Benjamin Harvey, the CEO of AI Squared, says he’s added investors including former TIAA CEO Roger Ferguson. Harvey joined Forbes senior writer Jabari Young at the Nasdaq MarketSite to discuss the startup’s Series A raise.
Read the full story on Forbes: https://www.forbes.com/sites/jabariyoung/2024/04/17/meet-the-ai-entrepreneur-who-used-linkedin-to-raise-138-million/?sh=60958bea5837
Subscribe to FORBES: https://www.youtube.com/user/Forbes?sub_confirmation=1
Fuel your success with Forbes. Gain unlimited access to premium journalism, including breaking news, groundbreaking in-depth reported stories, daily digests and more. Plus, members get a front-row seat at members-only events with leading thinkers and doers, access to premium video that can help you get ahead, an ad-light experience, early access to select products including NFT drops and more:
https://account.forbes.com/membership/?utm_source=youtube&utm_medium=display&utm_campaign=growth_non-sub_paid_subscribe_ytdescript
Stay Connected
Forbes newsletters: https://newsletters.editorial.forbes.com
Forbes on Facebook: http://fb.com/forbes
Forbes Video on Twitter: http://www.twitter.com/forbes
Forbes Video on Instagram: http://instagram.com/forbes
More From Forbes: http://forbes.com
Forbes covers the intersection of entrepreneurship, wealth, technology, business and lifestyle with a focus on people and success.
Read the full story on Forbes: https://www.forbes.com/sites/jabariyoung/2024/04/17/meet-the-ai-entrepreneur-who-used-linkedin-to-raise-138-million/?sh=60958bea5837
Subscribe to FORBES: https://www.youtube.com/user/Forbes?sub_confirmation=1
Fuel your success with Forbes. Gain unlimited access to premium journalism, including breaking news, groundbreaking in-depth reported stories, daily digests and more. Plus, members get a front-row seat at members-only events with leading thinkers and doers, access to premium video that can help you get ahead, an ad-light experience, early access to select products including NFT drops and more:
https://account.forbes.com/membership/?utm_source=youtube&utm_medium=display&utm_campaign=growth_non-sub_paid_subscribe_ytdescript
Stay Connected
Forbes newsletters: https://newsletters.editorial.forbes.com
Forbes on Facebook: http://fb.com/forbes
Forbes Video on Twitter: http://www.twitter.com/forbes
Forbes Video on Instagram: http://instagram.com/forbes
More From Forbes: http://forbes.com
Forbes covers the intersection of entrepreneurship, wealth, technology, business and lifestyle with a focus on people and success.
Category
🤖
TechTranscript
00:00It's called the last mile for machine learning.
00:03You can call it AI Squared, it's the name of the company.
00:05You can consider it the final connector, or maybe even a truck that makes your deliveries,
00:10or a washing machine.
00:11We'll explain it all with the CEO right now at the NASDAQ MarketSite.
00:15Hello everyone, it's Jabari Young, Senior Writer here at Forbes at the NASDAQ MarketSite.
00:22I am joined by the CEO of AI Squared, Dr. Benjamin Harvey.
00:26AI Squared is a machine learning, and machine learning and AI company takes all of that
00:33data and allows companies to make the final decisions of their business outcome.
00:37Did I say that correctly?
00:38Oh, 100%.
00:39Oh, that's phenomenal.
00:40Dr. Harvey, thank you so much for joining us, man.
00:41Appreciate the time.
00:42Excited to be here.
00:43That's great.
00:44Well, listen, man, I always start off the month with the theme of the month, and this
00:47theme of April is Financial Literacy Month, so I will ask you, what's the biggest tip
00:52that you can give people about financial literacy?
00:54You're a doctor, right?
00:55I know.
00:56Yeah, you better be good.
00:59Awesome.
01:00You know, so my background, I've got a PhD in computer science, but I've always had this
01:06entrepreneurial mindset, right?
01:09And one of the biggest challenges from going from a technical role inside of large enterprise
01:16organizations to being able to understand how to, for me, take a technology and understand
01:23how to go to market strategies across different organizations, understand the customer segment.
01:28So it's like business 101, right?
01:32But unfortunately, with a technical degree, many academic institutions don't really push
01:39the aspect of understanding the business side of technology.
01:43So really, when I think about, for the younger generation, the next generation of entrepreneurs
01:48that are out there, it's really important, as you think about the explosion of AI, to
01:53focus on the core technology.
01:55But also, if you have any entrepreneurial bone in your body, it's super important to
02:00also understand the business aspects, the financial aspects, and motivations of organizations
02:07as you go to transition from a core technical role to more of a owner of a business and
02:15really driving business outcomes.
02:17Would you say that tip you can give, would it be maybe learning instant profit loss sheets
02:22or anything in financial literacy?
02:24What would be the biggest tip?
02:25In one sentence.
02:26What's the biggest tip?
02:27Yeah.
02:28I mean, one of the things our investors really focus on are P&Ls, right?
02:34Really being able to understand that balance statement from end to end and really being
02:39able to create performer projections that can help you understand how you need to posture
02:45your business for ultimate success in the future.
02:47I got to go back home and read all of my business books, man, because I didn't focus too much
02:50on the profit loss.
02:51I was testing you there to see what you were going to say.
02:53Hey, man, listen, thank you so much again for joining us.
02:56You're kind of coming up here on a Forbes exclusive in a way, right?
02:59You guys are just coming off of your Series A fundraiser, a $13.8 million raise, and it's
03:07featuring investors including TIAA CEO Roger Ferguson, along with New Enterprise Associates
03:13and Ansa Capital, a New York-based venture firm here.
03:17$13.8 million Series A, total $20 million.
03:20You had a six million seed round in 2021, if I'm not mistaken.
03:24Take me inside of this $13.8 million Series A and what AI Squared is going to do in the
03:30AI space with this money.
03:32Yeah.
03:33You know, so just to start off with a little bit of the journey, kind of where we are now
03:40in the movie and how we're going to leverage the resources to really drive us to that
03:45next unicorn.
03:46Yeah.
03:47You know, so really, the impetus of how we came into fruition was really some work that
03:54started at the National Security Agency.
03:57Where you worked.
03:58Yep, exactly.
03:59So I was the Chief of Operations, Data Science, and I was also the Head of Data Science for
04:04the Edward Snowden Leagues.
04:06And in those positions, what we saw is that 90% of AI and machine learning technologies
04:13never made it into a mission production application, right?
04:17So when you think about, you know, there are thousands of data scientists that are actually
04:22building world-class artificial intelligence and machine learning capabilities and technologies.
04:28But when you think about, are they actually coming to life for the business and in the
04:34federal government for mission operations?
04:36So, you know, we really set on a journey to solve that problem so that we can really get
04:41those AI and machine learning technologies out of that experimental sandbox and into
04:46the hands of the people in the business, as well as from a mission operation perspective,
04:51the analysts and the military warfighter, which are the individuals that ultimately
04:55needed those insights the most to, you know, in many situations, help save lives.
05:00So, you know, really, you know, where AI Squared is right now, you know, we've been
05:04able to do, you know, amazing work where we're solving what we identify as this last mile
05:10of AI problem in Fortune 500 financial services organizations.
05:16We're working with, you know, great cybersecurity organizations like Rapid7.
05:20We're also working with, you know, great organizations like Johns Hopkins Applied Physics Lab.
05:27We're also working, you know, across, you know, the CPG landscape with Coca-Cola Florida.
05:33And, you know, it doesn't stop there.
05:35We've also been able to amass many federal contracts, you know, from the NSA to the NGA
05:42to the National Geospatial Intelligence Agency, NSA, National Security Agency, all the way
05:46through to in the Department of Defense, including the Navy, as well as the Air Force.
05:51So, really, at this point, it's about how can we take those use cases and these different
05:56customer segments and really drive some repeatability in the market so that we can see the ultimate,
06:02you know, full commercialization potential of the actual technology.
06:06So that includes things like, you know, how can we, you know, expand the product teams,
06:10you know, how can we, you know, really build a go-to-market motion, right, when, you know,
06:15historically, you know, it was founder-led sales.
06:18But now we want to really be able to bring in a CRO, build out a sales team across commercial,
06:23strategic, and enterprise reps and be able to take those use cases and consumer packaged goods,
06:28health and life sciences, cybersecurity, you know, and really push to see the extent
06:35of the agnostic capability that we built in this last-mile platform to really drive business
06:41across different customer segments.
06:42Now, you tell me something, I had, my head was spinning.
06:45Not now, right, not now, it's still spinning.
06:47Not now, but, you know, when me and you first spoke, right, about a week ago,
06:51you called me up and we were going through this.
06:54And I'm listening, and I turned the volume of the TV down.
06:56I'm like, all right, he's sitting on something.
06:57I don't know what it is.
06:58And I had to go through it all weekend, man.
07:00I'm talking to your investors.
07:02And I used an analogy, this analogy, that analogy.
07:04We settled, right, we settled on the washing machine analogy, right?
07:08And so if I had to go deep into your company, because I would tell me, I said,
07:11let's pretend I'm a third-grader.
07:13How would you tell me what this company is?
07:14And we landed on the washing machine now.
07:16So you go, you take all this AI data, you take the machine learning, you take the learnings,
07:20the yeses from the algorithms, you put it all in a washing machine, right?
07:24And your investor, I love what he said over at NEA, he said, well, it's not only a washing,
07:28it's an ultimate superior washing machine, because after you do this,
07:32after you wash that data again, the data that we're getting, right,
07:35that washing machine then dries it, then it folds the clothes for you,
07:38and it stacks it up nice and neat.
07:40Whereas the process where we are right now is that companies are washing this data
07:44and leaving it on the floor after they wash it, right?
07:46There's nothing to do with, and this is where the last mile problem, right,
07:50trying to understand how do we get products, new software, new tech,
07:54in the hands of our customers and employees.
07:57And AI Squared says, hey, we've solved this issue.
08:00It's almost like the final connector portion of it, right?
08:03Getting the piece of delivery to your house via the truck.
08:06I tell people, if you want to be rich, own a trucking company.
08:08You can buy nothing without a truck getting it to wherever it needs to get to.
08:12Did I describe AI Squared?
08:14Is it the washing machine for AI?
08:16I said that right?
08:17I love that.
08:17Great.
08:18My head is not spinning anymore.
08:19Great.
08:19I can stop watching the exorcism.
08:21Great.
08:21So that is the correct way is the washing machine for AI.
08:25I love that analogy.
08:27Great.
08:27And the reason I love it so much is because if you think about, as you mentioned,
08:32over the past five plus years, the landscape of AI has been really focused on, you know,
08:40building these world-class AI and machine learning algorithms, right?
08:45But if you think about it, the value for organizations is really leveraging the output
08:52of those models, the insights that are being derived from those algorithms to drive the
08:57decision-making within the organization.
08:59So in order to be able to derive insights, you first need data within the organization, right?
09:07But not just data from one place.
09:09You need data from multiple places.
09:11So the pieces of clothing could come from different parts of the house, right?
09:17In our perspective, the enterprise organization, and we have to be able to reach out to those
09:21different places where the data lives, put it inside of this washing machine with the
09:26algorithms, and then we're pushing out the insights.
09:29But as you mentioned, right, we're just not providing the insights to the end user or to
09:34the customer.
09:35We are actually, you know, when you think about, you know, the contextualization that's
09:40associated with making actionable decisions, we're, you know, providing it in a timely
09:46fashion.
09:47We're providing it where, you know, you have additional, you know, visualizations to really
09:53help guide you in making the right decision.
09:57So the analogy is perfect because we start with multiple sources of information, multiple
10:02models.
10:03We condense it down as we're churning and pulling out the insights, and we deliver those
10:08directly to that customer.
10:09Absolutely.
10:10Listen, as you enter from growth stage, right, or from venture stage to growth stage, I mean,
10:14not profitable yet, hope to be there one day soon, what's the biggest positive surprise,
10:19right?
10:20Because now you're the CEO of a company.
10:21Get into your background a little bit because it's quite impressive.
10:24But what's the biggest positive surprise that you've had and that you've learned running
10:29AI Squared as CEO?
10:33You know, I think through this journey, you know, one of the things that you learn is
10:42there's going to be so many things that happen that just go wrong during the entrepreneurial
10:49journey.
10:51And for me, you know, I started, you know, with a PhD in computer science from Bowie
10:56State University.
10:58I went to Harvard and MIT for some postgraduate work where I focused on, you know, building,
11:04you know, world-class AI machinery algorithms across large-scale cancer genomics data sets.
11:09And I ultimately ended up being recruited by the NSA, and I started a career as a federal
11:16civilian servant.
11:18And you know, ultimately for me, the transition from the federal government to, you know,
11:25a Silicon Valley startup, right?
11:28Because I knew all about problem mission fit in the federal government, but I had no idea
11:34what it means to actually incubate a technology, commercialize it, and ultimately go to market
11:41with it from a product market fit perspective, right?
11:47So for myself, you know, one of the biggest challenges, but also the biggest opportunity
11:55was to start a process where you're learning and you're having to put those lessons into
12:02action as quickly as possible so that you can ultimately drive the success of a business,
12:08right?
12:09And, you know, working at Databricks, you know, a shout out to Matei Zaria, Ali Ghosi,
12:15Jan Stoica, Arsalan, who were some of the co-founders that, you know, helped me.
12:22And I call Databricks, Databricks University, right?
12:25Because they ultimately taught me what was necessary to incubate a technology, commercialize
12:30it, go to market with it, build out awesome sales teams, go to market motions, product
12:35teams that could ultimately help you build a billion dollar unicorn, right?
12:40So, you know, the challenge was getting enough courage to leave the federal government, but
12:44I landed in an awesome organization working with the co-founders at Databricks, and Databricks
12:49University taught me everything that I needed to know to ultimately be successful at AI
12:54Squared.
12:55So would you say that positive surprise was the fact that the transition was a little
12:58smoother than maybe you anticipated?
13:02Great point.
13:03100%.
13:04I mean, you know, for me, you know, one of the things that I was able to do with Matei
13:09Zaria, who's the CTO at Databricks, you know, I sat down with him and I was able to tell
13:14him a little bit about the idea that I had, and he thought it had huge implications with
13:19regards to the last mile in how, you know, Databricks builds world-class models, and
13:25they want to make sure that the insights are actually being derived for their customers
13:28as well, right?
13:29Yeah.
13:30So, you know, with that, you know, Matei helped me make the right connections to A16Z, Nona
13:35Prize Associates, as well as Battery, and, you know, ultimately that journey was, you
13:40know, streamlined because of the proximity that I had in Silicon Valley, the network
13:46of individuals that I met, and ultimately the advice, as well as the direct connections
13:51that they provided for me to the right investors, and ultimately Pete Cincini from Nona Prize
13:57Associates, he ended up leading our seed round of funding.
14:00Yeah, that was Steve Jobs' lawyer's son.
14:03That's right.
14:04Pete Cincini.
14:05You got it.
14:06What's it like being a CEO of a company?
14:09That's such an amazing question.
14:11So it's not easy, I'll tell you that, right?
14:18And also from the outside looking in, you don't see the challenges, but also the amount
14:27of grit, the passion, the desire that's necessary from a day-to-day basis, and the relentless
14:33execution, right?
14:34I learned that from Ali Gosia at Databricks, the relentless execution that's necessary
14:39to be able to drive and build the next unicorn every single day, from whether or not it's
14:46the product teams, to the engineering part of the organization, to the sales and go-to-market,
14:51you know, the first, you know, seven figures of revenue that we created was founder-led
14:56sales, right?
14:58So you know, I tell a lot of the, you know, entrepreneurs that are interested in trying
15:01to, you know, raise 20-plus millions of dollars like we did, like, look, if you can't close
15:07the first 10 deals within an organization yourself getting started, it's going to be
15:13very difficult for you to be able to see the full potential of where you want to go.
15:17So you have to take on the persona of a sales engineer, but also be as good on the innovation
15:24side as necessary to be able to drive the organization, and when you think about those
15:28different facets, a CEO has to have intimate knowledge of every single area well enough
15:35to be able to help the organization have a vision, right, and really staying true to
15:41that vision, no matter what adversity comes your way, what challenges, staying true to
15:44that vision, right, because that vision is what's going to lead the organization, and
15:48you can't waver, you can't falter on that vision as well.
15:51Well, let's reflect on your vision a little bit, man.
15:53Talk a little bit about your background.
15:54You grew up in Jacksonville, Florida, right, one of seven kids, five brothers, one sister.
15:59What was it like growing up in Jacksonville?
16:00What was that like?
16:02Wow.
16:03You know, we call Jacksonville, Florida, if you ask people in the city, you know, the
16:13home of the Jaguars.
16:14The home of the Jaguars.
16:15We won't go there today, you know, but the analogy is, you know, they reference it as
16:23Jack and Kill Florida.
16:24Wow.
16:25Right?
16:26And the reason for that is because, you know, it for many years has been the murder capital
16:31of Florida, right?
16:32So when you think about my background, you know, I come from a socioeconomically disadvantaged
16:37background, you know, where, you know, I can remember one tax season, you know, my parents
16:44did taxes and, you know, they made $13,000 in a year, right?
16:49But, you know, all of those challenges, you know, built the character that I have today,
16:54which ultimately, you know, helps you have the compassion and the empathy as a CEO, right?
17:01That's necessary for you to build a great company.
17:04Yeah.
17:05As a kid sitting there, I know you said your daddy used to work at AT&T, if I'm not mistaken,
17:08and he used to bring home these computers, used to sit there and fix them and, you know,
17:12just get immense, immersed in that world.
17:15I say, you know, even as a child, you did that.
17:17You had to watch cartoons, right?
17:19What was your favorite cartoon as a kid?
17:22You know...
17:23Inspector Gadget!
17:24Inspector Gadget!
17:25Putting together devices.
17:26You know, you know, I'd say, you know, unfortunately, I didn't watch a lot of TV.
17:33Wow.
17:34Right?
17:35As a child.
17:36But when I did, one of the things that I really liked, I really liked, you know, Sonic the
17:42Hedgehog.
17:43Sonic the Hedgehog.
17:44Yeah.
17:45Sonic.
17:46What stood out about Sonic?
17:47You know, Sonic was really cool for me because of the speed and the pace that he would run
17:53at in order to get things done.
17:56Yeah.
17:57Right?
17:58And, you know, for myself, you know, I take on that, you know, that element of, you know,
18:02you can ask any of the employees in my organization, right, who the hardest working individual
18:08is and they're going to say, you know, Dr. Harvey does not sleep.
18:11Right?
18:12And the reason that, you know, I take on that, that aspect is because the team and the organization,
18:18right, they feed off of the energy, right, of the CEO.
18:23And I try to make sure that they understand the passion that I have, the grit, the will,
18:28the desire, and really, you know, that those attributes, you know, are the same attributes
18:33that I used to see as a young kid watching Sonic the Hedgehog.
18:36Yeah.
18:37Wow.
18:382009, man.
18:39You go really fast.
18:40Right.
18:42You went to Valley State University.
18:43You graduated from there.
18:44Right?
18:45You went on a football scholarship.
18:46Jerry Rice is alma mater.
18:47Played football and basketball.
18:48No NFL, no NBA.
18:49Why not?
18:50You know, that is another level with regards to, you know, how good of a player that you
19:01have to be to cross that chasm.
19:03So, you know, I was a, I was better at basketball than I was at football.
19:08But as a 6'1 cornerback versus a 6'1 point guard, you have a better shot at making it
19:14to the league, right, to the NBA or the NFL, specifically the NFL, as a 6'1 cornerback.
19:20Yeah.
19:21So for me, you know, the opportunity was how do I get out of Jacksonville, Florida, right,
19:28and leveraging a full athletic scholarship to do so while focused on a double major in
19:35computer science and pre-med, right, which is really hard, right?
19:39So ultimately, it's a, it was an opportunity for me.
19:43But at the same time, the NFL and the NBA was really far-fetched.
19:48But when I was a senior at Mississippi Valley State University, I had an opportunity to
19:54either pursue my senior year of sports, playing both basketball and football, or take a full
20:03year at Harvard and MIT in Harvard-MIT HST, which is the Health and Science Technology
20:09Division.
20:10Yeah.
20:11Which one did you pick?
20:12I ultimately, you know, even though I love the NBA and the NFL, I ended up choosing the
20:18Harvard-MIT program.
20:19And without that program, I wouldn't be here where I'm at today.
20:22Absolutely.
20:23Well, listen, that goes from, you know, you go to Bowie State, right, and you get your
20:25Masters and your PhD, stay within the HBCU.
20:29But I want to fast-forward your time to, you mentioned it earlier, NSA, right, National
20:32Security Agency, because there you were able to help lead the data scientists right there
20:37and be able to transfer that real-time data science and analytics, right?
20:43And you saw that lapse between getting that information to your brothers, who are two
20:47military personnel, one is a captain, one is a major, right?
20:50So you're seeing firsthand behind the scenes in the NSA about that lapse in information,
20:55which is where AI Squared was kind of conceptualized.
20:58Where are you, right?
20:59You're sitting at home, listening to Sade, drinking wine, like words going through your
21:03head that make you say, I can come up with a company that can solve this.
21:07You know, it really goes back to the point that when I was at the National Security Agency
21:16as chief of operations data science, so I ran data science for the entire operations
21:20directorate, it takes a lot of passion in order to think that you could come up with
21:30just an idea and ultimately make it come to life and create a technology that's dual-purpose
21:35in a way that could serve ultimately industry and the federal government.
21:39But the passion that I had behind solving that problem was because of Jeremiah Harvey
21:45and Joseph Harvey, which are my two brothers that were deployed to Southwest Asia and the
21:50Middle East.
21:52And ultimately, as the chief of operations data science, it was so important for me to
21:57try to figure out how could we accelerate and simplify how these AI and machine learning
22:04algorithm insights are actually being utilized by the intelligence community analysts and
22:09the military warfighter in the field, right?
22:12And if you think about my brothers, the insights that we could potentially gather could directly
22:20provide them with the information necessary to save lives.
22:23So understanding that problem and understanding how 90% of what the data scientists were building
22:29in the organization never made it into a mission production application, ultimately was sitting
22:34on a shelf, is when I made the decision to say, hey, not only am I going to build a small
22:39prototype inside of the organization that could solve the temporary, but I'm also going
22:44to create a company that could ultimately solve the larger problem for enterprise organizations
22:49as well as federal enterprise organizations.
22:52And ultimately, one of the cool things is AI Squared currently has a cooperative research
22:56and development agreement back with the National Security Agency where we're scaling out to
23:00multiple use cases within that organization.
23:02Yeah, I was talking to one of your investors, you know, over at Ansa Capital, and he's telling
23:05me, he's like, you know, it's almost like they've solved the problem that pharmaceuticals
23:09solved a long time ago, right?
23:10Imagine making all of these pharmaceutical drugs, these amazing drugs, and having no
23:14way to deploy them or distribute them, right?
23:17This is what AI Squared is doing with all of that information.
23:20But, you know, your dark period was one I think would probably stand out amongst entrepreneurs
23:24because we were talking, you take out a $20,000 credit card, right?
23:28You beg your wife to tap into that $500,000 retirement account, right?
23:33So that way you can build the prototype and then fund AI Squared, and then you get tapped
23:38out, right?
23:40And you have to go and you have to reinvent yourself from a mentality standpoint because
23:44when you're running out of money and you've got people that support you and they're relying
23:47on you and you've got this idea, things can get rough, man.
23:50What did you learn about yourself in that dark period?
23:53Wow.
23:54You know, entrepreneurs call it the valley of the shadow of death, right?
23:58And that's when, you know, for AI Squared as an organization, it was before the venture
24:04capital funding.
24:05Yeah.
24:07You know, how can we, you know, showcase that we have some traction and showcase that we
24:12can bring in some early revenue to actually even get the first dollars of venture capital,
24:18right?
24:19Or venture funding.
24:20And during that process, as you mentioned, the credit cards and shout out to the wife,
24:25the retirement that I cashed out to ultimately support the early developers inside the organization.
24:31And we still came to a point where, you know, the challenge is the mentality coming from,
24:36you know, the federal background is you think about how can you develop a company and build
24:41a company grassroots, right?
24:43So the mentality change was when we hit the valley of the shadow of death where we ran
24:49out of cash, no more runway.
24:52We hit our cash out date and, you know, you're starting to have conversations with different
24:58employees about, you know, hey, we may only be able to go, you know, the next couple of
25:02months before we have to hang it up.
25:04And going from there to getting your back against the wall where you have to make a
25:09decision and that decision was really, you know, calling, you know, Matei Zaria and telling
25:15Matei, Matei, look, you know, we've got this idea.
25:18We think it has huge commercialization potential.
25:21Please help us.
25:22Right.
25:23Matei making the connection to the, you know, A16Zs, the NEAs, the batteries, the tier one
25:30venture capital investment organizations that ultimately helped us get funding.
25:34But, you know, that valley of the shadow of death was really the point where you have
25:39to evolve.
25:40Right.
25:41It's about evolution.
25:42And that's why I tell the young generation of entrepreneurs, there's going to be a point
25:46where in order for you to be successful, you're going to have to evolve as an entrepreneur.
25:51And that evolution is actually what's going to take you to the next level in your entrepreneurial
25:56journey.
25:57Yeah.
25:58You guys kind of hit the scene before the chat GPT we right when investors kind of brought
26:02that up.
26:03But also you're entering a time now where money is tight, right?
26:08It's not like it's not a lot out of there's a lot of dry potter out there, investors,
26:11a little bit more conservative, higher interest rates and just being a little bit more a little
26:16bit more tight with their money.
26:18How were you guys able to go and raise a serious say, like, what did you say that was
26:21so convincing to them?
26:23Because you investors are currently they always say, no, this is not going to work.
26:27Tell me why it's not going to work.
26:29But you guys ultimately, again, you get to a serious say for that.
26:31Yeah.
26:32How'd you do it?
26:33I mean, if you think about right now, we're in this, you know, last year, right, where
26:41have spent tens of millions of dollars building these generative AI technologies or these
26:50large language models that are ultimately, you know, these very large algorithms, right?
26:58And these very large algorithms are, you know, trained on the universe of data on the Internet.
27:03But they're also there's a aspect of, you know, reinforcement learning with human feedback.
27:08But it's essentially, you know, if you're using chat GPT, for an example, it's essentially
27:13the opportunity for you to provide feedback, thumbs up, thumbs down.
27:18Is the answer correct?
27:19Is it wrong?
27:20And that feedback ultimately is also what the algorithm suppliers use to tune the model
27:28to increase the accuracy and performance.
27:30Right.
27:31So there was really a couple of things on the AI squared side that we were doing.
27:34Right.
27:35And it was about now that you have these very powerful generative AI, large language models
27:40from these different algorithm providers.
27:43How do you first, you know, when you think about the gold rush, it was the the pixes,
27:47the axes and the shovels.
27:49Right.
27:50So it's the same thing with AI squared, right?
27:52Where the picks, you know, pick the axe, the shovel that's associated with how do you accelerate
27:57how the insights from these, you know, very large language models are integrated inside
28:03of applications.
28:04And then the second thing, which is really important, is how do you start to leverage
28:08the feedback from the human in the loop to increase the accuracy and performance of the
28:14model in the workflows of the business?
28:16You got my head spinning again, Doc.
28:18No, I'm playing.
28:19Hey, listen, man, moving on right fast, I'm going to get you out of here on some rapid
28:24reaction stuff.
28:25Right.
28:26And again, you know, 13.8 million raised and you guys are going to use this money to operationalize
28:30the business.
28:31Right.
28:32And you kind of mentioned the fact that what stood out about AI squared was you guys had
28:37a corporate and government customers.
28:39Right.
28:40You're working with the U.S. Navy.
28:41You're working with the Air Force and a couple of other fintech financial companies, as you
28:45mentioned.
28:46But if you break down the AI as a software, as a service sector, AI as a service, I'm
28:51assuming that's what you guys fall under, right?
28:53And that's software publishing, AI as a service.
28:55Is that accurate in that way?
28:57Both.
28:58We provide the service from a perspective of once we deploy our technology inside the
29:02organization, we empower the organization with the tech and we also provide the services
29:08to help them build out to new use cases as well.
29:11Absolutely.
29:12Exactly.
29:13I will ask you this.
29:14And I thought, you know, again, one of your investors said it perfectly.
29:17I love talking to any guy.
29:18I got to talk to him again.
29:20But you know, he kind of mentioned how AI is going to it's another democratizing technology.
29:26Right.
29:27And not only that, but it's going to change the way that humans interact with computers
29:33forever.
29:34Right.
29:35It's forever going to change where creators are going to have amplifiers.
29:38That's what AI is going to be able to do.
29:40Right.
29:41Describe.
29:42And I have to ask you, or define.
29:43Everybody have their own definition.
29:45Define AI.
29:46What is it to Dr. Benjamin Harvey?
29:47Yeah.
29:48So, you know, when you think about artificial intelligence, there's a couple of different
29:53areas that all converge.
29:54Right.
29:55So, you know, think about Amazon, Alexa, you know, speech to text and text to speech.
30:01Right.
30:02But also you've got computer vision.
30:03Right.
30:04You've got agent based technologies.
30:07You've got the core aspect of machine learning as well.
30:12Right.
30:13So what you really have is a convergence of multiple technologies that are ultimately
30:19providing you with the ability to augment the cognitive decision making process of
30:25humans.
30:26Right.
30:27So it's all about, you know, how do you provide a human with additional insights from a machine
30:35learning perspective?
30:36But how do you provide a human with the addition to their skill set from a perspective of being
30:44able to solve problems with necessary information that comes from an algorithm to supplement
30:51their current internal knowledge?
30:52Yeah, absolutely.
30:53And I wanted to quote that Ken Griffin, right, the CEO of Citadel, he says, you know, the
30:57impact generative AI he was talking about, you know, is going to be call centers, translation
31:02work, producing content for Hollywood.
31:03So all of that stuff kind of falls under that, man.
31:06Advice to entrepreneurs.
31:07What would you tell them about AI and what to focus on?
31:10Software or hardware?
31:11You got a love for them both, doctor, right, because you building computers need that software
31:15or hardware.
31:16You're an entrepreneur.
31:17You're looking to maybe start something.
31:18What do you tell them?
31:19Yeah.
31:20You know, I'm a software guy at heart, but we we also provide a lot of the communication
31:26that's necessary to be able to scale the software on the hardware as well.
31:32But when you think about, you know, organizations that are starting right now, it's really about
31:38the algorithm itself.
31:39Right.
31:40It's not just about open AI.
31:41It's about the algorithm.
31:42Right.
31:43They built this large language model that has, you know, this powerful algorithm that's,
31:47you know, providing, you know, responses that are associated with anything that you can
31:52think of as far as a prompt.
31:53Right.
31:54So, you know, the opportunity now for entrepreneurs is to really think about how can you start
32:00to personalize those algorithms to really understand individual behavior, whether it's
32:07implicit behavior or whether it's explicit behavior, understand individual behavior so
32:12that, you know, when you create a prompt and you get a response, it's not just a response
32:18that's associated with, you know, the world view, but it's a response that is directly
32:23correlates with not only your behavior, but the knowledge that you have as an individual.
32:29And it provides you with more of like a copilot, right, for a specific individual.
32:36Right.
32:37So, when I think about, you know, as we talk to the entrepreneurs that are out there, it's
32:41really how can you take these, you know, large language models, democratize them not only
32:45for organizations, but personalize them for the end users that are actually going to be
32:50leveraging the results in their workflows.
32:52Yeah.
32:53I mean, listen, software, as you said, right, that publishing software publishing $528 billion
32:58sector in the U.S. alone, according to IBIS World, U.S. presidential race, right, last
33:03before I get you out of here, I'm good to great, without choosing sides, right, what
33:06do you want to see or hear from the candidates?
33:08You are a CEO of a company, an up and coming company, one that we hope matches Databricks,
33:12right, it's a $43 billion company, according to Forbes.
33:16What do you want to see from the candidates here, from the candidates?
33:20Yeah, you know, you know, one of the things that's extremely important for enterprise
33:26organizations is, you know, how do you create the right policies, the rules, the regulations
33:34that can allow, enable organizations to truly foster the AI technologies within the organization?
33:43Because, you know, many organizations, what happens is, is that the technology really
33:47takes off, but the rules, the policies, and the regulations are a little slower to catch
33:53up with the technology, and ultimately, once they go into effect, you have to pull back
33:58some features, functionality of the technology to abide with those rules and regulations.
34:04So one of the opportunities for us right now is to get ahead, right, where, you know,
34:09whether it's a sandbox, where we are starting to, you know, test those models, or test these
34:14companies' technologies and capabilities, so that we can ultimately have an approved
34:20set of AI technologies that are ready for the market, but ultimately, we have to be
34:26able to get ahead on the AI technology policies, right, the rules, regulations that are supporting
34:33organizations and how they can conduct AI in a trustworthy, in an assured manner inside
34:39these organizations.
34:40Yeah, European laws are already in full effect, writing AI laws, and again, they may have
34:43to scale back over there to kind of make sure that they're, you know, kind of staying within
34:48the operating procedures of what the law says.
34:51Good to Great Time, get you out of here.
34:52My favorite business book, right, Dr. Jim Collins, well, I'm not going to say a doctor,
34:56but Jim Collins' Good to Great Book, what's the difference between a good AI platform
35:02and a great one?
35:04You know, a great AI platform is use case agnostic.
35:10Use case agnostic.
35:11Right, meaning that no matter what use case an organization has, it's able to not only
35:18handle that use case, right, whether it's, you know, from multiple customer segments,
35:23but it's also a platform that connects the algorithm insights to the actual business
35:30operations, right?
35:32So it stops short, where you're just creating the technologies, but how do you ultimately
35:37leverage those insights to support the business or the mission in the federal government,
35:42where the results are actionable, they're relevant, they're timely, they're contextualized,
35:47and they ultimately enable the adoption, the increased adoption of AI across an enterprise
35:53organization.
35:54There you go.
35:55Smart, principled, risk taker.
35:56That's what one of your customers said about you when I was asking, right?
35:59Smart, principled, risk taker.
36:00I said, why?
36:02Because you're just giving PhDs away, right?
36:03So clearly smart.
36:04I appreciate the time, Dr. Harvey, congrats on the raise again, $13.8 million Series A,
36:10and you will operationalize the business on this, get a CRO, right, a Chief Revenue Officer,
36:15and all of that stuff.
36:16Y'all hiring, so I'm assuming.
36:17Now you got $13 million, you're hiring out of the sudden, right?
36:20Appreciate it, man.
36:21Listen, welcome back.
36:22I'll get you back to the NASDAQ market site, because AI is the future, and we're going
36:24to need all the expertise from you, right?
36:26And you're a smart guy, so you're going to prove us right.
36:29You got it.
36:30Appreciate it.
36:32We'll be right back at the NASDAQ market site.
36:33Thank you for watching.