Category Archives: Technology and its malcontents

The new division of labor: On our evolving relationship with technology

I, along with Alan Marshall and Robert Hillard, have a new essay published by Deloitte InsightsThe new division of labor: On our evolving relationship with technology1)Evans-Greenwood, P, Hillard, R, & Marshall, A 2019, ‘The new division of labor: On our evolving relationship with technology’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/the-new-division-of-labor.html>.. This is the latest in an informal series that looks into how artificial intelligence (AI) is changing work. The other essays (should you be interested) are Cognitive collaboration,2)Guszcza, J, Lewis, H, & Evans-Greenwood, P 2017, ‘Cognitive collaboration: Why humans and computers think better together’, Deloitte Review, no. 20, viewed 14 October 2017, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-20/augmented-intelligence-human-computer-collaboration.html>. Reconstructing work3)Evans-Greenwood, P, Lewis, H, & Guszcza, J 2017, ‘Reconstructing work: Automation, artificial intelligence, and the essential role of humans’, Deloitte Review, no. 21, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-21/artificial-intelligence-and-the-future-of-work.html>. and Reconstructing jobs.4)Evans-Greenwood, P, Marshall, A, & Ambrose, M 2018, ‘Reconstructing jobs: Creating good jobs in the age of artificial intelligence’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/creating-good-jobs-age-of-artificial-intelligence.html>.

Over the last few essays we’ve argued that humans and AI might both think but they think differently, though in complimentary ways, and if we’re to make the most of these differences we need to approach work differently. This was founded on the realisation that there is no skill – when construed within a task – that is unique to humans. Reconstructing work proposed that rather than thinking about work in terms of products, processes and tasks, it might be more productive to approach human work as a process of discovering what problems need to be solved, with automation doing the problem solving. Reconstructing jobs took this a step further and explored how jobs might change if we’re to make the most of both human and AI-powered machine using this approach, rather than simply using the machine to replace humans.

This new essay, The new division of labour, looks at what is holding us back. It’s common to focus on what’s known as the “skills gap”, the gap between the knowledge and skills the worker has and those required by the new technology. What’s often forgotten is that there’s also an emotional angle. The introduction of the word processor, for example, streamlined the production of business correspondence, but only after managers became comfortable taking on the responsibility of preparing their own correspondence. (And there’s still a few senior managers around who have their emails printed out so that they can draft a reply on the back for their assistant to type.) Social norms and attitudes often need to change before a technology’s full potential can be realised.

We can see something similar with AI. This time, though, the transition is complicated as the new tools and systems are not passive tools anymore. We’re baking decisions into software then connecting these automated decisions to the levers that control our businesses: granting loans, allocating work and so on. These digital systems are no longer passive tools, they have some autonomy and, consequently, some agency. They’re not human, but they’re not “tools” in the traditional sense.

This has the interesting consequence that we relate to them as sort-of humans as their autonomy and agency affects our own. They’re consequently taking on roles in the organogram as we find ourselves working for, with and on machines. This also works the other way around, and machines find themselves working for, with and on humans. Consider how a ride-sharing driver has their work assigned to them, and their competence is measured, by an algorithm that is effectively their manager. A district nurse negotiates their schedule with a booking and work scheduling system. Or it might be more of a peer relationship, such as when a judge consult a software tool when determining a sentence. We might even find humans and machines teaching each other new tricks.

As with the word processors, we can only make the most of this new technology if we address the social issues. With the word processor it was managers seeing typing as being below their station. The challenge with AI is much more difficult though, as making the most of this new generation of technology requires us to value humans to do something other than complete tasks.

The essay uses the example of superannuation. Nobody wants retirement financial products, they want a happy retirement, the problem is that ‘happy retirement’ is no more than a vague idea for most of us. We need to go on a journey through sorting out if what we think will make us happy will actually make us happy, setting reasonable expectations, and adjusting our attitudes and behaviours to balance our life today with the retirement we want to work toward. This is something like a Socratic dialogue, a conversation with others where we create the knowledge of what ‘happy retirement’ means for us. Only then can we engage the robots-advisor to crunch the numbers and create an investment plan.

The problem is the disconnect between how the client and firm derive value from this journey. The client values discovering what happy retirement means, and adjusting their attitudes and behaviours to suit. The firm values investments made. This disconnect means that firms focus their staff on clients later in life, once the kids have left home and the house is paid off. The client, on the other hand, would realise the most value by engaging early to establish the attitudes and behaviours that will enable the magic of compound interest to work.

As we say in the conclusion to the report:

However, successfully adopting the next generation of digital tools, autonomous tools to which we delegate decisions and that have a limited form of agency, requires us to acknowledge this new relationship. At the individual level, forming a productive relationship with these new digital tools requires us to adopt new habits, attitudes, and behaviors that enable us to make the most of these tools. At the enterprise level, the firm must also acknowledge this shift, and adopt new definitions of value that allow it to reward workers for contributing to the uniquely human ability to create new knowledge. Only if firms recognize this shift in how value is created, if they are willing to value employees for their ability to make sense of the world, will AI adoption deliver the value they promise.

You can find the entire essay over at Deloitte Insights.

References   [ + ]

1. Evans-Greenwood, P, Hillard, R, & Marshall, A 2019, ‘The new division of labor: On our evolving relationship with technology’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/the-new-division-of-labor.html>.
2. Guszcza, J, Lewis, H, & Evans-Greenwood, P 2017, ‘Cognitive collaboration: Why humans and computers think better together’, Deloitte Review, no. 20, viewed 14 October 2017, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-20/augmented-intelligence-human-computer-collaboration.html>.
3. Evans-Greenwood, P, Lewis, H, & Guszcza, J 2017, ‘Reconstructing work: Automation, artificial intelligence, and the essential role of humans’, Deloitte Review, no. 21, <https://dupress.deloitte.com/dup-us-en/deloitte-review/issue-21/artificial-intelligence-and-the-future-of-work.html>.
4. Evans-Greenwood, P, Marshall, A, & Ambrose, M 2018, ‘Reconstructing jobs: Creating good jobs in the age of artificial intelligence’, Deloitte Insights, <https://www2.deloitte.com/insights/us/en/focus/technology-and-the-future-of-work/creating-good-jobs-age-of-artificial-intelligence.html>.

Digitalizing the construction industry: A case study in complex disruption

I, along with a Robert Hillard and Peter Williams, have a new essay published by Deloitte Insights, Digitalizing the construction industry: A case study in complex disruption1)Evans-Greenwood, P et al. 2019, ‘Digitalizing the construction industry: A case study in complex disruption’, Deloitte Insights,<https://www2.deloitte.com/insights/us/en/topics/digital-transformation/digitizing-the-construction-industry.html>.. The case study elaborates on one of the examples we used in Your next future.2)Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>.

In that essay we made the distinction between simple disruption – disruption due to a particular disruptive technology, the thing the comes to mind first for most people when they think of disruption – and complex disruption – where the disruption is due to a confluence of (mainly social) factors. Think the telegraph (simple disruption) vs the global multi-modal container network (complex disruption). Many current disruptions – artificial intelligence, blockchain, etc – tend to be complex (rather than simple) disruption. We’re seeing an environmental shift, as individuals and firms realise that the current environment (with many things available cheap and on-demand) presents opportunities to find new ways to use old technologies to create new ‘disruptive’ operating models, rather than there being a massive wave of new technologies as many pundits claim.

One of examples we used to illustrate the shift was the building industry. There’s a lot of noise about technologies such as 3D printing or brick-laying robots disrupting the building industry, but this is unlikely as the industry’s product is the building process, not the buildings it produces. Builders will simply integrate these new technologies into their process if and when they become commercially viable. The invention of a new building process, however, where a builder uses old technologies in new ways to create a new, and superior, operating model has the potential to disrupt the industry.

Your next future mentioned a design for manufacture and assembly (DFMA) process – where a building is completely modelled in 3D before the model is split up and feed to numerically controlled machines in a factory, with the components shipped to the construction site for assembly – as potentially disruptive. Versions of the process current at the time of publication were roughly 30% faster than a conventional build (due to moving some work to the controlled environment of a factory where rain delays aren’t a problem, and enabling the optimisation of vertical transport on site). They were slightly cheaper, and had the potential to be much cheaper. And there’s the possibility to integrating new materials into the process, materials which couldn’t be used in a conventional process due to on-site restrictions.

Since that essay was published what was then a potential disruption looks like it might be about to tip into actual disruption. This is the subject of the case study.

In 2018 a project in the Melbourne CBD hit problems as the cranes and trucks required to move materials onto the site would block a lane that was the sole access to the homes of many local residents. The solution the builder (Hickory) came up with was to build at night: the machinery would arrive around 9 pm and lift DFMA components (via the Hickory Building System) onto the site, installing an entire floor in four-six hours. Once a floor is complete the floor below is weather-proof and there are no lives edges. The machines are gone before the residents wake. During the day the trades go through the completed floor and finish the interior. There was some skepticism as building is considered noisy, though a trial one night showed that the residents would hardly notice the nighttime construction.

And here’s where we might be seeing a potential complex disruption crystallise into actual disruption. The build proceeded, and the city council was so happy they are considering that all high-rise building to be done at night. This would, with the stroke of a pen, bar conventional builders from the market until they undertake the multi-year journey to develop their own operating model based a DFMA process.

The case study looks at the development of DFMA building processes, the challenges they faced and how they’ve been overcome, and the potential impact on the market. It also looks at how firms might also anticipate similar complex disruptions in their own market, pointing out that conventional market-scanning practices looking for disruptive technologies can actually be counter productive as they cannot predict complex disruption, and we’re in a market with there appears to be more complex disruption than simply disruption.

It’s an interesting story, and a local story which is nice, so head over the the Deloitte web site to read Digitalizing the construction industry: A case study in complex disruption.

References   [ + ]

1. Evans-Greenwood, P et al. 2019, ‘Digitalizing the construction industry: A case study in complex disruption’, Deloitte Insights,<https://www2.deloitte.com/insights/us/en/topics/digital-transformation/digitizing-the-construction-industry.html>.
2. Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>.

Your next future: Capitalising on disruptive change

I and a coauthor have a new report out on DU Press: Your next future: Capitalising on disruptive change.1)Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>. Disruption is something we’d been puzzling for some time as it’s a fuzzy and poorly defined concept despite all the noise it generates. It’s also concerning that few, if any, of the theories have much predictive power.

Our contribution is fairly straight forward.

First we make that point that disruption, as the term is commonly used, covers a broad range of phenomena. This creates tension between our desire for a comprehensive definition, one encompassing this broad scope, and the need for a precise definition, so that we are all clear on what we’re talking about.  Many academic theories (such as Clayton Christensen’s) come unstuck when it’s pointed out that the theory might refer to some disruptive phenomenon, but they don’t account for many other phenomena that can also be considered disruptive.

Consequently we must acknowledge that disruption operates are at least three different levels of abstraction:

  • At the highest level are long-term whole-of-economy shifts that disrupt all of us. The shift from stocks to flows – which we try and measure in the Shift Index2)Evans-Greenwood, P & Williams, P 2014, Setting aside the burdens of the past: The possibilities of technology-driven change in Australia, Deloitte Australia, viewed 26 October 2017, <https://www2.deloitte.com/au/en/pages/technology/articles/shift-index-key-findings.html>. – is one of these.
  • At the mid-level are disruptions focused on a sector or industry. Our colleagues in the US have be cataloging these in the Patterns of Disruption series.3)Hagel, ,J, Seely Brown, J, Wooll, M, & de Maar, A 2015, Patterns of disruption: Anticipating disruptive strategies in a world of unicorns, black swans, and exponentials, Deloitte University Press, <http://dupress.com/articles/ anticipating-disruptive-strategy-of-market-entrants/>.
  • At the lowest level are the things that disrupt us, our firm.

It was the observation that value used to be objective and defined relative to the market, in terms of product feature-function, but now value is more commonly defined subjectively, relative to the firm and the firm-customer relationship, that prompted us to look at disruption with a wider lens and make this subjective disruption the subject of a our essay.

Next we wanted to create a model of disruption that was predictive, which could be fed into a strategy-formation process to enable a firm to identify concrete actions that would enable a firm to prepare for a (potential) disruption and either capitalise on it or defuse it (i.e. neuter the disruption). The resulting model relies on three observations.

  • Disruption is degenerate. A single outcome, a disruption, might be triggered by a large number of different processes. This means that will be impossible to understand disruption by identifying and analysing individual contributors without considering the complex relationships between them.
  • Disruption is constructive. While technology is important to a disruption, technology alone is not enough and we need to also consider social and commercial forces as well that come together to trigger a disruption.
  • Disruption is subjective. A new technology might disrupt our sector or industry, but it may not disrupt us. The reverse is also true. Our concern is disruption to our business, not markets (via patterns of disruption), the economy (via the Big Shift) or disruption in general.

The result a model that shows us why we we cannot predict disruption by identifying ‘disruptive technologies’, but which does enable us to do something about shaping how we approach disruption.

We’re pretty happy with the result, which you can find at DU Press.

References   [ + ]

1. Evans-Greenwood, P & Leibowitz, D 2017, Your next future: Capitalising on disruptive change, Deloitte University Press, <https://dupress.deloitte.com/dup-us-en/focus/disruptive-strategy-patterns-case-studies/capitalising-on-disruptive-change.html>.
2. Evans-Greenwood, P & Williams, P 2014, Setting aside the burdens of the past: The possibilities of technology-driven change in Australia, Deloitte Australia, viewed 26 October 2017, <https://www2.deloitte.com/au/en/pages/technology/articles/shift-index-key-findings.html>.
3. Hagel, ,J, Seely Brown, J, Wooll, M, & de Maar, A 2015, Patterns of disruption: Anticipating disruptive strategies in a world of unicorns, black swans, and exponentials, Deloitte University Press, <http://dupress.com/articles/ anticipating-disruptive-strategy-of-market-entrants/>.

Cryptocurrencies are problems, not features

CBA announced an Ethereum-based bond market solution1)James Eyers (24 Jan 2017), Commonwealth Bank puts government bonds on a blockchain, Australia Financial Review.) It’s the usual sort of thing: it’s thought that blockchain and smart contracts will make everything so much easier and cheaper by improving transparency and making the exchange of goods (bond) and value (currency) atomic.

What caught my eye though was the following:

CBA created a digital currency to facilitate the payment for the bond through its blockchain, and Ms Gilder called on the RBA to consider issuing a digital version of the Australian dollar, which she said would provide the market with more confidence.

“For the blockchain to recognise its full potential as an asset register and a payments mechanism, you need a blockchain-friendly form of currency,” she said. “In the future, we would hope the RBA will look at issuing a centrally issued, blockchain-friendly digital currency, which would help because then the currency would be exactly the same as a fiat currency dollar in your account today just in blockchain form.”

James Eyers (24 Jan 2017), Commonwealth Bank puts government bonds on a blockchain, Australian Financial Review

As is all to often with this sort of thing, the proponents of the blockchain solution don’t understand how money works and consequentially don’t realise that statements like “a centrally issued, blockchain-friendly digital currency, which would help because then the currency would be exactly the same as a fiat currency dollar in your account today just in blockchain form” are just wrong.

To provide the atomic operation the article talks about (atomic asset and currency exchange), both asset and currency need to be blockchain native: blockchain needs to the the ‘database of record’ for both. Further, this means that the currency must to be issued on the same blockchain as the asset.

The most obvious solution is a private currency secured against some AUD held by an issuer / market maker. If we want our currency to be exactly the same as AUD then it must be backed by AUD – i.e. a unit of private currency represents a claim on a unit of AUD – otherwise we’re forced to deal with change rates.

The problem is that no-one will want to obtain the AUD required to issue enough private currency to support transactions in the market, so the solution isn’t economically viable. Imagine deploying a market-based solution that requires the market manager to hold the same amount of working capital as the total market valuation? That’s what they’re talking about.

The proposed “centrally issued, blockchain-friendly digital currency” doesn’t solve the problem as the currency wouldn’t live on the same blockchain. All payments would be off-chain via a gateway / oracle and therefore that security-value exchange would not be atomic, with enforcement all of value exchanges off-chain in the gateways / oracles. The nature of the currency doesn’t matter (“blockchain-friendly” is meaningless): for the operation to be atomic the currency and asset must be issued on the same blockchain.

We could support atomic transactions via Ethereum by issuing a currency on-chain (a “cryptocurrency”, as with Bitcoin) and then have an exchange rate between the AUD and on-chain currency. I doubt the bankers would find the currency risk acceptable though. Plus each market participant would need to maintain an account with enough on-chain currency to support their operations, so all we’ve really done is take the “working capital is total market value” requirement and spread it around the market participants, with an additional currency risk. I can’t see the market having a lot of confidence in that solution.

Consequently the blockchain doesn’t buy us much more than a bit of transparency, and there are cheaper and more efficient ways of supporting that without Ethererum. If we dump Ethererum and the cryptocurrency, and build a conventional distributed solution (R3 is default mode without a blockchain – smart contracts optional – should do), then the solution should be quite practical.

References   [ + ]

1. James Eyers (24 Jan 2017), Commonwealth Bank puts government bonds on a blockchain, Australia Financial Review.

You can’t democratise trust

I have a new post on the Deloitte Digital blog.

There’s been a lot of talk about using technology to democratise trust, and much of it shows a deep misunderstanding of just what trust is. It’s implicitly assumed that trust is a fungible asset, something that can be quantified, captured and passed around via technology. This isn’t true though.

As I point out in the post:

Trust is different to technology. We can’t democratise trust. Trust is a subjective measure of risk. It’s something we construct internally when we observe a consistent pattern of behaviour. We can’t create new kinds of trust. Trust is not a fungible factor that we can manipulate and transfer.

Misunderstanding trust means that technical solutions are proposed rather than tackling the real problem. As I conclude in the post:

If we want to rebuild trust then we need to solve the hard social problems, and create the stable, consistent and transparent institutions (be they distributed or centralised) that all of us can trust.

Technology can enable us to create more transparent institutions, but if these institutions fail to behave in a trustworthy manner then few will trust them. This is why the recent Ethereum hard fork is interesting. Some people wanted an immutable ledger, and they’re now all on ETC as they no longer trust ETH. Others trust the Ethereum Foundation to “do the right thing by them” and they’re now on ETH, and don’t trust ETC.

Why is blockchain so wasteful?

I have a new post up on the Deloitte blog, coauthored with Robert Hillard.

As we point out in the post:

Bitcoin Miners are being paid somewhere between US $7-$9 to process each Bitcoin transaction.

To do this they’re consuming roughly 157% of a US household’s daily electricity usage per transaction. Those numbers don’t suggest a sustainable future for Bitcoin. They suggest an environmental disaster. And this is by design. So why is Bitcoin so wasteful?

The root of the problem is that in a permissionless and anonymous environment — where anyone can mine — you need to pay the miners, otherwise few will mine. We also know that miners will invest up to the margin (which looks to be around 20% for Bitcoin) to obtain this reward.

You can structure the mining algorithm to favour CAPEX or OPEX, though favouring OPEX is preferred, as it reduces the tendency to centralise. You can also play with where the resources are consumed, either direct in the mining process as with Proof of Work, or more indirectly via Proof of Stake. However, you cannot escape the fact that ultimately Bitcoin works because it consumes real world resources.

This leaves you trapped between two conflicting goals:

  • make the mining pool as large as possible to increase the security of the ledger
  • make the mining pool as small as possible to make the ledger more efficient

The only lever you have to pull is the size of the reward: either via seigniorage, or transaction fees.

Again, as we conclude in the post:

Bitcoin is wasteful as it must be wasteful to work. It isn’t actually waste, it’s really just the cost of securing Bitcoin’s ledger. It is, however, a rather high cost when compared to a more conventional, centralised solution.

Image: Mirko Tobias Schäfer

Can blockchain save the music industry?

I have a new post up at the Deloitte Digital blog: Can blockchain save the music industry?

One of the trends we’re seeing across industry is for the market to split in two – low cost, and high value – with the mid-market dying. The mass market, where everyone bought the same thing, is dying, and we’re transitioning to a market where individuals make their own trade-offs between high and low cost.

This makes me wonder if the attempts to modernised the old mass market music model will work. Mycelia and Mediachain are distribution strategies in a world where the mass market is dying.

The future for the music industry might lie elsewhere.

Image: Anefo Nationaal Archief.

Blockchain performance might always suck, but that’s not a problem

I’ve been watching the Bitcoin scaling debate with some amusement, given that my technical background is in distributed AI and operational simulation (with some VR for good measure). Repeatedly explaining blockchain’s limitations to colleagues has worn thin so I’ve posted a survey of the various scaling approaches on the Deloitte blog,1)Peter Evans-Greenwood (5 May 2016), Blockchain performance might always suck, but that’s not a problem, Deloitte Australia blog. Available at <http://blog.deloitte.com.au/greendot/2016/05/05/blockchain-performance-sucks-not-problem/> pointing out why they won’t deliver – either separately or together – the 10,000 time improvement everyone is wishing for, and why this is not a problem. This post is the short version, one not intended for the general audience of the Deloitte blog has.

Continue reading Blockchain performance might always suck, but that’s not a problem

References   [ + ]

1. Peter Evans-Greenwood (5 May 2016), Blockchain performance might always suck, but that’s not a problem, Deloitte Australia blog. Available at <http://blog.deloitte.com.au/greendot/2016/05/05/blockchain-performance-sucks-not-problem/>

Bitcoin’s not broken

Cryptocurrency_Mining_Farm

A lot of high profile Bitcoin people are getting their knickers in a knot as they’re starting to realise that they don’t have any real control over Bitcoin and how it evolves.

As Wired points out,1)Cade Metz (2016/02/11), The Schism Over Bitcoin is How Bitcoin is Supposed to Work, TechCrunch. the current schism is more akin to a vote than anything else, and it is working as designed.

Bitcoin’s ledger is protected by an indirect consensus process. Rather than voting on which ledger is correct, with Bitcoin we prefer the ledger (the version of the truth) that has contains the most “embedded work”, as this should be the ledger with the support of the largest proportion of the mining community.

Bitcoin’s definition – its consensus process (protocol in geek, the whole transaction definition, proof-of-work thing) – is protected via a similar mechanism. Miners are free to adopt any version of the consensus process they chose; big blocks, small blocks, etc. We should also remember that there is no restriction on who can offer up a version; they don’t need to be from the “core team” or other blessed group of individuals.

Consequently Bitcoin governance – just like the state of the ledger – is based on the consensus of the miners. This is quite different from the governance models we’re used to in industry or government. It’s also a long way from the traditional open source world.

What we’re seeing is a bunch of high profile individuals getting in knots as they realise that they don’t have any real control over Bitcoin, which is working as designed.

Image source: Marco Krohn.

References   [ + ]

1. Cade Metz (2016/02/11), The Schism Over Bitcoin is How Bitcoin is Supposed to Work, TechCrunch.

The problem with platforms in the sharing economy

Platform_compressed-750x300

I have a new post up on the Deloitte Strategy blog.It’s the result of a chat I was having the other day with an economist colleague who opined that “platforms are an essential part of the sharing economy”.

As I point out in the post:

These platforms might be sufficient to kick-start the sharing economy, but they’re not necessary for its long term survival. There are alternative approaches to creating sharing economy solutions that do not rely on a centralised platform.

Platforms solve what we might call the discovery problem. When we’re creating a market it needs a mechanism for buyers and sellers to discover each other.

Rendezvous – where buyers and sellers meet at a common location – is probably the most common solution to discover. It’s also the one that firms prefer as it’s the easiest to monetise.

As I point out later in the post:

The recent emergence of blockchain – a distributed ledger solution – from the shadow of Bitcoin might be a sign that something has changed in the environment, something that is tipping the advantage away from centralised solutions and toward distributed ones.

This could be a big deal, as it blows a rather large hole in the business models of the sharing economy firms.

Check out the post and see the whole story.