Having read Jerry Neumanns excellent piece on the cargo-cult that “disruption” has become, I was wondering about what Jerry seemingly missed, and a lot of critics and defendants of capital-D Disruption seem to neglect or disavow: That disruption very seldomly happens from the bottom of a market.

To illustrate what I mean let’s take Jerry’s example of Intel’s market entry as a starting point:

Intel, after all, did not enter the microprocessor market by intentionally introducing a cheaper general purpose computer, they entered it by introducing a much more expensive slide rule…the 4004 was developed to power electronic calculators. The market for electronic calculators was small, allowing Intel the room to build expertise in CPUs, but Intel’s entry can’t be described by Christensen’s attack from below process unless you take into account facts not then in evidence: that CPUs would be used to build general purpose computers.

This is a strategy that we see implemented time and time again, and it’s staggering that it doesn’t receive more attention. I’ve taken to call this strategy »take rich people’s money to build your business.« And it often works. Jerry again:

Finding a foothold market for a new technology gave Intel the time and space to explore other potential markets for the technology, and even though the strategy itself was not disruption, Intel was successful.

To attack and change a well-developed market, your business needs time and experience to start competing in the market. Oftentimes this can be found in the relatively remote confines of adjacent market that incumbents don’t even realize are touching upon their core businesses until it’s too late.

Tesla comes to mind in this regard. Often claimed not to fall within the Disruption paradigm, the effects the firm has on sales numbers of other higher-end car manufacturers is staggering. True, this is not canonical disruption in that it serves novel user needs at a lower cost. It serves novel user needs at a significant premium. Nonetheless, that technology trickles down, and already changes the face of the auto industry to the point of classic big auto manufacturers trying to either partner with Tesla or cargo-cult what they perceive to be Tesla’s secret sauce. ¹ All the while Mr. Musk made Tesla’s strategy plenty clear, giving a playbook for this “disruption from the top”:

Build sports car
Use that money to build an affordable car
Use that money to build an even more affordable car
While doing above, also provide zero emission electric power generation options
Don’t tell anyone.

The adjacent component of Tesla’s strategy is the secondary use of the vehicle tech, namely the batteries. With the Power Wall (to complement SolarCity’s products) a notional car manufacturer suddenly walked into the market of energy firms and changed the rules. This is adjacent competition in action.

Another example is the case of the Nest Smart Thermostat. Often, industry analysts purely focus on device sales of the product, missing a significant revenue stream that Nest has uncovered adapting their product to adjacent markets. What did they do? They started offering Demand Response services to local utilities companies, contracting out what in the industry is called Negawatts, reducing electricity consumption during times of peak demand, and filled those contracts with their Rush Hour Rewards program, into which customers served by participating utilities can opt in. In essence, Nest then reduces the energy consumption of a customer’s AC during peak load, in turn helping stabilize the grid and reduce the need for utilities to fire up back-up plants.

So your fancy gizmo of a thermostat suddenly becomes a crucial instrument in energy policy, something that Power Companies have been trying to implement for ages, albeit unsuccessfully.

And while Jerry describes Disruption as a warning to the managers of incumbents, disruption and adjacent competition can serve as both, a warning to incumbents and a potential strategy for new entrants.

Time and again, you hear calls for a unified Internet of Things standard. The argument is essentially this: the value of the Internet of Things does not lie in the individual products themselves, but in the connections they can make, the network they can tap into. Without a universal standard, these products will live in “walled gardens”, restricting the network size, and following Metcalfe’s law, will restrict the total value of the network, as the number of connections between nodes is handicapped.

While this argument is sensible, it doesn’t incorporate the pathways on how we might end up at such a universal standard. After all, there are now a multitude of competing standards organizations that lay claim to being exactly that. The W3C has compiled a neat overview that currently counts 32 different consortia.

This puts developers of applications and devices for the Internet of Things in an impossible position that ultimately ensures suboptimal outcomes for the ecosystem. This is driven in large parts by incumbents trying to secure a favorable outcome in what appears to be an incredibly large future market with perceived winner-takes-all dynamics – the power laws of the internet apply – and thus strategically hedging their bets. Theo Priestly had a look into that over at Forbes and found:

While there are differences between their focus; for example industrial IOT use cases and smart home interoperability, what’s interesting to note is just how many camps these 6 vendors are playing footsie with. Cisco and Samsung are part of seven initiatives. IBM, Honeywell and Intel are aligned with five.

Why this makes sense for large platform providers to stave off potential disruptors is handily explained by Ars Technica

But this is typical of how Google operates. The company’s actions have shown it doesn’t really believe in focusing on a single solution to a problem, regardless of how much easier that would make things for users. It has to deal with external competitors in all sorts of areas, and Google seems to see no reason why competition can’t also come from within—Google products competing with other Google products.

It’s almost like every product category is just a big A/B experiment for Google. As Google’s search engine constantly gathers data from the Web to learn and improve, Google the company works much the same way. It provides multiple solutions to a single problem and expects the best one to win out over the other.

They’re spreading their investments in the hopes to have a presence in whatever platform or standard wins out. But while Ars Technica focuses on the user, that is not the target audience the standards groups are speaking to.

Any potential IoT standard rises and falls with developer adoption. It is third party manufacturers that need to be convinced to buy into an ecosystem and design products with that ecosystems standard in mind. It is application developers that need convincing to write apps for the platform. After all, that’s the point of “interoperable” standards.

But while it makes sense for incumbents to place their bets across many different standards, developers and manufacturers don’t have that luxury. Often it is simple cost and time constraints that requires them to focus on a single supported platform. How many startups do you know that launch iPhone first, with a vague promise to support Android later, and no mention of any of the also-ran mobile platforms?

But which platform to develop for? The optimal outcome for the ecosystem would be for manufacturers and developers to agree on a standard, forego short-term profits for growing the long-term market size. Growing the pie, rather than your piece of it. But with current market dynamics, the strategic choices are limited to guarantee suboptimal outcomes, as the dominant strategy for every market participant is to roll their own. In that way, they at least have a modicum of control over their underlying infrastructure, rather than being at the whims of a whole-market A/B experiment, of which they might easily back the wrong side.

What would be required for interoperability to happen is non-zero-sum thinking with the big platform vendors. And yet we have entered a spiral of zero-sum competition in what seems to be a crucial part of a still-emerging market.

What this will lead to is a necessary segmentation of the market. It is unrealistic, and a problematic aspect of the whole Internet of Things discussion, to have cluster such a variety of things as Industrial and Supply Chain applications, Automotive, Health and Home together. Each of these industries will likely develop their own set of competing standards, where winners will be easier to find, but a lot of deadweight loss will occur. (Think HD-DVD firms and customers.) The really valuable position to sit in such a scenario is at the fringes of respective verticals – being a gateway and translation point between them. We’re starting to see key player jockey into position for this.

Entertain, for a bit, the notion that you are one of the predominant players in today’s technology environment, amassing unheard-of fortunes, selling upmarket – some say luxury – goods on a nice margin, and generally being lauded as one of the best design-driven and customer-focussed organisations around. Also, the technology press and the analysts regularly beat you for having missed the biggest technological shift in recent memory.

The simile should be obvious, and the technological shift we’re talking about is »cloud«¹. What most of the “analysis” on Apple’s weakness in cloud misses is the notion – and if you’re in any way working in a field that creates stuff, you’ve heard it way too often already, so sorry – that focus is more about saying no than saying yes, that you, your career, your company will be defined more by what you say no to than you agree to, and that excluding things from your option space will open up opportunity to discover non-obvious solutions to problems presented to you.

What we’ve seen in last weeks keynote about the new possibilities in Apple’s ecosystem is exactly that: novel, non-obvious and deeply Apple-y solutions to a problem-space that appeared solved, but unsatisfactorily so.

Take the integration of phone calls and SMS into the operating system, based on proximity and without a cloud component. Sure, a service like this existed before, but it required that you hand over control of your communications to a centralised server instance that then reroutes your communication according to pre-set rules to either phone numbers or an app on your computing device, be that a phone, laptop or phablet. That is the cloud-centric way of doing things: put the smarts – the control, really – into the network center and distribute actions out to the network edge.

With their perceived Cloud-weakness, Apple was able to come up with a solution that doesn’t require that forfeiting of control to a centralised server instance to enable the same functionality.

It’s a long-standing quip in the technology sphere that of the big players only Apple, by virtue of its business model, is able to offer you some semblance of privacy. Their motivation, as Benedict Evans puts it, is to sell you smart, premium devices, whereas Google for instance wants to sell you dumb glass to access their services.

And this leads to this huge philosophical divide which we will see playing out as both Google and Apple, joined by many more companies, branch out to bring chips and connectivity into more and more parts of our lives. Do we put the smarts into the networks center, or onto the network edge? The gauntlet has been thrown. It’s onto every company that works in this field to choose its side. Choose carefully.

If you follow the chit-chat of twitter and the technology “press” you’re very likely to have come across Spurious Correlations, a site showcasing the absurdities of “big data”.

That is a very roundabout way of saying what a lot of critics of “data journalism” and “big data” have been shouting from the rooftops for a while now: just because there’s a correlation doesn’t mean there’s actually a connection between datasets. And that’s before taking into account that by overfitting and assorted other means of statistics, you can kinda manufacture correlations.

But that alone wouldn’t be very problematic. Statistics had to, and continues to, battle with this problem for a long time now. The problem arrives once we start collecting and correlating data on large scale with very concrete impact on everyday life.

Take for instance the latest coverage of the potential impact of health-tracking devices on the overall healthcare system and health insurance specifically. We’ve seen coverage of employers systematically equipping their workforce with fitness trackers to drive down health care costs and the providers of those fitness trackers starting to sell that data of insurers outright.

Amy McDonough, who oversees Fitbit’s employer program, wouldn’t comment on how Fitbit data would affect pricing negotiations between employers and health care providers, though health insurer Cigna said fitness trackers “may” have an impact on future group insurance pricing .

The Quantified Other: Nest And Fitbit Chase A Lucrative Side Business - Forbes

This is problematic on a couple of fronts: first, it perpetuates an existing bias for able-bodied employees, and secondly, it relies on the implicit assumption that activity as measured by the devices maps to better health outcomes, completely disregarding the individual capabilities of the persons tracked. Nevermind the extension of reach employers have over their employees.

And then there’s the question of what those fitness trackers actually measure and try to motivate you to do. We want people to be more active, to develop healthier habits (and this is foregoing the whole argument of us having to possibly address systemic challenges first before we attribute blame and try to fix behaviour at the individual level), and to generally »level up«. But on the design front, there’s very little discussion around how to actually encourage sustainable behaviours.

One other thing that I'm still a little fixated upon is what happens when wearable devices are successful. From a mental health point of view, one of the things that I realised about myself was that I was only really happy when numbers were trending in the right direction. When blood sugar and weight were coming down, that was great. But the wearable systems that we have at the moment aren't particularly good (and obviously that depends on how you define 'good') for what happens when the numbers are trending in the wrong direction. Or, what happens when you don't need to progress to a goal state, but that your goal state is instead a *steady* state. For me, stereotypical language like the Nike Fuelband includes exhortations to Crush It and Win The Day, which (again, this may be idiosyncratic) imply a relentless striving for betterment that may not be each and every user's goal. Maintenance, as opposed to excellence, requires a different type of design.

Episode Sixty Two: Wearables Unworn; Look At What They Want

And, to quote from another Dan Hon episode:

If you're sensing, then you'd better have either novel sensors or outstanding data manipulation, because the sensors have become pretty much commoditised now that it's trivial to whack a three-axis accelerometer in a thing and now that everything has a three-axis accelerometer in it, you'd better be able to deliver on the promise of your sensing which, to be honest, probably doesn't live up to what the public thinks of your device, no matter what you actually tell them. Fitbits and Fuelbands should measure *effort*, in a consumer's eyes, but don't, and the degree to which they're able to fulfill that promise relies upon your smarts with data. 

Episode Forty Three: Wearable Reckons; Mirror Neurons; More Google

Looping back to Spurious Correlations, what we have right now is the bearings of a systems where your employment chances and your health care expenditure are going to be in no small part determined by the measurements of little three-axis-accelerometers with no health-care grade certification, data scientists trying to make sense of the data but in large part no medical experience to interpret the data in a theory-driven way, and that is heralded as “the new way” for healthcare.

We need to do better.

(Disclosure: I’m currently retained by a startup to work in the connected health space. I’ve also previously contributed to the KANT HEALTH report)