Latest / Agentic Conversations (formally mlops.community) / AI in M&A: Building, Buying, and the Future of Dealmaking // Kison Patel // #315

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AI in M&A: Building, Buying, and the Future of Dealmaking // Kison Patel // #315 · Agentic Conversations (formally mlops.community)

• 0:00
  So name Keesan Patel, CEO and founder of Deal Room, host of
• 0:06
  the MNA Science podcast. Coffee.
• 0:08
  I like my oatmeal plattes. People of Earth, we're back for
• 0:13
  another ML OPS community podcast.
• 0:14
  I'm your host Demetrios. Today we're talking all MNA.
• 0:18
  We are also talking about the product life cycle and the
• 0:22
  inception of where and how you can validate injecting AI into
• 0:28
  your already fairly mature product.
• 0:33
  Let's get into this conversation.
• 0:41
  I know you wanted to talk about the founder's journey that
• 0:43
  you've had. I am particularly interested in
• 0:46
  that right now because it feels like every once in a while you
• 0:49
  go through the slumps in the founder's journey.
• 0:52
  And I have been or feel like I'm in a bit of a slump.
• 0:58
  And so it's good to hear the rocky cutscene and how you're
• 1:03
  going and doing your training. But giving it to me a little bit
• 1:07
  more straight and helping me to understand that it's not just a
• 1:11
  5 minute scene. It can last for days and weeks
• 1:14
  or months or years. So I'd love to hear just about
• 1:18
  like the inception of Deal Room and then how it's been so far.
• 1:24
  Yeah, so I come from an M and a advisory background, so started
• 1:29
  from the bottom. Long story, I guess, behind the
• 1:32
  story, behind the story, which is failing out of undergrad, you
• 1:36
  know, find myself in real estate trying to build a career to
• 1:41
  circumvent not having a college degree.
• 1:45
  And I ended up finding my way. I didn't find selling houses my
• 1:51
  jam. I just it's like an emotional
• 1:53
  sale. And that wasn't people don't
• 1:55
  want to buy houses from 21 year old kids.
• 1:57
  That's the thing. But somehow I ended up joining
• 2:00
  this startup whereas 2 guys and they were building like this
• 2:03
  business brokerage type of practice.
• 2:06
  Hindsight now I see that they were generating all these
• 2:09
  inbound leads online and they're mostly Indian descent people
• 2:12
  looking by like gas stations and hotels.
• 2:14
  And they probably figured, you know, we can barely understand.
• 2:17
  I want to hire this kid and let him deal with them.
• 2:20
  And that was it. It was like day one.
• 2:21
  I'm on the phones talking to prospective buyers, figuring out
• 2:25
  what they're looking to buy, contacting businesses that fit
• 2:29
  their profile, seeing if they wanted to buy those.
• 2:32
  If they didn't, can we list them for sale?
• 2:35
  And just got my first hand experience in what M AM NA was
• 2:39
  really early and it was funny because I had to go backwards.
• 2:43
  Like I would learn to put the deal together, but then I had to
• 2:45
  go learn how to do the analyst work.
• 2:47
  Like how do you actually put financials together?
• 2:49
  How do you do do all the modeling and and things like
• 2:52
  that? I did that and left after one
• 2:55
  year because they figured, hey, I already know everything here
• 2:58
  and this firm doesn't really have a clear strategy.
• 3:01
  So I left to start my own practice.
• 3:02
  It's probably still pretty young, like 2223 and built that
• 3:06
  up over about nine years, ended up to do most of my deals in
• 3:11
  hospitality. So I think at the peak was
• 3:14
  working with Kimpton Hotels, Extended Stay America, La
• 3:17
  Quinta, and then I ended up finding Fig like an opportunity
• 3:21
  to work with the financial institutions, work with a
• 3:23
  corporate regional banks on some buy side and then sell side for
• 3:26
  small community banks. That led up to the recession in
• 3:30
  2007 when everything got flipped upside down.
• 3:33
  I wanted to do something in tech and I, I had this idea of like
• 3:39
  buying these financial domains. And what I ended up doing was
• 3:43
  basically creating these automated blogs that would
• 3:47
  populate content based on the keyword of the domain.
• 3:51
  And I remember at the peak, I had like 500 of these finance
• 3:54
  keyword domains because I noticed the ad revenue was
• 3:57
  higher than average, like anywhere from 5 to 10 X higher
• 4:01
  than a typical web web domain. And so we ended up with about
• 4:07
  200 blogs that were powered up and we're building like a
• 4:10
  customized platform to power all these blogs.
• 4:15
  I mean, I didn't realize at the time.
• 4:16
  Like we're basically gamifying Google's algorithm to rank all
• 4:19
  these sites really high. Free LLM Days.
• 4:22
  And then one day they just made their Penguin up to and
• 4:27
  considered all our linking approach is back as link farming
• 4:32
  and then it blocked everything. So our climbing revenues went to
• 4:36
  zero. And I had a small team of about
• 4:38
  5 folks. We had to basically wind down
• 4:41
  the whole business. And the one thing from that
• 4:46
  experience was the first time I was working with software
• 4:48
  engineers, I was really intrigued how they were using
• 4:50
  Jira to manage developing our software platform.
• 4:55
  And that that led to this inspiration of why not build a
• 4:59
  project management tool for M&A. That's what got the the idea to
• 5:04
  start Deal Room in 2012. So JIRA for M&A, because every
• 5:09
  software developer loves JIRA. That's a probably better way to
• 5:13
  frame it. I should probably use that like
• 5:15
  what you do. But the, the fascinating thing
• 5:18
  here that I, I really like is taking something that could be
• 5:23
  done in a spreadsheet, right? Or that is traditionally done in
• 5:26
  a spreadsheet and just giving it something beautiful and helping
• 5:31
  folks along on their journey. As opposed to you need to have
• 5:36
  this knowledge and figure it out from the spreadsheet.
• 5:41
  That's true. I mean, if you look Fast forward
• 5:43
  from that time to now, the product has evolved.
• 5:46
  We differentiate ourselves that there's a lot of tools to sell
• 5:50
  businesses, but we are very much focused on the buy side.
• 5:54
  So if you look at our customers, they're large Fortune 500
• 5:57
  companies or private equity back roll ups that are consolidating
• 6:02
  some and you see them across the board.
• 6:04
  You see like H FAC service businesses, wealth management,
• 6:08
  accounting, private schools, you name it, managed service
• 6:12
  providers. There's just a lot of
• 6:14
  consolidation with those smaller fragmented industries.
• 6:18
  So those and then the product itself is like full end to end.
• 6:21
  Now it's managing your pipeline, managing your due diligence, all
• 6:26
  the back and forth requests for information, clarification,
• 6:29
  questions, doing your integration planning, actually
• 6:32
  executing your integration, any kind of like BI reporting you
• 6:36
  want to do. And now more recently, we
• 6:38
  started building some AI capabilities for extracting data
• 6:42
  out of contracts. Yeah, I want to get into that
• 6:45
  because it feels like a perfect fit.
• 6:47
  But the part of me that I I want to make sure we cover is in a
• 6:53
  deal in the deal life cycle that you just laid out, you're not
• 6:59
  having the marketplace for the companies that are for sale,
• 7:04
  right. And this is and you said you're
• 7:05
  you're focusing on the buy side. So I imagine these folks are
• 7:10
  trying to mine for gold out there in companies or small
• 7:14
  businesses that are willing to sell.
• 7:17
  We're, we're not, it's funny mention that we're not a
• 7:19
  marketplace, but you know, I and I feel like this is the, I feel
• 7:24
  like founders now should probably know better because
• 7:25
  there's so much content written about this.
• 7:27
  But back when I started, there was nothing, there was no
• 7:30
  startup ecosystem anywhere. Especially I originally was in
• 7:34
  Chicago when we started the, the business And I, I at the time
• 7:39
  it's like you, you start listing out your features that you want
• 7:43
  to develop. And this is like the definition
• 7:46
  of feature creep where you kind of had this idea to build a
• 7:49
  project management tool from Angie M&A.
• 7:52
  And then you end up with a list of like 100 foot potential
• 7:55
  features you could build. And then you're like, where do I
• 7:58
  start? And then we have the idea of
• 8:00
  starting with more of this marketplace model because we're
• 8:02
  like, we should probably start with connecting buyers and
• 8:05
  sellers. And then as they progress with
• 8:07
  the process, we'll, we'll build those features out as needed.
• 8:10
  Our MVV should be, you know, the marketplace and we, we did, we,
• 8:14
  we within six months, we got our initial launch for marketplace.
• 8:20
  We got about 1300 users on their 200 deals.
• 8:23
  And I looked at this and it was going through the deals and
• 8:26
  realized we just built the world's most sophisticated
• 8:29
  dumpster for deals because everything on there was just
• 8:32
  garbage just like nothing a person would really seriously
• 8:37
  consider it investing in. And that's where we had like go
• 8:40
  back to the drawing board. And I think around this time too
• 8:43
  is when all like the lean startup was, was taken off where
• 8:46
  there was a book called Running Lean.
• 8:49
  That was pretty cool because it was more of a practical approach
• 8:52
  on how to, you know, leverage the model.
• 8:57
  And so we started doing these customer development interviews
• 8:59
  and really trying to be as unbiased as possible.
• 9:03
  I think that's like the number one threat to an entrepreneur is
• 9:06
  yourself is that you're like in love with your idea.
• 9:09
  You have this natural biases that you're completely blind to.
• 9:15
  I think there's new books now like the mom test and things of
• 9:18
  like how do you bake? You want to frame questions to
• 9:23
  people in a way that's unbiased. I think it's a whole approach
• 9:28
  and and discipline of its own. Yeah, it's a skill.
• 9:32
  And then it's just doing that like doing the reps because
• 9:34
  there's so many entrepreneurs skip it.
• 9:35
  They just want to hurry up and start building something, get to
• 9:38
  market fast. But I honestly think this is the
• 9:40
  most valuable step in the entrepreneurial journey is
• 9:43
  validating that you're solving the right problem.
• 9:46
  And I think we've done this and we continue doing this, right.
• 9:49
  This is like now in our R&D, it's instilled.
• 9:51
  We have any new ideas of what we're trying to build for.
• 9:54
  We go through the same process and I've seen it where if you do
• 9:58
  this right, you can actually find more interesting problems
• 10:01
  to solve than what you originally set out to do.
• 10:03
  Oh, wow. Because if you just go around to
• 10:05
  people and you're like, OK, this is specific cohort of people
• 10:07
  that I think has this problem, I want to go solve it.
• 10:10
  But if you're remove that biases and you're literally going to
• 10:13
  people and saying, Hey, what's the biggest challenge you
• 10:15
  currently have doing X? You know, whatever it is, What's
• 10:19
  the biggest challenge you have doing hosting a podcast and then
• 10:23
  just let people speak up. And then you go through a series
• 10:27
  of those conversations and look for patterns.
• 10:30
  But that that's I think if you do it right, you can more times
• 10:33
  than not actually find some more interesting problems to solve.
• 10:36
  Then you go through the whole exercise like, OK, you know,
• 10:39
  one, do you do try to steer them to the problem that you're going
• 10:41
  to to address? Because if that doesn't happen
• 10:44
  naturally, that's one clue. And then when you do start
• 10:47
  bringing up and listing these problems, how do you weight them
• 10:50
  because it's a paper cut problem?
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  Or is this like a severed, you know, hemorrhaging, the thing
• 10:55
  you got really got to solve? That's what I was going to ask
• 10:58
  because that's where I tend to struggle.
• 11:02
  Is full of ideas and full of cool things that I want to
• 11:07
  build. But then at the end of the day,
• 11:10
  figuring out which one is going to be the one that I focus on
• 11:14
  for the next three to six months or rest of my life, depending on
• 11:19
  how successful it is, right? That that's where I will get
• 11:24
  caught up because it's like in that analysis paralysis of is
• 11:29
  this actually going to be worth it if I put my time into it and
• 11:33
  have a, a few friends that have told me like, well, look man,
• 11:37
  you can quickly test, try and get signals and then see if
• 11:42
  there's something there. And if not, it's not like you
• 11:46
  are that invested in it. So go on to the next one.
• 11:51
  Yeah, I would agree with that. The testing parts really
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  important and I think just the conversations I found the most
• 12:00
  valuable. I feel like some people are
• 12:01
  afraid to talk to people and they end up doing these highly
• 12:05
  quantitative approaches and they send surveys out and things like
• 12:08
  that and it's like terrible. You get a weird bias there
• 12:12
  because people that take the time to answer those already,
• 12:15
  yeah. Yeah, the different level or
• 12:18
  even I've seen folks that will use LLMS as their way to do
• 12:24
  this, this type of research. And it's cool for some, but it's
• 12:29
  definitely not equal to getting on a call with a perspective
• 12:34
  client. I can help.
• 12:35
  I can definitely help you guide you where it's like we do it.
• 12:39
  I'll use it to key up questions. I'm doing one right now for our
• 12:43
  next set of AI features and where I'm using AI to help
• 12:48
  really generate the question flow.
• 12:50
  And especially it'll give you too many.
• 12:51
  And then you got to go back and say, alright, like let's really
• 12:53
  prioritize this thing. I want to get down to about
• 12:55
  three or five key questions because the goal is to make it
• 12:58
  conversational. Like that's where you get more
• 13:00
  of the interesting insights is it's conversational because you
• 13:03
  want to peel back layers. And if you just kind of like
• 13:05
  tactically ask question after question after question one, it
• 13:09
  sucks for the other person, but then it's like too high level.
• 13:12
  The interesting insights are are sort of like when you start
• 13:14
  getting some real understanding, like why is that?
• 13:16
  Like, why is that that that be that kind of the the cliche
• 13:19
  thing, right? You asked why five times and you
• 13:21
  really get to some some clear root cause of of all this stuff.
• 13:25
  Then that's more of the important thing to to have in
• 13:28
  those conversations. You know, I also think that when
• 13:32
  you reach out to people, because you, if you can reach out to
• 13:35
  people cold and you're like, Hey, I'm trying to solve for
• 13:37
  this problem and wanted to get your feedback.
• 13:40
  You know, your experience and perspective would be really
• 13:43
  helpful. Just the response rate is
• 13:47
  probably an early indicator if you're selling something
• 13:49
  interesting or not. You know, you reach out to 100
• 13:51
  people and nobody responded. It's like, OK, maybe there's
• 13:54
  something there, you know, rethink that to try to see if we
• 13:58
  can amplify it a little bit and see, but that that, but you
• 14:02
  know, you should be able to get some conversations.
• 14:03
  And I think that's like the most valuable thing.
• 14:06
  Ghost get skipped all the time or or just taking lightly.
• 14:10
  You have like 5 or 6 conversations.
• 14:12
  I I think a better targets like 40 because then you can really
• 14:17
  identify some patterns, but also covered multiple cohorts of
• 14:21
  different prospects that you would tap into.
• 14:24
  And you might find like the way the original customer base you
• 14:28
  set out for isn't the right one, or isn't the one that really
• 14:31
  experiences the pain that you thought they did.
• 14:33
  I love that we're harping on this because it is so important.
• 14:37
  And I had a friend who has now created a fairly successful
• 14:42
  startup. They've been ramping like crazy.
• 14:45
  And he told me about how they came or stumbled upon the idea
• 14:51
  that they have and they were talking to CR OS and they went
• 14:55
  in with one idea. But in one of the calls, the
• 15:00
  first thing that they asked was, so what's top of mind for you
• 15:03
  right now? Or what's like the burning thing
• 15:05
  that keeps you up at night? One of those kind of generic
• 15:08
  open-ended questions that you can ask.
• 15:11
  And the CRO said one answer and they were digging in, digging
• 15:17
  in. And at the end of it, it had
• 15:19
  nothing to do with the product that they wanted to pitch them.
• 15:22
  But they said, hey, look, it's funny you should say that
• 15:25
  because we're actually working on something like that.
• 15:28
  Can we get together in two weeks and show you our demo of the
• 15:32
  product? And so in the next two weeks,
• 15:34
  they just didn't sleep and coded up an MVP showed them and the,
• 15:42
  and then they closed their first deal right there on that call.
• 15:45
  And they were, they realized, wow, we got some here.
• 15:47
  And so then they just kept doing it and saw that, OK, this is A,
• 15:51
  really easy to sell if we can show them the value here, and B,
• 15:56
  we need to start hiring people. That's a great.
• 16:00
  I love it. I call it being dumb because I
• 16:02
  can't meet you give this I suck a human nature.
• 16:05
  You get into a meeting and you're constantly thinking about
• 16:08
  the next thing to say. Where are you trying to steal
• 16:11
  the conversation? But if you just come in the flip
• 16:15
  that around. Just be is is like dumb.
• 16:20
  I get to the state where you're not assume whatever you know is
• 16:24
  wrong or you know nothing. And then you just like
• 16:26
  intensely, really listen. And then all you had is that one
• 16:29
  lead question, like what's on top of mind?
• 16:31
  What's the biggest thing you're struggling with right now?
• 16:33
  And you just really dig in and listen and understand that
• 16:36
  problem that that's going to steer you in a much better
• 16:39
  direction than having so many different things mapped out and,
• 16:43
  and where you plan to take the conversation.
• 16:45
  I was listening to a podcast with Gilly who created the
• 16:51
  incubator that Wiz came out of Wiz and a bunch of these
• 16:55
  cybersecurity startups. And he said that one of the
• 16:59
  questions that they have their startup members ask the
• 17:09
  potential clients is to basically to cut through the
• 17:14
  noise. Because what he was saying, if
• 17:16
  I'm remembering correctly, is that when you ask someone what
• 17:21
  their biggest problem is, it's very time specific.
• 17:26
  So this week I have a very big problem.
• 17:28
  Next week, if you ask me that same question, I have a
• 17:31
  different very big problem. And so it all depends on when
• 17:34
  you catch me and what mood I'm in, what I've been thinking
• 17:38
  about, what meeting I just had, etcetera, etcetera.
• 17:41
  And so he was saying there was, there were a few questions that
• 17:45
  he likes to have his startup founders ask, which is something
• 17:48
  along the lines of if you could RIP out any one piece of
• 17:53
  technology right now, which one would you RIP out?
• 17:56
  And the other piece was if you had to lose one piece of
• 18:02
  technology, but you would cry because you had to lose it,
• 18:08
  which piece of technology would that be?
• 18:10
  And so it's kind of getting outside of the box of, hey,
• 18:14
  what, what's on your mind? Yeah, I like feel like that
• 18:18
  approach a lot. I don't know why.
• 18:23
  I feel like both of them would be like Gmail.
• 18:25
  It's a plot twist. The same answer to the same
• 18:30
  question or the different questions.
• 18:32
  The biggest pain and the biggest joy.
• 18:35
  Yeah. So, I mean, I want to talk a
• 18:38
  little bit about how you've been creating these AI features
• 18:41
  because I think that flows in perfectly here and it is very
• 18:46
  related to going out. And I'm sure you went and talked
• 18:49
  to folks to try and figure out, OK, out of the million ways that
• 18:54
  we could put AI into our product, how and what's going to
• 18:59
  serve our customers the best? Yeah.
• 19:00
  So I started this journey last year and that I got to tell you
• 19:05
  like there's big thing I learned is when you have a company
• 19:10
  that's more mature because we're about 50 people in the company
• 19:13
  now, that same playbook of when you're a company of like 2 to 5
• 19:19
  does not work the same way. In fact, I'm still learning that
• 19:22
  if anybody listening has some tips for me, because that's one
• 19:26
  thing that'll that'll unfold in the, in this sort of journey.
• 19:29
  But the beginning part starts off really similar.
• 19:32
  And think at the time, I had about four different hypothesis
• 19:36
  on where AI would play well when it comes to mergers and
• 19:40
  acquisitions, specifically on the buy side, when you go
• 19:42
  acquire company. And I, I remember starting from
• 19:47
  talking to existing customers and saying, Hey, we, we got this
• 19:50
  AI stuff. And I remember I talked a
• 19:51
  handful outside of it as well, because you can get access to
• 19:53
  prospective customers and one of their take as well.
• 19:58
  And what when ended up was we quantify, we quantified the,
• 20:03
  the, the value was actually what, what, what, what, what we
• 20:09
  drew, what drew people in was the use cases where there was
• 20:13
  really clear value. And the number one thing that we
• 20:16
  found was people wanted to save legal expenses.
• 20:19
  So we kind of went through all these ideas and we ended up
• 20:23
  really focusing on contract analysis.
• 20:28
  Specifically, we started with this extraction.
• 20:31
  If you're going to go buy a company, every single company
• 20:34
  has a ton of employment contracts.
• 20:38
  If a ton of customer contracts and they have a ton of vendor
• 20:41
  contracts. And to sit there and go through
• 20:44
  them and mine for information. Because like, for example, the
• 20:46
  first thing, if I'm looking at a deal, I want to know it's in
• 20:49
  their customer agreements, if there's a change of control
• 20:52
  provision, right? If we're going to buy this
• 20:54
  company and they have 500 customers, I find out every
• 20:57
  single one of them has a right to terminate the contract.
• 21:00
  There's a huge risk item and I'm going to have to go to the
• 21:03
  seller and say, all right, before we close, like we need to
• 21:05
  address that because I, I can't buy a company where I don't know
• 21:09
  if we're going to have customers.
• 21:10
  So they all have the right to cancel their contract.
• 21:13
  Now, normally you'd have somebody like a junior legal
• 21:16
  person that would sit there and they would open up each document
• 21:19
  and do a control F and look for change of control.
• 21:22
  And they just keep doing that over and over.
• 21:24
  And you spend quite a bit of money just doing that, that
• 21:26
  extraction. And usually there's some handful
• 21:29
  of key things you're trying to get in all those sets of
• 21:31
  contracts. You know, you might have a set
• 21:33
  of leases and you want to know what the assignment provisions
• 21:36
  are. Consent comes up in a lot of
• 21:38
  agreements. You know, you have to notify
• 21:39
  people about the transaction happening.
• 21:42
  And this AI just does such a good job of, you know, taking on
• 21:46
  basically unstructured data and structuring it.
• 21:50
  And to be able to do that on, on a whole set or series of these
• 21:54
  very similar documents and sort of identify what's unique about
• 21:58
  it and come back to you and give you an analysis that explains
• 22:01
  all the different things that picked up that that we found
• 22:05
  just resonated so well. And then I think that was like
• 22:10
  where why we gravitated towards it.
• 22:12
  It's it's sort of the value of solving that problem.
• 22:15
  It was very easy to correlate to.
• 22:20
  An invoice? Did you have a preconceived
• 22:26
  notion of? Oh well, normally lawyer fees or
• 22:30
  legal fees can be up to 25% of the deal or X percent or 25 KA
• 22:38
  100K depending on how big the deal is.
• 22:40
  No, like I was not thinking about that all.
• 22:43
  I'm like the founder of Visionary thinking of stuff that
• 22:46
  would be cool, like it'll be so cool if AI could automate, you
• 22:50
  know, this part of the deal, if AI could help with this sort of
• 22:53
  planning stuff and, and you know, help you.
• 22:56
  Nobody cared about that all all this stuff, right?
• 22:58
  You think of cool stuff, but then when you sit there and talk
• 23:02
  to these people, you're like, wait a minute, they just care
• 23:04
  about saving a buck here and there.
• 23:05
  And if you like go back at everything and like, well, this
• 23:08
  would actually save you the most money.
• 23:10
  So that that's where we ended up putting our focus.
• 23:13
  And it was a good bet. Like that bet worked out really
• 23:15
  well. Like it was, it's great
• 23:17
  capability. But in now if I look at this
• 23:19
  year, so this kind of goes back to dealing with the mature team,
• 23:23
  because there's a point when you move through that process, you
• 23:26
  realize you can't do things the same way you did before, like
• 23:29
  just prototyping and and prototyping you can still do
• 23:34
  because you can work with a designer and get some mock ups
• 23:36
  and get feedback, right? You should do that, but there's
• 23:39
  like a mature team. So it kind of gets, it's got to
• 23:41
  get handed off back to them and it kind of goes through their
• 23:44
  whole cycle. You know, before you might not
• 23:46
  have, you might not have AQ and a cycle.
• 23:48
  Now you got to like all these different steps that they're
• 23:51
  there. And then you'll find yourself
• 23:52
  like breaking more things then. So then you can go on Jira.
• 23:56
  What's that it has to get put on JIRA?
• 23:59
  Yeah, so I got to. That's the point I realized
• 24:01
  like, OK, I got to step back and I'll step back and there's other
• 24:05
  things to work on in the business.
• 24:06
  So I, you know, but I think this is an interesting thing.
• 24:10
  I stepped back and six months or so ago was by and I realized
• 24:14
  that there wasn't a lot of progress in that initial
• 24:17
  features that we created. People use it, but it just
• 24:20
  didn't mature evolve when like you look at AI in general, it's
• 24:23
  like evolve really quickly. So that got me thinking like
• 24:27
  when I looked at it and the way engineering resources were
• 24:30
  allocated, you're competing with a lot of things.
• 24:33
  You're competing with a lot of requests that your existing
• 24:35
  customers have. Your sales teams has requests
• 24:39
  that they identify prospective customers, However, what what
• 24:42
  would help them sell? And then I looked at like like
• 24:45
  basically like 10%, it was only getting allocated to like real
• 24:48
  innovative parts of the product. And it's kind of an interesting
• 24:52
  thing I've noticed like you could be this new AI startup and
• 24:55
  you're just like 100% focused on it, which is a, they have their
• 24:59
  own set of challenges, right, 'cause right now the markets
• 25:01
  getting flooded with AI tools, just getting distribution is
• 25:04
  really tough because you're especially in like finance
• 25:07
  industry, people are pretty conservative.
• 25:09
  They haven't heard of you. There's that part, there's a
• 25:12
  security they're really sensitive of, of have you
• 25:14
  figured that part out? Just the whole nature of working
• 25:17
  with larger companies and things like that, those are like
• 25:22
  challenges the smaller companies have, then the bigger companies
• 25:25
  actually have their own challenges because it's all
• 25:26
  competing priorities. Like that's the whole dilemma.
• 25:29
  Like how do you really innovate? Well, so we actually, and we
• 25:32
  have the smaller sub product, but we ended up that didn't have
• 25:35
  a road map and that was like growth of slowing.
• 25:38
  So I said, hey, when I do a founder reboot on that product,
• 25:40
  actually moved the AI road map onto this sub product.
• 25:44
  And then I started like just building a small little team to
• 25:47
  really just focus purely on AI development.
• 25:51
  And it is, you know, we're able to accelerate so much faster by
• 25:55
  doing that because now we didn't have all these conflicting
• 25:57
  priorities you got to. And then we also like got a lot
• 26:02
  of the benefits of the maturity, right?
• 26:04
  We already have this mature platform.
• 26:05
  We already have security credentials all over the place.
• 26:08
  All those things are figured out.
• 26:09
  We already have some reputation in the market.
• 26:11
  There's already a base of customers.
• 26:13
  So it's it seemed like that was sort of finding that little area
• 26:16
  to to get that right balance was a good thing that now allows us
• 26:21
  to really accelerate this road map.
• 26:24
  How's the speed of iteration been since you did that?
• 26:27
  And if I'm understanding it correctly, it's like you created
• 26:31
  a sub product under the same brand so you're not doing
• 26:34
  anything. So it's a different brand
• 26:36
  actually it's called. Yeah.
• 26:39
  So you know, the back story on it real quick is basically we
• 26:42
  would turn away all these like small little companies and we
• 26:45
  realized like all they wanted was a data room.
• 26:46
  They just want data security product, right?
• 26:48
  There's Dropbox and things like that.
• 26:50
  But when you're doing M&A, you want to have audit trails, you
• 26:52
  want to watermark documents and very granular permissions.
• 26:55
  Those are like the differentiators of data room.
• 26:58
  Like we technically have the data room in our product.
• 27:01
  We had the idea of, of carving it out, making it to a
• 27:04
  self-service product and we did that like five years ago and
• 27:08
  didn't do anything, no marketing.
• 27:09
  It was just, you know, hey, if you're our main product was in
• 27:12
  the fit, we'll send you to our sub product.
• 27:14
  And that thing grew. It grew to like a million and a
• 27:16
  half revenue. So we're like this thing
• 27:18
  actually is doing extremely well for what little effort that we
• 27:22
  put in because we didn't do any like we don't, we put the stood
• 27:25
  the thing up. We never did any product updates
• 27:27
  or anything odd about it. And then you realize that, well,
• 27:33
  you're going to take that and create the AI features inside of
• 27:38
  that product. Let's let that be our R&D and
• 27:40
  kind of fork off like a little separate team.
• 27:42
  Now it's the same back end engineers, so it still rolls,
• 27:46
  you know, parts of it will still roll into the same engineering
• 27:49
  team, but they got their own free will on how they want to
• 27:52
  allocate, you know who's working on what.
• 27:54
  But everything else is completely separate.
• 27:56
  It's like it's own, you know, product, marketing, design.
• 28:00
  All those components are just the small little team, which I
• 28:03
  much prefer. Yeah.
• 28:05
  Yeah. I think that's like a whole
• 28:06
  founder of frustration of watching your baby grow.
• 28:09
  You know, people always talk about baby grow, but like baby
• 28:11
  grows and just get slow as shit. Like everything takes forever to
• 28:15
  get done. And that's to me the the hardest
• 28:17
  thing to to when. You're used to moving at the
• 28:21
  speed of a 235 person team and then you go to a fifty person
• 28:25
  team. I can only imagine.
• 28:27
  And then if you go to a 500 person team or a 5000 person
• 28:32
  team, it's got to be so painful. I, I, I just, I don't know, I
• 28:37
  mean, I, it worked out well when we brought in a COO, we brought
• 28:42
  in a professional person to run operations.
• 28:46
  That was God sent for me. Like it allowed me to focus on
• 28:50
  what I wanted to in the business and have somebody that really
• 28:53
  enjoyed doing all the stuff I hate doing, which is amazing
• 28:56
  people and you know, the operational components of it.
• 29:00
  So I, I think that's, but you still like, I don't know, you
• 29:05
  can still think of pulling my hair out at how slow things move
• 29:08
  and how much more inefficient it becomes as you grow.
• 29:11
  And it's just there's you, you want to be proactive to counter
• 29:15
  it, but you just can't. It's inevitable.
• 29:17
  It just who knows, maybe AI we're going to figure out AI
• 29:21
  agents that manage other a agents that'll do everything and
• 29:24
  maybe that'll be the counter move.
• 29:26
  Yeah, that's the dream. I am very skeptical of that
• 29:30
  happening anytime soon. And I see all these posts that
• 29:34
  folks are talking about like look at these companies that got
• 29:37
  to 100 million in revenue with only 10 people or 30 people
• 29:42
  etcetera, etcetera. And what I see as a commonality
• 29:45
  between those companies that people do not highlight in their
• 29:49
  posts is all of these companies are product LED companies and
• 29:54
  you can just give them a credit card.
• 29:56
  And so, yeah, they have 30 engineers that are just working
• 30:00
  on the product and then they have some virality and they
• 30:03
  don't need to necessarily hire a big sales team or hire the big
• 30:08
  marketing team. And so I've got a little bone to
• 30:12
  pick. I would say that's my only
• 30:13
  counter argument or not. Totally agree.
• 30:16
  I mean, there's some lottery winners out there, right?
• 30:18
  You just struck the right chord and things lined up for you.
• 30:22
  There's also this easy come easy goes 'cause there's something
• 30:25
  that kind of hack their way to, to get to the early traction.
• 30:30
  But the reality is of all the companies I looked at, they are
• 30:34
  struggling. They are really struggling with
• 30:37
  the, you know, I, I feel like there's some benefits of AI
• 30:40
  allowing you to get to market quickly.
• 30:43
  But there, there's still like a lot of stuff that just, I don't
• 30:48
  know how you can accelerate it. You know, there's building a
• 30:50
  brand and I, I think that's the thing I'd still would rethink if
• 30:55
  I were to do it again. Like the, for us, the podcast
• 30:59
  and social media is now driving about 50% of our new business.
• 31:03
  And if I were to, I mean, I, I'm just staying anchored in this
• 31:06
  industry because we have such a good foothold with those
• 31:08
  channels. But if I was to do it again, to
• 31:11
  start from scratch, a new industry, I would focus more on
• 31:13
  how, how I'm going to solve for distribution.
• 31:16
  It's one thing when you got followers listening to you, that
• 31:19
  helps quite a bit. But if you're entering a new
• 31:22
  area, I think that that's a bigger challenge is the
• 31:25
  distribution over the product. And that's like becoming really
• 31:29
  well known. That's why they have that saying
• 31:31
  right the first time founders focus on product and second time
• 31:35
  founders focus on distribution. I think that that's exactly I'm
• 31:40
  case in point with focus on distribution.
• 31:43
  Exactly. So I want to talk to you about
• 31:46
  pricing because I feel like that's another interesting
• 31:48
  piece. When folks are creating AI
• 31:52
  products, especially inside of a product that they already have,
• 31:56
  how do you look at pricing? Is it usage based?
• 32:00
  Is it just like, hey, here's this AI feature that you can pay
• 32:04
  X amount per month for and then you get access to that feature
• 32:08
  which later on your side, depending how much they use it,
• 32:13
  it can be a really good profit margin or a really bad profit
• 32:16
  margin? Yeah, that's a good question
• 32:19
  wanted I have like a really simple answer at all.
• 32:23
  I can tell you we take a mixed approach of just looking at
• 32:27
  what's trending because there there is like some truth that if
• 32:30
  there is some established status quo to pricing now AI is really
• 32:35
  emerging. So a lot of areas that isn't.
• 32:38
  But if there is sometimes you really don't need to like
• 32:41
  disrupt the pricing model because things could be priced
• 32:45
  really well. And if you're sort of
• 32:46
  everybody's kind of price similar like that could be a
• 32:49
  benefit and you just selling differentiators, you know, you
• 32:51
  don't have to sell yourself short.
• 32:52
  So hey, if there is some pricing model out there and it's like
• 32:56
  pretty aggressive and it's what's standardized out there, I
• 32:59
  wouldn't mess with that. I would adopt it and then just
• 33:02
  focus on how do you differentiate against
• 33:03
  competitors that would be a better win because otherwise you
• 33:06
  don't want to come in as the race to the bottom or you're
• 33:09
  trying to, you know, be the most cost effective solution unless
• 33:12
  there's a real strategy behind that, like you're aggressively
• 33:15
  going for market share because you've got that tailwind of
• 33:19
  acquisitions at a low cost. The other thing I think that
• 33:25
  we're realizing is we have a certain pricing model.
• 33:29
  It makes it more. If you have a pricey model
• 33:33
  that's unique, like in the data room, you tend to charge by
• 33:35
  data. It complicates things because if
• 33:39
  you're looking at just AI products and that's all they're
• 33:41
  billing for is this particular AI service.
• 33:44
  When you have a core product that's already existing with a
• 33:46
  certain pricing model and you can't just slap it in another
• 33:51
  say, hey, we had this new AI capability and we're just going
• 33:54
  to charge for it because they charge for that way that
• 33:57
  exponentially complicates your pricing.
• 33:59
  Exactly. So I, I think that's like where,
• 34:01
  where our struggle comes in, it's like, whoa, how do you
• 34:03
  figure that? So now for us, it's like we're
• 34:05
  actually looking to simplify it where it fits in the same
• 34:09
  pricing model, where it's a a bit more modulized, right?
• 34:13
  You're paying for a bucket of data for X amount per month.
• 34:16
  You're paying for where we have additional services, you know,
• 34:21
  you may have extra rooms that you pay X amount for monthly.
• 34:25
  You know, you may have some tear for better support.
• 34:27
  You're paying X amount monthly for now, like AI capability
• 34:30
  becomes that it's more of like a flat monthly fee.
• 34:34
  But then they yeah, your engineers will come raise a
• 34:36
  concern. Hey, by the way, somebody runs
• 34:39
  this, you know, prompt on this 300 page contract here.
• 34:42
  It's going to cost to take so much dollars.
• 34:44
  And if they keep doing that for so many of them, you know, we
• 34:47
  might lose money on this. And that that's where it gets
• 34:49
  interesting because the people don't like the complicated
• 34:52
  things. And I've noticed that with some
• 34:54
  of the companies where they've tried to do the per credit or
• 34:56
  per consumption based. I think it works at some level
• 35:00
  with like middleware, right? Or Amazon, we're just paying for
• 35:03
  our usage. But somewhere you might be
• 35:07
  dealing directly with end user. They don't like it.
• 35:09
  At least their ChatGPT which lets them prompt all day and you
• 35:14
  know. 20 bucks a month or 200 depending on what tier you have.
• 35:19
  Yeah, that's that's what I I'm always thinking about because
• 35:23
  you bring up a really good point on the complexity.
• 35:26
  And then sometimes I imagine if you're trying to sell this for
• 35:31
  the deal room, for example, this product LED product.
• 35:37
  And so it's I imagine it's just you swipe a credit card or you
• 35:41
  pick your tier and then boom, you're in.
• 35:45
  If all of a sudden you're like, well, if you have two terabytes
• 35:49
  of data, then if you want that AI feature, it's going to be
• 35:53
  this much or we'll give you X amount of credits.
• 35:55
  And we are assuming that you're going to use it this month.
• 35:59
  So that should be, you know, it just is like, wow, can I just
• 36:04
  have a simple solution that I say, all right, it's an extra
• 36:09
  1020 bucks. Cool.
• 36:11
  For this AI feature. I need it, let's try it.
• 36:13
  Or maybe not. And so that's why I've heard a
• 36:16
  lot of folks talk about how in iteration stage or in that
• 36:21
  product validation stage, it's very common.
• 36:26
  And it's almost like best practice these days to just
• 36:30
  outsource everything to a model provider API because you don't
• 36:35
  have to worry about any of the infrastructure.
• 36:37
  You don't have to worry about any of the headaches of serving
• 36:39
  these models and all of that. Once you validated it though,
• 36:43
  and you see people are using it and you see how much it's
• 36:45
  costing you, then you can say, all right, we should probably
• 36:50
  bring this in house or we should at least look at how we can
• 36:54
  drastically reduce cost. Can we use the smaller model and
• 36:57
  still get similar results? Can we try to do things that are
• 37:03
  not necessarily just pinging the API and?
• 37:09
  When, when you're describing that, I was thinking, could we
• 37:11
  have two products, right? Deal room is sales LED and then
• 37:15
  firm room is product LED. And you think about pricing so
• 37:19
  differently between those two products because sales LED, it's
• 37:24
  literally sales LED. Like, you know, let the sellers
• 37:26
  figure that out and they, they will, they'll come back and they
• 37:29
  just try to sell different ways and they will sell for a much
• 37:33
  higher rate. You know, they can sell AI on
• 37:35
  credit. They can do all kinds of things.
• 37:36
  They can sell it. And that that's what they do is
• 37:38
  they actually have, they end up with tiers of credit with so
• 37:42
  many whatever it is. I don't know if it's either
• 37:45
  documents. I think it's by the document.
• 37:47
  And and so they bucket it that way and then they they do, they
• 37:51
  do do get a good price for it. But then when it's self-service,
• 37:54
  that same model doesn't work at all because it, yeah, you're
• 37:58
  going to get sticker shock. And it's so, but in two people,
• 38:02
  you know, the way we got made it work is just like very
• 38:05
  transparent, very simple pricing.
• 38:08
  Because I think that's the frustration point we noticed in
• 38:10
  industry people, our competitors have like all these hidden fees
• 38:13
  and things like that. And that's, that's actually
• 38:15
  where they make a lot of their money off of.
• 38:18
  So we're, we're trying to, that's part of it.
• 38:20
  You know, how do we serve the best customer best?
• 38:22
  And I was eliminating the surprises.
• 38:26
  I've seen the other common thing that I imagine you've probably
• 38:29
  thought about or you are currently doing is just giving
• 38:33
  folks the product for seven day free trial.
• 38:37
  Hey, you've got Slack AI now in your Slack workspace for the
• 38:41
  next 14 days to try it out. You can do these things, blah,
• 38:44
  blah, blah. And then seeing if they want to
• 38:46
  continue, it's like, all right, cool, upgrade to this plan.
• 38:49
  It's 20 extra bucks a month or it's fifty extra bucks a month.
• 38:54
  I, you know, it's been interesting.
• 38:56
  I haven't seen that work a lot of times.
• 39:00
  Yeah, that's why I'm asking you because I personally, when I get
• 39:04
  that I'm just like, get the fuck out of here, AI, what are you
• 39:06
  doing? Like, I don't want this right
• 39:08
  now. If I want it, I'm going to go
• 39:10
  and find a way to use it, but not necessarily like shoehorning
• 39:15
  it into random products that I wouldn't really associate with
• 39:20
  AI giving me that much of A extra boost.
• 39:24
  So sometimes it's that that it's like like when I'm scrolling
• 39:27
  Instagram, I don't want to be thinking about work stuff.
• 39:31
  Isn't it funny how like our behavior, like our user behavior
• 39:34
  shifted because like I remember being, you know, like 1520 years
• 39:39
  ago, like I would try just new shiny things or try new shiny
• 39:41
  things because there wasn't a lot of new shiny things out
• 39:43
  there. Now you fast for today with all
• 39:46
  the cutting edge AI, which you think would like, oh wow, this
• 39:50
  is all game changer technology, but we're just so inundated with
• 39:54
  all these new shining things in our inbox, like it's out of
• 39:57
  control that even from the brands that we trust that are
• 40:01
  pushing the new shiny things, like we're still like, Nah, like
• 40:06
  at this point it's got to be something that I I want.
• 40:08
  Like I realize that there's this real aha moment of like, I'm
• 40:13
  going to benefit and and give value from that, which I think
• 40:16
  is like so much harder to reach nowadays.
• 40:19
  It goes back to what we were saying where you have to talk to
• 40:22
  folks and see what it is that they want so that you can
• 40:26
  hopefully get in front of somebody that also has that same
• 40:29
  pain and they go, oh, cool. I was looking for something like
• 40:32
  this as opposed to a lot of times, and maybe it's the way
• 40:35
  that it is messaged or maybe it's the way that it is put in
• 40:39
  front of me. It's just like I don't really
• 40:42
  care about this AI feature for some random app that I use.
• 40:47
  Yeah, that's, I think that's like the the so such a true
• 40:50
  thing like it is. The other thing that I wanted to
• 40:54
  ask you about was in the AI development journey of these new
• 40:59
  features for for deal room and it it's the other one is firm
• 41:05
  room firm room OK cool. The process of building a team,
• 41:12
  what did that look like? Because I'm fascinated by how a
• 41:18
  lot of ML engineers have now become AI engineers and how if
• 41:24
  you look at the job postings of AI engineers, it's basically
• 41:28
  like, oh, that's just a data scientist or oh, that is AML
• 41:33
  engineer, like a platform engineer.
• 41:36
  And all right, that is so AI engineer encompasses this whole
• 41:41
  world of what these old job titles were.
• 41:45
  Maybe it's a full stack developer that knows how to
• 41:50
  prompt a little bit. Maybe it is.
• 41:53
  I saw one today where it was like machine learning systems
• 41:56
  and basically it was a date of visualization job posting, but
• 42:01
  they were calling it this whole complex term.
• 42:05
  And so as you went and you said, all right, when you want to
• 42:08
  build AI capabilities, what team did you assemble?
• 42:13
  I worked directly with our VP of Engineering and let him figure
• 42:16
  all that out because I, I know that's what's going to happen.
• 42:18
  It's going to become messier and more confusing and I'll take
• 42:22
  design discipline as an example. Then again, like when we started
• 42:26
  this company back in 2012, I mean, just to find AUI designer
• 42:31
  was a unique thing. Like that was extremely hard to
• 42:35
  find AUI designer. Somebody just designs interface.
• 42:39
  Yeah, and. I remember that like just going
• 42:42
  through all this effort to find somebody and now if you look at
• 42:47
  like this design discipline, it's all over the place.
• 42:50
  Like there is just so many different facets of just the UX
• 42:53
  part UI. It's just.
• 42:56
  And it's still, yeah, they use the term product in there and
• 43:01
  it's it's just a lot more fragmented and confusing.
• 43:06
  And so you got to dig in and, and just figure out like what
• 43:10
  people, what do you actually need?
• 43:11
  Like really understand what are your specific requirements?
• 43:15
  And then what are people really good at?
• 43:18
  I, I think that's like, I think that's the part of it too.
• 43:21
  It's like you got to still write your good posts to make it
• 43:25
  clear, like what do you actually need?
• 43:27
  Because I, I couldn't see the fault of the, the business
• 43:30
  itself too, because we don't write good job descriptions.
• 43:33
  You put stuff out there that just, you know, you're asking
• 43:36
  for like every skill in the world that you expect the person
• 43:39
  to do. But I, I, I think if it's, you
• 43:42
  sort of use more of that narrative of like pulling them
• 43:45
  into the problem you're trying to solve and what, what do, what
• 43:48
  do you need? Like what are the skills you
• 43:49
  need? And that that's sort of really
• 43:51
  clear of like what do you prioritize the most of?
• 43:54
  I think that's the thing I've noticed more recently when we
• 43:56
  had to hire the, the designer for our work, you know, I, I
• 43:59
  knew the thing we lacked on the past project we used in house
• 44:02
  resource was the UX side of it. Now with that, you know, we had
• 44:07
  an opportunity to bring somebody new in.
• 44:09
  That was like the area we wanted to emphasize, but it was also
• 44:13
  like striking the right balance. Like, you know, we didn't want
• 44:16
  somebody that's just so strong on UX, but not so much on the
• 44:19
  actual UI design that that's tough too, because you get
• 44:24
  somebody like that and they're like maybe a little too much of
• 44:27
  a true UX architect that they just sit there in wireframes for
• 44:32
  to lend a time. It reminds me of basically
• 44:39
  building any team. You're trying to figure out, all
• 44:42
  right, if it's a football team or if it is your engineering
• 44:46
  team or if it is a Devrel team even, you're figuring out, OK,
• 44:52
  what qualities are we looking for?
• 44:55
  And I really appreciate how you break that down and say this is
• 44:59
  the goal that we're trying to accomplish.
• 45:01
  Where we had a bit of a hole or there was a vacuum is in this
• 45:07
  specific spot. It's our wide receiver, it's our
• 45:09
  striker and football, depending on which side of the pond you're
• 45:13
  on. It's our content creator for the
• 45:17
  Devrel team, whatever it may be. And and then you figure out that
• 45:23
  culture fit and you figure out like, we have to make sure that
• 45:27
  they have a little bit of crossover so they can still be
• 45:30
  in the team and on the team and contribute back to the greater
• 45:34
  goal as opposed to just off on their own in this little igloo.
• 45:38
  You know, there's a lot when you look at a business grows around
• 45:41
  the culture component because you got to define it, especially
• 45:46
  the bigger you get. It's just got to be very well
• 45:48
  defined to track the right talent.
• 45:51
  I think there's a lot of leadership stuff too that that's
• 45:54
  interesting to see how companies mature very differently.
• 45:58
  I think for us, like I'd have struggled with it a lot in the
• 46:00
  beginning. So I started reading leadership
• 46:03
  books and found that they are actually helpful in learning
• 46:05
  leadership. It's actually learn more from
• 46:07
  organizational psychology books. You kind of understand like what
• 46:11
  makes for this high performing work environment.
• 46:14
  I remember there's like 3 key elements.
• 46:16
  It was like a strong communication framework where
• 46:19
  everybody feels their voices heard.
• 46:21
  And then 2 was acknowledging or recognizing achievements.
• 46:27
  They just take the time people put in that for they, they want.
• 46:29
  That's a huge thing that people want to feel valued for the work
• 46:31
  they contribute or the, the work is being valued.
• 46:34
  And then that third was a Korean environment where people feel
• 46:37
  they work amongst friends. You know, they're really
• 46:40
  comfortable to have like improv conversations with people and
• 46:43
  just reach out when they need to.
• 46:45
  Those are the three, three things.
• 46:47
  And then then you get inside all the values, fun stuff of like,
• 46:51
  how do you define that for the company?
• 46:52
  And sort of like use that for as part of your, your expectation
• 46:55
  of behaviour and hiring and, and things like that.
• 46:58
  That's all fun stuff that matures as a company grows.
• 47:02
  And then I, for me, it was always like empathy, Like I was
• 47:04
  not very empathetic person. I think some people they may
• 47:08
  resonate with that, but that was like one thing I had to like
• 47:11
  really learn to get better at. And I think that was the thing
• 47:13
  that really contributed to doing when you have to manage people
• 47:18
  connecting with them in in that way.
• 47:20
  Wow, you're so soft spoken. I would have thought it was the
• 47:23
  opposite. You were going to tell me?
• 47:25
  That it's like a introverted, introverted, reserve shy person
• 47:30
  like a would avoid confront, confrontation and all these
• 47:33
  things may contribute so to podcasting helps you throw away
• 47:37
  some of it. Well, the last thing I wanted to
• 47:42
  ask you was about they say companies, great companies or
• 47:48
  good companies are bought not sold.
• 47:51
  Have you seen that to be true now that you've had so many
• 47:56
  companies get bought I imagine or or helped deals go through?
• 48:02
  I, I would say yes for one big reason.
• 48:04
  And I'm like recently did a whole series called buyer LED
• 48:08
  M&A and, and basically the theme is if a, if a buyer controls the
• 48:13
  process, they get better outcomes.
• 48:14
  But it all starts with the defining your strategy that hey,
• 48:18
  where, where you have a company strategy of where you want to be
• 48:21
  in 10 years. And if you look at an M and a
• 48:24
  strategy, what can you do or what businesses could you buy to
• 48:28
  accelerate that? You know, for us, it's at some
• 48:30
  point we want to get in Europe and we know just going to Europe
• 48:33
  from scratch is like a slog of a journey to do it unless you got
• 48:37
  the right leader that you can find, which is really hard.
• 48:41
  But we do keep tabs on businesses that are already
• 48:43
  established with that similar type of customer that we have in
• 48:47
  those European markets. And for us like that would be a
• 48:50
  good play for geographical expansion to help us accelerate
• 48:53
  that the geography so that that's where you have that
• 48:58
  clearly defined you approach those businesses.
• 49:02
  Those businesses are bought versus some banker, somebody
• 49:06
  comes to you and says, hey, here's this company in Europe
• 49:08
  for sale. And then you're trying to like
• 49:10
  rationalize why you should buy it.
• 49:12
  Or sometimes you that happens. You found your entrepreneurial
• 49:15
  like get deal happy. You can't like, hey, this will
• 49:17
  help us grow, but it isn't like clearly tied to the strategy.
• 49:22
  That's why it's so important to have that the, you know, real
• 49:25
  clear business strategy. But then as you see a fit for
• 49:29
  M&A as a tool, then you would have that defined M&A strategy
• 49:34
  and you'd have like a clearly defined criteria of the
• 49:37
  companies that would fit in there.
• 49:38
  Then you could be very proactive about like finding those
• 49:41
  companies and you get a better story to tell because you have
• 49:44
  like a clear understanding of why like Better Together makes
• 49:47
  sense. Because these companies that
• 49:49
  servicing those customers in Europe, like I, I can know that
• 49:52
  we would create a lot more value because more times than not like
• 49:55
  our ACV is a lot higher than what they're selling.
• 49:57
  They might be selling it like 5-6 thousand a year ACV and
• 50:00
  we're like 40K. Nice.
• 50:02
  So just to say, hey, look like if we came together, you know,
• 50:05
  we could serve as your customers, but we could probably
• 50:07
  upsell a lot of them, like half of them to this premier product
• 50:10
  and be able to really generate a lot more revenue.
• 50:13
  We'd be able to support you. We're really good at this
• 50:15
  marketing side from what we've done that's helped contribute to
• 50:18
  our growth. Can we run some of those same
• 50:20
  playbooks to your business that that sort of like, you know,
• 50:25
  there's more you can get into, right?
• 50:26
  Like the just the nature of doing a deal.
• 50:28
  Now all the sudden it's like very amicable.
• 50:30
  I I had this clear vision strategy, how we're Better
• 50:32
  Together, but we work together and, and manage the timelines
• 50:36
  and, and make it for a really smooth transaction.
• 50:39
  First, when a business is sold, it's like, here's this advisor
• 50:43
  that wants to sell a business as fast as possible.
• 50:45
  So you can go sell the next business as fast as possible and
• 50:47
  make as much money as they can at the end of the year.
• 50:49
  And so the timelines are super compressed.
• 50:52
  You don't get to do as much diligence.
• 50:53
  You didn't get to like think through of how you're going to
• 50:55
  integrate the tech as well as you should have.
• 50:57
  And then also you buy the company, go to integrate it.
• 51:00
  And it's like, fuck a lot of these thesis or hypothesis when
• 51:03
  what you thought they were. So I'm a big proponent of the
• 51:07
  bot oversold. Yeah, you talking about that
• 51:11
  reminds me of there's some book, it's like the failures, the
• 51:17
  worst failures are 101 mistakes, something like that.
• 51:21
  And it talks about all these M&A disasters on how you have
• 51:27
  gigantic M&A situations that go nowhere.
• 51:33
  And basically the companies, these big companies that buy
• 51:36
  other big companies have to write it off to 0.
• 51:39
  And you think about like The Who bought AOL?
• 51:44
  Have water. Yeah, very clear one where and
• 51:48
  no shade to anybody that's still using their AOL account, but I
• 51:51
  think we've moved on that. That was one.
• 51:54
  I remember being in there and then there was a few others that
• 51:57
  it just shows you have an idea of what can happen.
• 52:06
  And I think a lot of it is that like you're saying where someone
• 52:09
  will come to you and say this company is for sale, we could do
• 52:13
  XYZ. Wouldn't it be a great fit for
• 52:15
  your company? And then at the end of the day,
• 52:18
  it's not a good fit. And you got I I like that term
• 52:21
  deal happy, a little trigger happy on the deal.
• 52:24
  Deal fever, Yeah. Deal fever, man.
• 52:28
  That is it. So is there anything else you
• 52:31
  wanted to talk about before we jump?
• 52:34
  I think we got a good run. We kind of talked a little bit
• 52:36
  about the trials and tribulation of the founder journey.
• 52:40
  There's, you know, now this everybody's kind of piece
• 52:42
  mealing their their AI story into it.
• 52:46
  I think we're going through a big hype cycle right now.
• 52:49
  Companies are even when you look at M&A, they're sort of
• 52:52
  proceeding with caution. I think I've been particularly
• 52:55
  interested about like what's what's unique IP?
• 52:58
  They've seen a lot of these startups and when you sort of
• 53:01
  pull back the curtains, you're like, well, you got some like
• 53:03
  prompt instructions here, but that's like really easily, I can
• 53:07
  imagine somebody just copies the paragraph and it's like they got
• 53:10
  all your IP. Like that's not that's not good.
• 53:14
  You know that there's some of the providers of the data, you
• 53:17
  know, they know they have their own proprietary data set.
• 53:19
  They've obviously get valued really well in the market.
• 53:21
  So I, I think there's a lot, there's a lot of the way
• 53:24
  companies are figuring out and in valuing these AI companies.
• 53:28
  But we are going through a big hive cycle.
• 53:29
  We do see a lot of like very big multipliers on the, the venture
• 53:34
  investments. But you know, when you look at
• 53:37
  the acquisition side, like I said, there's a lot of
• 53:39
  skepticism still there and you don't see a lot of these AI
• 53:42
  companies just getting snatched up left and right.
• 53:45
  I think because of those high valuations of the VCs, like
• 53:48
  their exit expectations are going to be high.
• 53:51
  I think you're going to see few winners and a lot of losers in,
• 53:55
  in the coming years. And, and probably a real
• 53:58
  correction around how these companies are, are valued.
• 54:01
  And you'll probably like we talked about those different
• 54:03
  roles of AI engineers, you're probably going to see the
• 54:06
  classification of different types of AI technologies and
• 54:09
  they that they will have their own valuation according to how
• 54:13
  strong their IP is. Is there a certain subset of
• 54:18
  software that is getting snatched up left and right these
• 54:22
  days? Of software.
• 54:26
  Or doesn't have to be software, but I was thinking software some
• 54:32
  some type. I think there's still strong
• 54:35
  appetite for SAS that's, you know, following this traditional
• 54:39
  business metrics because if you look at the private equity
• 54:42
  world, there's a lot of the software focused private equity
• 54:44
  firms. But they, you know, they raised
• 54:47
  their funds even prior like years ago and they, they raised
• 54:50
  their funds on these, on this thesis that was from, you know,
• 54:55
  been like leading for the past couple of decades.
• 54:58
  That is like very much focused on on the trend with the SAS
• 55:02
  business model and it's been very favorable for valuations.
• 55:06
  So if you have that business that's in that 40% year over
• 55:09
  year growth segment and you're got good SAS metrics, you don't
• 55:14
  have churn issues and things, I think those tend to be like very
• 55:17
  highly sought after businesses.