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