, learned good ways to teach, but how did those good ways get propagated? With Duolingo, if they figure out something that helps people learn faster, they can scale it across the world tomorrow. And that's pretty exciting. One of the examples I like is the Fosbury Flop. So Dick Fosbury invented the high jump method to where you, instead of going and running and jumping forwards over the bar, you jump backwards over the bar.

(Sam Ransbotham at 00:43:03) In about two years, the whole high jumping world switched from forward over the bar people to backwards over the bar people. Now this is related. I'm not going to tangent here. You think about there's companies putting sensors in ski boots, and they're monitoring in real time how you're skiing. What's gonna happen is that somebody's gonna invent some crazy way of skiing that performs better in some way, and I don't know what it is. They're gonna record it. And the next day, their engineers are gonna look at it. And then the next day, it's gonna be in the advice that everyone else using that device is using. Does that make sense?

(Sam Ransbotham at 00:43:40) So we have the ability to push out what we know at scale quickly. Again, these are ways that humans can be better, and I think I'm super excited about that.

(Joel Beasley at 00:43:50) Yeah. My kids use Simply Piano. Have you come across this application? And they have really mastered how to break it down so I can put a four-year-old or a seven-year-old in front of that thing, and it'll walk them, it'll hold their attention while teaching them step by step how to play this piano. It's fantastic.

(Sam Ransbotham at 00:44:10) You see, this stuff is amazing because, again, the goal of that tool is not to play the piano or even to play music. The goal is to help you play music better. And I think that's just an untapped resource that we're really only getting started thinking about what we can learn from the machines.

(Joel Beasley at 00:44:28) Thank you so much for listening. And if you found this episode useful, please share it with a friend or colleague who you think would get value from it. And if you have topics that you would like to hear discussed on the podcast, either add me on LinkedIn or send me an email [email protected]. Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.