The CIO’s Guide to AI Governance and Risk

0:00We're facing a date landscape that is intent on trying to regulate this technology in different ways. And a federal government that is moving much more slowly on that front. There are differences of opinion at the federal government. There's a number of people, the federal government, who think we need federal laws to trump state laws around artificial intelligence regulation. And there are others who say we need a lot of states to be the laboratories of democracy. It's a confusing field, and the way in which this is going to impact companies really depends on where they sit in the whole AI value chain. Are they developers? Are we employers? Do they work with customers who are consumers or are they other businesses? All these things are relevant questions to how the policy landscape is going to affect their day-to-day reality. Technology has transformed how we live, work, and connect. And behind that change are visionary people with bold ideas. Welcome to the Bridgecast, where host Scott Kinkak explores hybrid work, AI, and are evolving

1:01future with tech's brightest minds, presented by Bridgepoint Technologies, the nation's leading tech advisory firm, experience a better way, the Bridgepoint Way. Hi, and welcome to another episode of the Bridgecast. I'm your host Scott Kinkak with us today. And joining me is Paul is the executive vice president of global public policy and government affairs at the software and information industry association. Paul has led public policy work across three presidential administrations. He's co-authored major recommendations at the National Security Commission on Artificial Intelligence. He served as a senior counsel at the Department of Defense. He stood up governance at federal commission on national and public service. This is quite a resume, Paul. Today we will explore how companies should translate fast-moving policy into operational strategy,

2:02how to engage constructively with governments around AI and cross-border data flows, and what responsible advocacy looks like in an era where tech policy and national security intersect. What all of that really means is you have your finger on the pulse. We're trying to figure out a little bit more about what's going on. Paul, welcome to the Bridgecast. Thank you so much for having me, Scott. It's a pleasure to be here. Well, super excited, Paul. We always begin with, tell us a little bit about you. Where are we speaking from? Tell us about the family. Where'd you grow up? Get us, let us know you a little bit. So right now I'm in Washington DC. I'm with the Software and Information Industry Association. We are at Trade Association representing nearly 400 companies across the technology and information spaces. They include some of the biggest tech companies in the world, major developers of AI models, all the way down to startups. We represent lots of companies in the education technology space and financial market information, publishers and range of other companies. Got it, makes sense.

3:02And did you grow up in DC? So I grew up in Michigan. That's a side Detroit. And I lived basically in the East Coast ever since I left for college. Gotcha. You like it more rough and tumble out here because you went to Harvard Law. You just stayed out, huh? I stayed out on the East Coast, but I love the Midwest. I love it. Well, fantastic. So you go Harvard Law. And if I have the LinkedIn profile right pretty much, I mean, you different companies started on the firm side, but it's been almost all in public policy and government facing efforts. Was that a design for you when you were in college? I guess what you always wanted to do or did you just kind of land there and here you are decades later? Not at all. It kind of, it took me some time to find the space, actually. I started my career as a lawyer in private practice. I worked for over a decade and then I went into government. But I think over that time, I realized that it was really the government facing work that attracted me. And now I work largely in policy. I'm still a lawyer, but I don't practice law.

4:05And I'd like that kind of front facing side of things a little bit more. So it just took me a little while to find my niche. I, you know, we get that story all the time. I love getting, I love here kind of how people landed where they didn't think that they were going to land. Paul, you've got a rare mix of government, legal experience, private sector advocacy. It's quite a resume. Hit me with an early career moment that shaped how you think about or how you approach public policy today. Yeah. When I was a lawyer in private practice back in probably 2012, 2013, I was working with a lot of tech clients on internal investigations. And at that time, the general public didn't really talk about AI, but we were actually dealing with investigations involving AI. And the one thing that really stuck out was the level of education, the need to really educate government enforcers, government lawmakers about this technology.

5:05They didn't really understand. And that's something that's stuck with me because a big part of my job right now is really explaining. It's educating. It's talking about what laws might do, what regulations might do, and what's actually happening in the world, what the effects of those laws and regulations may be. Got it. And so I think about that. I mean, my next question on here, you got to a little bit, was tell us a little bit more about the SIA. That's the wall. Right. So you are an advocate on behalf of, I think you said, 350 to 400 some technology organizations facing government effectively. Working with government. I'll say. And by the way, there's a little bit of an echo. So if I, if I sound strange, that's why, but we'll just keep going. It's fine. I'll try to talk past it. So we work constructively. We try to work constructively with lawmakers, policymakers at the U.S. federal level across the U.S. states and also internationally on issues like AI and privacy and cyber security,

6:07cross-order data flows, the whole range of things that affect companies that are in the business of technology or information. The funny part, we run into this all the time. I mean, we have many guests on here who are innovators in the AI space, security space, et cetera. I'm going to guess that several of them may also be organizations that you work with. One of the questions we run into all the time, and we are, we rarely ask in tech whether we should, just because we can. And I read, I read through your policy priorities, I got myself prepared for today. I think part of being an American, my part of being in this economy is this just sort of unbridled innovation and commercialism at the end of the day. We're always in the Ken. And I think it's hard sometimes to pause and think about the should. So is that really where you guys are, just kind of getting people to think that way and getting government to try to kind of catch up with what's actually happening, kind of

7:11an innovation in these organizations? Yeah. There's a ton of interest around making policy, making laws that affect the tech industry. And, and when I talk about the tech industry, I'm talking about the broader tech industry. Not just the big players, big tech, but really everyone out there. And so a part of our work really is try to explain where innovation is today and how we can foster more innovation. But also recognizing that when we're talking with different policy makers, they may be coming from a different perspective. They may be coming from a perspective where they're very concerned about something. And so we have to try to meet them where they are. I think a lot of our listeners are probably going, all right, what's coming down, right? So as we think about like the current legislative regulatory and policy landscape around AI, where are we in the US right now? And then what do you see kind of in the immediate horizon? Yeah, that's the billion dollar question, really.

8:14So, you know, one thing backing up in your audience probably all knows this. But AI is affecting every sector of society right now. And in addition to some of the big AI labs, we're also working with companies that were not traditionally technology companies, but are using AI in many different ways, right? To improve their back office functions, to streamline workflows, to make sense of information that just a large amount of information is out there for many different markets. And so we're working very closely at the federal level and at the state level on legislation and also other types of policies. And I think you're seeing a different approach from the federal and the state governments. Last year, there were over 1,500 bills introduced across the 50 states around artificial intelligence. Now a couple hundred past, some of them are very significant and some of them don't have

9:14regulatory effects, say, on companies. But we're facing a state landscape that is intent on trying to regulate this technology in different ways and a federal government that is moving much more slowly on that front. There are differences of opinion at the federal government. There's a number of people, the federal government, who think we need federal laws to trump state laws around artificial intelligence regulation. There are others who say we need the state's being the laboratories of democracy. So it's a confusing field and the way in which it's going to impact companies really depends on where they sit in the whole AI value chain. Are they developers, are they employers? Do they work with customers who are consumers or are they other businesses? All these things are relevant questions to how the policy landscape is going to affect their day-to-day reality. I noticed in the policy priorities that you guys have for this year, you talked quite

10:15a bit about advocating for federal legislation. I think first, to your point, to organize the states. We don't have 50 sets of rules that we have to apply. Obviously, that becomes the basis of trying to create some kind of uniform international code, which I would love to get into in a couple of minutes, but hold on that. One of the specific things stated, I want to challenge you a little bit, is advocating that the federal government have the sole authority for model oversight. That's going to make some people uncomfortable, at least in their first read. Tell me what that actually means in your mind, federal government having sole authority for model oversight. Just walk me through that if you would. Absolutely. We like to distinguish between different actors in the value chain. There are the models, the big models that are being developed, the chat GPT, the Gemini, the Quad, and then there are companies who are using AI in lots of different ways.

11:18We call those deployers, and there are lots of times when deployers are also developing or fine-tuning models themselves. A lot of these models and uses really depend on those larger foundation models that are being built by the larger AI companies. There's been a move, one of several strands of legislative activity in the States, but there's been a move to try to regulate these models from tier models at the state level. Last year, we saw laws enacted in California and New York, and fundamentally what these laws seek to do is minimize the risk of a catastrophe happening because of the way the AI model is used or deployed. This is something that we think is fundamentally the role of the federal government because it would be incredibly complicated and resource-intensive to have 50-state looking at the exact same thing and looking at things like, what is the national security risk here?

12:19What is the risk that these models will act in ways that we're not intended? What are the risks of a massive public safety event and trying to convene resources that compute resources and the expertise at each state is something that is extraordinary. It's probably not the best use of state resources, but it is something that the federal government itself can do and something that the federal government already is doing to some extent. Really, the key is regulate enough to organize up 50 states so that companies can operate, but not so much that it infringes on First Amendment rights, that it infringes on the ability to innovate that it somehow slows economic growth in the area. That's exactly right. Another component of this, which is getting a lot of attention right now in Washington, is how do you draw the lines between where the federal government should step in and where state governments should step in?

13:21When we talk about potentially 50 laws, we're talking about 50 laws on every single aspect of AI policy, and there may be other areas where it makes much more sense for the federal government to step in, but what are the challenges we've seen? This is where I'll speak a little bit to what I hope your audience is, which are companies that are innovating in different ways. Other things we see is a interest in creating laws that apply across the board to AI. But if we actually look at how AI is used in the real world, the sorts of rules or guardrails who may want around how AI is used in medical devices are going to be different than how we want to establish safeguards around AI used in vehicles or AI used in consumer-oriented chat plots. These are all different types of things just because they use similar technology, AI in this case, doesn't mean that you should be treating them all the same way.

14:25This is where the insight from companies that are building models for their own uses or adapting models for their own uses is absolutely critical to guide policymakers in the right direction. Here, the director of IT or CIO of a mid-market enterprise and you're leveraging these, you're not constructing a model but you're leveraging models to innovate inside your business right now. I think that you're rarely thinking regulatory in policy unless you're in banking or health care or you're taking credit cards. Those are the frameworks people in IT are thinking about, but outside of that, they're not really thinking about it. If you are that guy or gal and you're doing some innovation for your business, what should they be thinking about policy right now to make sure that because you made an earlier point that I thought was great. The government's moving slower than the states. The states are moving slower than innovation at the end of the day is where we are.

15:25What's a practical piece of advice for that IT leader who's trying to do something with these tools right now? What should they be thinking about so maybe they're not surprised later? Any company that's using AI, there's got to be some governance mechanism. So the engineers need to be in conversation with the lawyers who are in conversation with the business people. But that's kind of the going in presumption. So what I think matters, and you want the lawyers and the policy people to provide very clear guidance to the engineers on this stuff. But the areas where I think you're going to be looking are what kind of uses? How are we using AI? And are we using AI first question in any high-risk scenarios? High-risk scenarios generally include, and I'm basically seeing this on laws that have been enacted so far, this can always change. But are you using AI in a health care context? Are you using

16:27it to determine employment eligibility or access to financial services? There's a series of areas that are generally considered to be more high-risk, and other use to AI are generally considered to be lower-risk. And so if you're going to be implementing additional audit practices or explainability requirements, you really want to focus on those high-risk uses because down the road, if there is any potential legal liability, that's where it's likely to be. So I would say mapping data, mapping how you're using AI, and categorizing the types of AI systems you're using, where you're using them, and what sources of data you're using as well. Or using data that includes personally identifiable information, there are likely to be more requirements around that, either today or after the fact if something should happen. And so doing those kind of things and working very closely with legal

17:30to conduct assessments and something like an audit is really what you should be thinking of right now. Guy, so basically apply the same level of care to that sensitive data in AI, but you're not necessarily advocating that at least today, we should be thinking about other data sets. In other words, we're already worried about personally identifiable information. We're already concerned about medical, we're already concerned about financial information. If you're going to touch that, apply the same level of care effectively as not to put words in your mouth, but feels like a practical story to give to the average IT leader. Yeah, I mean, I think that was a great way to put it. We should switch seats because you explain that very well. You know, so you're already taking a lot of care around certain types of data and that's important. Keep doing that. And then on top of that, layer the AI, the AI piece, like where are you using AI? Now that makes sense. I want to go a level above federal now when I think about your policies,

18:31because one thing we've talked quite a bit about on this show is, you know, we're trying to do the right thing. And obviously, that implies all the things that we just talked about in terms of securing personal information, et cetera. Obviously, the idea that we're going to build a consistent framework across the states means the Fed steps in. Now you've got the next level. And this is stated in your policies to use that to effectively try to begin to build some international standards, or a coalition of the willing, if you will, around beginning to define standards around AI. Because this really isn't a technology that has place at the end of the day. That's what makes even the difference between the states and Fed's Fed hard to manage, right? There's no real place to this. The question that I have struggled with, and I'm sure you guys have thought about it, given that it's stated in your policy, is just around, you know, what? And we're going to regulate here in the US. I think it's a pretty good bet that the bad actors are not going to regulate, you know, which means then we're taking all the power out of our hands

19:35and putting it into the hands of the people who are not like, how do you think about that? And whether that's state actors or bad actors, you know, what have you? But if we're going to build an international coalition around how do you want to build standards around AI usage, you know, can you define that risk a little bit for us? Like how do you think about building a federal got, you know, or international guidebook? That's not going to get followed by the, you know, by the, by the people who are ill-intentioned. Again, I think about this at different layers of the AI stack. And if we are talking about, when we are talking about models, we want models to be reliable. We want them to be able to, to the greatest extent possible, do what we're asking of them. And no model is going to be perfect. And, you know, policymakers need to understand that you can't achieve perfection. You can't prohibit a model from doing certain things unless you cut off use entirely. The major AI developers right now are already adhering to a set of best practices. We'll say, I won't use awards standards

20:39because in this area, standards often imply these international technological standards that are created by bodies like the ISO and IEC that would be complicated. Some of your audience may be familiar with some of the cybersecurity standards coming out in this and some of the other bodies. And those are in the works. And so you don't see companies wanting to be completely ad hoc about everything because no one's going to use their models. If they're trying to sell the businesses, businesses need reliability. You know, the companies we work with who are in the financial market data space, they need their models to work. Otherwise, we're going to create a whole bunch of downstream consequences and they're going to lose customers. So it doesn't make business sense. It doesn't mean we're calling for rigorous requirements, but we do need some sort of check to build public trust and ensure that the models that aren't there are reliable. Now, it may very well be that models coming out of other

21:41countries are not as reliable and they aren't adhering to those standards. And, you know, the market should be able to exclude those models that are not reliable and not use them. There's a number of challenges we face here right now, but there is certainly pressure in other countries to try to improve the trustworthiness of the AI that is being used in those countries. And that is something that we need to tap into. So if we're talking big picture about the competitiveness of US technology versus technology for some other countries, this is something that we can rely on and we should be relying on. Speaking of things that may come back to haunt, you know, IT leaders, it's one of the questions that I get a lot. And you've made reference to it in a couple of spots in the dock that I read around copyright and intellectual property rights. And I think, you know, and we talk often, you know, AI is meant to be referential, not self-referential, but referential, meaning that everything is a derivative. And, you know, there was a specific statement I wanted to ask you

22:46about in the policies. It would basically that you basically said effectively that the current system is well equipped to handle the challenges posed by emerging technologies just as it has in the past. The flip side of it is, you know, this will be, you know, there will be precedent set around the use of, you know, derivative works in AI multiple years. Right. After the listeners of this show have been using models that are making derivative works, right? Like, so how do you guys think about, by the way, I agree, this isn't a disagreement, it's just a matter of like, how do we collectively maybe just breathe deeply that we're not running headlong into a copyright or IP infringement scenario just by using a public model at this point. And just how do you think about that in terms of, you know, the timing of using tools now when having precedent set, apparently probably two years from now, three years from now, you know, in some of these cases that are going to get brought leveraging a court-based system, you know,

23:47to settle some of this. I mean, that's a really good point, that it's going to take some time for the law to settle. And as, you know, you cited, we think the courts are best equipped to deal with this. We think making a change to copyright law right now in the United States would have dramatic implications. And we don't know what the effects would be. And look, this is a I work with not only model developers, but also with publishers and publishers who have really valuable content, right, that they need to get paid in order to continue to produce. Think of publishers of scholarly journals and education educational textbooks. And this is one place where everyone has kind of come together that we really do think that the courts are best equipped to do this. And it's not a place where Congress should be acting. And when we're talking about copyright in the United States, that is law that is made at the federal level. There is preemption of state laws. So the states don't

24:47enact their own copyright laws, though there are a number of states that are looking to create copyright adjacent type laws that would extend protections to things like image, name, likeness that may not be copyrightable right now. And so this is a very challenging area. And there's a political battle going on right now between the content industry, Hollywood, and some of the other parts of the economy. Yeah, the thing that scares me, I would just say, from my seat, is just ultimately this is going to come down in my mind to a definition of derivative work and what of the previous work ultimately falls under copyright at the end of the day. And I think about it, I mean, in my part time, previous full time in my life, I was a musician. And every piece of music is a derivative of something that somebody's already heard. Nobody has an original thought at the end of the day. We're combining uniquely pieces of

25:49things that have been created over millennia, frankly, in the human experience. But like, how do you, how do you adjudicate that at the end of the day? Now, rip in somebody's lyrics off, you know, licking somebody's, you know, intro off again, I get it. But like, you know, there's a whole debate raging in the AI industry around Suno and tools of this. Like they're producing works that sound completely different than anything else, but just having them leverage derivative works is a question mark right now. You know, I mean, I don't know that there was so much a question on there, but I'm curious if you have any thoughts in it. That's where I'm getting nervous because, you know, copying is one thing, but there's no question. Everything happening in AI is ultimately a derivative work of somebody's copyrighted content, even if it's undistinguishable at the end of the day. I think that's really where this thing's going to personally. I think this is where it's going to come to in the US courts. I think, well, I think you've really hit on it, is that there's a distinction between the inputs and the outputs and on the and those questions are

26:49determined at the output side and they're adjudicated because they're not easy questions and it's hard to create one rule to rule them all. And so that's why we bring up to courts and the court system is slow. I recognize that. But it's just a very difficult issue to deal with at a policy level. One more question, then we'll get into some fun. I'm looking at the time on this. Where's the time going? I could talk to you for two hours. You know, just thinking as a mid-market and enterprise IT leader, we kind of bounced around the outside of this. You had a good point around governance and that's probably your answer. But if you're a business that does not have a policy team, doesn't have a big legal department, you know, what's just the first practical protection step that businesses should be thinking about as it relates to AI? The very first thing what I said before is data mapping, AI mapping. Like, what kind of data are you using? What type of AI are you using? Make sure that you are in touch with your lawyers,

27:50presuming there's no policy team. They should be abreast of developments. And then you're going to have to plan out how you deal with the near-term requirements. So updating terms and conditions, doing more impact assessments, looking out for different things in your audits, and how you're looking forward. And looking forward is going to head on a number of different trends that we may see, and also the nature of the business. If you're a business that is working across borders, you need to really think about the likelihood that there's going to be more sub or AI requirements, data localization requirements. You're going to have trouble transferring data across borders, and you need to adopt more of a modular approach to how you're providing your service, your customers. It makes sense. What about, um, young to make a general statement, chat GPT is the number one shadow IT application of all time, right? And so, you know, and we're generally advising businesses on how to manage that, not to shut it down, but to use that for

28:51use case development. But it certainly creates exposure at the end of the day. Is a good just AI governance policy that's attached to sort of the acceptable technology use policy inside of a business generally? A, obviously, I think we agree. We would agree that it's important, but, you know, is that enough to cover most businesses in terms of rogue use of AI inside of the business? Oh, good question. So we're talking about use in the business. I mean, I think it's from a 19 perspective. You have to make sure that, um, you have to make sure that you're using an enterprise, uh, enterprise version of chat GPT or whatever it may be, uh, that your data, your queries, anything you put into the system is not being shared, not does not become part of the training corpus. So maintaining the privacy of your trade secrets, your proprietary information is number one. Um, and I think training people too. And this goes, this partly IT, but it also goes beyond that. And we're going to see AI, um, help people in so many different ways,

29:55workflows, and we need to make sure that people understand how to get the most out of that. And it's not going to be intuitive for everybody. I can continue. I mean, we're 30 minutes in. So I'm going to get to the fun, but this was interesting. I would love to have you back at some point in the future, because that was, I have, I have about two more hours worth of questions. We can keep going. Oh, well, good. Let's jump to some fun with the time that we have left. I'd love you to give me, maybe this one isn't as much fun. Give me one, maybe policy surprise, or something that happens in the industry that you maybe expect to happen in the next 12 months than IT leaders are not planning for. Oh, that's a good question. So, um, what IT leaders are not planning for. So I think that, um, these are probably going to have a downstream impact on IT leaders. I would say there is a huge push right now around AI infrastructure and data centers. And so I think you're going to see more federal action focused on that, um, trying to rein in states by focusing on data center licensing and making sure that we

30:58can actually build out. Um, I think that has also been a drive a lot of international AI policy as well. Um, AI, I believe will be a, will be a hot issue in the midterm elections. And it's not an issue that it's going to line cleanly with Democrats for Republicans. It's going to create alliances that are unusual. Uh, and how that plays out is not really clear, but, um, but I think it's going to become increasingly, uh, newsworthy as if AI isn't very, very newsworthy. And from my perspective, I would say just not to talk over you, but I, in cyber security, I think people are really becoming, uh, attuned to the cyber risks that, um, AI can pose. And that is something that's going to affect, um, IT leaders, for sure. Yeah. I mean, I, I love your take on that it's going to become part of the election, but it's not going to go along party lines, but I mean, particularly if you just take, we've been talking about policy and use, you just brought up a whole other animal, which is the capacity and more importantly, the power that's required in these data centers.

32:02You know, it's one of the things that we, at bridge point, we do for a living is help these companies find data centers right now where there's enough power and enough space to be able to actually stand these models up, stand these products up. Um, so yeah, I mean, that's one of the things where tech, infrastructure, you know, the whole green crowd, like everybody's going to kind of be smashing in the middle of this and it's not just around AI use, right? It's, it's how to power and locate it at the end of the day. That's a super interesting one. Let me, um, let's be controversial. Give me a contrarian view that you will hold around AI policy that maybe your peers or some of the folks in your industry might go, I can't believe that he just said that. That's good. Um, I, I think, so the debate is often, uh, the debate here at least is often positioned as there's two camps. There's the, the tumors and the boomers. Um, but maybe it's not contrarian. I actually think, where industry is largely is in the middle, like we recognize industry recognizes that we need

33:06to be promoting innovation as much as possible. Um, but innovation has to be responsible. Uh, and I really think that's where we are. And I think that doesn't come out enough in, in the, um, the debates we're part of. Is there a governmental responsibility to help businesses define what responsible AI actually is? I love the word responsible. This was not in my questions, but it just popped off because we talk about it all the time. Yeah, we talk about it all the time. Um, is there a government responsibility? Not really. But the government is a great convener. And over the past four or five years, um, there's really been a convergence on a lot of non, non legislative best practices. And, and I think that that is really the, the area that is going to grow a lot more. And that's what we're working on. You know, industries, standards, industry leading, we want this, we want US industry, um, to be seen as the best out there.

34:06There's a lot of competition. And the products are really, um, they're really good. People are addressing concerns that they hear from the public. Amazing. All right. Too last. And then we're going to get you off here. I'll get to the rest of your day going. Tell me one book podcast, something that's shaping your thinking right now. Oh gosh. Okay. Okay. So when I read, I read novels. Um, and they're not always science fiction. I need to give my mind off because I read the news, the tech policy news all the day. Um, but, uh, there are blogs that I follow pretty religiously, uh, tech dirt, uh, law fair. These are great blogs, tech policy press. And it gives me, um, a good pulse on what's happening in the, in the community. Do you care to share a novel you're reading right now for our audience? Is there anything interesting? Just finish your book called Euphoria by Lily King based off of the television show or something completely different. You guys absolutely nothing to do with the television show, which I did not stain. It takes place in mostly the 1930s and New Guinea. Um, but it's extraordinary writing. It's about human relationships,

35:12love. Yeah. All right. All right. I'm going to check that out. I'm going to check that out. Now, my last one, we ask every guest this question. Um, and you wouldn't believe some of the answers we've gotten. We reach some future, some dystopian future, um, whatever science fiction you follow, you can choose which dystopian future that is. But you are left with a phone that has only one app still working on it. Um, what might that app be if you had your choice? Google maps. Okay. You want to know where to go? I got it. I want to know where to go. I rely on it probably more than anything else. Fantastic. I love that. I guess getting around DC isn't exactly easy. So that's part of it, right? Yeah. It's, uh, we've gotten plenty. I, I always harken back to usually people go to their entertainment because we're like, if I'm by myself, potentially, I need my entertainment, um, or they're like flashlight, because I have no idea what the, what the situation is right now. Uh, Paul, for listeners who want to follow your work, access to association resources, like where can they go to find out a little bit more about the

36:12work that you're doing? Great. Uh, our website S I I A dot net. N E T is a great place to go. And we're also on Twitter, LinkedIn, um, I think we're S I A S I A or S I A policy. I love that. And I highly recommend, by the way, I mean, maybe you would love this, but I, you know, I downloaded this yesterday and read it. I thought it was great. It gave great perspective, at least a framework. This is the 26 policy priorities that you guys published. It's right on the website. You can download the PDF, uh, super helpful to get something in around it. So there's a recommendation there. I'll leave you with just one. If you could leave tech and business leaders with one sentence, just one about operating effectively in a fast changing technology and policy environment. Um, what would that, what would that be? Let's try to leave the politics aside and get practical policy done. God, ideally, the politics are always in the way of good policy. But that's how,

37:12that's how the city works. So policy, well, you know, I was just going to say policy, Trump's, uh, politics, but that's, that words charge these days. Let's just say policy over politics. How's that? Uh, I love it. Paul, thanks for joining us. This was a great conversation. Great. Thank you so much for having me on Scott. Absolutely. And that is a wrap on this episode of the bridge cast. If you found today's conversation insightful, if it gave you something to think about, please hit subscribe, leave us a review, share it with your team, and we'll be excited to catch you on another episode of the bridge cast. Thanks a lot for joining us. Listen, if you made it this far, there must have been some good content or you're just a fan or relative of mine. In either case, we appreciate you spending your most valuable asset with us, your time. We don't take that lightly. We'd also like to say one more time that we're appreciative of bridge point technologies and their belief and sponsorship of this show. We hope that if you or someone you know is thinking about your company's digital transformation, or simply the next IT project that you may not have the resources, budget, or time to get to, I'm sure the bridge point,

38:17one of the country's fastest growing technology, advisory, and procurement firms can help. Check out bridgepointtechnologies.com. Don't forget the e on bridge point, or simply reach out to me at skynka skynk at bpt3.net. Also take a minute, please, if you would, to give us a five-star review on your favorite platform. It helps give us the visibility to reach other people like you. Thanks for listening to this episode of the bridge. I'm Scott Kynka, and until next time, there's a lot of noise out there in business and in life to do what you can to be the signal. Thanks.