Michael, Mark, welcome to the Evolved Radio podcast.
Thanks for having us. Thanks Todd. So
glad to have you here. You know, there's been a lot of talk in the
industry lately about everyone calling for
the doom cycle of AI. There's a lot of conflicting
opinions about whether or not this is working. So we
wanted to kind of get into some of the details of like why that
is potentially a debate. And Michael, maybe start
with you. You have sort of an interesting perspective on this. Some of
the things that you saw in your travels in the medical
realm where you were before joining thread, do you want to give us kind of
a bit of your backstory and how AI is related?
Sure. To your point that where I've been in the past,
I've spent the last 10 years or so inside the digital health space which
has its own set of challenges outside of the AI piece. But
yeah, for the past prior five years before joining thread, I was working
at a, in a healthcare organization that was building
agents on a kind of a more classic rules based
structure versus what is really more commonplace
today based on the large language models and etc.
But yeah, look,
setting aside kind of the unique challenges of operating in the health
care space and kind of the
different set of rigidity, our clients today in the space that Mark and I
operate on have a set of standards and requirements that we need to
make. They're just kind of uniquely different in healthcare. But I think to your point
about at the beginning where you mentioned
this doomsday and is AI really delivering on kind
of the value prop? I think it really depends on how you
frame the question of that. I think where it may be
failing or perceived to be failing is where the promises
were ahead of its ability to begin with. And
the shiny promise is really easy to sell and that's easy to sell
in almost any industry regardless of
being healthcare or in more of a core technology space,
a place where there's no doubt it's creating value is
some of the back end systems, some of the analysis side of things.
Where I think some of the challenges have come in both in healthcare
and outside of healthcare, is where the
rubber meets the road on real specificity of problem solving,
solution solving, having the
AI or in our case an agent being able to take
an action that brings to resolution. And I think if you're able
to frame the ability of the product and
set the right promise rather than this,
you know, North Star. Always looking at this North Star
type situation, I think it's in a lot of cases been
more successful than maybe we Give it credit or some of the recent
articles. And also, no matter how you look at the
speed at which it's, at which it's moving, I mean,
Mark can certainly speak to, you know, what
thread has been able to develop in other agentic companies
in a much shorter period of time with a much
higher degree of fidelity, a much higher degree of solutioning
for the end user than we would have seen five years
ago. So kind of to end that rant. Yeah,
I think the promises have been too huge
and kind of inset the wrong expectation.
But the underlying, I think if you, you kind of said before we
got on here, you have a little bit different view. And I think I'm with
you. If the expectation is set correctly, it is. Has been incredible.
The advances and the ability to solve day
to day work problems are. It's quite
remarkable. Yeah, it's, you know, the,
the Gartner hype cycle sort of comes to mind here. We had chatted about this
before. We were talking about coming on and like, I don't know that we're
necessarily in the trough of disillusionment yet. Right. I think
it's maybe on the downward trend. Like I think to your point, there
was a lot of sort of unrealistic, unrealistic
expectations of what, what these things were capable of. But at the
same time, the part that I marry up with that is like how much we've
blown past even the most basic expectations
of what an AI should be capable of. Like I heard this conversation on a
podcast and they were talking about like, hey, remember the Turing Test? Like no
one, like that was hot for about five seconds as we ripped past
it and no one seemed to really notice. Right. So like, like
be in a way, like we're actually beyond, I think what the peak
of inflated expectation would have been like. We shot so
far past it that the, the, the drop to the trough of disillusionment
is sort of like almost passing through the workable
cycles, the workable workflows. But we're in this sort of
weird, messy middle of. We expect. Maybe it should be capable of
more because it's not AGI. I don't know. Like, Mark,
what's your take on this? We had a great AI
service unleashed weekly meeting today and I
love your point about Turing Test because we've,
we've now progressed. We are now at the Will Smith
eating spaghetti test and it's
insane. And we've all been like, oh, okay, it's okay, it's conscious and
like spaghetti doesn't crunch and there's steam coming off
the pasta. So we've, we've definitely blown
past that significantly.
What. And I think about this a lot and there's
like two big concepts I want to convey. This is not the
first AI cycle in human history.
This is the second AI cycle. We have
had a nuclear winter for AI after
the first big realization, right. I forget the
guy's name, but the first learning of neural
networks came from a biologist that was also mathematician and he figured
out the neural network and we made a massive leap
and then it just went quiet. Right. And
you know, to Michael's point, like we had to do a lot of
rule based conversation flows
and it went quiet and then the transformer
model from Google came out.
Hilarious that they did not monetize or productize that
and OpenAI did. So I think we're on the cusp of another
big, big
uphill and just like this
explosive growth with gen AI now
there's a lot here. Humans always
overspend. If anyone was around during.com
bubble, man, did we overspend for
pets.com to do something that was probably not
worth it, right? But what, what
was left behind that was infrastructure. What
happened was we went from a thousand dollar T1
lines to 50 DSL lines
and that infrastructure is why Google exists, is
why Amazon exists. So we are definitely
overspending. The question is what's going to be the
infrastructure that's left behind for us to build
upon? Yeah, no, I think that's a really good
prediction that like, you know, maybe we're kind of
misunderstanding what the point is right now. Right. And this kind of
goes to like, I, I kind of bring this up every time we're, we're talking
about AI on the podcast, but you know, I won't do the full detail
of this, but my, my AI sort of prediction timeline
is now three or four years into this. And I
predicted kind of three to five years was usable workflows
and we're well into that. Like there's a lot of very useful
creations, you know, thread certainly being one of them is a
very reusable work workflow use case. And it provides value. Like
those things absolutely exist and it moves on to sort
of later on it becomes something that starts to potentially eat service
desks after five to 10 years. And I think there's a lot of things that
you guys are cooking up that lend to that vision. And
it's not a doom and gloom type thing of like everyone's going to lose their
jobs because AI is taking over. It Definitely pushes people to
higher value work, right? It's commoditization of rote
work within those ecosystems. So if that's the case,
like that's part of what I'm curious about here and I have, I have some,
some opinions I can definitely lend to this. But I'm sure
your guys's perspective on, you know, like the HBR study that came out that
said 81% of AI projects
in business are considered a failure, right? Like, like
why, if we're sort of seeing the early
stages of this golden age of AI, why is everyone
predicting that, you know, we've overstated this and you know,
now everyone's like well crap, maybe we shouldn't have fired all those people
at sales and Microsoft because now we have to hire
80% of them back, right? Like what is the tension between those,
like is it, is it oversold or you know, is,
are we sort of missing the mark on what we're actually supposed to be achieving
right now? I think it's probably a little bit of both
and we have to be careful to not be a little bit of the
self fulfilling in that. Right? If you're a big major company,
leave them nameless. But any of them, or any company, large or small, who
you make a commitment and you go out and you get ahead of your
understanding of the practical application of
the technology in this case, you know, agentic that we're talking about,
then there's this pressure to live up
to. Oh, you said it was going to replace, pick a department. You said it
was going to replace your legal department. Well,
you know, if you're a public company, you better show
some evidence that that occurred because you've suggested it
happened rather than, because I think because of the,
some of the stuff we talked about, the promise is so
huge and the reality is
some of the stuff is so amazing that it's really easy
to take a look and say look, it fundamentally can do this. Well,
we can jump all the way to there. It's like, it's
like almost, it's, it's almost human nature
to kind of jump to the end of the, right to the end of the
rainbow or, or whatever it is rather than
understanding that it's not understanding, you know, being methodical
that everything is a step process, right? You've got to,
your legal team's got to know how to use it and where the value creation
is before you could not have one or, or
to the point you guys made, you said earlier, how do you up level
skill set, how do you get These people in any department doing
higher value creation things
rather than kind of time,
workforce burning less optimal things.
And I love that. Can I, can I add on? I don't know if you.
Man, there's two big things that are happening
economically. We went crazy on
cryptocurrency. We went through Covid.
We've inflated the market. We
needed a way to generate more value. I don't know what
would have happened if we didn't get the gen
inflection point. So I think big business
needed a soft landing. Right. Or they needed a way
of, of progressing forward. And I think,
I think we've saved all the crypto farms because I'm pretty sure all the GPUs
are now used for AI. But also there's
a lot of inflation in the economy. There's a lot. And so we're trying
to figure out unemployment
is low, salaries are high. So what do we do?
How do we, how do we fix the labor market? And I think there's this
big demand for us right now to figure out the digital work
because if we can do will heal our economy, at least in
the United States, it will heal our economy. So I think with
Harvard Business Review, they're obviously going after the large companies. So I think
that's why we're seeing that pressure there.
Yeah, I think, look, some of the challenges
right now, are you looking at a very high level.
There's a couple dozen companies driving this perceived. If
you look at the market, this market explos. Whereas the
reality for those major companies that have
their point, that have their AI plan mapped out and
they're building the data centers and they're building the hardware and
they're building the processing power. Those are very clear.
The vast majority of the companies in the US
around the world, and certainly our customers, they're figuring out
how to deploy it really effectively. And everyone uses it and
touches it on their computer, they do searches with it, they do whatever. But from
practical workflow utilization. What do you mean? We're taking my wedding
photos and making them anime is not productive to the economy.
Yeah, it's my favorite activity. We're still like the
average company. The business model isn't as clear as an
Nvidia or somebody who's building the data centers. In
addition to that, we're still settling in. Mark mentioned the broader
economy. Like what is it doing? Right. As these data centers
absorb the electricity and power, there'll be
other investments in new sources of power at the same time.
Until those are there, we all might see our Electric bills go
up. Right? Because supply and demand, right?
So if there's that, or terraforming the. Planet
to be a compositional brain. It's wild.
It's insane. I do have one thing I really like.
I need to get off my chest. Let him do it.
There's a big difference between a proof of concept
and production rate. There has not been
that big of a difference in traditional software development.
Like, sure, you haven't thought about all the workflows. You haven't thought about
all the use cases, obviously. However,
this is the first time we're talking about indeterministic
code. This is code that's making its decisions.
And it's a completely different game. It might look
great, but every time when we first started to
QA our voice agent, every time we'd pick up,
what would we say? I have a printer problem. I have a printer problem.
I have a printer. I was like, this is awesome.
It's so good. But I never thought maybe it's just
good at printer problems. Because when you get
into production and it's not a printer problem,
it's a lack of connectivity, it's
a broken dll. It just acts
completely differently. And so I think the
combination of the, the. The pressure economically to
find the next way to heal, but
also seeing those little POCs, they're like, yeah, I'm
Clara, baby. I'm gonna fire 700 people.
And it's not going so well. Maybe we should reverse
out of that. Because people actually have hard,
hard questions. And it's not all about, like, I need a refund.
Yeah. And this, this sort of gets to, like, my feeling. I have two things
that I think are sort of at the core of. Of those. Those numbers
of the percentages of failed AI projects in business. And
one, I think is. Is sort of the majority of it is
the historical percentage of failed
IT projects in general is. Is
catastrophically high. Right. So, like, sure
to say 81% fail. But if we're starting from a base baseline of
64% of IT projects fail, which is. Is a
general stat, then we're really not that far off the mark. So I don't feel
like this is as. As sort of as
diminishing to AI as a project as a whole. In business,
it's more to the point of AI, of IT is messy,
and the implementation often doesn't go the way that we think because of poor project
management, because of scope creep, because of people issues.
And I think where that gets married. Up with it's always people issues.
Project Management is as simple as who does what by when. And then you add
people and things get complicated, right? So like I think
the other core of this is that a lot of the AI,
like well, I'm using air quotes here for the audio listeners. Like
AI deployments and projects were largely just sort of,
hey, we turned on Copilot for you guys. Go forth and prosper,
right? And it was, or you know, we subscribed to Gemini. You
know, you guys, you guys now have access to these tools and then you go
back and you check with people who have not had any level of training
and have no concept of how to utilize these things in their day to
day operations. Like, hey, is that, that, how's that been? It's been
amazing, right? Like it's totally revolutionized your work. And they're like,
I got nothing. Like I didn't find it that useful. They're like, failed
project, I guess. AI didn't save our business, right? So I think those
are the things that are largely at work. And I've been even kind of amazed.
You know, I've been working on some, some AI projects and
as a part of this I've been interviewing a lot of MSPs and
technologists and saying if you had an AI army to
build some workflows for you to automate some things, to insert some
agentic capabilities in your day to day operations to save you money
and time, what would you do? And the level
of sort of sophistication and creativity in those
responses from highly technical people that are very aware of what their
business is have been very routine. It's like really like
we need to scratch a little deeper and think about some things that like that
we could really sort of leverage and blow the doors off of the capabilities
and allow people to do more. So if you assume
that the IT people that are immersed in this all day and have a pretty
good sense of what this stuff does, they can't come up with great use cases
for how to deploy it and how to you how to leverage it for workflows.
How are you going to expect that like Sarah from accounting
and Jim in the marketing group are any
further ahead in figuring out like exactly what they need from
a workflow standpoint. And this is where I think like
the core of this really comes down to is a lot of this kind
of needs to be programmatic and determined because just to say to
people you have AI, it will be amazing for your work
ends up as a dead end, right? So I'll sort of
end my rant there and leave you guys to sort of jump back in on
this. What do you think, Mark? I haven't earned. We
haven't earned secret here.
The reason for our success is because we
pair workflows with AI.
It's not about telling people to use AI when they need it. You
need to put it into the workflow. Right. The difference
between telling accounts payable
person, hey, I got you a Claude subscription. It's
awesome. And telling them that every
invoice that comes in will process added to the
database. And I'll tell you at the end of the
day what is the breakdown of invoices
from vendors. Let me help you prioritize.
So the key to success here is a workflow
that's deterministic. Like this is a real use case.
I need to figure like msps, man.
Reconciling vendor
invoices to billing to your customers is
insane. Yep. And of course you can give them
Claude, but you need to ingrain yourself in
that workflow. You need to get every invoice
and automatically process it and read the
invoice and break it down. Like that's been, that's
been so instrumental and I think that's what a lot of people aren't
doing. You're right. It's so, it's hilarious. Co pilot.
Great, Claude. Awesome.
But it's not part of a workflow. And what Mark's talking about, when it really
becomes just peripheral through society.
Right. To where it's just, it's when somebody's
used it all day and they don't realize they've used it because
it's just culturally part where it is not.
I'm taking a specific action that
will engage this AI thing.
Somebody said to me from my prior
career, healthcare moves in decades,
not in quarters or not in years. In
a lot of ways. Everybody we talk to is still early
adopter. Right. I mean to the average
330 plus some a million Americans out there, our. Industry is
an early adopter. Yeah. And the subset of people.
Yeah. And if you stick to the decade is what creates
change. I mean I think just about my kids, they're
adults now and simple things
like they want to know why I'd call
instead of just text them. I mean it was only
15 years that that wouldn't have been an option. So
maybe not quite the decade. Mark's kids
will not know a world where
AI isn't influencing almost
every piece of technology they touch.
They won't. For good and for bad both.
But again, kind of to my early where I started that
it's about kind of to where
Mark was going in that
it's embedding the solution using AI solution
in your day to day workflow. So it's just part of what
you're doing. And that's when
those big promises that maybe that article was suggesting the
81% aren't achieving. That's when we start to. We see the
reverse of that and it's 80% of them are achieving and maybe
20% or not. It's also last thing I'll say on
that. If we go really long term and we talk
about Mark, you were talking about the way people think about
planning a workflow or planning a solution. His kids
at their age, they won't know that that's just how they will think.
So everyone knows my kids are four and five. Oh. So yes.
Minor adults. Yeah, but right. You're absolutely right.
You're absolutely right. My kids like okay, you're tired of telling
me stories. Can you have the AI tell me a story about Sonic?
I'm like. That'S crazy.
Like how did you come to that conclusion?
You know, because it just. But I would say like this is really important.
Like this is really important. It's not
just AI, you need to get into workflow. If you
don't get it into workflow, you will fail.
It needs to be part of a workflow.
So let's build on that because like, like,
I mean I understand what I. Well maybe I'll preface that. I
think I understand what you mean. Right. Of like, like we actually map out a
full process. We understand what instigates a certain action
or some. Something, something. An activity
and then it leads through a bunch of actions and creates in some type
of endpoint. Right. Like
is that sort of all we're thinking about in a workflow? And understanding that
enough is being more detailed and understanding what are the
actions we take on initiation of something
towards an end goal. Is that kind of what. What you're thinking about when you're
talking about workflows? Yes.
Very high level. Right? Yeah. Business process mining.
Yes. Yeah. Right. Like there's a trigger. It's typically,
you know, someone reaching out. There's a communication layer. But I
think what the most important part that people aren't realizing
is that if you map out, if you just forget AI if you
just map out the whole business process, it starts to
branch heavily towards the end. Right. Always it
becomes wild. Oh, this customer is on this
plan. Oh, this customer. We promised them something.
We always promise. Right. Like everyone, everyone promises a customer
something. And so what I have found is that the
first two, three, four steps of a business process
are typically linear, but then what
happens is you hire a middle manager to be that,
that branching point, like, oh, this needs to go here, or
oh, we need to do this. What we're finding
is you can, you can automate 1, 2, 3, 4
and 5 is where you put your AI.
5 is where you say, oh, we promised this to a
customer. Do not bill them for this. 5 is where
you say, this is a security issue. Get
it over to the security team as soon as humanly possible.
And I love William. I will give him a shout
out forever. William from Integratech, he said a
great line. AI is here. And we want to use
AI to make service more human. It's not
about responding back to the other person that's triggered the
workflow, but it's about getting it to the right person
in the right context with the right information.
So like, that's been like our, our pillar.
So. And this kind of goes to like, I think the way that
maybe people are misusing the technology in a lot of ways. Like, I see this
as very similar to sort of
the other tools that get used and how people migrate away from them.
We talked about sort of the generational changes of how people utilize
technology. Right. And you know, I use Perplexity all day
and I barely use Google anymore, and that's a fairly recent
transition for me. But I know a lot of people, you know, their, their,
their default is to, to still Google stuff. You know, I have these conversations with,
with my wife and instead of her doing. I had the same conversation with my
wife, by the way. It's hilarious. Yeah, like she'll Google stuff and then ask
me to ask the AI. It's like, well, you know, you can do this too,
right? Like, so I think there's a lot of that of people are somewhat
comfortable with the way that they operate, the way that they deploy these technologies, the
way that they think about the activities that they need. And honestly, one of the
biggest shifts that I've had with AI was, you
know, for years I was still using it like
a search engine. Like I was sort of one or three shot prompting everything.
And then a person made a very sort of simple suggestion to
me. They're like, treat it like a co worker. And that was a huge unlock
for me. And just the way that I interacted with AI and made it a
lot more capable because the expectation was
just slightly different about how I engaged with it and
as I sort of explained this and we've had this conversation, I think
I'm more, maybe more convinced, like, you guys opinion on this.
Like, maybe we are on the slope of enlightenment and the Gartner
hype cycle because now we have a better understanding of how to interact
with and how to prescribe the technology's use,
right? To just sort of say, here's a tool and you know
you're going to do amazing work, right? Like, if you think of it in a
very sort of, sort of pedestrian way of like,
you know, here's a, here's a wrench, right, To a person who
needs a hammer. And they're like, great, I don't really understand what this does
for me. So I'm just going to go back and do what I was doing
before. And like, they're like, he doesn't understand, doesn't understand how to use this
tool, right? Like, this has been a total failure. Why wasn't this,
you know, creating massive outcomes the way that we saw it? I think there's a
lot of similarity to that. So I think your point about like, the definition of
understanding how the work actually flows through the organization
and where are the intersections where we can actually start to insert
some of these agentic actions and utilize
AI more as a co worker or a team member than
just sort of a tool unto itself. Any thoughts on that?
Yes. I have thoughts.
Michael, you want to go first or should I? You go, I'll do cleanup on
this one. Yeah, that's absolutely
right. And I think, you know, we just had our keynote
and we get this question a lot because Thread
is becoming a little notorious for automating, dispatching work.
We are for better or for worse. And
folks are asking us like, well, are you scared to talk to
MSPs? Like, aren't their dispatchers scared to get Thread?
And we had this deep reflection where we realized
where AI is today. We're not replacing
100% of any job, of any role.
What we're seeing is that we
are moving to a supervisor relationship with the AI.
It is a coworker, but it's very important to understand it's a supervisor
relationship. And when I found out that Waymo was doing,
it's all, it's, you know, autonomous driving, but
behind all those cars, there's a person with an
Xbox controller looking at nine cars.
And if any of them get stuck, they use an Xbox controller to get
out. Now, is it better than
hiring nine drivers? Absolutely,
absolutely. So I think the role, what's, what's Happening.
And again, like I said, like, we'll see
how many, how many more inflection points we have. But the relationship
is a co working relationship, but it's a supervisory
relationship. We're not going to get rid of all the dispatchers. We're going to
make the dispatchers that are the best, the supervisors.
We're not going to get rid of the technicians. We're going to make the best
technicians supervise their little army,
their digital workforce. Because you're right, they can reason,
they can talk, they can't innovate. And the world has
entropy. So how do we handle entropy? Humans are great at
entropy. So I believe it's
really important for us to recognize this. I
believe that jobs will be automated, like jobs to be done,
like prioritizing, categorizing or driving someone
a mile. But I don't think roles
will be fully destroyed. I think roles are going to become
supervisory roles. I like that vision.
I think, look to your point about where are we on the curve? If we
started that curve,
we're, we're really early because
even the really early adopters still, I'll still
get on an engineering stand up with Mark and the team
and there'll be a new release from one of the large models.
And we're still saying that group who live and breathe
it. Holy crap. Look at, look at what this one does.
You know, just the release three months ago
didn't do X in 18 months. Before that,
you know, seemed like an, you know, it's the difference between an
infant to a teenager and kind of its, its
capabilities. And that is, that's people
like Mark's engineering and product team and the company's team
living and breathing it. Whereas look,
I, I think about, you know, the
masses of people think about, just go through my family. I've got a lawyer
and my brother's a school teacher and one's a lawyer and one's a
works in retail. And like they are not,
they are. Not medical industry radiologists.
Yeah. They're just not what a radiologist organization standpoint. Like they're
not, they don't, they don't yet have the
opportunity to. And if they do, it's it's out of human
curiosity. Right. You know what I mean? And to your point about.
Yeah. Tinkering and to point about, about specific
workflows like my brother who has been a
schoolteacher for years, you know, we all know about the
challenges of teachers. Like did the paper get written? Did they write themselves? And
you know, so there's a whole other set of challenges. So certainly
he is. He is teaching himself and
educating himself on those practical applications, on how
it's impacting his environment and being sure that
like, you know, kids still have license
to learn and that kind of thing. But again, it's. It's fairly
isolated to the more not the
advanced stuff. So to your point of the book, I
Super early I think in that. In that curve
maybe not as early for the marks of the world even
for me. Todd, I want to flip it back on you. I have a question
for you. I have. I want to know your thoughts on this, if that's
okay. Sure. You know, Jensen is
obviously the CEO of Nvidia is. Yeah.
Fixing shovels. Win any gold rush. Yep.
And he said a quote and I was like, yes, we are not forgotten.
The IT department of every company is going
to be the HR department of AI
agents. Ooh, I like that.
Yeah. TNHR have been new
user onboarding termination. I. I just. I would love to
hear your thoughts about that. Yeah. In Jensen's
a brilliant dude. There's a lot I disagree with about Jensen's
management style, which is a separate podcast seems to have worked
for him. But holy hell, the. That
image I think is really smart because if I
pull that back to something that's a bit more familiar right now, and that being
the extrapolation from IT is outsourcing has become a lot
more practical for IT organizations post Covid because
it just removed all the geographic barriers on where people work
from. And one of the things that I insisted very early on
when I encouraged people to consider outsourcing was
that they said, oh, I've tried that. It doesn't work. I'm like, yeah,
but you probably abdicated a lot of the responsibility. And you
know, hey, we hired this person. They should work remotely. They're good
at what they do. And you know, none of that happened. But
you never spent any time with them. You didn't treat them like a staff member.
So of course the results were different. So the
extrapolation of that I think is now that we'll have an
AI workforce, we have to understand the.
The workflow analysis and kind of figuring out
like what the business patterns are and therefore how we can insert AI into it.
So I think there's a very natural marriage there of like the business
consulting aspect of it is becoming more prevalent because
the, you know, the just the break fix and the support requirements
are lower. And you know, once the AI start to
live inside the. The business's analysis gets complete and figure out
sort of where the agentic capabilities are and you know, building on
those workflows, there still needs someone to kind of manage and operate
those like from that supervisory position. Right. To hire them.
Yeah, exactly. Like we, we understand sort of like which agent
is particularly good at this type of role. And you know,
we manage some of the exceptions based on sort of a decision tree
because stuff happens, right. Work and people are messy and
you know, how do we step in when, when someone requires a bit of intervention
on that when it's dealing with, you know, an AI
team member. So I, I think that's, I think that's a cool vision. I really
like that as an, as an idea.
I couldn't agree more. Right. And there's so much opportunity for us. This
is why there's so much excitement in the MSP industry. Like we
are going to be the HR for, for agents.
And I, I just want to, you know, Todd, please, let's keep going.
But I just love the relationship between IT
and hr. Who's working and what tools do
they have to get leverage in their work. Where the tools
where the fire. Currently. I agree. Yeah. But look
what's happening in the industry. Rippling deal just
works. They're all starting to launch little like
device management, onboarding and off board.
I think that's going to be apart from the AI curve. I think that
the marriage between IT and HR is going to be an interesting one.
So I mean, obviously I could talk with you guys for hours on
this. I want to look. To sort of close it up. I'll sort of drop
one more here. That is really outside of sort of the
basics of what we're talking about here. But Mark, I'm sure you've put some thought
to this because I know that you live, eat and breathe this stuff.
What are your thoughts on RL AI like, especially
as we start to embody AI into RL
real life. Like, like robots basically being
human AI and starting to interact with, with the real world.
You know, I, I've been thinking a lot about this recently. You have any,
any thoughts on, on sort of the, the putting AI out
into the world and having it interact in that fashion?
We've, we've run out of training data. There's a big problem.
That's why I'm saying the last AI winter
perhaps may not be the only one
we've run out of training data because the
LLMs that we have today have learned everything they possibly can
from all of our Reddit posts and all the junk we've put online. It's
petabytes. It's great, but we're running out of data.
I think there's two angles here. I think that AI needs
eyes. It needs to, it needs to. You
know, our eyesight consumes more information
per second than any other of our senses.
So I think there's, there will be an effort to get
more information. So I think it's a natural progression of, of giving
it senses in a certain sense. And
I think that's really interesting. Now
what I'm really excited about is I think,
manufacturing. And
I'll go back like after we had a disaster in New York,
you know, the MSP I worked for, we bought the Sprint Data center
in Purchase, New York, and we ran a data center
and there's like predictive hardware failures
and you just need to swap out a hard drive from a SATA drive. It's
like we could figure it out. We just need someone to do it.
So I, I think that on the manufacturing side and on data center
side, I think we're going to see some of those,
some of those things automated that are not as important as, for
example, figuring out the energy problem or figuring out how to get
smaller in our silicon manufacturing or get to
quantum computing. But I, I
think there's a desire to feed more information into the
AI because we've run out. We have a big problem. We've run out. We're
now using synthetic data, but also to help on that
manufacturing side. Michael, what do you think?
I'm gonna go more human, philosophical.
That's what we work together, Michael. Perfect. I
think if those robots, humanoids,
real life, play a role in where Mark
went, where the agent, where it's inserting and improving,
obviously huge value there. I think on a human level,
I think we learned a little bit from COVID in those years. We
as people get a lot from interaction and being around each other.
So if it over a decade or more would create an environment
where we're doing less interaction with each
other, I think that's not great for
us just as people and as
relationship. I've kept referring to kids in the generational.
I mean, I'm not that old, but I mean, I can remember when my
daytime activity was go outside and play with your friends.
Like that was what after school was. And then one
generation back there, the video games and all, which is, which is great.
And it's developed our minds in different ways. But
on the human level, I hope we deploy them
where it makes us better and not
the other way around. Yeah, I share that sentiment.
As a technologist and someone who is very excited about this stuff,
I learned in Covid that despite the fact that I consider myself
an introvert once it was no longer an option to interact with
people, it affected me. I think you're absolutely right that
there. There needs to be. Still needs to be humanity in this. And I
think that'll be a tricky. A tricky tightrope for us as well as we start.
I love how. I love the angle that Michael took. And
Michael knows this. I don't have TVs. My kids have
zero screen time. I
understand. Considering how nerdy you are. Yeah, no, I understand.
I understand the problem. Yeah.
But I also, you know, like, maybe we can manufacture
stuff easier without people getting their fingers cut off. That's where
I'm like, that's important. Or a humanoid going down
and just sort of, like, pulling dead GPUs, slamming in a new one to the
array and moving down the line, basically. Definitely. Yeah, definitely.
Cool. Well, this has been really fun, you guys. Appreciate you coming on.
We'll link to everything thread in the show notes and
link to you guys on LinkedIn if anyone wants to reach out, but appreciate your
time. Thank you. Thanks so much. All right, people.