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The impact of AI on the workforce: A state-level case study · Practical AI

• 0:04
  Jerod: Welcome to the Practical AI podcast, where we break down the real world applications of artificial intelligence and how it's shaping the way we live, work, and create. Our goal is to help make AI technology practical, productive, and accessible to everyone. Whether you're a developer, business leader, or just curious about the tech behind the buzz, you're in the right place. Be sure to connect with us on LinkedIn, X, or Blue Sky to stay up to date with episode drops, behind the scenes content, and AI insights. You can learn more at practicalai.fm.
• 0:36
  Now, onto the show.
• 0:49
  Daniel: Welcome to another episode of Practical AI. This is Daniel Wightnack. I am CEO at PredictionGuard, and I'm really excited today to follow-up on a number of topics that that Chris and I have discussed over previous weeks related to wider AI innovation, what's working across, various industry sectors, how that's actually happening, how that's impacting workforce. Because we have with us today Chelsea Linder, who is vice president of innovation and entrepreneurship at TechPoint. How are doing, Chelsea?
• 1:23
  Chelsea: I'm doing great. Excited to be with you today.
• 1:26
  Daniel: Yeah. Yeah. I know you and I met a little while back because you were kind of making your way into, I think, what is your current role and focus, part of which is this AI Innovation Network, which you set up in Indiana specifically, and that's been very successful. But how does one get to get get to that point? Describe a little bit about your background and kind of how you end up thinking about these kind of wider AI innovation network things and workforce things.
• 2:00
  I know we'll talk a little bit about that later.
• 2:02
  Chelsea: Yeah. For sure. So my original background is actually in user experience research. So I did that work for a while, at a startup here in Indiana called Angie's List. And then I took a career detour to work in the venture capital space.
• 2:21
  So I was a partner at Generator, which is a startup accelerator and VC firm where we invested across the country. I got exposure to all the most cutting edge technology and innovation that was happening at the earliest stages through that impacting investing activity. So then when I joined the TechPoint team, TechPoint is a nonprofit and our mission at TechPoint is to drive Indiana's digital economy through talent, innovation and community. So when I joined about two years ago, we were talking a lot about what does that mean and what do we need to drive, especially through that innovation lens to ensure Indiana's economic success at our core. We're really an economic development focused organization.
• 3:12
  And of course, staying up to date on the most cutting edge use cases for AI was definitely one of those topics. And we had the opportunity through a federal program called the Growth Accelerator Fund Competition that's hosted by the SBA to apply, to do assessment of our community and identify areas of needs specifically around AI education. So I think that's when I met you, we were doing a needs assessment, mapping out the different stakeholders and resources we had across the state. And the thing we kept hearing over and over again was people across the state of Indiana wanted access to just know what other people in the state were doing to share best practices with each other, really get in the weeds and learn from each other's mistakes and each other's opportunities so that we can all, you know, rising tide lifts all boats. We can all support each other and making sure that Indiana can stay on the cutting edge of this new technology advancement.
• 4:12
  So we got the grant.
• 4:14
  Daniel: That's great. It was successful.
• 4:15
  Chelsea: AI network. Yep. Yeah. It was successful. And so we launched the AI innovation network to try to help, build that peer group and and convene those folks to share those best practices and keep each other up to date.
• 4:27
  Daniel: Yeah. And how long has that been going on now?
• 4:30
  Chelsea: So it's been going on for almost a year. We launched it in January.
• 4:34
  Daniel: Congratulations. Yeah.
• 4:35
  Chelsea: Thank you. Yeah. And we have, as of today, three ninety nine members of the AI Innovation Network.
• 4:42
  Daniel: Surely, as this goes out, because it's prerecorded, you will be at 400 already. So we'll just say we'll just say 400. I'll I'll I'll find someone to to register. So that's That
• 4:55
  Chelsea: sounds great. Yeah. So, you know, it's it's really cool to see that the market is interested in what we're doing and that we are providing the value to our community that we set out to provide. That's really cool.
• 5:08
  Daniel: Yeah, have you, as you were looking at this kind of need, of course, like in a specific geography, I mean, you think about where we're at, we're surrounded by the industries that are sort of being transformed by AI. So whether that's like life sciences manufacturing or logistics or all of those things of course are kind of surrounding us. As you did those needs assessment, as you pulled together, and we'll talk about specific things that maybe you're doing that have worked well or learnings that you've had that maybe you wanna pass on to others trying to do this type of work. But as you evaluated that, were there examples out there across the country of other kinds of things like this or what kind of went into the inspiration, I guess, for the kind of mechanics of what this could or should be?
• 6:06
  Chelsea: Yes. So we mostly drew inspiration from our other networks that we run at Tech Point, especially the Indiana Founders Network, which is a network that I launched the year prior to this one. And we have gotten a lot of great market success from that as well and designed this network to be similar to that one. But there are a couple of key differences. One of them being we really did intentionally wanna serve the entire state.
• 6:33
  And that can happen at an only in person convening. So we wanted this to be hybrid and we've been navigating what that means and how to do it well over the past year. But it has given us the opportunity to serve people in the middle of the cornfields, in the manufacturing floor, you know, all across our state. And that has been a really powerful, impact from the network.
• 6:55
  Daniel: Yeah, and for those out there, maybe kind of across the country that are maybe in an environment where certain people, maybe they are in SF or somewhere and they have an AI meetup every night, but there's certainly large parts of the country that are trying to figure out kind of how to navigate this. And of course, we'll talk about the workforce things later, but just in terms of the community and what's needed around this, what have you found to be some of those, let's say the core components of what people have responded to well and gotten value out of? So maybe that could inspire others in different areas that are trying to do some of this networking or maybe even spin up their own sorts of communities related to this technology.
• 7:45
  Chelsea: Absolutely. I think the number one benefit that we have here in Indiana is Hoosier hospitality. The way that that shows up for the AI network is that our members are extremely willing to share and be transparent about their work. And we very rarely come across instances where somebody says like, no, I need to protect my IP. I'm not willing to share.
• 8:10
  Or we just don't come across that kind of ego driven unwillingness to be open. And that is only to the benefit of the other members. Especially at the beginning, I think people were so excited to have an outlet to talk about their projects that we just had so many people raise their hands and say, I want to share what I'm working on. I would love to get the feedback from other experts in my field. I would love to help a new person learn how to do this or replicate this type of work within their organization.
• 8:40
  I think it's the power of having a strong community of helpers. And I don't know the secret to building a strong community of helpers, but I do think that that's really the foundation that we were able to build off of.
• 8:52
  Daniel: What do you think that eagerness I mean, I've even seen it on the podcast here with people that we've had on that are really wanting to, I guess they've felt pain maybe through some of their AI journey and maybe that's part of why they want to share things. What is your sense of, yeah, why is part of that eagerness there and what are they hoping to provide like in the AI innovation network that you run when people are sharing their stories about what's working, what's not working? Yeah, what are their motivations? What are they hoping to see?
• 9:27
  Chelsea: I think that, like I said, a big part of it is being able to learn, or identify opportunities to get better at what you're doing professionally. I think that's a big piece of it. I also think that we, as a community are we're focused on education. Indiana has like the most universities per capita, I think except for Massachusetts. And so for us, like being a lifelong learner, being in an environment of people who just wanna learn a lot is a big piece of it.
• 9:59
  And then thirdly, I would say one of the key topics that our advisory council who we have helping us with the content for the network, What we talked about at the beginning was a feeling of imposter syndrome of, I think I'm doing a good job, but am I actually? I don't have a lot of peers in this work that I can verify with. So a little bit of willingness to be an open book or show your work just to combat those feelings of imposter syndrome. We've started to try to kind of democratize the opportunities within the network, doing more live prompting or live coding activities, again, just to help with that feeling of imposter syndrome and make everyone see, you know, they're not the worst person in the room at whatever they're doing.
• 10:47
  Daniel: Yeah. It helps to be in a room with what you might perceive as very smart people, but in reality are other practitioners. And certainly I've noticed that over time where I have a very small sphere of knowledge, but others have different sort of spheres of knowledge. And when you, I think start to realize that, that we have a lot to learn from one another and I may be able to talk about whatever security related concerns with AI agents. I certainly can't talk about other things at the core of pharma manufacturing and how AI fits into that always.
• 11:30
  So like the, yeah, that sort of cross pollination is really exciting. Does that happen? Like what, describe for us kind of an AI innovation network event? Like, what would what would that typically look like? What could people expect?
• 11:48
  Yeah. How do people show up there and what what happens?
• 11:50
  Chelsea: Yeah. So we have kinda three different buckets, I would say, of events. So the first one is just purely networking. Just make friends, get to know people in your field. And so we facilitate that through different activities or different types of icebreakers to just help folks feel more comfortable in the room.
• 12:08
  Secondly, we do on a quarterly basis, a case study where we'll have a partner come in and present about a project that they've done under within their own organization or for a client. Then we kind of take it as similarly to how you would structure a PhD course, where we'll have the beginning half be about describing the case, describing the problem, break into small groups and have a discussion about how would you approach that problem and what tactics would you use and then wrap it up with what they actually did on the outcome side. And then we follow-up with a written documentation of that case study. We're trying again to just give that more in the weeds knowledge to the practitioners that are in the group. And then the third one I would say is more of that kind of open book sharing about projects or processes that folks are working on to be able to get more real time feedback or just show how an application may work inside of a different organization.
• 13:10
  For example, recently we had a fantastic presenter from Baker Hill come in to talk about how they encouraged adoption across their team, which is, of course, a really major issue that we're all trying to talk about right now, not only adoption, upskilling and rescaling up the workforce, and being able to share a shining star example of how it went well and what they learned. It that's just incredibly applicable to other organizations that are in the room.
• 13:49
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• 15:53
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• 16:00
  Daniel: Well, Chelsea, it's really great to see, this happening in my backyard and see some of this innovation network being formed. What lessons do you feel for maybe, again, others out there that are thinking about forming communities around this topic, around AI, whether that be on Discord or hybrid or via in person events. What have been some of the lessons you've learned that are maybe like minds to not step on or just like challenges that are part of it that you haven't totally figured out yet?
• 16:41
  Chelsea: I think the number one would be not assuming that what works for our community will work for your community. And that's why we have the advisory council. I am not an in the weeds AI practitioner. That's not my day job. And so I never would want to pretend that I was or lead the the content of the network in that way.
• 17:02
  So we built an advisory council and they've been instrumental in helping us identify where the acknowledged gaps are, how we can help build those and championing the network in general as well. So I would say step one, start by talking to your community and figuring out what they actually need. And then I think you have to kind of capture and hold onto the momentum as tightly as you can. So certainly at the beginning, we had a lot of excitement, enthusiasm, momentum for the network. With some things we did a great job capturing that and pulling it forward and with other things we didn't.
• 17:36
  So some of those initiatives have kind of slowed down or aren't happening anymore. So you kind of have to pick and choose, what are the things that are really providing the value you wanna provide that you can double down on? And then are there things that maybe aren't working out that you don't need to continue doing moving forward?
• 17:53
  Daniel: From your perspective, because you have been working on this over this past year, have you seen just kind of general trends or shifts in people's view towards AI within companies or people's education around the topic or the types of things that they want to talk about. Because that's something maybe for some of us that are in the industry or like building AI things, we have our own view of maybe what's on people's mind and that sort of thing, where they're at in terms of what they're thinking about. And that doesn't always map to actual discussions in companies, where people are at on their kind of educational journey, what they want talk about. Like, are they just trying to get their first chat interface up and like have that be a win? Are they thinking about, tying in AI functionality to existing products or building custom tools?
• 19:01
  Are they thinking mostly about, like you said, the adoption side of things? Have you seen that shift, over this past year and any anything to kind of highlight there?
• 19:14
  Chelsea: Yes, absolutely. I think, we're really uniquely positioned as TechPoint because we have working relationships with everyone from Eli Lilly, which was recently named the number one most AI ready company in the world.
• 19:29
  Daniel: Oh, I didn't know that. That's so that's awesome. Congrats to that.
• 19:32
  Chelsea: Really cool. Right? It's great. All the way down to, like, little baby startups or more main street lifestyle type of businesses that have perhaps been operating for many, many years and have basically zero technology adoption. Right?
• 19:48
  So we have the viewpoint of that entire spectrum. And what I've witnessed over the past year, or maybe even more than that, is a democratization of access, which has been really cool to see where, you know, Lily has all the resources and all the capabilities and incredibly talented and smart people to help get them to that place. And, you know, automotive shop down the road doesn't, but thanks to a lot of the initiatives that have happened both within Indiana and just in general with more education and awareness of AI, that small business has the same access and opportunity. And that's really cool to see because now we can see how a small business may be able to grow exponentially more quickly, increase their productivity, etcetera, in a way that they really never would have been able to before. With that in mind, I think what's happening is these businesses are realizing that this is a must do.
• 20:51
  And so now I'm getting more and more inbound questioning from businesses that probably don't even have a tech team at all, have never really thought about this before saying, I know I need to do this, How do I get started? And that's a really great position to be in to say, okay, well come to the AI Innovation Network. And then we'll introduce you to some of the people who are already leading the way here and they can share with you what works and what doesn't work and we can go from there. So that's been a fantastic thing to see. On the flip side of that, I think a big challenge that every business is facing right now is with the workforce and upskilling, reskilling, job security questions, all of that.
• 21:35
  So Tuckpoint just recently published a report about AI driven skills and the changes in workforce demands due to AI. And it's it's just some really fascinating data. Right? Like, job postings for generative AI engineers are up by seven times year over year. Fascinating data to think about.
• 21:57
  But at the same time, we're seeing higher levels of unemployment, higher levels of reduction in forces, all kind of also attributed to AI. There's a lot of contrarian data, but at the end of the day, I think we all can agree that we need to focus on upskilling and reskilling and that we need to do it in a way that can preserve the workforce as much as possible. And we're so lucky to have companies like Lilly in our backyard that are leading the way on that.
• 22:24
  Daniel: Yeah, this report is really fascinating. I encourage people and we'll link this in our show notes, but this is a report AI driven skills for Indiana's economy insights from employers and industry trends. Certainly something like at the heart of our country that is the reality kind of on the ground of what's happening, which is why I think this is a really great way to present this. So give us a little bit of a background on this particular report, kind of how it came about and the data behind it.
• 23:01
  Chelsea: Yeah. So if you recall, I mentioned that TechPoint achieves our mission through three pillars. So innovation, community, and talent. And the talent pillar has historically been about solving the brain drain problem we had in Indiana, helping retain and recruit workforce, especially tech workforce to come to our state or stay in our state once they graduate from one of our amazing higher ed institutions. And over the past couple of years, we've been seeing that problem not be the biggest problem for employers anymore.
• 23:32
  So it drove TechPoint to go back and talk to our stakeholders and employers across the state and say, okay, we sense that retention and recruiting of tech talent is not your biggest problem anymore. What is? And we kind of already knew what they were gonna say, but they absolutely validated our assumption that aligning the workforce with these new in demand skills is their number one talent problem right now. And so with that in mind, we started doing a lot more of this research and ultimately came up with the contents of this report. And what we found out is that right now we needed to focus on three things.
• 24:12
  We need to focus on integrating AI into the workforce training program. So including higher ed, as well as within an organization upskilling, We need to focus on doing that in an industry specific way because the AI skills in every industry, as you mentioned, like manufacturing, AI in a manufacturing floor looks very different than AI in pharmaceuticals. So we need to align that in industry specific ways. And then we need to expand adoption past our early adopter sectors. So we really need to push into some of those more lagging sectors like agriculture, etcetera.
• 24:49
  And then thirdly, we need to create cross sector AI knowledge as well. So we need to share best practices across all of our strongest industries. And the AI network is one of the ways that we can do that. But if we can do these three things, then we think we will be able to maintain a competitive workforce here in Indiana.
• 25:06
  Daniel: Yeah, some of these things, I encourage people to look at the report. I mean, there's some things just generally that are interesting kind of nationwide and some things that are kind of a case study of where we live specifically. But yeah, job postings for generative AI engineers up 7X is really interesting. Another thing though that you mentioned is, job postings requiring generative AI skills in other IT roles are up 35%. And I think you have some graphs around this.
• 25:41
  So there is this kind of generative AI engineers or AI engineers specifically that is a in demand thing. It kind of reminds me back when everybody was looking for data scientists and we were trying to figure out how to get data science into workforce education all of those things, but that is up, but even more so or at a higher rate are these other technology positions that are requiring skills in AI. From your sense, is that due to just the, I know you also talk about or show kind of a graph in the report of companies adoption of AI, of official adoption of AI, maybe not shadow usage of AI, but official adoption of AI kind of rising up to 10% or beyond. Do you think that's just a matter of them figuring out that, yes, this is here to stay and we need to not just have a we need to have a SIS admin that also knows how to use these AI tools. Like what do you think is behind that?
• 27:00
  Chelsea: Yeah, I think it's partially because it's becoming clear that it's a competitive advantage. And so companies are doubling down on requiring those skills. You know? It's hard to ignore when you hear somebody like the CEO of Atlassian being like, everyone has to do this. Right?
• 27:19
  So I think that's that's a big part of it. And then I also think that, with Gen AI, as you know, I don't need to tell you this, this is really just the tip of the iceberg when it comes to truly implementing AI, not just Gen AI within your business and that what businesses are really gonna see success from is not using a chatbot, right? It's a many layers deeper application. And so I think having these roles is kind of the preemptive steps towards moving into those deeper applications where we're truly talking about process automation, etcetera. So I think it's the tip of the iceberg and I feel like those roles are going to continue to require some level of skills.
• 28:04
  Similarly to now how a product owner would normally be required to have some level of coding skills. Right? So I think we'll continue to see that proliferate across organizations. I recently was, able to present to a group of CFOs of companies that had 50,000,000 annual revenue or more. And it was really interesting to see the spectrum of I absolutely will never trust AI to do my finance work to, like, I love it.
• 28:33
  It does so much of the things that I used to hate doing. And I think we're just again, we're gonna see that proliferate across every business unit or every department within an organization.
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• 30:22
  Daniel: Chelsea, just another interesting thing that I see in this report, which kind of stood out to me is you talk about kind of the skills gap, which some of those like highest skills gap around AI and machine learning, data analytics, big data, cybersecurity, but there's kind of chart of most in demand roles now. And it kind of stood out to me that software developer or engineer is still up there as the highest in demand skill, even higher than the AI machine learning engineer. And so I think there is this narrative that we hear, right? Everything is a kind of mixed bag. It's a messy situation, but narrative on the one side of the spectrum is that, well, AI is kind of doing all of these software engineering things now and we don't need as many software engineers to do the same amount of work, which means like maybe I need to be an AI engineer.
• 31:29
  It's not enough to be a software engineer and maybe this is a timing thing, but I don't know if you have any thoughts on that. That one stood out to me as kind of even in light of all the vibe coding things that are going on, software development is still and and being a software engineer is still a good gig to have at least at least here.
• 31:51
  Chelsea: Yeah. So this data is trailing data. So I do expect that that number will change in the future as we you know, we're scraping job descriptions and things that have already happened. Right? So I do anticipate that those AI focused engineering roles will take over as the number one most in demand.
• 32:15
  But I also have been doing a lot more research on this recently. And as an example, I spoke with an organization who had laid off all of their junior devs. And they had their mid levels working with AI to do the work that the juniors were doing. And at first, the mid levels loved it. They were like, this is great.
• 32:36
  I'm getting basically the same quality work as a junior dev would give me. And I don't have to manage a human being. I don't have to deal with the emotional side of it. I can be as blunt as I want to. Like, they're never gonna get upset with me.
• 32:48
  Right? And they loved it. Well, six months down the road, those same mid levels are saying, can we please hire some junior devs? Because Oh, wow. I am so annoyed at my AI, and I don't wanna have to be the one managing that.
• 33:01
  Right? I don't wanna have to be the one doing the prompting, getting it to fix all of its dumb mistakes, hallucinations, whatever. So I think that just speaks to this is a transition in what those jobs look like, not a complete removal of those jobs from our workforce.
• 33:20
  Daniel: Yeah, that's really interesting. It reminds me of a discussion, I forget what episode it was on, but we were talking about this kind of vibe coding work and the actual mental burden of that work for an engineer is very different from the kind of normal, oh, I'm typing things in my editor, even with auto complete or something like that, where you have like multiple things going on at once. You're constantly context switching between different agents doing different things. And to your point, you kind of get into this like master orchestrator mode. And maybe to your point, like there are engineers who like doing engineering and they don't want to be just those orchestrator type people.
• 34:08
  So it's almost like a different, it is a different cognitive load. It's a different day to day workflow and we haven't, certainly haven't fully navigated that yet and understand, you know, what will happen, right? Yes. When that comes about. But I think at least Chris and I's view on the podcast here, I think we've said a few times, I don't think software engineers are going anywhere.
• 34:37
  The dynamics and kind of the roles may shift, like you said, but there's still going to be a need for those resources. There may additionally be a need for these, you know, increasing need for these resources around AI engineers and that sort of thing.
• 34:51
  Chelsea: Yeah. I will hold fast forever that the number one skill you need to vibe code well is to already know how to code.
• 34:59
  Daniel: How to how to code. Yeah. Hey. Good good point. That's a that's a great quote.
• 35:02
  Yeah. I I love that. Yeah. And I think, I mean, even back when people were first starting, like in the seventies and eighties when people were programming, I think they were already talking about, well, computer programs are eventually gonna be able to program and we're not gonna have jobs. And that was quite a while ago.
• 35:24
  Now things are moving a little bit quicker now, I think you could argue, and maybe there's some differences, but I think we're at least safe for the moment. Absolutely. Yeah. I am interested in this graph you show about the share of businesses that are using AI. Could you talk about the kind of range of industries that that represents?
• 35:47
  Just to give people, because you're listening on audio, a visual of this, it's essentially an increasing trend to more and more, right? But this goes from September '23 up to July '25. And September '23 was less than 4% in US, slightly less in Indiana, and then kind of both converge up to around 10% in July 2025. So around 10% of businesses using kind of using AI. So could you help us understand like, what does that mean using AI, what industries, etcetera?
• 36:26
  Chelsea: Yes. Yeah. So that data is from the US Census Bureau and it's a survey, it's the BTOF survey. And forgive me for not remembering what that acronym stands for, but it is a survey they do every two weeks. So that's why we see this data.
• 36:40
  You look at the charts, it's pretty lumpy, because they're getting different numbers of responses, etcetera, from different industries, different stratification every two weeks. But what the questions are, there's two different questions that they ask to pull that data. They're very specific questions. So one of them is, are you using AI to build your product at your organization? And then the second one is, are you using AI to do business within your organization?
• 37:09
  So it kind of touches on your question, which is what exactly are they using it for? Right? And so I didn't have access to the data for those two separate questions. I think that would be really interesting to see. Assumption is that very few are using it within their products and very many more are using it within their business operations.
• 37:29
  That's where the data comes from. And it definitely makes me feel positive about Indiana's trajectory to see us kind of catching up with the rest of the nation. But then we could compare that to the data recently yesterday or a couple of days ago coming out of MIT talking about how 99% of AI projects fail or pilots fail. So I think it's one thing to say, yes, I'm doing it. And it's a completely different thing to say we're doing it successfully.
• 37:57
  And I think that's the data that we're gonna have to dig in more to find out more about and how we can leverage programs like the AI network or otherwise across the country to ensure the success of our adoption programs.
• 38:10
  Daniel: Yeah, yeah. And it is interesting, like the perception around these sorts of use numbers. My friend Scott at Chip dot ai, I think he says something like 90% of people are using AI or some high net figure, 80% or whatever it is, 80% of people are using AI, but 10% of businesses are using AI, which if the 80% of the people in the businesses are using AI, it probably just means a lot of companies don't know that their employees are using AI. Do you find that to be a common theme amongst these discussions of even just figuring out what people are doing because people, it seems like are just getting access to whatever tools they think will make them productive because they actually probably do wanna be productive in their jobs or make their jobs easier or whatever that is.
• 39:06
  Chelsea: Yes, absolutely. And I think that's true about individuals as well. I would say probably almost 100%, at least in The United States, are using AI. They probably just don't know. Right?
• 39:16
  That tends a percent
• 39:16
  Daniel: of people. Fair enough.
• 39:17
  Chelsea: Yeah. Probably don't know. And I think that's the same with businesses. As an example, I my my daycare, one of my daycare teachers started using Gen AI to write her daily status updates about my daughter at daycare. Nice.
• 39:34
  I was not cool at that for
• 39:36
  Daniel: probably For various reasons. Yes.
• 39:40
  Chelsea: And when I went to go and talk to that administrator of that daycare, she had no clue that that her staff was doing that. And certainly, a daycare doesn't have an acceptable use policy of AI. Right? So I think there's a lot of those types of circumstances, and the best thing that a business owner can do is regardless of whether you think people are using it or not, you need to have a policy. You need to set up the appropriate guide rails so people can do it safely.
• 40:08
  And I think that starts with giving approved access to everyone.
• 40:11
  Daniel: Yeah, yeah. I think certainly there's companies that try to lock down from the beginning, but it doesn't really stop it. It just sort of stops the maybe egregious use. I don't know. Yeah.
• 40:28
  Chelsea: It stops them from doing it on their work computer.
• 40:31
  Daniel: Yeah, maybe so. That's way a to put it. But yeah, I very much encourage people, please go and look at this report. It's very nice. We'll link it in the show notes and you can find it at the TechPoint website.
• 40:48
  There's a lot of cool stuff coming up that if you are in the Midwest or in Indiana, TechPoint's doing, they have this great Muir Awards, which I've been to, which celebrates a lot of innovation that's happening and people can find out about that and nominate people. There's events coming up. What are you kind of, as you look at all the ecosystem, Chelsea, of things that of course have spun up this past year, but maybe kind of wider, just what's happening and the shifts that you're seeing in terms of adoption of AI and case studies around AI. What's most exciting for you as you're looking forward to the next year of the AI Innovation Network, maybe related to the network, but also just related to your own view of like how the technology is developing?
• 41:38
  Chelsea: I'm really excited about, how AI is pushing people to be even more innovative and think about things in even more uncomfortable ways than they did before. And especially right now in Indiana, the conversation around data centers, I think is driving a lot of forced innovation where we have some of the best scientists in the world here in Indiana, and they're trying to figure out how we can make data centers more efficient and more ethical and sustainable and all of those things that there wasn't a pressure cooker to figure out until relatively recently. So I'm really excited to see that because, you know, if we can find a copper alternative that can stay cooled more efficiently, that's gonna have impacts across so many industries. AI is just you know, data centers are just one use case for that. Right?
• 42:28
  So that gives me, I always, challenges drive innovation. And so the more of these kind of hard conversations that we have to have, the more innovation will stem out of it. So I'm looking forward to seeing what the next crop of amazing innovations are that come out of this really pressure full time that we're in.
• 42:47
  Daniel: Yeah, that's awesome. Well, I'm sure that TechPoint and the AI Innovation Network will continue to produce great reports around this, but also engage the community as all of that's going on. So really, really appreciate you joining us, Chelsea. It's been a it's been an amazing, conversation and, of course, look look forward to interacting with you in my own community around, things that that are going on. And, yeah, thank you so much for joining us.
• 43:15
  Look forward to having you back again sometime.
• 43:17
  Chelsea: Thank you. It was great catching up.
• 43:26
  Jerod: Alright. That's our show for this week. If you haven't checked out our website, head to practicalai.fm and be sure to connect with us on LinkedIn, X, or Blue Sky. You'll see us posting insights related to the latest AI developments, and we would love for you to join the conversation. Thanks to our partner Prediction Guard for providing operational support for the show.
• 43:46
  Check them out at predictionguard.com. Also, thanks to Breakmaster Cylinder for the beats and to you for listening. That's all for now, but you'll hear from us again next week.