The Aboard Podcast

Paul Ford: Hi, I’m Paul Ford.

Rich Ziade: And I’m Rich Ziade.

Paul: And this is The Aboard Podcast, the podcast about how AI is changing the world of software. And, Rich, how are you today?

Rich: I’m doing well.

Paul: I want to share some thought leadership with you from our industry. I want to bring it on in and we can discuss it.

Rich: Good news or bad news?

Paul: Interesting news. Turns out that AI isn’t good for a lot of engineering teams. They’re struggling.

Rich: Whoa. Okay, let’s do it.

Paul: But it’s really good for some. So we’ll talk about that. Let’s play that theme song, and let’s go.

[intro music]

Paul: Okay. We’re with a company called Aboard. You and I, we’re the co-founders, right?

Rich: We sure are.

Paul: Aboard is a partner. We use AI. But basically, we are old software pros, and we have a set of really good custom tools for delivering software with AI in a very low-risk way. You come to us and you say, “Hey, I want to get in on this revolution. I heard you can get things a lot faster and cheaper, but I still want it to be really good.” And we take you seriously. We bring you in.

Rich: Yep.

Paul: We can turn around your old legacy tools. We can build something new and greenfield. It’s not just a matter of, like, strapping an LLM in front of something and crossing your fingers. There’s ways to use this stuff that are really good and really productive. So that’s what we do all day. And we build and we ship things for large firms, small firms, not-for-profits. All the people who need software.

Rich: Yep. The thing I’d add is that we don’t lead with a bunch of tech. We come in, we listen, we get to know your business, see where we can be helpful, and then we go from there.

Paul: Sounds great.

Rich: Reach out.

Paul: We were working in insurance, helping with policy management. We’re working—

Rich: Health.

Paul: Health. Helping people make better dashboards. Like, really grisly stuff, but we like to do it and we like to do it fast. That’s all you need to know about us right now. But you know what’s funny is that actually ties into today’s conversation.

Rich: Oh, talk to me.

Paul: I have in my hands a piece of thought leadership.

Rich: Ooh, it looks chunky.

Paul: It is chunky. It’s from CircleCI. Do you know what the CI stands for?

Rich: No.

Paul: Continuous integration.

Rich: I know what CI stands—it’s a hell of a thing to put it in the name.

Paul: It really is. It really is.

Rich: Yeah.

Paul: So tell the people what CI is, or I can.

Rich: Continuous integration is a style of building software where it’s less ceremony, less chapters in a book, and you just kind of keep going as progress gets made.

Paul: Just keep pushing that code out, right?

Rich: Keep pushing code out.

Paul: So this is a company, CircleCI, and it’s important to note why. So they work, and it’s done with another company called ThoughtWorks. It’s kind of like a big consultancy.

Rich: ThoughtWorks is a big consultancy.

Paul: And so what CircleCI has access to, what they do, is they provide services to all sorts of programming teams to ship code in a more reliable—

Rich: Streamlined, rapid way to get code out.

Paul: Lots of testing, lots of good stuff that way.

Rich: Yup.

Paul: It’s been around for a while. So what they have is insight into how people are actually deploying code these days.

Rich: They have the data.

Paul: And they had 28 million workflow deploys.

Rich: Sure.

Paul: That they were able to look at and see sort of what’s going on.

Rich: Okay…

Paul: And so I’ll give you some interesting stats. I think, clearly people are writing more code. So I’ll give you the number. 59%, year over year, throughput has increased—but just, throughput is throughput. Like, it’s code.

Rich: More lines of code.

Paul: That’s right. And so—

Rich: That’s a huge increase.

Paul: Very short—yeah. I mean, because it’s not a lot more engineers.

Rich: Right.

Paul: Right? So every time, every word I’m saying to you now, I would be saying 59% more words.

Rich: That sounds terrifying.

Paul: [laughing] Nobody, nobody can, I mean, we wouldn’t be able to get this done in 20 minutes.

Rich: Yes.

Paul: So more code is being written.

Rich: Okay.

Paul: But what they’re finding is that the number of bugs is going up, and the number of issues is going up. And what they’re finding is that there’s this huge split. The 95th percentile and above, of really productive teams? They’re off to the races. They are pushing thousands of changes. They are just all in and they’re moving so fast.

Rich: Okay, when you say 95th percentile, what, of what?

Paul: Of sort of high-velocity teams.

Rich: No, but are they higher quality, like, is it the top 5% of quality? Or top 5% of velocity?

Paul: We’re kind of working back from velocity here. I mean, they’re not really reading every line of code.

Rich: Okay.

Paul: But what they’re seeing is, like, you know, things, things fail, things have bugs, things have issues.

Rich: Yeah.

Paul: So what they’re seeing is that if you are a team that’s, like, all in on this and you’re kind of in that, that cream of the crop, it’s, like, an order of magnitude how much more you’re getting done, according to their metrics of getting things done.

Rich: Okay…

Paul: Everybody else, the tail gets really, really long. There’s more bugs, things slow down.

Rich: Yeah.

Paul: Projects stall. And what’s happening, and they make a really good point in this which is just like, because, okay, so you use the AI code. It’s magic. It writes some code for you.

Rich: Mmm hmm.

Paul: Right? And now you bring it into your continuous integration pipeline which means that you’re going to be tested.

Rich: Yeah.

Paul: You’re going to have all these things going on where we make sure—we automate the quality.

Rich: Yeah.

Paul: But that produces a whole lot of issues. And now nobody’s seen the code, because it was AI-generated.

Rich: Yeah.

Paul: There’s no magical way to get through this.

Rich: Sure.

Paul: And so all these hours are being spent cleaning up AI mess because you’ve been told you got to use these tools. And it is fast, it gets you done quicker. But it’s leaving you a big mess. And so I think the really good teams are the ones that can automate the mess cleanup. They have a real specific policy.

Rich: Yeah.

Paul: Like smaller changes, whatever. And so it’s an interesting time because what we’re learning is that, and I think we keep learning this in a million different ways.

Rich: Yeah.

Paul: The advantages of this technology are not equally distributed. It is somebody picking it up and going with it might have a lot of failure states, and some people might have a very—a relatively small number of people is actually really successful with AI coding.

Rich: Yeah. It’s not even relatively. You’re saying 5% are really cashing in all this productivity and all these capabilities.

Paul: I think it’s really confusing to us because we’re cashing in the capabilities.

Rich: Yeah. And I’m going to say something that can sound maybe a little arrogant.

Paul: Mmm hmm.

Rich: I don’t mean it to be.

Paul: Well, okay.

Rich: High-quality talent can really assert their knowledge and their ability to assess what’s being produced on these tools in a more deliberate way—

Paul: Mmm hmm.

Rich: —than mid-level talent. And I don’t mean that mid-level talent is, I don’t mean that to sound elitist. The challenge you have with this stuff is that the tools don’t just produce stuff because you push a button at the beginning of the day. You do a lot of things to guide how these things work.

Paul: Mmm hmm.

Rich: And if you don’t have a really thorough and intimate understanding of a good practices in general, right? And the truth is you can be productive without tons of good practice. Right? You can be productive. Python, bless its heart, is incredibly forgiving as a programming language. It lets you do stuff. It’s not going to bat you over the head with all kinds of rules. It’s kind of its strength.

Paul: It’s so purposefully simple that it actually drives a lot of very serious engineers batty.

Rich: That’s right.

Paul: Because they’re like, “No, no, this is nowhere near as complicated as it needs to be.”

Rich: Exactly.

Paul: Yeah.

Rich: Now you have these just incredible weapons-grade tools that can be massively productive. And if you’re not really asserting expertise and high-level concepts of how code should be structured, like, all the things that you, all the boring things about good practice, right?

Paul: Mmm hmm.

Rich: It’s a runaway train. And here’s the other reality is you’re producing stuff that you’ve not reviewed and you’re handing it into the CI process. And unless you have absolutely airtight confidence in how you got there, because of your skills, you’re really rolling the dice. And I think that’s what we’re seeing here. Like, what we’re seeing is productivity is not code by the pound here. Like, it doesn’t work. It just doesn’t work. It’s hitting that wall for tools like—CI, by the way, it’s worth saying out loud, CI isn’t just a cool way to like, all the colors blend together. It’s a process that actually applies rigor to the quality of what’s being integrated into the code base. Right? [laughing] It’s not, that’s the whole point of it is that, yes, you’re supposed to go faster, but there are a lot of toll booths that are going to stop the process.

Paul: So there’s a few things that come to mind. First of all, we are veterans of process in this industry. We have CI, we have CD, we have Agile.

Rich: Yes, yes.

Paul: We have Agile with Scrum and so on.

Rich: Yes.

Paul: And they all end up failing because no process can capture everything. No abstraction is perfect. Right?

Rich: Yes.

Paul: So I think there’s a little bit of that, which is just there isn’t a really good established process for working with all these new tools.

Rich: Yes.

Paul: There’s another point I want to make, too. Obviously we’ve talked a lot about Simon Wilison. He had a link to—you should go check out Simon Wilison’s website. Just type it into Google. But he’s really capturing the industry. And he had a link to, there’s a programming language that’s called Zig. It’s relatively a low-level programming language.

Rich: Mmm hmm.

Paul: It’s open source.

Rich: Okay.

Paul: It came in the news recently because Anthropic bought a company that uses Zig heavily.

Rich: Okay.

Paul: And the company, they make a product called Bun. It’s a faster JavaScript.

Rich: Now you’re making up words.

Paul: I know this is horrible, this part’s horrible, but just stay with me.

Rich: Okay.

Paul: So the Bun folks were, like, hey, we actually improved Zig, and we made this one part, like, four times faster. But just so everybody knows, you can go get the code, it’s all good, we’re still open source, but we can’t put it back into Zig because they have an absolutely no LLM rule.

Rich: Hmm. Okay.

Paul: And at first that sounds like maybe it’s open-source people just being their open-source selves.

Rich: Yeah.

Paul: But the Zig maintainers made a very interesting point, and I think it’s a point that the whole industry should internalize.

Rich: Mmm hmm.

Paul: They’re, like, look, you’re going to do what you want to do.

Rich: Yes.

Paul: We’re going to say no. And here’s why. Our job is to develop and create contributors.

Rich: Yeah.

Paul: We need to create an ecosystem around our code where people take ownership and do things. We will invest in contributors even if their early contributions are really messy.

Rich: Mmm hmm.

Paul: But investing in an LLM’s output doesn’t create contributors, it just adds more code.

Rich: Yeah.

Paul: We got plenty of code.

Rich: Yeah. [laughing]

Paul: I got code all over the place.

Rich: Yeah.

Paul: It’s more important for us to draw this very clear line and only let human work in.

Rich: Mm hmm.

Paul: And build people up—

Rich: Yeah.

Paul: —so that they can be part of this community. And less important for us to just have the best product as quickly as possible. So go to. There’s no rules. You go live your life over there.

Rich: Yeah.

Paul: But don’t expect it to be upstream here in the main branch if you’re going to just have a robot do all the work for you.

Rich: Yes.

Paul: Now literally the other company’s inside of Anthropic.

Rich: Yeah.

Paul: So, like, it’s a funny split. But I did hear that and I was, like, look, I may not agree with that top to bottom, but it’s very credible.

Rich: Yeah.

Paul: Right? And what they’re saying is, look, I don’t want this in my process because my process is to build up humans to be contributors who understand the code base completely.

Rich: Yes.

Paul: And I get that. I really do.

Rich: Yeah.

Paul: I don’t want to build that myself. I don’t ever want to live in that world again. But I actually do understand those boundaries. Let me read you just a tiny section from this.

Rich: Okay.

Paul: Because let’s focus on what’s working. Because you know what’s funny is, I read through this report, and everybody should go read it, it’s fine. But when you read the report, everything fails in the same way. Like, it’s just sort of, like, too many bugs.

Rich: Yep.

Paul: “The top 5% of teams nearly doubled their throughput year over year, from 6.8 to 13.4 daily workflow runs. The top 10 and 25% of the teams felt smaller but still significant increases.” So the median team increased throughput by just 4%. So what we’re seeing is like, boy, the advantages of this are just going to a very small number of people. And then the next one is, “The year’s most productive team delivered roughly 10 times the throughput 2024. Organizations running—” Just, if it’s an AI-focused company, they’re running thousands of workflows, just kind of just absolutely shooting code out.

Rich: Do you have any company names that are in the top 5%?

Paul: No, they’re being, they’re being fuzzy.

Rich: Yeah, I get that.

Paul: They’re being fuzzy. Yeah.

Rich: Look, I’m going to, I’m going to punctuate the point I made earlier. AI-generated code is an absolutely devastating vetting process, is what’s happening here. There’s something known as—

Paul: Explain what you mean there. And actually, I’ll give you a stat which I think is relevant. Each of the top 10 teams on CircleCI, which are probably mostly AI companies themselves, validate more than 10,000 changes a day.

Rich: Okay.

Paul: So this is like huge numbers of new code releases per programmer per day.

Rich: Yeah, yeah.

Paul: So back to your point.

Rich: Yeah. What I mean by “vetting” is that there’s always been something known as the 10x engineer in engineering, that if you distribute out the most junior to the best engineers, right? The best engineers are not 20% or 30% better than the junior or the beginner engineer, or the weak engineer. They are 10x as productive as others in their cohort. Right?

Paul: People get very upset about this concept.

Rich: It is very real.

Paul: But I’m gonna tell you, I’ll tell you what’s real about it.

Rich: Yeah.

Paul: I don’t know if I’ve ever met a truly 10x completely everything engineer.

Rich: Yeah.

Paul: But I’ve definitely met 10x in terms of like, oh, yeah, I do low-level streaming databases that take into account the rotation of the hard drive. The next person standing to their left cannot do that.

Rich: Yeah. But I also think I’ve seen it in terms of raw output.

Paul: Sure.

Rich: The person next to them is one tenth as productive. And they are a very good, credible engineer.

Paul: Yeah.

Rich: Like, I have seen it. I’ve, we’ve been fortunate enough to hire a few of them. And what you have in some companies, especially AI companies, who have just like thrown so much money at the top talent, they’ve kind of gathered all those people, is that their productivity there, because they know how to bring a tool this powerful to heel, that the others don’t. They simply do not. And, and that is, that is what I mean by a vetting process. These tools are not making the 1x or 2x engineer two to three times more productive. They simply are not.

Paul: I wouldn’t even focus on the individual engineer, because I think these tools could really help the individual engineer.

Rich: Yes.

Paul: I would focus on the, let’s call it the 1x process.

Rich: Yeah.

Paul: I think that’s more relevant than the individual.

Rich: That’s right.

Paul: Because okay, here’s how the AI companies are going.

Rich: Yeah.

Paul: They’re saying we’re yeah, of course we’re going to use these tools. Of course we are. All day long. Let’s go. What do you need? Do you need more tokens? Have more tokens.

Rich: Anthropic says this all the time, that they’re using Claude to come up with the next version of Claude Code or Claude Work—was it Claude Cowork?

Paul: Yeah, yeah.

Rich: Or whatever. And it’s like, “Look how awesome it is.” And it’s like, you have literally some of the best engineers walking the earth in your walls. Right? So it is the kind of tool that if you know how to gain control over its output, you’re going to be incredibly productive. If you don’t?

Paul: So now we have a problem. The problem is that results and the value—if this thesis is correct and it’s only that top percentage, and they tend to cluster around—

Rich: Yes.

Paul: Then are you out of luck if you don’t hire a bunch of million-dollar-a-year programmers who are really good at this one specific thing, because it’s an order of magnitude. We’re back to the—it’s 10x productivity by these metrics.

Rich: Yes.

Paul: If they’re using these tools wisely. Followed by this incredible long tail of not that productive.

Rich: Yes.

Paul: Or even less productive, in some ways.

Rich: Yeah. I have two thoughts about that. One is I think the tooling around AI will get better and people don’t talk about this a lot. The usability around these tools, how you can be productive with them. Everyone—like, no one has put, it’s so early in terms of the maturity of these tool sets, such that the mid-level engineer isn’t being empowered in a way where their outputs can be more productive. Also the LLMS are getting better. It’s just early. Like, I think you can get—will you get 10x out of the mid-level engineer? Maybe not, but you’ll get 2, 3, 4 as these tools get better and smarter about how they can help people

Paul: there’s more code, and there is more velocity overall.

Rich: Yeah.

Paul: There’s just a lot more, yeah.

Rich: I think if you’re pulling the lever and just letting you know the firehose of code come out, and you don’t know what’s coming out, and then you’re sort of saying a prayer and putting it into the integration flow, workflow? Best of luck, right? You just got to know what’s going on there.

Paul: Let’s be management consultants for a minute because it’s, here’s what this feels like.

Rich: For a minute?

Paul: For a minute. Here’s what this feels like. Top tier? “Hey, guys. Figure out the process that works.”

Rich: They’re having a blast.

Paul: “Go to it. Ladies and gentlemen, you are free.”

Rich: Yeah.

Paul: But no bugs, no defects. Use this thing as much as you want and just get 10x results. And if you need money, you let me know. Okay, so that’s top.

Rich: Okay.

Paul: Second tier. Lower tiers are this: “We got to use more AI.”

Rich: Who’s saying that?

Paul: The boss.

Rich: Yeah. Well, there we go.

Paul: The boss. And then everybody’s like, and people, and so the metric gets wrong.

Rich: That’s right.

Paul: The metric is immediately wrong. Which is, I think everybody’s saying, you got to put the AI in here to get us the results so we can be like those AI companies.

Rich: The boss is playing with the tools. That’s part of the problem. [laughing]

Paul: That is part of it. And because he gets it to make him a plan for, like, a new piece of software.

Rich: It’s not a plan. It’s just a bunch of pretty colors that, like, “Look at this. I’m almost done.”

Paul: It draws—

Rich: “Just finish it.”

Paul: So I had this experience. I’m working on a little side project to do, making a sort of climate analysis.

Rich: Uh-huh.

Paul: Climate analysis is very tricky. And I had to make a little document that would explain how to climate-proof your house.

Rich: Yup.

Paul: And I showed it to my wife who’s in construction, and I really almost didn’t survive the next five minutes.

Rich: Sure.

Paul: Because what I thought was fine regarding backwater valves outside your, in your sewer.

Rich: Uh-huh.

Paul: Was not accurately presented.

Rich: Sounds like you had a really fun weekend at home.

Paul: Everybody was having a great time. [laughter] And so, like, the subtlety gets lost. But it looks so real. It’s so confident that you assume that among all its many other things, Claude is obviously a master planner.

Rich: And we’re seeing that. Right? So these mandates are coming down from oftentimes non-engineers.

Paul: Who are saying, “Look, it drew me a picture of the interface. It looks pretty good to me.”

Rich: I mean, those managers should look at this paper and realize that the cliff of diminishing returns here. And the truth is this. And I’m going to say another thing out loud.

Paul: It’s a podcast, so you should.

Rich: I should, is, look, certain industries are just not gonna attract the best people. Like, this is the reality of it. Like, a top-shelf engineer wants to work on the coolest stuff. And there’s a lot of industries that need straight-up just people to handle the workflow of, reset your password at the bank in the Midwest.

Paul: This is why client services exist, my friend.

Rich: There’s that, too. So this is the other piece of advice I would give people is there is expertise that’s going to cluster around this. I think the professional services industry around this stuff can take off if you have the right people.

Paul: Here’s why: If we don’t get that 5%, what happens to us?

Rich: We get shown the door.

Paul: Yeah, that’s it. So it’s like you need to get people who are just like, okay, here’s how we use it. Here’s the process. I’m going to be frank. Process should not be secret with this stuff.

Rich: No!

Paul: Don’t trust anyone who’s like, “I have magic AI tools that will guarantee success.” We will sit with you and anyone good will sit with you and be like, “Here’s the 12 things we do, here’s the internal tools that we use, and here is how it’s very, very likely that I will be able to get you a bad version of your software followed by a good version of your software in the next six weeks.”

Rich: Yeah, yeah, yeah.

Paul: Right?

Rich: Look, I think if you’re going to give some advice to someone that’s in the middle of the pack here, like an engineer that’s in the middle of the pack is, I’ve been, sorry, this is going to be my catchphrase: AI punishes laziness. Like, it just does. And you could see that in an image that got spit out in 10 minutes rather than—I’ve seen art, I’ve seen videos, I’ve seen music videos where clearly someone spent hundreds of hours using AI, but they applied all their creative thinking to it and used it as more of a tool. Do not let it come up with what looks like really neatly, really tidy code and just pass it along. Like, you’re going to have to do the work of understanding what it’s outputting. And that’s work.

Paul: You know, what everybody needs to do is pick something where they’re truly an expert. It could be a hobby.

Rich: Sure.

Paul: And then get it to write in very, very clear, discreet, like, footnoted terms about that hobby.

Rich: Yeah.

Paul: And you will find…

Rich: You’ll learn where the edges are.

Paul: Yeah. And then realize that that hobby is everything. It’s really good for a lot of stuff.

Rich: Yeah.

Paul: But without the refinement and verification steps, which are what those companies have. That’s, I’m going to tell you—

Rich: They’ve built software to read through their software.

Paul: That’s right.

Rich: Like, the best shops do that. Right?

Paul: And that is why they’re getting those results. They’ve automated a lot of that.

Rich: That’s right.

Paul: They’re formally verifying. They’re following these very strict processes. And so they always get good stuff on the other side.

Rich: Yes.

Paul: Whereas your guys, your people are in there just kind of like, making do the best they can with Cursor and hoping it works.

Rich: Yeah. Frankly, when you hit the wall at the test phase, these are people that are using sophisticated tools. There’s a lot of businesses out there that are pushing code.

Paul: Yeah.

Rich: And just hoping for the best, because it looked pretty darn good, and we’re just gonna run with it, and that’s scary, too.

Paul: I gotta say, the worst part of all this is I think what’s gonna happen is everybody’s gonna go looking for that one tool that’ll solve it.

Rich: Yeah.

Paul: And it’s actually process and learning and accepting—

Rich: It’s boring stuff. The boring is creeping up on all of this stuff.

Paul: Yeah.

Rich: Like, there’s hard—writing well takes work. Creating art with these tools takes real work.

Paul: Boring just feels like home to me, though.

Rich: Oh, you’re an exciting guy.

Paul: I think.

Rich: In other ways.

Paul: Yeah. But I like boring software.

Rich: Any other juicy stats out of this paper?

Paul: I mean, no, no. This is not a juicy—I wouldn’t say it’s a very juicy stat.

Rich: It’s not a…

Paul: Yeah. No. What it is telling us though, and I think it’s really good to be, to remember is, like, for all the narrative about how the revolution is here. And God knows I’m part of that narrative.

Rich: Yeah.

Paul: Most people are not experiencing it the same way.

Rich: I think, it’s funny. I want to close with this thought. I think the revolution is complete at the desk.

Paul: What does that mean?

Rich: Everyone’s got it at their desk. Like, everyone is like if there’s a function that just can’t seem to run right because you’re a coder or if you want, or you’re stuck on a tagline for a slogan. It’s at your desk.

Paul: It’s Stack Overflow for everything. Kind of answers all your questions.

Rich: It’s kind of there to sort of maybe if you’re stuck and you want to get unstuck stuck or you just need a spark to keep going or you need to review a legal document. I think that that has been, that is complete. Right? That part of it is complete. I think at the organizational level, where the vetting process and the testing and here it happens to be a very clear process which is like, we’re gonna have to test this code before it goes out, or whether it be, we’re gonna change the way we work because we have AI now, that is, We’re at the, like, 3% mark of that change.

Paul: I want to—

Rich: Too early.

Paul: I want to close this with something kind of to think on. Right? Which, I think that’s important. And we’re trying to work on that, too. Everybody is.

Rich: Yeah.

Paul: Everyone has experienced this technology as an individual.

Rich: Exactly.

Paul: And it’s one of the ways that it really has blown up in our face. Because that’s actually how people sit there and they’re, like, it told me my scientific theories are my brilliant.

Rich: My God.

Paul: Yeah.

Rich: Yeah, yeah, yeah.

Paul: It’s incredibly affirming. It’s sycophantic. And the organization—remember when you start working and you kind of get that boss who sits you down and is like, “This isn’t it, buddy?” [laughter] Right. And it’s like, AI’s never gonna do that. It’s never gonna, like—so everybody’s having this experience where they’re getting their narcissistic supply fed.

Rich: Yeah.

Paul: They’re writing code, they’re drawing pictures, and nobody is swooping in and going, “Hey, that’s kind of garbage.”

Rich: Yeah.

Paul: Right? That’s what the organization needs.

Rich: Yeah.

Paul: Like, the organization doesn’t need all these little tiny people like in cubes going like, “I came up with everything!”

Rich: It’s also when you put forward the work to an org and it falls on its face, it’s very embarrassing. It’s not good, right? Like, you can try it at your desk and fail a bunch of times till you feel good about it. But a tool like this, it’s just going to light up everything red.

Paul: And we’re going to see, now that we have the giant AI companies which are all worth like nearly a trillion dollars?

Rich: Yeah.

Paul: They really want to make the enterprise work.

Rich: Yeah.

Paul: But their view of this is as a one-to-one thing that kind of scales up?

Rich: Yeah.

Paul: So I don’t know how that’s going to go.

Rich: It’s going to go, we should, we’ll have conversations about that. It’s where we are swimming right now, which is, how does an org metabolize all this so it’s useful?

Paul: I’m going to tell you: Orgs don’t bend to software.

Rich: No, they don’t.

Paul: Big orgs. Small orgs have to.

Rich: Yeah.

Paul: Big orgs, the software must bend to them.

Rich: Yes.

Paul: Even if it’s AI and it’s worth a trillion dollars.

Rich: That’s right.

Paul: So I think that’ll be really wild to see, and I’m going to enjoy that.

Rich: There’s another to bypass all of this and get incredible transformation into your organization.

Paul: That’s right. That’s right. And let me tell you, it’s to call…

Rich: 1-800—

Paul: ThoughtWorks? Is it CircleCI?

Rich: No!

Paul: No!

Rich: Jesus. That was going to be a smooth exit.

Paul: There’s no such thing as a smooth exit.

Rich: You made a joke.

Paul: We are a New York City shop.

Rich: Yes.

Paul: We’re a growing group of people, and we are solution engineers. And you say, “Hey, I need to do this thing.” So I’ll give you a couple examples. Right now, somebody called, they’re like, “Hey, we built this really cool app and it’s a scientific app, but we’re having trouble getting people to use it.” And we’re like, “Oh, this is great.”

Rich: Professors are using it.

Paul: Yeah. And we’re like, “We are going to make this really pretty for you. We’re going to make it pretty and we’re going to make it easy for people to walk in.” And when I say pretty, I mean good UX and so on. We’re going to do that as just like a prototype to see what happens next.

Rich: Great.

Paul: Okay? And then we got other ones which are much bigger.

Rich: Yeah.

Paul: Which are sort of like, “Hey, can you replatform? I need everything. I need, like, I need a new CRM, and I need a new this and I need a new that.” And we’re like, yeah, “We’re going to put it all together for you in one nice package.” So that’s who we are. That’s what we’re about. If you want to talk to us, you just send an email to hello@aboard.com, we’re on the list.

Rich: We’ll always take the call.

Paul: Always, always. You know the other thing, Rich, so we would love to hear from you. Not just about business, obviously, we love business. But we want to also hear about what worries you about this technology, what you’re learning, what you think we should be paying attention to. We’d love to hear about guests that we should have on. Maybe not, you don’t have to send us like yourself as a guest sometimes, that happens a lot. It’s pretty awkward. But beyond that—

Rich: Unless you’re awesome.

Paul: Yeah, that, too. Maybe we do want to hear from you. Anyway, please get in touch. Please ask us any question. We would love to do more advice columns.

Rich: How do they do that, Paul?

Paul: Oh, my God, thank you. Well, they can just send an email to hello@aboard.com. They can also check us out on YouTube. They can give us that beautiful subscribe and thumbs up. They can subscribe to the podcast, and they can give us five stars.

Rich: [whistles]

Paul: So there’s lots of ways to interact in really positive ways with us, and that’s what we’re all about.

Rich: Have a great day.

Paul: Bye!

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