Launching ChatGPT with Zack Kass, Former Head GTM at OpenAI

Today we're talking to Zach Kass, former head of Go to market at OpenAI, about the story of launching ChatGPT and how society can responsibly adopt AI. Thank you to DigitalOcean for sponsoring this episode. For simple cloud and powerful AI that's built to scale, visit digitalocean.com or just click the link in the show notes. You're listening to Joel Beasley, Modern cto.

You've been in AI for a long time time and you were working at OpenAI and go to Market and now you're doing like a lot of speaking and consulting helping companies make this transition. Is that like a fair overview or am I completely off base?

It's a totally fair overview. I, I realized that there is this massive, massive disconnect between how the industry itself perceived itself, how the industry saw what was coming and how basically everyone else saw what was coming from my mom to Jamie Dimon and that a lot of the change is going to happen in, you know, not from inside of these organizations. And so I have spent the last two years building an advisory business and also doing research at University of Virginia and Hong Kong University of Science and Technology, studying the future of work and trying to bring to bear 16 years of applied AI experience and also academic exposure. Now to everyone else, as I say, that's pretty cool.

And so walk me through a little bit of your professional background, like your credibility of those 16 years in AI.

Started my career at a company called Crowdflower which became figure 8. It was the first scale AI. Basically it was started by two Stanford researchers who were search scientists at Yahoo and figured out there were, I mean I don't know if your listeners are going to be familiar with search science, but they went from precision to DCG and DCG which was just a more accurate way of telling, informing the indexes on how to rank search results. And they could do DCG up to you know, 20 or 25 really effectively. And it was, it's actually what gave Yahoo almost a performance advantage. But the only way you could do it is with, is with humans at massive scale. And so Lucas built this sort of front page on Amazon Mechanical Turk again, I don't know how much you remember about this but early, early crowdsourcing technology to, to do, to build training data for, for the search algos and we turned that into a company and that, that, that was Crowdflower and Figure 8 and spent a while there with him as we watched sort of statistical machine learning gain more popularity as we watched the models proliferate, the number of companies that could effectively use these models, expanded and then went to a company, lilt, which was also started by two Stanford researchers using large language models to solve machine translation. And there I started to sell, basically I was one of the first people to sell large language models. And we were doing it in sort of this workaround way where we were selling enterprise translation services powered by, powered by the machine translation efficiency. So it was AI enabled services long before people were talking about it in that way and did that for, for a while and then graduated. Just before OpenAI launched GPT3, the head of applied called Lucas my, my first, my first boss and said, do you know anyone who can sell AI? And Lucas is like, there, there are not many people who have done it. So here, here's one of the guys and got to, you know, launch 3.3.5 for ChatGPT 4.0 and some other, you know, just amazing products at OpenAI. And we sort of watched as the world, as I went sort of from a world where no one cared about AI to a world where, you know, no one didn't care about AI.

Yeah, I remember that time very clearly because it happened while we were doing the show. That is super interesting. So when you were doing the go to stuff at OpenAI, I mean, was that hard or easy? Because like, for example, if I create a teleportation business tomorrow, everyone's like, holy crap, teleportation. And they're all just gonna flood to it. Was it. But was it hard or was it easy?

Both. So what I think is important. So the answer is both. The expectations when you're working at a company like OpenAI are exceptionally high. And so the, the amount of pressure that you put on yourself is basically all relative, right? It's like, it's like, it's like when you, when you talk to friends who grew up to very successful parents, it's like, well, of course you, you had so much going for you. How could this not be a really pleasant childhood? And it's like, well, yeah, it's because my parents expected me to outperform them and they had already outperformed their parents and so on. And so when you work at these exceptionally high performance organizations, the pressure you apply is all relative. And the, our ideas of growth wasn't, you know, 5 to 10 million, it was 20 million to a billion. Right? I joined OpenAI at 2 million in revenue and left at 2 billion in revenue. And that's, that's work. You know, it's like, that's, there's a lot that goes into that. And, and one of the key elements is, is maintaining talent density. So you need to hire an exceptional team and not compromise, which is just one of these incredible challenges. The other thing, and I think more to the point that, that maybe you're asking, is what is it like to work at a company where it's so exciting that you know the market is pulling you? And what I would remind people is OpenAI had been building important products long before we built ChatGPT. Dolly predated ChatGPT. GPT 3.5 predated ChatGPT. And we thought that we had cracked the nut with the science. And what's really interesting, and the thing that I would remind any CTO listening and any CEO or had a product, is that GPT 3.5 was released in June 2022. Right? So four months or a little more. Four months prior to the release of ChatGPT. And it's important to remember this because the concept behind ChatGPT was actually a chance to show people what the API was capable of. And yet with the idea that more people would use it, we were sort of sitting around, I was like, oh, why aren't people, why aren't people flooding the API? Why is it still so niche? And the model that ChatGPT was built on, GPT 3.5 was publicly available for four months, right? It was a slightly dialogue aligned model, but not much. And the major breakthrough was the application layer. And I tell this story over and over because it really serves as one of my three epiphanies at OpenAI that in fact the application layer just matters as much, if not anything, as much, if not more than anything else. And it serves as this constant reminder to anyone out there sort of feeling like they're stuck not knowing what to build, which is that building a great customer experience remains king, right? Like building a thing that people can use effectively, that doesn't require material. Behavioral change is really important. And that of course sparked a bunch of interest, but it still didn't sell itself, right? You still need to actually do a lot of the work, which is why OpenAI has invested so much in the last two years into the go to market organization. Because it's not sufficient to just have good technology. You must build great people to sell and support it. And so the answer, you know, is yes, it was easier than, you know, when people ask for go to market advice, I'm like, look, I can give you plenty. But not from OpenAI. That is an anomaly, but not because it was easy. Just because it was so different in scale and, and surprise. I was so.

It was like, they, like, to many people, they created intelligence, you know, like to the average person, like, whoa, we can have synthetic intelligence now.

Yeah.

And then there's a lot of pressure because if the average person, if the uncle, if the aunt, if the grandpa is playing with the technology, then it's easy for people to understand it. And then they can talk to their board and say, why aren't we doing this type of stuff at our company? And one of the conversations that happens a lot in our CTO space is pressure from the board to adopt AI and to show some return on AI, which doesn't happen like a whole lot. They usually let technology kind of operate and they have products and they, they need some returns and stuff. But this is a whole lot of pressure right now from, from board members and the average people to their CTOs about implementing this.

We're observing the consumerization of technology in basically real time. I mean, we've sort of talked about it for a while, but it really is happening at the edge now where state of the art technology, in this case synthetic intelligence, I like that term, by the way. But unmetered intelligence is arriving first at basically a teenager's pocket. I mean, I wrote about this the other day that more and more world leaders I'm meeting, they can remain unnamed. So they're continually willing to tell me about these stories, are learning about the next big thing from their teenage. For, for, you know, they go home to the dining room table and they look at their, their child's phone and they go, what is that? And this is the chat GPT effect. And it's actually, you know, I don't know where we're gonna go in this conversation, but I would. One of the cases I make for why building in the open is so important, you know, the Sora 2 came out and everyone, you know, I got phone calls from press and other people. How could they do this? They're flooding the market. It's, it's slop, it's, it's dangerous, it's scary. And I go, listen, there are two alternatives to this world. The first is that we don't build this at all. And I don't believe in that. I think that we should, I think we should build technology and, and sort of pursue frontier within, within reason, obviously, alignment and explainability. The other option is they build it in, in, in secret. They build it and they don't tell anyone, or they build it and they only tell a few Hollywood Studios, right. They sell it to the highest bidder. And what's amazing right now is we're building state of the art at, at the edge, at the consumer edge. And it's turning it, it's creating a very strange power balance because a bunch of CTOs who used to have early access to the best technology are sometimes late to the party because it's their board or their board's teenager who's discovering the latest, greatest thing, you know, on, on a YouTube video, on our, on a, on a, on a GitHub thread. And that's, I got no issue with that, frankly. I, I mean, I, I think that, you know, I say this with, with, with sympathy for the CTOs who are getting tremendous pressure to build things that are maybe not appropriate to build, which we can talk about separately. But I think it's pretty cool that everyone gets to see in real time how, where the technology is going.

And I get it, I have empathy for both sides. You know, you see this, you see your teen at the table using this new technology, and you were just in a meeting earlier where the software people are lagging behind. You know, it's like, why aren't we doing, why aren't we getting some of this, this intelligence into our company? Yeah. One question I, I was really curious to ask you is do you ever get into the legislation side of conversations at all in your, in your work?

So, so I'm gonna, I'll answer that. And I also want to speak to something you just said, which is, I think one of the funniest things about this moment. I mean, there's a. Yeah, you have to pardon the crassness, but there's that teenage sex joke about like, you know, the teenage sex is like, everyone talks about it. No one's, you know, no one's doing it. And AI can. More things, things change the more they stay the same, which is like, it's. The other problem here is it's, it can be so performative. It is so easy. Now the cost of creating value is, is coming down, but so is the cost of, of creating a little less than value. Right. So my, when I say that, what I mean is like, lots of people now are building these really cool demos that are doing that cost very little, that are really interesting to look at, but aren't, aren't actually doing a lot because they don't change behavior. They don't actually meaningfully produce a new outcome. And so then you get these situations where the board, board is now staring at something that isn't Even valuable, but just looks good. And they're like, why don't we have this? Which is, I think a lot of the demo wear going on right now. And it's not to say that AI can't do incredible things, but so much of the change happens in sort of like a traditional people way. And the reality is that just takes an enormous amount of work. So my sympathy goes out to the CTO doubly because I think they are getting pressure that is often sometimes unfair and misapplied. Even though this technology is super powerful, there's just still this incredible a misunderstanding of where it should be applied. As for the legislation question, I spent a bunch of time on this, A lot of. I got really interested in sort of the last three years on this, on this role that emerged, the responsible AI function and person, which sort of appeared sort of overnight. A bunch of people had been studying ethical AI their whole lives, it seemed,

of course, update your LinkedIn bio and that's it.

Yeah, so, so I got really fascinated mostly because I wanted to see where these, what these people were saying and, and what the grift was. And I'm not, and I don't, I use grift not. This is, I don't mean it totally pejoratively. I use it sort of tongue in cheek. But a lot of people overnight were like, look, I'll show you how to do responsible AI. And, and I, what I pieced apart is that there was a bunch of, you know, there's a lot of just ways to extract revenue from enterprises who were terrible, terrified of being irresponsible. Right. It's like the, the, the alternative for responsible AI is irresponsible AI, and no one wants to be that. So everyone has to go hire responsible or ethical AI people. But what I tried to do when I talk to CEOs, I spend a bunch of time on this and policymakers is pick apart what I think ethical AI is. And first I remind people that there's a lot of, there are a lot of laws in place in this world, and a lot of these laws are meant to and effectively govern what enterprises can do. And they have been applied sort of carefully across technology for a while. And a lot of these laws do not change. Right? There's just like a bunch of ethical practices that don't change because of AI. But there are a few that we need to reconsider and two of them unfortunately happen much further upstream. Sort of two of them happen really at the, at the point of research where the models are Built one is alignment and one is explainability. Explainability can happen a little bit further down. And the third, bad acting is critical and happens at the point of application and, and the policy to, to protect us. And I talk a lot basically about these three and not much else. When I talk about, when I talk about legislation I basically am like look, there's a bunch of stuff you're going to be inclined to policy. You should avoid all of it that is performative. And you should focus on making sure the models have an appreciation for the consequences of their actions and are aligned to human interests. There are ways that we can do this. You should enforce explainability, especially when the models are being used in precision high risk environments. Think loan approvals or, or even small claims courts. It what's important is not that the model gets it right every time. What's important is the model can explain itself. And then the third is bad acting. I don't think we fully appreciated that we are massively empowering a low resource bad actor. We spend so much time worrying about the nation states and the Bond villains and I am principally concerned about the psycho in the garage or even just the, the, the criminal who has figured out that they can do a whole lot more with a lot less. The cost of doing bad stuff is coming down. And so this is where I crusade and we've had success on sort of all three bases, both at the state, federal and international level. And I really try to encourage others to just talk on these terms because it would clarify a lot of this dialogue.

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Now back to the episode.

Can I share with you an idea and you can tell me if it's stupid, has merit, if it's kind of okay, but has holes. And then can we, can we discuss

Something the preface makes it sound like it's already going to be good.

It's already gone. Yeah, it's good. Okay. So I was thinking, I'm really into health, right? And so I always look at the nutrition labels. Like I want to know what's in the food that I'm eating. I try to eat as close to like real food as possible. And so I was like, this is great. All of these companies are legislated. They have to do this through the fda, put the ingredients and they appear in the order in which the largest quantity, right? Then I go and I use. Let's just not name brands. Let's just say every major brand LLM out there that a consumer can pay 20 something dollars a month for. And they all have these obvious default master prompts that we can't see. They're not transparent and they're injecting ideology of how the model should operate. Regard, I'm not saying which ideology. They have an ideology. And it's decided in this back room at these companies. And I can't see what I'm consuming now. I'm okay if one, things vary differently because of the culture or whatever. Like I'm okay that there's a bunch of models with a bunch of differences. What I'm not okay with is the fact that there's no transparency as a consumer for me to make a choice. Like I want to see the master prompt for each service and then choose which one I want because there might be some services based on the culture that I'm living in that doesn't align with my values.

You're describing explainability. I mean, that's explainability. This is explainability. So what you just described is, and you did it very, very thoughtfully, it is not a crazy idea. It's a super reasonable idea. And we should all, we should all require this. One of the things that people always ask me, Joel, is how do we reduce bias in the model. Now stop me if you've heard this before, but I have to take the opportunity to explain to people that bias is not actually a pejorative. Well, it's a pejorative, but it shouldn't be. The, the, the, the term bias itself is in fact neutral. And what I have to remind people is that all thinking machines, all humans, all, all, all thinking machines, all AI systems have bias. And they only work because they're biased. We only work because we're biased.

I think we're going to be best friends. Oh my gosh, the number of people I've had to Explain that to. It's unbelievable. I was like, you cannot function as a living organism without bias.

How do you live?

How do you survive? You're going to drink motor oil because you can't bias it against water. What is your deal?

Yeah, insert step brothers meme. Josh, you can, you can do the, you can do the step brothers meme there.

What they're really saying is values though, right? They're really saying.

No, sort of, sort of you, you, you almost hit the nail on the head there. And, and what you said was, are you going to drink a mortar or motor oil? No, you're going to drink water because you know that you need water. There are some things that actually we know we need. What, what where bias is super interesting is like we would be zombies. We would be basically functional zombies without bias, but we would perform the rudimentary actions that are, we are sort of hardwired to perform. And without being crass, you can sort of think of what they are where. What is important to remember is that there are a bunch of things that we are born knowing how to do. Breathing is one that. You don't need to be biased to breathe. You just, you need to have a brain that work. We, we, we sort of like have these pre, pre wired ideas of, of, of living. Humans over time have sort of come to, to, to understand lots of things. What's important is and remarkable about humans is that our brains have now filled with so much information that we cannot possibly unpack all the things that happen inside of our brain. And this is what I have to remind people, which is the problem in the world is not bias. The problem in the world is inexplicability. Humans are exceptionally inexplicable. In fact, not only inexplicable to each other, we're inexplicable to ourselves. A lot of the pain in living is not knowing why you feel a certain. Is wrestling with a feeling that you cannot make sense of. It's not that you are sad, it's that, you know, your depression doesn't, doesn't. Why are you. People always say, I don't know why I'm sad. And it makes it even more sad. Right? You know, everything's going well, but I can't, I can't find joy or I don't know why I'm so angry right now. Or I woke up really happy today. That's, that's amazing. I, you know, it must be, it must be the astrology. Humans are inexplicable. There is not enough therapy and ayahuasca in the world for any individual, you or me or anyone else, to unpack all the things that we believe. The wiring in our brain is so complex, and we store everything that's ever happened to us in there, and we could never access all of it. Moreover, Joel, even if we could, we would never admit it. We would never, ever tell another soul all the things we believe and why we believe them. There are just some memories that are so taboo that they should never be shared. And frankly, a lot of decisions we make are so sort of inappropriate from where they come from that. That it would be. It would be professional or personal suicide. Right. A judge would never say, I'm sentencing you to six months. Because I. I woke up on the wrong side of the bed this morning. I had a terrible. I. I didn't sleep well last night. I'm pissed off. And you get an extra six months. That happens all the time. That happens all the time.

Did you hear the study they did where they took the board of parole and they mapped the time? Did you see that one?

Yes.

And they found out that they would deny the parole before lunch because they got hungry. They were in a bad mood.

I have a friend who applied over and over to get a visa three times, and then they finally got a good. They finally found a good visa lawyer. And the visa lawyer said, actually, you're applying to the wrong office. That office is famous for rejecting it. Is that bad? Like, it's just. It's one of these situations where we live in a world that isn't. That is unfair, principally because we often can't explain it. It's not in that we think the rules are broken. Sometimes they are. Now, I say all this because the reason that bias is a pejorative is I think our association with bias as something inexplicable, like when a parent, you know, you'll be at a soccer game and. Or the scenes in the movies if you don't have kids, and some parent will say, oh, the coaches. The coach put their son in because he's biased. It's like, that's true. That is exactly how bias works. But no one ever says, oh, that really kind person picked that woman up or helped that woman with her bags because they're biased. We. We never say someone is biased when they do something good. We only say they're biased when they do something bad. Even though most bias, it turns out, is actually good. Like, most people do pretty good things throughout the day. We make mistakes. But like a lot of our bias tells us to do really nice things, and we never use that. We always say, oh, you know, you're so kind, you're so thoughtful, you're so gracious, but it's never your bias to do good. Now, I say all this because I'm on this crusade, as it sounds like you are, to remind people that bias is not a pejorative. It's not inherently bad. And what we live in is a world that is not filled with bad bias. We live in a world that is filled with bias. We cannot make sense of. That is just not how we are wired. And frankly, I don't want to unpack all the things in my life. There are plenty of memories that don't need to be. That don't need to be accessed. And so what we have an opportunity to do now with AI is actually to build an explicable world. We have an opportunity to build a world that makes more sense to us by building machines that are required to explain themselves not because their. Their decisions are perfect, but because we can make sense of their decisions. It is. The assumption should not be that machines will always get it right. The assumption should be the machines will always tell us exactly why they did something so that we can then actually,

yeah, okay, I like that it solves this. It's the same. Same end result.

That's right.

Nutritional labels versus just asking it like, hey, tell me what your, you know,

default thoughts are You, Joel, do you have kids?

I have three.

Yeah. Okay. So parents will always say to me, I'm really worried about, you know, LLMs teaching my kids. And I go, that's interesting. Have you ever been to a school?

Yeah, well, we homeschool, but.

Okay, enough, Enough said. I'm preaching to the we are best friends. I'm preaching the choir. My wife's a first grade Waldorf teacher and she's like, listen, Rudolf Steiner is like a pretty clear understanding of what should and shouldn't be taught. And still, you know, Waldorf teachers go off the reservation because their biases. And when parents are like, you know, what is this LLM teaching me? I'm like, nothing different than what a teacher might like. If you think ChatGPT gets it wrong, you should go into a classroom. And it's not. It's not because teachers are bad. It's just because the job is very hard. And what you never get a chance to do as a parent is say, show me all the things you believe. Show me all your values. Show me all your genetic preconditions. Show me all of your lived history so that I can know what it is you're biasing my child with. You don't get to do that. You gotta, you meet the teacher once or twice at a parent teacher conference and you go, oh, I, I like them. I think, right. I guess, but with the models. And I'm not saying we should, we should hand over teaching to alums. I'm not proposing that at all. I don't want to sound like I am, but I'm saying it gives us a chance to say, show us all the underlying decisions and information that led you to inform or that informed this curriculum or this suggestion or this response. And that is all we can ask for from, from the machines that are inevitably going to.

Can we legislate that though?

Yes. In fact, not only can we, we must. I am on a crusade that alignment, explainability and bad acting are the three things that must be legislated and probably the, the extent to which we should legislate. For now.

How is the bad acting not already legislated?

Well, we have not yet cons, so it's starting to be. But let me give you an example. The cost, the penalty for being caught trying to do mail fraud or wire fraud in the United States sort of scales on a per action basis. So obviously Bernie Madoff got got, you know, 500 years. But if you try to, if you get caught phishing someone on email, you can only be tried for the amount that they can improve that you were trying to phish them for. Now the reason why, Joel, you can answer, why is that flaw flawed? Well, because whereas historically you had to do like Robert Redford, Paul Newman, the sting in order to steal from people, you do these very elaborate financial fraud situations. And so you could basically say this person was caught doing this and this is the extent to the damage. Phishing is exceptionally inexpensive. So you, we have to assume like very, we have to assume intent to sell and, or, or you know, whatever, whatever the terminology and drugs are, which is, which should carry with it a much bigger penalty because the cost to doing it is so low. And so we have to create this new artificial penalty that doesn't exist. So if you get caught scamming someone over email or catfishing someone, you get slapped on the wrist. Sometimes it's a fine, sometimes, you know, some, some DAs will not try it. They'll figure it out on their own. I am advocating that in fact, if you get caught trying to steal from someone using technology, broadly defined, but deep fakes, etc, we should assume you're doing it to a lot of people. And we should assume that you are an incredible threat to the, the fabric of society and trust. And I advocate aggressively for very punitive policy to punish and principally to terrify people who consider using AI to tear at the fabric of society. Because if we don't, I think it's going to get really weird.

Dude. I did an interview with an insurance guy a couple years ago that does insurance for like data loss and wherever they lock up your data, you know, and hold it hostage. And he was saying you can buy these things like franchises on the dark web and just like let them run. And then you just basically provide support for the, you're the customer service for all the people you're scamming and locking up their data. And I, it blew my mind.

I think that we should try these people. Like, well, I, I don't, I don't want to lose followers, but I, I,

I, losing followers today. That's what the, that's what the engagement was. I was like, Zach, come on, let's rub everyone.

I really think that we should treat these, these crimes as, like, as, as crimes that tear the fabric of society. Like, these are not, these are. So Florida recently passed a law that, that, that I advocated for and helped with that sort of basically says if you get caught stealing from a senior citizen in Florida using technology, it's an extradition law, you get extradited to Florida and they are trying to put people away for up to 25 years for attempted financial crime. And my feeling is good, like do it, let everyone find out that this is a, this is a scary proposition. It is not something you should consider doing. And when, if you're a bad guy in a room with other bad guys sort of going through the list of the bad guy ideas, pass over the one around deep fakes, pass over the phishing one. And, and you know, we, we just, we do not want to devolve into a low trust environment. So much of what makes, especially this country, but, but so much of what makes the world livable is knowing that you can trust when your phone calls, you know, when you're, when your phone rings. It's knowing that you can trust a text message from a friend. It's knowing that you can respond to an email safely. And if we lose that, it's going to take a lot to get it back.

Yeah, I agree. I think we should make penalty in general. I'm going to make a blanket statement. I would like to see higher penalties, but I'd also like to see the Age changed. Like, I don't, I don't think you're an adult until you're 30. I mean, I don't think your brain, you might have been an early developer, but my brain didn't start really working until my late 20s. So I'm like, I think we should raise. This is not popular, but not many people talk about this specific topic anyways. But because like, look, when, if you're 13 and you're at home and you're screwing around hacking stuff, I don't think you should get 50 years, you know, but like, if you're 30 and you do that same thing, maybe because like, we need to know that there's serious penalties for doing it.

It's funny, I take the other side. I think that younger and younger people are more and more capable. I'm, I'm not. I agree that your brain isn't developed, but I actually think that part of the problem is that we, I think that we should, I think we should create more accountability for younger people. They're going to start to entering the workforce sooner. I mean, Alex Karp and Sam Altman have already done away with the college requirement for employment because they're like, young people are super capable. I mean, thanks to AI largely and moreover the tools that they have. I mean, it used to be, you know, when, when you and I were coming up, we didn't have a whole lot of tools to learn how to cope to, to learn how to program. So like the, the amount of harm we could do is pretty limited. I mean, smash mailboxes, you know, like.

Yeah.

You know, I throw the local golf flags into the, into the nearby lake. That's, that's okay. When people start, you know, doing things like chaining the other models to, to fish and then, and then scam and doing it at scale.

I mean, yeah, there's a, there's an argument for that. Yeah.

Scattered spider. Scattered spider. You know. You know that group that. The crypto. The crypto theft ring run by like four 19 year olds in Canada and the US got away, stole whatever $200 million. I don't know, man. You're gonna have, you're gonna have to convince me those are, those are not adults doing adult things.

Yeah, no, I mean, look, I. You're exactly right. No, no, no, I'm, I'm. That was not a incredibly strong belief that like was held really tightly. That was just an. In general watching how stupid and undeveloped kids are. Like just, I'm around a bunch of 8 year olds and stuff. Dude, I'M like, your brains are. You are not capable of decision making. I forgot that.

Like. Yeah, it's just. It's. It's. It's a long day as a parent, sort of reminding yourself they're out of school. Yeah.

Right now. So we homeschooled for the first several years. They're 8, 6, and 3. We homeschooled, and then we moved into. We lived on a farm, and we live, like, out in the middle of nowhere. We homeschooled, and then we moved into this neighborhood about three months ago. And their friends, the neighborhood kids, go to school. So they approached us three months ago, and they're like, hey, can we go to school? And I said, well, I got to go meet the teachers first. Cuz at their grade, they're with one person.

I got to know their biases.

Yeah, I did. And so I walked in. I walked in. They're giving us the tour. And the teacher goes, do you have any concerns or questions? And ar. My daughter goes. She goes, my daddy wants to make sure you guys aren't woke. And they laugh so hard.

But where are you? Are you like.

Yeah, Nashville.

Yeah. They probably. They had a good chuckle.

They had a good chuckle. Yeah, they. And so we. We actually sat down and had a conversation with the two teachers. One for my son, one for my daughter, and we talked about their values. And obviously we didn't see eye to eye on everything, but it was 80% there.

Yeah.

You know, And I was like, okay, I'm comfortable with. With my child spending seven hours a day with you.

That's great. How's it going?

They love it. My. My son, the younger one, my son's like, he likes it, but if we wanted to go to Disney World, he'd be like, we're going to Disney World. My daughter is like, I don't even want to go to Disney World. I just want to go to school. She is incredibly smart.

Wow.

And she's really taken to it.

Well, so that's awesome, Joel. And sorry you're there. It's a. What are their ages?

My daughter's 8. My son's 6, and my third, he's 3. So he's not in school right now. Yeah. What about you?

My daughter said to be born in 18 days.

Okay, dude, you're in it right now. You're on high alert.

But yeah, so. So baby coming and all of this is, like, super. I mean, it's so top of mind. And actually, the book. I wrote a book coming out January 13th and basically wrote it with my daughter. I started the process. We weren't yet pregnant. And then sort of as. As the book got closer and closer to the manuscript deadline, my, my. You know, the pregnancy became more and more real. And then kind of. It sounds like you just started thinking about everything from the lens of like, what is this experience going to mean for my child? Whatever it was. And so I now sort of find myself advocating for lots of things that I. I think I'd like, believed, but. Or known. I should say known.

But now I believe I cared about nothing. Before kids. I would tell people I'm apolitical. I'm a technologist. I create things. I spend all of creating products and businesses. I didn't get involved in any of the social stuff at all. And then I had kids. And then I. It just became unavoidable. Yeah, it's like. And you could kind of see how decisions and stuff between different parents and like, how their kids start to develop. And it's like, whoa, we're all raising our own little large language models. It's crazy. And we're all programming them. It's wild.

That point. On that point, I will say something that I think is unavoidable and hard for me to consider, but it's something I've thought about a lot, which is, you know, you and I are sort of this generation where. The front. The last generation where we didn't. We sort of grew up sort of with the Internet, but not. Not high speed.

Right.

So. So we. Right. What we learned, we were savored. I remember going on the, like, dialing into the 56k modem and going to a web page and being like, wow. I just read Ike, I am the. Someone asked because I am the smartest man alive, right? Grandma just sent me an email. I just read a webpage about, like, you know, who knows what. I just spend an hour on Ebaum's World. Ask me anything. And what I've realized in talking to young people is so much of what I attribute to the sort of. The angst in the younger generation today is, of course, one device addiction. I just think the screen is destroying lives in ways we don't yet appreciate or do, but haven't really priced in. But I actually think the other issue is we just know too much. It's like the large language models. We've. We've had these very powerful neural nets and they've developed quite a bit for. For a while. But all of a sudden, we spent our entire lineage sort of knowing nothing, you know, very knowing very little. Right. And then. And then within, you know, in a matter of 30 years, we said to every child on Earth, watch this screen for eight hours. We'll watch everything. All the horrors of the world and everything else, and then tell us how you feel. And by the way, you know, why are you complaining? Go enjoy your day. And the consequences of this are remarkable. Which is like, the model itself hasn't updated, but the rag. The amount of retrieval information has. And so you don't actually have a system that is effective at parsing any other than a prior model. It is. You know, it's the equivalent of a GPT, but it's now being asked to make sense of so much more information. And so I just sort of attribute so much of the current state of angst to just the fact that people know too much. They could be staring at all of the problems in the world, despite the fact that things constantly get better everywhere, all the time. I mean, some significantly. Yeah, we. We are filled with so much dread because we. We never figured out, you know, how to. How to retrieve the right information. And so we're filled with selection bias, confirmation bias, negativity bias, and my favorite rosy retrospection, which keeps telling us that the past is better than it actually was. And I'm like, it's not. The. The problem here is that the kids know too much. Like, they just know too much. And we should start acknowledging that we. We. We overexposed them to the horrors of the world and then ask them why they were having a bad day.

And it's true.

That's. That's. That is something I think about all the time, and I do not know how to escape that. I, like, I don't know. I don't know what I know. The world my daughter will come into is full of wonder. And I think we'll be on a better path than the one we're on today. And I also have to wonder, you know, how she's gonna. How we're gonna manage the neural net that. That is gonna be overexposed to just bad stuff.

So what I've seen over the past eight years of being a parent and doing this show and all of this, and for me, it just forces you to become a more involved, better parent. So I think the kids that have absentee parents when we were a child, they could get into some trouble and stuff, but not like real addictive trouble, you know, it's like. It's really hard. It's so accessible. It's so like. It's a Swiss army knife the screen can do everything. It can be the alcohol that you shouldn't get into, but it can also be the first aid kit that you need. It can, it can be so many things and it's so accessible and so cheap and it's all through one device. So that means if you, you are wild and free and you don't have, you know, parent involved parents as a kid, you can really go far down the wrong path. And for us raising the kids, we've just been, you know, making up our own rules as we go with, with watching their stuff. We've got the, they're all on Apple devices and we all have very, we're very involved with the restriction systems on the Apple devices and we, for, you know, they have limits to their different categories of applications. Like they can do their educational apps more than they can play some games.

Seems really smart. I mean the rules based systems clearly are, are tough. What when people ask how are you? You know, there's always the gotcha. What are you, how are you going to raise your, you know, are you going to give your daughter social media? And I'm like, look, who knows what the device is even going to look like by the time she arrives at that age. I said all I can hope is that she loves the outdoors. The only thing like I really, really want to over invest in the alternatives to a digital reality and I'm sure that the technology is going to carry her far. I'm just desperate to create a world where she is excited to be with friends and family in, in, in the outdoor spaces.

I can give you that answer right now. Tell me they doesn't matter what you tell them, they're just going to do what you do and they're going to love it. They'll just, they just want to. You're the hero and they want to do that. So if you're doing bad things, they're going to copy you and do bad things. No matter how much you tell them to do good things. If you do good things, they're going to do good things even if you told them to do bad things.

Good, good thing. Adley's my wife then.

Yeah. Yeah. So they're, they're gonna do. Is she, she into outdoors?

Exceptionally. Yeah. She, she's. I married, I married my. These are my spirit animals. As I say to people. There we go. You're talking about a woman whose phone can die and she won't notice for like a day.

That's great.

Loves camping, first grade Waldorf teacher and, and elite athlete who Is, you know, her idea of, of a late term pregnancy activity is to play volleyball for a few hours. So there you go. You know, it's. Yeah, it's, it's. It's all I could, all I could have ever dreamed of and I just didn't even. I had no idea how important it would be. I knew it was important and then AI started to catch the pace. It did. And now it's so unclear to me that like truly the future currencies are physical spaces. I mean there's just going to be so much digital abundance and we just all we have to impart on our children a love for community and outdoors.

Yeah. Well, we in Covid about three or four years ago, we sold everything, we bought an RV and we did a year long camping trip around the country.

Wow.

So the kids got outdoors, they were playing in the national parks every day. It was, that's why we homeschooled originally was so we could do the RV thing.

Favorite. This is I guess so reductive. But favorite. Favorite moment or, or, or a special moment that you capture from that trip.

Yeah, the most special moment is we had to, we were on our way to Nebraska and we had to stop in Tennessee and it was like close to summertime or at summertime. And we were born and raised in Florida, so that's where we were coming from, which is hot every day and muggy every day. And so we get to Tennessee and it's summer and it's cool in the evening. And I said, I want to live here. We then proceeded to continue to travel for nine, 10 months and we ended up buying and then within an hour of that place that we stopped in Tennessee.

Yeah. That's all that is a special moment.

That was a special. Yeah, it was just a one night stop while we were driving through and we just went on this walk and the people at the campsite next to us were cool and it just had really good vibes, for lack of a better term. And we're like cosmic.

Cosmic intervention.

Yeah. 100. Hey, side question, just for fun, what's the first piece of content you've ever put on the Internet?

Well, I mean in a public domain or private domain? Because private domain obviously emails, like early AOL email or aim.

The first public post that you could, you could track back.

I assume that it is the first Facebook post I ever did and I think it was, if I recall, it was a photo in senior high school. I took a selfie on a, on a traditional, you know, camera, digital camera of me and four friends in a Lufthansa. Remember those three, four, three. Well, I think they still exist. I don't go to the back of the plane much anymore, but those three, four. Three crazy economy cabins in Lufthansa where they just jam people in. I took a digital camera selfie. The whole row was my high school friends. Oh, it's nice. I love this photo. And then I posted on Facebook and I think I'm fairly certain the first photo I ever posted. I think I'd still be there. I haven't logged into Facebook in quite some time.

That is awesome. For me, it was 1996 and I had posted. I was trying to. To. I don't know if it was cheating or just being smart about my homework, but I had a report due on sea slugs and a guy had a blog on sea slugs. And I posted a message to him asking him essentially the questions that were in my worksheet about sea slugs. And he responded two years later. But that's like you could see Joel Beasley, you know. So I looked it up in the Wayback Machine and took a screenshot of it a few years ago. Cause someone was asking me about it and that's it.

I. I was pretty active on the Tom Clancy Rainbow Six.

Okay.

Gamer board in eighth grade. 2000 or net 2000 yet. Yeah, 2000 and 2001. I was pretty active on the Tom Clancy Rainbow Six message boards. I don't know why. I love that game.

Three was the best.

I loved that game. Yeah. I just remember telling everyone that I was going to play Tom Clancy and that everyone needed to like unplug the phones. I was like, listen, if the phone rings or if you pick up the phone, it's going to ruin this whole thing. So just let. I'm going. I'm dialing into the modem. I do remember. Yeah, I remember all that.

I want to talk. I know we only have about 15 minutes or so left, but I wanted to talk about the societal thresholds. I think you've written some white papers on this. Can you give me the brief overview

of that societal threshold? Yeah, we've written a few white papers now that have gained some momentum. Unmetered intelligence is sort of like the principal one. And that's. We predicate the book on this idea of, you know, if intelligence becomes a resource. What if we treat intelligence like a resource that is just declining now precipitously in cost and therefore increasing in abundance. And one of the questions that is born from unmetered intelligence is this idea of technological and societal thresholds. Technological threshold asks the question, what can a machine do? And a societal threshold asks the question, what do we want a machine to do? What are we willing to let a machine do? And societal thresholds and technological thresholds have always coexisted and often with a space, what we call the adoption gap between them. And sometimes that space has felt very pronounced, but basically never more so than it does today. And the reasons for this are sort of obvious. But before I go into it, I'll tell a story that I think explains societal thresholds because it makes it very real for most people. I was trying to figure out what is the best way to explain societal thresholds. And we studied a bunch of stuff and there are tons of really interesting examples. Nuclear power, GMOs, chemicals like DDT. Right. Things that we adopted and then actually rolled back over time. The elevator is my favorite. And the elevator story goes like this. Elijah Otis discovers a better way to move people up and down tall buildings. Previous to this, we'd been using rope and pulley systems in mine shafts and tall buildings. And in 1854, he introduces what he calls the people mover, a safer way to move people up and down these buildings. And it comes with basically a fail safe that catches the pulley if it breaks. So it is, it's a second parachute. And it's empirically a much much. It's just massive improvement that actually exists today in one form or another. This thing that if the mechanism breaks, it actually catches so elevators don't plummet. And he starts selling a bunch of these people movers. At the time, it's not called an elevator. And the people mover captures a bunch of attention from building owners and they buy a bunch, but. But a few. Within a few years, 1856, Elijah starts getting all these phone calls and it's, it's the building owners. They're saying, hey, no one's riding the elevator. And why not? People are afraid they're going to die still. So Elijah's met the technological threshold. He has built a machine that empirically is safe and effective at moving people up and down. But he hasn't met the societal threshold. He hasn't convinced people that they should use it. So he goes back to the drawing board and he comes up with three ways he thinks he can convince people to ride the elevator. The first is he puts a mirror in the back of every elevator so when you walk in, you'd be distracted by your own image. The second is he puts music in every elevator. So when you walk in, you'll be entertained. And the third is he puts an operator in the elevator, a physical person in the elevator, to give you a sense of human agency and control. And of course it works. It moves the societal threshold. People gradually start to adopt the machines and then the rest is history. We don't think twice. I actually, I like the story in particular because I do think twice. I am terrified of crowded elevators. That's a separate psychological problem. But it is this, you know, it's this funny reminder that like, just because something works doesn't mean that we won't want to use it. And when we, when I tell people the story, they laugh, you know, they're willing to chuckle at it. And of course, then I asked them, I said, well, okay, what do you think?

Our elevator is Tesla self driving right now? I want them to release it. I want them to. I know they can. It's great.

You nailed it. I mean, actually, I argue that of all the things that have been said about autonomous vehicles, not enough has been said because we're, we're fixated on all the wrong things. We're talking about how well it works or how will we make the roads, you know, forward compatible. What will it do to the economy? And I'm like, no, no, no, you're missing the whole point, which is that right now, globally, but pronounced in the developed world, autonomous vehicles, Joel poll at 25% approval, they are exceptionally unpopular. And actually it appears that they used to be more popular. Now, some of this is an Elon thing. Some of this is like this political flashpoint and, and it's, it's just like this bad confluence of events. But actually, in studying it, we, we sort of unearth some other very, very interesting truths that I think tell the story about AI societal thresholds. How and why will we adopt AI systems? And the first is, is that humans. So the first reason that AI societal thresholds move slowly as told by the autonomous vehicle, is that humans love control. We love control. And we, we understood this really well, having done a bunch of different things, but we went to Disneyland and we studied the ride. Autopia. Have you taken the kids to Disneyland, by the way?

Yeah, we have.

Okay, Disneyland in Anaheim.

No, Disney World.

Okay, so Disneyland and Anaheim features a ride that was built in 1954 called Autopia. It is one of the seven original rides at Disneyland. It is a cement track on which children age 3 through 8 ride around in gas powered go karts about 2 miles an hour.

Okay, I know what you're talking about

they operate on a track literally like this wide, 6 inches wide, that gives them this much wiggle room so you don't have to turn the wheel. And Disney kind of hates this ride. And in fact, they're finally updating it. But the reason it took them so long, despite the fact that the ride is very smelly, it. It actually emits a lot. So enough fumes that. That people working the ride have to change shifts regularly because it gives them headaches. It sounds bad, it looks bad. It's a cement track. It has no Disney affiliation. And the problem with this ride, as far as your, as far as Disney is concerned, is that since 1954, its inception, it has been the most popular ride for children age 3 3. And when you learn this, you appreciate the most remarkable truth, that more so than the Matterhorn, more so than Space Mountain, more so than the Teacups, kids want to drive a car. And it is this reminder that even a child who has no appreciation for what a vehicle is or what it, you know, what it offers them knows that controlling a machine is fun. And relinquishing that control is very hard for us. And this is just one of these incredible reasons that we are going to struggle, even if you hate the monotony of your commute, to actually give up the steering wheel or even if you appreciate how dangerous it might be. Second reason, technological thresholds are moving too quickly. So when we started doing the study, autonomous vehicles were actually not that good. Empirically, human drivers were seem to be safer. And in the sort of three years, in the time that we released the book, something amazing happened, which is that autonomous vehicles got really good. I mean, empirically better than human drivers. And we expected it to move the poles. Guess how much it moved the poles?

Little to none.

Not at all. In fact, it appears that autonomous vehicles were more popular three years ago. Yeah, and. And 25% of people now say that they are open to it. 50% say they are disinterested. And an incredible percentage say they would never. They would advocate aggressively against it. And this, to me, I used to think, oh, humans don't risk adjust. Well, that may be true, but actually something else is happening here, which is most people just have no idea how good autonomous vehicles are.

That's what it is. So for me, I went down in December, my wife came out and I was like out in the garage. And she goes, hey, did you hear that full self driving's out now? Because I'd always talked about getting a Tesla or trying one, because some of my Business partners had them, and I thought they were cool. And I was like, for real? She's like, yeah. She's like, there's one in Franklin, which was like two hours from us. It's where we live now. She's like, do you want to go look at it? And I said, yeah, let's just go experience. Because, you know, I'm modern CTL guy, right? I want to. I want to sit in the car and let it fully self drive me from A to B. That would be very cool and nerdy. So I did, and I walked out of the dealership with. With one that day because it was just. It was such a magical experience. Oh, y. Just a white model Y. And it was, you know, 500 bucks a month lease. And I was like, yeah, I'll take it just to, you know, exper. It's my favorite car. Like, I thought I would just have it. It would be, you know, one of the cars.

And it.

It's become my favorite car, hands down. I wish I could take it everywhere, you know.

So the. What you're describing is this reminder that most people actually have no idea how good autonomous driving is because it's happened so fast. Outside of Pittsburgh and Phoenix, San Francisco, Los Angeles, just very few people have ever been exposed to these things. And I experienced this in a very real way, sort of orthogonally. Recently, I was at a medical conference espousing the promises of radiology pathology in computer vision, talking about how we had made so much progress and it was getting so exciting. And when I was done at this talking about this, this doctor stood up, raised his hand, stood up in a huge room and said, zach, I'm sorry to embarrass you is how he started the question that, I'm sorry to embarrass you, but you're wrong. And actually, he took the time. He spent like a couple minutes sort of lambasting the technologist who had been talking about all the things that AI was going to do. And none of us appreciated what the actual plight of the patient experience or the doctor, and we should stop promising these panaceas. And he sat down, never really having asked a question. And I had the sort of gall to respond and say, sir, I'm sorry to embarrass you, but you're wrong. The study that you have cited is actually a year old. And as of two months ago, radiology pathology is basically solved in all scans except the cardiovascular chest scan, which is. Which is hard for reasons we can't yet figure out. But limbs, abdomen, brain, we are the GBT4O is outperforming the 95% of radiologists. And what I said was, I don't blame you for not knowing. It is all happening too fast. And if a radiologist who, you know, has to, who's clearly at a conference so he wants to learn more, who has to take regular boards, who has to stay up to date, doesn't know that what a technology is capable of and how it is impacting his profession, what chance does the average person have to know? And the speed at which the technological threshold is moving is going to serve as this sort of natural but very interesting rate limiter to the societal threshold. Now, I'll start by saying, Joel, before I tell you the third reason this one is okay, because I do think it gives us some appropriate pause. Like, it's not terrible to me that we don't immediately adopt everything. There are plenty of societal thresholds, I argue, Joel, that should never move. And we can have that conversation another time. But the third reason that societal thresholds move slowly as told by the autonomous vehicle, because of, you know, in the case of AI, is one that you can never unsee once you see it, and is sort of, you know, funny. But humans have a remarkable tolerance for human failure, and we have no tolerance for machine failure.

And that's why I did this interview. When I read that line, I was like, we're doing this interview.

Nice. It is this incredible thing that you cannot unsee. And it's why 1.3 million people can die on the global roadways every year, 95% by human error. And if a Waymo swerves into the wrong lane, people go, how dare they, how dare they endanger us?

Shut it down lines for 72 hours.

Yeah, shut it, shut it all down. Like drag, drag them in front of Congress. Let's get to the bottom of this. These, these, these dystopian technocrats. Now, I will say there are some features to this very strange anomaly. Well, this, this very strange bias there. I am using it. One of the features is that like, our, our expectations for machines is so high that, like, things like flight work exceptionally well when it could be really, you know, questionable. We are like, listen, we're going to inspect that plane every, every three months. And if there's any issue, it's coming out of the sky, you're going to get fined. Buildings and don't fall over. For the same reason. We have exceptional code around the machines that we build that, that run our lives. And to the such an extent that like any Manufacturer of modern goods worth its weight offers like three year warranties. Like people like, oh, they don't make things like they used to. I'm like, yeah, they still make things pretty good, right? I mean, like, there are a lot of laws that, that protect you from lemons and things that break that. In a world where like they're manufacturing like a million of these, 2 million of these, and they still work really well, that's amazing to me. But it is fascinating to me that we are so willing to watch ourselves do incredible damage to each other as long as it's an accident and human error. And the marginal likelihood of a machine that could save, in our case, again, 1.3 million lives is the marginal chance of failure is just too great for, for us. And I think there are two reasons for this that we talk about in the book. One is humans have this association with mechanical failure as systemic failure, which is interesting. We believe that if one machine can fail, all machines will fail in the same way. And of course, the opposite is true. If we can observe a machine fail, we can stop all machines from failing in the same way. And humans, as you well know, do not possess this feature, right? Humans fail all day, every day, in the same way, over and over and over, collectively and individually. And the other reason is that we have no empathy for machines. And I think this is fascinating, and I don't hate this one either, but we really do have empathy for each other, right? Watching someone make a mistake, we always, you know, we'll often imagine empathetic people, but, but many people will say, I, it could have happened to anyone. I, I understand. You know, it's like, that's tough, right? That's hard. Someone, you know, truck driver falls asleep at the wheel and people are like, God, we just asked too much of these truck drivers, right? We rightfully have none for machines. We expect precision from machines and we expect directionality from each other. And honestly, I think it creates a more human world. I prefer this to the alternative. But I also acknowledge that this is going to be one of these cases where so much good is going to be locked up behind a societal threshold because there is a marginal rate of, of failure that is just intolerable. And early adoption will happen on an individual basis, but it will take a long time to happen on a societal.

I agree. That's amazing. I like the rate limiter part. It's. I always think about the human's ability to adopt things in our. How difficult it is for us to change behaviors is sort of like a Safeguard for rapid advancement. Because right now we're having breakthroughs. You know, let's say they were happening on a five year basis. I feel like right now they're happening on a quarterly basis. You know, like it's been speeding up in my professional career and there's no reason for me to think that that wouldn't continue to stay in motion. And then in the next couple years we're having breakthroughs on a daily basis that are just. I can't even keep up with them. It's my full time job. I get paid to do this, to try to keep up with this stuff and I have a team. Team. I get paid to do it and I can't keep and my mind is still blown on a daily basis. It's wild.

Yeah. But I, I really do think that our, I think societal thresholds will serve a critical role going forward. They will, they will create an artificial rate limiter and it will give us all a chance to sort of update to society in a more natural way. But it is going to create some head scratching moments where we go, God, we would be a lot better off if we just, you know, if we allowed this or did this and gonna be, it's gonna, it's, you know, the question can a machine do something? Is just not going to be that important in, in 10 years. Right. The question of do we want a machine, do we let a machine do that thing is, is going to be way more interesting.

Yeah. Well, luckily we do have a biological limit in generations like iPhones. Right. Or generations of humans will be more prepared to, you know, handle this. But we are like, I'm in my late 30s right now. We are in the position where we're having an impact on this stuff for the next generations below us. So we have to at least think deeply about these things. Do the best we can.

Well, we're scratching the surface today and I do think social media, like others, serves as a reminder that not all societal thresholds need to, to stay crossed. Right. Like I think there are times where we can say oh whoops, we, let's dial like, let's dial this back. Let's like we, we get to, we get to live and learn and we can acknowledge that we, that we are still capable of making mistakes and the humility to, to, to, to admit that is going to allow us to actually, you know, undo a lot of wrong.

Let's get some rapid fire stuff here as we start to wrap up. Are you cool with that fire? Okay, you've got a book coming out in January of 2026. Tell me about the book.

Well, we talked about some of it today. It's, it's the answer to my attempts to help people make sense of the future. And I do. So I think the world lacks a lot of nuance, especially when it comes to AI. And it, it's, it's, it's the best attempt I have to give people the good, the bad and the, and the ugly from, from honestly a biased lens of hope I have. I was born with a, with an optimistic disposition. I studied history and found a lot of defense for it and computer science. And when you study those things, you learn that the world gets better all the time and then you learn why. And the book is my attempt to help people imagine a future that is better, acknowledging all the things that can and may break.

And is it available for order today?

It's available for pre order today. Thanks. You're leading the witness quite well. You can, you can host the link I'm sure on, on your site. It's on, it's on Amazon. It's also on my website so people can search.

Great. It's great. Renaissance.

Is that the next renaissance? AI and expansion of human potential. And I checked, it's doing pretty well on SEO and ChatGPT. So depending on your search preference, it will show up most places.

Yeah. And also guys, I'd like to give a shout out to Zach because you know, when doing this interview, when prepping for it, I got to watch some of the videos and stuff on your website and of you speaking and doing these conferences and this consulting stuff. I mean co. You got like a five star review from Coca Cola for like consulting and helping them figure out the future with AI. So I believe you do that. Right? You do consulting and you do speaking as well.

Yeah, we have a, I have a advisory practice and a speaking practice. We have a content obviously we have the book coming out and then, and then I have a company to be announced. We. I'm going back to operating so I just finished a fundraiser for a company to be announced soon enough that I'm really excited about that that aims to, aims to bring AI to everyone.

Oh, that's amazing. Now I'm an interviewer So I have 70 questions but I'll.

We can, we can do a follow up and it's, it brings, it brings to bear a lot of my experiences and is trying to help the Fortune 5000. A lot of companies that I worry about getting left behind. There's a concentration of resources happening right now and we're going to try to help everyone else catch up.

That's interesting. Maybe we talk after the show. I've been having conversations the past two weeks about how do we help companies in the 5 to 15 million revenue range adopt this stuff with a couple different people. So January 2026, the book comes out, and let's see. I also have. Let's do one. Let's do one of these questions for advice for tech leaders. What should a CTO prioritize when integrating AI into their organization?

It. Obviously, it depends on, you know, B2B, B2C. Are they building internally? Are they building externally? I try to remind companies, forward compatible infrastructure. And forward compatible infrastructure now is not just technological. There's organizational, there's cultural, building a company that is. That is amenable to change. The other thing I try to remind people is this idea of anchoring on your vision and mission and values. I think it's easy to be a candle in the wind when everything's changing. It's very easy for a CTO to pick up a new object just to satisfy the board with a shiny object. And I think it's critical now more than ever to say, listen, this is what we're trying to solve. These are the things that we alone can build, and these are the things that we must purchase. And acknowledging that that list may change, but our pursuit, our. Our panacea, our utopia will not. And if you're amenable to the ways and means, if you're very open to how you accomplish something, which I, you know, I've discovered a lot of CTOs are sort of. Sort of religious in their practices, actually, and be religious in your. In your vision. Be religious in. In your. In your. In your mission and purpose, and be very amenable to how you accomplish it, you will have a very good time.

Yeah, I love that. No, this is good. I. I like the way you think.

It's.

Zach Cass. Z A K K A S S dot com.

Is that C A C K-A dash S dot com.

All right, we got it. And we'll put links in the notes below. Thank you so much for doing this. We made a podcast. How do you feel?

Great, Joel. I had a ton of fun. And as you said, are we. Did we just become best friends?

I think so, yeah.

There you go. In, in. Insert, insert, insert. Step brothers.

Thank you so much for listening. And if you found this episode useful, please share it with a friend or colleague who you think would get value from it. And if you have topics that you would like to hear discussed on the podcast. Either add me on LinkedIn or send me an email. Joeloderncto IO Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.