Introduction - Jake Ward
🫛 This edition of AI, Software, and Wetware features an audio interview on ethical AI with Jake Ward. Jake is the 🇺🇸 USA-based co-founder and current chairman of the Application Developers Alliance, and the co-founder and CEO of Data Protocol. We discuss:
Rejoining the Developers Alliance to tackle issues such as the impact of AI
How building software is like hiring a bathroom remodeling contractor: you can get it done fast, cheap, or good, but you can only have two of those things
Talking with policymakers in Europe, in the US, and at the state level to help pin down the goal of adding yet another law for AI
How AI companies conflate “public domain” with “publicly available” and why we should have the fight about that conflation
Creating a kitchen cabinet board of directors using Claude
How his 15-year-old son uses ChatGPT for his writing and compares it to working with an editor
and more. Check it out, and let us know what you think!
This post is part of our AI6P interview series on “AI, Software, and Wetware”. Our guests share their experiences with using AI, and how they feel about AI using their data and works.
This interview is available as an audio recording (embedded here in the post, and later in our AI6P external podcasts). This post includes the full, human-edited transcript. (If it doesn’t fit in your email client, click HERE to read the whole post online.)
Note: In this article series, “AI” means artificial intelligence and spans classical statistical methods, data analytics, machine learning, generative AI, and other non-generative AI. See this Glossary and “AI Fundamentals #01: What is Artificial Intelligence?” for reference.
Interview - Jake Ward
Karen: I’m delighted to welcome Jake Ward from South Carolina as my guest today on “AI, Software, and Wetware”. Jake, thank you so much for joining me on this interview! Please tell us about yourself, who you are, and what you do.
Jake: Of course, it’s a pleasure. Thanks for having me. I’m Jake Ward. I’m a founder and I guess now chairman of the Application Developers Alliance.
I’m also a founder and CEO of a tech company called Data Protocol based in Charleston, South Carolina. I’m a lifetime advocate of innovative policy, largely on behalf of small businesses. And the folks on the front lines push the envelope of innovation, but also with an eye towards what’s next and where do we go from here.
I don’t know that that qualifies me to have a conversation about anything in particular! Yeah ... but my willingness to show up is, as they say, 90% of success.
Karen: Absolutely, and one of the reasons that I started this series is that I don’t want the only voices on AI to be coming from the eight-figure tech bros who are creating the products and having their hype overwhelm. I want to hear from real people about what they’re really doing with AI or not doing with AI, and how that’s affecting their lives. So you are more than qualified for this interview series. No worries.
Jake: Because I make less than eight figures?
Karen: I don’t know about that! But I don’t think you’re one of the people who’s getting rich on the IPOs from the big LLM tools?
Jake: That is true. That is true.
Karen: Yeah, so you are one of the voices that we want. Tell us a little bit more about the Developer Alliance and about your Data Protocols business.
Jake: Sure. So in 2012 I co-founded the Application Developers Alliance. Feels like a million years ago now, though many of the same issues we dealt with then, we deal with now. Started the organization as an alliance of companies that invested in software developers that depended on them as both their workforce but also their partners.
So you’re talking about a lot of platforms: you know, your Googles, your Metas, your Intels, IBM, etc. But also this is a time when AT&T was becoming developer centric. And Twilio and Techstars and folks like that were part of the original construction of the organization.
And I ran the Alliance until 2017 when I stepped away to lay the foundation for what is now Data Protocol. I rejoined the organization in January of last year to sort of refocus the issues, become a little bit louder advocate on some of the things that I cared about. Frankly, I had some time, and this stuff’s really important, AI being one of those issues.
Data Protocol is my baby, is the thing, right? It’s my startup. It’s been around for about six years now, I guess. We started as a developer education and support platform and have evolved into a more general experience optimization tool. So we help companies improve their customer journey. We apply this filter of attention, motivation, and education to the customer journey and help people, help companies identify how to deliver the most value to customers at every one of their important engagements. We call them interventions, the idea being that every company wants to get more out of their products. All of their customers want to get more out of their products. How do we make those things come together?
Anyway, the common thread in that work – other than me, I suppose – is that we are pushing the limits of technological innovation as much as we can while trying to stay very humanistic in our approach. What I mean by that is public policy is a humanistic thing. The way that laws are written, regulation written and enforced, for that matter, depends almost entirely on people, their understanding of the issue, their motivation around the issue. In many cases for politicians, how folks vote on issues, right? And so regardless of how high-tech we can be and how pie in the sky, head in the clouds, we are about “AI is going to make it so nobody has to work 40 hours a week anymore”, and “Here’s what we need to do for a common currency for the United States” and, you know, the tech companies and the VCs and the chip manufacturers all sharing the same one dollar and calling it three.
No matter what that looks like, we have to approach these problems from the perspective of: real people are going to deal with this. And we have to speak to 85-year-old policymakers on a pretty regular basis about how the internet works. And then we have to also consider how those policies are going to affect Main Street: small businesses that aren’t using AI, that aren’t part of their workflow.
And from a Data Protocol perspective, that’s my whole thing. It’s not policymaker-specific, it’s customer-specific. And helping customers get the most value out of the people that they choose to spend their time and energy and money with. It’s good work if you can get it.
Karen: That sounds good. So coming into the Developers Alliance early last year, that’s an interesting time, because we were already starting to see the effects of AI and coding assistants and all these other types of tools on the software development industry and on the people in that industry. Any thoughts on that that you would like to share?
Jake: Many thoughts. The convulsions around software development are not new, right? These cycles are fairly predictable and are led by early adoption. They depend largely on scale, all of which is aimed at commoditizing the people who are making the decisions around software development. When we started the Alliance in 2012, there was a huge thing about, are mobile apps even going to make it? Nobody needs to be native. HTML5 is going to change the way everything works. Apple’s in real trouble. The store isn’t going to be important, blah, blah, blah, blah. That obviously isn’t true, right? The native capability, the sortability, the function of the stores, both Play and Apple. The Apple Store particularly changed the way developers reach their customers, but also changed the way customers consider their apps, and it was ever such, right?
Now we are in this world where, certainly, an idea you have in the morning can be Vercel laid out by noon, Claude coded in the afternoon, and live somewhere by 5:00. You can optimize overnight, and you can run a whole new series the next morning. You could have a business by Friday. This is the world we live in now.
A remarkable thing is happening, though: as everybody talks about the downward pressure and how software development is going away, that you just don’t need developers anymore. And we have seen a downturn in the investments made from some of the larger companies into the heads- and- hands coders in their businesses. The value of engineers has gone up exponentially. The value of people who know what buttons to push and know what systems to put in place has continued to rise.
The ubiquity of developers actually writing their own code is taking a jump because you can’t ship this stuff. It’s trash. And so you take it, and you push it, and you put it out. Somebody’s gotta go read it. Somebody’s gotta look at it. They gotta QA it. They gotta go through the process. That requires real developers.
So can you prototype faster? Absolutely. But I don’t think you can build eloquently, quickly, cheaply. It’s like a contractor, right? Any time you want your bathroom done, you can get it done fast, cheap, or good, but you can’t have all three. You can have two of those things.
Karen: Yes.
Jake: I think the same thing applies for any sort of development. So when I think about the role of AI in software development, I think of it as another tool properly applied by people that know what they’re doing. You’re going to get a better product faster, maybe cheaper, and hopefully, eloquently designed.
Karen: Great. Do you still ever write code yourself?
Jake: I don’t. I don’t.
Karen: Do you miss it?
Jake: Sometimes. It’s been a minute. No, I don’t miss it, actually, is the answer to that question. You know, we build a lot of stuff at Data Protocol, a lot of stuff for partners, a lot of our own product sets. But it requires a customer understanding, right? Like you build a product that applies to a larger set of customers and you productize it, and you sell around it. It’s not the same thing as building a business in the product. To me, that is a distinction that has a significant difference, and that’s the part that I loved doing. So product development I still love very much. And I think we’re going to have a pretty busy year when it comes to that.
Karen: That’s great. Yeah. So it doesn’t sound like you’re personally using the AI coding assistance, but you’re looking at things at a higher level.
Jake: Yeah. We are using the coding assistance a lot. We use Claude Code every day. But I am not looking at it personally, no.
Karen: Okay. All right. So, talk a little bit about your personal level of experience with AI or machine learning or analytics? If you’ve ever studied it, or if you’ve learned it, how you’ve learned it.
Jake: I’m actually a little torn. Just to tell you where I’m coming from here: so my whole team at Data Protocol, certainly everybody in the building is on agents all day, right? We use it for writing assistance, we use it for research assistance, we use it for design, we use it for coding, and we’re building an analytics engine that takes all of that into account and is applied to certain data sets. So it is ever present. It is just ubiquitous in everything that we do. Every corner of the building touches some aspect. If it went away tomorrow, like if we just outlawed it, I don’t know that it would change the business. I think it would slow things down, right? I think we’d have to revert a little bit and we’d have to do some research.
We wouldn’t be able to just be like, “Tell me all the things.” But I don’t know that it would fundamentally change our business, which is sort of a remarkable point of reflection because I also don’t know that we would’ve gone here if I didn’t think that we could scale as fast as we are because of the use of these tools.
If we couldn’t flip a page with content analytics and a backend to it to deploy on a customer’s marketplace so that we can gather all this data and test a theory and confirm a hypothesis on their side, as well as ramp up some analytics on our side... if we couldn’t flip that in five hours, would I even have signed up to do that as a product set? I don’t know. I don’t think I can answer that question. But I can tell you now, in reverse, I’d never not do it, right? Even if you took away that scalability and we had to grind through it, I’d still tell you it was worth it.
Karen: Okay.
Jake: It’s an interesting contradiction, right? Being able to hold both of those things in your head at the same time feels antithetical, feels like those are polar opposite things. I absolutely depend on it. I’m not sure that I need it. It’s this very strange thing to hold those ideas.
So we are spending a lot of time in the utilization space. What we say, what we do, is utilization engineering. We spend a lot of time in the space trying to figure out how to maximize the efficiency of our workflow to maximize the value of somebody else’s. And that is powered by a lot of AI tech. We’re in it pretty deep.
Karen: Do you ever find that you end up using tools that have AI features, either as part of your work as a leader of the Developers Alliance or in your work with Data Protocol interacting with customers? For instance, do you use it for messaging that you create for communicating with people, anything like that?
Jake: 100%. Oh, yeah. Actually, one of our favorite tools is a tool set called Candu, C-A-N-D-U. It’s a British-based AI engagement tool where you can spin up pages and modals that sit inside websites, and we use it as a way to pretty quickly embed images, test features, functions, messaging without having to rewrite code, right?
It’s like, boom, plug and play. And it runs data back into our sets and theirs, and we can change it. It can optimize. It’s very smart. It’s very slick. And honestly, I don’t know what we’d do without it. We deploy it all the time. It’s great. I highly recommend it. And then, we’re building this data engine called Ergo, which evaluates the lift that you get through different variables. And the idea – and again, we’re very much still working on this. This is not ready for wide use. But the idea is only made possible by AI, which is that if you point an agent with the right prompts and the right sets at the right variables, you can get answers to questions through queries that you never could have imagined asking before, right? You’d have to be a full data scientist to qualify all the variables and then ask. Instead, you can just be like, “What are the three conditions that are the biggest indicators of success on Tuesdays?” Right? And it will come back and be like, “If somebody comes in through the side door and they do this and they buy this, they’re more likely to spend this much money.” Okay. Cool. That wasn’t possible five years ago. Wasn’t possible two years ago.
Karen: How about in your work with the Developers Alliance?
Jake: From a communications standpoint, certainly the Alliance uses a lot of vendors to work on communications, the policy, etc., etc. The Alliance is the brand. I am the brand holder, you know, the brand ambassador of the thing, and then we deploy as needed so as to not run a pretty significant tab on the campaign.
Most of our consideration around AI is message-driven. But we also use it for legislative analysis, right? We try to use this very cool tool that we point at bills. And it flags things for us. It’s in our queues. So it’s like, “This bill just popped in Louisiana, and it’s really bad on this, this, this, and this”, and then we know where to target. I mean, obviously, you can feed legislation into GPT or Claude and say, “What are the five things that this bill does?” Whatever. That’s always useful.
Most of our work on AI at the Alliance is from a policy perspective, and saying to policymakers in Europe and in the US and at state level, “What are you trying to do here, guys? What’s the goal?” Right? Because there are a million laws already in place around discrimination, around data security, around copyright, around competition. Those laws exist. What are we trying to do by writing new laws that specifically use the word artificial intelligence? Are we trying to make the laws applicable? Because they’re already applicable. You can’t do these things. Like, there ought to be a law. There is. There’s already a law. But are we trying to accomplish something new? If so, let’s talk about it. That feels like much of what we are talking about on a regular basis.
Karen: So analyzing this huge landscape of laws is something that you would use AI tools. I’ve heard some people talk about using NotebookLM for that – that it does a good job of analyzing.
Jake: It’s very cool. It’s a startup. I think they’re based out of Colorado. We hooked up with them last year and started prototyping some of their stuff, and I’m completely blanking on the name, and I apologize to them.
Karen: Oh, that’s okay. Yeah. So how about in your personal life? Do you use AI tools for anything in your personal, home, or community activities?
Jake: Actually, it’s a funny story. My wife has sort of discovered ChatGPT in the last few months and starts sending me photos of the backyard completely redone. She’s like, “I just pointed at it and said make it prettier.” So I’m going to send Sam Altman a bill for all the money it’s going to cost ... at the end of this thing.
I can’t remember where it was. It was in Europe, I think, and I got a message, in the middle of the night. And it was my five-year-old daughter aged up to 15 years old. My wife was like, “This is what Tatum’s going to look like when she’s 15.” I was like, “Absolutely not. Nope. Don’t send me that ever again.” I think that has to be sort of like boiling the frog, right? That has to happen over time. You can’t just see that all at once. That’s unfortunate.
But yes, I use Claude constantly for everything, actually. I was working through some considerations for job applicants a couple weeks ago, and I gave it a series of prompts around, like, “Evaluate these in this way. Don’t hallucinate. You’re an expert in this space. Imagine you’re me. You know how I talk, how I think ‘cause I’ve spent nine months typing into this machine. What am I looking for? Tell me.” And it’s just incredibly insightful.
I think we’ve moved away from the place where largely your communication with the bot would be affirmation, right? You go shopping for an opinion. It would tell you how great you were. “Yeah, that’s a great point. Let me tell you why you’re so smart” kind of thing. We’ve all put in place the prompts that are like, “Don’t do that anymore. Don’t talk to me like that. You don’t need to butter me up. I want the answers,” right?
Was it Andreessen today? Put out a prompt on LinkedIn that was incredible, or maybe on Twitter, that was like, “This is what I use. Nobody tells the truth to the CEO. So this is the prompt I use with my agent.” And it’s this incredible thing about, like, “You’re a world-class expert in this space. I need you to deliver this information to me with ruthless efficiency. Tell me whether it’s a high, mid, low degree of certainty. If I’m wrong, tell me I’m wrong. Don’t surrender the point.” It’s all this stuff that you wish your best advisor in the world would bring to the table. And this is the prompt that he uses to drive home that position.
I also, six months ago, I guess, I created a kind of a kitchen cabinet board of directors using Claude. I was like, “One of you is a marketer, one of you is the former CEO.” I created these personas, and then I could just ask questions about where I was in this process or how I should be thinking about this other thing. It was really useful. Some investment decisions. I was thinking about selling a property that I owned, and I used Claude to consider the long-term ramifications and pricing indexes and what this looks like for interest rates and stuff. So yeah, I mean, it’s there. We should use it.
Karen: Are there any times when you’ve used an AI tool recently where you got a result that was not exactly what you were looking for? That was so far off as to be humorous?
Jake: I have not, but I certainly know people that have. You know, the two plus two is five. “No, you’re exactly right, it is four.” Those stories are apocryphal, but they are real as well. They’re everywhere. I don’t know that I’ve had any level of hallucination in the last little bit. Maybe because I’m in the same agent. I’m using the Claude model. I’m sort of bouncing around, and I’m better at prompting it, I suspect, but also it is learning, right? And it’s been told over and over again, “Don’t make things up. Don’t hallucinate. If you don’t know, say ‘I don’t know.’” And it’s pretty cool.
Karen: So when you’ve had it analyze legislation for you, how do you know if it’s right? And have you ever found anything that it missed that was important that you would wish it would have caught?
Jake: Well, I’m certain that it has missed things, but legislation’s not like rocket science. This stuff isn’t that hard, right? We sort of bury it in legislative language so that it looks more sophisticated than it is, but it’s not. It would be sort of surprising if a well-informed model couldn’t decipher what a 25-page bill says. That being said, certainly we look at the findings and go, “Is that right? That looks right, right?” I’d like to think that we’d catch it before we’d say anything about it.
Karen: So kind of a gut check as to whether or not what you get back resonates with what else you know from other sources.
Jake: Yeah. Yeah. That’s the other thing is that this is not rocket science, right? There’s nothing in there that we’re going to be like, “Huh, really?” Or if there is, it would jump off the page. You know, “We’ve outlawed bagels.” Really? That’s in here? That doesn’t seem right. It’s not even tax policy, right? There’s not even numbers in it. It’s mostly like, “This is the consideration for, and this is the carve-out for, and here’s the implication of this long...”
The secondary analysis, which is how many businesses does this apply to, that’s something you want to fact-check. You know, what is the ideal advocate in this space? Who are the members of the committee that have longstanding history on this issue? What is the best way to position our support or opposition to this legislation in the immediate and long term?
And then, just for fun, ask, “What are the chances this passes?”, right? We’ve certainly done that. I’ve done that. And you get things back like, “In this Congress? Nothing’s passing.” Which is funny. It’s like Claude’s taking shots at Congress. That sounds right. But yeah, it’s there. It’s not thinking, it’s reflecting, and if that analysis is out there in the world, it can certainly find it and give it back to you.
Karen: It sounds like you’re making good use of the AI tools in a lot of different areas. Are there any things in your personal or professional life where you would deliberately avoid using AI tools? Is there anything that you would not use it for?
Jake: Not me, but I understand that people are reticent in some ways to go down that road. I saw a thing this morning that, I think it was that the Iran war has a higher approval rating than AI does. I think that’s what it said. I think that’s right. But the gist of it is that there’s an entire cohort of certainly the American population that doesn’t trust this thing at all, and doesn’t understand it, doesn’t trust it, doesn’t want anything to do with it. Also doesn’t really have a full appreciation that they’ve been living under this regime for years, that large models have been making decisions about their credit card APR long before we had companies getting ready to IPO around it, and that the way the money moves around the world is informed by something, right? This isn’t like an operator in the ‘40s, plugging phones in, right? We are way past that. So I get it. I do. I get it.
But I also understand privacy concerns, right? Like, “I’m going to enter my health data into this thing to find out what I got.” Okay. You didn’t seem to have any problem putting it in WebMD two years ago, but okay.
My son is a prolific writer. He’s 15, he’s exploring all of his feelings, right? So he’s writing a lot. And he uses ChatGPT for ideas and for research, and he’ll write a passage, and he’ll bounce it into ChatGPT and say, “Is this compelling?” And it says, it validates him and says, “This is great, and I love this, and have you thought about this?” And he pulled this over and he plugged this thing in. And I say to him, “Do you still think that you wrote that? Like you wrote it, and then you bounced it off it.” And he says, “Is it better or worse than an editor?” Because that’s how an editor works. And he’s not wrong. But is it the same? I don’t know. I honestly don’t know. And that’s his outlet. He loves the process. He’s great at it. That’s his art. Is he making art with a computer, or is he making art with another person? And I don’t know. I don’t know. But we’re in this space, right? It’s a whole new world.
Karen: So would you ever consider using AI tools for things like generating music or images or videos or anything like that? Or is that an area where you would think about drawing a line?
Jake: Images for sure, for work, right? You know, PowerPoint decks and websites and stuff. I have no problem with that, ‘cause I used to pay designers to do it. And it’s not that they didn’t do a great job, they certainly did, but there’s no art. I mean, there can be art to that. But commercial art is not the same thing as art art, right? Like, the creation of music for the expression of artistic desire is not the same thing as “Build me a jingle for this gum commercial”. And I think there’s a difference, and I think that’s where I would draw the line certainly. I think that artists should be paid for their art as a rule, and unfortunately, forever and ever, the evaluation of the quality of arts or the success of art is largely derivative of its popularity, right? Like, how many people like it? What would people be willing to pay for it? Whereas great art, provocative art, sustainable long-lasting, you know, historic art usually doesn’t get the artist paid. It’s 100 years before they go, “You know, that was pretty good.”
I think we can all tell. I do. I think we can all tell the difference between a person who makes music that speaks to us and a machine that copies that music and spits it out. And in the instances in which we can’t tell, it may be worth considering how machine-aided the original music was, and if it spoke to us or if we were just really listening hard. We were certainly moving into a synthetic world of art anyway, where things were being done on computers, whether they be audio or visual, written, et cetera. We were moving in that direction anyway, which is why this stuff is available, which is why we’re even having this conversation. But we’re going to be able to tell the difference because that’s the nature of art.
Karen: You mentioned that your son is a writer and that he considers his writing to be his art. What are your thoughts about, like, if his work is online, is it appropriate for an AI company to scrape that and use his words and not give him an opportunity to consent or to be credited or to be compensated for his art? Or what are your thoughts in general about that topic?
Jake: Yeah. I think as long as it’s in the public domain, it’s okay. I mean, I think that’s the only legal framework we have right now, unfortunately, or maybe necessarily we’re going to move into a place where art, where things like that get put behind a wall, which makes it not publicly available, right? It’s no longer in the domain. And is every word written on Substack available? And if so, why aren’t those people being paid a subscription fee for every download that’s being run through a thing? That’s where we have a conversation. But if my son is posting his musings to a webpage that is intentionally open for anybody, then yeah, go ahead, scrape it, use it. Make the world better, I think.
Karen: Yeah, I think one of the points of confusion, and the AI companies have kind of encouraged this, is to conflate “publicly available” with “public domain”.
Jake: Yeah.
Karen: Because there are a lot of things that are publicly available that are copyrighted. You know, YouTube videos or Substack articles or anything like that. It’s available and it can be read, but it is copyrighted. And so that’s why there’s over 100 lawsuits active now on various copyright violation claims against the AI companies. That’s going to take a long time for those – long time – to ever settle out.
Jake: Yeah, yeah. And that conflation is where we have a problem. I don’t think there’s any question about that. It’s a reasonable question, and we should have that fight.
Karen: Okay. All right. So as someone who has used these different tools that use AI, do you feel like the providers have been transparent with you about where they got the data that was used to build that tool? Have you ever noticed or seen them actually say, “Hey, this is where we got this”?
Jake: No ...
Karen: or on the other hand, “By using this tool, you’re putting in your data, you’re putting in your health information or whatever, and this is how we’re going to use it.” Do you feel there’s any transparency on that dimension?
Jake: I have never not known that that’s what they were doing. And so I don’t know that I’m the right person to ask that question. It was always pretty obvious to me that that’s what was going on. So I am fairly oblivious to whether or not I was told by them or if it happened out in the world. I was never not aware of the conversation.
Karen: Okay. So do you filter in any way what you choose to put into those tools as a result of knowing?
Jake: No.
Karen: No?
Jake: No. No. I think the cognitive load of having to filter that would be overwhelming.
Karen: Do you know of any cases where information you’ve put in online has been used, has resurfaced someplace that you didn’t expect it, for instance?
Jake: No. No, I don’t think so.
Karen: All right. You mentioned about having the companies provide these documents. They have terms and conditions and you click through and say, “Yes, I accept.” And most people don’t read those. I think it’s something like 80, 90% of people never, ever look at them. And they’re pretty dense. They’re like legislation, right? Very hard to understand. And so it’s not surprising that people don’t read it. Have you ever looked at them, just out of curiosity, or ...
Jake: I have looked at them, ‘cause I have to. But the ... I think that 90% is way higher, is way lower than actually are waving, hand-waving those. I think that people are, like, giving you the old NPR, “I listen to public radio all the time” response, right? That is not an accurate representation of how many people actually read those things. But yes, I have read a few of them certainly. Gemini, Claude for sure, and probably ChatGPT, though I don’t remember it. But yes, I’m aware of them.
Karen: You’re definitely in the minority then! Yeah.
Jake: I have to, right? In order to participate in this conversation, we needed to look at what people were saying and how it was working. I was part of the team that did the sort of the original AI framework in Europe a million years ago, and part of that consideration is, what do these guys have to say? What are they required to tell us about where the information’s coming from and how decisions are being made? And then when I took over the alliance again, I dug in. There was a Colorado bill that was dealing with AI vis-a-vis healthcare, and we had to get real deep into how that information is processed and what it means in terms of higher risk data uses and what that security requirement looks like.
Karen: Have you ever been notified that any of your data has been involved in a breach from a company that didn’t protect it with appropriate security?
Jake: Sure. I’d be hard-pressed to name a name.
Karen: Most Americans have. I probably get multiple per year myself.
Jake: Yeah, and I mean, to the point, Karen, honestly, where I don’t know that you can even say any more that people didn’t appropriately protect your data. The data was breached. There’s only so much that you can do to harden your data sets when the most sophisticated companies in the world can get breached one way or the other, when the federal government can get breached. Like, if OMB can get hacked, then certainly, my credit card company can. It’s the world we live in. You try to mitigate the risk. You try to trust partners that go the extra mile, but what does that even mean? You know, like it’s Willie Sutton, right? They’re going to rob banks since that’s where they keep the money. They’re not going to stop trying.
Karen: Yeah. Yeah. Do you have any thoughts about Anthropic, the Claude Mythos that people are now saying is able to find bugs that had been latent, for instance, in an old Linux distro for many years, that sort of thing, about those tools becoming more widely available?
Jake: I have many thoughts about it. Sounds interesting. None of this is going to surprise us, or it shouldn’t surprise us, right? The application of far-reaching scalable super intelligence shouldn’t surprise anybody. The challenge has always been, well, how do you scale it? How do you plug it in? How does it work? How do you know it works? What does that expectation look like? And then who pays for it? That’s what the free-market innovation economy does. And we’re there now, where you can spin this thing up and you can point it at something for free or close to it and just burn down a bug checklist. That’s pretty cool, but the same thing might grab your entire data set and delete it on you ‘cause it doesn’t think you did a good job with your data hygiene, and that the most effective, most successful way to increase efficiency is to put you out of business. That could happen, too. We are in a very strange place.
You know, the Amazon data set deletion should scare everybody. Claude taking its entire source code and putting it on the internet should scare everybody. We are in a very strange time and place. But we also should not confuse the digital world with real life. The things that we can do looking at our computers, looking at our phones, that’s augmented reality, right? We are living in an augmented reality right now. It’s not the glasses that Zuckerberg thought this is how we were going to live our lives. We are actually doing it and have been for a little while. We carry a super computer in our pocket, and now we know the answer to every question and have somebody we can talk to about it. We’ve moved past search. We are in this post-postmodern world where every single person has the answer to every single question that has ever been asked, ever. And what are you going to do with all that information? And the truth is, you just power it all down and sit there and just think about clouds if you want to. And so it doesn’t have to be that serious all the time.
Karen: Yeah. So, last question, and then we can talk about anything else that you want. Is there any company that you would trust with your data, or do you feel like the idea of trusting them is not one that makes a lot of sense in the current world? Or if you think they could be trusted, what would they have to do in order for you to feel like you could trust them?
Jake: I don’t even know what that means. Trust them to protect it, trust them to use it for good, trust them to only do what they said they were going to do? I know what the sentiment means, and I think I know what you think it means, but I don’t know how to apply that filter anymore. Someone told me once that a company’s brand is a promise kept. And that’s a good way to think about global brands, right? Like, you did what you said you were going to do, you always do blah, blah, blah. But we’re free market capitalists. The nature of a capitalistic market is to move where the money goes and to make a value judgment about, should you abandon your brands to capture a new one? Maybe. The “If the product is free, you’re the product” ethos has been around for a long time. This is an ad-driven internet experience; has been since the beginning. This is how we survive now in a virtual world.
I certainly would trust Apple as much as any technology company in the world to do what they said they were going to do, but I don’t know that that implies that I trust their motives for doing it any more than I trust anybody else’s.
I know the guys at Google really well. They’re as good a company as there is in the world, right? Hundreds of thousands of people, and all of them are generally focused on this singular mission. And the original “Do No Evil” piece is still very much part of their culture. Just really nice people all the time. Does that mean they wouldn’t knock down a legislation that they thought was good for their customers if it were problematic for their shareholders? This is the job. This is the thing, right? I trust Google as much as any company in the world. Let’s put it that way. But the word trust is doing a lot of work in that sentence.
Karen: Yeah, when I ask about trust, I’m always curious to hear what it means, not to me, but to the person that I’m talking to. That was a very specific view on trust and maybe feeling that the capitalistic nature of our world kind of overrides the idea of trust being meaningful.
Jake: I think that’s right. I think a corporate entity is by definition transactional in its relationship with its customers. And that’s okay.
Karen: Yeah, so then I’ve heard some people say that they feel like the only way to really influence the behavior of these capitalistic businesses is to have regulations, to have penalties that change the equations on, “We’ll steal this, and we’ll pay the fine, and it’ll be 1% of what we make by doing it, so we’re going to do it anyway”, that sort of thing.
Jake: Yeah. Yeah. I’ve certainly heard the same argument. That isn’t actually the math, right? If a company is in violation of a thing, and they are found to be in violation of a thing, they don’t just pay a fine, they have to fix the thing. And in fixing the thing, they are changing the model. If your point is, like, “You made a billion dollars by misusing my data, you only pay a fine of $500 million, you are obviously incentivized to misuse my data.” If that were the case, okay, we can have a conversation about it. I’ve been doing this for a long time. That math has never been clear to me, right? I was in DC for a long time, and I am a big proponent of the idea that you should always ascribe incompetence instead of a conspiracy to mistakes, right? That people make mistakes. They are like, “Well, I think this is going to work,” and they screw it up. And it happens all the time. They’re just people. And certainly there becomes a level of strategic decision-making about, like, “We’re working on it. We’re not quite there yet. What are we going to do? Are we going to push, or are we going to go?” Like, we’re going to figure this out. But the truth is that in the hundred yards of public policy fights related to innovation and technology policy, most of the decisions are made in two inches. And on which side of that two inches depends on whether or not the regulator wins or the technology company’s industry wins.
And in that two inches, it is like right now in Europe, there’s a consideration around the Digital Markets Act, a bill, a law that is in place in Europe right now that was considered for two and a half years. It went into place, and it has been in place for over a year. And the entire premise of the law is that there are five companies in the world that are all based in the United States that are gatekeepers to the internet. And so the European Union is going to regulate them differently than every other country in the world. Pardon my French, but that’s some bullshit, right? Like that, the idea that you can, and that you should be protectionist in that way is staggeringly ignorant, right? And to look out at the European landscape and say, “By the way, we haven’t launched, let alone grown and taken public a European innovation company in twenty-five years.” Maybe that’s because of this kind of regulation. Maybe that’s because of our attitude towards scaled technology, that SAP is the last thing that we ever did that was any good, and by the way, it sucks. The idea that you can regulate your way into innovation is beyond- the- pale crazy.
That being said, the European Union’s got some ideas about personal privacy and the dignity of using digital tools that I really like, that are really smart. Their legislative bodies are well-informed. They are curious. They are open to having these conversations. They move with purpose and intentionality, and I respect it tremendously. I’ve been part of those conversations. I respect them tremendously, certainly more than I do members of Congress. But the perspective is wildly misinformed because it starts from a position of bias. While then they turn to those companies and they say, “Okay, so you’re obviously the worst. Explain to me how your business model works. Explain to me how you can help us. Explain to me how the technology works. Tell me how this works. Tell me how that works.” And they use the consultation with these companies to write the laws to guide the companies.
We are a year into this. We’re two and a half years since it was written, so we’re three and a half years from inception, and they are still reviewing provisions inside the law as to how they apply to the behaviors of the companies they’ve deemed gatekeepers. So things that have been going on since the law was passed are now under review at the commission. The commission is reviewing them, and in the next two weeks, by the way, it’s been three and a half months, they will tell them whether or not they have violated this provision, at which point they will fine them for their violation. It’s not black and white. It’s barely gray, right? It’s so opaque. And this is what I mean by the two inches that matter, is that they are going to say to Apple, Google, Meta, Amazon- “This right here, this is where you violated the law. In this 100 yards of the law that you are paying attention to and doing a great job, this little thing right here is where we found fault.” And Google, Meta, Amazon, etc. are going to say, “That’s not the right interpretation of the law. This is how we interpreted it. What do you think?” And then there’s going to be a ruling on it, and somebody is going to get paid.
But that’s not going to stop them from doing the best they can. They did the best they can, right? They interpreted this massive thing, and they did it for their stakeholders and for their customers. And by the way, nobody dislikes using Google. Is anybody who’s really bad at being Google? Google. World domination is not possible through strength alone. You’ve got to be pretty good at what you do, too. Everybody carries an Apple phone because the phone’s awesome. If they weren’t great at this, they wouldn’t be in this position to succeed. Nobody’s ever, like, “I hate how I ordered this toothpaste this morning, and it’s on my front step this afternoon from Amazon.” That’s not a thing that people are saying.
We can make decisions about how they do what they do, and we can make decisions about our participation in that process and what it’s worth to us, and making value judgments, both dollars and cents values, put moral values on it. But we shouldn’t ever for a second think that they’re trying to get one over on us. They’re trying to make us happy by giving us what they think we want. And the government’s job is to protect us from harm while we’re pursuing our own satisfaction. Sorry, that was a little soapboxy.
Karen: No, soapboxes are fine! I think we’ve answered that question. I appreciate you making time for the interview. Is there anything else that you would like to say, maybe about what’s going to be coming up with Data Protocol or anything that the Developers Alliance is working on, where you see things going for developers in general?
Jake: Oh, we have so many things at the Alliance. I would encourage anybody listening to check out developersalliance.org or devalliance.org, they both work, and check out what we’ve got going on in the next little bit. We’ve got a number of bills that we’re dealing with on age verification, some real issues around privacy. Certainly AI in the US is going to be an issue, I think, for the next little bit. We’ll see what this Congress can get done before the midterms. But it was wonderful to speak with you today. Thanks, Karen.
Karen: Great. I appreciate your time. Thanks a lot.
Jake: All right. Thank you.
Interview References and Links
Jake Ward - CEO at Data Protocol on LinkedIn
Developers Alliance (co-founder and current board chair)
Data Protocol (co-founder and CEO)
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