This week: It’s hype time, fast and agile leadership, and the business of failure. Sandra Peter (Sydney Business Insights) and Kai Riemer (Digital Disruption Research Group) meet once a week to put their own spin on news that is impacting the future of business in The Future, This Week.

 

The stories this week:

The Gartner Hype Cycle for Emerging Technologies 2018

Leadership trends to watch til 2022

How failure became big business

 

Other stories we bring up:

Gartner’s great vanishing

Our discussion of the Gartner Hype Cycle for Emerging Technologies in 2017

World of Drones Congress 2018

Walking robot terrifies celebs but is BS

The self-driving car that will never arrive

A more realistic route to autonomous driving

Digital humans special @ Vivid Sydney Ideas 2018 with Rachel Botsman

Dilbert ‘Strategy is to nimble and agile’ July 02 2018

The ‘Terminator’ of startups 

When startups fail, investors recoup by selling patents

Not all failures are created equal

The FailCon conference

 

Join us September 21 for DISRUPT.SYDNEY™ 2018: Robots against the machine?

DISRUPT.SYDNEY™, in its 6th year, is Australia’s first and oldest disruption conference. In recent years we talked a lot about what makes innovations disruptive. This year we look at the other side of the coin: Managing for innovation, disruption and change from within. With two Q&A panels, parallel workshops after lunch, and an interactive futures session on ‘digital humans’ in the afternoon, DISRUPT.SYDNEY 2018 is shaping up to be another engaging highlight.

More information and registration

 

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Our theme music was composed and played by Linsey Pollak.

Send us your news ideas to sbi@sydney.edu.au.

Disclaimer: We would like to advise that the following program may contain real news, occasional philosophy and ideas that may offend some listeners.

Intro: This is The Future, This Week. On Sydney Business Insights. I'm Sandra Peter and I'm Kai Riemer. Every week we get together and look at the news of the week. We discuss technology, the future of business, the weird and the wonderful, and things that change the world. Okay let's start. Let's start.

Sandra: Today on The Future, This Week: It's hype time, fast and agile leadership, and the business of failure. I'm Sandra Peter, I'm the Director of Sydney Business Insights.

Kai: I'm Kai Riemer, professor at the Business School and leader of the Digital Disruption Research Group.

Sandra: So Kai what happened in the future this week?

Kai: It is this sweet time of year, that time of celebration between Easter and Christmas when Gartner releases its long awaited annual Hype Cycle of Emerging Technologies. And our first story comes straight from the horse's mouth. So we're discussing the Gartner press release: "Gartner identifies five emerging technology trends that will blur the line between human and machine."

Sandra: So indeed it is that time of the year. Every year we talk about what else is on the hype cycle and we have a closer look at what the hype cycle really is. Our listeners might remember that we've done this about a year ago just reminding everybody that Gartner actually looks at about 2000 technologies every year and picks a few that it places along this hype cycle. And the Gartner curve looks at whether these technologies are around the ascending part of the slope where there is an innovation trigger, whether they're at the top where there is a peak of inflated expectations and this curve is a bit like a sine curve. Technologies then fall down through a trough of disillusionment before they emerge on the slope of enlightenment and eventually get to a plateau of productivity.

Kai: Or so the theory goes. So every year Gartner picks what it thinks are the most remarkable, the most important, emerging technologies. And while we want to discuss a few of those we also want to have a look at what happens over time given that Gartner's model actually models time against expectations, it is worthwhile looking at what these hype cycles look like in previous years because there's some revealing things going on or as an article in The Register from this week puts it "Gartner's great vanishing: some of 2017's emerging tech just disappears".

Sandra: Ah. So at this point I think it's quite important that we remind our listeners that this hype cycle is really a qualitative tool. There is no single measure for how things get put on this curve or where they get put on this scale and Gartner consultants actually use a variety of surveys to do this but mostly this is forecasts made by the analysts who work at Gartner. They look at media, they talk to people, they listen to what industry is saying and then they basically just put technologies on the curve.

Kai: And each of those technologies that they put on the curve come with a time prediction of less than two years, two to five, five to 10, or more than 10 years before they reach the plateau of productivity or the point where they are in widespread use so not only does Gartner identify what they think are the most important emerging technologies they also make predictions of how long it will take these technologies to march along this curve up the peak into the trough and then over the slope to the plateau of productivity.

Sandra: We should note that last year we did a more in-depth analysis of how this curve has changed over the years and we noted that quite a few technologies just disappeared off the curve and they did this this year as well and we'll have a look at those. It's also worth noting that over the years quite a few really major technologies have completely flown under the radar on the hype cycle things that look quite trivial in the beginning were not picked up by the media but that are kind of foundational to the way we do business today, have not made their way to the hype cycle at all.

Kai: For example the DVD when it was announced was initially ridiculed but then in what was a rather short time saw widespread adoption without ever going through the motions that this curve lays out.

Sandra: Things like open source have never made it on to the hype cycle either. And they're really were foundational for entire communities that shared code, it enabled cloud computing models, it enabled a lot of things that have eventually made it onto the curve. Hadoop which was foundational for basically this generation of large scale data analysis and so on never made it onto the curve.

Kai: At the same time Gartner each year invents what are hip and trendy sounding names for technologies only to abandon those names a year after. And technologies are also often just rebadged. So what was known as deep learning and machine learning as two separate entries on the hype cycle, this year just shows up in the same place on the peak of inflated expectations as 'deep neural nets' (deep learning). So a lot of kind of reinterpretation of these terms and names is going on the hype cycle.

Sandra: But before we move on to the curve we should also say that the Hype Cycle for Emerging Technologies is the most well-known of the Gartner hype cycles. There is tens of these for different types of industries, at different points in time, different types of technologies but the Hype Cycle for Emerging Technologies is really the longest running one and the one that provides kind of this cross industry perspective that everyone refers to.

Kai: Yes and when people say the Gartner Hype Cycle that's usually the one that everyone is referring to. Now before we take a look at how this year's compares with last year's, let's take a look at some of the things that strike us as a little bit odd on this year's Hype Cycle. And the first thing I want to pick up on is that the slope of enlightenment and the plateau of productivity are completely empty and that's been going on for a little while now.

Sandra: We did have virtual reality on it last year which has now completely dropped off.

Kai: That's right. So while virtual reality according to Gartner was on the slope of enlightenment, inching its way forward to the plateau of productivity, this year nowhere to be seen. Has been dropped without comment and so nothing is enlightening, nothing is productive this year. Everything is kind of emerging, which is interesting and a little odd in itself because if this model was really predicting how things progress along the curve you would expect some of the things that have been there in recent years to now finally make its way to productivity.

Sandra: So before we move to an in-depth look at some of the technologies on the curve, I think one more observation is in order and that is we mentioned before that the way technologies are put on this curve is technologies that will mature in more than 10 years, five to 10 years, two to five years, or less than two years. And surprisingly on the curve we have no technologies that will mature in less than two years. Neither on last year's or on this year's curve. Good to know. Also compared to last year there seems to be almost double the number of technologies that are expected to mature in more than 10 years. You'll forget about this curve by then. Also a lot fewer technologies that are expected to mature in two to five years so pretty much everything is in that five to ten or more'' years this time around.

Kai: Yeah but what also stands out for me is that in the press release Gartner talks about technologies as if they were things in the world, as if they existed. For example, the first of the five trends that they identify is democratised artificial intelligence. And so they talk about technologies on the Hype Cycle such as autonomous driving which we haven't mastered yet, artificial general intelligence which more and more evidence actually questions whether this is possible at all, it's certainly not something that we have any clue of how to achieve yet, yet they are listed as emerging technologies when in fact they are just philosophical ideas at this point in time.

Sandra: Well they've also got flying autonomous vehicles on there right? And speaking of flying autonomous vehicles did you realise that the drones have dropped completely off this list.

Kai: Yeah as The Register puts it "the drones have crashed" so they are among a few technologies that were going strongly on the Hype Cycle last year and that are still being widely discussed. I mean you have just been speaking at a large drone conference.

Sandra: Yes, The World of Drones Conference in Brisbane which had people from around the world meeting up to talk about the future of drones and applications in a variety of industries.

Kai: And we do actually see real life applications of drones - a life saving drone has just made its first rescue here in Australia. So drones are actually making their way up the slope and onto the plateau so I can't understand why they wouldn't be there. So there's a number of other technologies that have as The Register puts it "plunged off the peak, perished on the plateau or tripped in the trough". Among them are things like human augmentation, augmented data discovery and knowledge crafts, arguably some of which might just have been names that Gartner invented but have never actually made it into the mainstream consciousness. But nonetheless there are things that just appear or disappear. So what strikes me for example is there's an entry reading 'smart fabrics' and it's sitting right in the trough of disillusionment - that wasn't there last year so magically Gartner must have realised hey here's a technology that everyone is really disillusioned about that we hadn't picked up on and let's put it on the curve but I wasn't there last year so I don't know.

Sandra: There's another new trick they've employed this year as well which I was quite surprised with which is some technologies have actually made it to both sides of the curve which I think is cheating a bit. One of those is blockchain which is not only currently falling off the peak of inflated expectations into the trough of disillusionment but it also appears on the left hand side now under the innovation trigger so it's moving up the slope. We've got blockchain for data security.

Kai: Well isn't that the whole point of blockchain anyway.

Sandra: And blockchain is not the only one. The other one that's now split is autonomous vehicles.

Kai: Well that's an interesting one. So autonomous vehicles was just sitting past the peak on last year's curve and now it has dropped half way into the trough named autonomous driving level 4, while autonomous driving level 5 capability is just climbing up the other side towards the peak. And that is a really curious one because it suggests that we're becoming disillusioned with level 4 capability but we're really kind of looking forward to level 5.

Sandra: People are disappointing so we should just take them out and have a level 5.

Kai: So a level 4 means a car can drive itself under most conditions but the driver still has to intervene whereas level 5 is fully autonomous vehicles where you might not even have a steering wheel anymore. But the Hype Cycle suggests that while we no longer fully believe that we're making progress on level 4 we're still really positive about level 5 which is kind of a little bit inconsistent in my view although you have a slightly different angle on this.

Sandra: Well level 4 actually is a bit more difficult to do because I need the tech not only to survey the outside of the car but if it's level 4 and I need to make sure that you as the driver can take over I need tech inside the car as well making sure that you are not actually playing Zelda while you should be driving.

Kai: Ah a little dig. I do love my Zelda on my Nintendo Switch and we're not sponsored by Nintendo I must say.

Sandra: Not that we would stop them if they ever tried.

Kai: No. Not that we would. But there was an article in The Outline just this week questioning the whole idea of self driving cars. And it's not the first one of its kind but there have been increasingly people who question the feasibility of autonomous vehicles as entities that can mix and mingle with people in the streets. So let's have a look at the article in The Outline briefly: it's titled "The self driving car that will never arrive" and the author Casey Johnston first of all points out recent problems, the Uber self driving car in Arizona we've discussed this which killed a pedestrian because as it turns out not only was the human driver not paying attention to what was going on but also the self braking capability in the car had been disabled because it just had too many false positives and it got in the way of the actual driving experience. And the author makes the argument that, and I quote, "laying the blame on a critical failure conveniently sidesteps the whole issue of whether a self driving car can adequately identify a thing it shouldn't run into" which should be almost the entire point of a car that drives itself.

Sandra: Which reminds me of another article that was in the news this week and this one comes from The Economist and it's titled "A more realistic route to autonomous driving" and a very good article in The Economist talks about the six month trial in Texas that's focusing on what self driving technology can actually do now. And it really looks at the startup that deploys minivans to transport people within a limited area of the city under very specific conditions, so taking into account what cars can actually do now and finding ways to deploy them where actually those limitations are controlled for.

Kai: But here I want to come back to The Outline article and the author there makes the point that experts in the self driving industry have now started to not blame the car's lack of success but what he calls non-negotiable aspects of reality such as that it is not that self driving cars are bad at driving it's that people are bad at walking and it quotes experts like Andrew Ng who say that if only we could change the build environment we could get people not to walk in the street but use crosswalks and observe the rules and the stop lights and everything else. In other words if we could make humans just a bit more like machines, machines would get on in the world much much better if they weren't encountering those erratic humans all the time or even better if we could just fence off the areas where self driving cars are driving without humans interfering but the author then makes the point that we have that technology already and it is called trains. So we're now at a point where we can do something with self driving cars but we're not really on track to achieving fully autonomous vehicles that can interact with humans in the natural environment which much like with artificial general intelligence raises the question whether we're actually on track to achieving this at all.

Sandra: Okay so blockchain, autonomous vehicles on both sides of the fence. I want to look at a couple that are kind of precariously perched on the peak of inflated expectations and one of those is digital twins. Digital twins have actually made their way up from last year, last year there were about halfway through the curve on the way up now they're at the very top of the Hype Curve.

Kai: And they're certainly faster than the smart robots which have barely progressed so digital twins are out running smart robots, at least uphill they do.

Sandra: At least uphill, exactly. So the digital twins are the only manifestation of that technology that made it onto the Hype Curve and surprisingly we know from conversations we've had previously on this podcast when we spoke about Digital Mike, we spoke about digital humans, we show that the technology that we can use to create a digital twin is actually the same type of technology that we can use to create fake videos, to create deep fakes. So a lot of technologies that potentially would have a much stronger impact on the business world have never made it onto the Hype Curve, even though it's the same type of technology. Quite surprising. And we'll put all of those conversations in the shownotes including our Vivid panel with Rachel Botsman.

Kai: But that brings me to talking about smart robots which we've discussed previously the Boston Dynamics robot dogs and the bi-pedal human-like robots that they have. But just this week a video made the rounds that according to the Independent in the U.K. terrified celebrities a number of which have re-tweeted this video which went viral on social media and it shows a bi-pedal human-like robot walking down a street for all intents and purposes looking very human-like and rolling its eyes and really quite terrifying but it turns out that behind this video is the same kind of technology that we see with digital twins because it's a computer generated robot that has been inserted into a video that was clearly shot on a smartphone and it is indistinguishable from the real thing. And so we think that we are much closer to the kind of computer generated visuals and those kinds of technologies than either smart robots or artificial intelligence because we're actually making sizable progress on the visuals front, both on the back of AI with image recognition and image generation where the technology really can show its strength and where it doesn't have to interact with the real human environment such as robots or self-driving cars would have to do.

Sandra: So realistic fake landscapes, robots, humans, modified video is what we would probably put on the slope of enlightenment and very slowly the plateau of productivity to be frank.

Kai: Which has been completely vacated as we noted earlier.

Sandra: That's why we're populating it.

Kai: That's right.

Sandra: And there is a real temptation now to just keep going through these various technologies on the Hype Curve and we could spend the rest of the podcast on this but we have to move on to other stories because it has been a week of predictions hasn't it.

Kai: Yeah. The next article comes from Forbes Magazine from one of their contributors. And really you know one thing that stands out here and we must mention is that Forbes is more and more becoming like a blog which publishes a wide range of views. And here's one that we found curious because we so disagree with it. It's called 'Leadership trends to watch for from now to 2022'.

Sandra: So this article really caught our attention with its eight trends for leaders while arguably there seemed to be fewer trends than emerging technologies... I don't know how to say this nicely. Most of this is is...

Kai: Bullshit. You want to say bullshit.

Sandra: Bullshit. Yes I do want to say bullshit.

Kai: Apparently it's based on 150 interviews that the author who shall be nameless has conducted and the first one is just very fitting, the first trend is 'leaders must pay attention to trends and predictions'.

Sandra: How is that a leadership trend?

Kai: No she's telling her readership that they must pay attention to what they're about to read, the other seven trends apparently, I think. Or maybe she's referring to an active engagement with the Hype Cycle that we've just discussed.

Sandra: Which would actually quite fulfill the role of the Hype Cycle which is probably to actually create those trends in the first place. So yes leaders must pay attention to trends and predictions. The second one is even more telling its 'Leaders and their organisation must become agiler'.

Kai: Agiler or more agile...r.

Sandra: Which just reminded me of this wonderful Dilbert cartoon from last month titled 'Strategies too nimble and agile'. Dilbert goes to a strategy meeting where he is told that our strategy is to be nimble and agile. And he asks 'Do other companies have a strategy of being clumsy and slow?'.

Kai: Exactly.

Sandra: Yes of course they have to be agiler. Nobody sets out to be clumsy and slow.

Kai: But then it goes on to tell the dear reader that really it's all about speed because number three is 'Organisations and their' people must accelerate their pace of learning. So not only do you have to learn, you have to learn fast...r. And the author tells us that because machines will take our work we have to update our skills ever more often otherwise we risk being unemployed.

Sandra: And quite fittingly the author does bring up a 1978 book on organisational learning that argues for agility and for continuous learning that provides a competitive advantage. So yes for the past 40 years this has been a trend, it seems to be a very long term trend or a just life as we know it.

Kai: And we don't want to discuss all eight of them but just want to point out that number five 'We need to identify and build talent at an increasing rate'. Employee engagement will continue to be important in volatile times so effectively what she is telling us is everything is going ever more faster and that's hardly new nor is it necessarily true as many people have pointed out. Reminds me of this book I found on a friend's bookshelf which was an economics book from the early nineteen hundreds which started with the sentence (in German but I translate) 'that we live in times of increasing speed and volatility'. So there you go.

Sandra: So whilst we are being a bit dismissive about this list this is for maybe the reason that many of these things are really basic leadership concepts that are not new trends, are not fads, are not anything else but just what good leaders do normally.

Kai: Number eight 'Effective leaders are conscious of the impact across a broad range of factors and stakeholders'.

Sandra: Just try to think about the opposite of these like how could the opposite be true. You know leaders are not conscious of a broad range of factors.

Kai: Communities must be disparate. Employees should be disengaged. Companies should be slow and clumsy. No.

Sandra: No. And whilst it's quite important I think to have these in mind and consider these, they are not trends in leadership, they are just leadership.

Kai: And they also don't really go to the heart of the matter of making us better leaders. So I think people who would look at this list and find it useful in becoming better leaders have a long way to go. But those who are actually in leadership positions would look at this and pretty much have similar thoughts to what we were just discussing. But leadership has become one of those fields where everything has to be new all the time. We have to kind of figure out the right formula. So in that respect it has become a little bit like technology, a hyped topic when in fact good leadership is often not that. It is not glitzy, it is not standing out. We know it when we see it and its got a lot to do counterintuitively with being a good listener, taking the pace out, reflecting about things, thinking about things and doing things that are not mainstream, that allow people to break out of the taken for granted routines.

Sandra: Let's move to our last story. On the business of failure. This story comes from The Guardian and it's titled 'The undertakers of Silicon Valley: how failure became big business'.

Kai: And in some way it brings together tech and leadership because it's all about the leaders in Silicon Valley who dabble in tech, take on new businesses and then frequently fail because we do know from studies that between 70 and 90 percent of all startups never return the investment put into them and therefore can be considered failure.

Sandra: The article is actually a story about a company called Sherwood Partners whose business is to actually take over the companies that have failed, make money out of the patents that they still own and basically dismantle the carcasses of failed Silicon Valley startups.

Kai: It's published in The Guardian and it's actually a republication of an article that appears in Logic which is a magazine about technology who have a whole feature issue about failure.

Sandra: And speaking of the leadership trends we had in the previous story, I actually went back to see who else was talking about this seemingly very large company called Sherwood Partners that does this for quite a few years now in the valley and found out that this story seems to keep coming back year after year so went back, there's an article about a year and a half ago in Tech Crunch calling it the terminator of startups that is seeing two to four company wind downs a week. Then I went back about eight years ago in Wired Magazine an article called 'When startups fail, investors recoup by selling patents' is basically the exact same story but something interesting changes in the language if you go back 10 years or so the failure narrative becomes increasingly positive. So it's increasingly a good thing to fail and we've started to glorify failure across the years. It's been something that wasn't really talked about back in the day, was something that would rarely crop up and then increasingly failure became a badge of honor.

Kai: And while we want to make this the topic let's just have one more look at this company which is quite extraordinary. So this company is run by two guys in their 70s and they have really created a business that provides the infrastructure for failing startups to the extent that it allows the venture capitalists, the investors and the founders of a company to just walk away from a failing startup. They sign over all the assets and everything to this company Sherwood Partners and they then basically do the rest. So Silicon Valley has found a way to even make failing not only a lucrative business but a very convenient and efficient affair.

Sandra: And that has indeed been the narrative out of Silicon Valley for the last few years. Failing is something that everyone goes through. It happens, you get right back up, you start a new company and the next one will be the million dollar company.

Kai: So failure has really become an ideology of innovation and disruption.

Sandra: But interestingly failure seems to not be equal for everyone involved. The article points out that whilst Uber might be hemorrhaging cash well that's just the sign of how visionary and how forward looking and how future focused the company is. Whilst if a taxi company is hemorrhaging money...

Kai: Then that's just a sign that the old dinosaur couldn't go with the disruption and they deserve to die. So failure is valued very differently when it's attached to either the incumbent who in the disruption narrative of Silicon Valley deserves to fail and the innovators who when they fail basically learn from their mistakes and then go on where it's just part of doing business.

Sandra: And this becomes an interesting conundrum for leaders of companies that have now started to age somewhat. So think about a company like Apple that we still see as an innovative company that should be allowed to fail, should be allowed to have a hit and miss every once in a while, yet is no longer allowed in the public narrative to have products or services or things that are not successful from the very beginnings, so failure for them even though it's supposed to be a culture that encourages failure, is just not something that is actually acceptable.

Kai: And failure is often something that becomes narrated in relative terms. So while the Apple Watch is regarded by many to be a failure, it is by far the most successful wearable device on the market and actually the best selling watch in the world but it doesn’t quite live up to Apple's previous standards and it hasn't made the impact on Apple's bottom line that people would have liked it to do when it was announced as the next big thing after the iPhone and the iPad.

Sandra: And this is a good time to point out that not all failures are created equal which also happens to be the title of an article by Amy Edmonson who is an academic at the Harvard Business School, who talks about the different types of failure and how it's worth thinking about the reasons for failure. She talks about the difference between complexity related failure where failure might be led by the fact that there is a lot of uncertainty in the environment. Just think about the startup that might have taken off during the financial crises it might have gotten all the elements right - the leadership, the technology, everything else but because there was a lack of funding during period it actually failed. The processes might be very complex and failure might result from its interaction with the environment. But that there are also preventable failures which are a very different kind of failure, which stem from lack of ability or from process inadequacies or from inattention or deviance that the individuals involved in the process might experience.

And then she also talks about this idea of intelligent failure where we might fail in the process of hypothesis testing where we conduct experiments actually to prove that an idea or design is or is not successful when we might perform exploratory testing, we want to expand our knowledge or learn about new things and fail in the process. So not thinking of all failures being created equal and in that sense not glorifying or demonising failure equally.

Kai: Yeah these are really good points especially the point about intelligent failure because what gets me is that I think we have actually taken the failure ideology a little bit too far in recent times. I think it's great that people are allowed to fail because all innovation needs are the psychological safety of being allowed to make mistakes. It's in the nature of pushing the boundary, pushing the envelope that we get things wrong and that from failure we can learn but the ideology of failure in Silicon Valley just tends to glorify failure for its own sake. There's this conference FailCon which has been going on for almost a decade and I always think if you were a regular speaker at this conference and you talk about your failures and you never succeed, I think you're still doing something wrong. It's not a great thing to fail all the time. I remember a talk once at a disruption conference where I was a speaker where a fellow panel member made the point that they were doing a lot of AB testing putting up a hypothesis and then testing of whether they're right or wrong and they said in most of the time we were wrong actually and I was thinking well you know if you have a 50/50 chance and then most of the time you're wrong, you're clearly not very good at this.

Sandra: But at least they're experimenting, at least they're testing, at least they're trying.

Kai: Yeah but the fact that you're trying something doesn't mean that you're doing this at random. You should be guided by some principles, you should be guided by what your customers want and appreciate. There should be some, as the author puts it, intelligent failure going on and not just random failure or you know frankly bullshit ideas we've had the Juicero Award previously on the podcast where you have these startups that are very cashed up, they are completely tech driven but they come up with these nonsense products or you have these hype waves where everyone jumps on an idea and a lot of money is poured into things that by and large are either trivial or not actually innovation. Just this week another article in Quartz pointed out that the latest thing in Silicon Valley where a lot of venture capitalists are moving is sparkling water and the narrative is that while sugar based drinks are taking a hit in public perception and legislation, everyone is moving on to what in the US is called Seltzers so sparkling water with a bit of fruit pulp in it maybe. But these are commodities. We're not talking real innovation. So a lot of money goes into something that is just hype in essence.

Sandra: And whilst we do see a lot of these eventually fail - Juicero famously has failed and has gone bankrupt - I think the last thing we want to touch on with this failure narrative is that failure always seems to be about the leader or the founder or the entrepreneur and none of these narratives actually reference the fact that most of these companies that fail are not a man or woman show.

Kai: Oh man really because that's the other side of the narrative. The permission to you know try hard and then fail is very much a macho male dominated narrative that coincides with the sexism in Silicon Valley that we've previously discussed because it's often boys who will be boys who are behind this narrative and my personal feeling is that women entrepreneurs will be judged much harsher when they fail than men. But that's not the point that you were alluding to.

Sandra: No I was alluding to the fact that most of these organisations actually employ a range of people many of whom work for very little money or work for free, for shares in the future unicorn company that will generate billions of dollars...

Kai: And when they fail they basically go home empty handed and this is an important point that The Guardian article also makes that venture capitalists, they work on a broad basis so they seed their money into many startups and they only need a small percentage of those to succeed so they will always come out on top. The founders will get a second chance. They're usually relatively young male entrepreneurs who will get a second or third chance in another venture. So they are in the "in circle" of people who will fall upwards who will positively fail forward. But then there's all these people who are not part of the "in circle" who just go home empty handed who are part of the venture that Sherwood Partners basically put under the hammer and then dissolve.

Sandra: So whilst having permission to fail leads to innovation, can lead to creativity, can lead to new insights and new ventures, we need to learn how to fail better.

Kai: And more equally.

Sandra: And this is definitely a topic we want to come back to. As I'm sure we will also come back to the Gartner Hype Cycle same time next year.

Kai: And that's all we have time for this week. Thanks for listening.

Sandra: Thanks for listening.

Outro: This was The Future, This Week made awesome by the Sydney Business Insights Team and members of the Digital Disruption Research Group. And every week right here with us our sound editor Megan Wedge who makes us sound good and keeps us honest. Our theme music was composed and played live from a set of garden hoses by Linsey Pollak. You can subscribe to this podcast on iTunes, Spotify, Stitcher, Youtube, SoundCloud or wherever you get your podcasts. You can follow us online on Flipboard, Twitter or sbi.sydney.edu.au. If you have any news that you want us to discuss, please send them to sbi@sydney.edu.au.