Latest / The Inspired Stories Podcast / How Greg Geehan Creates Healthcare AI Startups Through Scale Up Labs Venture Studio

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How Greg Geehan Creates Healthcare AI Startups Through Scale Up Labs Venture Studio · The Inspired Stories Podcast

0:04
Welcome to another edition of the Inspired Stories podcast,
0:08
where leaders share their experiences so we can learn from
0:11
their successes and be inspired by how they've overcome
0:14
adversity. My name is Anthony Kotaspodi and
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today's guest is Greg Gehan, Co Founder and Managing Partner at
0:21
Scale Up Labs, a venture studio dedicated to creating high
0:24
impact AI and smart systems ventures in the healthcare and
0:29
advanced manufacturing space. Their mission is to use
0:32
blueprint AI to spot real problems, build lean startups
0:37
and turn smart ideas into fast moving companies engineered for
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early wins and lasting impact. Now, under Greg's leadership,
0:46
Scale Up Labs has garnered attention for its unique
0:49
approach to launching AI driven solutions, guiding entrepreneurs
0:53
to transform innovative concepts into flourishing enterprises.
0:58
Greg also brings a wealth of experience from his roles at
1:01
Excel Hub, Venture Partners and Social Impact Partners,
1:04
supporting disruptive technologies and global growth.
1:08
He has successfully guided early stage ventures through funding
1:11
rounds and international expansions, highlighting his
1:15
knack for nurturing transformative projects.
1:18
Among Scale Up Labs, milestones is forging strategic
1:21
partnerships to accelerate IAI advancements in local and global
1:26
markets. Now Greg's dedication to
1:28
innovation helps shape the future of advanced manufacturing
1:31
and healthcare. And before we get into all that
1:33
good stuff, today's episode is brought to you by my company,
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AD back benefits.com. All right, back to our guest
2:05
today, Co founder managing partner, scale up labs.
2:08
Greg, I appreciate you making the time to Share your story
2:10
today. Yeah.
2:11
Thanks for having me, Anthony. And I want to make sure that I
2:14
got the pronunciation of your last name correct.
2:17
It's Guillen. Guillen.
2:19
Plus you're plus and. Everybody says it a different
2:22
way, so it's no problem. I appreciate your flexibility on
2:25
that. So Greg, you Co founded scale up
2:28
Labs almost three years ago. Can you quickly take us through
2:32
your career progression that kind of led you into this space?
2:37
Yeah, a lot of people ask me that because it's it, it's kind
2:41
of a winding, it could be a winding adventure.
2:43
You know, what I found when I got out of college was I didn't
2:46
know what I wanted to do. I got out in marketing,
2:49
information technologies, and I've never done anything in that
2:52
space beyond trying to help out my companies grow over the
2:56
years. But so I ended up, I got on
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September 11th, 2001 was kind of like as the world imploded
3:03
around us. I, I was backpacking Europe
3:05
right after school and came back and had no job prospects, you
3:09
know, the entire economy shut down.
3:11
So I did things like sales. I ended up teaching for a year
3:13
trying to figure out what I wanted to do.
3:16
And then I ended up at a nonprofit company where I ended
3:20
up meeting my wife, who was originally from Poland and kind
3:24
of started to understand the business context.
3:27
At that point. It turned out that I wasn't
3:29
making the money I needed to make and, and a very expensive
3:32
city like Boston in the, in that company.
3:35
And so I decided to, to make a move into, into some into the
3:39
private sector. And so I joined a start up and
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the start up was in healthcare and I'd never done anything in
3:45
healthcare, didn't really know a lot about it.
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And during that process, they'd just been acquired by a bigger
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company. And then they were bought and
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sold multiple times. It went public.
3:55
They changed their name three times.
3:57
They went private again, private equity owned.
4:00
And through this entire process, I, I kept taking on more and
4:03
more of the company from the operational and relationship
4:06
side. And I started to realize like, I
4:08
really like the energy of never having enough resources, which
4:12
can be very stressful. But, you know, understanding is
4:16
like, how do you make your resources work to the point
4:19
where you can hire the right people at the right time for a,
4:22
for really what it's a transformative technology.
4:26
Up to that point, I'd, I'd started a couple companies on my
4:29
own, you know, like early on, I was always doing things like
4:32
buying and selling gum at school, like in, you know, 6th,
4:35
7th grade and you know, so I had this like this, this idea of
4:38
like how entrepreneurship should work.
4:40
I didn't really understand it. And so while I, when I decided
4:45
to move on from that company and I started looking at
4:47
opportunities, I said, I'm going to move into start-ups.
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I'm going to, I'm going to join one earlier stage.
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Ended up not doing that at all, going to a bigger company that,
4:55
but what was really cool about it is that they were in
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healthcare, but they worked, it's Athena Health.
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They they, they were working with start-ups in healthcare
5:04
sector. So at the time they didn't
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incubator, they're investing in start-ups.
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There was, you know, a marketplace where we were like
5:12
connecting them in there was an accelerator.
5:14
And so it joined this really, really dynamic, interesting
5:17
space that was really helping start-ups to grow.
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And that was the. Funding coming from Athena so.
5:26
It was, yeah. You've got a pretty.
5:27
Solid foundation under you then. That that's right, that's right.
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And I joined in on the operational side, but I was
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working, you know, as a small team and we were all working
5:36
together. And from this, I, I, you know,
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Boston at the time was moving from, you know, being a little
5:42
bit of a venture backwater where there was a lot happening in
5:45
the, in the like pharma, tech, life sciences, things like that,
5:48
where there's a lot of investment happening.
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But it really started to shift and concentrate investments into
5:54
the Boston ecosystem, into things like digital health.
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And that's where I found my interest.
6:00
And I ended up because I was really connected to these
6:02
start-ups. I started to go into the Boston
6:06
ecosystem. And at the time, this is pre
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COVID, right? Right up until COVID when this
6:11
was happening, you could go into Kendall Square or downtown
6:14
Boston and there be on a Thursday night, you go to
6:17
Venture Cafe at the, at the Cambridge Innovation Center,
6:20
where it's entrepreneurs, executives, industry leaders,
6:23
educators coming together in one spot and, and just talking about
6:28
networking. Or there'd be presentations like
6:30
the country of, let's call South Korea or Canada or France.
6:34
They would bring start-ups over into this one, one location and
6:38
you go there for a couple hours, have a couple drinks.
6:40
And then you go to a law firm across the street who would be
6:42
hosting a, a pitch night for another, another sector that
6:46
they were supporting. And you could walk down the
6:48
street to another event that was hosted by by a venture, A
6:52
venture fund. So there's always things that
6:54
were happening here. And so I was able to start
6:55
bringing some really interesting technologies and companies to
6:58
the table at Athena. And through that, I started to
7:01
really get interested in investing.
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So I joined an an investment group that at the time was
7:07
looking for for people to Co invest alongside the fund next
7:10
Gen. venture partners. And I really started the group
7:13
in Boston that was, that was focusing on, on technologies in
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the healthcare sector. And so this got me really
7:19
interested into, into the, you know, how do you start to build
7:22
out, you know, networks within the in inside the startup
7:27
ecosystem, but how do you start thinking about from an
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investment perspective? So that, that led me to Excel
7:33
hub venture partners. So we actually ended up like Co
7:36
founder and I were looking first in Poland, which is where I have
7:38
connections. My wife is from there and there
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was an increasing ecosystem similar to what we saw here in
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Boston at the time. And we're saying, hey, if we, if
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we invest in a company in Poland, bring them to the US,
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you're going to see a 4X increase in the valuation of the
7:52
company just by bringing them to the US and getting investment.
7:55
Here and why is that just because sort of the cost of
7:58
Labor to sort of get it off the ground is is lower there?
8:02
Yeah, it's actually an ecosystem issue.
8:04
And so it's not just the capital, but companies that are
8:08
that are outside of certain ecosystems like let's call it
8:12
the West Coast, Silicon Valley, Boston, LA, New York.
8:16
You have you have a couple major issues. 1 is that you can get a
8:19
lot of early stage funding, usually government backed, but
8:23
you're not going to get the next the next funding round as
8:26
easily. There's just not as much money
8:28
in it. And so a start up gets this
8:30
early funding and they need to immediately get to revenue
8:34
production. And so EBITDA becomes the
8:37
driving factor in that company as opposed to growth.
8:40
And so in a place like the US, what we're saying is I'm going
8:43
to give you a bunch of money right now and I want to see you
8:46
grow 100 X so I can see my return because I know the next
8:51
round, there's an investor that's out there that will give
8:53
them, give that company a lot more money.
8:57
In a place like Europe or Latin America or really most other
9:00
places outside the US and even places in the US, you don't have
9:05
that next round. Or if you do, it's really small.
9:08
So the only way you can grow is to do it through revenue
9:11
production. And so when you, when you think
9:13
about it, it's like if you're using your revenue in order to
9:16
grow your company, you have far less of it because of your
9:20
margins, etcetera. You're still trying to still
9:22
trying to grow in order to capture that next big growth
9:25
cycle. So what happens is you come, you
9:27
bring a company that has a great technology, great founder, great
9:30
opportunity, global position like technology opportunity and
9:34
you bring them to the US and you find an investor here, they're
9:37
just going to give them a bunch of money and say grow.
9:39
All of a sudden you're, you're levelling that company up to
9:43
what AUS investment valuation would be because it's no longer
9:47
tied to EBITDA. It's now tied to, hey, what's
9:49
the future? And the future is big.
9:51
It's just we have a bigger market here.
9:52
We've been doing this a lot longer than than everywhere
9:54
else. Yeah.
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OK, we found this in Latin America.
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We found this everywhere. So we moved to Latin America as
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part of Excel Hub because you know, a lot closer time zones
10:03
and things like that. But there's great technologies
10:05
everywhere, great founders everywhere.
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And so how did this kind of eventually lead to the
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inspiration for starting Scale Up Labs?
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Yeah. So when Excel Hub was looking at
10:15
these companies come in and we ended up meeting our our other
10:19
Co founders. So there are a couple of
10:21
companies that came together. We are all thinking the same
10:23
way, all small businesses here in Boston, all multiple
10:26
start-ups in under their belts, investments, things like that.
10:29
And so we had started to find companies in Latin America and
10:32
Europe to bring to the US through our accelerator
10:34
programs. And we saw the same thing Forex
10:37
valuation increase. We proved it out a number of
10:39
times. It was really, we were getting a
10:41
lot of traction, great companies.
10:43
And, but we, we kept coming back to our roots of we're creators
10:47
at heart and we've all started multiple companies.
10:50
We, we like to take a company from like a -1 to 0 and then
10:53
zero to 1, right? And so like we, we, what we
10:56
realize is that we could try to force ourselves into being
10:59
traditional investors like venture capital, or we can go
11:02
back to our roots and actually start the companies from
11:04
scratch. And that's the concept of a lab,
11:06
right? Or venture studios, as you, as
11:08
you might call it. And So what we realize is that
11:12
we also have a really, really interesting value proposition
11:15
that not everybody has. Two, there's actually two of
11:19
them. One is that we have this
11:20
international, this international presence.
11:23
So we know how to get devalued or undervalued assets,
11:27
undervalued entrepreneurs that can make a real difference.
11:30
And two is that we're right at the forefront of major changes
11:35
within the AI sector, right? So it's not just AI as a tool.
11:40
There's a lot of development happening, but we actually sit
11:43
kind of in the MIT ecosystem. So my Co founders are helping to
11:47
develop the next version of what you'd call identified AI, the
11:52
protocols around how they're all going to communicate small, you
11:55
know, small language models to small language models.
11:57
And because of that, because we have the, this ecosystem here,
12:00
we can connect these great technologies to solve real
12:03
business needs into the US and make them stronger by bringing
12:08
these other tools in that we have access to.
12:11
And this creates a really strong value proposition.
12:13
And because of that, we're now able to do rapid prototyping
12:17
for, for new businesses. We have what we call blueprint
12:19
AI. It's a, it's a, it's like what
12:22
you call like an engine, right? We can take all these market
12:24
signals. We talk to corporations, we talk
12:27
to technology. So like these are our
12:29
international spaces as well as, you know, what are we hearing
12:32
here? And then we talk to investors
12:35
and say, what is it that people are, are, are investing in?
12:37
We're able to take that information and start to put
12:39
together a rapid prototyping process that does not require a
12:44
lot of resources on our side because we're using this IP that
12:48
we've created internally to create rapid prototypes.
12:51
We can get that into market in 30 days.
12:54
We then can figure out how's it responding and we can then make
12:59
iterations until we figure figure out if we have a real
13:02
product and then that product can become a company.
13:05
Let's unpack a little bit what you're saying about AI here.
13:09
You know, I, I talked to a lot of my guests about AI, but most
13:13
of us are using it in terms of like ChatGPT or, you know,
13:17
Claude or, you know, one of the other like similar large
13:20
language models out there. And I don't know, we feed in
13:24
some context, some text and it gives us some feedback and it's
13:28
super helpful. I hear a lot of my guests tell
13:30
me that it's, it's really, it's really improving their, their
13:35
efficiencies in a great way. But I, I feel like what you're
13:39
involved with and what you are seeing in the way that you're
13:42
using it is on another level that a lot of people outside
13:47
that space probably aren't really tied into.
13:50
So when you talk about AI becoming identified, I think
13:54
what you're saying is the AI agents that you can build to do
13:58
particular task. Can you maybe give an example of
14:00
that? Yeah, no, absolutely.
14:03
So AI is a tool and if you think about things like a large
14:10
language model, when you can sell it for $20, when you have
14:14
it for free or the premium version for $20.00 a month per
14:17
person, there's not a lot of value being created there.
14:20
There's, you know, it's something that has that can
14:23
create a lot of micro value to somebody if they know how to use
14:26
it. So how do you talk to your data?
14:28
Most people can use it to like create documents and things like
14:31
that, but most people don't realize you can actually use
14:33
them to talk to your data by importing your data into what's
14:37
called chat chip BT and then asking you questions.
14:39
It can actually produce a lot of a lot of information and a lot
14:42
of people aren't aren't familiar with that being being a real
14:46
value that you can create. In some ways, that model is
14:51
going to continue. Large language models are going
14:53
to continue to add value, but it's going to become more of the
14:57
within the fabric of how businesses occur.
14:59
It's like the Internet. It's going to be the Internet.
15:02
The future of value is going to be created on on different value
15:08
proposition. And that's really tied to a few
15:11
different things. One is agentic AI.
15:14
So these are agents and how do you build out agents that work
15:17
with other agents to create value on top of value.
15:21
So you have one agent doing one thing, you may have 33 agents
15:24
that take what that agent has, create other things and you can
15:27
create value to it. 2nd is small language models.
15:30
So small language models is when you take these bigger large
15:36
language models and you put them out onto the edge.
15:38
So this is in our company, we have a lot of data.
15:42
We can train models on our data that stays within our our walls
15:47
and we can structure value creation based off of our IP.
15:52
We can use that to create value as out to the outside world
15:56
products, you know, consulting, you know, other things that
16:00
other companies can consider to be value, right.
16:03
And then the the third one is around decentralized AI.
16:07
So decentralized AI is when is when you're communicating across
16:12
with other decentralized AI. So it's, it's, so it's, it's
16:15
taking out of the large language models and then figuring out how
16:17
to communicate across your small language models and your agentic
16:22
AI. So when we're working within a
16:24
space and we're looking at building a company, we're not
16:27
just taking can can I build a wrapper on top of Chachi PT.
16:30
We're actually building these proprietary models that create
16:34
value that use agentic AI in order to build value on top of
16:39
these small language models. And then we're working with
16:43
decentralized AI in order to communicate with other small
16:45
language models. So that's the future.
16:48
And if the companies are not actually putting that natively
16:51
into their businesses or figuring out how to create value
16:53
within their businesses, they're going to be left behind in the
16:56
longer in the longer. Term so the difference between
16:59
the small language model and a large language model it am I
17:02
understanding it correctly? A small language model is it's
17:06
trained on your particular set of data.
17:09
That's right. What it is that you have that's
17:11
sort of proprietary to your company.
17:13
OK. And so it's kind of walled off
17:15
in a way so that, you know, ChatGPT, you know, can learn
17:19
from kind of what I'm inputting to it.
17:21
And you know, that can in some way, shape or form perhaps get
17:25
disseminated to other folks. But on a small language model
17:27
that's it's kind of a, there's like a firewall around it, so to
17:30
speak. That's right.
17:31
From a value perspective, if you can enter it into ChatGPT and
17:34
get A and get a result, everybody can.
17:37
There's no differentiation. If you're training on your own
17:40
proprietary data, you can create something that has value for
17:44
your business. And so how does somebody create
17:46
their own small language model? Well, I, you know that there's,
17:50
there's technology experts who understand how all that work.
17:53
You know, it's not something you just go and do you need to, you
17:56
need to understand it and there's going to be a lot of
17:58
value that's created for, you know, the, the people who really
18:02
understand this in, in in the future.
18:04
I'm, I'm really, really fortunate.
18:08
I'm not, I'm not on the technical side.
18:09
I'm, I'm more on the business side, but I have three partners
18:13
within our organization that are really, really in the depths of
18:18
creating this next level of AI, what you call small language
18:22
models. And they are building that out
18:24
with us with the input across the entire organization.
18:28
Can you give an example of multiple agents, AI agents kind
18:33
of working on the same data set to, I don't know, kind of have
18:36
this multiplicative effect? Yeah.
18:39
So, OK, so we have a, we have a company called Meshify and
18:43
Meshify and the way that that it works is you have an agent.
18:48
The, the whole premise of this is that we believe that your
18:52
traditional structure around CRMS, let's call it, is going to
18:57
disappear as a, as AI becomes stronger.
19:02
Mostly it's because data is dirty and most of these in the
19:05
most of these systems and the way you're going to interact
19:07
with it is going to be very different.
19:09
It's going to be much more intuitive.
19:10
It's not going to be like put it into a certain table and then
19:13
we'll access in a certain way and somebody has to make a
19:15
decision on if it brings value to the company or not.
19:18
So Meshrify, what it can do is it can actually interface with
19:22
your Gmail, what's called Gmail. If you use a business Gmail, it,
19:25
it, it will actually look through all of your data.
19:29
It can, that's your emails incoming start to figure out
19:32
where there's, where there's opportunities.
19:35
It can then go into your internal, it's called your small
19:39
language model. So in a practical sense, let's
19:42
say that I have, you know, I'm trying to do an, I'm trying to
19:45
do invoices. I'm trying to create a quote or
19:47
an invoice or something like that, right?
19:49
And I know there's this, there's all this information.
19:51
I have 2000 SKUs. I need to be able to figure out
19:54
based on what they're asking for, what the right quote or
19:58
proposal could be and then get it out to the right people at
20:03
the right time. So inside the agent looks
20:06
through your emails, starts to look at where the value is.
20:08
You have other agents who are going out to your data, to your
20:11
internal data to figure out here are the right right potential
20:15
opportunities that would solve their problem.
20:17
You have other ones that are figuring out the value, other
20:19
ones that do research on the outside to figure out how you
20:22
craft a message and what it will do is overnight while you're
20:26
sleeping. It will start to craft out all
20:28
of these emails and proposals. When you walk in in the morning,
20:33
you can just look through it. You're going to find stuff that
20:37
you've forgotten about, you know, like an e-mail that you
20:40
missed, things like that. This is going to bring it to
20:42
light and help you really very quickly get off these proposals
20:46
to the right people, asking them for the right thing and putting
20:48
the right thing in there. And then it learns.
20:51
Then it learns over time how how did we do?
20:53
So it gets stronger. The future of this is on the
20:56
other side. If that other company that
20:58
you're trying to run a proposal with has a version to Meshify or
21:01
a similar one, the agents behind the scenes can actually
21:04
negotiate with each other because one has the value, one
21:08
knows the value on our side. You they can start talking and
21:10
start to say, hey, here's a potential, here's a potential
21:12
opportunity. So when you walk in in the
21:14
morning, you kind of have it already figured out.
21:17
Now you need to eval, you need to add your own value to it,
21:19
right? Like the future, future value is
21:21
not AI can't do that and it's not going to be able to do that
21:24
for a while. It can create, it's kind of it's
21:26
idea of it. Now you come in and you have
21:29
basically everything kind of figured out and you, you can,
21:31
you can solve it. So that's an example of how
21:33
agents can work behind the scenes and circumnavigate to CRM
21:37
or other processes, Google Drive, things like that.
21:40
That's really interesting. OK.
21:42
So I've got a better understanding of how multiple
21:45
agents can be working together sort of behind the scenes.
21:47
I've got a better understanding of the small language models.
21:51
Help me wrap my head a little bit more around decentralized
21:53
AI. So decentralized AI is when you
22:00
have basically other small models that are working outside
22:05
of an LLM. Consider an LLMA centralized
22:07
data source. And so a centralized data source
22:10
has lots of data. It's been trained on everything,
22:13
right? So call it your ChatGPT.
22:15
Now you start taking it and you put it onto your computers and
22:18
you're, you're doing training internally.
22:20
So you're creating your small language models.
22:22
Eventually you have things like on your phone, your phone has
22:24
more and more AI tied into it. You're going to start being able
22:28
to do processing and learning that's hyper personalized.
22:31
Hyper personalized means that when I pick up my phone and do
22:34
certain things with my apps and I have my day-to-day things that
22:37
that that it does, it's going to hyper personalized information
22:42
and the way that my my phone works with me now to connect all
22:49
of that right now you go into one centralized location, which
22:52
is the LLM and you say HITPT. Here's my prompt and it provides
22:55
output. Decentralized means when you
22:58
have like multiple, multiple small language models are small,
23:02
like multiple edge devices that are connecting, how do you
23:06
connect all of those to provide value?
23:09
So if I go into one place, how, how are how am I managing the
23:12
data coming from here into my small language model?
23:15
And then how and what are what are the ways that they're going
23:18
to be able to connect and communicate?
23:21
OK, so that that's super cool. Thank you for that explanation.
23:25
Now I really want to hear about Blueprint AI and your rapid
23:29
prototyping. Can you walk us through a
23:31
specific example? Because when I think of rapid
23:35
prototyping, I think of like 3D printers we're making, you know,
23:38
a physical part, but you're talking about it in a very
23:41
different way, like your rapid prototyping, like software and
23:44
AI companies. Right.
23:47
Yeah, it's so if you think about in the life cycle of a start up,
23:53
generally it's somebody makes a decision that they want to start
23:57
a company because they've seen some sort of problem or they
23:59
think they have a problem and they spend a lot of time
24:02
whiteboarding it, putting together pitched presentations,
24:04
starting to think through how to code it.
24:07
What is the problem, What's the what, what's the major problem
24:11
and what could be the solution that can solve that problem.
24:15
And that that's a lot of manual work that goes into it.
24:17
It requires somebody who kind of knows, knows the space.
24:22
Then you have the problem of figuring out does the customer
24:25
actually want it? And you have things like jobs to
24:27
be done. You have whole industries that
24:29
all they do is interview people to find out will they actually
24:32
buy the solution that you've put together.
24:34
Then you code it and you iterate it and you eventually put
24:37
something out into the market that you need.
24:39
Now you need to build one of the major reasons that so many
24:43
companies fail in the start up space, which is the majority of
24:46
them is because the ability to sustain that and create and
24:50
understand the value is housed within a certain individual that
24:54
is the entrepreneur. And so finding the right person,
24:58
the right problem, the right solution and the right ability
25:00
to execute on it is really is really hard.
25:03
So now what we can do within Blueprint, Blueprint AI is
25:07
actually to start to rapidly iterate on a lot of those
25:10
processes using a genetic AI. So for example, jobs to be done,
25:15
well known practice cost a lot of money, takes a lot of time.
25:18
We have actually built out internally the ability for you
25:22
to pose a problem or solution. And we can we can capture a lot
25:25
of that from like our thesis on on talking to, to corporates,
25:29
etcetera. And then what that does is
25:32
systematically use agents to start to figure out what is the
25:36
mission in the vision? What's the problem?
25:38
How do we think that? And before you even get to a
25:40
solution, it starts to figure out who's the persona and it
25:44
starts to use already known information out in the out in
25:48
the Internet, right? And it's like created these, it
25:50
creates A persona. Who is the customer?
25:52
Why are they the customer? What's their age, back story,
25:55
education, everything that you would normally try to figure out
25:59
and it will rapidly interview them virtually.
26:02
And So what does that mean? It, you know, it uses our tools
26:05
to say, if I was going to interview somebody with this,
26:08
what do I know about these types of people that can help me to
26:11
provide like a real answer and will actually create a whole
26:14
story and interview. And then that interview, you
26:16
have agents that will look at those interviews and start to
26:19
figure out what would be a solution.
26:21
So like, it figures out what are the problems that they have in
26:23
their day-to-day lives and what really pains them.
26:26
Because at the end of the day, nobody buys a product unless
26:28
there's that, unless there's a solution that solves a real pain
26:31
point, right? Like people don't want to spend
26:33
money unless it creates value for them.
26:35
So what the agent is doing is looking at those pain points and
26:38
trying to figure out what are those technologies that could
26:41
solve the problems. And it will create a whole list
26:43
of them, right? And then you're basically.
26:46
Creating a focus group out of thin air.
26:48
That's it. That's it.
26:50
Now what we figured out is our first version of it.
26:52
We're like, yeah, maybe this is like 50% there.
26:56
Like, you know, there's some good things.
26:57
It's really basic, like I could have figured that out on the
27:00
back of an app. Yeah.
27:01
And then we've slowly built it. We think we're closer like 7080%
27:05
there. But what, what that gives you is
27:07
that rapidly, within 3 to 5 minutes, I now have new business
27:12
ideas that solve really potential problems.
27:15
And we can use that internally to say, hey, now, now let's go
27:19
do our own research things that we know and we're going to
27:22
figure out if it makes sense or not.
27:23
We keep feeding that information into our, into our agents and
27:27
they're going to keep getting better and better and better.
27:29
So as everybody knows, you actually have agents that can go
27:33
and create code really fast. So for example, we actually
27:37
created some of this code within like, you know, you know, a
27:42
week, 3 days that would have normally taken months and months
27:45
and months to code. So now what we can do is very
27:47
rapidly go out and create a, create a workable solution of a
27:51
minimum viable product that we can bring out to people that we
27:54
think are the real stakeholders and get their reaction to it.
27:57
And if it doesn't work, what do we spend?
27:59
A few hours instead of months and maybe millions of dollars?
28:04
So where does the original seed of an idea come from?
28:08
Are you guys feeding that in or are you just sort of pointing
28:12
one of the agents in the direction of we're interested in
28:16
healthcare based software? Like you've got all these
28:18
different personas, go and come up with some ideas for us,
28:23
right? Well, remember that a lot of
28:25
this technology is still very new.
28:27
A lot of agentic AI is still like a it's still being figured
28:30
out. So for us, it's the same thing,
28:32
right? We have a lot of this IP that's
28:35
been developed, but we're continuing to build more
28:37
overtime. We're building these building
28:38
plots so that we can continue to use them as we spin them out.
28:42
So there's still a lot of manual piece upfront for us, which is
28:46
why we're very focused on energy, advanced manufacturing
28:50
and healthcare because we know the space really well.
28:53
And when we talk to corporations, so we actually
28:55
physically go out and talk to corporations, we generally
28:58
understand where there's major pain points.
29:00
And what we can do is feed that in to start to figure out what
29:03
are the, what are the underlying technologies that might be able
29:05
to solve some of those pain points.
29:06
So there's some manual work upfront, but what's nice is that
29:10
we can enter in just a high level like this is we think we
29:13
need to solve this and our agents will actually completely
29:16
refine it within seconds. Say, OK, this is what I think
29:20
you're saying. Here's what the here's how I
29:23
reconstitute that to have a, to be rarely concise on what the
29:28
problem is we're trying to solve.
29:29
And then it picks it up from there.
29:31
So we solved some manual manual prompts in between.
29:34
We can't do everything. Everything.
29:36
I mean, that'd be incredible. We'll get there.
29:38
Hope someday. But you know, a lot of the
29:41
inputs are still manual. So how many ideas are you guys
29:44
kind of tinkering with, I don't know, on a weekly or monthly
29:47
basis? I mean, given this rapid
29:49
prototyping process, I mean, I'm an entrepreneur.
29:52
Like I, you know, I know how our minds are wired.
29:55
Like you walk down the street and you see three opportunities,
29:58
you know, and, and it's like, well, one thing an entrepreneur
30:01
has to learn to do up until this point in, in history is to
30:06
focus. I can't, I can't go after all
30:09
those 3 new opportunities I just saw in the last 10 minutes.
30:12
I, I got to stay focused. That's right.
30:14
Now what you're talking about is, oh man, I got all these
30:17
ideas like let's get the agent started.
30:20
I'll, I'll, you know, come back tomorrow morning and, you know,
30:22
we'll have a prototype that we can go and show people.
30:25
So like how many are you guys kind of kicking around on a
30:28
regular basis? There's always ideas.
30:30
So what's, what's really what, what's really great about this
30:34
process is that when you get a whole bunch of these ideas that
30:37
come out is that in reality, you're going to try to distill
30:40
it into one, one major solution, right?
30:43
So like our goal right now is six to seven new companies a
30:46
year, right? So it's not huge numbers.
30:49
In order to get there, we need to create a lot of interest.
30:52
And so we have, we have internally have processes to
30:56
help us to figure out like which ones do we want to go after, you
30:59
know, your traditional like investment committees.
31:01
But for us it's more, it's more like from a business
31:03
perspective, is the problem big enough?
31:06
Is the solution strong enough? And we kind of go through these
31:09
processes and that helps us to really start to, to focus in.
31:13
So we may have, you know, 1012 ideas a year that we really want
31:18
to pursue that can start off with many, many more ideas that
31:22
are input into, into our platform for, right.
31:25
Like, for example, I'm working on one right now that I really
31:28
like in the therapist space, it's the referral referral
31:32
space. And there's a company that we
31:33
know that's created kind of a front end for it, but doesn't
31:36
have any of the AI infrastructure.
31:38
And I started looking into this through one of our, you know,
31:42
some people on the team who brought that to our attention.
31:45
And, you know, talking to like my chiropractor, he's like, oh,
31:47
we need something like that too. And so all of a sudden it, it
31:51
takes on a life of its own because you start to socialize
31:53
it and people give you feedback that helps you really start to
31:57
define it. And you can take all these
31:59
multiple ideas that are kind of floating out there and start to
32:03
really crystallize it into a real, real potential problem and
32:06
solution. And we create decks around it.
32:08
Like a pitch deck has to be created.
32:10
Otherwise we can't tell our story.
32:11
If we can't tell a story around it, then there's probably not a
32:14
lot of value in it. So we go through the same thing.
32:17
And then a traditional start up entrepreneur would do.
32:21
And you know, like, let's figure out the financial model, let's
32:23
figure out the pitch, like who's going to buy it?
32:26
Who are the stakeholders? And so it needs to like to get
32:29
to that six, we're going to have to do that a bunch of times
32:32
every year. What is the, I don't know, sort
32:35
of the leading key indicators that, OK, we've got something
32:40
here, This one doesn't have to go on the garbage heap.
32:42
This one is going to be one of the six or seven that we push
32:45
forward with this year. So clear stakeholders and
32:49
potential first buyers, right? So like we talk to corporations
32:53
all the time, small, small, all the way up to multinationals.
32:58
And one thing that you know, becomes clear pretty quickly is
33:02
when we start socializing it, will somebody actually buy it?
33:05
So we don't want to just go create and put a lot of cash
33:09
into creating something that doesn't have like your first,
33:13
your first early adopters already built in.
33:16
So that's really where we where we like the pivot starts to
33:19
happen. You can have as many ideas as
33:21
you want, but if nobody's going to buy it, then there's no value
33:23
in it. And so for example, we work with
33:26
like some industry organizations that work with small to medium
33:29
sized manufacturing companies in Massachusetts and more
33:32
nationally now as well. We think right now that small to
33:37
medium sized businesses are an underserved market.
33:40
If you think about most of these big, big tech start-ups, they,
33:44
most of them end up pivoting to some sort of enterprise solution
33:47
because there's a lot of money there.
33:49
But it turns out that only 6%, we think this is what the
33:53
industry says about 6% of US manufacturers are using
33:58
technology on a day-to-day basis.
34:01
Most, most manufacturing companies are still using paper
34:05
or no data like paper travellers.
34:07
They go around traditionally a, you know, like non AI type
34:12
technology companies. You need the big dollars coming
34:15
in from the big, big manufacturing companies in order
34:18
to justify the cost of develop doing all the development work.
34:22
Well, we can rapidly create AI that is a lot cheaper.
34:26
And all of a sudden we have this massive market that we can go
34:28
into and help to solve some of their, their most pressing
34:31
needs. So this gives us like this
34:33
direct feedback loop. And what we do is we, we narrow
34:36
it down and we say, OK, this is here's a bunch of ideas that we
34:39
think can solve the problem. We've heard that they'll
34:43
actually buy this. Let's move that into the into
34:45
the top of the tunnel, right? So you've got a lot of
34:48
relationships where you're going to show kind of these these
34:51
decks and these like early prototypes to say, is this
34:54
helpful? Is this something you would use?
34:56
No, no, no. Oh, yeah, we want that one.
35:00
OK. We've, we've heard a few yeses.
35:02
We've got some commitments. We're going to move forward.
35:04
What what does the process look like from there?
35:07
I mean, you've had the AI, you know, help you spin up the
35:11
coding, you know, kind of build, build the tool.
35:15
How long before? Like I don't know, the company
35:19
has launched. Is it like?
35:22
Everyone as you start getting some yeses.
35:25
Yeah. I mean, everyone is a little
35:27
different, right? So like we need to find a
35:30
management team for it, right? Look, we can't do everything
35:33
with a small set of resources that we have.
35:35
We run very lean. And so we have to go out and
35:39
find a team to actually like turn it into a real business.
35:42
And so that could take a little while.
35:45
It could be as soon as we get our first customer in, we need
35:48
to start doing rapid prototyping around it and therefore we're
35:51
going to spin it off. So like right now we have a
35:54
couple companies that have been incorporated, but we have a
35:57
bunch more that have not been incorporated, but are still
36:01
there's like AMVP within, within the structure of our lab that
36:06
can allow us to go out and make sales.
36:08
And eventually when we think there's enough critical mass,
36:11
we'll start to think about the management team.
36:12
So everyone is a little different because each company
36:14
is, is a little bit different. Our, our goal is to create, you
36:18
know, lasting companies that can, we can get an exit in 2436
36:23
months if we can get some sort of M&A or continue to grow it
36:27
over a longer period of time. In either case, it needs to be a
36:30
real business that has real, real management around it.
36:34
You guys started this scale up labs not quite three years ago.
36:39
So I'm going to guess you haven't had anybody sort of
36:42
graduate to that stage of an exit or?
36:45
Not yet, no. No, we have some that are, you
36:48
know, like health. Q is one of them that that we
36:51
think is starting to get enough traction where that might happen
36:54
in the next, you know, 12-14 months.
36:56
It's really extraordinary how we've seen health systems just
37:01
need and want what this is and at some point somebody's going
37:05
to gobble up. So we focus on denied claims,
37:08
you know, in the news, healthcare claims in the news,
37:11
I'm sure you've seen with United Healthcare and some of the some
37:14
of the other issues that happened.
37:15
You know, it's a massive, it's hundreds of billions of dollars
37:17
in waste. And on one side you have the
37:20
insurance companies that are leveraging in AI in order to
37:23
deny claims. In many cases, they're they're
37:26
denying them, but they're not very successful at having a real
37:29
reason why. It's just, it just does the work
37:33
on the other side is that you have health systems that are on
37:37
the verge of bankruptcy and they have something like 100 days of
37:40
operating capital sitting in, in these, you know, they, they do
37:43
unbelievable amounts of revenue in the door, but they don't get
37:46
paid out on a lot of it because they get they get denied.
37:49
And so because of that, they're always on the verge of
37:51
bankruptcy. And So what we're doing is, you
37:54
know, one of the, one of the issues that happens is that when
37:56
it comes back is the denied claim.
37:57
There's usually no information on why, it's just a generic,
38:00
this was denied because of and you need to have somebody go in
38:04
and work on each of those claims to figure out if they're going
38:07
to resubmit them. As a result, it's like $54 per
38:11
claim in overhead. And the vast majority of them,
38:14
especially the small dollar amounts, never get resubmitted
38:16
because they don't have the resources to do it.
38:18
On the AI side, it's very, very, you can take a lot of complexity
38:22
and start to figure out what are the anomalies that are causing
38:25
these to happen. And from that we're able to
38:27
start clustering and figuring out where the why these were
38:31
denied so that they can be adjusted and sent back very,
38:34
very affordably and, and effectively.
38:37
Longer term, we're going to become almost like a copilot
38:39
where we're saying, hey, we know based on the information you put
38:42
into this claim, this is going to get denied likely because of
38:45
this, this and this. Make the change now.
38:47
So it never even comes back as a denied claim.
38:51
That's pretty exciting. And I'm gonna guess your target
38:54
customers and maybe even target acquirers would be like these
38:58
health systems that are health. System.
39:00
They're, they're cash strapped, right?
39:03
That's right, yeah. And they they need the extra
39:05
revenue and they need to not have the collection process be
39:09
so labor intensive. That's right.
39:12
And we have some major, major health systems like brand name
39:14
ones that are talking to us right now.
39:16
And we're, we're doing MVPS and we're proof of concepts and
39:20
it's, you know, there, there's a major need for it.
39:24
Where do you see scale up labs going?
39:28
I I would ask two to three years, but that that seems like
39:31
an eternity. I'm just going to say 6 to 12
39:33
months. What?
39:34
How do you see things kind of unfolding here?
39:37
Well, so I mean our goal is 6/6 companies a year.
39:41
Within a couple years we'd like to see something like a billion
39:43
dollars assets under management from the companies we've
39:45
created. You know, there are a lot
39:48
remains to be seen to do that, right.
39:50
But if we can create rapid, rapid opportunities and rapid
39:54
value creation, then that is foreseeable within the next
39:57
couple years. In order to get that within the
39:59
next 6 to 12 months, we need to raise more capital.
40:02
Into the company itself. So we've been bootstrapping most
40:05
of this for, for years and for the last year and a half or so
40:09
and we've had some really good success.
40:11
We've been in the middle of a of a fundraise selling equity in
40:17
the in the lab itself in order to really put a lot of fuel
40:21
behind these other, these companies that are coming out of
40:23
the labs. And we've been pretty successful
40:26
on it. You know, we have the first
40:27
couple investors in and we've been talking to some family
40:30
offices to close the rest of it. We still have a little ways to
40:33
go. So, you know, we are looking for
40:36
family offices that want a seat at the table in the forefront of
40:39
AI, but really specifically strategic partnerships that you
40:44
know, can can provide input to us and then also be interested
40:50
in the in the outcome of the types of companies we create,
40:53
not just from an economic perspective, but the value that
40:56
it's creating within the industries.
40:58
The reason we're looking at family offices is we're not
41:00
looking to sell our company in seven to 10 years, which is what
41:03
venture needs, they need an exit traditional venture family
41:07
offices. So what we're doing is we want
41:09
to continue the capital capitalization, you know the
41:11
equity that they own in the in the business to continue to
41:13
grow. But along the way, as we have
41:15
exits from the companies that are coming out of the lab, we're
41:19
going to be providing some of that back to our investors to
41:21
make sure that there's cash flow back to them as well.
41:25
What does a potential strategic partner look like?
41:27
What's that profile? So family offices or
41:32
corporations that are in advanced manufacturing, energy
41:35
or healthcare or care about those spaces, it could be
41:39
cybersecurity or other like fintech that that hit those
41:42
particular industries. And one of the things that that
41:47
I think a lot of corporations or family offices or investors
41:51
realize is that AI is going to completely change industries.
41:55
You know, since 1954, eighty, 8% of all Fortune 500 companies
42:00
have gone out of business. 54% of those are since 2000.
42:05
The velocity is increasing. And So what you're going to see
42:07
is AI is going to be the next big thing to really drive
42:10
businesses that don't figure this out.
42:13
They're going to see them being disrupted by, by the new
42:17
businesses that come out of it. Think about it as like ACRM.
42:20
If you no no longer need to have these, these databases, there's
42:25
a major, major corporations that are in the CRM space.
42:28
If they're not embracing that, they're going to have problems
42:30
in the supply chain space. So manufacturing in the supply
42:34
chain space, the companies that are not utilizing AI in order to
42:37
figure out how to better utilize the, the materials that are
42:41
coming in to produce better output are going to have a
42:44
disadvantage against the ones that do.
42:46
And so if you don't realize and have a seat at the table and
42:50
understand how these changes are coming, you're going to
42:53
potentially have some issues. So when I think about strategic,
42:56
I want, I want groups that care about that and not are only
43:00
coming in to give us money because we because we need cash
43:03
as fuel, rocket fuel, but are actually going to participate.
43:07
It doesn't have to be all the time, but like sit in our round
43:10
tables, give us their feedback, what are they hearing
43:13
potentially become first customers, potentially become
43:16
acquirers down the road. That's fine.
43:18
Co invest in the companies that they think are the best
43:20
companies coming out of the studio, but the ones that that
43:23
are going to participate along the way has the most value to
43:26
us. Could you see like an existing
43:28
CRM company coming in with your idea and saying, hey, we see
43:33
where this is going, we don't want to be left behind.
43:35
We understand that our old business model set up
43:39
arrangement is probably going to die off at some point.
43:43
Let's get involved here. Yeah, I mean that, that could
43:46
easily be it, right. Like a lot of, a lot of
43:48
companies have some sort of CVC corporate venture capital
43:52
already set up and some of them are willing to come in and pay,
43:56
pay a little money every every year to have a seat at the table
43:59
or do an investment into form of equity.
44:01
So we we're actually talking with a number of multinationals
44:04
now that want a seat at the table and you know, we're happy
44:08
to talk to anybody and see if there's a fit.
44:11
Is it a fit for like an existing fund to come and invest in you
44:17
guys or is that not really a thing that you're looking for
44:21
it? It it could private equity
44:23
potentially so, but from a venture perspective, you know,
44:27
we're not looking to exit our company in, you know, 6 to 10
44:30
years. In some cases an Evergreen, if
44:34
it's an Evergreen fund that can sit, it can wait it out and get
44:37
returns like revenue in or cash flow in from, from the sales of
44:41
companies that could be a fit. It needs to be the right thesis,
44:44
though. Often times there's really,
44:46
really specific thesis in the funds on what they're allowed to
44:49
invest in or not. We're willing to talk to them,
44:51
but we're also not going to compromise and say, hey, we're
44:53
going to be exiting or IPO ING in, you know, in seven to 10
44:57
years. Maybe that happens.
44:58
You never know. You know, hopefully we get big
45:01
enough that that's that that's a thing.
45:02
Eventually we're going to switch more to a, a, a fund model
45:06
similar to like what you see with flagship pioneering.
45:08
Eventually. Originally they were funding
45:10
them things themselves. They got multiple billion dollar
45:13
funds that they're spinning up in order to support the
45:17
companies that are coming out of their venture studio.
45:20
We would probably do something very similar to that as we get
45:23
bigger in that case, if that. 'D.
45:26
Be more venture class. Yeah, I get what you're saying.
45:30
I want to shift gears, Greg, and talk about a serious challenge
45:33
that you've overcome in your life, personal or professional.
45:36
Maybe it's both. What was it?
45:39
What did you learn? How did you get through it?
45:44
I, there's, there's challenges in every part of business.
45:49
My biggest personal challenge is I, I'm not 100% sure I, I have
45:54
this, but I, I'm, I'm like, I'm under, I'm undiagnosed ADHD.
46:01
Everybody in my family has it like I've never technically been
46:05
been, but all, all the characteristics are there.
46:08
And this has been a big, big challenge for me over the years
46:10
because focus can be a big challenge and I can be very,
46:16
very focused on, on things for a short period.
46:19
But I also have this shiny object syndrome, which is very
46:22
indicative where every time I see something new that looks
46:26
cool, I want to drop everything and go after that.
46:29
And so finishing things can always be a big, big challenge.
46:32
And for a long time I was starting like before, before
46:37
scale up labs, I would go and I'd start, you know, like I, I'd
46:40
put together pitch decks around this idea and that idea.
46:43
And then my wife would say, why are you going after that?
46:45
That doesn't make any finish the one you already you already
46:47
created. And this would also challenge me
46:51
a lot when I was in the corporate world, because in the
46:53
corporate world, it's much more by the books.
46:55
And it's like, I always felt different.
46:58
And so somebody would say, we need to do this incremental
47:00
change in order to get this small incremental increase.
47:03
I'm like, oh, let's just go like invest in this company or let's
47:06
go start this new, new line of business and let's go.
47:08
And it just clashed. And so for a long time, I was
47:13
always trying to figure out like, how do, how do I focus
47:15
myself in a way that allows me to create maximum value without
47:21
stopping my creativity or my, my, my interest level.
47:25
And it, it, it took, so I have, as you looked on my LinkedIn,
47:31
you said, I have all these different businesses and it's
47:34
taken over the years a while for me to narrow it down to one
47:37
business, which is scale up labs.
47:39
The cool thing about scale up labs is it allows me to go and
47:42
explore a bunch of different opportunities and other people
47:44
to come in and help me focus. So having a great support system
47:48
around you is really, really important.
47:50
Going out and finding a, a great mentor, somebody who who can
47:55
help you to figure out your next steps, your next path is really
47:59
important. Figuring out what's important to
48:02
you that you that you can actually stick with and have
48:05
goals that you can go after. Whether if it's like I want to
48:08
be a millionaire by this time, that sounds like a cool goal,
48:12
but what, how do you do it? You need to come up with a, an
48:16
actual plan, step back and like figure out where are you
48:18
creating the value creation and then how do you stick with it?
48:21
It has to be something that's interesting to you and creates
48:23
value for those around you in order to get you to that end
48:26
goal. And if you don't do that, you're
48:28
going to be distracted, right? So that's been my personal
48:31
challenge that I've, I've struggled with for since the
48:34
beginning of my career. And if I really look back at it,
48:37
it like, if I'm really honest with myself, it was all through
48:39
school as well. I could only do concentrated
48:42
short spurts. There's a lot of great things
48:45
with that too. It's also my superpower, I
48:46
guess. And I'm glad that you mentioned
48:49
that because I I hear this from entrepreneurs all the time.
48:54
Most entrepreneurs I would guess have some level of ADHD and it
49:00
is at times a curse and other times it is exactly your
49:05
superpower. It's a huge blessing.
49:08
We had son diagnosed with ADHD and somebody turned us on to
49:13
this book called ADHD is Awesome and for.
49:19
Anybody. I haven't read it.
49:20
It's well, I'll tell you what, for guys like us, the audio book
49:25
might be better for you. That's how I consume most of my.
49:28
Books I, I figured and it and the audio book is very
49:32
entertaining because one of the co-authors very talented ADHD
49:39
off the charts and you know, his energy and sort of ping ponging
49:43
all over the place really comes through.
49:45
And so I, you know, what I've seen both in my own experiences
49:50
as well as, you know, other folks that I've got to meet over
49:53
the years is when you do have a founder person, you know, who is
49:58
like that, lots of ideas, you know, all kinds of directions we
50:01
can go. If.
50:03
They do have a partner or a support system like you're
50:06
talking about that is is not wired that way.
50:10
They're not the idea of people, but they're great from an
50:13
operational perspective and they can kind of help put some
50:16
guardrails around things, right. So that it's not, you know, oh,
50:20
Greg came up with four new ideas today.
50:23
You know, he's going to have us chasing all of them at the same
50:25
time. And I'm curious.
50:28
The problem is though, Greg, with, with the technology that
50:31
you've built, now you can chase so many of these ideas at once,
50:34
right? So probably all the, the
50:37
corralling that you've done with yourself and, and your, your
50:40
massive brainpower over the years.
50:42
Now you're like, no, now I can just go free because can help us
50:46
chase all of those. It does help but.
50:50
One thing that I found, and I'm curious if you've ever
50:52
implemented anything like this for yourself, is when I come up
50:55
with a new idea, before I bring it to my team, I have a one week
50:59
waiting period. And what I do, what I make
51:01
myself do is I write down the idea, type it out, whatever.
51:04
Because as I do that, I start to see some of the holes.
51:07
I can go back and fill them in. And sometimes I never finish
51:11
with that document before. I'm like, yeah, this isn't
51:14
right. Sometimes I get done with that
51:16
document and I'm like super excited about the idea, but I
51:19
still make myself sit on it for a week.
51:21
If I come back to it a week later and I'm still just as
51:24
excited, OK, that's when I bring it to the team.
51:27
I like that actually. I, I have not implemented
51:30
something like that for myself, like mentally thinking that I
51:35
need to do that. I probably have forced myself to
51:39
do that just share necessity in the past.
51:42
And it's a really great strategy because there's a number of
51:45
things that I've started and then just put down and never
51:47
picked up again. It's probably better I never
51:49
tried to bring them to the team. Yeah.
51:53
What else have you tried that has kind of been helpful with
51:56
you and corralling your superpower?
52:01
So I've got to say like I've actually become a lot more
52:04
operational over time, force and necessity, right?
52:07
Because there's, there's always things that have to happen.
52:10
Being able to like put something down in writing to your point,
52:16
but something that puts like a process in place is really,
52:20
really important to me. So lists, lists, lists, lists.
52:23
I have, unfortunately, sometimes I have like 5 or 6 running lists
52:26
and I can't remember where my lists are.
52:28
But as long as, so like I'll, I'll, every morning I'll wake up
52:33
and I'll take my old list if I can find it from the day before
52:36
and start to write down again. Here's the things that I need to
52:39
accomplish today. And then I go through that
52:42
throughout the day. I, I, I refer back to it and
52:44
say, did I did I actually set up this meeting?
52:48
Did I send out this presentation?
52:50
Did I do this? Did I do that?
52:53
And this drives some of my business partners crazy a little
52:55
bit, but I've, I've actually taken to putting things in my
52:58
calendar as well. So as one of my, as one of my
53:02
partners says, he goes, if I want to know what's on Greg's
53:05
mind, I look at his calendar and see what he's put on a blocker
53:09
on to take care of for that day. Because I'll do it like, you
53:12
know, 2 in the morning, I wake up and I'm like, oh, I need to
53:14
do this. And I'll open up my phone, go to
53:17
my calendar, put a blocker in it to do it.
53:18
You go, oh, OK, this is, this is what's on Greg's calendar.
53:23
This is what he has to do. And you know, maybe I can help,
53:25
maybe I, maybe I can help, but I have a as long as I'm writing it
53:29
down and tracking it, I feel like that that can help me to,
53:33
to reset myself and say, am I accomplishing what I set out to
53:36
accomplish? I like it.
53:38
I use that calendar one myself especially and it, it's super
53:43
helpful because even if I end up getting distracted and I don't
53:46
get to it in the moment, I've still got a running list of
53:49
these alarms that popped up from my calendar, sort of like my
53:52
constant To Do List that follows me around, you know, before I,
53:55
I, I dismiss it. How about some daily habits,
54:00
goals, rituals, Things that help keep you on track, get you
54:04
started, keep you focused? So for me it's exercise.
54:09
So you know, they always say, you know, when, when you're,
54:11
when you're an entrepreneur, I have two, two kids, one's a
54:14
teenager about to drive, the other one's almost a teenager.
54:18
And you know, so family is extremely important and things
54:21
get really, really busy. And so you have, you have your
54:24
family, you need to make sure you take care of, you have your
54:26
friends, you have your, your business, you have your health,
54:30
all these things in, it's really hard to be able to focus on all
54:33
of them. So I actually have built things
54:35
into my life in order to make sure that I, I, I take care of
54:39
myself, my body. And so really specific things
54:43
like I drive to the train station, I take the train
54:47
station in, but I get, I could take it right to my front door
54:50
of my office. I'd have to switch a train.
54:53
But instead I get off and I walk 28 minutes from a different stop
54:58
to my office, clear my head and listen to audiobooks.
55:03
Think when I leave, I I walk 28 minute, 28 minutes back.
55:08
And then when I get home every night, I either go for a walk
55:10
with my wife or I go to the the gym with my son.
55:13
And does it help? Yeah.
55:16
I I mean, I double keeps me in check.
55:18
But what I, what I really find is that that exercise really
55:22
stimulates a lot of, a lot of things in my brain that allows
55:27
me to really like think a lot clearer.
55:30
When I walk into the office after exercising for 28 minutes,
55:32
I'm like ready to go where if I, if I sat on the train and just,
55:36
you know, close my eyes for a few minutes, I would not have
55:38
the same, same way of doing that.
55:40
And plus I I feel better at the end of the day.
55:43
I love that and I do something a little bit similar.
55:46
I live about a 15 minute walk from my house.
55:50
There's a garage here at my office.
55:52
I could drive and I could park, but even in the worst of weather
55:56
I am walking to and from and every day.
55:58
Some days that's the only physical exercise that I'm able
56:01
to squeeze in. That's right.
56:02
But I also really like that transition time where now I'm
56:06
here, I'm in work mode. When I leave here at the end of
56:09
the day, I sort of have that decompress time where I kind of
56:12
let it go and I get ready to go be a dad and a husband.
56:15
And then I do that kind of in reverse on on the way back yet
56:18
so. Yeah, that's great.
56:20
And and when you get it out of the way as part of your
56:23
day-to-day, when you get home, you have time for your for your
56:26
family. No, it's if you, if you, if
56:29
you're, if you're going home and just spending all your time now
56:31
focusing on exercise and forgetting about your family
56:35
because it's outside of work, then then you miss out on that.
56:37
Right. So like I try to bring
56:38
everything into my my day and structure it in so that I I
56:42
don't have an excuse. Yeah, Greg, as you look back on
56:47
your career up to this point, what do you think is the best
56:49
decision that you made for yourself along the way?
56:55
So there was, there was a defining point where I was
56:58
working for a company while starting Excel Hub and then
57:02
moved transitioning that into scale up lab.
57:04
So again, my shiny object, I was trying to do too many things and
57:09
I noticed my output and my the value I was trying to create was
57:12
suffering. And one of the best things I did
57:15
was start to really focus. So got left the company.
57:19
It was, it was a tough decision. It's nice to have a steady
57:21
paycheck and healthcare paid for and all that.
57:24
All you want to do is start a company and you need to focus on
57:26
it. You're, you're not going to make
57:28
the traction unless you're focused.
57:30
And so I moving moving into this full time into scale up labs a
57:36
few years ago was probably one of the most important things I
57:39
ever did because we went from like slowly growing it to like
57:43
getting it off the steroids and being able to sit side by side
57:47
with my partners every day, all day really just allowed us to
57:50
really do that. And this is the focus, the focus
57:52
thing. I forced myself to focus where
57:55
shiny objects had always kind of been.
57:58
I built an interesting career around it.
57:59
I did a lot of cool things. I've been involved with a lot of
58:02
great things. It helped that I'm in Boston
58:05
where we have this ecosystem, right?
58:07
That helps. But I was not focusing myself
58:11
was holding me back. And when I finally realized that
58:14
it, that was kind of the next the next opportunity for me to
58:19
to to progress in my career in in in the company.
58:23
Nice, Greg. I've just.
58:26
Got one more question for you, but before I ask it, I want to
58:28
do 2 things. First of all, everyone listening
58:31
today, go ahead and hit the follow button on your favorite
58:33
podcast app so you can continue to get more great interviews,
58:36
great content like we've had here today with Greg from Scale
58:39
Up Labs. Greg, I also want to let people
58:42
know the best way to either get in touch with you or to follow
58:46
your story of that of Scale Up Labs.
58:48
They've heard something today and it's intriguing to them.
58:50
They want to learn more. Maybe they want to be a part
58:52
What's what's the best way to make that happen?
58:55
Yeah, so the best way you. Know check out our website scale
58:59
up labs dot VC is interesting, but if you want to connect in
59:03
and discuss more, we're always open to that.
59:05
And I'm a big, big proponent of LinkedIn.
59:08
We use LinkedIn for so much and we we post a lot of what we do
59:12
on LinkedIn. And so if you wanted to follow
59:16
us on on LinkedIn, it's scale up labs and you can find us there.
59:20
But also if you want to be personally connected, I'm just
59:24
let me know that you listened listened to the podcast.
59:27
I get a lot of a lot of requests every day and I don't
59:30
necessarily take all of them, but anybody who's listened to
59:33
the podcast and wants to connect, just mention it in a
59:35
little note and I'll I'll, I'll know that it's that it's
59:38
interesting that that that you want to hear more about what I'm
59:40
doing. And I'm happy to bonus value for
59:43
those folks who made it to the end of the.
59:44
Interview you. That's why you get that.
59:46
You get that special connection with Greg.
59:48
I'm under. Gregory Gian I look just like
59:50
this on the picture and Gian since I slaughtered.
59:53
The pronunciation of it, let's give the spelling, which is
59:56
GEEHAN, and we'll include a link in the show notes.
1:00:00
But for those that may be driving and listening to this,
1:00:02
there, there you go. OK, So last question for you,
1:00:05
Greg, you and I reconnect in a year and you're celebrating one
1:00:08
thing super excited, very happy about it.
1:00:11
What do you what do you hope that is the?
1:00:16
One, the one thing, OK, so it's going to be tied to some
1:00:23
successful exit of, of a company that comes out of our studio.
1:00:27
That's, that's what we're going to consider a success.
1:00:29
You know, we're building it because there's value that can
1:00:31
be created. But the end of the day is, you
1:00:33
know, we're trying to build this also to make sure that we can
1:00:35
provide for our families and provide value to the ecosystem
1:00:38
and provide value to our stakeholders.
1:00:40
And if we've successfully launched and sold a company in a
1:00:43
year, I'm going to, I'm going to be very, very happy with that.
1:00:46
Clicking your heels at that point.
1:00:48
That's it. I get it, Greg, I want to be the
1:00:50
first one to thank you for sharing both your time and your
1:00:53
story with us today. I really appreciate it.
1:00:55
Welcome. Thanks for having me.
1:00:57
Folks, that's a wrap on another episode.
1:00:59
Of the Inspired Stories podcast. Thanks for learning with us
1:01:01
today.