[MUSIC PLAYING]
You're listening to Deal by Deal, a McGuire Woods podcast.
Deal by Deal invites you to conversations
with experience independent sponsors
and other private equity professionals.
Join McGuire Woods partners Greg Haver and Jeff Rucker
as they explore middle market private equity M&A
to provide you with timely insights and relevant takeaways.
[MUSIC PLAYING]
Hello, and welcome to Deal by Deal,
a podcast for independent sponsors and other investors.
In the lower half of the middle market.
My name is Greg Haver.
I'm a partner in the Chicago Office of McGuire Woods.
I focus on M&A.
And I'm excited for this episode.
I'm joined today by Paragh Vaish of Next Now.
Some of our episodes, we talk about meaty legal topics,
like QSBS or Hart Scott-Redino filings or things
like that.
For this episode, I'm excited because we're
going to talk about AI.
And we're going to talk about how Paragh helps private equity
investors and others use AI to really take
their portfolio companies to the next level.
So really excited Paragh took time out of his schedule
to join us today.
But why don't we kick off Paragh with just an intro
to you and your super interesting career that kind of led
you to this point?
Yeah, Greg.
Thanks for having me.
It's an honor to be here.
And I'd love to speak into your community.
I've met numerous folks in the private equity world
and the investment banking world over the last two years.
And it's interesting that it's not at all my background.
I'm a technologist.
As you said, it's been an interesting career.
So began my career in L.A. at the Walt Disney Company
in technology capacities with ESPN and the Walt Disney Studios.
After that, I went to Microsoft.
That's where product management really
was born around the early 2000s, followed
by a division of eBay, which many folks
know StubHub, that was the division.
The transition after StubHub's work
got particularly interesting.
So I went into a role as the head of digital product
and design for Tesla.
That is a role that is as large and as stressful as it sounds.
It probably shaped five years off my lifespan,
but it was well worth it to get that chance
to work side-by-side with Elon and John McNeil,
who is the president of the company.
He was my manager.
The window of time is 2017 and '18, when the Model 3 was just
coming out of production hell, as many people know.
And we were figuring out how to scale the digital assets
of the company to complement the physical development
of the vehicle, among solar and other product lines.
After that, I went to Google.
I got a very rare opportunity, which
was effectively a one-line sentence job
description, which was build whatever you
want for the next two years.
And so I chose to work on a product that
was based off of search data benefited
from the role that Google was playing in the cloud space
and used machine learning, all of which
was a precursor to AI.
So I could say that in the 2019/20 range,
we were identifying some of the early patterns and philosophies
behind what constructive the AI world were in today.
After that, I went into a role as chief product officer
for Weapons Detection Company.
This is Evolve Technology.
It's a publicly traded company in which the essence of it
is a modern version of Weapons Detection
at schools, hospitals, sports stadiums, casinos, et cetera.
You can think metal detector for your purposes,
but imaginative Tesla made it.
It would alarm only on the bad stuff
and all of the good stuff just flies right through.
And that creates the ability for people
to get to where they're trying to go,
or to be able to scan people in environments
where their previously wasn't the possibility
of doing a TSA-style prosecution.
So that has all led me to create next now.
But I'll pause there Greg, because that's the background side.
That's the storied career.
Happy to answer any more questions about it.
Go deep on some of the stories.
Yeah, it's super interesting.
I mean, when you mentioned that you were at Tesla
during the era of rolling out the Model 3
and then at Google in the era of really the precursor to AI,
I mean, just how interesting a moment's in time to be there.
I'm curious, I kind of know the culture of big law firms
and private equity investors,
but maybe compare the culture of Tesla at that time
to Google at that time,
were they worlds apart or lots of similarities?
And I know we're off on a tangent here
from middle market investing.
Yeah, it's worlds apart is the right answer.
So Tesla moves with incredible speed.
They're oftentimes project timelines in Elon's mind
that are one day or one week.
There's not really other timelines.
So a really big project is one week.
Most things are one day.
And so--
I hope my clients aren't listening to this
that everything is either one day or one week.
No, I'm just kidding.
The opposite being true at Google,
which takes a very methodical approach,
oftentimes it verifies that what is being built
does not cross lines that might be interpreted
as antitrust violations or being viewed as a monopoly.
Those type of things tend to slow the company down a bit.
The group I was in had the ability
to rapidly develop and experiment and break things we did.
So it had some resemblance up.
But overall, Google has a little bit of a different pace.
Things have changed a bit.
I think in the most recent time
as I keep in touch with my colleagues,
it is an embracement and how liberal you can be
in the use of technology is common to both.
So if I were to juxtapose to Tesla and Google to private equity,
I would say it's on the most extreme perspective
or end of the spectrum of using technology.
That's Tesla and Google.
Private equity oftentimes is a very conservative approach.
So I'm observing that difference
that you're calling OutGreg and have the spectrums
in the technology world.
And now I'm seeing it in the private equity side as well.
Yeah, that's a good segue to tell us
about the clients you're working with now
and what you're helping them implement.
Absolutely.
So we work with middle market private equity firms
and there's really two functions that we play for them.
And maybe it's worth be giving a brief overview
of what next now is and context of what we do for them.
So next now is an AI product studio
that builds discrete and specialized AI capabilities
that cannot be delivered through off the shelf technologies.
So if you can do it through cloud and JGPT,
that is not something we would build for you.
So we look for high value, very discrete capabilities
that give you the 10X results.
That's what we strive for.
We focus on the metric first, verify
that what we're trying to achieve is a worthwhile outcome
and then go get it with extreme creativity and extreme rate
of pace.
So now to your question, private equity firms
have engaged us in two fundamental ways.
There's the internal operations of a private equity firm
becoming more efficient.
Much of that is document-based,
connecting into data and different systems,
moving at the rate the world should work and move,
which is revealing the right opportunities, the right people,
and having AI give you a remarkably high starting point.
So take, for example, one where you have Sims that
are passed around quite liberally.
They're verbose, sometimes hundred pages,
many charts and tables.
Imagine if you can have a distilled version of that
that is edited by a human verified, the data is accurate,
and served up to you on a platter for you
to do what you do next with it.
When you compare that and some people on this call
and this podcast might hear and say,
well, I do that through cloud today.
And the answer is yes, you do.
And you might experience about a 95% accuracy rate.
And the challenges you may not know what the 5% is
that is inaccurate.
Well, that's where we come in.
So we have developed the capability
to be more than 99% accurate because of the verification,
the variety of AI models being employed at the same time
to assess a document.
So that's the one side of how we work private equity firms
is internal operations, highly accurate,
remarkable efficiency gains by connecting systems
that currently don't connect together
and delivering you things that you may not
have thought were possible before.
The other side is the portfolio companies.
So EBITDA is king.
We focus on top line revenue improvement or cost reduction
to realize that EBITDA gain.
There are so many different stories that I can describe.
I'll illustrate one that's very good example
of how a private equity firm can utilize AI
for a remarkable advantage for a portfolio company.
So one of the PE firms who work with has an investment
in a company that sells playground equipment.
Very simple business, jungle gyms, swings,
that's those kind of things.
And they sell it to K through 12 schools across the country.
But parks and rec and all those things as well.
So private equity firm asked us,
what can you do for this company?
We saw that they were generating about 80 leads per year
through a very traditional marketing means.
This meant conferences where superintendents
or facility managers gather.
And they tend those conferences and have a booth
where those people walk by and you scan the badge
and that's considered a marketing qualified lead
so they're getting 80 per year.
So we're a fresh pair of eyes on the problem.
We don't know much about K through 12
let alone playground equipment.
But we looked at that problem statement
and found that their target audience
being K through 12 schools,
how might you know when they're the market
besides them walking by your booth?
And we found is that after COVID,
8,000 of those 13,000 school districts
stream their board meetings on different video platforms,
YouTube being one of those.
One click off of every YouTube video is the transcript
of the video.
And we take all those transcripts for all 8,000 schools
and scan for questions or answers
that relate to their interest in playground equipment.
So something like, are they expanding their campus
that might warrant playground equipment?
Is there expressed displeasure?
Are they taking out funding for?
You name it, you can see how you can get to dozen questions.
And we deliver to our customer a 50 word summary
whenever it's a yes with a link the timestamp,
map it to the sales territory
and the sales rep and drop it into the CRM system of choice.
So now this is taking the company
that previously was thinking biopically
about how they did lead generation
and saying there's a broader world
and a broader set of data
that is available to you for the taking.
They are getting 22 leads per week,
delivered every Monday morning
from the prior seven days
of those 8,000 school district board meetings.
Mathematically that results in over 1,000 leads per year.
So 80 per year versus 80.
This is a thousand is the 10X that we were seeking that we delivered on.
That's incredible.
I mean, it's such a creative idea as well to go on to YouTube and find those meetings.
I mean, a question I have is, how do you get to that creativity and to that great idea?
I imagine it's a collaboration between you, maybe the PE investor and existing management,
then you might have to battle against some inertia or some resistance to AI.
I mean, how are you able to come up with these great ideas?
Ultimately, the background I described at the start of this podcast is probably a lot
to do with it.
You mentioned, you know, working at Tesla and how is that against Google.
There's a principle at Tesla around taking first principle approach to problem solving.
And so if you apply that, if you take Elon's words about first principles, which he's
using everywhere and Starlink and other parts of SpaceX and so on, the first principle
here is my customers, the school boards and where they present and how can you understand
you know, their interest and intent. And that leads to then the solution, right?
It leads to the opportunity to find the solution.
So if you take that approach, which is well written about many books and folks have spoken
about first principles and you truly look at the right metric to go solve for at a company,
these opportunities are sitting there for the taking.
It takes a little bit of that breath of understanding of different industries and the drivers of
a business to then reveal those opportunities.
And that's where we propose things, run experiments very quickly, show results and private
equity recipients or their portfolios, they are oftentimes just wowed by the output of
what's possible.
And that's where, you know, a little bit of the back and forth and results and the improvement
of the idea.
That's really interesting and kind of taking a step way back when I think about why I like
to work in what I call the lower half of the middle market where my clients are buying
businesses, typically from founders and really taking them to the next level.
It's because there's real progress and real changes that my clients can make day one after
closing and, you know, it's funny a decade ago, it related to professionalizing the management
team, accounting, books and records and sales, et cetera.
And now it just seems like you have a whole nother tool that should just be a almost standard
practice.
Day one, how can we improve this in the business we just bought?
I mean, it's obviously going to vary business to business.
But if I'm thinking about as an investor, my playbook, what are some broad themes that
you see that can be improved with AI that aren't quite as specific as the school ward example,
but just like general, you know, concepts AI can improve with across businesses generally.
Yeah, I think the income statement is a very good place to start and that's where you can
rank your top expenses and you can, as an approach, look at each line item and we ask
the question very directly, if this number on the expense side of the income statement
is 50% lower than what it is today would it be worth it to you?
And that's a very good approach to then finding cost reduction opportunities to then say,
okay, yes, it's worth it at half that number because it changes your margins, you might
become a rule of 40 company, whatever the result is, but you now focused on the right area
to then reveal the opportunities, same on the revenue side.
So that's one approach you can take.
Another approach is you look at a business and say, where is their time spent by human
capital, by labor, and that could be in the form of processing deals or understanding RFPs
responding to RFPs, whatever it is, but if you see where you're putting people time and
anything that those people are doing that are related to documents or responses, meetings,
there's something there that can be had that can improve it, which is either, and I like
to favor the idea of if you put 10x more leads or improvements there, you'll get improvement
in your business, you could also realize some potential cost savings or scale.
You have five people doing this and they're supporting 25 customers, well, how can those
five people support 100 customers in the presence of AI?
So those are the philosophies and principles that I would use, but I think the income statements
are a very good place to start.
One thing to add on, Greg, is that as a private equity firm makes an investment from taking
a founder led business and then having ownership over it, there's also the flip side, which
is what is the AI risk to this business, right, and so maybe you don't make the investment
because you spot this chance that this business could be fundamentally disrupted in the presence
of AI.
That is another way to use capabilities and skills of AI to make those assessments, you
know, where that downside risk is.
So hopefully these are a bunch of different ways to get to the answer.
You're having conversations even with potential buyers that might be looking at an industry
and saying, "Is there an existential risk to this industry before I make the buy?"
That's absolutely right.
And it's both sides and maybe there's an opportunity as well because you see that they're
doing, you know, a 22% margin, but in the presence of AI, you can get up to 38%.
You can do all the analysis before making the investment, you know, be much more informed.
Right.
And I'd love to get your view on it.
This is a little bit of a touchy subject, so maybe we can cut this out if we need to,
but the fear was a couple years ago that AI is going to eradicate huge swaths of jobs,
white collar jobs, entry jobs, et cetera.
And I think that maybe the data is more of a mixed message.
And I have law firms.
I think we're one potential area where AI was going to come and take all of everyone's
job and my job included, and we're not seeing that at the moment.
You know, when you're implementing AI with port codes, how do you see like total job
replacement versus sort of, if I have eight things that are part of my job, AI is doing
three of them or five of them really well, and I'm still doing the other five to eight,
et cetera.
The way I think about it is opportunity creation before I think about job replacement.
And the data supports that there's tremendous amount of opportunity creation and new jobs
and roles being created today.
And new companies and individuals who are becoming founders that previously didn't think
it was possible.
There's all kinds of growth stories there.
But specific to a portfolio company, the essence of my message here is if you could do
more with the same, then the business is healthy.
And the reason I say the same is that the people that you have that in your question are
the ones at risk of being sustained in their role.
Those are the ones who are effectively training the AI to do what it is the outcome we're trying
to achieve.
And if those people feel like they're going to be replaced, there is human nature does
kick in at times where they try to disrupt the project from being successful.
So it's upon the founder of the owner of the business to ensure that those people are
assisting the AI to do the work such that you can get 10x more opportunities in front
of them or more business or they will benefit from doing so because the project or the execution
or that technology will just go better versus if you have folks who are constantly worried
they'll find ways to sabotage it and things don't go as smoothly as you'd expect.
That makes sense. I mean, we're looking at it a similar way here at McGuire Woods in
that, you know, it's increasing the opportunity.
It's making us faster and better at certain aspects of our job, which frees us up to take
on even more work and expand the pie, really.
To me, I've heard the term jagged frontier as it relates to AI sometimes.
There's certain things that it does really well.
And certain things that you think it might, but it actually doesn't do a great job as
far as like, and thinking about the menu of 20 things that I do as a lawyer on a daily
basis or an engineer does on a daily basis.
I guess a question is what has kind of surprised you as far as an AI function where it just
did fantastic and maybe what's been a disappointment where you thought it might do an incredible
job, but actually it was a bit disappointing.
So inherent in your question is an interesting thing that I think a lot of people are not
able to get over.
So they'll go and use cloud or chat GPT to try to achieve something.
And it might be a miss or doesn't deliver what they're expecting.
And they stop there and say, well, AI is not there yet.
Okay, and that's a disappointment.
And that's precisely where we start next now.
And so the capabilities of chat GPT and cloud are phenomenal and they should widely be
utilized by private equity firms.
No doubt about it.
There are limitations as well.
And that limitation spot is where the remarkable opportunity is.
So I would ask everyone to look at it and say, well, it didn't do what I wanted it to
do.
The most likely outcome or reason why is that you needed multiple models to be employed
to be able to do the end result you're trying to get to.
And that's what we construct are the series of models.
So in the pleasant surprise category, I'll describe one story where we had a portfolio
company of private equity, they are rather tech savvy, utilize cloud, pounded on it many
times over to try to get the ability to analyze blueprints.
Okay, this is blueprints of a new construction building.
And they were trying to figure out where their product would be deployed in these new buildings.
So it's a multi day call it 10 day process to analyze those under the current capabilities
of their people.
And they tried cloud and it didn't work for them.
Yeah.
And just a little bit more detail.
So we're talking.
We're talking they were using a large language model and they were just using it off the
shelf.
That subscription.
And you come into a company, you're building sort of a harness on top of an LOM or maybe
just going a little bit more tech details for those that are interested in it.
Sure.
So you're right.
The description is the off the shelf.
So you open cloud, you pay $20 a month or some, you know, capacity level.
And essentially what they were trying to do is, like I say, analyze blueprints and new
construction buildings for where their product would be deployed.
And so you can imagine that scenario you upload a PDF, you type out a series of questions,
you say where these 25 keywords present in this document.
And that's the scenario.
And it did not deliver what they are presently doing using human talent to assess those documents.
And so now you have a gap and you say, well, AI is not there yet.
The reality is is that there are different models from the large language model companies
that do certain things better than others.
And so you don't know that.
It's a very, you know, I didn't buy it in a curtain.
type of a thing as to which model does what really well. So just as a simplified example, certain
models can scan through text incredibly well. But those models may not do image evaluation
very well. So they'll not scan through the image of the diagram that's on the same page.
And then vice versa, some will analyze images incredibly well, but they'll miss on text.
And so as a novice person in private equity, you most likely don't know which model does
what well. And you're certainly not going to jump to the stage where you're going to have one model
do something really well and then flip to the other one to the other thing really well and combine
the two outputs at the end. That's a bit too much effort and very technically savvy people might
be able to do it. So what we do as you said, like a harness, we'll look at what model does what well
and employ those in the scenario to achieve the end output. So in this particular example,
the ease with which we can evaluate those blueprints and do what their current present team does
represented a 10x decrease in the rate at which they can evaluate an opportunity and figure
if they get a bit on that project at all. So we're able to analyze both the text and the images
and you know get to that in result. So we say like the pleasant surprise is your initial question versus
the what didn't achieve meet the mark. I now believe that anything is possible. I truly believe
that anything is possible. We're limited our creativity of what to go after and the way that you get
to the creativity is by getting to the number that you're trying to influence. So it's combining two
of your questions there of how do you have one of the mechanics and then I do believe the range is
nearly unlimited. If you can understand that these models do different things well and then work
with somebody who can do that. No, that's fantastic. Look, Paraga, I really appreciate your time
today. I work with and think about AI quite a bit at least for an M&A lawyer and so this has been
really fun. Before we go though, for our listeners who are primarily again, you know buyers of
middle market companies and operators of them post closing, anything we didn't cover today that
is kind of critical as they think about AI and how they're going to use it over the next couple of
years. Ultimately, the AI strategy of a company has to be more than using the off-the-shelf tools
because the off-the-shelf tools will give a company the ability to meet parity with their
competitors. What I mean by that is, one day, Claude and Chad CBT will be just as ubiquitous as
Excel and Word and PowerPoint and Outlook and so on. And no one thinks of those four things as a
competitive advantage these days. And so the ability to use Claude and Chad CBT are becoming ubiquitous.
You might think you do it better than others, but you'll never know. You use Outlook better than
others. You never know. So the world is broader than what those off-the-shelf tools offer,
in particular in private equity because you're opportunistic to seize improvement on EBITDA
and to get a return on your resume as quickly as possible. The way you get outsized returns and
differentiate from the competitor companies in that space is by doing unique custom AI executions
that you cannot do from the off-the-shelf tools. That's where differentiation is created,
that's where improvement in EBITDA happens and you get the end result that you're seeking in the
trade that you're in. It's such a good point. Well, thanks again, Prague. Where can people
find you and next now and learn more? NextNow.ai, the phrase there is we are
employing the next technologies now on your behalf. LinkedIn is a great way. PerageVesh,
our website has all of our details. We have case studies and video demonstrations of things we
built that can exhibit more than what I've shared here, Greg, or my email address,
[email protected].
Well, great. This is really fun. Thanks again, Prague. Of course. Thank you.
Thank you for joining us on this episode of Deal by Deal, a McGuire Woods podcast.
To learn more about today's discussion and our commitment to the independent sponsor community,
please visit our website at McGuireWoods.com. We look forward to hearing from you.
This podcast was recorded and is being made available by McGuire Woods for informational
purposes only. By accessing this podcast, you acknowledge that McGuire Woods makes no warranty,
guarantee or representation as to the accuracy or sufficiency of the information featured in the
podcast. The views, information, or opinions expressed during this podcast series are solely
those of the individuals involved and do not necessarily reflect those of McGuire Woods.
This podcast should not be used as a substitute for competent legal advice from a licensed
professional attorney in your state and should not be construed as an offer to make or consider
any investment or course of action.
PodcastTranscript AI