WEBVTT
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So today I was fortunate enough to interview someone who's a founder, chairman, and investor in businesses that have exited for over£7 billion over the last decade.
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Mel Morris CBE was on stage and we had the opportunity to talk about his career, the number of innovative things he's doing, and also his new corpora platform where he's looking to develop new scientific discoveries through AI.
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It was an incredible opportunity to hear from Mel.
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It's a pleasure to have you here, Mel.
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You may not know that Mel has chaired, founded, and invested in businesses that have exited at over seven billion dollars worth of businesses.
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So he is an incredible person to have here with us today.
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I've been a customer of at least one of your businesses.
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So throughout the afternoon, let's figure out see if we can figure out which one it is.
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Did you buy a cybersecurity product then, David?
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No, no, no, no, no.
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But didn't you have a dating business?
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Yeah, well, let's not go there.
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You could be embarrassed by that.
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It's not that one.
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Just to be clear, but it's not that one.
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So look, I'm going to start out, if I may, by just asking the simplest of questions, just to explain your entrepreneurial journey and how you've got here, if that's okay.
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Yeah, I don't want to bore you because it's spanning now close on six decades.
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Not six full decades, but six actual decades.
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Historically, anyway.
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My journey was really quite interesting because I wanted to work with computers from day one, right from leaving school.
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And that was really difficult because I was only 16 at the time.
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And I went to look for a position to start writing computer software.
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That's what I wanted to do.
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But there was a job in computer operations which I was going to look at, and I couldn't do that either because I wasn't 18.
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I couldn't work shifts.
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So I ended up decalating output from a printer and then delivering around the offices.
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And after about three minutes, probably three months, but somewhere between there, I got extremely bored with that.
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And one of the women who worked there said, Why are you so miserable?
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I said, I want to write computer software.
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My husband is the computer manager for EW Bliss, part of Gulf from Western.
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Why don't you go and see him?
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So I said, I'd love to.
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So I go and see him, a gentleman called Tony Bohr, and he said to me, Well, why don't you just take a week's holiday and come and see how you get on?
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We'll teach you and see how you see how you cope.
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Sure.
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Only one problem.
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I've got no holiday left.
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They said, Well, you know, can't you take a week?
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I said, I won't take a week off sick, I'm not sick.
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I tell what I'll do, I'll basically have my noses in, I'll come to you for the week.
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I know if I don't shape up, there's no job, but I sooner do it that way.
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I was offered the job three days in.
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I ended up with a guy who was a computer science graduate, which was very rare in those days, and he was a real superstar at writing software.
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But he wanted to be lazy and he wanted to teach me as fast as he could so he could put his feet up and I could do all the work.
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And I loved it.
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Absolutely loved it.
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It was such a great mentor.
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And I realized from that age I could write software.
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And what I found with software is it's a great challenge of coordination, correlation, planning, doing all those things, ideation.
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And I had a knack of writing software that was extremely efficient, very small and compact, and that set me on a journey that put me through all of my businesses.
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One of those companies ended up going from software into hardware, or at least a blend of the two.
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So I got into hardware design and we're producing products that basically was what the first ever intelligent caching disc array, which ended up selling for over$100 million worth of product very quickly.
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So I moved to the States to work for Wang Laboratories, had the pleasure of working directly for Dr.
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Ann Wang over there, who was a fantastic inventor.
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I mean, this guy was a refugee from Shanghai.
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He was the person who created the patent of hardcore memory, which put IBM into the computer space.
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With the money he got from that, he formed Wang Laboratories.
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And he later suffered from cancer of the esophagus, and he was off work for about 18 months.
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Came in one day, and I'm upstairs in this meeting, and we're debugging a problem with a computer system.
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And the people in the room, most hadn't really worked with him.
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But he's asking, What problem?
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What problem?
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So one guy says, Don't worry, doctor, we'll figure it out.
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He says, What problem?
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I said, excuse me.
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Please tell him what the situation is.
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So he walks around the room with all these circuit diagrams, never seen them before in his life.
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He walks around, he gets his cane out, he slaps it down, goes, problem here.
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Fetch board and soldering iron.
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And he figured what this minute problem was that people have been working on for months.
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And I just thought, wow.
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And that set me on the path.
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It said, no matter how good you become, you're never going to be as good as him.
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And that inspired me to do all sorts of things in pursuit of trying to at least be better than I was.
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I worked for Wang doing computer tuning, going down the biggest customers, helping them get those, and then built a business doing that.
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After that was a business called Minitech.
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Then that then became ProMetrics, doing PC software, and then that got bought out by Platinum.
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I became New Product Development Manager.
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That sold.
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I then went to doing New Date.
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After that, I got involved in four other companies, one in social media listening, one in Google Advertising and those sorts of markets.
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One in terms of intelligent sensors, one in terms of today, which is Corpora.
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And so many businesses en route to that.
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In UD, two of the people who worked for me there, one was a guy called Toby Rowland, one was a guy called Ricardo Zacconi.
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Became close friends to both.
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They wanted to buy this small star to be in Sweden doing computer games.
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Computer games.
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I didn't join them.
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They said, Would you be chairman?
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I said, Yeah, would you help us raise money?
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Yes.
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We scoured Europe looking for funders to back what became king.
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No takers.
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For several years, no-takers.
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Until one point, they're about out of cash and they said, What are we going to do?
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I said, I suppose I've better write a check.
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So I wrote a check to Keith Monday, and I stayed as the chairman of that company until it went public on the MISE in 2014.
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And Candy Crush was about probably 10 years into the creation of that business.
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So, but we knew it was going to be a success from day one.
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Lots of things to talk about at some other time.
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But that was the journey.
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And but the King story isn't about me.
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I provide the finance, I chaired it.
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It was about the team that worked in that business.
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Ricardo was a fantastic CEO.
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Sebastian Knutzen was the head of design for what went on.
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I just had the pleasure of working with a great team and helping backing them in any way I could.
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Wow, that is quite a journey, isn't it?
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And um, you know, there's some really interesting things to pick out there.
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I mean, the giving up your job on the basis of hoping that you're gonna get it's gonna work after three.
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I mean, that is really bold and brave.
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Um, and I love the humility, you know, the bringing together the people, it is fantastic.
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Um Candy Crush might be the one where I'm a customer, I've got to be honest.
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Um I'm not sure I did.
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It's too late now, I'm afraid.
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Um, but actually, interestingly, it was known as an incredibly innovative business, and I think it'd be interesting if you if it's okay to perhaps bring out why it was so innovative.
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Yeah, um, I mean the game was important, but it's not, in my opinion, the major part of that story.
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The game was important because it was a game that could be played by anyone pretty much of any age.
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So it was ubiquitous in its in its market opportunity.
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But but the secret, in my opinion, was again coming back to Ricardo and the development team, and and we'd figured out that we were doing computer games that were effectively tournament games.
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So you could sign up to play online pool against other people, and it would be a tournament.
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So lots of people would enter the game, sometimes two, three, four thousand people.
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They'd all pay a few euros to play to get to one prize that one person would win the tournament, and that one person would probably walk away with the check for three, four thousand euros.
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But maybe three thousand people played that game to get to that point.
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How many people wanted to talk about it after the tournament had finished, do you think?
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Do you think it was 3,000 or do you think it was one person who walked away with the check?
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And and we figured out that you can't scale.
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You can't scale that.
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You can't scale that.
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There's no viral link to that.
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Because 2,990 people are absolutely fed up because they're wasted their money.
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Particularly those that got closest to the finish line.
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So we the business model was fantastic, it moved from being a tournament.
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Think of it as a cup.
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Those of you into soccer or rugby will know on a cup, it's a quick death.
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You you go in a round, you lose, you don't go to the next round.
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So on a cup, there's one winner.
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But you think about the Premier League, and it's a league.
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You come number two, it's a pretty good place to be.
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Actually, if you come number 19, it's a pretty good place to be.
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Even better if you're 16.
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So it's one of those things where we wanted a league.
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And then we looked at companies like FIFA, who had the brilliant model, which was well, you want to play FIFA, which one are you on?
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I'm on 2026, so I've only got 2023 still, it's not as good.
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It doesn't have all the new players, the new teams in, and so on and so forth.
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So we wanted a league, but we wanted a perennial game.
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So, what do you call a perennial game?
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You call it a saga.
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So can you crush became a saga?
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It's never going to end.
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It's going to have more levels than you could ever count.
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And where does the league come from?
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It's pretty easy, it's going to be your friends.
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And because everyone can play it, people will begin taunting one another on what level they've got to.
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So the model was you can play the game, two, three, four lives.
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When you run out of lives, you can sit and wait for an eternity while your friends are getting ahead, or you can pay, or you can refer n number of your friends to get more lives.
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It was the ultimate expression of a viral game.
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The business model made the game, it made the company.
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And at peak, we almost got to ten million dollars per day in revenue.
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But only around three or four percent of people ever paid.
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David didn't.
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It's disgusting, really.
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I think my kids did.
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So so it was the game had to be something that anyone could play, from grandparents right the way down to grandkids.
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They could taunt each other.
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Where are you at, grandma?
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Oh, yeah, how many of this?
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Okay, I'm at 4,022.
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And and the frustration of having to wait got so bad that I know David actually got his credit card out on several occasions to get ahead.
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All right, and that's what happened, and the rest was history.
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Is that a good answer?
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It it's an amazing answer, isn't it?
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I mean, you think about some of the things that have happened in the last decade, you know, the Roblox and the Fortnites.
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Um, you started all of that in terms of them thinking about it as an ecosystem, thinking about it as member get member, community, uh, the freemium model, getting people hooked involved, sorry, and then and then figuring out how to, you know, it really was so innovative, um, which is incredible.
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And kind of over this last decade, we've seen a lot of changes with the tech, and you know, I can't do a full sentence without somebody mentioning AI to me at the moment.
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Um, it's very difficult to get through.
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I'm just interested in your perspective in terms of AI, the challenges, the opportunities, and where you see that going.
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Okay, just a quick show of hands.
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Has anyone ever heard of a paper called Attention is Everything?
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Okay, all right.
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So that paper, and you should answer that.
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If you if he can't, he should be fired.
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Okay.
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Otherwise, you're you're a complete facade, otherwise.
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So that paper was was authored by about four or five people in Google at the time.
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Uh, one of the guys, a guy called Leon Jones, uh, younger cunz, a couple of others as well.
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And that was the paper that basically figured out transformers.
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I don't mean the things, the robots that turn into whatever cars and things.
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It was the transformers that became the model of how we can actually predict the next word in sequence of a body of text, of a, of a, of a topic.
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And we saw that paper and we looked at it, thought, wow, this is really interesting.
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We went on a different path.
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But that paper was absolutely the starting point for today's incarnation of the tools we see today, ChatGPT.
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AI, its origins go way before that, but that was the trigger point.
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Then ChatGPT, October, November 2023, you know, they they launched.
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I think they're launched in some ways too soon, but it's typical of American companies.
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Let's grab the market.
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It may not be fully baked, it may not be quite ready, but we'll get it out there, we'll get a footprint.
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We put loads of money about promoting it, and by gum, did it take off?
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Absolutely took off.
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I mean, even Eclipse candy crushes growth in people terms, and and they were able then to get their valuation up, which let them borrow more money and away they would go.
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And Google did the typical thing that you see in these things when someone does something that causes, I think it was a half a trillion of value, shifted around the market in the space of a day or so.
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It shifted the market, it changed the whole dynamics of it.
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So Google is saying, hey, our stock price just tanked because these guys have launched this.
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But we've been working on that, we were the founders of this thing.
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What are we going to do?
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We'll rush the market with our offering, and it was an absolute disaster.
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It was an absolute disaster.
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And then Microsoft decided we've got to jump in.
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Then all the rest started to come to market.
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But what happened was, had they not have launched then, we wouldn't be where we are today.
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It was not yet ready, it wasn't done, it didn't matter.
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It showed enough of the potential of what was there.
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And there's a lesson from this for all of you, which is that in Britain, and I'm a big, big, big, guilty person for this, we obsess, we over-engineer, we over-develop, we spend too long trying to get it perfect.
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The Americans don't do that.
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They slap it out in the market, and they'll go through n iterations of it in a space of three months if they have to, but they'll corner that imagination of the market.
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And that's what happened.
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And so from then, the rest is almost history.
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But the acceleration of AI in the last three months eclipses what happened in every point prior to that three months from when ChatGBT was launched.
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And it's off the charts, and we cannot afford to ignore it.
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The implications of it for universities, for everyone in everyday life is huge.
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We've got to rein in the energy demands and all those things, but it's absolutely massive.
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I hope that helps.
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Absolutely.
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I mean it's really interesting when I hear you speak because it's this intersection of kind of customers, business model, the behaviour, the tech.
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You're able to kind of see across all of those different things, and it's that intersection that I think build really good deep tech businesses.
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Um, I'm obviously interested in your latest deep tech venture.
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Um, as I understand it, you've been ingesting all of this scientific information into Copora, and you know, I think it would be amazing if you could just tell us a little bit about that.
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Yeah, so you know, attention is everything.
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That that was the paper.
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That that that changed how I looked at things, it had a massive influence on what we were doing.
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We were on a parallel path looking to basically understand a massive corpus of information, hence the name corpora.
00:16:52.320 --> 00:16:54.559
Corpora being the plural of corpus.
00:16:55.279 --> 00:17:01.039
And when we set out, we wanted to try and use whatever technologies were around to get a head start.
00:17:01.279 --> 00:17:03.679
So we said, like, we want to do this at scale.
00:17:03.840 --> 00:17:06.000
So we said, what is it at scale?
00:17:06.160 --> 00:17:07.519
What do you mean by that?
00:17:07.839 --> 00:17:11.519
And an avi was, well, someone said, well, is that a few terabytes?
00:17:11.599 --> 00:17:13.279
No, no, no, much bigger than that.
00:17:13.440 --> 00:17:16.240
Well, is it is it maybe a is it a petabyte?
00:17:16.400 --> 00:17:17.680
No, much bigger than that.
00:17:17.839 --> 00:17:22.960
We want to be looking at hundreds of petabytes, maybe even an exabyte one day.
00:17:23.200 --> 00:17:31.200
And we want to be able to therefore take that size of corpus and and understand what's in it, correlate it, graph it.
00:17:31.279 --> 00:17:35.519
So we looked at graph databases, Neo4J, DGraft, you name it.
00:17:35.759 --> 00:17:39.759
We looked at vector databases, we looked at PineCone, all these things coming along.
00:17:40.079 --> 00:17:46.640
None of them would scale to anywhere close past probably a few terabytes, and it's very little further forward today.
00:17:46.720 --> 00:17:51.759
So you have the classic challenge of scaling out is the only way you can go.
00:17:52.000 --> 00:18:01.119
It didn't scale up enough, and if you wanted to scale out from a few terabytes to handle hundreds of petabytes, you're talking about thousands of servers.
00:18:01.359 --> 00:18:03.200
It's an absolute nightmare.
00:18:03.359 --> 00:18:07.440
And if it's a graph, you've got to segment your graph across all those things.
00:18:07.519 --> 00:18:08.400
It's a challenge.
00:18:08.640 --> 00:18:14.079
So we decided, as a bunch of lunatics, we are, that we'd actually set out to look at scale up.
00:18:14.400 --> 00:18:26.559
How can we ingest, understand, fully map, correlate, graph, vectorise, do whatever we need to do with a massive corpus, and we have to do it at speed, otherwise we'll spend 20 years trying to do it.
00:18:26.960 --> 00:18:30.799
So we obsessed over the efficiency of our technology.
00:18:31.039 --> 00:18:36.240
Our entire platform today runs on less than 8 kilowatts of power.
00:18:36.720 --> 00:18:41.279
Entire platform, mapping over 200 petabytes of unstructured data.
00:18:41.839 --> 00:18:45.519
And then the trick was then we wanted to compress that.
00:18:46.160 --> 00:18:50.079
Models compressed data, but they don't expand it again.
00:18:50.480 --> 00:18:55.359
They don't have the ability to give you the attribution of where that information came from.
00:18:55.599 --> 00:18:57.759
We wanted a lossless compression.
00:18:58.240 --> 00:19:10.240
So we've got a compression that gets data down to about one and a half to two percent of its size, but we can fully expand it, knowing exactly where it came from, taking it to the exact article, what it says, where it is.
00:19:10.400 --> 00:19:38.559
And then the next piece was condensing that view of that information into a form that's fully graphed, fully vectorized, all those things that you want to do, and be able to answer questions very precisely by being able to look at all of the totality of information around that topic, corralling it, condensing it, and getting it down to what we call the net unique view of information on that question or topic.
00:19:39.119 --> 00:19:41.359
And we do that in milliseconds.
00:19:41.599 --> 00:19:47.599
Once you've got to that, meanwhile, Chat GPT launches and it threw us into absolute turmoil.
00:19:47.839 --> 00:19:50.960
Oh my goodness, this is gonna change the world.
00:19:51.119 --> 00:19:53.759
This is gonna change how people consume information.
00:19:54.000 --> 00:20:01.519
People are gonna think it's all real and they're gonna get running away with, oh, I've got my nice medical diagnosis from Chat GPT, it looks fantastic.
00:20:01.680 --> 00:20:03.519
It says I've got nothing to worry about.
00:20:03.680 --> 00:20:06.880
Meanwhile, it's all really just a facade of what's there.
00:20:07.200 --> 00:20:27.680
So we said, but if we can take our information, the ability to corral the information of a small topic, a really precise set of questions, and we can pull the net unique view together and then decompose it again to use a small model to be able to represent that, we can make a tiny model look like it's five times the size of Opus 4.7.
00:20:28.240 --> 00:20:30.400
And that's what we built, and that's what it does.
00:20:30.480 --> 00:20:34.799
And we've demonstrated to a few people today that wouldn't stop asking questions.
00:20:34.960 --> 00:20:35.920
Am I right, Helen?
00:20:36.559 --> 00:20:37.039
Absolutely.
00:20:37.119 --> 00:20:38.400
They wouldn't stop get tested.
00:20:38.480 --> 00:20:39.359
Well, how about this then?
00:20:39.519 --> 00:20:40.480
Try that with it.
00:20:40.799 --> 00:21:09.279
And so corporate AI now can take you from a simple challenge statement to produce a really elaborate problem definition to doing a fully ideated solution, to validating that solution, to working out a development roadmap to tell you what the staffing should be, what equipment you need, what the use cases are, what the patent application to protect it all should be, and what the pitch deck is for grant funding or for your VCs.
00:21:10.400 --> 00:21:17.279
And it's basically a complete scientific frontier research platform in a box, one-stop shop.
00:21:17.359 --> 00:21:22.160
Now the entire process, I think less than 30 minutes is a good example of it, Andy.
00:21:22.319 --> 00:21:22.400
Yeah.
00:21:22.480 --> 00:21:22.720
Yeah.
00:21:22.880 --> 00:21:23.119
Yeah.
00:21:23.359 --> 00:21:24.000
Wow.
00:21:24.640 --> 00:21:26.079
That's pretty amazing, right?
00:21:26.240 --> 00:21:35.279
So to bring it to life, are there any challenges or questions that you could share that you've asked it and you've got, you know, things that have excited you?
00:21:35.599 --> 00:21:37.200
Oh, loads, loads.
00:21:37.359 --> 00:21:46.240
So on route to this, funny story, en route to this, uh, there's a lovely guy called Daniel Hayes, and I've got to know him really well.
00:21:46.400 --> 00:21:49.119
And he's now met him on a few occasions as well.
00:21:49.200 --> 00:21:55.759
Uh, and he represented this little group called UCR, Universal Collaboration Research.
00:21:56.000 --> 00:21:59.759
And we weren't ready, the platform wasn't launched, even in its prior incarnation.
00:22:00.319 --> 00:22:02.640
And Daniel said, we'd really love to use your platform.
00:22:03.119 --> 00:22:04.160
We've heard what you're doing.
00:22:04.319 --> 00:22:10.400
We'd like to use it to try and see what we what the patents are in this space, what the intellectual property is.
00:22:10.640 --> 00:22:11.599
And I said, We're not ready.
00:22:11.759 --> 00:22:12.160
We're not ready.
00:22:12.240 --> 00:22:12.880
Go away, we're not ready.
00:22:12.960 --> 00:22:13.359
We're not ready.
00:22:13.440 --> 00:22:14.400
We'll let you know where we are.
00:22:14.559 --> 00:22:18.160
He kept going at it and go, and by the way, that's a great quality.
00:22:18.400 --> 00:22:21.759
For you, the entrepreneurs in the room, do not get put off.
00:22:21.920 --> 00:22:22.799
Learn from kids.
00:22:23.039 --> 00:22:24.640
I want an ice cream daddy.
00:22:24.720 --> 00:22:25.519
You can't have one.
00:22:25.680 --> 00:22:27.119
But I want an ice cream daddy.
00:22:27.200 --> 00:22:30.480
And they'll keep up in the game until you give them an ice cream.
00:22:30.559 --> 00:22:30.880
Okay?
00:22:31.200 --> 00:22:33.839
Now, when you're a grandparent, of course you give it them straight away.
00:22:33.920 --> 00:22:35.039
You don't even wait, do you?
00:22:35.200 --> 00:22:36.480
So that's our job.
00:22:36.640 --> 00:22:37.119
All right.
00:22:37.279 --> 00:22:41.519
So you're in this scenario where you know he kept pushing.
00:22:41.920 --> 00:22:49.119
And he said to us, we want some queries on a thing called, remember it well, multi-layer ceramic bus bars.
00:22:49.279 --> 00:22:56.160
Now we're only a stone throw away from JLR, and some of you in the room probably would actually have an understanding of what that probably means.
00:22:56.319 --> 00:22:56.640
Okay.
00:22:57.119 --> 00:22:58.960
I didn't know much about it at the time.
00:22:59.039 --> 00:23:06.319
We ran some queries, and I had 10 minutes, ran some queries through, sent them to him, and I go, what's that about?
00:23:06.559 --> 00:23:07.759
That's interesting.
00:23:08.079 --> 00:23:12.079
Two minutes later, wow, ten minutes later, can we have a call tonight?
00:23:12.160 --> 00:23:13.359
I think, here we go.
00:23:13.519 --> 00:23:17.759
I told you it wasn't ready, I've done you a favour, and you're just like those guys that worry.
00:23:17.839 --> 00:23:18.799
You just want more.
00:23:18.960 --> 00:23:20.640
You just want to keep going, that's it.
00:23:20.799 --> 00:23:26.079
So we had a conversation, we weren't, it didn't have a license, we had no business model.
00:23:26.240 --> 00:23:28.720
We said, okay, we'll let you use the platform.
00:23:28.960 --> 00:23:42.640
Where they've ended up is in a really interesting, and this is, and by the way, we were so impressed by them, we actually provided some grant funding, we provided some pre-seed funding, they've had less than a million pounds of the funding.
00:23:42.880 --> 00:23:57.119
They now have a working prototype of a device that can take the output of a solar panel, and I'm talking about a current state-of-the-art solar panel, and boost the usable output by 2.4 to 1.
00:23:58.240 --> 00:23:59.599
2.4 to 1.
00:23:59.759 --> 00:24:09.440
It would make solar the cheapest form of energy generation by a country mile, better than fully amortized nuclear, you name it.
00:24:09.920 --> 00:24:14.799
But besides that, they wanted to do that with ceramics, that's where they started.
00:24:15.039 --> 00:24:26.799
They also created a new, more efficient method of creating plasma, sparks that are 7,000 degrees and above, with the amount of energy you get from an electric kettle.
00:24:27.359 --> 00:24:32.160
And with that, they can now produce ceramic materials that can do wonderful things.
00:24:32.319 --> 00:24:41.279
And our problem is they can do so much that our problem is actually, what are we getting all the people to allow us to implement all these things and do these things with it?
00:24:41.519 --> 00:24:45.759
But they've done that, and the platform's been a big part of that journey.
00:24:46.079 --> 00:24:47.039
Does that help?
00:24:47.359 --> 00:24:54.240
Well, yeah, I mean it's not quite cold fusion, but we're getting there, I guess, in terms of um the energy.
00:24:54.400 --> 00:24:58.480
Um does it do, I mean, we talked about the scientific side of things.
00:24:58.559 --> 00:25:04.559
You know, if we were asking it for peacekeeping in the Straits of Hormuz, I mean, can you go in that side?
00:25:04.640 --> 00:25:05.599
Can you go humanitaires?
00:25:05.839 --> 00:25:06.640
Can you go politics?
00:25:06.960 --> 00:25:08.799
Is this guy a plug for Neo?
00:25:09.680 --> 00:25:10.319
Is he?
00:25:11.119 --> 00:25:14.880
Obviously, Neo got to him and said, I didn't quite have enough time to quiz Mel this morning.
00:25:14.960 --> 00:25:16.799
Would you just bring that into the talk?
00:25:16.880 --> 00:25:25.519
You know, so Neo is one of the guys on the peacekeeping force in in Warwick University, and this morning we gave him a license key for what we call curve.
00:25:25.680 --> 00:25:29.119
Curve is the real-time viewer on this data set we have.
00:25:29.279 --> 00:25:35.599
Now, I talked about 200 petabytes, but it's actually looking at changing data sets in the world in real time.
00:25:35.759 --> 00:25:39.359
So every day there's like three or four million new articles for news coming in.
00:25:39.519 --> 00:25:43.119
There's new patents, new SEC filings, you know, all come in real time.
00:25:43.359 --> 00:25:46.799
Curve is the window into that, and Neo wanted to have a license key for that.
00:25:46.880 --> 00:25:47.680
So give him that.
00:25:47.759 --> 00:25:49.680
And he asked exactly that question.
00:25:49.920 --> 00:25:56.000
I said, look, this is a scientific thing for trying to work out problem definitions and coming up with solutions.
00:25:56.240 --> 00:26:00.319
But I tell you what, I'm going to take the risk and try it on these this question.
00:26:00.480 --> 00:26:05.279
And it'll probably blow up in my face, all right, and it probably won't work, but we'll try it.
00:26:05.359 --> 00:26:09.279
I love live demos, they're they're really horrendous, but we do them all, don't we?
00:26:09.359 --> 00:26:10.000
So, you know.
00:26:10.240 --> 00:26:16.160
So, but if you if you pull it off, it's worth a thousand PowerPoint presentations.
00:26:16.319 --> 00:26:17.759
There could be smoke and mirrors.
00:26:18.079 --> 00:26:26.240
So the challenge was how would we bring about a mediation in a conflict between Ukraine and Russia was the one he gave me.
00:26:26.559 --> 00:26:35.599
So we tried that, and it's come up with a pretty good stab at all the things you'd have to look at and then to come out with how you would actually implement that.
00:26:36.000 --> 00:26:53.359
So it's it's not where I would like it to be, David, on that, but it's a few hours at most to be able to just change the generics to do that, so we can then take on probably the politics if not all the humanities, but certainly the politics side of that.
00:26:53.680 --> 00:26:54.079
Wow.
00:26:54.240 --> 00:27:03.599
I'm um I'm I'm conscious that we haven't got a huge amount of time, so I'm gonna go from that global right down to a sort of very specific question.
00:27:03.759 --> 00:27:07.200
So we've got some amazing deep tech founders in the room.
00:27:07.359 --> 00:27:12.079
I wonder from your own experience what advice you might give to a deep tech founder.
00:27:12.880 --> 00:27:27.359
I think it's interesting because you know, been having been there before and having been there and and failed when I had no money, okay, which is a really difficult place to be because you you don't know where you're going to go with it.
00:27:27.680 --> 00:27:41.759
And having been there where I failed and had enough money to survive that and just regroup and go again, I would say the following that you have to have a pretty good indication, not of feasibility.
00:27:42.000 --> 00:27:46.160
Forget feasibility, go for viability.
00:27:46.559 --> 00:27:48.160
You want viability.
00:27:48.400 --> 00:27:59.200
It's no good coming out with a deep tech innovation that requires 10 times as much energy than another solution but has zero pollution because no one's going to use it.
00:27:59.519 --> 00:28:03.759
So you've got to think: is it actually viable?
00:28:04.079 --> 00:28:09.039
Viable doesn't just mean it works, viable means it's commercially valuable.
00:28:09.279 --> 00:28:13.599
It has a market opportunity that you can identify and go with.
00:28:13.759 --> 00:28:21.359
And then you've got to decide, even if I do this, how does that, how do I get to that go-to-market piece?
00:28:21.599 --> 00:28:24.960
And sometimes it's not something you can do yourself.
00:28:25.200 --> 00:28:30.400
Sometimes you've got to look, well, maybe it's a licensing deal with someone who understands that market.
00:28:30.880 --> 00:28:41.920
Now that might not satisfy your grandiose view of valuation, but it but it's going to give you a fast-track path towards that, and and you probably could get somewhere close as well.
00:28:42.240 --> 00:28:45.440
So think about what it is you're doing.
00:28:45.680 --> 00:28:46.960
Is it feasible?
00:28:47.200 --> 00:28:48.480
Is it viable?
00:28:48.880 --> 00:28:57.519
If it's viable, what's that bridge between where it is now to go to market and and be wary of that VC trap?
00:28:58.400 --> 00:29:05.359
You can easily spend, if you if you think you've got your idea pretty well sourced, you might even have a bit of a prototype.
00:29:05.680 --> 00:29:15.279
You could waste three, four, five, six months of your time writing documents to try and get a VC to respond to you.
00:29:15.440 --> 00:29:18.960
Even when they do, they'll always say, Yeah, we'll talk to you.
00:29:19.039 --> 00:29:21.920
Let's look at it, we're quite interested in this, and it goes nowhere.
00:29:22.240 --> 00:29:26.319
The emotional roller coaster that brings is huge.
00:29:26.640 --> 00:29:27.440
It's huge.
00:29:27.599 --> 00:29:32.079
And if you talk to four or five of them, and they all say no, you'll doubt yourself.
00:29:32.400 --> 00:29:37.119
If you're confident about what you want to do, have the passion, keep going at it.
00:29:37.200 --> 00:29:38.559
I want an ice cream, Daddy.
00:29:38.720 --> 00:29:40.079
All right, I want an ice cream.
00:29:40.240 --> 00:29:40.960
You can't have one.
00:29:41.039 --> 00:29:44.319
I want an ice cream, and you just keep going at it.
00:29:44.480 --> 00:29:49.200
But don't talk to too many until you figure it out how you do it.
00:29:49.359 --> 00:29:53.759
And don't necessarily treat what they say to you as the things you need to do.
00:29:54.160 --> 00:29:58.160
If it was as simple as that, they'd have backed you and just said, just do these few things.
00:29:58.480 --> 00:30:00.240
So they don't know the market.
00:30:00.559 --> 00:30:03.119
VCs are followers of fashion.
00:30:03.359 --> 00:30:05.839
Look at the AI investments now.
00:30:06.000 --> 00:30:12.960
The people on the fear of missing out that are coming in behind it, they'll probably have no chance of getting many times their money back on it.
00:30:13.519 --> 00:30:15.039
VCs follow fashion.
00:30:15.200 --> 00:30:21.839
If yours, what you've done, is what you've done is really unique, really novel, they won't understand that.
00:30:22.079 --> 00:30:23.440
Don't expect them to.
00:30:23.599 --> 00:30:27.440
So when they ask you, what do you think the total addressable market is of this?
00:30:27.599 --> 00:30:28.559
Walk out.
00:30:29.359 --> 00:30:32.960
If they don't know the market, you're going to aim at well enough to figure out that's huge.
00:30:33.119 --> 00:30:41.599
If you think the guys that did Uber, we've worked out how many taxi journeys there are in the world, and we think actually that the market for this thing is pretty big.
00:30:41.759 --> 00:30:46.240
In fact, in London, it's probably worth about 2,000 a year, maybe 2,000 a month.
00:30:46.480 --> 00:30:47.359
It's bloody huge.
00:30:47.519 --> 00:30:50.160
We're going to disrupt the entire taxi marketplace.
00:30:50.400 --> 00:30:51.680
I don't know how big it is.
00:30:51.920 --> 00:30:53.680
Have you got the ball to the back it?
00:30:53.759 --> 00:30:55.279
That's all I need to know.
00:30:55.519 --> 00:30:56.559
And what did they do?
00:30:56.640 --> 00:31:08.480
They did a smoke and mirror show by arranging for a car to be just around the corner to demonstrate it to, I think it was Bob Cagle from Benchmark at the time, to invest in it to say, look, here's how it works.
00:31:08.640 --> 00:31:10.480
Oh my god, they must be pretty big already.
00:31:10.559 --> 00:31:12.319
The car already arrived.
00:31:12.880 --> 00:31:16.240
But he was sold, invested literally within a week of that.
00:31:16.480 --> 00:31:18.960
He didn't have to go into a big PowerPoint presentation.
00:31:19.119 --> 00:31:22.720
Here's what the cars look like, here's the branding, here's how we're going to market it.
00:31:23.039 --> 00:31:25.279
Going to put an app out there, everyone's going to want to use it.
00:31:25.440 --> 00:31:28.240
And it's disrupted that market massively.
00:31:29.440 --> 00:31:30.079
That's great.
00:31:30.240 --> 00:31:30.720
I love it.
00:31:30.799 --> 00:31:37.039
Um, my last question, because I'm conscious of time, but I promised I'd leave a bit of time for any questions from the audience.
00:31:37.200 --> 00:31:40.079
So start thinking if anybody's got a question.
00:31:40.400 --> 00:31:43.680
My last question is really in this investment space.
00:31:43.839 --> 00:31:49.759
So obviously, bootstrapping versus investment, how do you know when you should go to investor?
00:31:49.920 --> 00:31:50.880
When do you need one?
00:31:51.039 --> 00:31:52.480
What's the trigger for you?
00:31:52.799 --> 00:31:58.319
I I don't think there's a binary view of that or even a litmus test you can apply.
00:31:58.480 --> 00:32:01.920
Uh and I'll take Corporal as a classic example.
00:32:02.160 --> 00:32:05.599
So we saw ChatGPT go to market.
00:32:06.079 --> 00:32:11.440
We saw perplexity, and I don't have a lot of, I don't have a lot of respect for the product.
00:32:11.599 --> 00:32:12.000
They were.
00:32:12.079 --> 00:32:15.440
I think now they're figuring out lots of good things, they're doing some great work.
00:32:15.680 --> 00:32:18.559
But at the time we thought, wow, we need to go in this bandwagon.
00:32:18.640 --> 00:32:22.319
We've got a tool that's better than all these things, we should put it out there in competition.
00:32:22.799 --> 00:32:25.839
We didn't have the resources to market that, to compete with that.
00:32:26.000 --> 00:32:29.359
We were never going to get the the toe hold that was required.
00:32:29.519 --> 00:32:30.880
Well, we'll go and raise some money.
00:32:30.960 --> 00:32:32.640
So we did our business plans around that.
00:32:32.720 --> 00:32:37.279
We went down the VC courtship, and no one wanted to back us.
00:32:37.440 --> 00:32:39.920
And you know, they were absolutely right.
00:32:40.160 --> 00:32:41.359
They shouldn't have backed us.
00:32:41.440 --> 00:32:43.440
It wasn't worth backing on that plan.
00:32:43.759 --> 00:32:50.960
And they did me a big favor because they forced us to find the route that we could go to market with, and we can now do it without them.
00:32:51.119 --> 00:32:55.359
And now and we've moved from a licensing model, we don't license our technology.
00:32:55.519 --> 00:32:58.480
We might talk about license with Yuli who said that.
00:32:58.559 --> 00:33:00.319
But we're not licensing our technology.
00:33:00.400 --> 00:33:00.720
Why?
00:33:00.880 --> 00:33:06.079
Because the value of what it produces will be democratized by a licensing model.
00:33:06.240 --> 00:33:17.680
And I will not democratize a product that can go from a simple challenge statement to a fully ideated solution with patent application in less than half an hour for 20 bucks a month.
00:33:17.920 --> 00:33:25.039
Because some kid in a bedroom using it will actually put loads of people out of business by just filing patents on the back of that.
00:33:25.279 --> 00:33:26.319
I won't do that.
00:33:26.480 --> 00:33:39.839
So our model is we either allow people to use the technology for a share of the IP, or we invest and we also allow them to use the model the product for a share of the IP, and that's a medium to long-term gain.
00:33:40.000 --> 00:33:40.799
We have no revenue.
00:33:40.880 --> 00:33:42.319
I'm not bothered about that.
00:33:42.480 --> 00:33:45.279
The revenue is just a facade, it's just another measurement.
00:33:45.440 --> 00:33:58.079
But we're building value in allowing companies that can utilize our technology to accelerate their value, to bring their product to market one, two, three years sooner, and for probably a fifth of the cost.
00:33:58.319 --> 00:34:01.039
And in there, we can actually extract value.
00:34:01.359 --> 00:34:04.559
But I mean I'm talking about small bits of value across a broad base.
00:34:04.960 --> 00:34:06.640
And that's the model we're now on.
00:34:07.440 --> 00:34:08.000
Thank you.
00:34:08.719 --> 00:34:10.639
Any questions from the audience?
00:34:11.039 --> 00:34:12.239
I think you first there.
00:34:12.960 --> 00:34:13.760
Thank you very much, Mal.
00:34:14.000 --> 00:34:15.679
It's been inspiring today.
00:34:15.760 --> 00:34:26.239
Uh if you know my question is, um, when and how did you decide to do your philanthropy work, your charity work like in SAR?
00:34:26.400 --> 00:34:26.639
Yeah.
00:34:26.880 --> 00:34:28.159
Where where did it come from?
00:34:28.639 --> 00:34:30.480
I I think just just giving back.
00:34:30.800 --> 00:34:37.119
All right, and if you've ever seen the movie, you know you you'd know exactly what that's that's all about, which is a fabulous movie, by the way.
00:34:37.440 --> 00:34:41.039
So I I I'd made a lot of money over lots of businesses.
00:34:41.280 --> 00:34:44.000
And my attitude was I'd like it to go to some good.
00:34:44.239 --> 00:34:44.559
Right?
00:34:44.639 --> 00:34:52.800
So we created this this trust which has got a very wide remit, medical sporting and research technology, and it has a very wide remit.
00:34:52.880 --> 00:34:54.000
It gets involved in all sorts.
00:34:54.320 --> 00:34:55.920
We sponsor sports people.
00:34:56.239 --> 00:35:01.599
Uh Genevieve Somerville will represent us in the Olympics next year, on the the Winter Olympics.
00:35:01.840 --> 00:35:04.480
Sponsored her since she was about 11 years old.
00:35:04.639 --> 00:35:06.960
Uh we've got tennis players we sponsored.
00:35:07.119 --> 00:35:16.000
We've got someone who was in the Olympics this year, Winter Olympics, who's actually uh basically a solo bobsleigh athlete.
00:35:16.239 --> 00:35:21.519
All right, she lives, she was born in Derby, and uh, you know, and there's no snow there, by the way.
00:35:21.599 --> 00:35:23.519
So you can imagine the work she goes into.
00:35:23.760 --> 00:35:26.639
So we put money in, but they put lots of hard work into it.
00:35:26.719 --> 00:35:38.320
We sponsor medical projects, we sponsored pure technology projects, uh, and we go where we think there's a value to be had, not in a return, but in terms of what's the impact of those investments.
00:35:38.559 --> 00:35:47.119
And and and it's great to do that, and it's it's carried us into all sorts of conversations that I feel honored to be part of because of that.
00:35:50.400 --> 00:35:56.079
So I have a question which is around the conflict between values and interest.
00:35:56.719 --> 00:36:12.320
So if you're a founder and and you found a product where you generally market, why can't we still do it here when you when you just have to try what kind of market in the UK why can't you plan and do it there?
00:36:12.800 --> 00:36:15.679
So is there a conflict between values and what do you think of that?
00:36:16.239 --> 00:36:19.360
I think that uh I'm a big sovereign fan.
00:36:19.760 --> 00:36:20.000
Okay.
00:36:20.480 --> 00:36:36.400
I think that and and you guys are uh on on my journey I see working with universities like this and others, I think, as being a key component of how we actually survive as a nation, how we thrive as a nation.
00:36:36.719 --> 00:36:42.480
And and you know that that means more to me than making 10x what we might make doing that.
00:36:42.639 --> 00:36:45.360
Um so you know, enough is enough.
00:36:45.519 --> 00:36:47.360
One pound more than enough is plenty.
00:36:47.519 --> 00:36:47.760
Okay.
00:36:48.239 --> 00:36:49.679
So you can go to the States.
00:36:49.760 --> 00:36:54.719
That might be the reason you go there, isn't because of the market, it might be because of the backing.
00:36:54.960 --> 00:37:03.760
And I think we've got a lot of work to do in the UK to increase, to accelerate, to make the fit of that backing process better.
00:37:04.000 --> 00:37:06.639
Uh, and I think and we've seen some great initiatives.
00:37:06.800 --> 00:37:10.000
I met with a lady called Josephine Kant three three weeks ago.
00:37:10.159 --> 00:37:13.199
She's one of the founding members of D Sit Sovereign AI.
00:37:13.440 --> 00:37:14.400
Fantastic initiative.
00:37:14.480 --> 00:37:19.840
So it's happening, but the answer is because I want to I want to try and help other things.
00:37:20.320 --> 00:37:21.679
The ground, so values first.
00:37:21.920 --> 00:37:22.000
Yeah.
00:37:24.639 --> 00:37:26.400
I mean, it's a great place to wrap it up.
00:37:26.480 --> 00:37:29.440
I have to say, it's fantastic to hear Mel speak, isn't it?
00:37:29.519 --> 00:37:31.760
What an amazing, inspirational person to have in.
00:37:31.920 --> 00:37:34.320
So I think he deserves a round of applause.
00:00:02.720 --> 00:00:14.400
So today I was fortunate enough to interview someone who's a founder, chairman, and investor in businesses that have exited for over£7 billion over the last decade.
00:00:14.800 --> 00:00:31.199
Mel Morris CBE was on stage and we had the opportunity to talk about his career, the number of innovative things he's doing, and also his new corpora platform where he's looking to develop new scientific discoveries through AI.
00:00:31.440 --> 00:00:34.320
It was an incredible opportunity to hear from Mel.
00:00:34.640 --> 00:00:36.560
It's a pleasure to have you here, Mel.
00:00:36.719 --> 00:00:45.200
You may not know that Mel has chaired, founded, and invested in businesses that have exited at over seven billion dollars worth of businesses.
00:00:45.359 --> 00:00:48.960
So he is an incredible person to have here with us today.
00:00:49.359 --> 00:00:52.159
I've been a customer of at least one of your businesses.
00:00:52.240 --> 00:00:55.600
So throughout the afternoon, let's figure out see if we can figure out which one it is.
00:00:56.719 --> 00:00:59.119
Did you buy a cybersecurity product then, David?
00:01:00.079 --> 00:01:00.960
No, no, no, no, no.
00:01:01.200 --> 00:01:02.719
But didn't you have a dating business?
00:01:02.960 --> 00:01:04.640
Yeah, well, let's not go there.
00:01:05.920 --> 00:01:07.280
You could be embarrassed by that.
00:01:07.519 --> 00:01:08.239
It's not that one.
00:01:08.480 --> 00:01:10.000
Just to be clear, but it's not that one.
00:01:10.159 --> 00:01:18.640
So look, I'm going to start out, if I may, by just asking the simplest of questions, just to explain your entrepreneurial journey and how you've got here, if that's okay.
00:01:18.959 --> 00:01:23.760
Yeah, I don't want to bore you because it's spanning now close on six decades.
00:01:23.920 --> 00:01:26.879
Not six full decades, but six actual decades.
00:01:27.120 --> 00:01:28.560
Historically, anyway.
00:01:29.040 --> 00:01:37.359
My journey was really quite interesting because I wanted to work with computers from day one, right from leaving school.
00:01:37.599 --> 00:01:42.719
And that was really difficult because I was only 16 at the time.
00:01:42.879 --> 00:01:46.480
And I went to look for a position to start writing computer software.
00:01:46.640 --> 00:01:47.680
That's what I wanted to do.
00:01:47.840 --> 00:01:54.159
But there was a job in computer operations which I was going to look at, and I couldn't do that either because I wasn't 18.
00:01:54.239 --> 00:01:55.359
I couldn't work shifts.
00:01:55.439 --> 00:02:00.959
So I ended up decalating output from a printer and then delivering around the offices.
00:02:01.200 --> 00:02:06.400
And after about three minutes, probably three months, but somewhere between there, I got extremely bored with that.
00:02:06.560 --> 00:02:09.599
And one of the women who worked there said, Why are you so miserable?
00:02:09.680 --> 00:02:11.759
I said, I want to write computer software.
00:02:12.080 --> 00:02:16.400
My husband is the computer manager for EW Bliss, part of Gulf from Western.
00:02:16.560 --> 00:02:17.599
Why don't you go and see him?
00:02:17.759 --> 00:02:19.360
So I said, I'd love to.
00:02:19.599 --> 00:02:28.240
So I go and see him, a gentleman called Tony Bohr, and he said to me, Well, why don't you just take a week's holiday and come and see how you get on?
00:02:28.319 --> 00:02:30.560
We'll teach you and see how you see how you cope.
00:02:30.800 --> 00:02:31.199
Sure.
00:02:31.439 --> 00:02:32.159
Only one problem.
00:02:32.479 --> 00:02:34.400
I've got no holiday left.
00:02:34.639 --> 00:02:36.319
They said, Well, you know, can't you take a week?
00:02:36.479 --> 00:02:38.639
I said, I won't take a week off sick, I'm not sick.
00:02:38.800 --> 00:02:43.759
I tell what I'll do, I'll basically have my noses in, I'll come to you for the week.
00:02:43.919 --> 00:02:48.000
I know if I don't shape up, there's no job, but I sooner do it that way.
00:02:48.240 --> 00:02:50.240
I was offered the job three days in.
00:02:50.400 --> 00:02:59.759
I ended up with a guy who was a computer science graduate, which was very rare in those days, and he was a real superstar at writing software.
00:03:00.000 --> 00:03:06.319
But he wanted to be lazy and he wanted to teach me as fast as he could so he could put his feet up and I could do all the work.
00:03:06.400 --> 00:03:07.520
And I loved it.
00:03:07.759 --> 00:03:08.719
Absolutely loved it.
00:03:08.800 --> 00:03:10.400
It was such a great mentor.
00:03:10.639 --> 00:03:14.719
And I realized from that age I could write software.
00:03:14.960 --> 00:03:24.159
And what I found with software is it's a great challenge of coordination, correlation, planning, doing all those things, ideation.
00:03:24.479 --> 00:03:33.280
And I had a knack of writing software that was extremely efficient, very small and compact, and that set me on a journey that put me through all of my businesses.
00:03:33.520 --> 00:03:39.039
One of those companies ended up going from software into hardware, or at least a blend of the two.
00:03:39.199 --> 00:03:52.080
So I got into hardware design and we're producing products that basically was what the first ever intelligent caching disc array, which ended up selling for over$100 million worth of product very quickly.
00:03:52.240 --> 00:03:57.439
So I moved to the States to work for Wang Laboratories, had the pleasure of working directly for Dr.
00:03:57.599 --> 00:04:01.280
Ann Wang over there, who was a fantastic inventor.
00:04:01.439 --> 00:04:04.639
I mean, this guy was a refugee from Shanghai.
00:04:04.879 --> 00:04:12.159
He was the person who created the patent of hardcore memory, which put IBM into the computer space.
00:04:12.400 --> 00:04:15.520
With the money he got from that, he formed Wang Laboratories.
00:04:15.759 --> 00:04:22.639
And he later suffered from cancer of the esophagus, and he was off work for about 18 months.
00:04:22.879 --> 00:04:29.439
Came in one day, and I'm upstairs in this meeting, and we're debugging a problem with a computer system.
00:04:30.079 --> 00:04:33.920
And the people in the room, most hadn't really worked with him.
00:04:34.240 --> 00:04:36.480
But he's asking, What problem?
00:04:36.720 --> 00:04:37.439
What problem?
00:04:37.920 --> 00:04:41.120
So one guy says, Don't worry, doctor, we'll figure it out.
00:04:41.199 --> 00:04:41.920
He says, What problem?
00:04:42.079 --> 00:04:43.040
I said, excuse me.
00:04:43.279 --> 00:04:45.759
Please tell him what the situation is.
00:04:46.000 --> 00:04:50.639
So he walks around the room with all these circuit diagrams, never seen them before in his life.
00:04:50.959 --> 00:04:55.279
He walks around, he gets his cane out, he slaps it down, goes, problem here.
00:04:55.680 --> 00:04:58.000
Fetch board and soldering iron.
00:04:58.480 --> 00:05:04.480
And he figured what this minute problem was that people have been working on for months.
00:05:04.800 --> 00:05:06.560
And I just thought, wow.
00:05:07.040 --> 00:05:08.399
And that set me on the path.
00:05:08.480 --> 00:05:12.000
It said, no matter how good you become, you're never going to be as good as him.
00:05:12.240 --> 00:05:17.279
And that inspired me to do all sorts of things in pursuit of trying to at least be better than I was.
00:05:17.600 --> 00:05:24.399
I worked for Wang doing computer tuning, going down the biggest customers, helping them get those, and then built a business doing that.
00:05:24.560 --> 00:05:26.959
After that was a business called Minitech.
00:05:27.199 --> 00:05:33.519
Then that then became ProMetrics, doing PC software, and then that got bought out by Platinum.
00:05:33.600 --> 00:05:35.439
I became New Product Development Manager.
00:05:35.600 --> 00:05:36.639
That sold.
00:05:37.120 --> 00:05:38.720
I then went to doing New Date.
00:05:41.680 --> 00:05:51.519
After that, I got involved in four other companies, one in social media listening, one in Google Advertising and those sorts of markets.
00:05:52.000 --> 00:05:58.720
One in terms of intelligent sensors, one in terms of today, which is Corpora.
00:05:59.199 --> 00:06:02.319
And so many businesses en route to that.
00:06:02.639 --> 00:06:10.079
In UD, two of the people who worked for me there, one was a guy called Toby Rowland, one was a guy called Ricardo Zacconi.
00:06:10.639 --> 00:06:12.480
Became close friends to both.
00:06:12.639 --> 00:06:17.439
They wanted to buy this small star to be in Sweden doing computer games.
00:06:17.680 --> 00:06:19.040
Computer games.
00:06:20.879 --> 00:06:21.680
I didn't join them.
00:06:21.839 --> 00:06:22.879
They said, Would you be chairman?
00:06:23.040 --> 00:06:24.560
I said, Yeah, would you help us raise money?
00:06:24.720 --> 00:06:25.199
Yes.
00:06:25.519 --> 00:06:30.560
We scoured Europe looking for funders to back what became king.
00:06:30.879 --> 00:06:31.600
No takers.
00:06:31.920 --> 00:06:33.680
For several years, no-takers.
00:06:33.920 --> 00:06:36.720
Until one point, they're about out of cash and they said, What are we going to do?
00:06:36.800 --> 00:06:38.959
I said, I suppose I've better write a check.
00:06:39.120 --> 00:06:46.800
So I wrote a check to Keith Monday, and I stayed as the chairman of that company until it went public on the MISE in 2014.
00:06:47.600 --> 00:06:53.759
And Candy Crush was about probably 10 years into the creation of that business.
00:06:54.079 --> 00:06:57.439
So, but we knew it was going to be a success from day one.
00:06:57.600 --> 00:06:59.759
Lots of things to talk about at some other time.
00:07:00.079 --> 00:07:01.519
But that was the journey.
00:07:01.680 --> 00:07:04.800
And but the King story isn't about me.
00:07:04.959 --> 00:07:06.879
I provide the finance, I chaired it.
00:07:07.040 --> 00:07:10.079
It was about the team that worked in that business.
00:07:10.240 --> 00:07:12.800
Ricardo was a fantastic CEO.
00:07:13.040 --> 00:07:16.560
Sebastian Knutzen was the head of design for what went on.
00:07:16.800 --> 00:07:21.279
I just had the pleasure of working with a great team and helping backing them in any way I could.
00:07:21.439 --> 00:07:23.120
Wow, that is quite a journey, isn't it?
00:07:23.199 --> 00:07:25.920
And um, you know, there's some really interesting things to pick out there.
00:07:26.000 --> 00:07:30.399
I mean, the giving up your job on the basis of hoping that you're gonna get it's gonna work after three.
00:07:30.639 --> 00:07:32.879
I mean, that is really bold and brave.
00:07:33.040 --> 00:07:37.920
Um, and I love the humility, you know, the bringing together the people, it is fantastic.
00:07:38.079 --> 00:07:42.319
Um Candy Crush might be the one where I'm a customer, I've got to be honest.
00:07:42.480 --> 00:07:45.920
Um I'm not sure I did.
00:07:50.240 --> 00:07:51.920
It's too late now, I'm afraid.
00:07:52.079 --> 00:08:01.759
Um, but actually, interestingly, it was known as an incredibly innovative business, and I think it'd be interesting if you if it's okay to perhaps bring out why it was so innovative.
00:08:02.160 --> 00:08:09.680
Yeah, um, I mean the game was important, but it's not, in my opinion, the major part of that story.
00:08:09.920 --> 00:08:15.199
The game was important because it was a game that could be played by anyone pretty much of any age.
00:08:15.360 --> 00:08:19.040
So it was ubiquitous in its in its market opportunity.
00:08:19.519 --> 00:08:30.959
But but the secret, in my opinion, was again coming back to Ricardo and the development team, and and we'd figured out that we were doing computer games that were effectively tournament games.
00:08:31.120 --> 00:08:36.240
So you could sign up to play online pool against other people, and it would be a tournament.
00:08:36.320 --> 00:08:40.639
So lots of people would enter the game, sometimes two, three, four thousand people.
00:08:40.799 --> 00:08:50.960
They'd all pay a few euros to play to get to one prize that one person would win the tournament, and that one person would probably walk away with the check for three, four thousand euros.
00:08:51.679 --> 00:08:55.600
But maybe three thousand people played that game to get to that point.
00:08:56.240 --> 00:09:00.480
How many people wanted to talk about it after the tournament had finished, do you think?
00:09:00.720 --> 00:09:04.639
Do you think it was 3,000 or do you think it was one person who walked away with the check?
00:09:05.360 --> 00:09:08.320
And and we figured out that you can't scale.
00:09:08.639 --> 00:09:10.159
You can't scale that.
00:09:10.399 --> 00:09:11.600
You can't scale that.
00:09:11.679 --> 00:09:13.519
There's no viral link to that.
00:09:13.679 --> 00:09:18.799
Because 2,990 people are absolutely fed up because they're wasted their money.
00:09:19.039 --> 00:09:22.399
Particularly those that got closest to the finish line.
00:09:22.720 --> 00:09:29.039
So we the business model was fantastic, it moved from being a tournament.
00:09:29.200 --> 00:09:30.320
Think of it as a cup.
00:09:30.480 --> 00:09:34.559
Those of you into soccer or rugby will know on a cup, it's a quick death.
00:09:34.960 --> 00:09:38.480
You you go in a round, you lose, you don't go to the next round.
00:09:38.799 --> 00:09:41.200
So on a cup, there's one winner.
00:09:41.519 --> 00:09:45.679
But you think about the Premier League, and it's a league.
00:09:46.000 --> 00:09:48.240
You come number two, it's a pretty good place to be.
00:09:48.399 --> 00:09:52.159
Actually, if you come number 19, it's a pretty good place to be.
00:09:52.320 --> 00:09:53.679
Even better if you're 16.
00:09:53.919 --> 00:09:58.000
So it's one of those things where we wanted a league.
00:09:58.240 --> 00:10:05.600
And then we looked at companies like FIFA, who had the brilliant model, which was well, you want to play FIFA, which one are you on?
00:10:05.759 --> 00:10:09.440
I'm on 2026, so I've only got 2023 still, it's not as good.
00:10:09.519 --> 00:10:12.879
It doesn't have all the new players, the new teams in, and so on and so forth.
00:10:13.120 --> 00:10:17.120
So we wanted a league, but we wanted a perennial game.
00:10:17.360 --> 00:10:19.200
So, what do you call a perennial game?
00:10:19.360 --> 00:10:20.799
You call it a saga.
00:10:21.200 --> 00:10:23.519
So can you crush became a saga?
00:10:23.600 --> 00:10:25.039
It's never going to end.
00:10:25.200 --> 00:10:28.159
It's going to have more levels than you could ever count.
00:10:28.480 --> 00:10:30.720
And where does the league come from?
00:10:30.879 --> 00:10:33.039
It's pretty easy, it's going to be your friends.
00:10:33.200 --> 00:10:38.879
And because everyone can play it, people will begin taunting one another on what level they've got to.
00:10:39.120 --> 00:10:43.840
So the model was you can play the game, two, three, four lives.
00:10:44.080 --> 00:10:54.240
When you run out of lives, you can sit and wait for an eternity while your friends are getting ahead, or you can pay, or you can refer n number of your friends to get more lives.
00:10:54.720 --> 00:10:58.480
It was the ultimate expression of a viral game.
00:10:58.879 --> 00:11:02.720
The business model made the game, it made the company.
00:11:02.960 --> 00:11:10.240
And at peak, we almost got to ten million dollars per day in revenue.
00:11:11.039 --> 00:11:14.639
But only around three or four percent of people ever paid.
00:11:14.720 --> 00:11:15.679
David didn't.
00:11:17.200 --> 00:11:18.080
It's disgusting, really.
00:11:18.399 --> 00:11:19.360
I think my kids did.
00:11:20.639 --> 00:11:28.720
So so it was the game had to be something that anyone could play, from grandparents right the way down to grandkids.
00:11:28.960 --> 00:11:30.320
They could taunt each other.
00:11:30.480 --> 00:11:31.600
Where are you at, grandma?
00:11:31.679 --> 00:11:32.879
Oh, yeah, how many of this?
00:11:33.039 --> 00:11:35.519
Okay, I'm at 4,022.
00:11:35.919 --> 00:11:44.320
And and the frustration of having to wait got so bad that I know David actually got his credit card out on several occasions to get ahead.
00:11:44.480 --> 00:11:46.960
All right, and that's what happened, and the rest was history.
00:11:47.120 --> 00:11:48.639
Is that a good answer?
00:11:48.799 --> 00:11:50.320
It it's an amazing answer, isn't it?
00:11:50.480 --> 00:11:55.840
I mean, you think about some of the things that have happened in the last decade, you know, the Roblox and the Fortnites.
00:11:55.919 --> 00:12:13.759
Um, you started all of that in terms of them thinking about it as an ecosystem, thinking about it as member get member, community, uh, the freemium model, getting people hooked involved, sorry, and then and then figuring out how to, you know, it really was so innovative, um, which is incredible.
00:12:13.840 --> 00:12:23.440
And kind of over this last decade, we've seen a lot of changes with the tech, and you know, I can't do a full sentence without somebody mentioning AI to me at the moment.
00:12:23.600 --> 00:12:25.679
Um, it's very difficult to get through.
00:12:26.000 --> 00:12:32.240
I'm just interested in your perspective in terms of AI, the challenges, the opportunities, and where you see that going.
00:12:32.639 --> 00:12:34.480
Okay, just a quick show of hands.
00:12:34.799 --> 00:12:39.039
Has anyone ever heard of a paper called Attention is Everything?
00:12:39.679 --> 00:12:41.120
Okay, all right.
00:12:41.360 --> 00:12:43.519
So that paper, and you should answer that.
00:12:43.600 --> 00:12:45.519
If you if he can't, he should be fired.
00:12:45.759 --> 00:12:46.080
Okay.
00:12:47.120 --> 00:12:49.759
Otherwise, you're you're a complete facade, otherwise.
00:12:49.840 --> 00:12:55.519
So that paper was was authored by about four or five people in Google at the time.
00:12:55.679 --> 00:13:00.159
Uh, one of the guys, a guy called Leon Jones, uh, younger cunz, a couple of others as well.
00:13:00.399 --> 00:13:04.720
And that was the paper that basically figured out transformers.
00:13:04.879 --> 00:13:08.480
I don't mean the things, the robots that turn into whatever cars and things.
00:13:08.639 --> 00:13:19.360
It was the transformers that became the model of how we can actually predict the next word in sequence of a body of text, of a, of a, of a topic.
00:13:19.840 --> 00:13:24.480
And we saw that paper and we looked at it, thought, wow, this is really interesting.
00:13:24.559 --> 00:13:25.840
We went on a different path.
00:13:26.080 --> 00:13:34.240
But that paper was absolutely the starting point for today's incarnation of the tools we see today, ChatGPT.
00:13:34.480 --> 00:13:39.759
AI, its origins go way before that, but that was the trigger point.
00:13:40.159 --> 00:13:46.639
Then ChatGPT, October, November 2023, you know, they they launched.
00:13:46.720 --> 00:13:50.639
I think they're launched in some ways too soon, but it's typical of American companies.
00:13:50.799 --> 00:13:52.159
Let's grab the market.
00:13:52.320 --> 00:13:57.360
It may not be fully baked, it may not be quite ready, but we'll get it out there, we'll get a footprint.
00:13:57.519 --> 00:14:02.159
We put loads of money about promoting it, and by gum, did it take off?
00:14:02.399 --> 00:14:03.360
Absolutely took off.
00:14:03.440 --> 00:14:12.799
I mean, even Eclipse candy crushes growth in people terms, and and they were able then to get their valuation up, which let them borrow more money and away they would go.
00:14:13.039 --> 00:14:25.279
And Google did the typical thing that you see in these things when someone does something that causes, I think it was a half a trillion of value, shifted around the market in the space of a day or so.
00:14:25.519 --> 00:14:30.080
It shifted the market, it changed the whole dynamics of it.
00:14:30.240 --> 00:14:35.200
So Google is saying, hey, our stock price just tanked because these guys have launched this.
00:14:35.440 --> 00:14:37.600
But we've been working on that, we were the founders of this thing.
00:14:37.679 --> 00:14:38.480
What are we going to do?
00:14:38.639 --> 00:14:42.080
We'll rush the market with our offering, and it was an absolute disaster.
00:14:42.240 --> 00:14:43.360
It was an absolute disaster.
00:14:43.440 --> 00:14:45.440
And then Microsoft decided we've got to jump in.
00:14:45.679 --> 00:14:47.519
Then all the rest started to come to market.
00:14:47.600 --> 00:14:52.320
But what happened was, had they not have launched then, we wouldn't be where we are today.
00:14:52.559 --> 00:14:55.600
It was not yet ready, it wasn't done, it didn't matter.
00:14:55.840 --> 00:14:59.440
It showed enough of the potential of what was there.
00:14:59.679 --> 00:15:15.600
And there's a lesson from this for all of you, which is that in Britain, and I'm a big, big, big, guilty person for this, we obsess, we over-engineer, we over-develop, we spend too long trying to get it perfect.
00:15:15.840 --> 00:15:17.519
The Americans don't do that.
00:15:17.679 --> 00:15:27.279
They slap it out in the market, and they'll go through n iterations of it in a space of three months if they have to, but they'll corner that imagination of the market.
00:15:27.600 --> 00:15:29.039
And that's what happened.
00:15:29.200 --> 00:15:31.679
And so from then, the rest is almost history.
00:15:31.919 --> 00:15:41.440
But the acceleration of AI in the last three months eclipses what happened in every point prior to that three months from when ChatGBT was launched.
00:15:41.600 --> 00:15:46.639
And it's off the charts, and we cannot afford to ignore it.
00:15:46.879 --> 00:15:51.600
The implications of it for universities, for everyone in everyday life is huge.
00:15:51.759 --> 00:15:57.600
We've got to rein in the energy demands and all those things, but it's absolutely massive.
00:15:57.759 --> 00:15:59.120
I hope that helps.
00:15:59.440 --> 00:16:00.080
Absolutely.
00:16:00.240 --> 00:16:09.200
I mean it's really interesting when I hear you speak because it's this intersection of kind of customers, business model, the behaviour, the tech.
00:16:09.600 --> 00:16:18.320
You're able to kind of see across all of those different things, and it's that intersection that I think build really good deep tech businesses.
00:16:18.559 --> 00:16:22.639
Um, I'm obviously interested in your latest deep tech venture.
00:16:22.720 --> 00:16:33.279
Um, as I understand it, you've been ingesting all of this scientific information into Copora, and you know, I think it would be amazing if you could just tell us a little bit about that.
00:16:33.679 --> 00:16:36.399
Yeah, so you know, attention is everything.
00:16:36.559 --> 00:16:37.679
That that was the paper.
00:16:37.840 --> 00:16:43.120
That that that changed how I looked at things, it had a massive influence on what we were doing.
00:16:43.279 --> 00:16:51.360
We were on a parallel path looking to basically understand a massive corpus of information, hence the name corpora.
00:16:52.320 --> 00:16:54.559
Corpora being the plural of corpus.
00:16:55.279 --> 00:17:01.039
And when we set out, we wanted to try and use whatever technologies were around to get a head start.
00:17:01.279 --> 00:17:03.679
So we said, like, we want to do this at scale.
00:17:03.840 --> 00:17:06.000
So we said, what is it at scale?
00:17:06.160 --> 00:17:07.519
What do you mean by that?
00:17:07.839 --> 00:17:11.519
And an avi was, well, someone said, well, is that a few terabytes?
00:17:11.599 --> 00:17:13.279
No, no, no, much bigger than that.
00:17:13.440 --> 00:17:16.240
Well, is it is it maybe a is it a petabyte?
00:17:16.400 --> 00:17:17.680
No, much bigger than that.
00:17:17.839 --> 00:17:22.960
We want to be looking at hundreds of petabytes, maybe even an exabyte one day.
00:17:23.200 --> 00:17:31.200
And we want to be able to therefore take that size of corpus and and understand what's in it, correlate it, graph it.
00:17:31.279 --> 00:17:35.519
So we looked at graph databases, Neo4J, DGraft, you name it.
00:17:35.759 --> 00:17:39.759
We looked at vector databases, we looked at PineCone, all these things coming along.
00:17:40.079 --> 00:17:46.640
None of them would scale to anywhere close past probably a few terabytes, and it's very little further forward today.
00:17:46.720 --> 00:17:51.759
So you have the classic challenge of scaling out is the only way you can go.
00:17:52.000 --> 00:18:01.119
It didn't scale up enough, and if you wanted to scale out from a few terabytes to handle hundreds of petabytes, you're talking about thousands of servers.
00:18:01.359 --> 00:18:03.200
It's an absolute nightmare.
00:18:03.359 --> 00:18:07.440
And if it's a graph, you've got to segment your graph across all those things.
00:18:07.519 --> 00:18:08.400
It's a challenge.
00:18:08.640 --> 00:18:14.079
So we decided, as a bunch of lunatics, we are, that we'd actually set out to look at scale up.
00:18:14.400 --> 00:18:26.559
How can we ingest, understand, fully map, correlate, graph, vectorise, do whatever we need to do with a massive corpus, and we have to do it at speed, otherwise we'll spend 20 years trying to do it.
00:18:26.960 --> 00:18:30.799
So we obsessed over the efficiency of our technology.
00:18:31.039 --> 00:18:36.240
Our entire platform today runs on less than 8 kilowatts of power.
00:18:36.720 --> 00:18:41.279
Entire platform, mapping over 200 petabytes of unstructured data.
00:18:41.839 --> 00:18:45.519
And then the trick was then we wanted to compress that.
00:18:46.160 --> 00:18:50.079
Models compressed data, but they don't expand it again.
00:18:50.480 --> 00:18:55.359
They don't have the ability to give you the attribution of where that information came from.
00:18:55.599 --> 00:18:57.759
We wanted a lossless compression.
00:18:58.240 --> 00:19:10.240
So we've got a compression that gets data down to about one and a half to two percent of its size, but we can fully expand it, knowing exactly where it came from, taking it to the exact article, what it says, where it is.
00:19:10.400 --> 00:19:38.559
And then the next piece was condensing that view of that information into a form that's fully graphed, fully vectorized, all those things that you want to do, and be able to answer questions very precisely by being able to look at all of the totality of information around that topic, corralling it, condensing it, and getting it down to what we call the net unique view of information on that question or topic.
00:19:39.119 --> 00:19:41.359
And we do that in milliseconds.
00:19:41.599 --> 00:19:47.599
Once you've got to that, meanwhile, Chat GPT launches and it threw us into absolute turmoil.
00:19:47.839 --> 00:19:50.960
Oh my goodness, this is gonna change the world.
00:19:51.119 --> 00:19:53.759
This is gonna change how people consume information.
00:19:54.000 --> 00:20:01.519
People are gonna think it's all real and they're gonna get running away with, oh, I've got my nice medical diagnosis from Chat GPT, it looks fantastic.
00:20:01.680 --> 00:20:03.519
It says I've got nothing to worry about.
00:20:03.680 --> 00:20:06.880
Meanwhile, it's all really just a facade of what's there.
00:20:07.200 --> 00:20:27.680
So we said, but if we can take our information, the ability to corral the information of a small topic, a really precise set of questions, and we can pull the net unique view together and then decompose it again to use a small model to be able to represent that, we can make a tiny model look like it's five times the size of Opus 4.7.
00:20:28.240 --> 00:20:30.400
And that's what we built, and that's what it does.
00:20:30.480 --> 00:20:34.799
And we've demonstrated to a few people today that wouldn't stop asking questions.
00:20:34.960 --> 00:20:35.920
Am I right, Helen?
00:20:36.559 --> 00:20:37.039
Absolutely.
00:20:37.119 --> 00:20:38.400
They wouldn't stop get tested.
00:20:38.480 --> 00:20:39.359
Well, how about this then?
00:20:39.519 --> 00:20:40.480
Try that with it.
00:20:40.799 --> 00:21:09.279
And so corporate AI now can take you from a simple challenge statement to produce a really elaborate problem definition to doing a fully ideated solution, to validating that solution, to working out a development roadmap to tell you what the staffing should be, what equipment you need, what the use cases are, what the patent application to protect it all should be, and what the pitch deck is for grant funding or for your VCs.
00:21:10.400 --> 00:21:17.279
And it's basically a complete scientific frontier research platform in a box, one-stop shop.
00:21:17.359 --> 00:21:22.160
Now the entire process, I think less than 30 minutes is a good example of it, Andy.
00:21:22.319 --> 00:21:22.400
Yeah.
00:21:22.480 --> 00:21:22.720
Yeah.
00:21:22.880 --> 00:21:23.119
Yeah.
00:21:23.359 --> 00:21:24.000
Wow.
00:21:24.640 --> 00:21:26.079
That's pretty amazing, right?
00:21:26.240 --> 00:21:35.279
So to bring it to life, are there any challenges or questions that you could share that you've asked it and you've got, you know, things that have excited you?
00:21:35.599 --> 00:21:37.200
Oh, loads, loads.
00:21:37.359 --> 00:21:46.240
So on route to this, funny story, en route to this, uh, there's a lovely guy called Daniel Hayes, and I've got to know him really well.
00:21:46.400 --> 00:21:49.119
And he's now met him on a few occasions as well.
00:21:49.200 --> 00:21:55.759
Uh, and he represented this little group called UCR, Universal Collaboration Research.
00:21:56.000 --> 00:21:59.759
And we weren't ready, the platform wasn't launched, even in its prior incarnation.
00:22:00.319 --> 00:22:02.640
And Daniel said, we'd really love to use your platform.
00:22:03.119 --> 00:22:04.160
We've heard what you're doing.
00:22:04.319 --> 00:22:10.400
We'd like to use it to try and see what we what the patents are in this space, what the intellectual property is.
00:22:10.640 --> 00:22:11.599
And I said, We're not ready.
00:22:11.759 --> 00:22:12.160
We're not ready.
00:22:12.240 --> 00:22:12.880
Go away, we're not ready.
00:22:12.960 --> 00:22:13.359
We're not ready.
00:22:13.440 --> 00:22:14.400
We'll let you know where we are.
00:22:14.559 --> 00:22:18.160
He kept going at it and go, and by the way, that's a great quality.
00:22:18.400 --> 00:22:21.759
For you, the entrepreneurs in the room, do not get put off.
00:22:21.920 --> 00:22:22.799
Learn from kids.
00:22:23.039 --> 00:22:24.640
I want an ice cream daddy.
00:22:24.720 --> 00:22:25.519
You can't have one.
00:22:25.680 --> 00:22:27.119
But I want an ice cream daddy.
00:22:27.200 --> 00:22:30.480
And they'll keep up in the game until you give them an ice cream.
00:22:30.559 --> 00:22:30.880
Okay?
00:22:31.200 --> 00:22:33.839
Now, when you're a grandparent, of course you give it them straight away.
00:22:33.920 --> 00:22:35.039
You don't even wait, do you?
00:22:35.200 --> 00:22:36.480
So that's our job.
00:22:36.640 --> 00:22:37.119
All right.
00:22:37.279 --> 00:22:41.519
So you're in this scenario where you know he kept pushing.
00:22:41.920 --> 00:22:49.119
And he said to us, we want some queries on a thing called, remember it well, multi-layer ceramic bus bars.
00:22:49.279 --> 00:22:56.160
Now we're only a stone throw away from JLR, and some of you in the room probably would actually have an understanding of what that probably means.
00:22:56.319 --> 00:22:56.640
Okay.
00:22:57.119 --> 00:22:58.960
I didn't know much about it at the time.
00:22:59.039 --> 00:23:06.319
We ran some queries, and I had 10 minutes, ran some queries through, sent them to him, and I go, what's that about?
00:23:06.559 --> 00:23:07.759
That's interesting.
00:23:08.079 --> 00:23:12.079
Two minutes later, wow, ten minutes later, can we have a call tonight?
00:23:12.160 --> 00:23:13.359
I think, here we go.
00:23:13.519 --> 00:23:17.759
I told you it wasn't ready, I've done you a favour, and you're just like those guys that worry.
00:23:17.839 --> 00:23:18.799
You just want more.
00:23:18.960 --> 00:23:20.640
You just want to keep going, that's it.
00:23:20.799 --> 00:23:26.079
So we had a conversation, we weren't, it didn't have a license, we had no business model.
00:23:26.240 --> 00:23:28.720
We said, okay, we'll let you use the platform.
00:23:28.960 --> 00:23:42.640
Where they've ended up is in a really interesting, and this is, and by the way, we were so impressed by them, we actually provided some grant funding, we provided some pre-seed funding, they've had less than a million pounds of the funding.
00:23:42.880 --> 00:23:57.119
They now have a working prototype of a device that can take the output of a solar panel, and I'm talking about a current state-of-the-art solar panel, and boost the usable output by 2.4 to 1.
00:23:58.240 --> 00:23:59.599
2.4 to 1.
00:23:59.759 --> 00:24:09.440
It would make solar the cheapest form of energy generation by a country mile, better than fully amortized nuclear, you name it.
00:24:09.920 --> 00:24:14.799
But besides that, they wanted to do that with ceramics, that's where they started.
00:24:15.039 --> 00:24:26.799
They also created a new, more efficient method of creating plasma, sparks that are 7,000 degrees and above, with the amount of energy you get from an electric kettle.
00:24:27.359 --> 00:24:32.160
And with that, they can now produce ceramic materials that can do wonderful things.
00:24:32.319 --> 00:24:41.279
And our problem is they can do so much that our problem is actually, what are we getting all the people to allow us to implement all these things and do these things with it?
00:24:41.519 --> 00:24:45.759
But they've done that, and the platform's been a big part of that journey.
00:24:46.079 --> 00:24:47.039
Does that help?
00:24:47.359 --> 00:24:54.240
Well, yeah, I mean it's not quite cold fusion, but we're getting there, I guess, in terms of um the energy.
00:24:54.400 --> 00:24:58.480
Um does it do, I mean, we talked about the scientific side of things.
00:24:58.559 --> 00:25:04.559
You know, if we were asking it for peacekeeping in the Straits of Hormuz, I mean, can you go in that side?
00:25:04.640 --> 00:25:05.599
Can you go humanitaires?
00:25:05.839 --> 00:25:06.640
Can you go politics?
00:25:06.960 --> 00:25:08.799
Is this guy a plug for Neo?
00:25:09.680 --> 00:25:10.319
Is he?
00:25:11.119 --> 00:25:14.880
Obviously, Neo got to him and said, I didn't quite have enough time to quiz Mel this morning.
00:25:14.960 --> 00:25:16.799
Would you just bring that into the talk?
00:25:16.880 --> 00:25:25.519
You know, so Neo is one of the guys on the peacekeeping force in in Warwick University, and this morning we gave him a license key for what we call curve.
00:25:25.680 --> 00:25:29.119
Curve is the real-time viewer on this data set we have.
00:25:29.279 --> 00:25:35.599
Now, I talked about 200 petabytes, but it's actually looking at changing data sets in the world in real time.
00:25:35.759 --> 00:25:39.359
So every day there's like three or four million new articles for news coming in.
00:25:39.519 --> 00:25:43.119
There's new patents, new SEC filings, you know, all come in real time.
00:25:43.359 --> 00:25:46.799
Curve is the window into that, and Neo wanted to have a license key for that.
00:25:46.880 --> 00:25:47.680
So give him that.
00:25:47.759 --> 00:25:49.680
And he asked exactly that question.
00:25:49.920 --> 00:25:56.000
I said, look, this is a scientific thing for trying to work out problem definitions and coming up with solutions.
00:25:56.240 --> 00:26:00.319
But I tell you what, I'm going to take the risk and try it on these this question.
00:26:00.480 --> 00:26:05.279
And it'll probably blow up in my face, all right, and it probably won't work, but we'll try it.
00:26:05.359 --> 00:26:09.279
I love live demos, they're they're really horrendous, but we do them all, don't we?
00:26:09.359 --> 00:26:10.000
So, you know.
00:26:10.240 --> 00:26:16.160
So, but if you if you pull it off, it's worth a thousand PowerPoint presentations.
00:26:16.319 --> 00:26:17.759
There could be smoke and mirrors.
00:26:18.079 --> 00:26:26.240
So the challenge was how would we bring about a mediation in a conflict between Ukraine and Russia was the one he gave me.
00:26:26.559 --> 00:26:35.599
So we tried that, and it's come up with a pretty good stab at all the things you'd have to look at and then to come out with how you would actually implement that.
00:26:36.000 --> 00:26:53.359
So it's it's not where I would like it to be, David, on that, but it's a few hours at most to be able to just change the generics to do that, so we can then take on probably the politics if not all the humanities, but certainly the politics side of that.
00:26:53.680 --> 00:26:54.079
Wow.
00:26:54.240 --> 00:27:03.599
I'm um I'm I'm conscious that we haven't got a huge amount of time, so I'm gonna go from that global right down to a sort of very specific question.
00:27:03.759 --> 00:27:07.200
So we've got some amazing deep tech founders in the room.
00:27:07.359 --> 00:27:12.079
I wonder from your own experience what advice you might give to a deep tech founder.
00:27:12.880 --> 00:27:27.359
I think it's interesting because you know, been having been there before and having been there and and failed when I had no money, okay, which is a really difficult place to be because you you don't know where you're going to go with it.
00:27:27.680 --> 00:27:41.759
And having been there where I failed and had enough money to survive that and just regroup and go again, I would say the following that you have to have a pretty good indication, not of feasibility.
00:27:42.000 --> 00:27:46.160
Forget feasibility, go for viability.
00:27:46.559 --> 00:27:48.160
You want viability.
00:27:48.400 --> 00:27:59.200
It's no good coming out with a deep tech innovation that requires 10 times as much energy than another solution but has zero pollution because no one's going to use it.
00:27:59.519 --> 00:28:03.759
So you've got to think: is it actually viable?
00:28:04.079 --> 00:28:09.039
Viable doesn't just mean it works, viable means it's commercially valuable.
00:28:09.279 --> 00:28:13.599
It has a market opportunity that you can identify and go with.
00:28:13.759 --> 00:28:21.359
And then you've got to decide, even if I do this, how does that, how do I get to that go-to-market piece?
00:28:21.599 --> 00:28:24.960
And sometimes it's not something you can do yourself.
00:28:25.200 --> 00:28:30.400
Sometimes you've got to look, well, maybe it's a licensing deal with someone who understands that market.
00:28:30.880 --> 00:28:41.920
Now that might not satisfy your grandiose view of valuation, but it but it's going to give you a fast-track path towards that, and and you probably could get somewhere close as well.
00:28:42.240 --> 00:28:45.440
So think about what it is you're doing.
00:28:45.680 --> 00:28:46.960
Is it feasible?
00:28:47.200 --> 00:28:48.480
Is it viable?
00:28:48.880 --> 00:28:57.519
If it's viable, what's that bridge between where it is now to go to market and and be wary of that VC trap?
00:28:58.400 --> 00:29:05.359
You can easily spend, if you if you think you've got your idea pretty well sourced, you might even have a bit of a prototype.
00:29:05.680 --> 00:29:15.279
You could waste three, four, five, six months of your time writing documents to try and get a VC to respond to you.
00:29:15.440 --> 00:29:18.960
Even when they do, they'll always say, Yeah, we'll talk to you.
00:29:19.039 --> 00:29:21.920
Let's look at it, we're quite interested in this, and it goes nowhere.
00:29:22.240 --> 00:29:26.319
The emotional roller coaster that brings is huge.
00:29:26.640 --> 00:29:27.440
It's huge.
00:29:27.599 --> 00:29:32.079
And if you talk to four or five of them, and they all say no, you'll doubt yourself.
00:29:32.400 --> 00:29:37.119
If you're confident about what you want to do, have the passion, keep going at it.
00:29:37.200 --> 00:29:38.559
I want an ice cream, Daddy.
00:29:38.720 --> 00:29:40.079
All right, I want an ice cream.
00:29:40.240 --> 00:29:40.960
You can't have one.
00:29:41.039 --> 00:29:44.319
I want an ice cream, and you just keep going at it.
00:29:44.480 --> 00:29:49.200
But don't talk to too many until you figure it out how you do it.
00:29:49.359 --> 00:29:53.759
And don't necessarily treat what they say to you as the things you need to do.
00:29:54.160 --> 00:29:58.160
If it was as simple as that, they'd have backed you and just said, just do these few things.
00:29:58.480 --> 00:30:00.240
So they don't know the market.
00:30:00.559 --> 00:30:03.119
VCs are followers of fashion.
00:30:03.359 --> 00:30:05.839
Look at the AI investments now.
00:30:06.000 --> 00:30:12.960
The people on the fear of missing out that are coming in behind it, they'll probably have no chance of getting many times their money back on it.
00:30:13.519 --> 00:30:15.039
VCs follow fashion.
00:30:15.200 --> 00:30:21.839
If yours, what you've done, is what you've done is really unique, really novel, they won't understand that.
00:30:22.079 --> 00:30:23.440
Don't expect them to.
00:30:23.599 --> 00:30:27.440
So when they ask you, what do you think the total addressable market is of this?
00:30:27.599 --> 00:30:28.559
Walk out.
00:30:29.359 --> 00:30:32.960
If they don't know the market, you're going to aim at well enough to figure out that's huge.
00:30:33.119 --> 00:30:41.599
If you think the guys that did Uber, we've worked out how many taxi journeys there are in the world, and we think actually that the market for this thing is pretty big.
00:30:41.759 --> 00:30:46.240
In fact, in London, it's probably worth about 2,000 a year, maybe 2,000 a month.
00:30:46.480 --> 00:30:47.359
It's bloody huge.
00:30:47.519 --> 00:30:50.160
We're going to disrupt the entire taxi marketplace.
00:30:50.400 --> 00:30:51.680
I don't know how big it is.
00:30:51.920 --> 00:30:53.680
Have you got the ball to the back it?
00:30:53.759 --> 00:30:55.279
That's all I need to know.
00:30:55.519 --> 00:30:56.559
And what did they do?
00:30:56.640 --> 00:31:08.480
They did a smoke and mirror show by arranging for a car to be just around the corner to demonstrate it to, I think it was Bob Cagle from Benchmark at the time, to invest in it to say, look, here's how it works.
00:31:08.640 --> 00:31:10.480
Oh my god, they must be pretty big already.
00:31:10.559 --> 00:31:12.319
The car already arrived.
00:31:12.880 --> 00:31:16.240
But he was sold, invested literally within a week of that.
00:31:16.480 --> 00:31:18.960
He didn't have to go into a big PowerPoint presentation.
00:31:19.119 --> 00:31:22.720
Here's what the cars look like, here's the branding, here's how we're going to market it.
00:31:23.039 --> 00:31:25.279
Going to put an app out there, everyone's going to want to use it.
00:31:25.440 --> 00:31:28.240
And it's disrupted that market massively.
00:31:29.440 --> 00:31:30.079
That's great.
00:31:30.240 --> 00:31:30.720
I love it.
00:31:30.799 --> 00:31:37.039
Um, my last question, because I'm conscious of time, but I promised I'd leave a bit of time for any questions from the audience.
00:31:37.200 --> 00:31:40.079
So start thinking if anybody's got a question.
00:31:40.400 --> 00:31:43.680
My last question is really in this investment space.
00:31:43.839 --> 00:31:49.759
So obviously, bootstrapping versus investment, how do you know when you should go to investor?
00:31:49.920 --> 00:31:50.880
When do you need one?
00:31:51.039 --> 00:31:52.480
What's the trigger for you?
00:31:52.799 --> 00:31:58.319
I I don't think there's a binary view of that or even a litmus test you can apply.
00:31:58.480 --> 00:32:01.920
Uh and I'll take Corporal as a classic example.
00:32:02.160 --> 00:32:05.599
So we saw ChatGPT go to market.
00:32:06.079 --> 00:32:11.440
We saw perplexity, and I don't have a lot of, I don't have a lot of respect for the product.
00:32:11.599 --> 00:32:12.000
They were.
00:32:12.079 --> 00:32:15.440
I think now they're figuring out lots of good things, they're doing some great work.
00:32:15.680 --> 00:32:18.559
But at the time we thought, wow, we need to go in this bandwagon.
00:32:18.640 --> 00:32:22.319
We've got a tool that's better than all these things, we should put it out there in competition.
00:32:22.799 --> 00:32:25.839
We didn't have the resources to market that, to compete with that.
00:32:26.000 --> 00:32:29.359
We were never going to get the the toe hold that was required.
00:32:29.519 --> 00:32:30.880
Well, we'll go and raise some money.
00:32:30.960 --> 00:32:32.640
So we did our business plans around that.
00:32:32.720 --> 00:32:37.279
We went down the VC courtship, and no one wanted to back us.
00:32:37.440 --> 00:32:39.920
And you know, they were absolutely right.
00:32:40.160 --> 00:32:41.359
They shouldn't have backed us.
00:32:41.440 --> 00:32:43.440
It wasn't worth backing on that plan.
00:32:43.759 --> 00:32:50.960
And they did me a big favor because they forced us to find the route that we could go to market with, and we can now do it without them.
00:32:51.119 --> 00:32:55.359
And now and we've moved from a licensing model, we don't license our technology.
00:32:55.519 --> 00:32:58.480
We might talk about license with Yuli who said that.
00:32:58.559 --> 00:33:00.319
But we're not licensing our technology.
00:33:00.400 --> 00:33:00.720
Why?
00:33:00.880 --> 00:33:06.079
Because the value of what it produces will be democratized by a licensing model.
00:33:06.240 --> 00:33:17.680
And I will not democratize a product that can go from a simple challenge statement to a fully ideated solution with patent application in less than half an hour for 20 bucks a month.
00:33:17.920 --> 00:33:25.039
Because some kid in a bedroom using it will actually put loads of people out of business by just filing patents on the back of that.
00:33:25.279 --> 00:33:26.319
I won't do that.
00:33:26.480 --> 00:33:39.839
So our model is we either allow people to use the technology for a share of the IP, or we invest and we also allow them to use the model the product for a share of the IP, and that's a medium to long-term gain.
00:33:40.000 --> 00:33:40.799
We have no revenue.
00:33:40.880 --> 00:33:42.319
I'm not bothered about that.
00:33:42.480 --> 00:33:45.279
The revenue is just a facade, it's just another measurement.
00:33:45.440 --> 00:33:58.079
But we're building value in allowing companies that can utilize our technology to accelerate their value, to bring their product to market one, two, three years sooner, and for probably a fifth of the cost.
00:33:58.319 --> 00:34:01.039
And in there, we can actually extract value.
00:34:01.359 --> 00:34:04.559
But I mean I'm talking about small bits of value across a broad base.
00:34:04.960 --> 00:34:06.640
And that's the model we're now on.
00:34:07.440 --> 00:34:08.000
Thank you.
00:34:08.719 --> 00:34:10.639
Any questions from the audience?
00:34:11.039 --> 00:34:12.239
I think you first there.
00:34:12.960 --> 00:34:13.760
Thank you very much, Mal.
00:34:14.000 --> 00:34:15.679
It's been inspiring today.
00:34:15.760 --> 00:34:26.239
Uh if you know my question is, um, when and how did you decide to do your philanthropy work, your charity work like in SAR?
00:34:26.400 --> 00:34:26.639
Yeah.
00:34:26.880 --> 00:34:28.159
Where where did it come from?
00:34:28.639 --> 00:34:30.480
I I think just just giving back.
00:34:30.800 --> 00:34:37.119
All right, and if you've ever seen the movie, you know you you'd know exactly what that's that's all about, which is a fabulous movie, by the way.
00:34:37.440 --> 00:34:41.039
So I I I'd made a lot of money over lots of businesses.
00:34:41.280 --> 00:34:44.000
And my attitude was I'd like it to go to some good.
00:34:44.239 --> 00:34:44.559
Right?
00:34:44.639 --> 00:34:52.800
So we created this this trust which has got a very wide remit, medical sporting and research technology, and it has a very wide remit.
00:34:52.880 --> 00:34:54.000
It gets involved in all sorts.
00:34:54.320 --> 00:34:55.920
We sponsor sports people.
00:34:56.239 --> 00:35:01.599
Uh Genevieve Somerville will represent us in the Olympics next year, on the the Winter Olympics.
00:35:01.840 --> 00:35:04.480
Sponsored her since she was about 11 years old.
00:35:04.639 --> 00:35:06.960
Uh we've got tennis players we sponsored.
00:35:07.119 --> 00:35:16.000
We've got someone who was in the Olympics this year, Winter Olympics, who's actually uh basically a solo bobsleigh athlete.
00:35:16.239 --> 00:35:21.519
All right, she lives, she was born in Derby, and uh, you know, and there's no snow there, by the way.
00:35:21.599 --> 00:35:23.519
So you can imagine the work she goes into.
00:35:23.760 --> 00:35:26.639
So we put money in, but they put lots of hard work into it.
00:35:26.719 --> 00:35:38.320
We sponsor medical projects, we sponsored pure technology projects, uh, and we go where we think there's a value to be had, not in a return, but in terms of what's the impact of those investments.
00:35:38.559 --> 00:35:47.119
And and and it's great to do that, and it's it's carried us into all sorts of conversations that I feel honored to be part of because of that.
00:35:50.400 --> 00:35:56.079
So I have a question which is around the conflict between values and interest.
00:35:56.719 --> 00:36:12.320
So if you're a founder and and you found a product where you generally market, why can't we still do it here when you when you just have to try what kind of market in the UK why can't you plan and do it there?
00:36:12.800 --> 00:36:15.679
So is there a conflict between values and what do you think of that?
00:36:16.239 --> 00:36:19.360
I think that uh I'm a big sovereign fan.
00:36:19.760 --> 00:36:20.000
Okay.
00:36:20.480 --> 00:36:36.400
I think that and and you guys are uh on on my journey I see working with universities like this and others, I think, as being a key component of how we actually survive as a nation, how we thrive as a nation.
00:36:36.719 --> 00:36:42.480
And and you know that that means more to me than making 10x what we might make doing that.
00:36:42.639 --> 00:36:45.360
Um so you know, enough is enough.
00:36:45.519 --> 00:36:47.360
One pound more than enough is plenty.
00:36:47.519 --> 00:36:47.760
Okay.
00:36:48.239 --> 00:36:49.679
So you can go to the States.
00:36:49.760 --> 00:36:54.719
That might be the reason you go there, isn't because of the market, it might be because of the backing.
00:36:54.960 --> 00:37:03.760
And I think we've got a lot of work to do in the UK to increase, to accelerate, to make the fit of that backing process better.
00:37:04.000 --> 00:37:06.639
Uh, and I think and we've seen some great initiatives.
00:37:06.800 --> 00:37:10.000
I met with a lady called Josephine Kant three three weeks ago.
00:37:10.159 --> 00:37:13.199
She's one of the founding members of D Sit Sovereign AI.
00:37:13.440 --> 00:37:14.400
Fantastic initiative.
00:37:14.480 --> 00:37:19.840
So it's happening, but the answer is because I want to I want to try and help other things.
00:37:20.320 --> 00:37:21.679
The ground, so values first.
00:37:21.920 --> 00:37:22.000
Yeah.
00:37:24.639 --> 00:37:26.400
I mean, it's a great place to wrap it up.
00:37:26.480 --> 00:37:29.440
I have to say, it's fantastic to hear Mel speak, isn't it?
00:37:29.519 --> 00:37:31.760
What an amazing, inspirational person to have in.
00:37:31.920 --> 00:37:34.320
So I think he deserves a round of applause.
