Meet The AI Entrepreneur Who Used LinkedIn To Raise $13.8 Million

it's called The Last Mile for machine learning you can call it AI Square it's the name of the company you can consider it the final connector or maybe even a truck that makes your deliveries or a washing machine we'll explain it all with the CEO right now at the NASDAQ Market site hello everyone this is jab Young Senior writer here at Forbes at the NASDAQ Market site and I am joined by the CEO of AI squar Dr Benjamin Harvey and now ai squar it is a machine learning right and machine learning and AI company takes all of that data and allows companies to make the final decisions of their business outcome did I I say that correctly 100% that's phenomenal Dr Harvey thank you so much for joining us man appreciate the time excited to be here that's great well listen man I always start off the month with uh the theme of the month right and this theme of April is financial literacy month so I would ask you what's the biggest tip that you can give people about financial litery you're a doctor right I know you got your PhD so you you better be good awesome um you know so so my background I've I've got a PhD in computer science and but I've always had this entrepreneurial mindset right and you know one of the biggest challenges from you know going from a a technical role inside of large Enterprise organizations to being able to understand how to you know for me take a technology and understand go to market strategies across different organizations you know understanding customer segments so it's like it's like business 101 yeah right um but unfortunately uh with a technical degree many um academic institutions don't really push the aspect of understanding um the business side of technology so so really when I think about you know for the younger generation and next generation of entrepreneurs that are out there you know it's really important as you think about you know the explosion of AI to focus on you know the core technology but also if you have any entrepreneurial bone in your body it's super important to also understand the business aspects the financial aspects and motivations of organizations as you go to you know transition from you know a core technical um role to more of a you know owner of a business and really driving business outcomes yeah would you uh would you say if that tip you can give would it be you know maybe you know learning in profit loss sheets or anything in financial L here what would be the biggest tip in one sentence one sent what's the biggest tip yeah I mean you know one of the things our inv ERS really focus on are you know pnls pnl right really being able to understand you know that balance statement from in to end and really being able to um create performer projections that can help you understand you know how you need to posture your business for ultimate success in the future yeah now I got to go back home and read all my business books man cuz I didn't Focus too much on the profit loss now I was testing you there to see what you're going to say oh hey man listen thank you so much again for joining us and you're kind of coming up here on a on a Forbes exclusive in a way right you guys are just coming off of your serus a fundrise f front raise a$ 13.8 million raise and is uh featuring investors including TIAA CEO Robert Roger Ferguson um along with new Enterprise Associates and uh an Capital New York based uh Venture firm here 13.8 million series a total 20 million you had a 6 million seed round in 2021 if I'm not mistaken take me inside of this 13.8 million serious a and what AI squar is going to do in the AI space with this money yeah you know so you know just you know just to start off with a little bit of the journey you know kind of where we are now in the movie and how we're going to leverage the resources to really drive us to you know that next unicorn yeah um you know so really you know the Imp the impetus of um how we came into fruition was really um some work that started um at the National Security Agency where you worked yep exactly so I was the um chief of operations data science and uh I was also the head of data science for the Edward Snowden leaks and um in those positions what we saw is that 90% of AI and machine learning Technologies never made it into a a mission um production application right so when you think about you know there are you know thousands of data scientists that are actually building world class artificial intelligence and machine learning capabilities and Technologies but when you think about are they actually coming to life for the business and in the federal government for Mission operations so you know we really set on a journey to solve that problem so that we can really get those Ai and machine learning Technologies out of that experimental sandbox and into the hands of the people in the business as well as from a mission operation perspective the analysts and the military war fighter which are the individuals that ultimately needed those insights the most to you know in in in in many situations help save lives so you know really you know where AI squar is right now you know we've been able to do you know amazing work where we're solving what we what we identify as this last mile of AI problem um in Fortune uh 500 financial services organizations um we're working with you know great cyber security organizations like rapid 7 um we're also working with you know great organizations like John's Hopkins um Applied Physics lab um we're also working you know across you know um the cpg landscape with uh Coca-Cola Florida and you know it doesn't stop there we've also been able to amass many federal contracts you know from the um NSA to the NGA to the National geospatial Intelligence Agency NSA National Security Agency all the way through to um in the department of fence including the Navy as well as the Air Force so really at this point it's about how can we take those use cases in these different customer segments and really Drive some repeatability in the market market so that we can see the ultimate you know full commercialization potential of the actual technology so that includes things like you know how can we you know uh expand the product teams you know how can we you know really build a go to market motion right when you know historically you know um it was founder Le sales but now we want to really be able to bring in a cro build out a sales team across commercial strategic and Enterprise reps and be able to take those use cases and consumer package Goods health and life sciences um cyber security um you know and and really push to see the extent of the agnostic capability that we built in this last mile platform to really Drive business across different customer service now you tell me something I I had my head was was spinning not now right not now it's still spinning not now um but you know when me and you first spoke right about a week ago you called me up and we were going through this and I'm listen I turned the volume of the TV down I'm like all right he's sitting on something I don't know what it is and I had to go through it all weekend man I'm talking to your investors and I used an analogy this analogy that analogy we settled right we settled on the washing machine analogy right and so if I had to go deep into your company because I was telling you I said listen pretend I'm a third grader how would you tell me what this company is and we landed on the wash machine analysis you go you take all this AI data you take the machine learning you take the the learnings the yeses from the algorithms you put it all in the wash machine right and your investor I love what he said that over at Nea he said well it's not only why it's an ultimate Superior washing machine because after you do this after you wash that data again the data that we're getting right that washing machine then dries it then if they folds the clothes for you and it Stacks up nice and neat whereas the process where we are right now is is that companies are washing this data and leaving it on the floor after they watch it right they there there's nothing to do with and this is where the last mile problem right trying to understand how do we get products new software new tech in the hands of our customers and employees and AI squar says Hey we've solved this issue it's almost like the final connector portion of it right getting the piece of of of delivery to your house buy the truck I tell people if you want to be rich own a treking company you can buy nothing without a truck getting it to wherever it needs to get to did I describe AI squ is that the cor is it the washing machine for AI I said that right I love that great my head is not spinning anymore great I can stop watching The Exorcist great so that is the correct way it's the washing machine for for AI I I I love that analogy great um and the reason I love it so much is because if you think about um as you mentioned over the past five plus years um or the the landscape of AI has been really focused on you know building these worldclass Ai and machine learning algorithms right but if you think about it the the value for organizations is really leveraging the output of those models the insights that are being derived from those algorithms to drive the decision- making within the organization so in order to be able to derive insights you first need data within the organization right but not just data from one place you need data from multiple places so the pieces of clothing could come from different parts of the um of the house right and in our our perspective the Enterprise organization and we have to be able to reach out to those different places where the data lives put it inside of this washing machine with the algorithms and then we're pushing out the insights but as you mentioned right we're just not uh providing the insights to the end user or to the customer we are actually you know um when you think about you know the contextualization that's associated with making actionable decisions we're you know providing it in a timely fashion we're providing it um where you know you have additional um you know visualizations to really help guide you in making the right decision so the analogy is perfect because we start with multiple sources of information multiple models we condense it down as we're churning and pulling out the insights and we deliver those directly to that customer absolutely listen as you enter from growth stage right or from Venture stage to growth stage not profitable yet um hope to be there one day soon what's the biggest positive surprise right because now you're the CEO of a company get into your background a little bit because it's quite impressive but what's the biggest positive surprise that you've had that you've learned uh running AI sare to CEO yeah you know I think through through this journey you know one of the things uh that you learn is there's going to be so many things that happen that just go wrong during the entrepreneurial journey and and for me you know I started um you know with a PhD in computer science from uh Buie State University I went to Harvard and my for uh some postgraduate work where I focused on you know building you know um world class AI machine learning algorithms across large scale cancer genomics data sets and um I ultimately ended ended up being recruited by the NSA and I started a career as a federal civilian servant and you know ultimately for me um the transition from the federal government to you know a a Silicon Valley startup right because I knew all about problem Mission fit in the federal government but I had no idea what it means to actually uh incubate a technology um commercialize it and ultimately go to market with it from a product um a um product um Market fit perspective right so um for for myself you know one of the the biggest challenges but also the biggest opportunity um was to start a process where you're learning and you're having to put those lessons into action as quickly as possible so that you can ultimately Drive the success of a business right and you know working at data bricks you know um a shout out to U mat zarya Ali GOI Yan stoa aralon uh who are some of the co-founders that you know helped me you and I call datab bricks datab bricks University right because they ultimately taught me what what was necessary to incubate a technology commercialize it go to market with it build out awesome sales teams go to market motions product teams that could ultimately help you build a billion dollar unicorn right so you know the challenge was getting enough courage to leave the federal government but I landed in an awesome organization working with the co-founders at data bricks and data bricks University taught me everything that I needed to know to ultimately be successful um at uh AI Square yeah so would you say that positive surprised with the fact that the transition was a little smoother than maybe you anticipated what great qu great great Point 100% I mean you know for me you know um you know one of the things that I was able to do with uh mat zarya who's the uh CTO of data BRS you know I I sat down with him and I was able to tell him a little bit about the idea that I had and he thought it had huge implications with regards to the Last Mile and how you know data bricks builds worldclass models and they want to make sure that the insights are actually being derived for their customers as well right so you know with that you know matate helped me make the right connections to a16z new Enterprise Associates as well as battery and you know you know ultimately that Journey was you know streamlined because of the proximity that I had in Silicon Valley the uh network of individuals that I met and ultimately the advice as well as the um direct connections that they provided for me to the right investors and ultimately Pete andini um uh from newer prise Associates he ended up leading ourc round the funding yeah was Steve Jobs's lawyer son I'm not misten um what's it like being a CEO of a company wow such an amazing question so it's not easy I I'll I'll tell you that right and also from the outside looking in um you don't see the the challenges but also the amount of grit the passion the desire that's necessary from a day-to-day basis and the Relentless execution right I learned that from Ali goia data bricks the Relentless execution that's necessary to be able to drive and build the next unicorn every single day from whether or not it's the product teams to the engineering part of the organization to the sales and go to market you know the first you know you know seven figures of Revenue that we created was founder Le sales right so you know I tell a lot of the you know entrepreneurs that are interested in trying to you know raise 20 plus million millions of dollars like we did like look if you can't close the first 10 deals within an organization yourself getting started it's going to be very difficult for you to be able to see the full potential of where you want to go so you have to take on the Persona of a sales engineer but also be as good as on the Innovation side as necessary to be able to drive the organization and when you think about those different facets a CEO has to have intimate knowledge of every single area well enough to be able to help the organization have a vision right and really staying true to that Vision no matter what adversity comes your way what challenges staying true to that Vision right because that vision is what's going to lead the organization and you can't waver you can't falter on that Vision as well well let's reflect on your vision a little bit man talk a little bit about your background you grew up in Jacksonville Florida right one of seven kids five brothers one sister what was it like growing up in Jacksonville what was that like wow you know you know um you know we you know we call Jacksonville Florida um if you ask people in the city you know the Jaguars the home of the Jaguars we won't go there today you know but but the analogy is you know they reference it as Jack and kill Florida wow right and the reason for that is because you know it for many years has been the murder capital of Florida right so when you think about my background um you know I come from a socioeconomically disadvantaged background um you know where um you know I can remember one tax season you know my my parents did taxes and you know they made $133,000 in a year right um but you know all of those challenges you know built the character that I have today which ultimately um you know helps you have the compassion and the empathy as a CEO right that's necessary to you to for you to build a great company yeah as a kid sitting there I know you said your dad used to work at AT&T if I'm not mistaken uh and he used to bring home these computers he used to sit there and fix them and you know just get immen uh immersed in that world but um I say you know even as a child you did that you had to watch cartoons right what was your favorite cartoon as a kid you know inspect the gadget gadget putting together devices you know um you know I'd say you know unfortunately I I didn't watch a lot of TV wow right as a child um but when I did um one of the things that I really liked um I I really liked um you know Sonic the H Hedgehog Sonic the Hedgehog why Sonic what stood out about Sonic you know um Sonic was was really cool for me um because of the the speed and the pace that he would run at in order to get things done yeah right and you know for myself you know I take on on that um you know that element of you know drive you can ask any any of the employees in my organization right um who the hardest working individual is and they're going to say you know Dr Harvey does not sleep right and the reason that you know I take on that that aspect is because the the team and the organization right they they feed off of the energy right of the CEO and I try to make sure that they understand the passion that I have the grit the will the desire and really you know that those attributes you know are the same attributes that I used to see as a young kid watching Sonic the Hedgehog as wow 2009 man you go really fast right fast Mississippi Mississippi valy State University you graduated from there right you went on the football scholarship Jerry Rice as Amma moer um play football and basketball no NFL no NBA why not you know um that is another level uh with regards to um you how good of a player that you have to be to cross that Chasm so you know I was a I was better at um basketball than I was at football but um as a 6-1 cornerback versus a 6-1 point guard you have a better shot at making it to the league right to the NBA or the NFL specifically the NFL as a 6-1 quarterback right so for me you know the um opportunity was um how do I get out of Jacksonville Florida right and Leverage vering a um a full athletic scholarship to do so while focused on a double major in computer science and Premed right which is really hard right so um ultimately it's a uh it was an opportunity for me um but at the same time the NFL and the NBA was really far-fetched but um when I was a senior um at Mississippi Valley State University I had an opportunity to either pursue my my um my senior year of of sports um playing both basketball and football or take a um a a full year at Harvard Harvard and MIT in um Harvard MIT HST which is the health and science technology division yeah which one did you pick uh ultimately um you you know I even though I love the NBA and the NFL I ended up choosing the Harvard MIT program and without that program I wouldn't be here where I'm at today absolutely listen that goes from you you go to Bowie State right and you get your masss and your PhD uh stay within the HBCU but I want to fast forward your time to you mentioned it earlier NSA right National Security Agency because there you were able to help lead the data scientist right there and be able to transfer that realtime data science and that analytics right and you saw that lapse between getting that information to your brothers who are two military personnel one is a captain one is a major right so you're seeing firsthand behind the scene in the NSA about that lapse in information which is where AI Square was kind of conceptualized where are are you right are you sitting at home listening to sha day drinking water like where's going through your head to make say I can come up with a company that can solve this you you know um it it really goes back to the point um that when I was at the National Security Agency um as a chief of operations data science so I ran data science for the entire operations directorate um it it takes a lot of uh passion in order to think that you could come up with just an idea and ultimately make it come to life and create a technology that's dual purpose in a way that could serve ultimately industry and the federal government but the passion that I had behind solving that problem was because of uh Jemiah Harvey and Joseph Harvey which are my two brothers that were deployed to Southwest Asia and the Middle East and ultimately as the chief of operations data science it was so important for me to try to figure out how could we accelerate and simplify how these Ai and machine learning algorithm insights are actually being utilized by the intelligence Community analysts and the military war fighter in the field right and if you think about my brothers um the insights that we could potentially gather could directly um provide them with the information necessary to save lives so understanding that problem and understanding how you know 90% of what the data scientists were building in the organization never made it into a mission production application ultimately was sitting on the shelf is when I made the decision to say hey not only am I going to build a small prototype inside of the organization that could solve the temporary but I'm also going to create a company that could ultimately solve the larger problem for Enterprise organizations as well as Federal Enterprise organizations and ultimately one of the cool things is AI squared uh currently has a Cooperative research and development agreement back with the National Security agency we we scaling out to multiple use cases within that organization yeah I was talking to one of your investors you know over at an capital and he's telling me he's like you know it's almost like they've solved the problem that Pharmaceuticals solved a long time ago right imagine making all these pharmaceutical drugs these amazing drugs and having no way to deploy them or distribute them right this is what AI squar is doing with all of that information but um you know your dark period was one I think would probably Stand Out amongst entrepreneurs because we were talking you take out a $20,000 credit card right you beg your wife to tap into that $500,000 retirement account right so that way you can build the Prototype and then fund AI square and then you get tapped out right um and you have to go and you have to reinvent yourself from a mentality standpoint because when you're running out of money and you got people that's supporting you and relying on you and you got this idea things can get rough man what did you learn about yourself in that dark period wow you know entrepreneurs call it the the Valley of the shadow of death right and that's when you know for for AI uh for AI squar as an organization it was before the Venture Capital funding and it was you know how can we you know showcase that we have some traction and showcase that we can bring in some early Revenue to actually even get the first uh dollars of of of of vent of venture capital right or Venture funding and um during that process as you mention the credit cards and shout out to the wife the the retirement that I cashed out to ultimately support the early developers inside the organization and we still came to a point where um you know the challenge is the mentality coming from you know the the federal background is you um you think about you how can you develop a company and build a company Grassroots right so the the mentality change was when we hit the Valley of the shadow of death where we ran out of cash um no more Runway uh we hit our cash out date and you know it's it's you're starting to have conversations with with different employees about you know hey we may only be able to go you know the next couple of months before we have to hang it up and going from there to getting um your back against the wall where you have to make a decision and that decision was really you know calling you know mat zarya and telling mat mat look you know we've got this idea we think it has huge commercialization potential please help us right and mat making a connection to the you know a16 Z's the neas the the the batteries the the the the tier one venture capital investment organizations that ultimately helped us get funding but you know that Valley of the shadow of death was really the point where you have to evolve right it's about Evolution and that's what I tell the Young Generation of entrepreneurs there's going to be a point where um in order for you to be successful you're going to have to evolve as an entrepreneur and that evolution is actually what's going to take you to the next level in your entrepreneur Journey yeah you guys kind of hit the before the chat GPT wave right one of the investors kind of you know brought that up but um also you're entering a time now where money is tight right it's not like it's not a lot out of there there a lot of dry Potter out there investors a little bit more conservative higher interest rates and just being a little bit more uh a little bit more tight with their money how were you guys able to go and raise a serious a like what did you say that was so convincing to them because you investors are currently they always saying no this is not going to work tell me why it's not going to work but you guys ultimately again you get through a serious a for that yeah how'd you do it I mean if if you think about right now um we're in this you know last year right where organizations have spent tens of millions of dollars building these generative AI Technologies um or these large language models that are ultimately you know these very large algorithms right and these very large algorithms are you know trained on the universe of data on the internet but they're also there's a aspect of you know reinforcement learning with human feedback but it's essentially you know if you using chat GPT for an example it's essentially the opportunity for you to provide feedback thumbs up thumbs down is the answer correct is it wrong and that feedback ultimately is also what the um algorithm uh suppliers um use to tune them model to increase the accuracy and performance right so there was really a couple things on the AI squared side that we were doing right it's really about now that you have these very powerful generative AI large language models from these different algorithm providers how do you first you know when you think about the gold rush it was the the pixes the axes and the shovels right so it's the same thing with AI squared right where the pcks you know pick the axe the shovel that's associated with how do you accelerate how the insights from these you know very large language models are integrated inside of applications and then the second thing which is really important is how do you start to leverage the feedback from the human in the loop to increase the accuracy and performance of the model in the workflows of the business I got my head spinning again doc no I'm playing Hey listen man uh moving on right fast I'm get you out of here on some uh rabid reaction stuff right and again you know 13.8 million rais and you got going to use his money to operationalize the business right and one of your investors you know kind of mentioned the fact that what stood out about AI Square was you guys had uh corporate and government customers right you're working with the US Navy you're working with the uh air force uh and a couple of other fintech Financial companies as you mentioned um but if you break down the AI as a software as a service sector AI as a service I'm assuming that's what you guys fall under right and that's software publishing AIS of service is that accurate in that way both both we do both service um from a perspective of once we deploy our technology inside the organization we we we empower the organization with the tech and we also provide the services to help them um build out to new use cases as well absolutely exactly uh I will ask you this um and I thought you know again one of your investors said it perfectly I love talking to any I got to talk to him again um but you know he kind of mentioned how AI is going to it's another democratizing technology right um and not only that but is going to change the way that humans interact with computers forever right it's forever going to change where creators are going to have amplifiers that's what AI is going to be able to do right describe and I ask you or Define everybody have their own definition Define ai what is it to Dr Benjamin Harvey yeah so you know when you think about artificial intelligence um there's a couple of different areas that all converge right so um you know think about Amazon Alexa you know um um speech to text and text to speech right um but also you've got computer vision right you've got um agent based Technologies um you've got you the core aspect of machine learning as well right um so what you really have is a convergence of multiple technologies that are ultimately providing you with the ability to augment the cognitive decision-making process of humans right so it's all about you know know how do you provide a human with additional insights from a machine learning perspective um but how do you provide a human with um the addition to their skill set from a perspective of being able to solve problems with necessary information that comes from an algorithm to supplement their current internal knowledge yeah absolutely and I wanted to quotes that uh Ken Griffin right the CEO of Citadel he says you know the impact generative AI he was talking about it's going to be call centers translation work producing content for Hollywood so all of that stuff kind kind of falls under that man advice to entrepreneurs what would you tell them about Ai and what to focus on software or hardware and I know you got a love for them both doctor right cuz you building computers that software or Hardware you're an entrepreneur you're you're looking to maybe start something what do you tell them yeah you know I'm I'm I'm I'm a software go guy at heart um but we we also provide a lot of the communication that's necessary to be able to scale the software um on the hardware as well um but when you think about you know organizations that are starting right now it's really about the algorithm itself right you think about open AI it's about the algorithm right they buil this large language model that has you know this powerful algorithm that's you know providing you know uh responses that are associated with anything that you can think of as far as a prompt right so you know the opportunity now for entrepreneurs is to really think about how can you start to personalize those algorithms to really understand individual Behavior whether it's implicit Behavior or whether it's explicit Behavior understand individual Behavior so that you know when you create a promt and you get a response it's not just a response that's associated with you know the world view but it's a response that is directly correlates with not only your behavior but the knowledge that you have as an individual and it provides you with more of like a a co-pilot right for a specific individual right so when when I think about you know as we talk to the entrepreneurs that are out there it's really how can you take these you know large language models democratize them not only for organizations but personalize them for the end users that are actually going to be leveraging the results in their workflows yeah I mean listen software as you said right that publishing software publishing $528 billion sector in the us alone uh according to Ibis World um US presidential race right last before I get you out of here on good to Great uh without choosing sides right what do you want to see or hear from the candidates you are a CEO of a company a upand cominging company one that we hope matches data bricks right it's a 43 billion doll company according to Forbes um what do you want to see from the candidates here from the candidates see yeah you you know um you know one of the things that's it's extremely important for Enterprise organizations is you know how do you um create um the right um policies the rules the regulations that can um allow enable organizations to truly Foster um the the AI Technologies within the organization because you know many organizations what happens is is that the technology really takes off but the the rules the policies and the regulations are a little slower to catch up with the technology and ultimately once they go into effect you have to pull back some features functionality of the technology to abide with those rules and regulations so so one of the opportunities for us right now is to get ahead right where um you know whether it's a Sandbox where we are starting to you know test those models um or test these companies Technologies and capabilities so that we can ultimately have a um an approved set of uh AI technologies that are ready for the market But ultimately we have to be able to get ahead on the AI technology policies right the the rules regulations that are supporting organizations and how they can conduct conduct AI in a trustworthy and an assured manner inside these organizations yeah European laws are already in full effect writing AI laws and again they may have to scale back over there to kind of uh make sure that they're you know kind of staying within the operating uh procedures of what the law says good the great time get you out of here my favorite Business book right Dr Jim Collins u i if say doctor but Jim Collins good great book um what's the difference between a good AI platform and a great one you know um a great AI platform is use case agnostic use case agnostic right meaning that no matter what use case an organization has it's able not to to not only handle that use case right whether it's you know from multiple customer segments but it's also a platform that connects the algorithm insights to the actual business operations right don't stop short where you're just creating the Technologies but how do you ultimately leverage those insights to support the business or the mission in the federal government where the results are actionable they're relevant they're timely they're contextualized and they ultimately enable the adoption The increased adoption of AI across an Enterprise organization there you go smart principled risk taker that's what one of your customers said about you when I was asking right smart principal RIS I said why they're not just giving phds away right so clearly smart um appreciate the time Dr Harvey congrats on the raise again 13.8 million Serius a and you will operationalize the business on this get a cro right a chief Revenue officer and all of that stuff y'all hiring so I'm assume now you got $13 million you you hiring out a appreciate man listen welcome back get you back to the NASDAQ Market site because AI is the future and we're going to need all the expertise from you right and you're a smart guy so you we're going to prove us right you got it appreciate Dr Harvey here at the NASDAQ Market site thank you for watching

Channel: Forbes

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Keywords: Forbes, Forbes Media, Forbes Magazine, Forbes Digital, Business, Finance, Entrepreneurship, Technology, Investing, Personal Finance, Benjamin Harvey, CEO AI Squared, Black owned tech companies, LinkedIn, how to leverage your LinkedIn profile, afrotech, AI, AI the latest

Id: kXmmw6yvPV0

Length: 36min 34sec (2194 seconds)

Published: Fri Apr 19 2024