ERP106 - AI for MSPs

Welcome back to Evolved Radio Podcast, today we are diving into the groundbreaking revolutionary world of the AI. My guest today is, Jimmy Hatzell, a man who's not just fascinated by the capabilities of AI but also turned that fascination into a MSP focused AI startup. Together, we're going to explore the ins and outs of AI in the world of Managed Service Providers.

Jimmy and I reflect on how far the ecosystem has advanced in such a short period. We nerd out abit on the underlying technology behind generative AI, and how it's prompting millions of users and companies to latch onto the LLM bandwagon.

Jimmy takes us through his personal journey with AI - from a eureka moment with his father-in-law to starting his own company, focusing on how small businesses can maximize their use of AI.

We'll look at the breathtaking pace of technological evolution versus the sluggish crawl of regulation and guess what the future holds.

Will MSPs manage LLM infrastructure? Will we see a return to on-premises solutions? Stick around for predictions and our deep dive into these predictive waters.

This episode is brought to you by Evolved Management Training Courses.

Online courses specifically crafted for MSP needs. A Service Manager BootCamp course, a project manager for MSPs course, an MSP security fundamentals course, and an IT Documentation Done RIght course.

What is Evolved Radio?

Evolved Radio Podcast: Interviews with technology experts, industry thought leaders, business leaders and other interesting minds. Exploring the evolution of business and technology.

Jimmy, welcome to the Evolved Radio podcast. Thanks

for having me on. So happy to be here. Appreciate it. Awesome.

So this is gonna be great. I think we'll we'll just sort of jump right

into it. I think an interesting place to start is just

a reflection on the sort of modern version of

LLMs and AI. I mean AI has kind of existed forever in a

lot of ways like I remember very early on my computer

days the, the SoundBlaster sound card that I got came with like

the Parrot AI or the and like a

it was like a psychologist that you could talk to and it would repeat your

questions and ask you how you feel about that. Sort of like what I imagine

is sort of the very first iterations of this. But, like, the very

modern version of the LLMs that that are really revolutionizing

everything right now have only kind of existed for about

2 years which I find kind of mind blowing, because of all the

things they're impacting. So, any thoughts on sort of,

like, that this sort of first two year stage and

and how things are sort of progressing so quickly just to open

up? Oh, I remember the first time I used

chat gpt, it was, like, using the Internet for the first time or high

speed Internet for the first time or something like that. Like, it was just,

oh, this is gonna change everything we do.

And sitting back and watching it like magic, like, I couldn't believe that

it was doing what it was doing, and it could talk like a person and

it could answer things really quickly.

And, you know, seeing that definitely opened my eyes to

the future. And here I am, you know, less than 2 years

later starting a company. So, like, sir, centered around the

thesis that, like, this is the chain gonna change all areas of business,

and people need help to do it. Mhmm. But I I mean, I can't

believe that it's only been around this long either. Like like I,

it's, it's funny working in AI. I've, I've been, you know,

publicly had this AI company for about a month now and you

go to technically implement something. And by the time you're

done some new version of this out and you, and had you started it a

month later, you would have done it a different way. And that's how fast things

are moving in this space, and it's just incredible. It's it's I'm so grateful to

be a part of it because it's so fun. Yeah. It is wild how quickly

change and the things are changing. I did wanna ask you, like like, what

was the sort of the moment in time? Like, do you remember sort of a

particular point or moment or evening where you're like, you know

what? I'm gonna start my own AI company. What what was that moment? Do you

remember? You know, I I I don't know that

there's a particular moment, but there is

definitely a number of

interactions that I could remember where I would show AI to

people and I would see their reactions.

So for example, I, you know, I worked in cybersecurity and I did a lot

of, demos of using AI to hack

people. And the laughs that I would get, the

inquisitiveness that I would get, the questions that I would get people stopping me afterwards,

then became a hobby and just showing my friends. I I guess one

moment where it was like, yes, I'm definitely gonna start a company on this was

I build a proof of concept and

I build a proof of concept, and I had my father-in-law test it. My father-in-law

is in his seventies, and he is the

executive secretary for his mosque. And he was doing a fundraising campaign and

there was a, a group of people who raised their hands saying that they would

donate to something, but he had to, you know, send file communication to

actually get them to mail the check or whatever it was. And I

gave him a proof of concept of, of early hats, before it was

hats. And, you know, I I told him, why don't you try

using it on this? And then he called me 2 days later and said,

Jimmy, Jimmy, Jimmy, what's what's that website that you

gave me that your your your AI? I used it yesterday,

and it wrote me this really great email. I I sent it out to everyone.

Yeah. I wanna use it again. Wanna give it to to everyone else on the

team here. And I was like, woah. This is a

man who doesn't, like, use 2 computer screens. He uses

to use this thing in, like, a number of minutes and immediately into

production. Like, saw the the life changing or the the way which

is in completely changed, you know, his his nonprofit, like,

the business. And that was, like, okay. This is gonna

have really lasting change. And, I I think that was

definitely a big catalyst for me to take the jump and and go full time

at it. Okay. Very cool. Yeah. It's I mean, just quickly on

that. Like, it is it is wild sort of the the the adoption of

this. Like, I can't remember the stat. You may know this, but, like,

ChatGPT's, like, acceleration to, what was it, a a

1000000 users or something like that is sort of this benchmark that they've used for,

like, adoption. You know, Facebook, Instagram were all these sort of

meteoric rises and then absolutely eclipsed

by chat gpt adoption. Do you remember that stat? Yeah.

It was, I think it was a 1000000 users in 2 months or something like

that, and then a 100000000 users, soon afterwards.

So they I mean, they did it in, like, like,

it's in order of magnitude each time faster than it happened, like,

each time that happened. And it was like

2 months or something where the previous was like, you know, 12 months.

Yeah. Yeah. It's so wild. Cut. So I guess that that leads

well to sort of the other thing I wanted to to sort of dig into

here is like like LLMs are all over the place, right? Like, you

know obviously everyone knows ChatGPT, Microsoft

Copilot is now kind of, like, rising up. And now,

you know, Google was caught a bit flat footed, largely

I understand because of sort their concern around safety and rollout.

So it was like these tools existed. They just hadn't made them public. Some people

argue as to whether or not they felt it might cannibalize the search business. I

think that's there's there's probably some validity to that. So they've got now Gemini,

rolling out. And then there's, you know, Meta's developing their own

internal. There's lots of even open source LLMs.

So I understand you are sort of, like, building kinda your own model,

and I'm I'm interested in that. Like like like, why your own model versus just

sort of leveraging off the existing LLMs that are out there?

Yeah. No. That's a great question. Actually, what we're doing is we're

empowering users to tune, or customize their

own models. So we we aren't necessarily interested

in in creating our own model. Maybe we will in the future.

But we aren't beholden to one model. So every

model that you've mentioned, Gemini, llama 2, which

is Facebook's open source model,

GPT 3.5, GPT 4, even Claude,

by Anthropic, we're using internally.

And so there's there's different ways that you can customize,

each of these models to do what you need do,

whether that be through context or through rag or actually tuning the models.

And we're in the business of helping small businesses,

do that through their MSP. I see. Okay.

So I yeah. I misunderstood this. You it's not that you built your own model.

It's kinda like a a department store maybe

for, for different models for different purposes. Is that a good analogy?

Sort of. It it's like it's like,

there's a lot of work that goes into the customization

rollout and access to data, of actually

using a model at scale inside your own business or

efficiently, whether that be business process efficient

or cost efficient or both, and we're a platform that helps

you manage all of that end to end. Okay.

With that, like, maybe getting into some of the technical details but like,

if you do training on one of your models and you're doing something in sort

of this space but you're using a different model for something else are you able

to sort of port some of that over through through your guys' platform?

Is that some of the benefit there? Yeah. So so you can do that in

a couple different ways. You can you can change

the context so you can move let's say you write a really good prompt or,

you have an application that's built around, specific prompts that

you you've built inside of our, UI. So so in our system, we

basically have a a UI for prompt engineering for MSPs, and they could

publish apps for their end users. You can switch out the underlying

models, very easily, in in doing it that

way. The only limitation is the context window. So

for example, like, Claude has a 100 k,

token context window. A token's like a syllable. So you can think of it as,

you know, whatever, 25,000 words or something

like a very, very long context window where, GPT

3.5, you know, there a lot of those, was 16

k or, 32 k

tokens. So, you know, 1 fourth the size. So that would be one

limitation of it. Another something that we're doing in the future

is is, retrieval. So it's it's RAG,

where basically the LOM can look inside of a a database,

or some sort of dataset and then spit out factual information,

or reference that information. We we you you

those are interchangeable as well where you just have to have the the vector database,

configured properly. The the instance where you can't,

is actually tuning. So the difference between,

training and tuning a model, basically,

OpenAI and, you know, meta, these developers of these

foundational large language models, they get all this training data

in and they, you know, publish the model. And then afterwards, when you

feed it additional information that's generally called tuning, that's

very expensive to do and expensive to run because you're not using

foundational model anymore that could be on a shared server or shared

resource or something like that or even if it's read only.

So so that gets you know, you're paying

GPU, like, processing power to actually, tune

it, and then you're paying for access to it as well, whether that be through

our platform or another. So, in in those three

scenarios, outside of tuning, you can generally switch the

underlying, foundational model. And that's very important

because say say you, are an

MSP and you do tons of tons of work

creating an amazing dataset on how to run an MSP business,

or perhaps one of your customers is in the

widget factory business, and they come up with a huge, a

phenomenal data that or a set of prompts or whatever it is,

on how to, create, you know, build widgets in the widget

factory. When a new foundational model comes out, if you

invested everything in tuning, you have to pay all of that

money again to to retune the data set. So, you know, you say

you're doing it on llama 2, then llama 3 comes out, llama 4. Whereas,

with with rag or with, you know, cleverly using context

windows, you you don't have to do that. So there's a

lot of trade offs between speed, efficiency,

cost, complexity, future proofing, and just using

LMS internally. And I think I think there's a lot of, like,

clear for clarification, you mentioned RAG. I'm not familiar with the the sort of

the the terminology there. What does that refer to? I

believe the

So you can use GPT 4RAG, and, that is a

version of the model that's able to look inside of a database.

So, say you had, all of

your SOPs stored in a database somewhere,

gpt4RAG could go and and say

instead you say, hey. How do I

restart this data server or whatever?

It could just make something up based on its foundational knowledge and

and, you know, probably 80% of the time it would be right

because it's pretty good. Like, g p t four is pretty good. But with

reg, it could actually look inside of a database and and bring

back a PDF user manual and say based on, you

know, page 6 of, you know, this latest user

manual,

Yes. Depending on yes. Exactly. So so

that's like and and you you get,

like, say say you could get, like, 80% accuracy on complex tasks with just foundational

models, like, they're pretty good.

RAG, and you'd get similar results for for for tuning

for tuned models, maybe even worse results in some cases. Some of the studies

I'm seeing that you're actually getting better results from right because it's, you know, it

can be more factual. So, you know, they're

like, there's technical trade offs between them all. Okay. That's cool.

I appreciate the background on that. It gets into some of the use case context

that I wanna get into as well. I I do wanna call out, like, I

have I have this conversation with anyone whenever I'm talking about AI because to

me, it's sort of like what I find the most fascinating

about this technology is like I kind of relate it

to like, it's a bit of a parlor trick. Like if you really

understand how it works like once I started digging into this and understanding the

technology I was really blown away by by sort of

how it actually works under the covers. So, I I know I'm not

exactly an AI expert so I'll go through this and then you kinda correct me

if I get anything wrong here. But, I had my brother reach out to me

and he's like, hey. Do do you know much about sort of chat GPT and

all this stuff coming out? And I was like, yeah. Like, it's pretty amazing.

I can't remember how we got into this, but I I I want I I

guess sort of started telling him, like, this is kinda how it works. Like, it's

actually more fascinating. The way I describe this is, like, usually if you find how

a magic trick works, it kinda takes away the magic. You're like, oh, well, okay.

Well, it doesn't doesn't feel quite so special. Like, I don't wanna know how the

magic trick is done. I enjoy the magic. And this is sort of this exception

to that rule of my mind of, like, what chat gpt and LLMs do

is amazing. Like, it's magical in a lot of ways, But it's more magical if

you understand how it actually works in that it's not smart at

all. It's it's purely a prediction engine and it's really, really

good at just sort of guessing what's next in a sequence based on on

sort of those those tokens of, like, word groups and stuff. Right? So it's like

this new numerical valuation that it sort of figures out on

the fly. Right? And I've I've done some prompts and stuff like that where I've

actually sort of broken it open and it gives you the v b script window

where it starts actually generating. Like, it's not just the typewriter text where

it actually starts like like filling in text and replacing text and going

all crazy as it as it sort of does this this sort of multi line

prediction. So I find this really mind blowing if you understand like

it doesn't understand what it's actually replying in a lot of ways, like,

the contextual awareness. It's it's really just sort of number sequencing

based on these groups or, like numbers assigned to

words, which to me, like I said, is is actually kinda more

incredible that it's actually able to do what it's do without, without being

intelligent at all. Right? Like, do I mostly understand that

correctly? And are you as fascinated by how that works as me?

No. No. You you do. You are. And and so it's

even like so chat gbt is a chat implementation of

a, text completion. So what what

it actually is is every time there's a

question response, it's just a longer, set

of text completion being set in. So there's a system

prompt for chat gbt, and it's it's literally

says you are chat gbt. You can do,

you know, these different things. You should answer users.

The question and answers follow this this format and then

system or or its user and then it's chat

gbt colon. Right? And then the user

sends first response, and it says user colon.

And then it and then it it it leaves a

blank spot for chat gbt colon, and then jet gbt fills in that text.

And then it just keeps sending the same thing back over and over and over

again. So, like like, you're not like, people think of it

as, like, oh, I'm sending off, like, something and then it's, you know, listening to

everything I'm doing and blah blah blah and sending it back. It's really just, you

know, one. It's like a text file that just keeps getting a little bigger where

it's like user response user response, which I

I mean, once I saw that I was like, oh, really? Yeah. Yeah.

It's wild. Like like like I said, it's just it's crazy how it works under

the covers. So, yeah, getting getting them into into the

nerdy weeds, but, you know, we're we're a technical group that generally listens to this

podcast, so I'm sure people will will also find this

somewhat fascinating. We'll switch to,

a bit on the I guess the practical use cases, Right? So, like,

you're you're building this specifically for MSPs. And, again,

like like, I'd love you to just sort of expand on that. Like

like, why why a model or this platform

for MSPs in particular? What did you envision as being possible

with that? So it's actually for

MSPs to get in the AI business. So it's for MSPs to bring to their

customers and naturally use it themselves inside their own

business. The reason for that is I've seen,

yeah, different mega trends in the past. You look at the move to cloud,

which in a lot of cases increase the cost per

seat, not necessarily the total revenue, but the cost

per managed user or managed device, about

50% in in additional revenue, through that

that transition. And in many cases, a move to a managed

billing model or the introduction of recurring revenue.

And it took a while to get there. It meant

much because, you know, talking to someone, hey. We're gonna move

your server your exchange server out of the closet into Office

365. Like, it was a it wasn't the easiest conversation to have. There's a lot

of businesses who were reluctant, this move from capital

expenditure model to operational expenditure model.

But, you know, MSPs were the only group of people capable of handling

that transition for small businesses, while big enterprises, you know,

hired large IT teams to do it internally.

Similar thing happened in cybersecurity, but it happened a a little bit faster.

So, many MSPs again increased their per

seat, revenue by about 50%,

over maybe 5, 10 years with the,

introduction of more cyber security services. And I'm talking about

changing from I just offer, you know, a a web

root or, you know, Sophos or like, I just offer 1 antivirus

as part of your managed package in addition to your RMN to, you know,

you're getting email protection, you're getting endpoint protection, you're getting sock

services, MFA, like, the whole suite of it, all the

the tools and services that m MSPs have been adding.

Cybersecurity, hard sell. Very difficult. I've been in the

cybersecurity sales training business through, you know, working at Scout for a

while where, you know, you can make great cybersecurity products for small

businesses for MSPs to deliver, but you still have to help the MSPs with their

biggest problem, which is actually convincing people that they actually need

need the damn thing. And and AI

is just different. So it it it happened

cybersecurity happened faster with the movement to cloud, and I think AI is

gonna be a similar scenario where small businesses are gonna need help.

They're all gonna need to integrate AI into their business, whether it's

through, you know, what we see today with interactions with large

language models and those use cases, which, you know, you might have been asking

about, like, writing job descriptions, doing SOPs,

documenting things, summarizing conversations, helping with customer service

workflows, lots of text heavy, tasks.

But I think it's gonna happen way faster because AI is show,

don't tell. And it's an operational

10 xer versus

a cost center. It's still, you know, it's still a cost, but but it's

it's much easier. Hey. You know, you know, this thing that's been taking

your employees, whatever, 3 days to

do here with AI, they can do it in in 20 minutes. Like,

who's gonna say no to that? So I think the explosion

with MSPs is gonna happen way faster than anyone's ready for.

That's interesting so like I guess it's sort of dual purpose like there's definitely some

things you can do to leverage the the models

internally like you said like finding relevant SOPs for a particular issue kind

of you know copilot for MSP

type data, but also, you know, how is how is that

being leveraged and utilized, for for the client base. Right? So

I I like, I had a conversation with someone recently on the podcast

and and it had occurred to me that, like, I I was surprised

I hadn't thought of this earlier of I think what you're sort of starting to

describe here is, like, the consulting opportunity around how you actually

roll out, implement, and leverage AI in in,

as an MSP in your client businesses is gonna become very

relevant very fast. Right?

Well, I think right now, if a small business needs help writing a prompt,

who can they go to for help? Yeah. Yeah. That's a good point.

I mean, MSPs are gonna get those questions eventually.

And I would bet your average level 1 technician

is better at writing a prompt than the average CFO of a,

of a, you know, $10,000,000 small whatever.

$5,000,000 a year small business. Probably safe safe

bet. Yep. But like what about like, you know,

like you leveraging AI, I suppose, in in in their workflow,

even some training around like Copilot, like you're rolling out 365,

you know, hey, we're gonna add Copilot for you guys and, you know, some

training around how to utilize that. I think those are, you know, some

interesting use cases, very valuable to the client as well.

What about sort of, like, more complex integrations of of

language models in, you know, workflows, right, like interactions with,

their clients or, you know improvements in workflows internally.

What about some of those more complex use cases? Have you thought put put some

thought towards what those would look like potentially? Yeah.

Yeah. So so for our, what what

we're building at ads and when we're releasing, I don't know, it might be out

at the time of the release of this episode, Is,

part of this Just quickly on that, what's what's your release date? In

in the 1st March is our is our first product just going out. So

Perfect. K. So by by this the time this is live, this is very

likely to be live. So right out and check it out. There you go. There

you go. Yeah. But what we have is an is an,

AI app builder where you can, do all the prompt

engineering work and build all the inputs, into a dynamic

prompt. You know, if then then this type of thing, and then

publish that to the end users. The end users just upload a file, press

enter, or, you know, type in yes, no, maybe so.

So so examples are marketing. Right? Marketing is a is an example because it's

synonymous with all all small businesses, and small businesses generally struggle

with marketing. They don't have enough money to pay an outside firm or they're paying

an outside firm. They're not doing a great job.

Say, the very least, every time you release a new

product, you wanna post about it on social media, and you don't have

anyone to write about it

on social media. So you you copy and paste

your your MSP or, someone internally builds an app,

that generates the social media posts, and it takes a specific input,

whether that's a PDF of the product documentation that you're releasing

or an email update or just a summary describing it. And

they add some additional system context about the business and who it is and the

tone that they like to use. And then the end user, all they're doing is

copy paste. I want this to be specialized for ins

for Facebook, for Instagram, for LinkedIn. I want it to be this

length and and press enter. Another use case

might be for job descriptions. Right? You could very easily like, that's a

one to many, type scenario, example where

an MSP could build, a an app

that generates, job description where you input the title of the

job, the salary, whatever the, expected inputs

are, and then, it it, you know, generates

the whole job description for them, and the MSP could populate that to

all of their different

of aspect of these models is like like there's often people

talk about sort of future jobs are going to be prompt engineering and like maybe

that actually gets sort of like like, sort of born out of this and and

sort of becomes less prevalent than it currently is. But I don't think people really

understand how different and how much better

information you can get from these systems if you engineer the prompt

just right. Like using sort of like like language variables and things like

that. So like maybe just, like, a a quick bit on this,

like, kinda your perspective because, like, I think a lot of people understand this is

just, like, the ChatGPT interface. I ask some questions, I ask it to build me

a, you know, a job description and it comes back with

some pretty good stuff like like better than what most people would write

but the difference between that and having a really good

prompt that is designed in a way that actually outputs

something that is like a 100 times better than what it would just generically

spit out. I think it's something people don't quite understand. You wanna

expand on that? Yeah. So with

some of the newer models you can have a very large context window like I

said earlier. So you can provide 5 examples of

of job descriptions, and and write ups on them. You can

provide nuances and, you know, if it's like this then do this

and and, really spending time to make a very

good prompt. But the the the thing that's changing each

time is just, you know, the title and a couple words about the

summary of what the job is. So we're we're

actually, releasing a a course and it should be

released actually. As this is released, you go to our

website and get access to it on hats.ai,

on on how to do basic prompt engineering and and the basics around

it and and how to do it well.

Yeah. I mean, I think it's a skill that's extremely

relevant. You could think about how when Google first came out, the people who could

get Google right away and a lot of those people own MSPs now.

Right? They could get the information they needed and other people would just type in

the wrong thing. So it's a similar skill.

I can relate to that actually because I used to work at an ISP way

back in the day, and we used to run these open houses where we, like,

sort of educate people how to use the Internet and how to use search engines.

I used to challenge people in the room when I was doing these sessions of,

like, name something. I can, like, I can find relevant information

somewhere and and they're like, oh, okay. Whatever. Like, how to build a box for

ferrets? And I'm like, alright. Here you go. Like, can they it would spit it

up. Like, like, who's the lead for this year's f one? And I'm like,

easy. Here you go. Right? And, like, people were kinda blown away by this because

they couldn't find that information. They'd have to they'd really struggle. And it was sort

of you're right. Like, that's early prompt engineering is a good Google

search. Like, maybe it's, again, less prevalent now. But, you know, it's a I

think an interesting analogy. I guess like the one of the other

sort of elephants around this is is data security. Right? I think is a really

important, aspect of this and I'm curious sort of how you guys are considering this

in in the product that you're building because, you know,

MSPs hold a lot of, sensitive data and they're

you know, I guess one of the another one of those things that people don't

quite understand about these models is if you're using just sort of like

free version of Chappy GPT you're potentially providing information to

be fed back into them into the the training model. Like it's

not this is this stuff is not as sort of of private as maybe

people assume it would be. So, like, what are your thoughts about your product? How

you're building it for sensitivity and care around

potential prevention potentially sensitive data, that

MSPs will be holding and wanting to leverage in this but are sort of

cautious around security implications. Yeah. I I

think just AI rollout as a whole or the goal AI

transition, the biggest problem that we're seeing is,

data readiness. So, for example, why not just turn

on, Copilot, right, on on

an organization? Like, Microsoft makes you do a whole data readiness thing where

it's like, okay, this thing's gonna have access to all your PDFs, like, that

you have in these folders. Like, did you, you know and

and the concern is right, like, I'm a I'm a marketing intern and I

say, how much budget should I allocate for,

q 2, for this program? And it says, well, the

CMO's salary is is, you know, this much money

and based on this, you know, spreadsheet that I found.

So so you need to be really careful there. I think

we set this platform up, to be a

secure, safe, alternative to sort of everything

out there where we're not trying to monetize your data. We're trying to put you

in the in the AI business where you have very granular control.

And and a big part of that and, you know, it's it's a lot more

work on our end to make to keep the data separate from the

models. So we you can very easily switch, models

in the future. Right. Or as you iterate or as you build.

Another piece of it, I think is just the

the use cases. So people are very quick to make publicly

facing, applications with with generative AI, and that's how

you get problems like the GM dealership or

the Chevy dealership that, you know, like, people

are getting it to generate Python scripts and sell them Teslas

and Elon Musk is screenshotting it. And then all of a sudden GM's got a

big PR problem because some random, you know, Chevy dealership and I don't

know where. Right. Like set up a chatbot on their public facing

website. Like, I I I you

need control over what users are doing,

and you need unification especially in, like, a customer

service environment. So that's why, you know, we build things one to

many where you can control things at the MSP level or at the

admin level and edit prompts, as a whole. But

also, you know, educating users, like, this is your first

draft. You should review this before you set it publicly.

Start with use cases like marketing and maybe not, you know,

entering code into your, right, production,

Linux terminal that, you know, hasn't been tested or vetted.

So it's, you know, it's a way to do things faster and then a way

to do things a little better. But, like, we have one product that is

external facing, for example. It's a it's a phone customer service agent that you

can call and it can take notes, transfer you, create a ticket,

you know, send email, whatever. And the amount

of guardrails we have to put in to, you know, end the

conversation if it starts drifting, like, it's a lot. And and

there's, like, AI, large language model security,

is is just beginning because these tools were used

internally for so long. Right. So I think that there's gonna be a

like, there's gonna be browser plugins to prevent people from

putting social security numbers or company information at a chat gbt that

pop up. Like, there we got a ways to go on all this.

Yeah. No. It's a part of the I suppose part of the issue of of

such a fast evolving field is like, you know, this stuff takes time and

has to the the the guardrails and safety has to evolve as it

evolves as well. Right? Yeah. I mean, it's like

imagine that we took, you know, like like,

it's it's it's almost like it's like, email

was not set up for security, and here we are still using the

same version of it. Late I mean, yeah, we've we've improved a

little bit. Right? Still evidenced by the fact that the

number of fishing and, you know, user training that's still required. It's an

inherently unsafe system for sure. Yeah. Like, it

wasn't necessarily designed with bad actors in mind. And I and and to some extent,

I would argue that that the initial large language models,

weren't, you know, designed with that. And and now there's talk of,

you know, we need a, a sovereign AI,

like like, or AI infrastructure should be,

regulated at the national level, where you have, you know, say,

the large language model and the, GPUs that run them that are,

you know, dictated by the federal government and, you know, maybe the Saudi

government has a different one. And things will probably go that

way, and I'm not, you know, like, I'm I'm I'm in no position to

comment. Maybe some, I don't know, like, FNAI company, but that's about it. I could

have an opinion, I guess. Yeah. Yeah. Yeah. Yeah. I I should say I'm

not, an expert in foreign policy and Sure. Yep. Live

liberty, like, civil liberties and and

copyright and all of that. But one thing I know for sure

is the technology will

evolve faster than the regulations. Agreed. So it'll it

it almost doesn't matter. Yep. Like, OpenAI is

getting sued by the New York Times, but everybody's already using them. So Right. I

think. Yeah. I mean, like, you know, like, Meta

and, all of the the social platforms have been around for

a long time. They still haven't figured out how to regulate them either. Right? So,

yeah, I don't think they're gonna be, I mean honestly maybe they're they're quicker with

AI at least they're they're trying to work on it on some regulations around it

but agreement on how that's actually gonna play out I think will be

pretty pretty, messy for a little while yet. I guess

that naturally leads to, you know, prediction time, right?

Predictions are are terrible because, you know, if you get them wrong then people may

remember. If you get them right, it'll see it'll seem, obvious in in

hindsight but, I would love to sort of since you've put a lot of time

to this and and you're passionate about the field I'm I'm curious about sort of

how you feel AI, writ

large will impact and change the the the MSP

business model for, sort of in the next five to 10 years? That's

a I think in 5 to 10 years,

MSPs will be managing, potentially

tuning large language models and their,

relevant insert in infrastructure around them like vector databases,

for the majority of their customers. And and that

may be, like, every small business, has

some version of a customized large language model that they

use inside their business. And the

MSPs are managing the infrastructure for that. I also think that there's going to

be a movement back to private cloud and on prem for some of this

stuff. And the most qualified

people I know to go set up a server room or a server

rack with a bunch of GPUs and virtualization and,

you know, management

ongoing maintenance of it. But, and and I

also think that, you'll start to see super winners in

in in in industry pop up. So I don't know if it'll

happen in MSPs, but say one of these really big MSP platforms

gets really smart about, organizing their data, and

they they create, you know, this thing that everybody's been trying

to sell. Like, the early AI people have been, you know, dreaming of of

this AI that solves tickets for you automatically and can do your

job. Like, say, say, say somebody figures that out and then starts

licensing that out to all the other MSPs. And they're no longer in the MSP

business anymore. Right. They sell all that off. They're just in the data

business and own, own the dataset that, you know, can solve tickets. Like that

stuff's going to happen in every industry,

I think. Yep. Yeah. I think there's

it'll be wild to sort of see you, like, there's there's sort of, like, some

of these things I think are are somewhat predictable and then there's gonna be

absolute wildcards that in, like, 3 to 5 years especially we're like,

oh, interesting. Did not see that coming. Right? So

it would be a fun space to watch for sure.

Awesome. Well, this has been great, Jimmy. I appreciate your time and, best of luck

with this. I think it'll be really fascinating to see how this evolves

and, really, really happy to see, you in particular as one of the

people sort of at the at the head of this spear head of the spear

for the MSP industry in in evolving the AI workload

for them. Thank you so much. Really appreciate it. Really

enjoyed the conversation today. Alright. Take care.