Paul Ford: Hi, I’m Paul Ford.

Rich Ziade: And I’m Rich Ziade.

Paul: And this is Reqless, R-E-Q-L-E-S-S, the podcast about how AI is changing the world of software. It’s brought to you by the amazing software AI-empowered business-app-development platform Aboard, launching soon—

Rich: That’s a mouthful.

Paul: Launching soon. We’re about to see a good demo right after this meeting.

Rich: It’s worth sharing with everyone that if you’ve wanted to see our faces, we’re not sure why, but you can now. We are—full video of us staring into cameras is on YouTube. So you can, you can find us there. We’re also going to make some YouTube Shorts that are going to go, just go viral.

Paul: We’re kind of, we’re kind of limping in the light here. [laughter] By the time you hear this podcast, it might be, like, four hours before it’s up on YouTube. But what happened is the people who are really good at marketing have taken us aside and said, “Listen, YouTube’s the big platform.” And I hate it. You don’t mind, you don’t mind people looking at your big shiny head. I don’t want to be, you know—

Rich: It’s not that I don’t mind. I just don’t see the people. [laughing]

Paul: Yeah.

Rich: I can talk to a room of 2,000 because I just. It could be three. It’s kind of the same.

Paul: I feel like a small but overweight dog standing in front of a jury of a hundred thousand people [laughter] who are just very disappointed with me because I peed on the floor.

Rich: Oh, you’re at the Westminster Dog Show.

Paul: That’s what I am. That’s how I feel at—

Rich: Anyway, let’s get a little more attitude here, Paul. All right, well, welcome. Let’s play that theme music.

Paul: All right. Good, good, good.

[intro music]

Paul: So, Rich, you were going to ask me what I’ve been working on.

Rich: Well, I think it’s worth it to take a minute and talk about how we work so differently with this stuff these days.

Paul: Okay. “This stuff.”

Rich: I’m—

Paul: When you say “this stuff,” you mean…?

Rich: Just grappling with how to understand AI, leverage AI, and go to market with the thing that people want. Right? We’re both kind of aiming for the same thing. You tend to open a development environment and you just start dancing, you’re a freestyle jazz music-making guy.

Paul: My way into the tech industry was I wanted to make publishing platforms. I built them on my own 20 years ago, and then I realized what content management was.

Rich: Yup.

Paul: And then I turned that into a consulting job, and then I went and became, built content-management tools for magazines and. And so, like that, that’s my way, my way of learning is just to jump in.

Rich: Yeah, yeah. And by the way, I like to jump in, too. But what I tend to do is step back out and think about sort of the business context. So I’m not in there, like, you know, wearing an engineer’s hat like you are. I think it’s worth saying. I’m just not. And I think that benefits us. I did go in because I wanted to see where the edges were, but then I came back out. But how is it going? Are you having a good time?

Paul: Well, I mean, honestly, our business has gotten really busy, so I have a little less time to mess with this stuff?

Rich: Uh huh.

Paul: Than I did. But, you know, there’s another aspect, and actually, before we move on from that subject, you did some research recently, and you found something important, which is how much, how much of the software-development process is actually writing code?

Rich: Less than 50%.

Paul: 45%, according to some, I remember, I can’t remember where you found—

Rich: No, it was a Meta study. It was actually a pretty big study. It was like 40 to 50, so I just took the number 45. But it’s, I mean, less than half.

Paul: What’s the rest?

Rich: That’s a big deal. Project management, product management, design, UI/UX design. If it’s games, model design, actors, motion graph—3D motion capture of, like, you know, special effects and stuff. Like, gaming, it’s even smaller, interestingly, because it’s like movie production, if it’s a serious game. So DevOps, data modeling, data architect, security, blah blah blah blah blah…

Paul: So to put the last part of the conversation into context, right, like, I go in and I start with code and it does—now, look, it’s 45%. It’s the biggest chunk.

Rich: It is the biggest chunk.

Paul: It’s really amazing that a bot can, you can give it words and it will produce anything at all. Like, it’ll make code. Oh my God, we’re in the future. But when software is, like, that, plus 20 other roles, plus a lot of processes and then users actually using it and it iterates and changes from there. And I don’t, like, that’s where we’re working right now. And I think, like…

Rich: It’s complicated.

Paul: But what’s happening—

Rich: It’s complex, and there’s a lot of different skills, right?

Paul: It’s sort of like when it draws you a picture of, like, a bear and you’re like, “Wow, drew me a picture of a bear.”

Rich: Yeah.

Paul: It’s not really a good picture of a bear. It’s wild that it’s there, but it just kind of sucks. Right now, ChatGPT rolled out a new version and everybody can make Studio Ghibli-style images like, “Oh my God, it’s Totoro, but it’s Andrew Cuomo. Oh, whoa!”

Rich: Ooh.

Paul: [laughing] Cuomo-ro. And…okay? You know, but the thing is, is they all suck and they’re really generic and you’ve seen, by the minute you’ve seen one, you’ve seen them all. Like, they’re really interesting for about five minutes, you’re like, “That’s wacky.”

Rich: Yeah.

Paul: And then by minute 10, you’re like, “Uh huh. Uh huh.”

Rich: Yeah.

Paul: Like, we’re getting tired—

Rich: It’s pretty predictable pretty fast. It’s actually fascinating.

Paul: We’re getting really tired of the joke.

Rich: Yeah, yeah, yeah.

Paul: I think everybody’s getting a little tired of the joke, except for, like, Sam Altman, who’s really into it. So, so anyway. Okay, that’s another giant conversation. So what I’ve been working on recently, remember we had Clay Shirky on the podcast?

Rich: Yeah!

Paul: Love Clay. Clay can just go. He’s—

Rich: We gotta have him back on.

Paul: —right in there with us. I love Clay. And so Clay has this database that he works with. It’s called IPEDS, which is a terrible name for a data product, and every time I type it in, Claude has, like, stopped me because it thinks it might be pedophilia content.

Rich: [laughing] Oh God.

Paul: But it’s called IPEDS.

Rich: Yeah.

Paul: So I’m wrestling with that. IPEDS is a database that colleges have to put all the data into the box about gender breakdown and what course is offered and—

Rich: Oh, I see. So it’s a statistical database that, like, gathers demographic data across universities for one giant blob of information, so you can tell—

Paul: Number of faculty, number—like, not just demographic, like, a whole lot of information. Links to all the mission statements.

Rich: Got it. This is a shared—this is an openly available data set that is funded by, I’m guessing, public-sector money.

Paul: At least so far. Right?

Rich: Yeah.

Paul: And so like, so… And if you’re, if you’re somebody like Clay, you’re really interested in this data set because it helps you, you’re at a really big university, and it helps you understand, like, where higher-ed is headed.

Rich: Sure.

Paul: You can think bigger thoughts. But the data has a web interface and it’s—it’s all very downloadable and freely available and it’s filled out, like, it’s mandatory that colleges fill this out as part of the—

Rich: Okay.

Paul: I guess, probably that’s how you keep your accreditation. So it’s a really good data set, but it literally was started in 1981. And you can feel it. Like it’s—the column names are very arbitrary, they’re only eight characters wide. Like, as far as you—and it’s only available in Microsoft Access formats about I think like 700 megs. So it’s not a huge amount of data, but it is a blob of data. It’s very well documented. There is an Excel file that describes every field and what’s in it. But you know, it’s like really hard to work with as raw data. You can use their web interface, but if you just kind of wanted to do your own thing? Mapping, or—

Rich: You’d have to put a lot of time in.

Paul: You gotta—

Rich: Scrub the data, normalize lookups, so it’s not looking at codes, all of it.

Paul: All of that. So I worked, I’ve been, I worked on this for maybe, like, 12 hours, and I have made frankly immense progress. Like, I’ve made six weeks of—

Rich: When you say working on this, you’re trying to come up with a clean, usable interface to traverse the information?

Paul: Very good question. I’m going to get really nerdy for a minute and then you ask me questions to translate it into civilian talk. Okay?

Rich: Okay.

Paul: Okay. I’m taking the Access database and I’m Claude, the—

Rich: Okay, so it’s a Microsoft Access database.

Paul: Yes, and I am feeding it—and so I used Claude to write a tool that uses some open-source tools to turn that into a SQLite database—

Rich: Okay.

Paul: —which is much more tractable, I can say, so that’s a different format, but it’s much easier to script and work with from the command line.

Rich: Okay.

Paul: And then from there, I’ve been using Claude to look at samples of the data, look at the data dictionary, and create a more usable version of this data set.

Rich: A more human-readable version.

Paul: But still very programmer, like everything is named with underscores, but it’s legible like a civilian would, would recognize.

Rich: Okay, and you did this because you’re just a good friend. Just a good guy.

Paul: But also this is an ugly migration problem. It’s just ugly enough.

Rich: Yeah—

Paul: It’s realistic—

Rich: It’s a good way to test out the capabilities of these incredible new tools.

Paul: Well, and it’s also we’re building a product that accelerates software development. But what happens, and we were just talking about this, what happens after the software exists?

Rich: Got to get the data in.

Paul: Yeah, nobody shows up. Very, very few people show up empty handed when it comes to business software.

Rich: Yep.

Paul: Sometimes they do, but very often, and I think that’s another aspect of these tools. It’s like they kind of don’t have a migration story.

Rich: Yeah.

Paul: In general, they’re just like, oh, you’re going to build a new app? And it’s like, well, what’s going to go in the app? Stuff!

Rich: Yeah.

Paul: Okay. So I’m now at a, actually, kind of at a block because the data dictionary is so large that in order to make the tables readable, because there’s hundreds and hundreds and hundreds of columns, I actually can’t get Claude to look at the whole thing because it’s too big.

Rich: Mmm.

Paul: So, I mean, there’s a context window. So now I’m working on a way to, I have to get it to write code. I’m going to make a database of the column names, have Claude write code to rename all the column names based on a query to the database. Like, we’re in Kooky Town. But I do feel that given probably another 12 hours, I’ll get to a really good place.

Rich: Hmm. So you—

Paul: Well you know what’s really nice about this? I had Clay give me a couple prompts, or a couple queries, and a lot of the things that we’d like to do with this, like writing the SQL queries around geographic locations? Is hard. I’ve done it before.

Rich: It is hard.

Paul: And here, if I give it the schema, I’m going to be able to get it to write the queries and I’ll be able to do things with this database that really, really would have been like hours and hours of work in minutes.

Rich: Yeah.

Paul: And that, like, there’s a good target here. Anyway, so that’s where I am right now. And you know what that makes me—you know what, you know what that is, if we’re going to draw a larger lesson from it.

Rich: Mmm.

Paul: You ready?

Rich: Yeah?

Paul: AI is getting a little boring.

Rich: That’s where you were taking me. Because I was thinking to myself, I just started to see listener drop, listeners dropping off in real time, as you’re talking to us.

Paul: Like a bad CNN poll during a debate? Yeah.

Rich: I was wondering where you were going with this, and I think, I think, I think you’re right. I think you’re—what about, like, all the spectacular headlines about, you know, robots that can dance?

Paul: If we take a breath—first of all, we got to be clear. Boring is good in technology.

Rich: Mmm.

Paul: I remember once we were working on a project and there was a client who was not a real technically aware person. And I went to a meeting with this person and I said something along the lines of, “I promise you the platform we’re building is really good and boring.” And she felt I was referring to the design and the interface and that I was promising to deliver crap.

Rich: Mmm hmm.

Paul: And what I was actually promising to deliver was stability and long term maintainability.

Rich: Mmm hmm.

Paul: There’s a language difference between the two. And she reamed me out and I had to go to Cipriani and get yelled at for an hour and a half because that’s what you get to do in client services.

Rich: Well, this is how you grow as a client services person, Paul.

Paul: Never said that again. Tell you that.

Rich: All right. But I mean—yeah.

Paul: Yeah, boring is good. And here’s—ChatGPT just rolled out Studio Ghibli and photorealistic bears and whatever the hell, right?

Rich: Mmm hmm.

Paul: There’s a—but if you really take a step back, compared to when that first version of all this stuff showed up and it just felt like the computer had come alive in a new way, like it did when it went from windowing interface, or mobile in your hand. Like just this new modality I’ve never seen before. It’s all incremental now. And they keep promising AGI and—

Rich: Honeymoon’s over? Is that kind of what you’re saying?

Paul: We’re adding new features to a database. It’s very comprehensible.

Rich: Mmm hmm.

Paul: It’s getting more and more comprehensible.

Rich: Mmm hmm.

Paul: It’s less like boy, we don’t—you know, because when this first came out, everybody was like, “Wow, we don’t really know how it works.” And we kind of do now.

Rich: Yeah.

Paul: Like, it’s layers and yeah, there’s opacity in the way that it’s scrunches the data. But you can learn how this works. You can figure out all the pieces. And increasingly you can build predictable systems—not necessarily on top of LLMs, it’s really hard to make them perfectly predictable, but you can do the same kind of heuristic processes that you do when you’re bringing in real world data.

Rich: Hmm.

Paul: And analyzing plain text and speech. You can do those to get stuff into a classic computer model where people are moving cards around on a screen or assigning tasks to each other or all of that stuff. And so there’s all this gee whiz and all this novelty.

Rich: Mmm hmm.

Paul: But I’m telling you, let me ask you. And we can pressure test this really easy, easily. Fast forward six months, fast forward a year. Is Sam Altman saying, “Hey, amazing news, ChatGPT can now replace your operating system. You don’t need Windows anymore.”

Rich: No.

Paul: He’s not. Why not? Because—

Rich: I think it goes back to, I think it goes back to… Well, first off, I said that very confidently. I don’t know anything. I don’t know what’s cooking on the 46th floor of OpenAI. But I, I want—

Paul: It’s actually vegan bean burgers.

Rich: Yeah, I mean, obviously. [laughter]

Paul: Yeah.

Rich: I mean, look, I think when you say it’s getting really boring, what you’re really saying is, eventually these things peter out and you’re kind of on your own with a toolbox. And the same old challenges kick in. And what you end up finding is—

Paul: And also at that point, Richard, like, the maximum amount of market value has been extracted from the novelty.

Rich: Yes. Yes.

Paul: Like, IPOs are happening, and now we’re all left, it’s like, it’s like waking up after the honeymoon. It’s just like, “Oh, I guess we’re married.”

Rich: I don’t want to discount the fact that when you did hit the wall, you got to the wall in, like, three days instead of five months. Like, the fact that you even got to the wall. Now you’re at the wall. The thing that, if there’s one thing to take away, and if there’s one thing our graphics design team can use for the cover of this podcast, it’s this.

Paul: Right. Because we have to make YouTube covers now. It’s like a whole thing.

Rich: You can’t dip back into the magic bucket. It’s weird when you dip back in, it’s sort of like glitter. It’s like glitter you buy from Michael’s art supplies. It’s—the magic is real. But when you go really far off to the edges of your journey and then you’re like, “I need more magic. I gotta get this across the line.” It falls, and you’re left in a very lonely place.

Paul: Well, no, I wrote about it—

Rich: Essentially called Microsoft Access Studio. [laughing]

Paul: I wrote about it in the newsletter, and I’m like, “This part right here is just programming.”

Rich: Exactly, exactly. Now let me ask you. Now here’s the thing. Here’s where it’s really good. And a lot of people have given this advice. Simon Willison has given this advice, which is, when you are in no man’s land, we should name this place that AI got you and then left you there.

Paul: It’s sort of the DMZ between human computer and…

Rich: Yeah. And—

Paul: Yeah.

Rich: You’re going to be okay for a while because. Because for some reason AI dropped a power bar in your pocket so you can kind of keep going. And you’re kind of in this weird place. And when you’re in that weird place, the way to use AI is to build you little micro tools to make little steps forward. If you go back in and say, “You got it wrong, you didn’t finish the job. Go back and finish the job.” The thing about AI is like, “You’re right, I got it wrong. Let’s try that again. But this time it’ll be in Clojure.” Like, it’ll just—

Paul: Yeah. Clojure is a language, by the way, for people like, this is… [laughter] This is vibe coding. Vibe coding is like, “Ah, you know, I’ll hit it with the same hammer, see what happens.”

Rich: Yeah. And the truth is, you’re technical. You understand the little, the smaller steps you should take. You know which little functions and little components you need to bring to the forefront to get moving again. That is not trivial. You’re talking about this like, “Hey, you know, I’m kind of vibing through it.” You’re not. You’re 30 years of very thoughtful technical thinking that is now being brought to the forefront. Essentially, the wheel has been handed back over to you. That is just the reality of it. Is that boring? I think it’s interesting that we’re having this conversation while everyone else is talking about how they built businesses in 45 days with AI and how AI will eventually break into our homes and take our children. Like, that is, that is the stranger conversation. And the truth is—

Paul: That’s fine. I’m a little tired of that conversation.

Rich: Yeah. [laughing]

Paul: I mean, I just, I think, and I’m not alone, I think people are getting a little bored.

Rich: I hear it less, by the way. I think you’re hearing that conversation a little bit less nowadays.

Paul: What I think is really interesting, you always say technology is about skipping steps, Right?

Rich: Mmm hmm.

Paul: It’s one, you know, trot that one out, Richard, And what you just said was really interesting, because I think it’s like, hey, it lets you skip a bunch of steps.

Rich: Tons.

Paul: It uses a plain language interface, produces a bunch of stuff and lets you skip a bunch of steps. But you need to actually get better at identifying what steps it doesn’t let you skip. And then you got to kind of craft—get in there and you have to figure out how to get from A to B.

Rich: That’s right. Let me ask you a question.

Paul: Okay.

Rich: You asked me a future-state question. Let me ask you a future-state. Will you be able to type in. What is it called? Pedo-Bass?

Paul: [deep sigh] IPEDS.

Rich: [laughing] Okay, IPEDS.

Paul: Yeah, it’s…yeah.

Rich: Will I be able to type this prompt?

Paul: Sorry—and by the way, it’s the Integrated Post Secondary Education Data System.

Rich: Cool.

Paul: So just couldn’t be worse.

Rich: Really cool.

Paul: Couldn’t be worse.

Rich: Yeah, it’s terrible.

Paul: It’s got a nice website. They’re good. You can go access it.

Rich: No, they’re doing their thing. All right, ready? Here’s the prompt.

Paul: Okay.

Rich: Build me a web-based IPEDS browser that masks away all the, like, technical aspects of it and you know, glossary mapping and all that and makes it human readable, excuse me, and human usable for non-technical people. And—

Paul: That’s my prompt?

Rich: That’s your prompt and. Oh, okay, okay. In a year?

Paul: I don’t think so.

Rich: Two years. Wow, that’s a ridiculous—forget the two-year question.

Paul: I’ll tell you what, here’s what actually has to happen for that. I don’t, I don’t think you get these one-shot solutions without a lot of subsystems. So when you describe that, what I immediately go to is one of, like, Claude or Anthropic or OpenAI or Microsoft or whoever. Like, you can’t get that out of one company, out of one solution.

Rich: Yeah.

Paul: I don’t think we’re there.

Rich: Mmm hmm.

Paul: So they have to go acquire all those little layers. Because it’s about layers. It’s about traversing through different sort of ways of understanding stuff and expecting—

Rich: Yeah, but even gluing those layers together is not trivial. It’s not like you can throw them all in the box, right?

Paul: What is the outside chance in two years? It could be, like, maybe through acquisitions a set of problems like that could fall to a prompt.

Rich: Mmm hmm.

Paul: I would not—I would, I will absolutely place very big bets. And in fact that’s what I’m doing with you right now.

Rich: Mmm hmm.

Paul: I am placing a big bet that development can be accelerated and in the hands of many, many more people. 10X, 5X, whatever, some, some X, sometimes 100x. And that you can build tools and use those tools much more quickly than before.

Rich: Mmm hmm.

Paul: Absolutely believe some of that’s happening. We’ve proved out some of it. Like, we’ve worked with people doing things for them and I’m 100% convinced that that’s coming. But the thing you just described?

Rich: Mmm hmm.

Paul: I almost think it’s a meaningless question. Not because what you said was meaningless, but because there’s so many little abstract things and little bits you have to tinker with in order to even understand what that means, what you just said.

Rich: Mmm hmm. Mmm hmm. Yeah.

Paul: And so you will get an opinion, it will write you a report or generate a bunch of code in two years. But the odds that it’s actually what you need or want or that it’s the right thing for your user or so on and so forth are really, really low.

Rich: Mmm hmm.

Paul: It might work, but it might not, it’s just, there’s no, there’s very little chance that it’ll be the thing you want, unless you go back and iterate on the prompt and get it just right. And again now you start to—that’s going to be what programming is.

Rich: Do you—so, okay, all right, so you’re not seeing AI really eat away at—like, you don’t see how it can bring it all together, is what I’m hearing. There’s so many little problems along the way, it has to venture out and, and deal with them all, right? Like—

Paul: I think a more subtle point and then, you know, we should probably, we should probably wind this one up. But that’s not how humans are. Humans can’t wrap anything up ever. Right? If you had told me five years ago, I’ll be able to tell a computer to make me a Studio Ghibli image from any photograph, I’d be like, “Ah, that sounds really like an impressive product.”

Rich: Mmm hmm.

Paul: And now there’s a glut of that trash everywhere. And everybody’s angry about it or excited about it or whatever, [laughter] but it kind of doesn’t mean anything. Right? Like, the next thing will be, “Can it make me…” You know, somebody’s gonna make like Totoro, but it’ll be Goodfellas.

Rich: Yeah, yeah.

Paul: And then, you know, Joe Pesci will be in it. And we’ll be watching that. We’ll be like, “Oh my God.” There is no end game with this stuff. There’s no, like, one-shot solution. Because the minute I come up with the thing you just described, somebody’s gonna look at it, including me, and go, “Wow, it’d be really interesting if I could put this kind of query in. I don’t really like the way the mapping works.” And then the product isn’t done anymore.

Rich: I see—

Paul: Because it’s a human artifact.

Rich: You’re, you don’t trust done. And the understanding of done.

Paul: Nothing has ever been done. The only thing that gets something done. What is the one technology that completes a task?

Rich: What?

Paul: A deadline.

Rich: Yes. A commitment to someone else. Yeah.

Paul: Yeah. It’s another kind of deadline.

Rich: Yeah, yeah, yeah, yeah. It could be a customer, it could be a boss, it could be whatever.

Paul: I got a busy early next week. Okay? But I should finish this thing and share it with Clay and put it on GitHub, and just sort of like make it a project and be done with it.

Rich: Yeah.

Paul: Okay, so I’m gonna, I’m gonna let you do something I never let you do.

Rich: Oh no. Okay.

Paul: I’m gonna let you give me a deadline.

Rich: All right. Tuesday.

Paul: Well, I just said next week early is busy. You know, I’m actually presenting something to you I’ve been working on on Tuesday.

Rich: Oh, yeah, that’s right. Friday.

Paul: Friday’s good. Friday’s really good.

Rich: Friday.

Paul: Friday. I have a working SQLite implementation that I can walk you through.

Rich: Man, we could have a podcast on deadlines, but let’s spare the masses.

Paul: Because for all the good of AI, it’s actually not getting it done.

Rich: There you go.

Paul: Neither am I.

Rich: Yup.

Paul: Yeah, right. I needed that. All right, for Friday, let’s see how it goes.

Rich: It’s nice to see your face on video, Paul Ford.

Paul: Oh my God. And there’s—we’re gonna have to learn, like, I have to learn to look at the camera, and I’m staring—

Rich: You don’t have to, I’ve seen, I’ve watched a bunch of podcasts. Nobody looks at the camera. They’re all, like, looking around.

Paul: I’d like to make something good that doesn’t look like two flibbertigibbets hanging out in the apartment.

Rich: Yeah, yeah.

Paul: But that’s actually what this is right now, so. [laughter] All right, well, maybe we’ll just add some videos to it and call it a day. Richard, it was great talking to you. In nine minutes, we get to go to another meeting.

Rich: Always great. Give us five stars. Give us thumbs up. If it’s thumbs, give us thumbs. If it’s stars, give us stars.

Paul: Yeah, you know what? It’s time.

Rich: We’re on all the podcasting platforms, and we’re on YouTube, and this is, this was just faces talking. Talking heads. But eventually, we’ll have a lot, it’ll be a lot more visual and interactive. We want to, we want to dial it up. It’s gonna be fun.

Paul: Well, we’ll even have more guests. You’ll see their heads.

Rich: And check us out at aboard.com. Sign up for the newsletter. You’ll get notifications and early invites on the very cool future cut of the platform. So aboard.com.

Paul: Yeah, that’s true. We’d love to get some beta testers nearby. All right.

Rich: Have a wonderful week.

Paul: Bye.

Rich: Bye.

[outro music]