IBM™ Master Inventor, Author, Business Advisor
Neil Sahota

"Always try and disrupt yourself before someone disrupts you."

- Neil Sahota

Neil outlines what he looks for in a start-up

Talk Takeaways

An interesting conversation with “Master” Neil Sahota where he shares his journey as IBM’s Master Inventor, as United Nation’s Subject Matter Expert, and as an Angel investor. With his extensive experience in the realm of Artificial Intelligence, gain awesome insights on AI powered solutions, AI for Good, data structures, and his unique personal startup stories.

About

Neil Sahota is an IBM Master Inventor, United Nations (UN) artificial intelligence (AI) Advisor, author of the book Own the A.I. Revolution., and Professor at UC Irvine. He is a business solution advisor to several large companies and sought-after keynote speaker. Over his 20+ year career, Neil has worked with enterprises on the business strategy to create next generation products/solutions powered by emerging technology as well as helping organizations create the culture, community, and ecosystem needed to achieve success such as the U.N.’s AI for Good initiative. Neil also actively pursues social good and volunteers with nonprofits. He is currently helping the Zero Abuse Project prevent child sexual abuse as well as Planet Home to engage youth culture in sustainability initiatives.

The full #OPNAskAnAngel talk

Jeffery:
All right everybody, welcome. Today we are with Neil Sahota, and I’m actually super excited because — neil, I have watched a lot of your content, I’m a big fan. I’m not sure if you get to hear that all the time but I do enjoy the conversations that you’ve carried and all the great things you do but just for the purpose of their audience and I guess I’m going so fast here — I should say welcome and thank you for joining us at OPN’s Ask An Angel. It’s brilliant to have you and thank you for joining us.

Neil:
Hey, my pleasure. Really excited, Jeffrey, thanks for the opportunity.

Jeffery:
For sure, so the best way for us to start is if you can give us a little bit of an intro background on yourself where you kind of came from. From your IBM days and everything else all the good things that you’ve done and then one thing about you that nobody would know

Neil:
Okay that the last one would be tough. So I actually started off as an entrepreneur my final year as an undergrad. I went to starting a company with a couple of buddies we made every mistake you could possibly make, we live in a time where he had the great access to mentors, accelerators, incubators, and we learned a lot and I went up relating that to going to becoming a management consultant. Figured if I can’t do it for myself at least I could figure out how to do it for other people and so going down that path I worked with global fortune 500 companies on business strategy, new product development, opening new markets, and that kind of reinvigorated my desire you know help startups and so I want to going back to the shop world but as an investor and advisor So I’m actually part of five different VC funds, I’m obviously an angel investor, but you know i’m solving problems, helping people, I wind up developing some intellectual property on something we call artificial intelligence now and that got me a call from IBN R&D asking my work and next thing I knew I was helping out to build Watson and the jeopardy challenge and want to building the ecosystem for AI, where 70 of the companies are actually startups. They don’t come with the you know what are we truly trying to fix here because nothing’s broken attitude, they come with the more disruptive you know ideas. Five years ago I was asked to give a keynote to the United Nations, spoke from world leaders and I wanted that helping them start up the AI for good initiative which is where here’s an AI on emerging technology for the sustainable development goals, what spooled off of that recently is last year we started the UN’s innovation factory. So it’s a social entrepreneurship initiative where social impact entrepreneurs get a very global stage to showcase their work, their ideas, help find teams, we have affiliations with you know VCs and all that stuff basically give them a helping hand because when they’re trying to solve local problems they often have global solutions.

Jeffery:
I’m dumbfounded I don’t even know what to say next. That’s brilliant, that’s awesome. well and where are you located right now? Currently yourself I know you work with the University of Irvine all that great stuff in California, are you residing in California as well?

Neil:
I do. I live in Orange County California. I used to say that the whole world was my office but only virtually these days.

Jeffery:
Oh that’s good. So I kind of want to go back to the IBM Watson side because you mentioned that 70 of the companies that are using Watson are startup companies and I find that kind of phenomenal, a big phenomenon, because it needs data and there’s companies that have been around hundreds of years that are still collecting data and they would be perfect to go on IBM Watson and we’ve worked with watson many times. We own a software company we’ve been doing that type of research and data for a long time but I find that fascinating that IBM has taken such a route to drive startups into their data, what is the holdback for big business in providing you guys with this data because that’s what’s going to benefit all these startups in creating amazing products

Neil:
For the I mean it’s a bit of a protecting the king of the hill attitude for them, they have a successful business, they don’t want to cannibalize it, they don’t want to take too much capital risk, but you know it’s complacency. I have to say that way I get that a lot of people are used to thinking of computers as automation, now we have AI that does more in automation. But it’s kind of like Kodak right back in the day. You know if you people actually were Kodak they were the kings of film and they were for over 100 years what killed them was digital camera. In the 1996 when cameras, these cameras came out, they’re like it’s a bad delivery place, film quality’s not great. Of course so what happened — the irony is that Kodak invented a digital camera, Steve Sapp soon invented in 1976 codex like we don’t want to cannibalize our business, we’re making good money, why do we fix something that’s not broken that’s the same challenge we have today. All these big companies like I got something I’m making good money why should I go do something new. Why should I change the way we’re doing things period. It’s like well you know adapt or die, Jeffrey.

Jeffery:
Totally. It’s kind of falls back on the Nortel, was very similar to this blackberry . Blackberry is a king of this, but they’ve and same with Kodak, they’ve made this big shift but they still protect their data. Like even if you look at blackberry, they now run a lot of the automation for car software for automobiles, massive, still doing a billion dollars in revenue but they’re looked at as being small Kodak. Billion dollars in revenue but you still think that they don’t exist but they’re doing pretty good ,i’d be all right with a billion dollars in revenue. You’re profitable you know, like so I think there’s an opportunity to coach them, to kind of get through with all that but the bigger piece here is that there’s a huge opportunity for startups. There is a lot of data and Watson really carries that, and obviously you being on the side of the AI is there something of optimism or is there something that you can really say to get these startups to really dive into what IBM’s offering here?

Neil:
I would tell everyone that you know Watson is not designed just for big companies, it’s actually designed with the little startup companies in mind. The people that have an idea of trying to figure out what to do. We’ve got the great legs to build dictionaries that are data sources for people to use but at the end of the day Watson AI all technology is a tool to figure out the best application for the tool that’s why we did this. it’s like let the experts bring in their domain knowledge but you got to think differently right. if you don’t disrupt yourself someone will disrupt you. Well we found that the big companies they want to disrupt themselves so the startups did so we enabled them, we empowered them to actually do that.

Jeffery:
I think it’s brilliant now with the the way it’s structured. How does this really benefit the startup ? So if I’m out talking about this which is — hey you guys got to connect to the IBM side of things, what’s the best way to sell them through on this because I think it’s huge,. And anything that you can get data from,i know it’s not free data, but anything that you can really collectively tie your algorithms in and just get smarter faster, what’s that sell feature what do you guys do to kind of move people through that quick pipeline?

Neil:
It’s the partnerships that we’ve built, it’s the access to data that people would never normally able to get to I mean if you’re going to do something like in med tech or health tech, it’s like you don’t want an AI to be able to read you know MRIS, you need lots of data for that. The watson ecosystem was built with you know the cleveland clinic as a partner the mayo clinic is a partner so there’s resources and channels to actually get the data you’re looking for that one of the big smartness things we actually did we set up a content repository to actually enable that kind of matchmaking

Jeffery:
I think you just sold me right there on something. I don’t think I heard in any of your talks because your bang on is that if you’re looking to fix something of a problem in this industry like data in MRI reading, We’ve got the partners that we’ve already pulled the data from that’s going to benefit you. And all the things that I’ve done in th IBM side of things they never brought that up as being the sell feature it wasn’ we’ve already went and scrubbed and brought all this data in it was just we have the data but knowing that it came from that clinic, or that the data was actually that powerful I think that that would actually make a bigger difference for me to say hey what am I doing I should pay the money get this data because it’s 08:5 solve my problem quicker and make my business move faster.

Neil:
You’re actually touching upon a really important point Jeffrey because truth and technology, truth and trust in technology is key but as technologists or engineers we just we already we kind of know it and we don’t think about the the average person or layperson. And so it never pops into our head to say oh yeah oh yeah that data came from a mayo clinic right not realizing like oh my god that creates so much reassurance.

Jeffery:
Knowing that it came from there that makes a bigger difference right, it’s like you said trust. But it also makes me realize like man, i’m trying to get this data and I’ve been knocking on the Mayo Clinic’s door they’re not answering. Well why am I doing that when IBM has it, I can just pay to get it. So I think that segmentation makes a big difference in the world of startups especially when you’re moving quicker. And I know we built a product this is a few years back, maybe I don’t know eight years ago we called it simplify and I was actually in my old email yesterday and I was going through and I was like oh my god that was from 2014. We designed this whole system CRM system that would tie into Linkedin, they were all for it and then they shut us out and they integrated someone that did the exact same thing we did. We were pissed. But regardless we couldn’t do it, do anything I don’t know why maybe it is too young at the time of looking at it but we tied into so many domain systems because the data that we needed in order to operate was Linkedin, it was not the better business bureau but some other application that just host hundreds of thousands of content but they were going to charge us like a hundred thousand dollars a month as a startup just to be able to pull and scrub this data and we’re like we’re a startup, how are we going to get a hundred thousand dollars a month to scrub this data together so at the end of the day there’s bigger better options and knowing where that data comes from is going to solve a lot of your problems quicker.

Neil:
A hundred percent right. I think we all know data is the new oil and people are hoarding it. They want to raise money if they can’t use it but the great thing about corporate ventures now is they’d rather bet on the entrepreneur, let them take the risk, give them a little money see if they develop something and there’s one of the resources to build a market great. You know maybe you buy them for a premium, if they don’t the company is like it was lower risk than you trying to do yourself.

Jeffery:
Yup no exactly and there’s a lot of companies that are moving quicker in the system right like they’re getting bought within the first year and a half and they really don’t have that much to offer but what they do have is that they tested the water, they got the data, they got the access, built the apis, and that’s good enough, so you know what buying for 20 million and build that up to 100 million dollar business in three years no problem because you can add in your own resources and make it bigger right.

Neil:
100%. That’s how you I thought big companies do crazy things now. They trust the entrepreneurs

Jeffery:
It’s opened the door which wasn’t the case five ten years ago right

Neil:
Oh my god, no.

Jeffery:
So in this journey that you’ve gone on from building the AI system and working within Watson and you’ve kind of progressed obviously a lot from there and now you’re kind of globally talking about everything you’ve done, you did make some predictions on how the AI system was going to work and operate inside of Watson in healthcare, has any of that changed from when you first talked about it to where we are today, this is like almost five years later have you seen a big shift and saying you know what man did Watson ever drive this forward or are you still kind of straight in your head going man why are people not moving quicker in this space?

Neil:
It’s a mix of both. We’ve seen really great progress in terms of you know diagnosis and personalized treatment plans and precision medicine, we’ve seen better things in terms of like bedside manner and just overall quality of care but looking more things around research not so much. It was about four or five years ago we had a case. There was a woman in Japan, she fell ill, she saw a doctor like 20 years, he couldn’t figure out what’s wrong with her, start seeing specialists, they tried all tests, long story short seven months later they had no idea what’s wrong with her. One of the doctors finally said we should try Watson. Right and so Watson came in, looked at all the great work the doctors did, look at her genomic structure, family history all that kind of stuff, asked a couple questions and said she has two rare forms of leukemia that’s the diagnosis. The doctors didn’t believe it but they tested for it it was positive for both. But now that they knew, they could put on a proper treatment plan she started making recovery, but again it never really spiked up from there. And I think it’s just, it’s the truth and trust and technology that we think that people are better at being a doctor than a machine. and we actually have the stats showing that a human doctor has a one out of seven chance of misdiagnosing you whereas AI doctor has a 100,000 chance. And the funny thing is people say I get the human, we’re only right, we’re only human but that’s unacceptable for a machine, they think it should be perfect. And it’s like there’s no way right? Machines will never be perfect. It’s like saying like the failure rate of an airplane should be zero percent. No there’s acceptable failure rate, it’s really small number but there is one yeah but on the flip side we’ve been able to do things like using like IOT, we can read like muscle tendon motion and allow a person to control a robotic arm right, we know the brain can still send signals we don’t have to be like Elon Musk can put chips in our brain and decode that we use an AI to decode muscle motion and allow you to control a robotic arm. that way there’s now — we’re destroying mobility so on one hand kind of stuck in neutral other hand some great advancement.

Jeffery:
And I guess that’s all gonna be dependent on the entrepreneur and how well they understand the systems being a first time or second time founder. How much they’ve learned in that process of building a company to know where to get the data, where they’re actually the problem, they’re trying to solve and a lot of the time and I’m going to say this is probably a business, not business acumen, but it’s the space that you live in that the problem that’s going to be solved more in the health care space is usually going to be a doctor or somebody that’s really heavily embedded in that the problem is they’re a doctor. So how often are they going to be out of a job and deciding hey you know what I saw this problem when I was a doctor I should solve it. So you’re not getting enough of that information coming back to an entrepreneur. I saw this great article or it was Linkedin post, and it was a lady that was in healthcare for 20 30 years, she was a doctor and she stepped out and decided to become an entrepreneur and build a successful health care practitioner business. And when you looked at it you would not have thought how was that possible but she saw a bigger problem than what she could solve when she was just a doctor. So if you take all of these other areas in the world in business, a lot of the lacking innovation comes because the people that are working in the space are comfortable happy and don’t need the change and you’re not getting in anybody that’s young and that spent 10 years to become a lawyer, spent 10 years to become a practitioner to decide you know what I don’t want that life I want to be a tech entrepreneur. So it’s these Watson systems have to collect that data turn it into something of value because you’re not seeing the problem unless you step out of the environment you’re in.

Neil:
Oh you’re 100 right. I hate to say it that way. Look how many technologists you know understand the daily challenges of a doctor, a nurse, a lawyer, or even an accountant right. It’s with AI – it’s the marriage between the technology, expertise, and the domain expertise. The most successful AI companies honestly weren’t started by a super smart technologist right. They had some combination of the technology, the domain expert, or they had guys that
actually were doctors or they actually were lawyers.

Jeffery:
Well you brought them in so they become advisors or they become part of the business but at the end of the day they’re still that practitioner, they’re still the doctor, they’re still doing what they were trained to do

Neil:
So not not always. Like I’ll give you, I’ll give you a legal example, Jeffrey that there are actually three lawyers that I met about four years ago. Now they’re just like we were talking about something else and they’re like what’s the deal with AI, they like all this stuff about AI and so we started talking about like we should do something, so we kind of talked through it during lunch they came up with an idea and they formed a company called legal nation. So they built essentially an AI associated lawyer. And they’re not technologists they hired really smart people to help them do that but they thought this would be a little side thing they wound up selling off their practice and devote themselves full time to their startup because they just saw a better opportunity. They got great traction you know help them get Walmart and target all these big companies as clients. And say like this is way more fun, and it’s way more lucrative but they’re like I actually feel like i’m changing my industry right that’s what they’re like you know it’s not just me trying to win a case now, it’s like me trying to help a whole bunch of people with their legal needs.

Jeffery:
Yep. Oh that’s brilliant and we need more of that too right but I think that’s again it’s a shift, it’s tougher. But I think that AI or the machine systems that are pulling in both sides of the information human and data combining it together you’re going to get way faster outcomes and more people shifting the way they work, so speaking of that how does that shift the way the work world is actually embracing ai, have you seen that this is scaring people or you know it’s creating new jobs, new opportunities, is there going to be that desolate 10 years from now where there’s nobody working and the computers are taking over everything and my next girlfriend is going to be a robot like is that kind of the mentality we’re going after?

Neil:
I can assure everybody that the machines are not taking over, we’ll have plenty of work to do the goal was never to replace people but essentially offload some of the admin grunt work supplements. So that we can focus on the things we’re good at the creativity, the imagination, the first of a kind stuff. You really can’t teach a machine so believe me, I get it, I would tell you that you know even as recent as probably about 2014 we were getting tons of death threats, people thought we were going to put all people out of work but something happened about two three years ago there were kind of this inflection point where people they were like you know what I can be that the passenger and freak out about what people are doing or I can be the driver, I can try and help shape things whether it’s for my company, my community, you know somebody I work for even and so now I don’t hear the question so much about well AI take my jobs, I get the question all the time what should I be doing with AI? How do I figure that out?

Jeffrey:
I like it and you’re right that’s really should be the question right, how do I work with it not against it? And it’s same thing how do I get my data into watson and work with the system instead of preventing it so that I can’t grow and nobody else can understand how to grow in this space either.

Neil:
This stuff’s not going to disappear right, Jeffrey. So you know it’s like, again, like it’s a tool some people think of it like AI’s like a hammer, you could use it to create something or use it to destroy something. The choice is on us as people.

Jeffery:
Well said I like that .I so now you’ve kind of progressed into AI for good and it’s a great little term I guess or a huge term because based on where you are we just talking about how people are afraid of AI and now you’re like no no is this for good man so maybe tell us a little bit more about AI for good and how that’s changing the way people are lining up to AI

Neil:
I’m a big proponent about trying to leave the world at least as good as if not better than I found it. And so when I was asked to speak in front of the u.n five years ago I was warned that like most of the world leaders think, it’s going to take over the world and eradicate humanity you know terminator time, I’m like okay. So I went up, I talked a little bit about what AI is, but I showcased a lot of examples where I was using for public service and that could be applied to some of the sustainable development goals and my speech went up being pretty well received. And that night at the reception I was approached by the secretary general and two other people and like Neil you kind of opened our eyes, up we never actually thought about using AI for public service or for public good right we just don’t have something to be afraid of like there’s a lot of momentum of the member nations can we figure out something to do and you know working with the the you know the secretary of staff we came up and said we can use AI to help bridge the gap on sustainable development goals. We help people that are homeless, that are hungry, you know protect life and fight climate change we decided to dub this ai for good. Because you know back then everyone was thinking terminator time and that really seemed to spark, we ran off trying to create some awareness put together a summit very quickly it was very well received but today we have over 116 active projects going on. We have a huge network and ecosystem of partners that volunteering time and resources got people coming in with you know project ideas, got solutions out there, and health care, financial services you know upskilling people for jobs I mean it’s been a real game changer.

Jeffery:
That’s brilliant. I got introduced to AI for a good probably around the same time that you were pushing it out because it did get some I guess world attraction which I thought was pretty amazing and a couple years later in Whitby which is Durham region outside of Toronto, one of the guy Isaac one of a great, great entrepreneur, he created a hackathon weekend AI for good and we’ve been supporting that except for this year due to COVID we couldn’t run it, but four years in a row and it was trying to find ways to use ai to help people. So they were doing medical, health care, like different aspects of it and it was just a weekend. People cram in there for 36 hours build out a cool little application but it’s following off exactly what you’re saying right, it was how do we do something with this ai that will uh better humanity and better the way we work with products and some of the stuff that came out of it was pretty phenomenal and it’s pretty exciting so kudos for starting that I think it’s brilliant and it’s helped a lot of people.

Neil:
Well, thank you. That was the goal. I’m glad you’re your friend is doing that and I encourage everyone just there’s one small thing I was willing to do take a look and find it yup now you solved a big problem I which is really cool

Jeffery:
So how do you find that now in the the better part of the last five years since ai for goods come out and 160 projects which is phenomenal how have you found that has shifted the entrepreneur’s side of things do you find that entrepreneurs are really attacking this totally different than they were the previous five years even when you were an entrepreneur have you seen that the whole landscape has changed and that it’s nobody’s looking at service, no one’s looking at anything they’re just like hey I gotta find some of those ai or how do you see now what does it look like to you

Neil:
It’s actually changed the way we perceive data. I think the unique thing here is we talk about the importance of diversity and inclusion and different perspectives, now we have essentially global participation. People are looking at things differently and sharing their perspectives we’ve been able to figure out things that like looking at satellite images we can now predict where poverty will occur all right and it’s just the different mindset and that okay you think okay i’m looking at these pictures and stuff but you get people from different parts of the world you look at parts in like central Asia, parts of Africa, where they’re like well you’ll notice the places where people have money like they don’t have lights. Right and they’re like you know that’s it’s a sign like okay but that just tells us where probably the empire was starting like but if you look like we were looking at the data and you can see places where lights get shinier so to speak and lights get dimmer and you know we started crunching the numbers you know the machines look at the pattern saying like where the lights get dimmer is areas where people start becoming impoverished. Right it might be a true political thing it might be or might be something else you actually see this trend over time so that months in advance you can start realizing oh my god this place is becoming a trouble spot economically. What can we do and so it’s that I think unique perspective everyone’s bringing the table even though we all kind of have the same data now we’re actually really tapping into the value from it really unlocking that thanks to these different mindsets.

Jeffery:
No, that’s brilliant. There was and again we all read so much stuff I don’t I couldn’t tell you pinpoint where this data came from in my head, but there was a great article or podcast or a book or something. They basically said if we wanted to understand how a country was doing if we could access their energy grid we could tell you where that country was now and going to be in six months. and the reason being is that the amount of power they’re diverting to warehousing, to manufacturing plants, we could actually tell you when they’re going to go into a recession. And this would actually be better data, better information, than the produced GDP numbers that would be coming out and we’d actually be able to predict what was going to happen in the next five years amongst all countries. And the one big thing that we can’t have access to is that no one will share that data because of those reasons.

Neil:
That’s the problem (inaudible) it’s the new oil everyone wants to try and monetize. We have a big edition in the United Nations trying to create a data repository where people give data away right for for this type of use, particularly healthcare data. Because we know that if we can aggregate that together we can accelerate things around medicine. I mean a lot of people ask me like with COVID, how come we’re not tapping more into AI and it’s like if we actually had more data we could solve some of these things faster. Yeah nobody wants to share right I get the rates for the vaccine, the monetization, but it’s like you probably could have cut all this stuff down and you combine AI with general design we can actually predict the mutations of viruses, we could predict viruses we’ve never seen before, because we know the genomic structure. We could actually start creating vaccines for viruses that don’t exist yet I mean imagine being able to prevent epidemics let alone pandemics Jeffrey.

Jeffery:
It would be pretty amazing and just have a database sitting there and be like okay someone tried to release this one oh that matches this – done. Throw it in – done. That was so easy. Yeah I agree that there’s a lot of areas that could be really explored if people were more open to sharing content data and information. I get there’s privacy but I think at the end of the day it’s more of people trying to protect their domain. and a lot of that domain can actually benefit and help others.

Neil:
Like I agree but you think about it unfortunately everyone wants to be a data company now. Right yep people, likely facebook is not really a social media company they’re a data company, google they’re not like a search engine, they’re a data company, and everyone knows there’s probably some value if I can’t find it so I’m not just going to give it away and we wind up hurting ourselves in the long run because of that —-

Jeffery:
For sure and this kind of goes into my my next question which was now you have this carbon footprint or carbon offsets, so do you find that AI might be actually tricking the system in some instances for big business to be able to find loopholes and ways to offset their initiatives to be able to hit the 2025 changes that they need to make because they can offset their carbon footprint by buying over here but using AI to find loopholes in these areas so they’re going to be able to offset all these carbon tokens and be able to win at the race. I’m just curious as to how you think that’s all going to shift because right now we’re in a serious part where the world’s trying to adjust and fix the climate and to fix all these problems and everybody’s coming out saying well we’re going to reduce all of our carbon by this year because we’re going to offset it by buying over here but are they really solving a problem? Or are they just creating a worse one?

Neil:
They’re not. I mean you’ve got to be committed to an actual solution. I haven’t heard of anyone using AI to try and find loopholes, but there’s a lot of work I know going on in terms of arbitrage. they’re looking for those opportunities, it’s usually with the stock market but the carbon offset market works very similarly.

Jeffery:
Yep. Well that’s what I meant by loopholes which is yeah I i think that a better word of saying it but it’s still loophole —

Neil:
You know, you’re absolutely right. And i’m sure there’s unfortunately some companies that are paying lip service to what they want to do and they’re doing exactly that but I actually think that problem will get solved ironically because of people. Because especially like generation z, they’re very passionate about social impact making positive change, they don’t want to work for a company that plays lip service to these things. I’ve actually seen it they don’t care so much about the salary or the position, they want to go somewhere where they the values are the same and they’ll feel like they make a difference and so companies that continue to do that they’re not going to attract the top talent, right. They’re not going to become the best say the best companies anymore, so it’s in everyone’s interest for them to actually just try and do it right. And if you’re gonna invest all that time and money looking for loopholes with AI, why not then tap ai and help find those opportunities where you can actually reduce your own carbon emissions.

Jeffery:
Agreed. Well it is only a discussion. So I won’t say that I know any companies doing it but in my mind I think hey if people have got the tools they’re going to find loopholes and it’s terrible but I know the last one I read was on amazon and how they were trying to find ways and they were coming out saying they were going to be 100% carbon neutral. You’re like how? You guys deliver all over the world massive warehouses is that even possible? Well we’re going to offset over here, well that offset probably is going to cost you way more money not you but somebody else so you’re really fixing the problem. So but I do agree on the one aspect is that uh the younger generations coming in are looking to make sure that they’re working for a business that actually is taking care of them.

Neil:
Yep. People are gonna help solve this problem

Jeffery:
Very good. So I know we’re gonna, and I just love talking about all these cool things you’re up to so the other question that I had that was I guess fits into this whole realm of angel and early stage which is all the things we’re kind of focusing on here which was that you’re a master inventor. And you define what that means because I just thought that was the cool tag I would just tell everybody that’s it and I would walk away (inaudible) drop the mic

Neil:
Well there’s only about 300 master inventors in IBM. And they’re essentially people that have created very revolutionary or maybe evolutionary technology essentially something that has a global impact and was generating billions of dollars for the company so because of my work in artificial intelligence and all that I was actually named an iIBM master inventor.

Jeffery:
Sick. Amazing that’s like getting sir in front of me, or being knighted like that to me feels like the same thing.

Neil:
So it’s a great, it’s a great honor. I was very humbled

Jeffery:
Yeah. It’s amazing man, I’m actually not going to call you Neil anymore, I’m just going to call you master, like master yoda, master Sahota. There, done. Just put the right term in – you’re good. Oh that’s brilliant. Well, so I guess the we’re going to transition we got to talk about startups, we got to get more into that space so now that you’ve kind of gone through this great journey in your career where does this put together the startup side of things? You said you worked as a consultant, you’ve been working with startups along the way so where does this kind of put you now? What is your favorite thing to do and what have you been really focused on in the last couple years and how does that work in the entrepreneurial space?

Neil:
My role as an angel investor as well as you know being part of five VC funds either as an investor or advisor or both it’s kind of helped unleash some of the potential out there because a lot of the early investing in emerging technology was more hype than reality. and you know like like blockchain was a great example where people I’m doing blockchain and you hear them talk it’s like I don’t think you even know what blockchain means but there’s so much money getting thrown at it and then the problem becomes is well all these guys all these VCs lost money and so they get soured on the whole area they don’t actually pursue real opportunities. So I’ve really been working to try and bridge that gap again and say look there’s actually a lot of untapped potential out there you just need to understand how to find it right. Don’t fall for the buzzword, it’s not rocket science, again it’s yeah are they solving a problem or value proposition and fulfill needs but it’s what they’re talking about is it actually feasible. That as you were talking about earlier starts a lot with data starts with the team itself and you have the subject matter experts.

Jeffery:
And I think you carry a lot of background in this so just like most people say that doesn’t sound like AI that sounds more like a matching data play or that sounds more like what’s the other one that’s more common when it comes to AI — predictive modeling when it’s not really ai. It’s not using the ai engine to the algorithm extent of what you’re trying to do and building up enough data stream to start breaking that down so I guess you can probably jump into a lot of that right away and understand it quickly and and even help maybe structure a company so that it would be using AI more properly in that instance.

Neil:
100 % that I think a lot of people struggle with. They think it’s a lot of if then statements — and it’s like nits not you do not program an AI like that there’s actually very little programming the AI figures stuff out on its own, you just give it some rules on how to make decisions called the ground truth they give it lots of data to learn from right it’s going to wire its own algorithms. That’s the way real ai works and it’s tough for people to get over that hump but people that actually do that’s how they’re unlocking insights. There’s a reason why AI can answer questions we don’t know the answer to because it’s actually learning like we learn.

Jeffery:
But you have to start it somewhere. So I guess the good thing is that if they can look for master Sahota they’ve got somewhere to go there. I think that’s brilliant!

Neil:
100% and look if people are skeptical there’s a bunch of artists using AI right now. Right totally non-techies create all new forms of music. If they can create that, if they can do that, then entrepreneurs only can change the world

Jeffery:
Oh wow that’s pretty cool. I like it. All right well now we’re going to jump into the rapid fire questions because I think we could talk for hours on AI, and data, and structure of companies because I’m a big fan and Ireally enjoy that conversation but for the audience, we really got to tap into some of these investor questions. So we’ll do those quickly and then we got a couple more questions for you but all right to start, so what’s your favorite part of investing?

Neil:
It’s seeing the ideas the entrepreneurs bring. So it’s so good to see how they want to disrupt and change the game

Jeffery:
I love it. How many companies do you invest in per year?

Neil:
About 10, about 10 a year.

Jeffery:
Okay, brilliant. You’re a rock star. I like it. Any verticals you like to focus on?

Neil:
No, no real verticals. It’s just more of a great idea, a great team, I mean I even you know listen to a pitch about sustainable apple cider. so it’s not so much the industry it’s are you solving a problem and you have the right team to do it.

Jeffery:
Yup I like that. Apple cider is good. I do a shot every morning. Do you any due diligence requirements that you look for when you’re diving into these companies?

Neil:
Yeah a lot of the standard stuff around financials, market size opportunity, but at the end of the day one of the biggest things for me is really the strength of the management team. Are they capable and if they have gaps, is there a way to fill those gaps or not.

Jeffery:
And do you have a timeline on investments? Is it one week, three weeks, a month, two months?

Neil:
Depends on the size and complexity of the investment. If you’re someone’s talking like we’re raising a hundred grand, you’re not going to spend a month doing due diligence. It’s probably a couple of hours of conversations and review to do something like that. If you’re coming in saying we’re looking for 10 million then it’s going to be a little bit more fine-toothed comb process.

Jeffery:
That is true. Outside of DD and you mentioned a couple right right now which is your into the team, is there any things that you look for outside of the paperwork, outside of just putting money in, what’s the thing you go for?

Neil:
Aside from the team, if they have customers or potential customers I love to talk to them and see why they think about it because I’ve seen too many startups where they think they know the customer but they never actually talk to them. So talking to the customer helps me understand are you fulfilling an unmet need.

Jeffery:
I like it uh do you lead rounds?

Neil:
Sometimes depends on the route.

Jeffery:
Okay any preferred terms? Pref shares, common safes?

Neil:
Again it just depends on the opportunity. I mean flexibility is the most important thing it depends what stage the startup’s in.

Jeffery:
Okay, I heard you like Canadian companies, is that true?

Neil:
Canadian companies are awesome.

Jeffery:
Making sure the audience knows that he likes all companies in the world but he does like Canadian companies. Do you take board seats?

Neil:
Sometimes I do. If I feel I can actually contribute value then absolutely. Otherwise I’m happy to be an investor or sometimes just being an advisor.

Jeffery:
Okay and on the investments you do make, do you do follow-ups?

Neil:
Sometimes. So there’s actually two funds I’m part of that’s actually our strategy. We keep betting on winners so as companies keep making milestones and get more traction we keep reinvesting every round.

Jeffery:
okay that’s awesome. What other ways to help startups outside of financials?

Neil:
Lots. I mean we try and plug in halls the management team at least temporarily like if they don’t have a finance guy provide that. Tap into our networks for potential clients or street partners like you know. We had a toy company where we brought some of our manufacturer resources in to lower their production costs. So at the end of the day the philosophy is for me to be successful I have to help you make you successful. That means revenue, that means strategic partnerships, it means cost management, the whole ball wax.

Jeffery: I like that line. Your bang on. If you want to, if you want to succeed in the world you got to find ways to make other people more money than you make.

Neil:
That’s right.

Jeffery:
Any companies that you have in mind that you want to share that you openly want to say this company’s doing awesome, big fan?

Neil:
There’s two. One I mentioned, legalmation which was the AI lawyer one the fantastic traction quickly especially with big companies then what I’ll mention is cyrano.ai. And so it was started by a therapist and a neurolinguist and they’ve basically found a way for ai to decode language. So imagine that you’re talking to someone and the ai will tell you like this is their level of commitment to what they’re talking about this is the way what they care about the most here’s how you speak to them even the words you use right for this person. You need to focus on the feature just for you to focus on the value. This wouldn’t focus on the fun but I look at it as like you know even with your significant other who I love the most the world we still have arguments and I just have that little ai coach like hey Neil don’t be saying that to her right she doesn’t — that’s not going to resonate which that’s not what she cares about. And don’t use these words, use these words instead. Just to improve the ability to speak to one another, speak to another person’s language I think that’s just huge. They just launched a zoom plug-in so it’s gotten really good traction on the downloads. Because we live virtually we lose some of the physical cues this has actually been a good bridge to help actually accelerate the building of strong relationships. So cyrano.ai, I see big things for them

Jeffery:
How do you spell it?

Neil:
it’s like Cyrano de Bergerac. So c-y-r-a-n-o

Jeffery:
I like it. All right I’m gonna look into that. It’s interesting you say that because well about a week ago I read this article and it was changing the way that instead of apologizing for the way that something occurred, acknowledged that they accepted you for the issue instead of saying hey i’m sorry I’m late say I appreciate that you were able to be here a few minutes without me, i’m happy to start now. So I started actually trying to change my mindset around that so every time I go to type or say something I shift from saying I apologize because or I’m sorry because I feel like one why am I always doing that if I’m the one always late or I’m the only one whatever the reason is so I try my best to say unless it’s obviously right off the cuff but how do I shift my dialogue to enhance it without using an excuse but using something to enhance it so it takes the guard down and gets that person more interested quickly by doing something funny or saying something interesting. And I can say that it actually works I feel less bad about any of the circumstances i’m in because I’m not always coming out saying the same thing every time right.

Neil:
There we go, proof positive from Jeffrey.

Jeffery:
I like it. All right cool. So the next question we have outside the rapid fire questions you did great, yeah no complaints everybody’s gonna be happy with those, the one question that we do have is in all the years you’ve been doing this you’ve gotta come across some sort of business that has just blown your mind away from they were on the verge of bankruptcy or going to fail and then they just did something, COVID hit and they took off and life was grand or reverse anything like that, any kind of heart string stories that you got that you can share because I always find those little tiny motivation stories are the best.

Neil:
Tons of stories like that. But let’s definitely share a positive one where we saw this company come in and pitch and they came and said like we’re like snapchat, they were talking about like these localized filters based on your geolocation information, and you know the guy told us he had done this because of his 12 year old daughter, and you know there were some health issues with her, but she was really into taking pictures is why I got into it. Honestly we passed on them right we’re just like because we the q and a session like you’re going to come in against snapchat and all these things and you have patents and maybe something I give credit to the CEO right because he was like I’m very serious about this he came back after we declined him and said look can I just I would love to talk to someone understand what we’re doing wrong and what do we need to do to be on the right track because I really want to make this company successful. and they’d ask would someone provide any input and I said look i’ll do it, I was one of the most vocal people during the Q&A, I’m happy to do that. And so I sat down with him and his management team, and they cut they started just walking like just walk me through what you guys are doing right I told you here’s the feedback walk me through you’re doing so I understand, I spent two and a half hours with them. After at the end, I’m like you guys are not snapchat right you guys have actually found a better camera right, you’re what you’re doing and what you’re messaging are completely out of sync. And so I wound up helping them out over the course of the next three weeks to kind of reconfigure their value propositions and the whole messaging and then brought them back for a pitch session. We decided we would you know not be like oh now I get it I see the value and so we said we’d fund them and we brought two other funds to the table so the guy actually was being oversubscribed for his round.

Jeffery:
Oh it’s amazing.

Neil:
Yeah so then we took them to some of the telecoms we knew, and so he went from thinking like this is kind of a pipe dream to now he’s you know he’s getting unsolicited acquisition offers

Jeffery:
Brilliant. Yeah well nice work. These are the good stories, positive pushes. I like that.

Neil:
Yep.

Jeffery:
Okay so now we’re gonna move to the more personal side and because we skipped over on the first question we’re gonna get it again but there’s a few personal questions just to top this off because I’ve learned through other podcasts and other things I’ve done that in order to connect with someone you got to learn a few little positive things about them that nobody would know. So you still have to answer the one question that nobody would know and then I’ll jump into the next two three questions.

Neil:
One thing that nobody would know, I never talk about it but I can actually make pizza from scratch. Like I make my own dough, I literally take the flour, I make my own dough I have a special technique it has to sit overnight, get my my fresh cheese or sometimes I’ll try to make the cheese myself but I i really love pizza I don’t get a chance to cook very often but not many people know that I can actually make pizza entirely from scratch.

Jeffery:
Brilliant. Well now that I know that next time in California I hope I get a pizza

Neil:
We’ll make you some, Jeffery

Jeffery:
Yes! All right man I like it. That’s brilliant. Okay, second personal question what’s your favorite sports team?

Neil:
The yankees. Man I grew up just a few blocks away from yankee stadium as a kid

Jeffery:
Oh that’s awesome. I was a Yankee fan at one point but I decided that I couldn’t get to a game living in Toronto so much so I decided that this wasn’t really working out for me I couldn’t be a distant fan. So I became a blue jays fan and New York is our rival so we don’t like you. so we go against the yankees, we’ll always get crush, you guys actually we don’t it’s about a 50 50 split but yankees are a pretty good team.

Neil:
You know they’re predicting the blue jays to win a division next year.

Jeffery:
Oh yeah?

Neil:
Seriously.

Jeffery:
I like it. We have some pretty big sluggers on the team. I think we’ve got a young team that can really pull it through. It’s gonna take about a year, two maybe three before they really peak out but from our old team to really you know getting a lot younger it’s made us a lot stronger.

Neil:
So I think a good pitcher and probably a veteran infielder or maybe outfielder and I think you guys are pretty much there.

Jeffery:
Agreed. Yeah Yankees will always have a good team because they always have to pay background to make sure that they can buy who they need to to run the team. But in the last i’d say the last five, six years they’ve really become more of a dynamic team they’re not just paying players to come they’re actually putting players in that fit the role to work as a team. And I think that’s what’s made the Yankees even better of a business.

Neil:
Well they’re tapping into the data, they’re using sabermetrics to do a degree even for team chemistry. So I gotta tip my hat to cash moon he’s doing a fantastic job.

Jeffery:
Agreed. Yeah. No I i totally agree, sometimes I waver when I’m watching both play and I’m like wait I used to be a fan I can’t — blue Jason. Second question what is your favorite movie and what character would you be in that movie?

Neil:
My favorite movie is actually Yojimbo. It’s a japanese movie by Akira Kurosawa.

Jeffery:
Okay.

Neil:
And I would definitely be the nameless samurai because I always feel like everyone’s got their kind of worrying nature and trying to get their stuff and you know I’m always seem to try and kind of bridge the gap. Not just play peacemaker but say hey let’s find the consensus, let’s expand the pie kind of thing you know

Jeffery:
I like it and it’s called Jimbo?

Neil:
Yojimbo.

Jeffery:
Yojimbo. I’m looking this up see if I can watch that. A big fan of a lot of TIFF. We go to TIFF every ear watch a lot of the stuffs. I’ve seen ton of Japanese film makers so big fans so I’m gonna look this way. Yeah that’s the reason why i’m asking the film question it’s also so I can find new ones. And I’ve had some good some good films too. Some shockers too I was like really that’s your favorite film but you know what everybody’s going to have their own so that’s good I’m going to look that one up And I’m going to look up the uh nameless samurai that’s very cool – awesome. Well Neil, or Master Sahota, I had a really good time chatting with you today. Honestly I think that you’re doing a lot of great things. AI for good has been a huge initiative, always exciting to hear that you’re chatting with the UN and getting people more aligned to your initiatives and the things that you guys are trying to solve. We’re big fans. So I’m glad that you had the time today to chat and the way we like to kind of end things off is that we like to give you the last word. Anything you want to say to an investor or to entrepreneurs, the platform’s yours and yeah please share away.

Neil:
I will tell everyone the best advice I can give you is always trying to be uber yourself before you get code act. Meaning always try and disrupt yourself before someone disrupts you. because someone will try and do that one day so you might as well be the one.

Jeffery:
i like that, I’ll write that one down too. Well you’re a good man, thank you very much as I always do took lots of notes. i’m going to love to have you back in some time but I appreciate all the insights provided. Brilliant and keep up the great work

Neil:
That sounds awesome Jeffrey, I had a blast. Would love to come back anytime.

Jeffery:
Awesome thank you very much for all that. Well that was brilliant! Fantastic Neil Sahota or as we were referring to Master Sahota. And man amazing only 300 people are designated with this master nventor so that’s pretty sick and I think overall Neil brought a lot of great things to share about AI and how the structure works and of course their old side of things on how they make investments. Really cool 160 projects on the go right now working with the UN and everything that AI for good. That’s amazing, absolutely awesome either way. He did a great job! It was a great chat and thank you everybody for joining us and have a fabulous week.

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