Paul Ford: Hi, I’m Paul Ford.

Rich Ziade: The Yankees shit the bed.

Paul: We’ll come back to that. What’s your name?

Rich: I’m Rich Ziade.

Paul: And this is The Aboard podcast. It’s the podcast about how the Yankees are shitting the bed [laughing] to the Blue Jays. But also it’s about AI and software. But to the Blue Jays.

Rich: Mmm.

Paul: I don’t care much about baseball, but I know how bad that was.

Rich: Play that theme song.

Paul: [weary sigh]

[intro music]

Paul: All right, Rich, so if you had one chance, what software do we need to build for the Yankees?

Rich: Software where—I hate Aaron Boone. He’s the manager of the Yankees.

Paul: Tell the people, many of our listeners will not know anything about baseball.

Rich: I’m a Yankee fan.

Paul: Okay.

Rich: So get that out of the way.

Paul: Okay.

Rich: Goodbye, Boston listeners, I guess. I don’t know. Who cares?

Paul: Nah, they still listen.

Rich: Yeah.

Paul: Honestly, people understand that you love a team they hate. That’s like—

Rich: That’s true. No, they just look absolutely terrible in Canada.

Paul: Well, it’s also the worst moment for them to just completely fall apart.

Rich: It’s not fall apart.

Paul: Share the, share the scores. Just so we have this.

Rich: Okay, so by the time of this, this recording, the Yankees are down 0-2 in a best of three series.

Paul: Okay.

Rich: It’s the American League Division series against the Toronto Blue Jays. Yeah.

Paul: [laughing] I know it’s a serious team, but it’s the Blue Jays.

Rich: AKA the Maple Syrup Tappers.

Paul: Wow. So they sound really, really bad. It must have been a really close game.

Rich: Blue Jays, they’ve had a good year. They had the exact same regular season record as the Yankees.

Paul: Oh, wow. They’re really good this year.

Rich: They’re good. They’re good. They’re a good team.

Paul: So you make little Canadian jokes, but they’re pretty solid.

Rich: The reason I love baseball is the best teams with the most expensive payrolls?

Paul: Mmm hmm.

Rich: Can only win like 65% of their games.

Paul: Mmm hmm.

Rich: Like you can’t be, you can’t dominate in baseball. I think about baseball when I think about starting businesses. I kid you not.

Paul: Because…?

Rich: It’s just you oscillate between absolute uncertainty and other days, you feel like you’re going to dominate the world. Like, it’s actually wild. And… But the Yankees suck right now.

Paul: The other thing is that it’s incredibly boring and everybody complains about it.

Rich: Yeah, I don’t mind that.

Paul: Yeah.

Rich: I’m kind of used to it.

Paul: No, but—

Rich: It’s trying to power through the AI-generated slop.

Paul: Yeah.

Rich: Like, that everyone is consuming.

Paul: I’m talking about business though, not about…

Rich: [laughing] Fair enough.

Paul: Yeah, no, it’s painful…

Rich: They’re both boring.

Paul: Yeah.

Rich: And you might not succeed at either.

Paul: So if you wanted to build software for the Yankees right now, first of all, what software would you build?

Rich: I would build how-to-fire-a-manager software.

Paul: Like, a manager CRM to go find a new one kind of thing?

Rich: Yeah.

Paul: Yeah.

Rich: I’m not an Aaron Boone fan. He’s the manager of the Yankees, so…

Paul: Okay, so you would like to…create a manager finder. We could actually do that. We could do a good job.

Rich: It’d probably do a pretty good job.

Paul: If you go to Aboard and you make a manager finder for the Yankees, go ahead and share that link back with us.

Rich: Yes, yes.

Paul: We’d love to see it. So go check us out at aboard.com. We can probably save baseball with our amazing enterprise software—

Rich: Baseball’s doing okay, I think. I don’t know. I can’t tell.

Paul: Anyway, you type the words in at the prompt. It makes you an app. You see the app.

Rich: Yes.

Paul: That is as simple as that can be.

Rich: How was your weekend?

Paul: Not as baseball-filled as yours, but it was pretty good. We… Back on the bike lately.

Rich: That’s great.

Paul: I like going through, I like going through the city like you, I go out to the Rockaways. I go the long way out through Shirley Chisholm Park.

Rich: So you’re not weaving through crazy traffic.

Paul: I do that sometimes, but I do that in the mornings if I’m coming in.

Rich: Yeah.

Paul: But like, nah, the weekends, I want a good, steady ride.

Rich: Yep, yep, yep, yep.

Paul: So out I go to the Rockaways and then I do, I pull a little trick. Okay? Because coming back is kind of a drag.

Rich: Yeah.

Paul: I jump on the ferry. Take it for, take it all the way around the Rockaways. It’s a long ride.

Rich: F-E-R-R-Y, not F-A—you don’t stop at an edible store and have…

Paul: [laughing] I don’t have… There’s no magic in my life. Trust me. [laughter] And so I go, I go all the way around the Rockaways—

Rich: The Staten Island Ferry, or one of this high-speed, cross-river—

Paul: One of the high-speed ones. And it takes about 40, 50 minutes. And I go—

Rich: It’s fun.

Paul: You see Coney Island and Seagate.

Rich: Hops—

Paul: Yeah, yeah, you go, you stop in Brooklyn, then you stop at Wall Street, and then I rode my bike over—you take your bike on the ferry and then you ride your bike over the Brooklyn Bridge and go home.

Rich: Ends on Wall Street, back over the Brooklyn Bridge, you’re home.

Paul: That’s a delight.

Rich: That’s a beautiful day.

Paul: And then Sunday was…

Rich: Football!

Paul: Garage sales!

Rich: Oh boy.

Paul: The social event of the calendar in my crunchy Brooklyn neighborhood.

Rich: Yeah.

Paul: Because we live in a lot of freestanding houses.

Rich: Uh huh.

Paul: We’re down south a little bit.

Rich: Okay.

Paul: And so we made $670 for charity. We’re gonna use that to feed people in Flatbush.

Rich: Nice. That’s actually nice.

Paul: I sold all the old prints from our last company that had been on the walls.

Rich: Ohhhhh. Interesting.

Paul: Because they’d been sitting in my attic for three years.

Rich: And you found buyers?

Paul: Everybody wanted them. People want stuff for their walls.

Rich: Yeah.

Paul: They have blank walls. There’s one that was like, pictures of books, and a teacher really wanted that for a classroom.

Rich: All right.

Paul: So we moved them at $10. Like, we just kind of, nice frames…

Rich: Yeah. It’s all good.

Paul: It was good.

Rich: It’s good to repurpose stuff.

Paul: So that’s our lives, you know, little sharing, little bringing people in. That’s nice, isn’t it?

Rich: Yeah, yeah.

Paul: All right, good, good. Now we’re going to hit three things fast in this podcast.

Rich: Okay?

Paul: Okay. Labor.

Rich: Mmm hmm.

Paul: Security.

Rich: Mmm hmm.

Paul: And slop.

Rich: Mmm. These sound interrelated.

Paul: [laughing] Not really.

Rich: Okay.

Paul: No, I was just gonna.

Rich: Okay.

Paul: This is what’s in the world right now.

Rich: It’s all through the lens of AI, I’m assuming.

Paul: So I’m gonna—

Rich: We’re not going to talk about labor in general. [laughing]

Paul: I don’t really want to do that in the next, like, five minutes.

Rich: Yeah, okay.

Paul: Here is from the Budget Lab, associated with Yale. Yale—it’s budgetlab.yale.edu. Because you know that’s a good time.

Rich: Mmm hmm.

Paul: That’s like, that’s like my Tinder right there.

Rich: Oh, man. The kitchen birthday parties that happen there. [laughter]

Paul: The little jokes. The little, like, famous economist jokes.

Rich: Okay, what do they have to say over here?

Paul: That’s so Ricardo of you. “Evaluating the Impact of AI on the Labor Market: Current State of Affairs” by Martha Gimbel, Molly Kinder, Joshua Kendall, and Maddie Lee. October 1, 2025.

Rich: Okay.

Paul: This is kind of, this is what I love about economics.

Rich: Yeah.

Paul: There’s no news.

Rich: No.

Paul: Okay? So if you are out in the world and you are reading about AI and what it’s going to do to the economy and to jobs, what do you think is happening?

Rich: The robots are going to take over jobs and people will be out of work. That’s, like, the most reductive sentiment.

Paul: It’s happening right now, right?

Rich: Supposedly.

Paul: Supposedly. Okay. According to this, and there is, I’m going to go to figure 11 and tell you what I see in figure—figure 11 and figure 22 are the hot ones in here. So you can go to that website. You can go scroll on down.

Rich: Lots of charts and graphs.

Paul: Mmm! Lot of chart and graphs. So what basically what this, this report keeps doing is going, like, “Okay, we looked at it and it seems a little early to be drawn all these conclusions.”

Rich: Yeah.

Paul: Okay, so figure 11 is this almost flat line, and it’s “change in the proportion of workers and occupations exposed to AI.”

Rich: Explain that. I don’t understand what you mean.

Paul: This one, so, okay, we go to conferences and everybody’s like, “It’s still early, man. It’s all really early.”

Rich: Yeah.

Paul: And you and I have an AI company.

Rich: Yes.

Paul: And we use this stuff all day long.

Rich: Yes.

Paul: And we’re technologists and we’re like, this is the biggest change coming to our industry. Correct? We’re in a tiny, tiny bubble.

Rich: Yeah.

Paul: And so when you look at the actual, like, the broader economy, people know about this stuff, they’re using it, they’re playing with it. Right? But essentially, aside from the technology industry, which is where we are, the tech-industry bias, and also I think the media-industry bias, because it’s so, the generative stuff is so powerful?

Rich: Yeah.

Paul: Means that it seems much, much, much, much bigger than it really is.

Rich: Yes.

Paul: In the economy. And so if you look, there’s, and I’m going to tell people, go to it, go down, go down to figure 22. You see the, got a little like, [New York accent] go down the figure 22. Go on down there, take a look. I don’t know what happened. Maybe I rode my bike to the Rockaways too long. But that’s the gap between actual and expected AI usage by major occupation. So it’s like,

“Hey, you guys using AI or not?”

Rich: Yeah.

Paul: Okay, so I’ll give you an example. No one’s really expecting forestry people to be using a lot of AI.

Rich: Yeah.

Paul: And guess what? They’re not.

Rich: Sure.

Paul: Okay? Office and admins? Not expecting too much, but they’re actually using it quite a bit.

Rich: Yeah.

Paul: But really everybody’s kind of within the range of expectations. Like, nobody is kind of surprising us by our AI usage. With one exception.

Rich: Engineers.

Paul: Us.

Rich: Us.

Paul: Us.

Rich: Sure.

Paul: So our bias—

Rich: It’s our toy, right?

Paul: Yeah, and I think like, when you’re talking to people, the bias that people have towards their own industry, and tech is so big, it feels like the world. And when you work in tech, it feels like the world.

Rich: Yeah.

Paul: But man, if I’m looking at even legal management, management, here are the industries where it seems to really be mattering. Sales, office and admin support, management, and engineering, and a little bit of, like, CFO support.

Rich: It’s worth noting though, there Is no revolution happening except in engineering. Like, it’s happening in a few of these other sectors.

Paul: I do think, like the research reports and stuff are changing stuff up.

Rich: Sure.

Paul: Like, I think consulting, it’s a trillion-dollar industry, and the way we’re hearing about it is people do a bad job reviewing the outputs and then they submit them to, like, a judge or a client and they get in big trouble.

Rich: That’s a problem.

Paul: Yeah, it is. And big consulting firms are getting caught. But I think, like, we have to take this in context, right? You and I, we’re talking about this transformational change. I’m writing about it, all this stuff is happening, it’s very real. But when we say it’s early, that’s not just us being, like, “It’s okay, you have a lot of time, we can handle the change.” It’s really early.

Rich: It’s very early. And I would also—

Paul: Like, how long do you think, to really bake AI into culture every day, like the internet has been?

Rich: Yeah, here’s where I land. I consider myself actually a transformational expert.

Paul: Yeah, that’s fair enough.

Rich: Pre-date AI.

Paul: Change. Change.

Rich: I’ve gone into large organizations where leadership has asked for significant change. And I’ve witnessed—and I’m speaking about myself, but this is—

Paul: And you represent, when you show up, it’s because all other attempts at that change have failed.

Rich: I am AI.

Paul: Yeah, but seriously, like, you’re not good news.

Rich: I’m not good news.

Paul: Even if you’re, you’re not necessarily going in to, like, lay people off. It’s just like they weren’t able to make the change themselves, so they had to call somebody else in.

Rich: Correct. I want to make three points. The first is people who become experts in particular ways of working are very resistant to change—not because they consciously are worried about their jobs. They are resistant to change because they’ve gotten incredibly good at it, right?

Paul: Yeah.

Rich: And when they get, become incredibly good at something, they revert to it. And the gravitational pull of doing it that way is incredibly strong. At an atomic level—in organizations, there’s something else that happens which is if you take all of those atomic units of expertise and put them together in one place, it embeds itself in the culture and how they work. And so systems and processes are in place around those atomic units of expertise.

And so now it’s not just about one person resisting. Organizationally, and this is like sociological and political in some ways, the organization is literally optimized to work a certain way. And so you’ve introduced this absolutely, without a doubt, transformational change that the org, frankly, subconsciously or unconsciously looks like it’s resisting. It’s not that it’s resisting, it’s that literally everything is calcified. The processes of calcified, the systems of calcified. Like, there are literally systems that exist because of the way people work that have to get uprooted for this change to really be transformational.

So how long does it take for that system to show, for those systems to be uprooted and for people to be retrained? I’m not even gonna guess on that. The third point I make is tangential, but I think it’s worth making, which is I would be wary of stats like these because I think people are still pretty embarrassed and ashamed about using AI a lot. So the data is based on, I’m guessing, conversations and whatnot.

Paul: Well, no, they’re there already. They factored that in.

Rich: Okay.

Paul: I think we really are seeing slower change than we thought.

Rich: My first two points I think are the more profound ones. It’s just, like, old habits not only die hard, they, they thrive. And they are defended. And so both at a personal and individual and organizational level, it takes a lot to uproot them.

Paul: You know, I actually, I use a metaphor for this, which is it’s sort of like you get the liver transplant and then you wait to see if the body rejects it or not?

Rich: Yeah.

Paul: This is, that’s what happens.

Rich: It’s disgusting.

Paul: It’s a transplant of this new technology into the culture. And very often the body is like, “I just don’t want any part of this. I’d really actually rather not survive.”

Rich: Let’s put a pin in this section. We’ve got two more to cover. This way. It is hard to get people to get off of a spreadsheet.

Paul: I mean, look, to be—

Rich: Let alone embed and integrate this whole new way of working.

Paul: It’s not just that. Large orgs and successful people in large orgs have a risk-based mindset, not a growth-based mindset.

Rich: Great point.

Paul: The growth is baked into the organization and there are incremental margins that define the thing. That’s so different from startups, that’s so different from tech world.

Rich: Absolutely.

Paul: And so tech world comes in and is like, “Don’t you want growth and margins like us?” And the CEO is just fricking drooling because he’s going to get that, he’s going to be able to buy—

Rich: Get those efficiencies!

Paul: Oh, that summer house!

Rich: Cost savings!

Paul: Summer house is screaming to him, right?

Rich: Yeah.

Paul: [impersonating a sentient summer house] “Come to meee!” And so he’s like, “Let’s get this tech growth jammed into our carpeting company.” And everybody on—

Rich: Doesn’t work.

Paul: Everybody on the floor is like, “If you screw this up, I won’t be able to get you the margins with the suppliers!”

Rich: People resist. People resist.

Paul: Yeah, they really do. And you know, so anyway, here we are.

Rich: All right.

Paul: Change is gradual.

Rich: Number two! Topic two.

Paul: Number two, this one is a wild one. So, okay, I’m going to give you a back, I’m going to give you a quick background, share the news and then see what you think about it, Richard.

Rich: Okay.

Paul: Okay. Daniel Stenberg. Okay, first of all, there is a low-level piece of technology. It’s one of those things that kind of the world is built upon. It’s called cURL. You ever run across cURL?

Rich: Oh, of course.

Paul: Okay. The C—don’t worry about the C. The URL is the important part. cURL is a command-line tool that lets you get data from the internet.

Rich: Yep.

Paul: Okay. It’s not just a command-line tool, it’s a programming library. So it’s—

Rich: Pretty foundational.

Paul: Things that talk to the internet on your phone or computer often use cURL. So it is infrastructure for internet stuff.

Rich: Yes.

Paul: Getting, sending, doing things.

Rich: Yes.

Paul: So our whole API-driven world, our web world, our spidering world, a lot of cURL all over it. So it’s a big deal. And for this reason, its security profile is very, very important.

Rich: Yes.

Paul: Okay. Because a vulnerability in cURL is a vulnerability in the internet.

Rich: It’s piping.

Paul: It’s like a browser. Right? But like all open source projects, it’s really just a few people and kind of underfunded, and so on and so forth.

Rich: All the usual.

Paul: The creator of it has been besieged by AI-generated bug reports seeking bug bounties. “Here’s what’s wrong. I found this on Buffer Overflow.” And he’ll be like, “That’s like a library we don’t even use. Like, what are you doing here?” And then they’re like, “Oh, sorry!” And then they’re kind of out.

Rich: Okay. But it’s not a human…?

Paul: No, not really.

Rich: That is submitting the report.

Paul: It becomes really clear—

Rich: It may be a human.

Paul: No, he’ll, like, reply and then obviously LLM, you know, “Thanks, that’s a great point. I also noticed that—” You know, just sort of like the…

Rich: And why do they do this?

Paul: Because there’s bug bounties on the other side.

Rich: Explain what a bug bounty is.

Paul: Oh, if you find a good bug, a security bug, you can get a little Money for it.

Rich: From the foundation that’s supporting the…

Paul: That sort of thing.

Rich: Yeah.

Paul: Right. Like, that’s where we’re at.

Rich: Many people might not know what that is. Okay, so they’re trying to like, get some money.

Paul: And he’s been out there—but the reports are bad. They’re just kinda, they’re hacking around.

Rich: It’s spam.

Paul: They’re spamming him with this very technical-looking spam.

Rich: Yeah.

Paul: Right? And so he’s kind of become somebody who in public is, like, “This is a mess. We have to stop taking these reports. I’m really sorry. I want you to find security bugs in this platform. It’s really good for the world.”

Rich: “You’re wasting my time.”

Paul: “Yeah, but I can’t take care of bot output so that you can make your money.”

Rich: Well, I gotta ask: Are the reports bad?

Paul: They were.

Rich: Okay.

Paul: They were. But then the cURL guy—

Rich: Yeah.

Paul: —is like, “Man, wow. Okay. Josh Rogers sent us a massive list of potential issues in cURL that he found using a set of AI-assisted tools.” So this is like, now we’re not just like hacking with ChatGPT. This is somebody who’s like, “I’m going to use AI to analyze this code.”

Rich: Is he part of the working group that’s—

Paul: No, it’s a guy.

Rich: It’s just a guy.

Paul: Guy.

Rich: He knows cURL well. He often contributes. Maybe he’s in the community.

Paul: So what was the story, and this is an inflection point. What was the story is, man, they’re just kind of spamming, these guys. But you still need humans in there with a screwdriver. I mean, there’s always been tools to automatically look at code.

Rich: Sure.

Paul: And look for security issues.

Rich: Sure.

Paul: But essentially, the LLM version of that was like, “Oh no, God, shut up.”

Rich: Yeah.

Paul: But now the guy just put 22 bug fixes in with security issues and other issues.

Rich: He used AI wisely.

Paul: Yeah.

Rich: To interrogate the code base, find holes, plug them up.

Paul: Yeah.

Rich: So the response to all that spam is, if you use it right, you can solve problems.

Paul: Yeah, this is real, right? And so I think, to me, that is a real inflection point, because so far we’ve been seeing there are companies doing this, there’s all kinds of stuff going on with this, with security profiles and people using LLMs to analyze code and fix bugs and look for security issues and so on. This is one of the most vetted pieces of code on the internet, right?

Rich: cURL?

Paul: Yeah.

Rich: Yeah, yeah.

Paul: Yeah.

Rich: I mean, widely, widely.

Paul: You got to dig in real deep to find, like, I could not, I don’t know C that well, I’ve been programming for 25 years. I’m not going to find a bug in cURL.

Rich: Yeah.

Paul: Okay? But the robots, intelligently guided, find dozens of, some of them pretty serious—

Rich: Credible.

Paul: Credible bugs to harden and improve the product. Now those are reviewed by humans and brought in.

Rich: It sounds like the robots that were unleashed were unleashed by very knowledgeable, thoughtful engineers that knew what they were doing.

Paul: That is correct.

Rich: That is the key. I think it wasn’t just, like, pointing an LLM in their direction. It was properly put forward.

Paul: You know what you’re seeing, and this is, we talk about Simon Willison a lot, but he’s starting to write about parallelization using multiple agents at a time.

Rich: Mmm hmm.

Paul: This is when you start to get exponential weirdness in engineering. So here we are talking about labor. Right? Computing is one of those things where you can turn that labor sort of knob up, up, up, up, up with this stuff because the better it gets at it, that just means you can do more of it at the same time. It’s really hard to, because you have to be really strategic.

Rich: Yeah.

Paul: So this person—probably, this is a real smart person, obviously, they probably could have dug in there and found some bugs on their own.

Rich: Yeah.

Paul: But they couldn’t have gone through every single bit of code, every case, every condition.

Rich: Of course.

Paul: Every library, and really found all that stuff. But they can do that 100x and then they can review those outputs.

Rich: I think we’ve talked about this before, which is good, thoughtful, senior-level expertise, architect-level expertise, use these tools much, much more effectively. The tools are a trap if you try, if you think you can shortcut. And we talk about this in the context of vibe coding a lot. But a good, thoughtful engineer that knows what lane to put AI in and knows where to establish the proper guardrails can be incredibly productive. Which is interesting. This is a message to the youth.

Paul: Yeah.

Rich: Of the world, the young people of the world. These tools are pushing, putting forward so much magic and convenience in their promise. But it turns out you’re going to still have to put your head down and understand pretty tricky, difficult comp-sci concepts to really use them effectively. That is still worth your time. Go get that master’s in comp sci, because AI is not going to go there just yet. In fact, you will be, it’ll just be a tool for you.

Paul: I think that’s real. I mean, you need the human in the loop on this one.

Rich: I think it’s more than just the human, because I think a mid-level engineer won’t know any better.

Paul: No, no, no. You need the expert in the loop.

Rich: You need the expert.

Paul: Honestly—

Rich: So go be an expert, is the message. This is my Schoolhouse Rock moment, by the way.

Paul: I think you’re seeing it, right? So like, expert in the loop is the future. And so that’s a little different than I’m going to become a junior engineer and kind of trudge through this industry. What it means is you want to apprentice to an expert as soon as possible.

Rich: Beware of the shortcut.

Paul: Yeah.

Rich: It is not real.

Paul: Yeah, it’s true, it’s true. But the upside is, like, the expert will have a lot of opportunity because…

Rich: He becomes a superhero. If you’re an expert and you’ve got these tools in hand? I am a very experienced product manager, product thinker. I know how to use these tools productively, and I use them very, very differently than just putting stuff in prompts.

Paul: Anyway, so we’ll put the link to the report on that, and I should actually be clear, like, there they, there might be security vulnerabilities, but mostly, I mean, it’s a very vetted code base and it’s cleaning it up, cleaning it up, cleaning it up. And so, like, one side, a lot of people kind of hacking around, making a mess. The other side, people using this stuff to clean something up that everybody thought was already pretty clean.

Rich: Yep. All right, I need a palate cleanser. Paul. This was a little nerdy for my taste.

Paul: All right, all right. Well, you know, here’s the thing. This was the week of slop. Like, slop just started showing up everywhere. Because—

Rich: Cardi B has a new album?

Paul: No, I like Cardi B. Don’t do that. Don’t do that.

Rich: The music?

Paul: I like Cardi B.

Rich: Do you really?

Paul: Yeah, I do. I mean, I don’t listen to it day to day, but I like, I like her vibe.

Rich: Oh, I like her vibe, too. But the music?

Paul: I listen to the stuff that kind of gets into the pop culture, I’m not, I just, no, one of the albums is really good.

Rich: Really?

Paul: Yeah, the first one was really good.

Rich: Okay, okay, go back to slop. I apologize. I clearly hit a nerve here.

Paul: Not a nerve.

Rich: A Cardi B nerve.

Paul: She can handle it. So Sora from OpenAI and then Facebook has Vibes. What’s Vibes? You know?

Rich: Yes.

Paul: Okay. What is it?

Rich: It uses AI to generate, I guess, five-second, like, just goofy videos for your phone.

Paul: Just AI-generated—

Rich: Type some stuff in and it’ll just generate a thing.

Paul: Groundhog flying like Superman.

Rich: Okay.

Paul: You know, that kind of thing. So I gotta say, like, it’s just slop Like, I’m looking at the Sora ones right now, and it’s just, you know, they’re pouring coffee and there’s bugs, pixelated bugs, and jellyfish.

Rich: Uh huh.

Paul: And this is like their best. This is the best they can do. It’s like a ground, it’s a guinea pig at the swimming pool kind of thing.

Rich: Okay.

Paul: Just these prompts. Just this sort of— [disgusted noise]

Rich: Yeah.

Paul: It means nothing. It doesn’t even look that cool. Apparently the new version of Sora is really good at putting your face into things so that you can do stuff with your friends, and cool videos, which I think will be okay for the group chat.

Rich: Sounds like a filter-type thing. Sounds like a feature.

Paul: I mean, we stopped using any AI images for the most part. I think every now and then we might do one almost ironically.

Rich: Yep.

Paul: In our marketing because they look so bad. They’re kind of glazy and…

Rich: Glossy and weird.

Paul: It just looks like you took the easy way out.

Rich: Yeah.

Paul: Right? And it’s obviously AI after a while, you just kind of see it. But I will still occasionally create an image and drop it in Slack or in the group chat to kind of, like, punctuate a point?

Rich: Yeah.

Paul: And everybody rolls their eyes.

Rich: Yeah.

Paul: Right? I think that’s the future for this, for creative work with this stuff. Like, it just doesn’t seem to be getting to that, like, meaningful level that we want to see.

Rich: Here’s my take on it. If you have unlimited inventory, nothing is interesting.

Paul: Yeah, that’s the problem.

Rich: If you showed me one of these, take us out of the AI slop revolution. Take us out of AI for a minute. Right?

Paul: Mmm hmm.

Rich: And you showed me one of these, I’d be like, “Oh, wow, that’s interesting.”

Paul: Yes.

Rich: Because I’m not seeing it a thousand different ways all day. Like, it’s a numbness that is kind of taking hold. It’s kind of the same with social media, which is everything’s gotta get dialed up over and over again. You gotta kinda constantly reach for the stimuli hit, right? And I think my problem isn’t the output. Because look, it’s kind of crazy that these videos are being produced so quickly and they’re big and colorful and interesting, but if you give me an endless supply of them, then you’ve commoditized everything and nothing can be interesting, because my brain can only ingest so much at a given point in time.

Paul: Well, you know, look, as a hobby, I keep trying to learn music production.

Rich: Yeah.

Paul: And there are a million plugins and there are a million ways to do it and a million sounds and a million tools.

Rich: Endless abundance, right?

Paul: And over and over, every lesson from every professional, there seem to be two things. Limit your choices and be, and when you practice, go really slow.

Rich: Yeah.

Paul: That’s it.

Rich: Yeah.

Paul: Like, there’s kind of no other principle that is, that matters.

Rich: Yeah.

Paul: Limit your choices. A lot of times it’s like, go for the first, best take possible, record it and move on to the next.

Rich: Yeah.

Paul: Because… And so what we’re ending up in is this world of infinite possibility, infinite visual style, infinite characters, hippos running around, dancing.

Rich: It’s a trend. It’ll wash away.

Paul: Well, I think when the constraint system shows up.

Rich: Yeah.

Paul: And people can start to understand how you’re creating within the constraint system?

Rich: Yeah.

Paul: For a while it was exciting because people were learning how to prompt, right? And so it was like, “What prompt gets you that?”

Rich: Yeah.

Paul: But now we kind of all know that.

Rich: Yeah.

Paul: And there’s no, nothing, there’s no boundaries to push against.

Rich: Yeah.

Paul: Except can I make it do porn? Or can I make it be more racist? People, because they try to lock those down.

Rich: It’s funny, this is kind of related to what we were saying earlier. James Blake came out with a video recently.

Paul: He’s a kind of interesting…

Rich: Vocal…electronic…

Paul: Indie electronic artist.

Rich: Yeah. And does weird stuff. He came out with an album was just ambient sounds. So he came out with this video called “Like the End,” which is just clips of AI video.

Paul: Mmm hmm.

Rich: Right? And I liked it.

Paul: That’s sort of dystopian.

Rich: Dystopian. But what I liked about it and which is, what I would that was, this is my response to slop is that he, he injected, he treated it as a tool, as a creative tool rather than as final production. Right? And so what he did was he had a message to share about the world.

Paul: Mmm hmm.

Rich: He wrote a song that has nothing to do with AI, and he tried to cobble together visuals that are interestingly almost a commentary about us and about generative content. And it’s dark and dystopian and kind of fun and funny.

Paul: I liked it too. It has images like porta potties exploding.

Rich: He put a little bit of himself into it rather than wrote a sentence and said, “I’m gonna put this on YouTube along with my music.” And this clearly didn’t happen in a minute. Could it be better? Will we look back on it and say this is a great work of art? No.

Paul: I don’t know. People might get really excited about it in the future. Who knows?

Rich: Who knows? But putting some effort and energy and a little bit of yourself into a thing? We’re not going to create creative works with a sentence. Because if you can, then everyone will, and then nothing is interesting.

Paul: No, no. We want constraints and we want human intelligence and rarity.

Rich: We want human individuality, in a way, to inject itself into the tools we use. Right? And the truth is Photoshop, I don’t know if there was a big hubbub about Photoshop showing up and people creating art with that.

Paul: Oh, no, absolutely. There were also unions protested synthesizers.

Rich: Oh, there you go. People are always going to see these things as threat. And the truth is it always reverts back to, well, “What did you bring to it? How did you use it as a creative tool?” Rather than as, like, automated output that’s just spitting out stuff all the time.

Paul: All right, so. So you’re not inherently anti-slop?

Rich: No, I’m not interested in it. I mean, I’m not—do I want it to go away? Like, as a form of protest? Who cares? Everybody’s drooling looking at their phones anyway. Maybe this is better than all the toxic nonsense that’s out there. I don’t know. All I know is it’s just nothing interesting will come of it. And I think it’ll just kind of get, melt, it’ll melt into everything else. Like, it’s just not that interesting.

Paul: Yeah, no, I think it feeds existing fires. Like there’s a lot of slop of, like, you know, Donald Trump riding an eagle.

Rich: Yeah, I mean, yeah, I mean, I’m sure I don’t know what their rules are in public figures. That’s usually a sensitive thing. It’s boring. It’s like, you know what it’s like? It’s the visual equivalent of like auto-generated filler music for malls.

Paul: Mmm hmm.

Rich: That’s all it is. It’s just this forgettable stuff that you’re just gonna hear. It’ll go into your ears and then you’ll move on and you’ll want Panda Express because you’re at the mall.

Paul: I think, collectively, that the term slop locked in.

Rich: I think it’s a perfect—

Paul: Perfect word.

Rich: It’s a perfect word.

Paul: How do you get past slop?

Rich: Forgettable strip mall garbage.

Paul: Well, the good news if we take it back is that most people are not even there yet. We can just relax for a minute. We got a second to process this and understand it, because the fisheries industry? Not really all over AI. [laughter] Your fish are free of slop right now.

Rich: Yeah.

Paul: Not your code, though. Your code’s getting a lot of slop.

Rich: Yeah.

Paul: But that might—actually, it might be anti-slop, too, because it can clean up the code.

Rich: Look, last thought. I’m sounding like curmudgeonly old guy. Maybe also one of these—

Paul: You’re sounding like vaguely pro-AI guy who’s a little sick of this stuff.

Rich: This is boring.

Paul: Fair enough.

Rich: Put some sweat in, man.

Paul: You know what you need? You need systems and tools that enforce the restraints, because then people will do good stuff.

Rich: That is a common theme across everything.

Paul: Yeah. All right, so…

Rich: Check us out at aboard.com. We make software that makes software using AI. We took a really different approach. We sort of taught it how to become a really good, thoughtful partner and consultant. And you can try it at aboard.com.

Paul: We sent Pinocchio to Deloitte, is what that sounds like. But, yes, it is. It’s a good tool. Check it out and send us an email at hello@aboard.com. We love to hear from you. We like your feedback, whether it’s, whatever the feedback, we welcome it all. Unless it’s on iTunes, where we really want you to give us five stars. So, you know, just keep that in mind.

Rich: Do it.

Paul: Bye.

Rich: Have a lovely week. Bye bye.

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