Innovation signalling has some similarities with its counterpart virtue signalling in terms of authenticity in terms of behaviour and the projected image. An organisation looks to demonstrate its ‘high degree’ of innovation with actions and projects with the external image firmly in mind. There may be an internal learning, or business benefit to this as well, but the image projected is the main objective.
As I wrote this post a collaboration between Moncler and Adidas dropped putting innovation signalling at its core involving both involving NFTs and AI generated designs and models.
Open Sea has an NFT where the owner gets a Rolex watch on submission of the NFT. This has since been extended into the US market by CRM Jewellers in Miami.
AI has its place for instance, simulating and optimising product design based on physical properties. NASA has used AI for just this purpose in conjunction with additive manufacturing techniques for small production runs of parts needed for the space programme.
It’s not just the luxury sector
This might read like I have been picking excessively on the luxury sector. I use them as exemplars mainly because their examples are so high profile. But there are examples in other sectors. For instance, Walmart partnering with IBM to use block chain to track individual lettuce heads from farm to customer trolley.
There were similar partnerships that IBM hatched with Unilever, Nestlé and Dole Foods as well, but the fruits of these projects were not publicised to the same degree.
You can find similar posts here and this metaverse discussion paper that helps to cut through the blockchain and metaverse hype.
An Outfit to Match Your Chain – Google Drive – Highsnobriety has interviews where these intersect with luxury and fashion. It will be probably handy for a couple of client presentations
Why strategy should embrace execution | WARC – The Nike ‘Nothing Beats a Londoner’ campaign was a really long process – about a year. In the beginning we had a vision to get really local. Then about halfway through the process, the terrorist attacks happened in London. And a picture emerged of a man fleeing the scene with a beer in his hand. Everyone inside London said that’s what it means to be a Londoner: no matter what happens, they hold onto their beer. And off the back of that, I wrote the line ‘nothing beats a Londoner,’ which wasn’t supposed to end up as the final line but it did. It just gave the creative more depth and a place to springboard from. It changed the energy of the work.
The more voters hear no-deal warnings, the more they support it | The Times – Much of this is simply because voters have heard it all before. Trust in politicians and the media, also seen as responsible, is at record lows. The legacy of the 2016 referendum campaign runs deep. Promises from both sides, from the infamous £350 million a week to forecasts of a recession, still endure as easy-to-reach examples as to why you should not trust anything a politician says. For up to about a year after the referendum, a handful of voters would repeat a number long forgotten in Westminster — that the Remain campaign had said leaving would make households £4,300 a year worse off. This was the archetype of nonsense, largely because of its precision. How could anyone know in such detail, to the nearest hundred, with such certainty, what the effect would be? It can only be a lie. But the aversion to anti-no-deal messages is about more than distrust. Where there is support for no deal in the country, it is fused to a deep sense of patriotism. A feeling that we are British, we have endured so much and thrived, of course we will be okay if we leave without a deal
Off-White, Vetements and The Paradigm of Luxury – “Disruption is evolution. Defining the word ‘luxury’ might be a start for defining disruption and evolution as the word and the concept of luxury has different meanings following the demographics of peoples and cultures according to age, race, religion, gender, ethnicity, income, and education”
Apple’s September 10 event ‘By innovation only’ marked the autumn season of premium smartphone launches. It is also a bellwether of what we can expect from the technology sector.
Mark Twain’s ‘History doesn’t repeat itself, but it rhymes’ fits especially well in the smartphone business. From a consumer perspective Apple’s 2019/20 iPhone range is basically the same phones but with more camera features. Other vendors are going to come out with handsets with more camera and 5G modems.
All of them are going to be trapped in the same pictures-under-glass metaphor. The smartphone industry as a whole (with the iPhone as bellwether) is trapped in its own version of groundhog day.
5G? Not so fast
Whilst 5G sounds good on new handsets, there’s five points to consider:
Early generation handsets for a new wireless standards tend to have poor battery lives
5G phones are only as good as 5G networks
There aren’t applications to make use of 5G networks
A lot of mobile usage happens on home or other wi-fi networks. 5G is competing with your home broadband connection rather than your patchy cellular connection
5G isn’t really about smartphones
When you see all launches (like this picture from the Huawei Mate 30 launch); just remember the five points above and process the slick technology spin through this lens.
In Huawei’s case they’re basically launching very pretty €1,000+ 5G Mi-Fi hotspots with point-and-shoot camera functionality, since they’re an Android phone without access to Google services. The Porsche Design variants come out at closer to €2,500 – ideal for bored, but patriotic 土豪.
Price inelasticity
Apple’s iPhone X and XS models tested the the price elasticity of premium smartphones. The market spoke. This year’s prices have stayed the same rather than increasing. You could argue that the value proposition has increased through a year’s worth of bundled services. Of course, its only worth anything if you use the services.
Differentiation through services
Seven years ago I was sat in a hotel restaurant in Seoul and overheard Flipboard going through a pitch they wanted to deliver to Samsung. Samsung eventually tried out Flipboard and free content subscriptions to help sell the Galaxy S3.
Apple decided to build their own free subscription model based around streaming video. This is to:
Differentiate its new devices from competitors
Provide a recurring revenue stream from iPhone users with older devices
Utilise the massive data centres that Apple has been building for the past decade
Built to last
The use of superior materials has resulted in iPhones lasting longer. Add this to pricing and for many people, their first iPhone is a pre-owned iPhone. They are handed down in families or to older relatives. This has built Apple a large user base. The big question is whether they can turn this footprint into services.
There is a tension between new phone sales in a saturated marketplace, versus a growing base of service users.
How Streetwear Is Driving Innovation in Hardware | HYPEBEAST – the innovation in hardware that Hypebeast is concerned about is fasteners clips and connectors in clothing and accessories. Some streetwear brands are using zips in a similar way to Vivienne Westwood during punk. They are borrowing from technical clothing, military gear and alpine sports for inspiration. More design related content here.
Branding
Cause Marketing Isn’t Working for Young People – Adweek – according to DoSomething’s survey, “Nike still only secured a 60% aided awareness of an association with any cause at all and only 27% with racial justice.” – doesn’t work unless it goes beyond a single campaign. This also has implication for this work and ZBB
Ralph Lauren’s CMO on retelling its brand story to ‘reach the kid looking for Supreme’ | The Drum – “We’ve marketed those in a very bespoke, very direct way to newer audiences. We can market on one hand to someone who’s been into the brand for 20, 30 years, who wore Polo Sport back in the 90s, but we can also market that product mix and that story to a younger kid who’s looking for Supreme. – hype isn’t only about media targeting yet according to this puff piece its all about digital media technology which is BS. It indicates a wider lack of focus there on craft, planning style insights, design, curation, the move to online ‘drops’ on certain collections
Anki shutting down despite $200 million in funding – Axios – bigger question consumers must start to have about having cloud in the product, is it really that smart as a model. I personally don’t think so. Also should cloud and product be sold by different companies a la Alexia and Harmon Kardon etc
I started thinking about a post on innovation, after an agency meeting about a possible project. My friend Nigel Scott has been researching the venture capital industry. His ideas fired some of the thoughts in this post.
It caused me to reflect again on innovation and the way we think about it.
Innovation rewards hard work?
We are often told that innovators work really hard and strive to achieve their goals. In Where Wizards Stay Up Late – there is a description of Silicon Valley culture. Late nights by engineers and takeout food was considered one of the factors that drove the early Internet. Engineers were building new technologies as they went at break-neck speeds.
The problem is that for many jobs there is no 9-to-5 now. When I worked in agencies 12+ hour days were typical depending on the client load. Yet we weren’t pulling Cannes Lions award-winning work out of our butts.
In China, many companies now work to ‘996‘. That is 9am to 9pm, 6 days a week as core hours. This is basic a minimum requirement for engineers. Somewhere like Huawei, try to build a ‘wolf’ mentality. They work their staff much harder and they’re expected to retire at 45 – presumably physically and mentally burned out.
Working hard is a hygiene factor, technology has made it that way. Your typical Uber driver is gamed by the driver app to put in excess of 12-hours/day. Both knowledge and unskilled workers would have a similar level of time poverty.
Innovation is like buses
For long-suffering public transport users in the UK many services are compared to buses. Due to road traffic and scheduling, there would often be an overly long time for a bus to arrive. When it eventually did, there would be another two following very closely behind.
You can see a similar thing with innovation.
Whilst we’re used to thinking of John Logie Baird as the inventor of television – and Baird worked very hard on television. The reality is that television was based on a series of inventions from the middle of the 19th century onwards.
There are at least 20 different inventors who had some claim to coming up with the light bulb. But Edison did manage to create the first commercially successful bulb. British school children are taught about Joseph Swan’s carbon filament bulb. This was let down by the vacuum process in manufacturing and poor quality electricity supplies so the bulbs didn’t last very long. Swan had solved his bulb’s problem and changed the filament.
It was only at this point that Edison started his research into electric light bulbs.
More recently, I was talking to an agency about a piece of work that didn’t come off in the end. The discussion turned to a drug that was very recently launched. The problem was that although they were first to market, they weren’t the only inventors. A large rival had launched drug approvals for their product in markets were original firm hadn’t focused on for its initial approvals. Another two companies were immediately behind them and likely to drop their prices (and profit margins) to make up for later market entry.
If one thinks about the modern computer with its graphical user interface. This was created by layers and layers of innovation. Doug Engelbart, whilst working at SRI International demonstrated the following to an audience of government officials in 1968
GUI interface
Mouse pointing device
Text manipulation
Collaborative editing
Video conferencing (a la Skype)
The Xerox PARC (Palo Alto Research Center) refined Engelbart’s concepts further with a complete modern office by 1973. Steve Jobs and his team got into see it, which drove work on the Lisa and then the Macintosh. Microsoft got in and eventually came up with Windows. Microsoft also learned from building software applications for the Macintosh.
Digital Research invented their own GUI layer called GEM. GEM was demoed at Comdex in 1984; right about the time Apple launched the Macintosh. Commodore launched the Amiga in 1985 and also added multi-tasking – the ability to run two or more apps at the same time.
These are just a few examples for the sake of brevity. But the inventor slaving away in isolation to come up with something, uniquely innovative is not rooted in evidence. Yet intellectual property law gives lie to this myth. I don’t want to belittle the work done, but it is as if there is a certain amount of predestination to invention based on prior innovations.
Innovation happens
This predestination of technological progress is something that Kevin Kelly labeled the Technium. In his book What Technology Wants he posited that technological progress can be slowed, but nothing short of an apocalypse can stop it completely. Here’s what Kevin Kelly said in an interview with Edge.org when supporting the launch of What Technology Wants:
The technium is a superorganism of technology. It has its own force that it exerts. That force is part cultural (influenced by and influencing of humans), but it’s also partly non-human, partly indigenous to the physics of technology itself.
We understand the innovation process?
Nigel Scott has done some research on the historic records of venture capital companies. And a key finding was the Silicon Valley venture capital firms do a ‘random walk’ on Sandhill Road. It implies that much of the advice dispensed is survivor bias or post-rationalisation.
You hear the phrase ‘pivot’ which means changing the model to profitablity. Old time VCs used to talk about investing people or teams, which explains why research by Boston Consulting Group found that women get less funding than male entrepreneurs.
Venture capitalists have the monetary incentive and the budgets to develop a thorough understanding of innovation, yet they don’t seem to apply it successfully. Which begs the question – how much do we really understand about innovation?
Six decades into the computer revolution, four decades since the invention of the microprocessor, and two decades into the rise of the modern Internet, all of the technology required to transform industries through software finally works and can be widely delivered at global scale.
Over two billion people now use the broadband Internet, up from perhaps 50 million a decade ago, when I was at Netscape, the company I co-founded. In the next 10 years, I expect at least five billion people worldwide to own smartphones, giving every individual with such a phone instant access to the full power of the Internet, every moment of every day.
On the back end, software programming tools and Internet-based services make it easy to launch new global software-powered start-ups in many industries — without the need to invest in new infrastructure and train new employees. In 2000, when my partner Ben Horowitz was CEO of the first cloud computing company, Loudcloud, the cost of a customer running a basic Internet application was approximately $150,000 a month. Running that same application today in Amazon’s cloud costs about $1,500 a month.
As one can see Andreesen’s title is a bit of a misnomer. Software is only the front end of a technology stack that is transforming the world. That transformation started before the web, before broadband infrastructure; with the rise of integrated circuits. Machine learning is doing some impressive things, but they are part of a continuum. Machine learning in data mining is building on work done in academia in the 1980s. It is replicating work done in the 1990s on decision support systems and business intelligence software.
Even, back in the early 1990s, commercial chemical labs were using software to guide product development. Rather than having to test every combination religiously; you started inputting formulations and results. The software would then extrapulate possible combinations and narrow down on an ideal formulation much quicker.
As for machine learning in consumer products; it mirrors the late 1980s. Fuzzy logic came out of a 1965 research paper by Lofti A Zadeh at the University of California, Berkeley.
Japanese manufacturers built lifts that optimised for traffic flows of people. Microwaves that set its own timer for defrosting an item. Washing machines customised spin cycles based on the drum load. Televisions adjusted their brightness based on the ambient conditions of the room. (When similar technology was rolled out on early Intel MacBook Pro screens and keyboard lights it was billed as game changing). It removed a lot of blur from camcorder videos. All applications that are not a million miles away from smart homes and consumer technology today. They improved energy efficiency, with precise lighting, heating or cooling.
A western analysis of Japanese technology companies; usually cites their ‘defeat’ by Silicon Valley as an apparent lack of software skills. I’d argue that this lacks an understanding of Japanese software capabilities. From gaming to rock solid RTOS (real time operating systems); Japanese products met Andreesen’s software definition. The Japanese didn’t manage to sell enterprise software in the same way as Silicon Valley. It is something to bear in mind given the current glut of machine learning-orientated businesses in Silicon Valley. Does it mean that we won’t have the type of general AI applications that we’ve been promised in the future? No far from it, though a technological idea often takes several tries before it breaks through.
What becomes apparent is that software making an impact is merely the last stage of previous innovations. The problem with Andreesen’s model is that it portends what Judy Estrin described as innovation entropy.
Andreesen’s model couldn’t exist without:
Packet-switched networks – 1960 (RAND)
Unix-type operating systems – mid 1960s (MIT, AT&T Bell Labs, General Electric)
C programming language – 1972 (Unix development team)
Optical fibre networks – 1965 (Telefunken)
Internet router – 1966 (UK National Physical Laboratory)
ADSL 1988 (Bellcore)
DOCSIS 1997 (CableLabs)
So the core technologies that Andreesen’s software relied upon to eat the world was between 15 and 50 years old. It also relied on a massive overinvestment in optical fiber. The dark fiber was done as part of a telecoms boom that occurred around the same time as the dot com boom. Software isn’t eating the world, its just the cherry on top of innovation that’s gone before. More importantly, software seems to be an end point and doesn’t seem to extend the base of innovation further.
A second problem is that semiconductors phenomenal progress in integrated circuits is slowing down. Part of the problem is that more money is being dumped into disrupting the supply and demand for service industries, rather than funding start-ups who will power the next wave of underlying innovation that future software will rely on.
