John Straw:

060 – Technonomics and Why My Profits Might Just Disappear with John Straw

John Straw:

060 – Technonomics and Why My Profits Might Just Disappear with John Straw

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In this episode, we are joined by digital and marketing entrepreneur, John Straw, who describes himself as a “technonomist” – someone exploring the cutting edge of technology and looking to understand where it fits from both an economic and commercial perspective. John is a Senior Advisor at McKinsey and IBM, as well as an author, speaker and investor with over 30 years of experience in IT and digital transformation.

  • The journey John sees towards “programmatic enterprises” in which the availability of data and artificial intelligence allow for organizational control on a totally different level than possible today
  • How this journey takes us from decision making via experience and intuition to experience augmented by data to data augmented by experience to simply by data. And how, as per previous major shifts (think of the introduction of the PC) this happens not as a “big bang” but as a more gradual or “stealthy” process
  • The advice that John uses when personally investing in new technology businesses and his two-part rule which he advises business leaders to use in renovation and innovation implementation

Key Takeaways and Learnings

  • How companies are using “layered” data to improve their renovation and innovation activities
  • How new technologies, and the pace of their development, provide opportunities for scale for all companies’ renovation processes
  • Why transformational innovation activities (“breaking” the existing business) need to go “in the garage”, away from the innovation “killers” of process and politics

Links and Resources Mentioned in this Podcast


In this episode, we are joined by digital and marketing entrepreneur, John Straw, who describes himself as a “technonomist” – someone exploring the cutting edge of technology and looking to understand where it fits from both an economic and commercial perspective. John is a Senior Advisor at McKinsey and IBM, as well as an author, speaker and investor with over 30 years of experience in IT and digital transformation.

John Straw

John Straw is a digital veteran with 33 years in IT and marketing. His current roles include senior advisor to Mckinsey and Co, IBM Watson IoT and his own portfolio of investments. In 2017 he became a NED for FTSE100, Provident Financial plc. He is a prolific blogger on disruptive technology and a contributor to The Economist on AI.

What Was Covered

  • The journey John sees towards “programmatic enterprises” in which the availability of data and artificial intelligence allow for organizational control on a totally different level than possible today
  • How this journey takes us from decision making via experience and intuition to experience augmented by data to data augmented by experience to simply by data. And how, as per previous major shifts (think of the introduction of the PC) this happens not as a “big bang” but as a more gradual or “stealthy” process
  • The advice that John uses when personally investing in new technology businesses and his two-part rule which he advises business leaders to use in renovation and innovation implementation

Key Takeaways and Learnings

  • How companies are using “layered” data to improve their renovation and innovation activities
  • How new technologies, and the pace of their development, provide opportunities for scale for all companies’ renovation processes
  • Why transformational innovation activities (“breaking” the existing business) need to go “in the garage”, away from the innovation “killers” of process and politics

Links and Resources Mentioned in this Podcast

Welcome to the Innovation Ecosystem podcast. With me today is John Straw who is an author, speaker, an entrepreneur and an investor. So, welcome to the show, John.

Thank you.

So, how do you answer the dinner party question, ‘What do you do?’

Well, my wife’s answer would be somewhat different to my answer. My background is I’ve done four startups with four exits, I am a current senior advisor to McKinsey, wrote a book, did the Thomas Cook digital turnaround etc. etc. – this is what happens when you get old. I think what I would describe myself as being is a ‘technonomist’ which is combining essentially the cutting edge of technology, we’re trying to understand where that fits from an economic base and from a commercial base as well, and the inevitable merging of those two systems because that’s where we’re going versus 1999 when it was actual technology and technology standing alone which is why the bubble burst. Now, it’s a completely different proposition. It is now completely intertwined, that technology is intertwined with where we are now and in the future technology will become more and more dominant as part of that relationship.

So, is that to do with the fact that it’s not just the technology that exists but it’s also the business models behind that?

Yes, absolutely. I spend a lot of my time literally going around the world talking to business leaders about how what any product offering looks like now it’s got to have technology merged with it so that it provides a level of scalability. Let me give you an example of that. So, our friends over at Uber, despite their corporate governance issues, they are actually very, very innovative and they did something really quite remarkable that was really simple. I’m a big believer in the API, the application programming interface commonly, and they did something, a classic example of that. Six months ago, they launched their commercial API into the open market and what happened was virtually immediately Hilton embedded that API into its app. So, I found it by going and booking Hilton and I noticed as soon as I booked it there was a new button there and it said ‘Uber’, and what Uber had done was integrate via an API into my diary and into the Hilton system, so by pressing that button, all of a sudden it removed the friction of the journey going to that particular hotel and leaving from it to go to another meeting, and that’s a brilliant example of integration. But the really important part about disruption now has become the adoption curve and in this case, that was disruptive and is disruptive because Hilton updated their app to five million users inside of twenty-four hours, so all of a sudden Uber gets additional incremental exposure to disrupt an existing friction system, which is people getting to the hotel, and they did it almost on an adoption curve which is almost vertical. Hilton win because they get a small commission from each ride, Uber get the distribution to go with it. Classic example of the API economy, the technology converging with the adoption curve. That’s disruptive.

And who are the losers? I guess it’s the minicab firms around servicing the Hilton, right?

Yep, any of the old-fashioned companies out there which can’t do the integration then they are going to lose and they’re going to be excluded from that journey.

So, was there anything that they – I mean, a lot of our listeners are probably more – I don’t think we have many taxi drivers who are listening but people who are in the old economy, perhaps, in more industrialized business to business incumbent companies and I guess they might well be listening and thinking, ‘Well, how can I see that kind of thing happening?’ So, as you talk to your executive, your leaders in these companies, what are the really good ones doing in terms of filtering out the signal from the noise, because there’s a pile of noise right out there around this? 

Oh, how politely put! Yeah, very politely put. So, I think you’ve got to distill this down to taking the technology down to use cases like the one I just explained because when I do a talk, I think the first thing you’ve got to do is actually let people understand in fairly simplistic terms about what AI is, what the Internet of Things is, but without getting into the technical details and you’ve then got to rapidly move that into real-time use cases that exist now which is, as I say, like the one I’ve explained, and such is the pace of technology combining the business model with the technology, there are a gazillion use cases from everything from AI robots all the way through to 3D printing etc. and there are many organizations now that are combining all of these elements. Adidas have just launched a factory in Germany that’s combining everything. So, it’s combined all the stuff that I talk about, the five pillars of disruption, so it’s combined Internet of Things, 3D printing, artificial intelligence, in this case, augmented reality, and very, very clever sensor technology, all in one factory with hardly anybody in it and producing very, very high-quality products because everything is controlled end to end.

And you touched on a very, very important phrase, ‘with hardly anybody in it.’ Presumably, this is the gig economy, the huge displacement of manual non-cognitive tasks by AI. How are you seeing that?

So, two responses to that. The first one is is that if you can write a very tight job description for somebody, that job is actually going to be replaced by AI and I’m going to give an example of that in a second. The second part of this is that I think that the press is building up the idea that AI is going to come in, it’s going to arrive one morning and everybody’s going to be out of work. Well, it’s not, because AI is arriving by stealth into everything that we do, you’re just not going to see it. You’re going to be talking to a call center, I talk a lot about call centers and about chatbots, in the next two or three years you won’t know whether or not you’re talking to a human being or you’re talking to a bot, that by the way is called the Turing Test, and we’re about to go past that. So, I think this whole AI thing – a classic example, so my next door but one neighbor is a surgeon and he’s very clued up and we got talking about the NHS and the way it’s going to be changed by AI and he said, ‘My first example here would have to be radiologists’, because radiologists do pattern recognition, they’re looking at an X-Ray and they’re looking to find an anomaly and what they’re doing there is that they’re using their own experience of let’s say twenty-five years to actually go find that anomaly, and what at the moment is happening is that we’ve got a lot of radiologists out there unknowingly training, called machine learning, AI systems to recognize what tumors are, and of course, when the AI systems get beyond a certain point they’re infinitely capable than human beings because an AI system can look at gazillions of images really, really fast, use the machine learning and then actually start to learn themselves and become a lot more accurate, and this surgeon was telling me already jobs are being affected by this but it’s very stealthy. It’s not going to arrive as if it’s an army and it’s not going to certainly even as a visible ripple. It will just slowly happen. It will be in augmentation first over the next two to three years but then it will become a displacement of jobs after that in an ever-increasing number.

I guess in the same way that it happened with PC’s, right? I remember when PC’s came into the workplace and they were supportive of the activities we were doing but more and more the technology just runs behind the scenes now.

Yeah, absolutely. Unfortunately, I actually remember when typewriters came into the work scene.

When they came into the workplace? No, I can’t.

Yeah, yeah, absolutely.

But going back, you’ve talked about use cases, this stuff coming in by stealth, so the CEO or an executive running a business unit in a large industrialized, maybe a regulated company, what should they be doing? Let’s call it a public company where they’ve got to continue to deliver against their quarterly numbers, against the expectation they’ve set to Wall Street. What are the really good CEO’s or leaders doing to figure out what’s actually happening, when it’s going to happen, is it going to impact them or is it going to be a replacements problem given the timeframes of some of this?

Right, that’s a relatively short answer and a four-hour diatribe to go with that. OK, so let me divide this up a little bit. So, there’s a lot of talk about digital transformation floating around and has been for about three or four years. McKinsey have got some really great solid frameworks for our transformation but to me, I think we’ve moved on a little bit and let me give you an example. I think in the nineties we used to make decisions, whether or not it’s plc listed business or whether it’s a small business, we used to make decisions as senior managers working on experience and intuition. Hopefully, in the noughties, we moved that on to experience augmented by data. Now, where we should be is decisions made with data augmented by experience and within about ten or fifteen years the decisions will simply be made by data, in many cases by trading platforms, buy site platforms, sell site platforms etc. So, as an organization, as a chief executive in an organization, I should be moving my organization to making those decisions at this moment in time supported by very, very accurate data so that there is very little room for margin and you can do a lot of scenario planning with a lot of AI help, but there definitely should be a graduation where the decisions become data orientated. That leads you to something I call the ‘programmatic enterprise’, where you have such control of your organization because of technology and an end to end sense of perspective, and you have an AI system that you can turn around and say, ‘OK, I want to increase my EBITDA by 1.77% in the next quarter. Tell me how to do it.’ and the system will come back to and give you various scenarios about how you actually go achieve that. So, I think that’s one point, that’s what I’d be thinking about from a CEO’s perspective. The second part of that is is that the disruption is upon us, it is, and this is what I talk to lots of corporates about, the fact that, ‘Actually, let me give you some examples of some startups out there that are currently disrupting your business, you just haven’t discovered them yet.’ So, coming back to this whole use case thing, we used to talk in 1999 at the bust, we used to talk about the theory of disruption, we are now talking about the practicality of disruption. Most organizations don’t realize they’re doing so. The biggest challenge that every single organization I go to talk is an encumbrance of overhead, right? I’ll give you an example. Ford have got 224,000 full-time employees that they need to pay every single month irrespective of whether or not they’re being disrupted by Elon Musk.

And that’s before their pensioners of course, right?

That’s before the pensioners, right? The flip side of that coin is that again our friends at Uber have got 4000 employees that they need to change every month so when Uber get disrupted they can actually flip their business over because they have not an encumbrance of overhead, and overhead is the really big problem to any form of agility for a major business. OK. So, let me extend into the part of the conversation about legislation and legality. So, you take legislation, I’m not a lawyer but I do sit on the board of a plc in London. Take any legislation. Legislation is built upon the basis of a bunch of senior people that get together with their experience and create subjectivity within that – they create legislation which is subjective by its nature which is going back to my previous argument about experience and decisions being made on that. We’re seeing an environment now where data is becoming to the point being so accurate it becomes incontrovertible. Therefore, I am hoping that governments around the world are starting to think about making legislation around data, data-based legislation rather than subjective human based legislation. That means that potentially we can move further forward more rapidly, certainly in pharmaceutical, certainly in financial services, to be able to cope with the level of destruction that’s going on because if we don’t there are some rather catastrophic implications for society I think in the next five to ten years let alone business, because if society gets disrupted business will consequently get disrupted as well.

So, as you talk there are a few things going through my mind. One, just going back to the data sources, I remember reading in 2009/2010 I think one of the reasons that the stock market in the US bounced back so quickly they called it the SAP recession because the CFO was able to fire up the SAP system and figure out where their costs were and they cut very, very quickly and I also met with someone, a professor of IMD two weeks ago and he’s seen the board materials that go to General Electric and he was saying that he’d never seen anything like it because the quality of the information and the set, you know, but many, many companies are nowhere near that. They are legacy companies where the technology might be strung together, UBS for example – well, we don’t need to name names but there are some large companies that have enormous overhead but they just can’t move for you the fear of destroying it.

So, I think it’s really interesting you used the example of GE. In the US where there is not that much legislation to protect employees then you’ve got the ability to rebound very quickly by cutting cost. In the European marketplace, that is completely the inverse of that, so that you took a motor manufacturer, I was talking to an executive of a motor manufacturer quite recently and giving a presentation about disruptive technology and he came to me afterwards and said, ‘Right, which industry should I move into when I move out of the automotive business?’ It’s a household name manufacturer and he said to me, ‘I can’t move from an innovation perspective because I don’t have enough money to be able to properly innovate.’ Most the stuff he puts out in the marketplace is spin, and he said, ‘The problem is I can’t build prototype businesses very easily because to build a prototype business I need to staff it with people but if the prototype fails, I need to get rid of the people by moving to another part of my business, so I acquire overhead and then I have to shift it around when I have a failure’, and by the way, failure is not really a word that I like very much because I like fail and learn as a combined word accordingly and a lot of businesses are getting their head around that but this overhead thing and the lack of flexibility in European labor laws is going to kill a lot of labor markets I think in the short-medium term.

And what was your advice to him? Which industry? Or should he go to the west coast and bang on the door of Tesla? What was – I was curious about how-

So, here comes the controversial bit. In the good old bad old days of early internet everybody’s feeling was, ‘Oh, we’ll get rid of all the intermediaries’, the insurance brokers and stuff like that which mostly in many cases we’ve actually done. What nobody anticipated was that they would be replaced strategically by intermediation by the big tech companies, and by the way I don’t they’re called tech companies anymore I think they should be called platform companies, and I was talking to the chief executive of a very large medical pump manufacturer, I hadn’t realized how big, that marketplace is worth billions, and I said to them, I said, ‘Well, actually I know a technology company as it happens IBM with their Watson AI system that’s going to come into your marketplace, you’ll be able to connect your pumps into it and they’ll be able to tell you comparatively how your pumps are performing specifically down to the individual’ and he just turned around, he said, ‘Oh, we’ve been waiting for that for years!’ About thirty seconds later he went white because what’s happened here and what’s happening is that the profit is being moved around the value chain and it’s being moved into the big technology companies, right? And that’s happening on an hourly basis. In fact, there’s a rumor floating around Amazon this morning that they’re going to go compete with Fed Ex and UPS because they built such a system and it’s so good because they’ll buy some technology but they’ll move into that marketplace, so strategically, and the EU’s got their head around this in a really big way, of course, we’re now leaving the EU – anyway, I shouldn’t go on on that one – so, actually what’s happening is we’re seeing a polarization of profit into the big technology companies and this is the flywheel effect, so the more profit they make, the average salary of a Silicon Valley twenty-two-year-old programmer is a $250,000 dollars a year. Go compete with that. And you can’t, and you get the glamor of working for a tech business-

And the company still has got margins of 98% or whatever on top of that. So, Charlie Munger behind us would say the moats that are behind the FANG’s are almost indefensible now that the Facebook’s – and I guess he’d probably say the same thing about, what was it the BAT’s in China, the Alibaba and Tencent and Baidu-

That’s right.

I presume they operate in a different regulatory environment I guess with the Chinese government because the Chinese government I think that can influence how they choose to but-

Well, I think you can regard their businesses as the Chinese government is embedded in business so much rather than with the US tech companies, the government is a long way away from those businesses. That’s the fundamental difference.

So, the controversy here is that the disintermediation has been replaced by a very, very small number of hugely profitable almost bulletproof organizations unless the regulators decide to go after them?

Yes, and Margaret Vestager, I can’t even pronounce her surname, who is Head of Competition or the EU is really going after the Apples and the Googles of this world and I think that when you’re talking about royalty payments and management charges, all the soft accounting mechanisms being moved all around Europe according to which are the most favorable tax opportunities, I saw there was one coming out of Amazon, they were going to get fined because they were doing stuff with Luxembourg which apparently the EU regard as being noncompetitive. So, data is fluid and when data becomes fluid, right, at that particular point your profit becomes fluid as well because you can move it around to actually anywhere where it’s much cheaper and you try to unpick that, it’s very, very hard. It’s like AI in the fact that when you get an answer out of an AI system you’re getting an answer out of a black box and you can’t really understand how the decision was actually made, and that’s exactly where we’re going. I think the EU has got its heart in exactly the right place where it needs to be because it can see the economic threat. How far it gets with these prosecutions is going to be an entirely different matter and of course, it’s going to be a five-year timescale before anything really comes out by which particular point the game has changed a hundred times since.

So, just going back to the executive who went white when he realized that all the margins are going to disappear to one of these big players, how did you encourage him to get his color back in his cheeks, because these are difficult times if you’re an incumbent in a legacy business where you perhaps have been invested in technology and you haven’t got a value proposition to attract a decent programmer because they’re all disappearing off to make a quarter of a million bucks, I mean, what do they do?

So, I’ve done quite a lot of work led by McKinsey in this area and they’ve got some really intriguing stuff going on. So, you firstly have got to divide your organization and look at your organization to decide whether or not you are a renovator or whether or not you are an innovator, and I think the sort of clients that you were talking about earlier on, many of those you’d categorize as being renovation, and when I’ve seen it, when you try to put innovation priorities into a line of business, BAU businesses on a ninety-day reporting cycle, it just doesn’t work.

So, just so I’m clear, renovation versus innovation, is it the same as incremental versus disruptive innovation or is it-


It is?


Because I’m curious, we had the VP for Creative Disruption in Electronic Arts, that’s not quite as right as his job title but I’ll get it in a minute, but Electronic Arts which is at the forefront of gaming, of virtual reality, his view was that 80% of the innovation they do is incremental, so, I’m just trying to map that with your point around this-

Yes, but that tracks into an interesting number where the amount of startups that succeed or fail is-

One in five, right?

It’s about one in five, yes, so that’s not far away from what happens but bearing in mind that he was a tech business, it wasn’t a car manufacturing business. So, if you look at renovation, my terms of renovation are product modification, service modification, whatever it happens to be, backed up with technology that scales, so there has to be a rule in renovation that it’s got to be technology that will provide a level of scalability, that would be part one. Part two is when you move into the innovation side. So, the innovation brief is, ‘Right, go and break my existing business’ and you can’t put that internally, you have to put that into a garage in Hewlett Packard terms, you’ve got to put it in a garage and you’ve got to not let your existing senior line management, your fifty to fifty-five-year-olds anywhere near it because they will try and kill it to the point that when it becomes successful then they will try and support it and take credit for it, this is natural human behavior, but you really have got to put it outside in a garage because normal process will just kill it. Process and politics kill innovation.

So, there is an organizational response to this and then what about the resource allocation issue? A CEO focused on quarterly numbers with a big LTI which he hopes is going to best in the next five years so that he can go and sit on a beach vs the board which maybe has got a far longer time horizon, how are you seeing people manage the resourcing, not the organizational issue but how much money are people putting behind renovation versus innovation in incumbents by order of magnitude?

It’s nearly impossible to answer that question. So, renovation, what I’m seeing, is just coming out of normal line budgets. It’s just a slightly changing different set of objectives to go with that. Innovation I’ve seen is as little as a million dollars and I’ve seen twenty, thirty, forty, fifty million dollars, and the rule of thumb, the bigger the number the more eyes will be upon it, therefore, the opportunity to quote-unquote ‘fail’ becomes larger and more visible. That means that innovation in itself becomes almost a self-fulfilling prophecy of death, right? So, in my case what I tend to do is I tend to take lots of small projects and actually see how quickly – so let me give you an example. So, Dollar Shave Club, a business where he just wanted, this guy, fabulous guy, wanted to upset the men’s razor market by skipping out the retailer and turning razors into a subscription pricing model. So, here is the way that I would do a startup model and I invest in a lot of startups. I have seen this, and that is that he went and spent five thousand dollars on making a video, put it on YouTube to find out whether there was any demand for a men’s razor marketplace by subscription. As it happened, in the first week he got half a million views, allowed him to raise a bunch of VC capital, four years later he sold it for a billion dollars to Unilever. Now, I think that’s a really classic example of what I describe as being bootstrapping, and the danger point is as soon as you actually apply too much cash to any new project then the ability for it to fail becomes greater. So, I like to see stuff really bootstrapped earlier on so I’ll really give you a great example of this. We talk about, lots of industries talk about building startups into minimum viable products. So, I was listening to Doug Gurr talk the other day, he’s the Chief Executive of Amazon, he’s McKinsey alumni, he said, ‘We build minimum lovable product’ and I think that’s a really great way of looking at it because that way you know that, firstly, it’s user engagement. So, I have a saying when I’m investing which I say to myself is, ‘I would rather invest in a business with a dollar in revenue with a hundred users using the product a thousand times a day than a business with a thousand dollars in revenue with a hundred users using once a day’ because in the former you can see what the eventual value looks like.

So, it’s stickiness, it’s customer intimacy-

It’s engagement, yeah.

Interesting, and I guess if you look at the platforms that you talked about earlier on that’s one of their characteristics, right? You become addicted to Amazon Prime or Facebook or whatever.

Yeah. I’m completely addicted to Amazon. Not as addicted as I am to Apple which is an entire proposition which actually takes a significant size of my wallet now.

Yeah, yeah. Well, Amazon, if they’re doing what you’re saying they’re doing, I think they were going after some, not pharmaceutical, but drug retailers in the US now-


It’s this wave upon wave of what they call in ecosystem language as a ‘trophic cascade’ where you have these falling dominoes within different industries and they’re just gobbling it all up.

That’s right.

Yeah, yeah.

And in fact, actually, the share price, I think, of Walgreens fell 5% on that rumor floating yesterday and you know, when they took over Whole Foods they bought it for thirteen billion dollars but they did it out of petty cash because their share price raised more than forty billion dollars on the day that they actually did it, so again it becomes that flywheel effect, the more you do, the more you get.

Yeah, and at the heart of this is data, right, the value of this data? What’s your view on how long data will remain a premium product versus become commoditized? How do you see that evolving over time?

So, the world is full of data. There’s more data now than probable atoms in the universe. The question is is we talk about – I don’t – but people talk about big data. I hate big data just as an expression, and often with an audience, I actually ask them, ‘OK, so who knows what big data is?’ and they all put their hands in the air. ‘OK, subsequent question – who knows what to do with it?’ and everybody sits around with blank faces looking rather embarrassed. So, I regard it as being layered data and layered data is where you start to layer literally layers of data like weather data on top of travel data. Uber is really good at doing this, by the way, to do predictive analytics with it. So, where this will go is every business has got a data exhaust and probably substantial and that is, data that is useless to them because they either can’t use it, won’t use it or don’t understand the reasons for using it, and we’re going to end up with data markets. So, I’ve just invested in one in the US which is producing an eBay for data, so it will categorize the data with a metadata set and say what type of data it is, what velocity it is, the volume of it etc. and you will bid for that level of data that you want. The question is with data is how creative are you? Because at the moment organizations tend to look at data in almost a two-dimensional level. Actually, you really need to be looking at three-dimensional and fourth dimensional in terms of the time and the way that you can actually use it, so creativity is really key here because data layered on top of data will produce something really interesting. When I was at Thomas Cook we did an experiment where we took some Instagram data. We took an Instagram heat map so we were finding where people were taking pictures at three am in the morning in Spain and inevitably that was going to be a nightclub. So, what we did with that is we then layered on top of that Thomas Cook hotels that were near to those particular data sources. Instantaneously, you’ve got the ability to appeal to a bunch of twenty-four-year-olds who are quite happy to be running around taking pictures at three am in the morning and then the messaging to those twenty-four-year-olds is actually, we have a Thomas Cook hotel three hundred meters away from that particularly large nightclub – remind me not to stay in that particular hotel, by the way – but you get the point about the data environment is that – you know, I was doing an interview yesterday and somebody said to me, ‘What would you take as one of the three things you could take to a desert island?’ and my instant reaction to that was a Venn diagram. I’m not taking any technology with me, I don’t need that but I want a Venn diagram because in the Venn diagrams you can find some really interesting market opportunities.

Interesting. So, as I said at the beginning you’re an author, a speaker, an entrepreneur and an investor. Now, you touched on one of your investments. I’m curious, do you take off your advisor hat and put on an investor hat when you’re looking at opportunities or is the world you know-

I can’t divorce the two, and actually, I wouldn’t want to divorce the two because every time I look at an investment opportunity I think, ‘What value can I add beyond dumb cash into that?’ so, therefore, divorcing the two would seem to be counter-intuitive.

Yeah, and you talked about the failure rate of startups. A little bit off topic, but what are you looking for beyond the technology – and you touched on the product and the cash flow – but what are you looking for in terms of the people, the processes, the vision, the aspiration, the obsession? Any things that really are vital for you?

Yeah. So, before the technology, I’ve got a specific way that I invest and, firstly, I only invest in B2B businesses which is the startup businesses. I’ve done four of them, they’ve all been B2B. The second part of that is I will no longer invest in businesses where the founder is less than fifty years old which is a pretty outrageous thing to say and the reason why it’s important is because they have the experience to be able to do this, and the ones that I invest in have got the experience and the energy levels and the passion levels which is a great combination. I’m sorry at the risk of being controversial, twenty-four-year-olds, lots of energy, lots of creative ideas but there’s a missing experience part there which can be deadly.

Yeah, OK. So, that’s the kind of person you’re investing in which is very easy to profile I guess. Is there anything else that you look for in a startup team?

Yeah, big vision.

Big vision.

Got to be a big vision but – so, I invest in businesses where I would expect the exit between fifty and two hundred million and that is – I don’t want to listen to a vision which is, ‘I’m going to change the NHS’, right? I might want to listen to a vision which actually says, ‘I’m actually going to change scanning or x-rays in the NHS’. In other words, I don’t want a Saturn shot, I want a Slough shot.

Sorry, you don’t want a Saturn shot, you want a Slough shot?

Yes. So, in other words – well, perhaps a Moon shot that sits between – but you see what I’m saying here is I – actually, the Saturn shot is commonly a seven to ten-year exit cycle. I’m too old to do that, I’ll be dead by the time some of them exit. I’d rather actually spend the money beforehand, at least my ex-wives would, so I’m looking for something a little bit in between and there is one issue here which I think is becoming really prevalent which is why I think that we’re edging into a bubble, and that is that I see so many startups in the city where they’ve had VC’s invest and the VC’s will come along and they’ll be looking for a quarter of a billion dollars exit and they’ll be looking commonly to selling it to an existing company. When I walked into the plc that I worked for their reaction to that was, ‘Yeah, we can understand why you’d like us to buy this particular business but do you think we’re stupid? Because the city will crucify us if we go and buy a technology business for two hundred million dollars with no revenues’. It’s not going to happen. So, I think the reason why we may be edging into a bubble is because the VC’s are encouraging a scenario whereby we’ve got a few exits a very large price rather than a lot of exits at a much smaller price which is where, I think, that the marketplace will be.

Yeah, OK great. So, John, let’s bring it back on track with the three questions that I sent to you earlier on today, or a couple of weeks ago actually. First of all, what have you changed your mind about recently?

Oh. I think that I’ve started to change my mind about the way that government understands the technology implications of where they’re going forward and I’ve had a number of conversations with government and provide a little bit of advice, whether they take notice of it is entirely different, but I’ve been quite surprised certainly by the British government in terms of its understanding of the implications of AI, advanced robotics etc. and they are pretty much, they’re not on the cutting edge of it but they’re certainly not that far behind and I’ve been a little bit surprised about that. Whether or not they can do anything about it when you have a weak government, when you’ve got distractions and Brexit, distractions of stuff that’s going to happen when Mr McDonnell comes into power etc. is an entirely different proposition and that’s why I think democracy is probably very badly broken because it’s not going to work for the working population for very much longer. So, I have changed my mind about that, I think there is a fairly large understanding of technology and the implications of it than I expected it to be.

Second one – do you have a personal work habit or practice you can share with our listeners that helps you become more effective in what you do?

Yeah. I learnt this technique several years ago. I’ve got a habit of repeating back questions, so if somebody asked me a question what I will try and do is, my normal reaction is, ‘Can I read that back to you?’ so that when I read that back to them we can see what the level of alignment becomes because so much crap appears as a direct result of people not listening to each other or not understanding each other, perhaps, more to the point. When I say, ‘I’m going to read back to you what I think you just said’ that’s the point when you flush out misalignments and the whole communication thing happens that much better.

Excellent. It also buys you time if you’re trying to think up an answer, right?

Well, I couldn’t subscribe to that particular thought in one second! Obviously, I haven’t had to do that during this interview because your clarification has been so good.

Thank you very much. So, final question now. We touched on this before about the concept of failure but what’s your most significant failure or low? What have you learned from it and how have you applied that learning?

So, my significant low was that in my last startup which was sold in August of last year, I didn’t pay enough attention to negotiating the terms and conditions of the VC putting money into it and it caught me out, it caught the shareholders out when we came to the point of the exit so, the lesson is actually to pay a lot more attention to the detail than you think. The difficulty is that when you’re a startup guy you’re in such a rush to actually build product that I think a lot of them count on the fact that you’re not going to pay attention to detail so, that was the first thing, and then the second part of that was actually what I saw in the term sheet ended up being very different in terms of the actual outcome in terms of contract. I am not a great fan of VC’s under these circumstances. There are a couple out there which are really great, that are really helpful and really add value but I’m of the view that a lot of private investors offer significantly more value through their networks and through their knowledge than many VC’s do, but my advice to entrepreneurs is to check the contract terms because there is some really nasty stuff like preference shares that can really hurt original investors on exit.

Yeah and often you don’t realize for, it could be a number of years since you’ve taken the money until you actually realize and that can be a little bit of a sobering moment-

Oh yeah.

That sounds like what happened to you.

Yeah, absolutely.

So, where can people get in touch with you?

Oh, easy. but the easiest thing to do is just Google John Straw and you’ll see it all there.

And you’re also on Twitter and LinkedIn and we’ll put those in the show notes as well.

Yeah, absolutely.

Well, John, it’s been great to talk to you. Natasha, who works with McKinsey, recommended you having heard you several times, so thanks very much for your time.

Thank you.

Safe travels and we’ll let you know when this is ready to go.

That’s great, thank you, Mark.

Thanks, John. Cheers.

That’s lovely.


Thank you. Bye-bye.

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