top of page
Search

When AI Rewrites Work: Jennifer Borchardt on Jobs, Power, and the Human Cost

  • May 11
  • 56 min read







What happens when AI stops being a tool—and starts redefining what work actually is?


In this episode of Signal & Noise, Brett House and Rio Longacre sit down with Jennifer Borchardt—UX leader, systems thinker, and newly minted Signal & Noise Executive Voice contributor—to unpack one of the most urgent questions of our time: What does AI mean for jobs, identity, and society itself?


Drawing on decades of experience at firms like Sapient, Slalom, Wells Fargo, and U.S. Bank, Jennifer brings a rare perspective that blends design, behavioral science, and real-world systems thinking. This isn’t a surface-level conversation about productivity gains—it’s a deep dive into the structural shifts already underway.


Together, they explore:

  • Why the labor-based economy may be fundamentally incompatible with AGI

  • The rise of the “hyphenate worker”—and the slow death of specialization

  • How AI is unbundling work, eliminating entry-level pathways, and reshaping career trajectories

  • The uncomfortable truth about who benefits—and who gets left behind

  • Why most companies are still wildly unprepared, despite the hype

  • The growing tension between innovation, regulation, and power concentration

  • And the deeper question few are asking: If work disappears, what happens to meaning, identity, and purpose?


Jennifer also reacts to major industry frameworks, including the OpenAI “Industrial Policy for the Intelligence Age” and the Stanford University AI Index, highlighting the gap between bold policy visions and real-world human impact.


This episode is equal parts optimistic and unsettling. Because while AI promises unprecedented productivity and wealth creation, it also forces us to confront a harder reality:

Work isn’t just income. It’s identity. And we’re about to rewrite both.


🎙️ About Jennifer Borchardt Jennifer is a UX and digital transformation leader who has spent her career at the intersection of design, technology, and human behavior. She recently joined Signal & Noise as an Executive Voice contributor, where she explores the societal implications of AI and the future of work.


📖 Companion Article Don’t miss Jennifer’s long-form piece on Signal & Noise: “Architecting Resilience in the Intelligence Age” — a deeper exploration of the ideas discussed in this episode.

If you’re building, hiring, leading—or just trying to stay relevant—this conversation is required listening.

Because AI isn’t just changing how we work. It’s changing why we work. Read the full transcript bellow:

Brett House (00:00)

Hey everybody, welcome back to Signal and Noise. This is Brett House. I'm joined by my co-host Rio Longacre. And today we are super excited to have Jennifer Borchardt whose name I ⁓ previously completely butchered is northern, a northern German name is what I learned. ⁓ On our show, she's a new executive voice on Signal and Noise. A UX leader who spent her career at the intersection of design, technology and real world user behavior, helping


Rio (00:06)

Stay there.


Jennifer Borchardt (00:13)

Close enough.


Brett House (00:30)

Organizations make sense of increasingly complex digital and AI systems, just the types of topics we like to get ⁓ deep into on Signal and Noise. so, Jennifer, welcome to the show. Thrilled to have you.


Jennifer Borchardt (00:41)

Thank you. Thank


you. So happy to be here. I think today's going to be fun.


Brett House (00:46)

Yeah, so we're going to get really wonky and I'll try to hang in there. I'm running low on the mental horsepower at the end of the day on Friday. We're East Coast. Yeah, Rio is taking his high vitamin B, high caffeine energy drinks in large volumes and it's snowing outside. ⁓ just so I'll finish my intro and then you can introduce yourself. So you were an UX leader. spent your career in places like Slalom Consulting. That's where you met.


Rio (00:53)

That's what Red Bull is for.


Well, it's snowing outside,


Jennifer Borchardt (01:02)

It's snowing. ⁓


Brett House (01:14)

Rio and Drew Burdick, was on our show, ⁓ a previous guest, who's a UX expert. So you've sort of made your name in UX. You've worked with other very large organizations. And some of them are blanking on me right now. ⁓ Yeah. US Bank.


Jennifer Borchardt (01:16)

Yes.


Rio (01:17)

Previous guest.


Jennifer Borchardt (01:30)

of them. Wells Fargo, US Bank, Finality,


Goldman Sachs worked for Pulitzer Sapient. Wells Fargo, yes.


Brett House (01:37)

Wells Fargo, right?


And you've been entirely involved


in sort of UX and digital transformation, right? On thinking deeply about what this means. And it looks like you've gotten really involved in AI and consulting for AI and its impact on work, ⁓ which is a hot topic in the industry right now. ⁓ And we are going to be looking at a report.


Jennifer Borchardt (01:48)

Yes.


Yes.


Brett House (02:09)

that just came out by OpenAI called Industrial Policy for the Intelligence Age Ideas to Keep People First. It's a fascinating report. And Jennifer, you mentioned also that who came out with the second report that Stanford.


Jennifer Borchardt (02:22)

Stanford, their 2026


AI report.


Brett House (02:26)

Yeah, so


Rio (02:26)

And that one


just came out, it's hot off the presses,


Jennifer Borchardt (02:28)

Yes,


which is a counterbalance to, to open AI's paper, which incidentally, I think, I think the title of the AI paper is not, is not dissimilar from the Unabomber Manifesto. I tried to look that up before, before this podcast.


Rio (02:45)

Interesting. Well,


it's a pretty, it's a jarring document for those who have not read it. But it's a good read. I've not read to Stanford one day. I'm excited to read that. I wonder if you could maybe just tell a little bit about your background and Brett hasn't touched on, then I'll, I can maybe, I can maybe touch on a little, like add a little color to what we want to go into today about this topic.


Jennifer Borchardt (02:47)

Good morning.


Okay, well I started my career at, I'm a med school dropout, ⁓ hitchhiked to San Francisco and started my career at a little startup. I had a little bit of a detour, but I started my career really at a little tiny startup called E-Trade where I learned.


all about financial services and load financial services. at ⁓ that, by that point, I had experienced doing building early websites and.


Monet monetizing user experience. And at that point, this is a very long time ago. If you were in UX, wasn't even a term, right? You, you were a UXer. You were a web, a web designer, but also. Yeah. And by the way, I have no creative training at all. As, as I said, throughout my very regulated career, I have a creative job, but really my biggest areas of creativity are in taxation and accounting.


Rio (03:52)

You're graphic designer or a web designer, right?


Jennifer Borchardt (04:08)

staying within the law. But I worked at E-Trade as a web producer, which was a designer, content strategist, project manager, front end developer. And I actually had the opportunity through that job to work all over the world. I lived in some really amazing countries, including Berlin, which is where my last name is from, so the legend goes. After that, after I spent...


Brett House (04:08)

wow. Yeah.


Jennifer Borchardt (04:35)

many years as what felt like many years that he tried. came back stateside and worked for Wells Fargo in San Francisco where I was a ⁓ information architect slash UX designer slash UI designer slash whatever is needed.


Rio (04:53)

And


that was a new profession, like kind of like, or disciplined back around that time. Like I is right.


Jennifer Borchardt (04:57)

Yes,


yes, and it was right around that time that I realized, oh wait, I'm actually using my degree. I have an undergrad degree in neuroscience and also studied economics and public health and all of that.


Brett House (05:11)

Yeah, your educational


background makes me wonder what I was doing in high school. been Harvard, MIT, Oberlin, Stanford. you know, throw Princeton in there if you really want to. But tell me a little, like that's pretty fascinating. There's some pretty high-profile schools in that list.


Jennifer Borchardt (05:24)

No tiger.


Yeah, education has always been very important to me. I'm very grateful for the opportunities that I've had, and especially to Wells Fargo and US Bank for paying for my graduate education. Yeah, nothing like tuition reimbursement. But I was actually going to grad school full time while I was working full time at Wells Fargo, which was such a great experience. ⁓


Brett House (05:45)

Nice.


Jennifer Borchardt (06:00)

I think that the spirit that led me to do that, to basically give up my entire life for a number of years so I could work at Wells Fargo, right at the time that they acquired Wachovia Securities, acquired Wachovia, and I was one of the leads to manage the integration of their investment in wealth management business, it was when I started graduate school.


And that's what introduced me to Charlotte all the way back in 2008. So while the financial crisis was crisis saying I had a super interesting in media and exciting role. Bringing, bringing two very, very big businesses together and caring for the user experience and design part of it, which was, which was, which was wonderful. ⁓ it was hard work and it was worthwhile work and.


Rio (06:30)

Okay, still making sense now.


Jennifer Borchardt (06:55)

Not a lot of people would say this, but I actually appreciated being part of a merger like that. ⁓


Brett House (07:02)

Yeah, because you can


see the human challenges, the organizational challenges, not just the systems integration, but yeah.


Jennifer Borchardt (07:06)

Yeah, and.


Rio (07:07)

I think the human ones are the bigger than the technology


or systems ones.


Jennifer Borchardt (07:11)

Well, I would say more important because that is the part, the human part is what's overlooked. It's also the most invisible, so you can't really fault someone for not seeing it, but it's definitely there. You can feel it and it is there. Any merger, any technology, any technology integration, any process improvement is not going to be successful or sustainable unless you care for the human part of it.


Brett House (07:41)

Yeah, and break down the silos and the fiefdoms.


Jennifer Borchardt (07:41)

And.


Rio (07:41)

Well, yeah, I


think it's usually overlooked, right? Which is why it becomes a singular point of failure in a lot of these mergers that's overlooked. I think that's the case. So it's interesting. See, that's how you got to Charlotte. OK, makes sense. And that's how you met.


Jennifer Borchardt (07:50)

Yes.


Well, no,


that's how I was introduced to Charlotte. What got me to Charlotte is many, years later, fast forward to 2020, even before the pandemic. It was actually pre-pandemic, right? So end of 2019, beginning of 2020, I was like, you know what? San Francisco's changed a little too much. I'm ready for something else, but I'm not going to move just anywhere.


Rio (08:11)

This pre-pandemic, okay?


Jennifer Borchardt (08:24)

Where shall I move? And as someone who is devoted to the ones and the zeros of it all, I spent a considerable amount of time over a year creating a data model to help me decide where am going to move to? And I did it in Excel. Now if I was doing...


Brett House (08:42)

Yeah, you did this


before it was a lot easier within, you know, Hermes or OpenClaw or Claw.


Jennifer Borchardt (08:46)

If I had quad for Excel,


my bags would be packed tonight. No, but actually what came up is that I could live pretty much anywhere in the world. And that is another story. And I've lived a lot of places. So number one was Raleigh, North Carolina. Number two was actually Charlotte, North Carolina, but not a statistically significant difference. And I said, you know what? I know Charlotte.


Charlotte has a better airport ⁓ number of direct flights from the air. Yes. And I really do not like driving. ⁓


Rio (09:19)

Yeah, Raleigh's hard to get to. Yeah, it's true.


Brett House (09:25)

And we were


talking before the episode, it's a small town compared to places like Berlin that you've lived and even San Francisco and Johannesburg.


Jennifer Borchardt (09:30)

Yes, Berlin and Johannesburg and


London and New York and San Francisco. Let's not forget that. And I'm just very much a city person, even though I grew up in a very, very small town. I'm glad that I got to live on all ends of existence. But anyhow, I had decided to move to Charlotte. Thank you, Excel. Thank you, Data Model.


I had all of these, I have all of these parameters. Where am I going to move to? And I re and then I, then I stumbled upon Topgolf and now I'm not a golfer by the way. And I promise you, this is relevant. A lot of, I realized that Charlotte was the only city in the U S that had two Topgolfs, except for Dallas where Topgolf is headquartered. And a lot of the metrics that they used to decide where they were going to open their locations aligned with.


what I was looking for. And so, hey, two top golfs, means I'm gonna load up my cats, get on a plane, and go to Charlotte.


Rio (10:38)

that's really cool. really like Charlotte. So I started going a lot when in my previous work, we had a big office, we worked, mean, for the audience, we actually crossed paths at Slalom, ⁓ for, a couple of years. And you were on Drew Burdick's team and Drew was a guest of the pod. Drew's great. And actually was introduced to Drew through Ronnie Battista, whom you know. And Ronnie is like an OG in the design world. He run, he's like professor at Rutgers.


He runs the Chubb Design Lab in Jersey City. Ronnie is amazing. Ronnie and I worked together for a long time. So I reached out to Ronnie initially about being on the pod. he was like, that's not really not my thing, but I know who you should talk to. And he gave me your name, Jennifer. So I'm really glad he did. And UX world is not a big world, right? End of the day, right? Everyone's just one degree of separation away. I think the timing is great at this because...


And I don't think it's coincidental that with your UX background and you know, not a designer either, right? But I've worked with UX and design leaders for years and years and years. used run pretty big design teams, but I think it's no coincidence having worked with many designers that like some of the people at the forefront of AI actually have a design background, whether it's design strategy, design thinking, or actually were a designer. I think it's no coincidence. ⁓ and maybe we can get into a little bit because I, really, like we've gone to the point where


And we've been talking about AI as a tool, right? Something that makes people's work more efficient, help teams work faster. We've talked about this podcast a lot about how we can prototype things and get things done with vibe coding and vibe designing up in like a couple of days, it would have taken weeks. But like the bigger picture is not just a tool. It's actually like changing how everyone does work. It started to change what work actually is. And that paper that Brett you referenced that was came out from open AI.


It talks about how is this going to change society? If it's changing work, it's redistributing, if it's creating a ton of wealth or redistributing it to a smaller amount of people, if it's changing work, maybe even eliminating some jobs. I don't know if there's too much evidence about that. We can get into it. But I think there's some. What does this mean for society? And really, the crux of the paper was we cannot be looking at incremental change to something that's as, if this is the next industrial revolution, we should be looking at


wholesale changes to the way we run our societies, the way our governments potentially even operate. So that's what the white paper, I guess you would call it, was proposing. And that's really what we wanted to talk about today. So it's a really cool topic. I think that, coming with your unique background, we're thrilled to hear what you have to say. Brad, I don't know if you want to kick off the first question or Jennifer, if you want to respond to it, let's go.


Brett House (13:22)

Yeah, so maybe I'll, yeah, so back to that paper and the idea around, you know, open AI central claim. And Jennifer, tell me if this is correct, is that super intelligent AI is arriving fast enough and with enough upside and enough disruption that market adaptation, sort of commercial adaptation or capitalist adaptation won't be enough that you need to institutionalize almost a new deal for this new AI sort of tech-driven age, which is


Jennifer Borchardt (13:48)

Yeah.


Brett House (13:53)

interesting and they also mention how it shouldn't be. Yeah, and this is the OpenAI paper. It's called just Industrial Policy for the Intelligence Age Ideas to Keep People First. And I didn't read the Stanford paper that you referenced, but it's interesting that this call is coming from OpenAI because one of the primary parts of the thesis is that it shouldn't be concentrated in the hand of a few companies, which is ironic considering that OpenAI is writing it and putting this up.


Rio (13:55)

This is from the OpenAI paper, right?


Jennifer Borchardt (13:57)

Yeah.


Rio (14:21)

One of the frontier models,


right? Yeah.


Brett House (14:22)

Yeah, so, but what were your kind of key takeaways in terms of what this paper's thinking about in proposing?


Jennifer Borchardt (14:23)

Yeah.


I mean, I think it's actually a proposal for a new world order. And OpenAI is finally saying out loud, but I think a lot of people have been whispering about, that the labor-based economy is incompatible with AGI. And by publishing this paper when they did, OpenAI is effectively positioning themselves at the center of this public-private partnership that they're proposing. And


They're also effectively attempting to wedge themselves and co-author the regulations that are going to govern AI and govern themselves. And the Stanford paper, 2026 Stanford AI index, it actually, I think, provides a counterbalance to OpenAI's paper. OpenAI talks about this sweeping policy. It really does speak in generalities, but not enough detail for my taste. Whereas Stanford highlights what they called the


jagged frontier. It's this. So while AI can solve these very complex, sticky problems like PhD level physics, it still cannot reliably read an analog clock. And that's reliability gap, right? And this is a friction. I think this is really important because this is the friction that open AI ignores. It's looking here while


Rio (15:30)

What do they mean by that?


Jennifer Borchardt (15:57)

say willfully ignoring what's over there. I also think that not just the New World Order, it's kind of a manifesto ⁓ for a post-labor economy. This is something which starting to be, people started to talk about a few years ago with UBI, Universal Basic Income, which incidentally, OpenAI did not mention specifically. OpenAI is signaling that the social contract that is


centering taxes and the social contract that's centered on payroll taxes, like work-based taxes and labor-based productivity is or will soon be obsolete. And instead, moving to capital-based taxation in a public wealth front that is specifically not UBI, by doing this, they're admitting that they expect automation to completely decapitate traditional revenue streams.


And this is kind of like a radical restructuring of society disguised as an innocent policy paper.


Rio (17:02)

Yeah, this is I thought was interesting because basically they're saying it's going to have massive productivity increases and economic upside, for, you know, in the aggregate. But this is going to displace jobs and industries. This is going to concentrate wealth and power in a few hands and probably like at the same time, introduce some some new risks. I mean, it seemed to be like that. That's really what it was saying. And I felt that that was kind of based on, yeah, those are possibilities. I mean, certainly if.


Jennifer Borchardt (17:22)

Yeah.


Rio (17:29)

some jobs will be eliminated. mean, like you think about like when, you know, when computers came out, you know, certain jobs were eliminated, right? When, like, you know, there used to be switchboard operators, right? I mean, those jobs disappeared, but a lot of more jobs were created by these technologies, right? Graphic design didn't exist, you know, illustration did, but graphic design didn't even exist like 35 years ago. So, I don't know. I think it felt like it came to, it was making some assumptions to me to be devil's advocate that.


Jennifer Borchardt (17:50)

Yeah.


Rio (17:57)

are certainly likely, right? I don't know if there are, I don't know if like there's consensus that that's what's, that it will eliminate more jobs than it'll create in the short term, at least.


Jennifer Borchardt (18:06)

Yeah,


it doesn't really get into that. actually across the two papers, across OpenAI's and Stanford's papers, the only explicit mention of job loss is Stanford's paper said that 20 % of junior developer jobs have gone away. The implicit understanding that those jobs are not coming back. yeah, the Stanford paper did. ⁓


Rio (18:27)

So they had numbers to back that up, like 20 % already gone.


Jennifer Borchardt (18:34)

Having spent so much, beginning my career in Silicon Valley, something I heard all the time that they're still saying, they even say it here in Charlotte, move fast and break things. But I believe that this paper marks a shift from move fast and break things to collaborate with the state to build the moat. That is a fundamental shift.


Rio (18:58)

I think that was another thing about the paper that I did have some questions about too, right? ⁓ Like they were claiming, it was going to concentrate powered wealth in the hands of a few, which, that's certainly likely, right? But I think that overlooks my personal belief is over time. I don't mean a lot of time. Over the next, let's say, three to five years, I think AI will be commoditized. I think token prices will collapse, right? And I do think instead of AI power, AI


Jennifer Borchardt (19:19)

Yeah.


Yes.


Rio (19:27)

wealth and power being concentrated in the hands of a few who like OpenAI, Google, the few that own these frontier models. I think what we're going to see instead is regular companies adopting AI to make their businesses more profitable, more efficient.


Jennifer Borchardt (19:34)

and the rest of the process.


Rio (19:50)

and it'll increase productivity everywhere. And I think AI will become more commoditized. That's just, don't know. I don't think we'll be as concentrated as these alarmists are saying, but I could be wrong.


Brett House (19:58)

Yeah, well,


well, and I think there's a democratization in usage, right? In some of the highly technical work, to your point about ⁓ developer jobs going away. I was just talking to a startup founder from Northwestern who's in San Francisco, starting his own company. He majored in computer science. And I was like, how ironic that that was the job, the safe job that was not going to be eliminated.


But to me, I don't think it's three to five years for frontier models to be commoditized. I think they're being commoditized right now and I'm experiencing it. I just installed Hermes or Hermes or Hermes as like a super agent on my machine. it's running locally on my machine and it runs through either, I have two frontier models. I've got Claude, both coding and general LLM, co-work.


Rio (20:39)

I'm shortening locally, right Brett?


Brett House (20:51)

and GPT Pro with Codex. But I could bring in any model at all. And then this super agent is basically prioritizing which model to use for what. So there is specialization, but I'm actually relying more on the orchestration and the management of multiple models, which to me suggests that they are being more or less commoditized. And you're relying on them for specialized compute, more or less.


Jennifer Borchardt (20:55)

Mm-hmm.


Yeah, definitely think that specialization is going away. Specialization for human beings is going away. And as workers, we are entering the age of the hyphenate. Maybe this is what it is from now on. Like you guys said, the hyphenate means you're not just a designer. You're not just a developer.


Brett House (21:39)

The hyphenate, define that,


Yeah, yeah,


Rio (21:45)

That's interesting.


Brett House (21:45)

yeah.


Jennifer Borchardt (21:45)

You are


a technology minded strategist. You are a human centered technology.


Brett House (21:50)

Yeah, Totally,


there's a job that I was just reading about the other day. was like the rise of the marketing engineer. And I'm like, that's interesting. So that's a technically astute builder, almost like a product engineer type, but that is leveraging those capabilities for marketing related outputs, which I thought was interesting.


Jennifer Borchardt (21:58)

Yeah. Babbagey athlete.


Yes,


it's missing some. So there is immense potential and opportunity. It is there. No question. It's super exciting, but without, without, without, without a lot of, we'll say, ⁓ support that, that opportunity, I don't believe is available to everyone. It is available to people who already have expertise.


So people like us, the Gen Xers and the older millennials, I would say that they're probably in the strongest position right now because we've had the opportunity to develop judgment and expertise and critical thinking. Yes, system, yes, system thinking.


Brett House (22:55)

In systems thinking and strategic thinking, Rio and I talk about this on the show all the time, right? Where you're bringing that knowledge


to be able to conduct. You're not just a violin player, which is specialization. You're the conductor of an entire orchestra, but you got to know what the different parts are and how they fit together as a whole, right?


Jennifer Borchardt (23:05)

Yes.


Yes. Understanding,


understanding the system, the holistic experience and what it isn't just, just what does good look like? Because I think AI is creating things that look good. I don't think they actually are, are architecturally.


and have architecture integrity, they look good. We know what excellent looks like. We know what it feels like. We know how to describe it while we're still, while, while prompting is still a thing. And I don't think that even prompting is going to be a skill we're going to need forever. As long as we can describe what does excellent look like, we can, we can orchestrate technology. We can orchestrate these agents. can describe


what the ideal state is going to be and we can continue to iterate on it.


Brett House (24:04)

Yeah, well, and it just-


Rio (24:04)

It's funny, always


thought prompt engineering was such a dumb term, right? So I agree with you, like, yeah.


Brett House (24:07)

Yeah, there's a


Jennifer Borchardt (24:07)

I knew it was going to last 30 seconds.


Brett House (24:10)

prompt, but there's all the memory system that you're building. That's what I'm learning like building an agent. Like you're almost like birthing a child in a way. It's a strange analogy because you're literally building a memory system and you're training it with those markdown files, right? And it's not the prompt that trains it on what to do, what not to do. And I do this for everything from narrative writing to technical writing to...


Rio (24:12)

Yeah, this.


Brett House (24:33)

⁓ proposal building and I give it these extensive instructions. It's almost like the semantic layer Rio that we talked about in terms of you putting that on top of it as a middleware on top of a data infrastructure to tell the agents how to interpret the data. You're sort of doing the same thing for your own personal or work agents, right? You're giving them the context and the rules of the road.


Jennifer Borchardt (24:39)

Mm-hmm.


Rio (24:53)

Brett, I think


that's it. think what you're referring to hits a nail on the head. Like it's the scaffolding, right? So like if you look at like the unbundling of work, I know it's a topic we wanted to cover today because I mean unbundling work, the premise here is AI does certain things. It can't do your whole job, but it can do certain things in your job really well, probably much better than you, right? It can, right? Or at least faster and more efficiently.


Jennifer Borchardt (25:03)

Yeah.


Rio (25:16)

If, if it's doing certain things better, where it kind of breaks apart, but you need the scaffold thing to know, okay, how do you stitch together these things that these agents or these large language models can do? How do I tell them what to do? Guide them, have that, have that, that, that map that I can lay them on top of and then get a, get a good final product out of that actually makes any sense. I think that's interesting. And I don't know. What's your thought Jennifer about how that impacts work and people.


Jennifer Borchardt (25:40)

Well, that's also higher


order work, right? That is not something an entry level person knows how to do. They don't know how to orchestrate. That is something you get with experience by having the opportunity to make a lot of mistakes, learn from it, continue to iterate.


Brett House (25:43)

Yeah.


Jennifer Borchardt (25:58)

That is why people who are just entering the workforce, people who are making career changes by choice or not by choice, we're at a significant disadvantage. And even people are already pretty good at what they do. Right now, we will arguably need fewer of them. then what happens to the people who haven't had the opportunity to develop those skills? There needs to be something that exists. And honestly,


Brett House (26:23)

Yep.


Jennifer Borchardt (26:27)

I don't even think that giving people the resources to meet their basic needs is even going to be enough. Work is not just money. Work is meaning and purpose and place and identity. And once you lose all of those other things, even if your material needs are met.


Brett House (26:35)

Yeah.


Rio (26:38)

purpose.


Brett House (26:39)

Yeah.


Jennifer Borchardt (26:49)

You have, you have a broken society full of broken people. And how do you care for that? That is the very important and very human part of this that Stanford open AI really none of these technology minded companies are really thinking about.


Brett House (27:07)

Well, in the


paper, they claim they want more policy, right? And the question is sort of where does that policy come from? They sort of reframed AIs. Yeah, it's not an infertile, it's like, yeah, they say it's sort of an infrastructure and distribution. And I think distribution probably infers distribution across society, across people. It's not just the technology problem. so knowing how slowly ⁓ governments, ⁓


Rio (27:14)

Well, in a paper from them, right?


Jennifer Borchardt (27:15)

Yeah, exactly.


It's kind of a public partnership, right?


Yes.


Brett House (27:34)

in the systems that power governments shift and adapt to technology change, certainly not at the speed of how technology is ⁓ sort of accelerating today. What are your thoughts on that? mean, is it coming from the government? Because you can't have, the ones that you're regulating can't do the regulation.


Jennifer Borchardt (27:40)

Mm-hmm.


⁓ Yes,


but that's actually what opening eye I think is, if not explicitly, implicitly hoping for. I think that this is something that is so big and so pervasive that everyone needs to be involved somehow.


Brett House (28:04)

Yeah.


Jennifer Borchardt (28:15)

It's not just going to come from government. It's not just going to come from private enterprise. It's not just going to come from individuals because that wouldn't be fair. Have an individual decide how are you going to be viable in this upside down new system. It is very much a warning.


Brett House (28:32)

It's sort of a warning, right?


Jennifer Borchardt (28:39)

But I don't think that there's an easy answer, but I'm glad that this came out now because it starts the conversation. There are some, there are some. ⁓


Rio (28:48)

Yeah, think it's good that it's doing that, right? And it's


interesting too, like, they call for some things that, mean, like, for example, they call for shorter work weeks, portable benefits being tied to, you not being employer-based, but I actually think would be great, right? If we could move away from this system that makes absolutely no sense in this country where your benefits are tied to your employment, which is really an anachronism based on some decisions we made in the 50s, right? To, yeah, right.


Jennifer Borchardt (29:02)

Yes.


Brett House (29:11)

Yeah, and coming from the founder mentality, it's extremely


expensive when you're founding a company to pay for insurance. Jennifer, you know what that's about.


Jennifer Borchardt (29:15)

Get us.


Well, what


I thought was interesting is that they mentioned using this wealth fund to supplement health insurance. All right. But what they didn't say is using those same funds to create a public single payer healthcare system. I I know.


Rio (29:40)

Okay. Well, yeah. So they think


about healthcare, right? I mean, end of the day, healthcare, if you, like the costs in our country, I mean, we can get a whole multiple podcasts about healthcare if we really wanted to, but the costs are so crazy. We pay twice as much as any other developed country in world per person, right? For healthcare because of the crazy way the system's designed. So it's like, I agree with you. If you're going to tackle, you're going to start throwing money at it. Like, like, I think they're probably structurally some things that could be solved that would have a bigger.


Jennifer Borchardt (29:51)

Yeah.


Yeah.


Rio (30:09)

much bigger benefit.


Jennifer Borchardt (30:11)

I mean, if not now, when in times of disarray, in times of chaos, emerged the greatest opportunities. If we're going to chuck the system, let's take this as an opportunity for all of the innovation, for unprecedented innovation, unprecedented wealth to truly reimagine something rather than just tweaking these systems that are already in place that we already know and that have very obvious problems.


Brett House (30:41)

Yeah, that's the optimist view. mean, it's also during periods of uncertainty and chaos. It's the shock doctrine, not to, the Naomi Campbell shock doctrine. It's a little progressive left, but the notion is that during periods of chaos, people take advantage and they force policy through when nobody's looking. And that's also the danger and the risk from a policy perspective is that.


Jennifer Borchardt (30:49)

Yeah.


Yeah.


Well, so I think that the shock doctrine applies not only to societies and organizations, but also to individuals. When everything is taken from you and it feels like you have nothing to lose, that is the time where you really innovate and reinvent and are forced to create.


Brett House (31:15)

Yeah.


Yeah, it's like, it's the abundance mindset, right? Versus like the scarcity mindset of just jump.


Jennifer Borchardt (31:27)

Well, yeah, a lot


of people are, a lot of people live in a scarcity mindset and AI is threatening and actually taking away a lot of people's livelihoods, uncertainty, it's uncertainty.


Rio (31:40)

but here's...


Well, I think it's, I think


the jury's out, whether it's taken away people's livelihoods. mean, I think that, yeah, but.


Jennifer Borchardt (31:48)

Well, some people, some people, like certainly nobody


on this call, but there are some people whose futures are very much more at risk than they were before this. And what's that?


Rio (32:01)

But here's what, like, but Jennifer,


here's what, but here's what it gets me worried about some of these prescriptions that come out of this, right? Like you look at if the Biden administration had been able to implement its AI policy, which is basically we're going to restrict AI to like three hyperscalers with their large English models. That was it. No one else is to be allowed to develop it. It was going to be highly restricted and locked down. And that would have been a terrible decision in retrospect because they had no idea like this explosion of vertical LLMs and, and like locally running LLMs and open source. They would have missed all of that right now. would have happened.


Jennifer Borchardt (32:22)

Thank


Yes.


Brett House (32:26)

Yep.


And Mark Andreessen


was screaming from the rooftops, right, whether you love them or hate them, about the regulation that was limiting our ability to be competitive in a race that we have no option but to compete and to win. It's the new space age. It's the new, you know, whatever you want to call it, Cold War. Yeah.


Rio (32:34)

Yeah.


Jennifer Borchardt (32:43)

Yes.


Rio (32:47)

If it is a new industrial revolution, mean,


like, you know, like for sure, like we don't want to be, we don't want to lose. If it is the industrial revolution, it's a race between us and China. We don't want to lose it. Because I think that we, we all agree that we'd probably rather have models created here, running the world over models created by a totalitarian regime. Right. So, ⁓ but I don't know. So, but, but it's, but, but, but I agree with you that there is going to be some job displacement. I mean, I don't know, I don't know how quickly and will the jobs be created? I mean, what do think? Will the jobs be created quicker than they'll be eliminated or, or not?


Jennifer Borchardt (33:02)

swear.


Yeah.


I do believe that jobs are going to be created. Jobs are being eliminated, but I don't think that job destruction is going to happen as completely as people think it's, as people are saying it's going to. And I also do not believe that the jobs are going to be created as fast as they need to replace what's being replicated.


Brett House (33:42)

Yeah, so the job elimination fear, do you think


that's a bit short-term or something? This notion of sort of keeping people first, does it really mean anything if AI is all about driving efficiencies?


Jennifer Borchardt (33:51)

Well, I think


the question is which people are being kept first because I don't think it's everyone. There are a lot of uncomfortable truths and assumptions that I think are going to start becoming spoken. And as someone who's a firm believer in leaning into those uncomfortable conversations, let's get it out there and talk about it.


Brett House (33:57)

Yeah.


Yeah, it's.


Jennifer Borchardt (34:18)

be the benevolent force that you want to see in the world. And it's going to be additive. It's not just going to be you or me or someone else. It's going to be people acting collectively to help ensure that the benefit is going to be as broad-based as possible. are organizations that are really


really working hard for this, one of which is ASAP, the AI ⁓ safety awareness project. ⁓ It's an organization I recently got involved with. They're doing some very important work and it's super grassroots. It's talking to people who are normally not part of these conversations. Students, senior citizens, local policymakers, just.


Brett House (35:05)

Yep.


Jennifer Borchardt (35:07)

open, not even being prescriptive, but saying, hey, here's what we know right now. We want you to be part of the conversation. What do you think? And helping steward these conversations, hopefully into benevolent policy.


Brett House (35:15)

Yeah.


Rio (35:18)

One, one.


One recommendation I really liked in the report was like giving more people access to AI and AI tools. thought that was like that made so much sense to me. Thoughts on that?


Jennifer Borchardt (35:27)

Yes, I


agree. Yeah, I mean, that's how the benefit is going to be more widespread. there are still people who, even though these tools are available, are not going to have access for one reason or another. We need to care for them too. How do we allow them


to still benefit from all of this innovation and discovery and new wealth creation and efficiency and time savings? In other words, how do we create the tide that raises all boats?


Rio (36:15)

How do you see some


of the changes in how it's impacting specific roles or jobs or the workforce? Do you see any differences from this compared to, let's say, any other industrial revolutions of the past or technology advancements in the past that you think makes this different or is it the same?


Jennifer Borchardt (36:32)

I think there's a lot of similarities. I'm certainly seeing a lot of similarities between this and the ⁓ internet, the rise of the internet, but there are also some important differences. We didn't have hyperscalers during the rise of the internet. We do now. And these hyperscalers are much, much better positioned to not only


extract the value from this and benefit from the future, but they are creating the future. That's not true of a lot of companies. This, I believe, is the first time that we have self-improving technology in a meaningful way. The internet didn't improve itself. Any improvements were the result of human effort. Human effort is still necessary part of the system now, but


these AI tools in these models are improving themselves. That is new, that is different. And so what do we do with it?


Brett House (37:40)

Well, how do you like as as society sort of adapts, because if you think back to sort of the growth of the Internet, there was a lot of the telecommunications were leading the basically groundwork of laying down the broadband cable network globally, right, which stood the test of time. So that cable infrastructure didn't immediately. Yeah, didn't immediately.


Jennifer Borchardt (37:52)

Yeah, the fiber.


Rio (38:01)

Yeah, short-term they went bankrupt, but within 10 years it was all being


used.


Brett House (38:04)

Yeah,


it's all being used. so because it didn't lapse ⁓ in sort of quality, the opposite problem with the hyperscalers right now is going to happen, right? Because they're laying down all of this tech infrastructure with GPUs and server farms that is quickly out of date, right? And so it's not going to, you 10 years from now, once five years from now. Yep.


Jennifer Borchardt (38:05)

Yeah.


Yeah.


Yes.


Rio (38:26)

It's a five to seven year obsolescence for lot of these chips, yeah.


Jennifer Borchardt (38:27)

I mean, even a year from now,


a lot of this stuff is going to be obsolete. then later in the innovators dilemma for these companies.


Brett House (38:36)

Yep.


Jennifer Borchardt (38:38)

It's a constantly moving target.


Brett House (38:42)

Yeah, and do you get into


like a debt cycle and a debt spiral because you've built out ⁓ and can't quite fully leverage this technology because it is obsolete, like you said, Rio, in a couple of years.


Jennifer Borchardt (38:51)

Yeah,


and this might be too alarmist, but if there is in fact a death spiral, is that the death of these companies or is that the death of society and a lot of people? I think it's somewhere in between. I don't think it's going to be an extinction event, but it is going to fundamentally change society in ways that I don't think anybody is ready for.


Rio (39:19)

Yeah, well, they call it seems like the opening. didn't again didn't read the Stanford one. I'd be curious how that difference is. But they did call out a lot of risks in the open. I won risks about job displacement, misuse, erosion of democratic norms and democratic power because of these things. A lot potential loss of human control to AI in the future. These are things they call it that I mean, some of them are pretty scary and ⁓ kind of dystopian. But but. don't know, thoughts on that. I mean, do you think these are


Jennifer Borchardt (39:45)

you


Rio (39:48)

legit things to be worried about, are they just using this maybe to insert themselves into the debate and help craft policy?


Jennifer Borchardt (39:55)

I mean, I think that's all true. mean, by being the first mover into this conversational space, they're also positioning themselves to drive policy. And they're saying it's to avoid regulatory capture. But this private-public partnership they're talking about is necessarily going to create regulatory capture unless there are other forces which prevent it. OpenAI is positioning for


sovereign AI, right? But you can't, how do you govern a black box? There's not a lot of transparency. And as transparency declines, and Stanford even noted this in their report that their labs are hiding their training data. This is a black box. ⁓


Brett House (40:33)

Yeah.


Yeah, and so yeah,


the regulators don't have the know-how or knowledge or visibility into these systems to be able to effectively implement any sort of legislation that would control or monitor.


Jennifer Borchardt (40:46)

Yeah.


Rio (40:55)

Yeah, would you want Bernie Sanders voting on anything related to technology? I


mean, seriously, he's got what's the bandana. I I don't doubt he's ever used a computer, right? mean.


Jennifer Borchardt (41:01)

I mean,


there's a lot of politicians I do not want to have anywhere near technology. do think we're seeing in real time how different AI ⁓ companies are recognizing and approaching this differently. Anthropic is seeking legitimacy by acting as something of a white knight, all right?


they are seen as the most moral or ethical AI company. Maybe that's true, maybe it's not. OpenAI, on the other hand, is seeking legitimacy through state alignment rather than open source accountability of some of these other models.


Brett House (41:48)

Well, the state alignment was to fill the gap that was left by Anthropic when they said, we're not going to agree to those terms, Pentagon. Yeah, right. And so, isn't that how capitalism functions? If competitor A leaves opportunity A, competitor B enters, and they claim...


Jennifer Borchardt (41:54)

Okay.


Rio (41:55)

That's good. Yeah, I didn't want to talk about this. I'd love to. This is love your thought on it. Yeah.


Jennifer Borchardt (42:07)

Sure.


Rio (42:08)

But you don't call them a national


security risk. I mean, it's crazy.


Jennifer Borchardt (42:10)

Yeah,


that that that I think was unnecessary. Yes, there was there was a vacuum, there was an opportunity. And open AI took at took it. What was the right thing to do? Was it open AI stepping in? Was anthropic right to say, listen, we need to need to have some guardrails here before we agree to this. I don't I don't think I don't think we're going to know what the right decision was until until we until we're able to look back a bit.


Rio (42:40)

Yes, interesting. I did listen to a couple of pods about that. you know, I guess, I mean, I think they took it too far. Completely agree that that was unnecessary. apparently part of the rationale was. Anthropics said, OK, well, these things are nonnegotiable, but don't worry, we can override them. All you have to do is apparently Dario said, oh, you just call me.


If you need me to override, you know, this call me, they're like, we're for a war. We're not going to call you like three in the morning. Like, you know, our troops are going to die. can you, Dario, can you override your AI so to like.


Jennifer Borchardt (43:06)

Simple point of failure.


Brett House (43:12)

We gotta change this script, this command.


Rio (43:14)

I mean, yeah, so


I, in all fairness to the administration, who I don't agree with a lot usually, like I do understand their decision when it was framed that way.


Jennifer Borchardt (43:24)

Yeah, and again, I don't think there is a right answer. And sometimes there's not a right answer. And what you have to do, your responsibility isn't to make the right decision, but to make a decision and then work to make that decision the right one. This very well could be such a circumstance. So, yeah, go ahead.


Brett House (43:43)

Yeah, and well, in


Rio (43:43)

Yeah.


Brett House (43:45)

Claude, just, you know, they've been in the news recently about there was a Claude mythos, right, which they're saying is potentially a more dangerous.


Jennifer Borchardt (43:50)

Yes.


Rio (43:53)

was


able to jailbreak itself like every time without, like from a completely locked down environment, apparently it's wild.


Jennifer Borchardt (43:56)

Yeah.


Brett House (43:59)

Yeah.


Jennifer Borchardt (43:59)

So yeah, that I think is a problem. Because even though Anthropic did, what sounds like was a very responsible decision to limit its release to companies whose best interest is to keep things safe and secure. The open source models are nine months, 12 months behind. So this is a can being kicked down the road.


The issues that Anthropic is seeking to avoid right now, they're avoiding them right now, but what happens when these capabilities are in a publicly available open source model? What do we do then? And how do we spend the next nine to 12 months, assuming it takes that long, to figure out what the right solution is?


Brett House (44:41)

Yeah.


Rio (44:53)

Yeah. And for those who missed it, I mean, what we're referring to here, there was this latest version of Claude code, right? I forget which one it was, but apparently it was able to analyze its own source code, rewrite its own props, iterating these changes to weaken its guardrails all by itself, right? Which is, you know, Jennifer, your point. Yeah. Mythos. Yeah.


Brett House (45:08)

Yeah, it's Opus 4.7, right? It's called Mythos. Yeah, so


Jennifer Borchardt (45:12)

No, I mean,


it's different, no.


Brett House (45:13)

it's basically developing some level of ⁓ self-preservation, right? Like how? Right? I almost named my agent, my Hermes agent, Hal.


Jennifer Borchardt (45:18)

Yes, like hell. Yeah.


Brett House (45:28)

Then I thought that's way too dystopian. So I went with William Gibson's Eidoru character, Rei, or lei, in how they say it Japanese, REI, which was like female artificial intelligence that is kind of a main part of the character of a couple novels.


Rio (45:33)

Nice.


But with Hal,


Jennifer Borchardt (45:42)

I mean, my...


Rio (45:43)

the only reason Hal went off the rails was they asked it to lie. It was unable to comport itself in a rational way after they asked. They said, you must lie to these humans and couldn't do that.


Jennifer Borchardt (45:47)

Yeah.


Brett House (45:47)

Yeah.


Jennifer Borchardt (45:56)

Yeah, I regret now dismissing science fiction most of my life. was like, it's like, this is not enough science and too much fiction. Little did I know this was actually a predictive documentary.


Brett House (46:02)

I know.


Yeah, and if you think about


like, if you think about like there's Arthur C. Clarke 2001, and then you have the William Gibson novels, Necromancer and Eudoru and all those. he, I mean, William Gibson was writing those novels on a typewriter, not a word processor, not a computer, but a typewriter. And he was predicting artificial intelligence. Isaac Asimov with iRobot and a lot of those.


Jennifer Borchardt (46:16)

I guess them all. Yeah.


No.


Yes.


Rio (46:35)

Well, the three


Jennifer Borchardt (46:35)

⁓


Rio (46:35)

laws


Jennifer Borchardt (46:35)

shit.


Rio (46:35)

of robotics, what is it like? cannot harm humans, right? They must listen to humans and then they must like preserve their own existence in descending order, they must, was pretty, mean, AI systems being designed using those same laws. I mean, they just show how prescient that was, right?


Jennifer Borchardt (46:40)

yeah.


Brett House (46:52)

Yeah.


Jennifer Borchardt (46:53)

And


I think that demonstrates how fundamental these issues are. These are fundamentally human problems. And they just have fancy wrapping right now. And there's a lot of layers on top. we, as human beings, always knew these things were going to happen. Or at certain humans could predict that this would happen.


Brett House (47:17)

Yeah. unlike


like Star Trek, we were talking about this musing in this beforehand and in geeky fashion where remember Data, how like you could just push that there was like a button on his back that you'd have to like, it was like in his ribs and you could turn them off if Data like went off the rails and Data for those that don't know was the next generation sort of fully human Android that was created. I can't remember his founder, but like one of the early Star Trek ⁓ characters actually built him.


Jennifer Borchardt (47:23)

Yes.


It's a great booth.


Rio (47:43)

His next generation,


Brett House (47:44)

Yeah, and the next generation and the thing with AI now with the hypos. Yeah, exactly. Exactly. Was you can't turn like I was thinking like, well, you could just turn it off. because of the distributed tech infrastructure globally.


Rio (47:48)

and built his evil brother, Lor, as well.


Brett House (48:00)

right from server farms to GPUs, I think it'd be pretty much impossible to turn it off, right? Because it could always move to other data warehouses, other places if it has some level of sentience and self-control and is thinking about self-preservation and reproduction, right? I don't know if you can control that, right? You can't just turn it off.


Jennifer Borchardt (48:22)

On the subject of reproduction, there's another science fiction creator I'd like to add to the mix, and that is Spike Jonze. He's of the modern age. 10, 15 years ago, he created that little movie, Her, which seemed plausible. It's like, sure, I guess this could happen, but this is also pretty crazy. Well, have you all seen Her? Yeah.


Brett House (48:31)

there we go.


Rio (48:49)

Her is excellent. is, I mean,


he nailed it. I mean, it's it's better today than it was when it came out, because it's been so really accurate.


Jennifer Borchardt (48:53)

Right? Yeah.


So one or two days ago, the New York Times had this video of an adult son interviewing his mom. His mom's probably in her seventies, has an AI companion named Max. And he's really worried about her. He's really worried about his mom. And it's this 30 minute conversation where you get to hear both sides of this concern.


And I think I came away with the same conclusions from this interview as when I saw the Spike Jonze movie. What is love? What is emotion? Those are real emotions. Those are real feelings, real connection. But how much of it exists or needs to exist between two organic


Brett House (49:40)

Yeah.


Jennifer Borchardt (49:51)

beings and how much can exist between one organic and one virtual or even two virtual? This is a real question now.


Brett House (49:58)

Yeah, and Spike Jonze was in a sense, he was


imagining what it feels like to live with AI. for us, those of us that kind of have gotten into it, it does feel that way. I mean, not quite yet, but there's this, yeah, my wife is telling me every day that you're obsessed. And I'm like, but it's fascinating in the capabilities. I don't think I'm quite, there's no emotional relationship. It's really a task master.


Jennifer Borchardt (50:04)

Yes.


Yeah.


Yes.


Brett House (50:27)

But ⁓


it certainly changed how I interact with technology.


Jennifer Borchardt (50:31)

Well, but so you may not be obsessed, but there are people who are because it may be the only way that they can comfortably connect to something outside of themselves. And that is a very important need. There's this other movie, it wasn't AI related, but it was Lars and the Real Girl. Again, it speaks to what are the fundamental human needs that everybody has and how are those satisfied?


Brett House (50:42)

Yeah.


Jennifer Borchardt (51:00)

And how do other people treat the satisfaction of needs if it doesn't align with how people commonly do it? Today, having an AI companion may be an outlier. I would say within a year, it'll be very, very common.


Rio (51:14)

But yeah, speaking of needs, I thought it was one interesting thing about the report too, that it really points to a future where there's so much prosperity. I mean, this is the hypothesis in there, right? That it can be evenly distributed. And they didn't really call out UBI necessarily, but they kind of hinted that there may need to be, yeah, there may need to be some redistribution of this incredible wealth that AI is going to unlock. I mean, that's a...


Brett House (51:31)

Universal basic income. Yeah


Rio (51:40)

It's a good thing to talk about. And I agree, Jennifer, we should probably be talking about it starting now. mean, personally, I wonder if too much of a society is on basic income. that somehow like sap creativity and people's purpose that you mentioned before? Or would it be a huge unlock? Like in Star Trek, there's no money, right? In Star Trek, you can create your food by clicking a button. science fiction does...


Jennifer Borchardt (52:04)

Yes.


Brett House (52:05)

And for those of the show,


Jennifer is going to have a little date with William Shatner, ⁓ as what I've heard in Charlotte, where he's coming to speak. He's going to...


Rio (52:09)

Ha


Jennifer Borchardt (52:12)

Yes, the rest


Rio (52:12)

Wrath of Khan, right?


Jennifer Borchardt (52:15)

of Connie is going to be there live on stage answering questions.


Rio (52:19)

So


jealous.


Brett House (52:20)

Yeah, and I watched the Artemis II ⁓ landing when, did you see his interview with, I love when William Shatner gets on, because he's so passionate, not super technical, but he's super passionate about what this really means for the future of mankind. So he should be a pretty fascinating guy to hear.


Jennifer Borchardt (52:35)

So you wanna hear something


weird. You know how I said I grew up in a small town in Ohio? The astronaut that was in charge of reentry went to my high school. Random.


Brett House (52:45)

really?


Rio (52:46)

That's awesome.


Yeah.


Brett House (52:48)

So back to your question, all the people that I'm talking to that are doing the builders, like the Drew Burdix, who we mentioned earlier from Stealth X, and our friend Mike Visanzino, who's running a company called Clover AI, is just launching. But he's a builder, and he's going into a lot of organizations, speaking of grassroots, typically non-tech organizations. I just talked to a chief AI officer from a company called Visionary AI, and he's an older guy that's been in this sort of data.


Jennifer Borchardt (53:06)

Yeah.


Brett House (53:15)

warehousing, he worked for Wells Fargo for many years, Bank of America, and Tech Space, and he goes into dental institutions and home improvement companies and financial services industries, ⁓ typically lower tech. And the typical feedback that I'm getting is that most people, especially within certain industries, but I would say generally across the board, are far behind ⁓ what the kind of...


Jennifer Borchardt (53:24)

Yeah.


Brett House (53:42)

⁓ the builders and the developers and the people that are really leaning in are getting to like we're talking about open AI and I am sorry, open claw and Hermes and stuff like that. Whereas the average employee, even at tech companies is just doing basic prompts. Like there's not advanced use cases.


Jennifer Borchardt (53:48)

Yes.


Yeah.


Yeah.


Rio (54:01)

Apparently, adoption of


AI Google is very low. Someone was talking about that this week. At Google. At Google.


Jennifer Borchardt (54:06)

Adoption of AI at Google is love.


Brett House (54:06)

Google, oh, at Google, thought you said Google, yeah.


Jennifer Borchardt (54:11)

to say.


Brett House (54:12)

So if people within organizations are sort of behind the curve, like how are you advising companies on this? I mean, are you starting at the very simplest level to kind of help bring them along this journey?


Jennifer Borchardt (54:26)

I mean, that's where it starts. Just getting people familiar with the basics in actually getting their hands dirty. That's where it starts. Getting comfortable with it, seeing what it can do for you on a small scale. But ultimately, and I know that this is something which Drew talks about, and I completely agree with him on this, in order for it to be sustainable and scalable and worth the investment, you need to be very clear about


What does success look like? How do you measure success? Where do you identify the initial use cases to prove success and get ⁓ a return on the investment and use that to kind of snowball and take on more and more. That's how it starts.


Brett House (55:12)

Yep, you can boil the ocean on day one.


Rio (55:13)

So yeah, I was interested


you say that because of the Google thing, just to cut you off for a second. So apparently Steve Yagy tweeted this the other day. AI adoption of Google is the same as at John Deere. And the reason why he was saying that, and I think it kind of validates what you're saying here is it's not like they're not allowed to use cloud code because it's the enemy, right? And there's been a hiring freeze at Google for the last 18 months. They're not bringing out.


Jennifer Borchardt (55:35)

Okay.


Rio (55:40)

anyone else from the outside. So Brett, people who are using open claw and Hermes and all these other, and the latest cloud, they're not going into Google. So they're just using their own tools. There's no pressure to use them. so, so I think it is, it's fascinating times. So there's something about being, being able to try things out and bring them and experiment, right?


Jennifer Borchardt (56:00)

I also think that this comes down to incentive structures. When the cost to, sorry, when the benefit of using something becomes greater than the cost of not using it, that's when adoption happens.


Brett House (56:15)

Yeah, yeah. And we had George Busy, who was at Horizon as an EBP on a couple of shows ago. And he was saying how ⁓ it just hasn't worked. That in most use cases that you align ⁓ AI to, it's just failed the litmus test. That there haven't been any sort of outcomes that people can point to that are meaningful. And I thought I was a bit surprised by that.


Jennifer Borchardt (56:33)

Yeah.


Yeah, I think people have a skewed view of what can AI actually do? Like really getting down to brass tacks, what can it do? From a UX perspective, I think AI is immensely successful at creating what I call Tmoo UI UX. It's something that looks good, it's passable to the novice. looks like, hey, this is magic. But to anyone who...


But as soon as you go below the surface a little bit, or someone who knows what X doesn't look like, it breaks. It breaks. doesn't care for the edge cases. And someone who's never actually designed and shipped a product, they don't even think about edge cases. They only think about the happy path. Life is all edge cases. It's all about edge cases. And it is a tool.


Rio (57:11)

To a design professional, right?


Jennifer Borchardt (57:37)

And I think it's always going to be a tool. It's an increasingly powerful tool. And I think that the high watermark of what is uniquely human is gonna continuously change, but it's ultimately always going to be a tool. And as it's constantly evolving individually, and we're talking about the job market and viability and all these things that are changing.


Anyone who is curious, who has good judgment, good critical thinking, is a lifelong learner and really truly dedicated to that. Those are the people who are going to be okay. There's always going to be a place for those people. And that is not limited to where you went to school, it's not limited to your age or to anything else. If you are curious, if you are...


willing to take a chance and try new things and learn and fail and be wrong. That is what it's going to take to be viable in this changing workforce.


Brett House (58:40)

Yeah, and I think it's important for people to realize that it's not as easy, despite the claims on X and in LinkedIn, that I built a CRM in five days or 24 hours. ⁓ It's not that easy, right? And some of the technologies that do exist, I've had this debate recently about CRMs. Like you can replicate certain vibe codes, certain capabilities that maybe as an SMB, a small business or a mid market would serve your needs.


Jennifer Borchardt (58:49)

Yeah.


No.


Yes.


Brett House (59:07)

But the amount of rigor and validation and test and learn that happens with these companies, let's say it's a HubSpot CRM or some other technology, through years of practice, hard to replicate that in 24, 48 hours. So there's a lot of like...


Rio (59:23)

Yeah, if you want to capture leads, store


Jennifer Borchardt (59:24)

it.


Rio (59:25)

records, know, manage cases and send a couple emails, you could probably do that by vibe coding. If you know what you're doing within a few days, but yeah, if you want to have an enterprise validate system that does these things, it can be used by multiple people in multiple environments. Jennifer, your point, this user tested and has gone through the rigor of, of, of of a deployment.


Brett House (59:30)

Yeah.


Yeah.


Jennifer Borchardt (59:42)

Yeah, and I want to be clear, user tested on actual human users. This whole thing around synthetic users and synthetic data is a disaster.


Brett House (59:42)

Yeah.


Yeah.


Well, synthetic


data can be used effectively in advertising to kind of chart and predict engagement rates and.


Jennifer Borchardt (59:56)

⁓ no, sorry. I'm not,


yeah, I'm not saying it has no value or no utility, because it certainly does within certain cases. But I'm afraid there's going to be temptation to use it for everything. And that is what we don't want. That is what we cannot have. But yeah.


Brett House (1:00:15)

Yeah.


Yep. Yeah, it's a...


Rio (1:00:22)

So get your product,


get your product in hands of actual, like real people.


Jennifer Borchardt (1:00:26)

Yeah. And to your point about vibe coding, can, you can create something that works sort of, but does it, doesn't manage edge cases? Probably not. What about security? What about vulnerabilities? What about malicious code? And you, as soon as you introduce anything, financial payments, we'll forget about it. So there's, yes. And also, and also accountability.


Brett House (1:00:39)

Yep.


Yeah, speaking of governance and heavily regulated industries where.


Jennifer Borchardt (1:00:54)

As of right now, you cannot hold an AI accountable. You cannot hold a machine or technology accountable. You only can hold people accountable. And so for that reason alone, there always needs to be a human in the mix.


Brett House (1:01:09)

Yeah. So, and are you seeing?


Rio (1:01:11)

I imagine there's going


to be a lot of court cases deciding this too in the future, right? About like liability, about, you know, because we've already had some about IP and content and ownership of that. But I think in terms of liability, as these systems get smarter, that's only going to start bread. I'm I cut you off.


Jennifer Borchardt (1:01:15)

But yeah.


Brett House (1:01:27)

Yeah, no, no. And how for companies that you're consulting with, ⁓ how do they build guardrails? also sort of it's like you have to build guardrails, but you also have to in sort of governance around how AI is used. But to your point about Google, how do you actually democratize the usage of AI? So what I've seen firsthand is that oftentimes it starts even today, you know, in 2026, April of 2026, even when it's even within tech organizations, it's within the IT department.


Right. And then they, they start to, you know, so other teams start to raise their hands to say, Hey, I want to access to this and I want to access to this. But I find that it's kind of a slow rollout process and there's whole parts of the organization. I saw this in commercial organizations, customer success sales that had no little to no other than free sort of LLM access access to kind of, ⁓ you know, more powerful AI capabilities. Cause those were generally managed and partitioned by the.


by the IT team. Are you seeing ⁓ organizations using these things kind of ubiquitously? Because I just found the rollout to be very slow.


Jennifer Borchardt (1:02:31)

Not, not, not


ubiquitously. No. mean, you know, granted I, I've historically worked with regulated organizations and they, need to be very careful about these things. There are certain areas in certain business units and teams that are, that are using, using these, these more powerful models, but by and large, they don't get access to the, to these frontier models and they're using.


Brett House (1:02:42)

Yeah.


Yeah.


Jennifer Borchardt (1:02:57)

It's not even that they're using the publicly available ones because they're not allowed to do that. It's these internal Waldorf versions that are actually quite sufficient for basic tasks. But the expectation is that you're going to be using them for more than basic tasks. Even at some of these large companies, their AI policy, some of them are still use AI. They don't provide any sort of guidance or support or training.


Brett House (1:03:02)

Yeah.


Jennifer Borchardt (1:03:26)

or training track beyond that. Although companies are definitely moving away from that, but there's still a considerable number of organizations that that is their AI strategy.


Brett House (1:03:37)

Yeah, it's basically replacing like Google search. Go and do a search on X approved ⁓ application. Yeah.


Rio (1:03:43)

Or here's copilot, right?


Jennifer Borchardt (1:03:45)

Yeah,


I actually think co-pilot, so I don't use co-pilot because I, you know, I work for myself. I actually think co-pilot has or had the potential to be the most useful AI tool, but it depends on, on clean, well organized data in order for it to work. And that is the part that is missing. So you have to have good data and good information in order for it to.


to create good outcomes, but you also have to have people who know what good looks like and are given either within themselves or given from leaders and influencers in the organization. What specifically is AI usage going to look like? How do you know when we're doing this right? What are some checks and balances we're going to put into the system?


Brett House (1:04:41)

Yeah.


Jennifer Borchardt (1:04:43)

And those are, I wouldn't even say that the nuances, they're bigger than nuances, but those are the things that are still evolving. The types of companies, yeah, and really.


Brett House (1:04:51)

Yeah. And it's the education layer is critical, right? It's less


about the technical layer and more about education and then, and then test and learn and start to roll this up in a structured way.


Jennifer Borchardt (1:04:59)

Yeah.


I actually think that the small humans, human aware AI native companies like, like Drew Celtic's company, those, those are actually the companies that I think are best positioned to be successful. They don't, they don't, they don't have to unlearn much. Unlearning is much harder than learning. Unlearning and relearning is much harder. Yeah.


Brett House (1:05:22)

Yeah, that's a really good point, right?


Rio (1:05:24)

Yeah, you don't have the legacy thinking


or legacy tech or legacy anything, right? You're just, you're starting fresh.


Jennifer Borchardt (1:05:28)

Yes.


Brett House (1:05:28)

But all the way back to your


original point around the mergers and acquisition, when you get companies merging, they have to unlearn patterns of work, patterns of organization. People talk about it as fit or culture. could be a whole bunch of things, systems. And they, you, lot of times have completely different approaches, kind of clashing.


Jennifer Borchardt (1:05:32)

Yes.


Yes, culture. Yeah.


It,


which is why I think the business, you were talking about before the, the, the consultant, the consultant or the firm that was going after these small medical practices. That is, that is a very good business model because you're going into someplace where the stakes are arguably lower. And there's not. Yes.


Brett House (1:05:56)

Yeah.


And the use cases are much simpler. Faster time


to value. ⁓ You keep it super simple. Yeah.


Rio (1:06:12)

There's a lot of low-hanging fruit, right? Everything's low-hanging


Jennifer Borchardt (1:06:13)

Yeah.


Rio (1:06:14)

fruit.


Jennifer Borchardt (1:06:14)

And you don't have these super expensive, super complicated legacy tech stacks. You can just introduce a new system. It's not quite as easy as that, but there's, there's, there, there are a lot, a lot fewer headwinds.


Brett House (1:06:19)

Yeah.


Yep. So ⁓ should we go into quick hits? Yeah. So I'll start. What's the biggest misconception about AI and jobs right now?


Rio (1:06:32)

Do quick hits.


Jennifer Borchardt (1:06:33)

Sure. All right.


that AI is going to eliminate all jobs tomorrow. It's eliminating some jobs now, but it's not happening as fast or as broadly as people think. There's still an opportunity, a lot of opportunity and more time than you think to level yourself up and find your niche. I think AI is broad enough now that there's a lot of, it's too much for one person.


to focus on, but find your niche. Find what is meaningful and interesting and what do you naturally gravitate towards and how does AI fit into that? And that is your learning path.


Rio (1:07:20)

What's one role in design or product or just UX generally that you think will disappear faster than people expect?


Jennifer Borchardt (1:07:29)

production design.


Rio (1:07:31)

Is it already gone?


Jennifer Borchardt (1:07:33)

I don't think, I mean, I think it exists, but not nearly to the extent it did even a few months ago. So any, well, make a banner.


Brett House (1:07:39)

Yeah, and define production design.


Yeah, yeah. Well, and I've talked to a lot of traditional designers, production designers that say, it hasn't been replaced. you know, and yet they're still building from scratch PowerPoint decks or Google Slides decks. Yeah, it hasn't reached them. And I tested out Gamma the other day, created an outline ⁓ in Hermes and...


Rio (1:07:44)

resizing banners, like,


Jennifer Borchardt (1:07:46)

Yeah.


It just hasn't reached them yet.


Brett House (1:08:05)

and popped it into gamma for PowerPoint. Cause my designers telling me, oh, you got to still build this because you know, the tools they were trying to go directly from the LLM to like production of something that the sales team could use. And you pop it into another AI capability, gamma. the output was just stunning. I'm like, you've just eliminated any need for me to ever manually build the PowerPoint deck again. And I just saw this a couple of days ago and I was like, this is astounding. This is incredible.


Jennifer Borchardt (1:08:23)

Yeah.


Yeah.


Anything, anything that requires any level ⁓ of, of specific of specificity that needs to be replicated AI cannot do that. You still need, you still need a talented designer to do, to do this. think, I think, I think something else that's going away is the, ⁓ is someone with just a single title. An interface only designer, a UI designer that is going away. They are being automated out of existence, but somebody who


Brett House (1:08:56)

Yeah.


Jennifer Borchardt (1:09:01)

who can practice AI enabled across a number of UX, across a number of experience capabilities and can then extend it to business and technology or marketing and compliance. Those, that is where the job is going. The single title, the single career path, the, it's going away. It's generalists.


Brett House (1:09:21)

The end of specialization. Yeah.


You heard it here on Signal of Noise. So if you had to bet, does AI create more opportunity? think I the answer to this question, but I won't ask a leading question. Create more opportunity or more displacement over the next 10 years?


Jennifer Borchardt (1:09:39)

I think


ultimately more opportunity, but it's not going to come easily or painlessly. This opportunity is going to come after a generational shift in how we view and define work. And I don't think that this is even 10 years. I think in two or three years, maybe even fewer than that. Yeah.


Brett House (1:09:51)

Yeah.


Yeah, and it's a paradigm shift in how institutions


and government operate, right? How regulation operates, how, you know, I'm not sure we're going to have, ⁓ you know, universal income in the next two years, but it's certainly going to require a paradigm shift in how people think and partition sort of AI in their lives.


Jennifer Borchardt (1:10:00)

Thanks


Rio (1:10:13)

Well, thing, one thing's


wrong. I've never seen technology advance this quickly. So I think that is unprecedented, but I think what will be slower is this diffusion throughout companies, throughout society. mean, that, I agree with your point earlier, probably a little slower than we're predicting now, but it's going to happen.


Jennifer Borchardt (1:10:20)

Yes.


Yeah, not everyone lives in the AI world that we do and that your listeners probably do. There's an actual world out there that doesn't look very different. What's that?


Brett House (1:10:35)

Have you ever talked to a lawyer? Have you ever spoken to a lawyer?


Have you ever spoken with a lawyer? That's a, I have a thing, I, like something, something, something evil within me, the little devil on the left side of my shoulder or whatever, just wants to see the legal industry completely disrupted. And I, I, I'm putting that on the record because there is no justification for $2,500 an hour or $1,500 an hour.


Jennifer Borchardt (1:10:41)

Okay. I just did the other day, actually,


my god, yes. me, ⁓ right?


Brett House (1:11:03)

often for work that's lot of billable hours that's being given to a second year associate. And so to me, those types of industries, and you and I have talked about this sort of billable hours model. The people at the top, can orchestrate and conduct and have the strategic systems thinking and the connections and know the letter of the law from decades of experience. That's a different play. It's everybody underneath them that are doing all of this rote work.


Jennifer Borchardt (1:11:08)

Yes. ⁓


Yeah.


Brett House (1:11:32)

⁓ you know that there


Rio (1:11:33)

Well, consulting


is interesting. I think that we're already seeing it be impacted, right? And I'm even telling clients, I don't want to do any time material work. A lot of strategy work is selling, Brett, you're making fun of PowerPoint production. A lot of it is selling, I joke with friends when they say, what do you do? Part of what we do is sell expensive PowerPoints, right? But it was never about like,


Jennifer Borchardt (1:11:36)

Okay.


Okay.


⁓ That's amazing.


Rio (1:11:58)

this PowerPoint slides, it's about the message they're conveying, right? But I think getting to that message is different because a lot of the tasks that we used to bill hourly for, AI can do much better more efficiently, right? So I think that consulting is changing. I tell clients, let's just do fixed fee. You tell me how much this is worth to you and we'll produce it within the timeframe you want. I'm not going to tell you this is the team we need because that's irrelevant.


Brett House (1:12:01)

Yeah.


Jennifer Borchardt (1:12:14)

Yeah.


Brett House (1:12:18)

Yeah, that's a point.


Jennifer Borchardt (1:12:19)

I 100


% agree with that. And that's subject to PowerPoint. One of the things that I love that's happening right now is that AI is taking over PowerPoint production. what it's not taking away, what it cannot take away, what humans still need to be able to do is storytelling. If you can tell a good, compelling, relevant story, you're going to sell, you're going to be successful.


Brett House (1:12:32)

Thank God! How many hours of our lives?


Yeah.


Rio (1:12:47)

⁓


Brett House (1:12:48)

yeah,


Rio (1:12:48)

And


Brett House (1:12:48)

and the best squee- Exactly. Exactly.


Rio (1:12:49)

that takes the years. That takes the years they've experienced to have the expertise and the credentials and like the, and like in the presence to be, get up there and tell that story and sound credible.


Jennifer Borchardt (1:12:49)

This is a opportunity to find out. Yes. Yeah, right?


Brett House (1:12:57)

It's not about the slides.


It's not, yeah, and it's, you know, the greatest quote of all time about PowerPoint is for people that have no power and have no point. Right? Because it's not about the technical sort of build of a slide that it's about the message behind it. So it really doesn't matter ⁓ what it looks like. Yes, you want to be buttoned up and professional. That stuff can be automated. It's about, yeah, to your point, what


Jennifer Borchardt (1:13:05)

I'm gonna read that a little bit, just feeling it.


Rio (1:13:06)

I've never heard that as funny.


Jennifer Borchardt (1:13:14)

No.


Rio (1:13:23)

But, but you'd be shocked


how many hours of consulting at the big, the strategy, like the MBBs, um, like that people used to waste. Like I used to see people mess around with slides for four hours per slide. I mean, I told, I, I'm not kidding. Right. I, so I tell people each slide should not take you more than half hour max absolute max. Like he may be even quicker than that. Right. I mean, it should take you a few minutes, right.


Brett House (1:13:45)

Well,


and I took a pretty in-depth proposal for go-to-market strategy for an independent agency, took that proposal, basically got a summary of that proposal through Hermes, and told it to prep it for slide development, right? And it created that, and then we popped that into Gamma.


And within five minutes, was just one of the better decks that I've seen produced. I mean, it just, was incredible. Maybe not brand on, but just colors, layout, workflows, structure buckets. Yeah. And I'm like, and I covered all the key themes in a way that was smarter than I've seen any person honestly create on a marketing team or otherwise. I'm like, that's powerful. Yeah.


Rio (1:14:22)

The structure of the thoughts to presentate, yeah, for sure.


Jennifer Borchardt (1:14:32)

But, but you need, you needed good inputs and good data to


make that a valuable slide, a valuable deck, right?


Brett House (1:14:38)

Yeah, yeah.


yeah, and that, you, I'm saying again, that's where we all think like, yeah, he's making this easy. Yeah, there were hundreds of hours of training, of educating, of all this, all these inputs that are going into the agent that's making a decision as to, hey, this is how the proposal structure should work. This is what we're seeing in the proposal based on my entire business strategy and every, and all the documentation behind it.


Jennifer Borchardt (1:14:57)

Yeah.


Rio (1:15:02)

And on this slide,


it's missing this, please add this in and restructure it and I'll expand that. So it takes a lot of like back and forth and prompts and work within SWAP mutating system. I that's where we are today, at least.


Brett House (1:15:08)

Yeah.


Jennifer Borchardt (1:15:10)

Yeah.


And then it changes something you don't want to have change and you need to be so prescriptive. it's like, should have done this myself, but I do think that that type of frustration is decreasing with every new.


Brett House (1:15:21)

Yeah.


Yeah, and people like


throw up their arms. I mean, I've got people and we'll finish in this, I've got I know people like we're all of the age where at least, you know, some of my friends are like, you know, I rather just, you know, fast forward to retirement than have to learn all this stuff. And I'm like.


Jennifer Borchardt (1:15:44)

I mean, I get it.


Rio (1:15:44)

This stuff is fun. mean, at one hand I get


it, but this is most fun I've been having at work in a bunch of years. It really is. I'm doing stuff I haven't been doing. I've been like actually building deliverables for clients more than I have in a bunch of years. But because I'm using AI, all the the dreary part of it is kind of quicker and almost gone. So actually having more fun at work now than I have in a while.


Brett House (1:16:05)

Yeah.


Jennifer Borchardt (1:16:05)

Now


what we need AI to take away is all those meetings.


Brett House (1:16:09)

Yeah.


Rio (1:16:10)

Yes, it's in


an avatar. Why not?


Jennifer Borchardt (1:16:12)

Yeah.


Brett House (1:16:13)

Exactly. Well, this has been


great. Thank you, Jennifer. And we're certainly thrilled to have you as part of the Signal of Noise family, as part of our Executive Voices ⁓ Network. Hopefully you'll be writing and maybe appearing in some more podcasts. Yeah, and maybe even some AI education because we realize, you know, I think that's a big play. A lot of the build companies like StealthX and others I've seen have gone into sort of the education angle because so many of the lower tech industries, even the higher tech industries to the Google point.


Jennifer Borchardt (1:16:25)

⁓ I'm already working on something.


Yeah.


Yes.


Brett House (1:16:42)

need that help on how do we actually rule this out and how do we start small and what's the framework and how do you spread the wealth of knowledge in a controlled way within big organizations or smaller organizations.


Jennifer Borchardt (1:16:48)

Yeah.


I mean, that is the promise of technology, right? If it's in responsible benevolent hands, then everyone can get the benefits. So I'm gonna stay optimistic that that's where the story's eventually going to go.


Brett House (1:17:11)

Yeah, it's not it's not going to end up in, you know, Blade Runner or Terminator. Yeah, or Battlestar Galactica. Right. It's kind of funny that all of this stuff, I know, and I'll end with this, but I think I just can't I can't fathom or put my wrap my head around how fast stuff has changed within our own lifetimes. I think we're all like Gen Xers here. Right. I mean, my God, I mean, you know, when Rio and I first moved to New York City, we were getting Internet delivered through disks.


Rio (1:17:11)

Love it.


Terminator, right? Skynet, yeah.


Jennifer Borchardt (1:17:16)

RoboCop.


Good night.


with them this year.


Brett House (1:17:41)

and modems with AOL, via AOL. so to fit.


Jennifer Borchardt (1:17:41)

An AOL?


Rio (1:17:45)

know, Brett, I had a Commodore


64 with like a dial-up modem, my first modem. used like, know, that noise it'd make, would call in. It was just, I mean, it was crazy, right? I was, yeah.


Brett House (1:17:52)

Yeah, you used to fat


Jennifer Borchardt (1:17:54)

I should make them my


ring cut.


Brett House (1:17:55)

like and just to see all of this happen I mean from the internet age the length of broadband to the internet age to the growth of digital advertising and everything around that to You know, we're like on the cusp of quantum computing ⁓ Going mainstream, right? It's it's pretty incredible. I think how fast all of this stuff is happening and it's within our lifetimes, which is mind-blowing


Jennifer Borchardt (1:18:08)

Thank you.


Yeah.


Brett House (1:18:19)

Well, this has been a terrific conversation. We could go on forever. On a Friday afternoon for late Friday afternoon.


Rio (1:18:21)

Yeah, this is fun.


But you've got to get to see Wrath


of Khan and meet Captain Kirk.


Jennifer Borchardt (1:18:27)

I do. Yes. I'm going to play


for Mr. Shatner. So I gotta run. It's been real. No, thank you so much. I'm excited. Thank you.


Brett House (1:18:32)

Yeah, it's going to be quite an experience. Yeah, Wrath of Khan, it's a classic. And so thanks everybody


for, if you've made it this far for joining us on Signal and Noise for a terrific podcast. Visit us at www.signalinoise.ai. You can read articles, watch videos, listen to podcasts. It's all there. And you can also find us on YouTube, Spotify, Apple Podcasts. And we are putting out a ton more content.


and we will be at possible in the very near future. So ⁓ talk to you soon and thanks for joining us.


Rio (1:19:06)

Thank you.



bottom of page