Latest / You've Been Heard / 453- Make Approved AI Easier to Use w/Mason Herbel

Transcript

453- Make Approved AI Easier to Use w/Mason Herbel · You've Been Heard

• 0:00
  Speaker A: I think at a basic level, if you're allowing AI in your organization, your policy needs to be you're only allowed to use these approved apps which we purchase enterprise plans for. When we have a data processing agreement. You can be a little bit more lapsed about it and be like, hey, if you have one that you like to use, we'll buy it for three months, we'll put the company card on it, we'll get the enterprise one and we'll evaluate in a few months to say like, how much were you actually using it? What were you doing in it? Could that have been done in the other apps that were not double paying? But having that low friction to it is really what keeps people from trying to skirt the line because if you tell them not to use it, they're going to just use it on their cell phone and turn the WI fi off on their personal device and keep using it. The cat's out of the bag. We can't undo it, we can't get rid of AI. So you may as well make it easy for people to use in a secured manner.
• 1:11
  Speaker B: After talking with IT leaders, I've come to discover one thing. And it's the same thing, you know, it's the same thing everyone else knows, whether they want to admit it out loud or not. Nothing gets done in any company without it touching it. Nothing. But here's the thing. When everything's on fire, when nothing's working, when the ransomware hits, when the whole business grinds to a halt, it is completely indispensable. You're the most valuable asset in the company. But when everything's running like a well oiled Tesla, you're a line item, a cost center on a spreadsheet. You've done the work, you've built the career, you've earned your seat at the executive roundtable. But having a seat at the table is one thing. How much nicer would it be to actually be heard? This is. You've been heard.
• 2:04
  Speaker A: I have had an interesting entry into the IT world. I would say I built my first computer around, I think I was 10. My mom will say I was 8 years old, though she likes to give me more credit than I deserve. But I forewent a birthday party at a young age to buy computer parts off of Newegg instead. Little bare bones kit and watch. And I was just obsessed with tech from a young age, so built my first computer. I started making websites around age 12. I would like mirror templates off of these template websites that you can use and probably not super ethical nowadays to like download somebody's template and then modify it and then sell it to somebody as a website. But when you're 12 and you're making a, a website for your friends, esports team, it is what it is. And I really learned at an early age. I don't like programming, I like, I like putting this stuff together, making it happen, configuring systems. But the building software is very tedious, right? It's one thing to write a script over the course of three days, it's another thing to build an entire desktop application that takes three years to come to fruition, right? So When I was 18 I got my first IT help desk job at a small dental IT provider in Houston. We had about 250 clients all over the greater Houston area. Grew quickly there. Became the help desk manager after about two years. Was able to get my hands into everything from solving weird active directory replication issues between some of our bigger clients or one time we had a doctor take his server like on a field trip. He took it home, he was moving to a new office, he took it home for the weekend, brought it back to his new office and it wouldn't boot, it would just blue screen Windows server. So I had to run some things on his hard drive to like fix it up. Spent all day just getting the Windows bootloader to work again. I don't know why, but it wouldn't boot. And from there. I know this is a very long winded answer, but at an early age always loved computers and was able to just level that up into other positions. I did sales for a little while, I did the whole IT contractor thing for a little while and then one of my family friends, it's not who you know, but it's who you know that knows what you know. Was head of HR at an engineering company that was very upset with their msp. They've been using an IT provider, managed service provider for a couple years and they were at that point where they could justify bringing it in house. And so they took a chance on me as their head of IT specialist at first and I was able to just take the reins there. And they had five on site active directories, servers. We moved all that up to Azure. AD did an entire entre migration. Just completely got rid of on site ad because they had a very distributed workforce to begin with. Five offices, 40 employees across those and then another 15 just dotted around the US. Nobody wanted to have to turn their VPN on to like receive the newest updates from the group policy so it just made sense to do a cloud migration there. Learned a lot along the way and then moved on to another position out here in California. And now I would say I'm on my third head of IT position. So that's what led you guys to find me on LinkedIn, I guess.
• 6:03
  Speaker C: Well, cool. From 18 until now. Seems like a pretty quick journey actually, to rise through the the ranks. What do you think was the thing that helped you grow and for others to see the value in you and promoting.
• 6:28
  Speaker B: If your Internet goes down and you're calling carriers yourself, that's not strategic. IT we help teams centralize monitoring proactive tickets and escalation across every location without capex. Less noise, more uptime, make it boring again. Just go to you've been heard.com and answer the seven questions to simplify, streamline and save.
• 6:50
  Speaker A: There's a couple things that go into that. I think the first one is always being curious. Even if it's not something that you could even wave a finger at, you may as well ask and say, how does that work? Right? When it was my third week at my first at that dental IT job and I was still 18 and I'm 24. Yes, I'm 24 now, had to do the math real quick. And third week there, they were installing a new server at a client and I just said, what's the process for plugging that in? I assume you guys are already setting up all the GPOs and everything, and I just have to go plug it in, transfer the software. And because I asked those questions, they were like, tag along, come see. And then it ended up being that I was thrown into the deep end on that one. And the lead tech that was gonna go with me had to go to a different job. And I was there by myself calling the software provider and saying like, hey, I have this old server, I have this new server, help me move the database and just got it done. So I think the willingness to ask and look stupid gets you front of mind with your superiors so that they think of you when they're like, hey, who could go fill in this gap? What about the super curious guy that always wants to learn? And then the second one is sometimes you have bad bosses that really don't see what they have in you. They like to silo you. You're getting done what they asked you to get done. And when you try to be proactive, they'll be like, hey, wait, what business initiative was that work for? Right? There's nothing that we thought needed you to go look up the best antivirus provider this month and it's like, guys, we don't even have antivirus yet. Like maybe we should get an edr. So asking but also having the right people is a big thing for sure as superiors.
• 8:58
  Speaker C: Okay, you mentioned or talked about that change from the five on prem. Well, actually you said it was five locations, five on prem ad servers and then migrated into Azure Active Directory and then leaned into it as it went from Azure Active Directory into entrepreneurs. Talk about some of the experiences and the challenges of doing that. And are you finding yourself building more cloud centric stuff or have you run into an organization yet that's like, no, we want everything on prem.
• 9:36
  Speaker A: So I like that question because every 10 years it's like cyclical. People are like all cloud and then they go back to on prem and then they go back to cloud. And what really works is is hybrid. You need to buy things and host them locally either in a colocation or get a good tier one circuit piped into your building and build a room with an extra split unit in there for air conditioning. When something is going to cost, I don't know, my threshold's like 10,000amonth. If you're already spending 10,000amonth on cloud services, you could probably buy a 2 or $300,000 HPC High Performance Compute system from Dell, Lenovo or Cisco and amortize that over three years, lease it, whatever, and be in the black making a return on that investment. But when it comes to the Azure ad Entre project specifically, they had a bunch of old Dell T3 20s or whatever, they were all running Windows Server 2012 and the options were do we do that capex of 30 or $40,000, get a bunch of servers, buy all the device cows, user cows, whatever. Or do we take the three softwares that run, which was like a licensing server for their GIS program, a backup script for their cloud storage because they had already migrated everything from SMB to, to something called LucidLink, which is mainly for creatives. Like they have a Premiere Pro plugin, they have an After Effects plugin. It's mainly for like video workflows and creative workflows. But it's a bit by bit storage system. So it will download the whole project file to your computer, but then it will only transfer and upload the portions that you change or download the new portions of the file when somebody else made a change to that file. And this was an engineering firm, so all of their CAD files and everything were in LucidLink. They had this fancy lib fuse magic that LucidLink had done with the software that goes on the computer to make it seem like it's a local drive even though it's a cloud drive. It would take a hundred gigs of local cache to download all the recently opened files. But before I came on they had already did the file storage part. So now all that lives on these servers is some very basic programs for their licensing and then the group policies and the active directory. So I said let's rip it out. This is only going to be $100 a month virtual machine with all the bells and whistles backups enabled on Azure. And you're already paying for Azure ad because they had Microsoft 365 Business Premium which comes with all of these features already. So they're paying for it anyway. And it was a month or two project to schedule with everybody. But I went on to each person's computer and backed up their user profile to OneDrive, enabled that sync, made sure that their Chrome or Edge profile was synced, removed them from the ad, rebooted their computer, had them log in with their entre instead, joined it and some of them took an hour, some of them took four hours. I just did that with every single employee, which you can't always do, but at that organization of 50, 60 employees it was the best option honestly. And now I've been able to move on from them. They call me like once a month with an issue. The COO has kind of taken back the IT stuff and they have one on site part time tech that's like an intern. And I know I've gone on a few different tangents on this answer, but it really is that per case analysis that you have to do to decide whether it's cloud or on prem. And I have another example with an AI workload with much bigger numbers too.
• 14:00
  Speaker C: If you. Yeah, and I'll Rusty, let me just throw this out there a little bit. Most of the people that I talk to are a little closer to my age and have like 20 plus years of experience. So it's fresh to hear from you having that six years of this working experience and being having raised yourself into the leadership. So I'm interested in your perspective and how it differs from the what I've had to go through because the technology changes and what I've seen over the last 24 years, 25 years and compared to what you've seen over the last six years. But tell me about the AI stuff, tell me about what you AI workload.
• 14:42
  Speaker A: I, I do Want to touch on that though? It is interesting and I kind of envy the people that started 20, 40 years before me because like you guys got to really tinker with computers. My first computer was already like an i5 3570K. We were already in the Intel I series. Right. So I never got to.
• 15:06
  Speaker C: You never got to deal with 56 megs of RAM. And that means the high end computer.
• 15:13
  Speaker A: Exactly. So there's two sides of the coin there for sure. But at one of my jobs, it was an AI based edtech company, we had this awesome product, gives students a place to learn more quickly. Their first product was actually like an essay writing app. The co founders, and it went viral on the Internet four or five years ago. They were getting payments, people were using the product, but they didn't really feel good about what they were doing. So they scrapped that and they replaced it with something that you would upload your notes to. You take pictures of your textbook, you let it record your lecture in the lecture hall or what have you. Maybe you're watching a video, you let it record off to the side, just like meeting notes apps do. And it will synthesize all that into flashcards, practice quizzes, it'll even make a PowerPoint. And you'll have a virtual tutor that you talk back and forth with to help you learn your class content more quickly. You're not spending three hours writing out index cards for your 400 vocab word like nursing exam. Right? You just put the textbook pages into study fetch and it'll fetch all that and turn it into all of these different learning modalities. And you would pay us the same amount as ChatGPT or Claude, 20 bucks a month. We have a chat feature as well, so you could ask it other things, but it would always steer back to like, hey, actually you left off on this flashcard set. Let's continue. At one point during a final season, we were spending hundreds of thousands of dollars on API billing. Because when you build an AI app, they charge you per token. A token is three to five characters and people would upload 300 page PDFs and we would scan all that. And that's millions of tokens that were synthesizing and they call it embedding into the vector databases for the LLMs to search in that infinite dimension array. And like six months before they made the decision to do it, I said, hey, like we spend all this money, we should probably look into buying a couple million dollars worth of GPUs, putting it in a beta center and then we own our platform and we're able to do extra model training. And that's when you turn from a ChatGPT wrapper, as they call it, into a company making your own AIs because you can retrain these public open weight models with your data. So we, we had all these learning outcomes because we would ask people what they made on their test after using study fetch and then we can use those inputs and outputs to make a typically worse AI better for our use case. And then there's just all these benefits that you can get from self hosting GPUs, even that a couple million dollars scale versus a billion dollar scale like SpaceX or Facebook is doing. Right. Essentially we spent millions of dollars on API costs making Google rich, paying Google cloud, when we should have been investing sooner in the infrastructure planning and getting quotes and laying out our roadmap for how we can consolidate these AI costs into a capital expenditure that can be written off and all of these things. Around the time that I left them and came over to my current job, one of their principal engineers had the same epiphany six months after I did. I was new at the time when I suggested it. So they were kind of like we're moving too fast, we don't have time for that.
• 19:17
  Speaker C: Right.
• 19:17
  Speaker A: Even though the co founders are younger than me, which is part of the problem. I guess it's like another double edged sword of having great ideas moving super fast, but not having worked at a large enterprise before, seeing how things are done typically, which is good because you have new ideas on how to do things, you're not trying to break old habits, but sometimes it gets in the way of things. And that's what I alluded to earlier is when you don't have good bosses, the they hire you, they give you the title, they give you the paycheck, but they don't want to let you run with it because they don't have enough experience delegating and seeing the fruits of that delegation. So they, they kind of pigeonhole.
• 20:13
  Speaker C: So how do you or what would you recommend to those that are listening? How do they deal with that? Is it just a, you know what, you're better off just scrapping it and going finding a new place or a better boss or are there things that you've learned, experienced in ways to do with that and to try to help shift that old guy like me, shift my perspective to see and empower you to let you go and do?
• 20:44
  Speaker A: Sure, I think there are a few things, but the main one Is this quote from President Reagan. He essentially says, a man has no limits when he does not care who gets the credit. So that same principal engineer that had the same epiphany, I was in his ear the whole time, I was like, hey, look at this AI model on hugging face. Hey, look at how much an 8 GPU cluster costs. Maybe we do spend enough to go buy one. And you know it's a good idea and you know it's going to benefit the business. I've always been a company man. Even when I do get frustrated and I might go on LinkedIn and rage, apply to five jobs or something and say, get me out of here. It's against my nature to actually silent quit or anything like that because I want to see the people around me succeed and do well and for everybody to have a great time. And the just numbers keep going up. Right. So the other folks around you that might have a little more pull and going leadership, that it's not just your idea.
• 21:57
  Speaker C: It's interesting you state it that way. And I think back to part of where we started the conversation of what are the things that helped you learn or helped you succeed and find your way to a leadership chair. I think that right there is one of the key things that helps us is when people see that we're there to help them. I'm not looking to win for me, I'm looking to win for us. I'm looking for how do I help the organization, how do what we do or how does that enhance or add to. I think that's a critical piece. And so often with these conversations and when I'm talking to people, it's, well, you got to listen to the business, you got to understand the business. I'm not hearing you say that, but you're showing that you need to. But you're hitting on some of the other key pieces that help you grow and bring value.
• 22:58
  Speaker A: Yeah, I think when you work at places that have a large or all of the employees as high performers, very flat organizations like the one I work at now, where our head of engineering has 20 direct reports and those people might have zero or three people under them, there's not these massive tree branches with all these limbs in our org chart, I think that lends itself to like everybody understands the business. It's kind of a prerequisite to work there that you need to understand how do we make, how does the business operate. And especially it being a cost center almost all the time, we need to increase productivity across the rest of the business. And there's no way you're going to increase productivity of a CAD workflow if you've never looked over the shoulder of one of the engineers that uses CAD and been like, what did you just do? You joined those two pieces of the assembly. What does that even mean? Right. You need to understand those business processes for sure. And all of the habits I've formed are definitely based around that, even if I didn't say it upfront. It's a very important to be the I think I heard this on one of the other you've been heard podcasts. It's important to be the IT department that people want to come to and share what they're working with on instead of be the one that they dread calling because they know they're going to take their computer for an hour and a half.
• 24:37
  Speaker C: And it definitely is. I've worked at both of those types of organizations and it's definitely it's a gift and sometimes it's a dual sided gift. But when the organization looks to you or looks to it for the solutions that everybody comes to you looking for help and they're expecting that level of help. That's a gift. But it's also huge drain too because now everybody's looking to you and you're getting taffy pulled in so many different directions. So it can be both. But recognize it for the true gift of they trust you and they want, they're looking for your advice. You've been heard right?
• 25:23
  Speaker A: You can definitely spoil people by being too good of a IT personnel. It's true. And then you just have to hope that the leadership above all those people that are coming to you and putting all these things on your plate, recognize the impact and then they'll let you hire other people to help. Because I've seen where yeah, sure, you're great and people are coming to you and bringing all these problems to you and you're helping them solve it. But then something else falls to the wayside like the conference rooms, speakers not working when that should be one of the basic things you're doing. Or making sure that a new onboardee's laptop is ready Monday morning. Instead of working on all the cool shit, you also have to make sure that you get all the basics out of the way and that if you are working on all the cool shit, leadership will hire other people to help you with that stuff.
• 26:21
  Speaker C: So if you don't mind, what exactly is the goal of the organization that you're at now and what are your plans when it comes to the thoughts of okay, you talked about AI at a level that I haven't had a chance to talk to too many people about. What are you thinking on when it comes to AI and the new organization and trying to help them there or is there already a defined path?
• 26:52
  Speaker A: No, there's not a defined path currently. It's definitely felt like I was hit with the bucket of cold water when I went from the AI native startup to this other startup. Granted, also startup, but a few years older and a little further along and a little more mature in their business processes. So Parallel Systems is a train manufacturer, but it's not a normal train. It's not one of these diesel engines where they'll have two or three on the front pulling 800 cars and it takes a mile to stop. Ours is a self contained rail car with batteries, motors, lidar. If you've ever seen a Waymo on the street. I don't know if they have those everywhere now. Yeah, or a Zoox. Exactly. It's an autonomous rail car. You put one container on it, you can have a bunch of them in a platoon moving along together. You can see on our LinkedIn and they'll go up to 500 miles and then you'll still have an 18 wheeler take it for the last mile. But it is cheaper than an 18 wheeler by mile. It is getting a lot of trucks off of the interstates. If this all continues to go well and our business continues to thrive, we'll have these out there in the wild. And a prime example of where this will help will be the shipyards. They sometimes they have the railroad tracks right next to the shipyard. But it can take up to three days to build a train to maneuver all the rail cars and put them in the right order and then hook them up. And it requires dozens of acres of rail yard to maneuver all of this stuff. So at a shipping yard draying. So they take the containers off of the ship and they put them on an 18 wheeler and then that 18 wheeler could be sitting there for like an hour waiting on his container to come off the ship. So he had to drive however far to get there. He has to wait there probably running his engine for AC depending on where he's at and what time of the year it is. So burning gas costing money and then he has to take it to where it's going which is probably onto a train and then going somewhere else. And there's thousands of tens of thousands of these containers on these ships parallel will have this self contained car, the shipping management system that they already have. Telling them this container contains. This is coming off this ship this day will ping the parallel brain. And that rail car will be there at the time that it needs to be automatically. And then somebody with the crane. The cranes aren't automated yet. That's not our business yet. So somebody puts it on there and then that takes it 500 miles, possibly further, because we can fast charge overnight and then take it another 500 miles on existing railroads. It all runs on existing railroads. We don't need any third rail or anything fancy. And then wherever it needs to go, the Walmart 18 Wheeler comes and picks it up. He can go home to his kids the same night because he's only taking it 10 or 50 miles. He's not taking it 700 miles anymore. So again, it's just cheaper, it's faster, it's more economical, it gets drivers home sooner. It's reducing traffic, it's reducing emissions. It's just a win, win, win product.
• 30:41
  Speaker C: Well, what I'm thinking is it sounds like it's almost an autonomous chassis. So those containers come off of the ships and then they go onto a chassis on that dredge. And then the train cars that they go onto are versions of the chassis also, where they can stack them on that and have multiples or two of them on a one rail car. And what I'm envisioning is that bottom part of it that's between the container and the wheels that are on the track is where all of the battery and the brains and everything else. Exactly right there. And then it's just moving that individual cargo container.
• 31:23
  Speaker A: Exactly.
• 31:24
  Speaker C: Yeah. That's cool. Interesting. Because I've been in transportation or the majority of my career, so I've been working with those 18 wheeler trucks. And some of what we were doing was we were taking and doing innovative things of like bringing the. The rail car and. And the one that they set it on for the trucks that are moving it. Because the containers themselves usually don't have wheels. But we were running the 18 wheelers that had the wheel. So they would pick that up with the crane, Set it on the flatbed rail car and then move that and intermodal and the ones that are coming off the ships and then going on to rail and then going on to an 18 wheeler. That's intermodal too, because it's multiple types of modal transportation. Except we thought we were innovative when we were working with the rail yard to take our trailers with the tires on them, Set it on that flatbed, and then run it.
• 32:23
  Speaker A: Yeah.
• 32:24
  Speaker C: Wonder about the puzzle you've got. So you've got 50 of these parallel cars at one of the shipyards, and you start loading them up. But one like the 15th in line has a priority, and he needs to get to the front of the line and the inner shuffle of all of those interconnected cars. And they're not platooning, but they're solving the problem themselves, I assume to help get that one to the front of the line so that it can head down the track.
• 32:56
  Speaker A: Yes. So there is a lot of work happening right now with the dropping off of cars, just like you're saying in the rearranging. The beauty of it as well is it's also safer for the existing railroad workers because it's not a linkage in between the cars. There's this other mechanism. They've come up with these two pressure plates that go together to tell the cars, like, how close they are to each other. They're never actually interlocked together with the really big clasps that they use. Apparently, people lose fingers weakly and people get hurt a lot in between those rail cars and our system, like you said, we have 50. Number 15 needs to get dropped off. The first 14 are going to speed up a little bit, give some room. And they're going to know where the switch is on the track because we're using GPS with Super, like up to 2 centimeter accuracy on our GPS. And then the 16 onward will get it some room as well. And then somehow, I guess a lot of the switches are manual. So I guess the shipyard or whoever's receiving it would know that they're waiting on that one. They would be there to switch it. The first 14 would keep going a little bit. Maybe they slow down to wait for their friends to catch up. Number 15 gets put onto the other rail section. Yeah, the diversion. Thank you. And then the switch gets put back. So the plan is to sell the vehicle to the rail, to the operators. We're not. We don't want to take money for your container. We want to sell the vehicle and then sell the software and then let them operate it. So the person doing the switch would tell his comm center, hey, I put the switch back, send them on their way, and then whoever hits the button, hits the button. And then the next cars keep going, and then they catch up with their friends. So there's so many different ways to skin the cat. That's how I speculate that it will be done. But by not having those linkages in between the cars, it enables us to do these Drop offs and things like this.
• 35:19
  Speaker C: Chassis is the word I couldn't remember because chassis is what the container gets put on, whether it's for the train or whether the 18 wheeler.
• 35:29
  Speaker A: Right, that makes sense.
• 35:31
  Speaker C: So we started off on this and obviously you're going to be leveraging AI for some of those decisions. For the 14, you know, we were talking about car number 15, trying to get to the front and all of those pieces and knowing where they are and what they need to do and some of those communications. But what are your plans for like the AI within the organization or how are you. Maybe the other question is, all right, so you've got the engineer who doesn't have a cloud account, doesn't want a cloud account and is still bringing value to the organization. How are you working with those kinds of individuals and what are your thoughts around trying to work around that? And all of the governance, the guardrails and anti hallucinations, how are you dealing with that kind of stuff?
• 36:23
  Speaker A: Yeah, it's very interesting. It's a little bit of an uphill battle. It's not as bad as I thought it was when I first got here. Only HR and finance are using AI. How are none of the programmers using it? And then I learn, okay, a few of them are using it. They have the cloud code plugin on their Visual Studio code, but they've never heard of Cursor. And I think the reason they have been hesitant is because our head of infrastructure and cybersecurity, my boss and his first question was what you ended with there and how do we govern this? How do we make sure it's not going crazy? The hallucinating is kind of up to the user to trust but verify. But the data loss protection, the making sure that users aren't using their own personal AI subscription and uploading a confidential PDF into it, things like that, there are a lot of tools for that. Cloudflare has a really cool one. If you've ever used Cisco, Umbrella or Zscaler or any sort of device level firewall, Cloudflare has their CloudflareOne platform which started as Cloudflare Access and Cloudflare Warp. But with COVID everybody started working from home and your normal network perimeter was no longer easily trustable. Barrier. You can't just say like hey, they're in the office, they're allowed to go access this, that and the other. We know we're monitoring their traffic because now they're working from home three days a week and we have no control into their Comcast or Spectrum modem. So you need to take the firewall and move it from the network to the endpoint. Cloudflare was one of the first big ones to do this and more than just DNS filtering, like Umbrella would do. And they will monitor every packet. You can set all these different rules. You can figure things out. I start with minimum DNS level filtering. So if we disallowed ChatGPT and they try to go to ChatGPT on their work device, it's going to be like you're blocked, go to Claude or vice versa. But then even deeper than that, they've launched some features by working with the AI providers and also their own forensics and reverse engineering to inspect even the MCP calls. So when you have their proxy enabled with their agent on endpoints, it will see the HTTP request body that is falling to an MCP server. Right. So like when you link up Gmail to your chat GPT, it's using an MCP server to make those API requests to Gmail, to read your email and everything. So they figured out a way to filter those out and then they can scan for pii or sensitive information or things that you've set as words to flag, things like that. So the tech stack is catching up in terms of catching people. But I think at a basic level, if you're allowing AI in your organization, your policy needs to be you're only allowed to use these approved apps, which we purchase enterprise plans for and we have a data processing agreement. You can be a little bit more lax about it and be like, hey, if you have one that you like to use, we'll buy it for three months, we'll put the company card on it, we'll get the enterprise one, and then we'll evaluate in a few months to say, like, how much were you actually using it? What were you doing in it? Could that have been done in the other apps? Or we're not double paying. But having that low friction to it is really what keeps people from trying to skirt the line. If you tell them not to use it, they're going to just use it on their cell phone and turn the WI fi off on their personal device and keep using it. The cat's out of the bag. We can't undo it, we can't get rid of AI. So you may as well make it easy for people to use in a secured manner.
• 40:38
  Speaker C: It's interesting, I've run into a couple of the AI security groups and our companies and one of them was, oh, it's just we've got A plugin for your browser. And we'll stop them by you have to install this plugin on each of the browsers. And I'm like, that's not gonna work. I need this at a deeper level than that because somebody's gonna go to their phone and ask how to get around it and then just get around it.
• 41:06
  Speaker A: Exactly. Tools like, like Cloudflare one or Zscaler. I'm sure Cisco Zero Trust Network Access has come up with a way to do this. They, they hijack your DNS settings and your proxy settings, whether it's Windows, Linux, macOS. They do whatever they're doing in the background to hijack that and they proxy it through their servers. What I like about Cloudflare is they have like 400 points of presence all over the US so there's typically a data center that's doing the packet inspection that's within a hundred milliseconds round trip time. So it's really not impacting the users too much.
• 41:43
  Speaker C: I have been enjoying this conversation. I hope it's been entertaining for you. Unfortunately, we're running tight on time. I did prep you and warned you about a question that I was going to ask. So make that prediction. What do you predict that we're going to be talking about in 18 months that we're not talking about today? What do you think's going to pop up?
• 42:06
  Speaker A: I've been mulling on that throughout and I think it's the same answer that first popped up in my head, which is open weight models, which I mentioned briefly earlier. But these models have all these different parameters and categories of things that they know in their memory that they were trained on. And the edge right now that the frontier models have is they have all this brain power that they've hired for hundreds of millions of dollars and poached from this, that and the other company to help them create those category weights. So they're doing these trainings and they're putting these weights to the parameters. I'm very butchering that. But the open source models with the open weights that you can see how they trained it. You can see if they gave this type of behavior more emphasis versus another one like ChatGPT being overly nice. You would be able to see that in the weight data if it was open source, but it's closed right now. So as the open source models get better, the deep seats, the Gemma's, the Llama models, then it will become more accessible and it will become more transparent. We might not even need as much regulation around it if it's just open source and the graybeards that have been doing it for a while that care about security on GitHub and everything or making sure that it's not going to go hack the world. That is the thing we're going to be talking about in 18 months. I think that anthropic and chatgpt and they're trying to build all this hype and like all this FUD around their model being the scariest and the most capable but all of the publicly available ones are catching up for sure.
• 44:06
  Speaker C: Yeah. And actually one of the things that I've been surprised about is how many of these models people are being able to are starting to be able to install on personal machines and have self contained or barely contained and be able to do other work with those things. I was talking to a former colleague and they were, he was telling me about how they were doing that to comb through all of the different logs at an organization to find different things and they were finding indicators faster than the MSPS or MSSPs and that are using some of these larger models as part of their MSSP offering. So it was kind of interesting. So I like that. I think you're right. I think we will be talking deeper and for sure more than just tokens. Right now there seems to be a lot of discussion around tokens and then of course the gray beards like myself trying to figure out the governance models and how do we put the guardrails in and how do we teach people to use it responsibly and all of those kinds of things. So I appreciate your time Mason. I invite anybody that has listened to this and is interested, reach out to Mason through the you've been heard.com community. And if you got questions about these kinds of topics or want to learn more or you got some ideas that you want to bounce off of Mason or try to help him help parallel get even better. Hit him up in the community. Hit us all up.
• 45:43
  Speaker A: Yeah, of course. Tag me in there. And I really enjoyed this conversation. Like I appreciate it and I feel heard.
• 45:50
  Speaker C: Cool. Right on. Appreciate your time. Thank you for your expertise and for sharing with us.