The big picture of radical decentralization

In a previous post I wrote about my belief that the world was ultimately moving to large-scale, public, decentralized technology models, and these would give rise to global, public, decentralized platforms for enterprises.

The impetus for that post was the announcement of the Enterprise Ethereum project, and my focus was on blockchain and the debate around permissionless or permissioned ledgers.

Blockchain, however, is only part of the picture. Today we are seeing a grand convergence of several technology mega trends that, working together, will make these future platforms extremely smart, fully autonomous, hyper-connected, fully decentralized, and very broad-based.

While it is the technologists who are building these platforms, it will fall to business decision makers to figure out how best to use them commercially. Certain industries are racing ahead in thinking about the radically new business models this future will bring. Others – including financial services – seem to me to be lagging.

I think that’s a mistake, as I intend to discuss in a later post. Here I would like to look at this convergence in some detail, as I think enterprises really need to understand the new environment they will eventually being doing business in.

 

All together now

 

Today, as people have recognized when for example talking about the fourth industrial revolution, we have at our disposal the various technological ingredients needed for radical automation and radical decentralization.

Most prominent among these, at least in a commercial context, are artificial intelligence (including, but not limited to, machine learning), big data, the Internet of Things (IoT), and edge computing.

Advances in each of these fields represent extremely interesting new technological capabilities in themselves. But to be truly useful for platform building, they need to work in tandem. That’s because they have a number of dependencies.

For example, thanks to artificial intelligence we can teach computers to think for themselves and make autonomous decisions orders of magnitude faster and, at some point, orders of magnitude better than we can.

But thinking machines first need to be educated – either by being fed a steady stream of information so they can learn on their own, or by being given robust enough models of the world to allow them to make intelligent choices without our help. The prerequisite for this is having enough information around in digital form with which to train our machines. This was impossible before big data.

Once our machines can “think”, we will want to “do”. To drive true large-scale automation, our AI decision makers will need to manipulate real-world devices outside of themselves. But this only works on devices that can receive messages, understand what they are being asked to do, and autonomously carry out their instructions. This was not possible before the IoT. And, as we are learning, for IoT-enabled devices to be able to react quickly, and so be useful in a decentralized world, they will not be able to wait for data and instruction from the cloud. Hence the current interest in developing edge computing, in which data and computation takes place on the devices themselves (the “edge” of the network) and not in central nodes. This prediction was described by Peter Levine, a partner at Andreessen Horowitz in his talk “Return to the Edge and The End of Cloud Computing”. In following video, Peter discusses the pressures that our pushing toward edge computing and away from the cloud:

 

 

Last but not least, no decentralized platform can be built if the nodes on the network, whether machine or human, can’t easily, securely, autonomously, transparently, traceably and quickly share data. Where can we look for a technology to allow them to do this? To the blockchain or other distributed ledgers, of course. For this reason, I think blockchain will play a key role in the coming convergence, as the communications, trust and auditing hub. But it is only a part of the picture.

 

New world, new model

 

There is no doubt that the radical decentralization and automation this will enable will have a radical effect on business models too. The new environment will just be too different for business as usual.

I expect that, thanks to far greater integration of value chains or between businesses and customers, business verticals will blur. The silos between industries will also come down.

Enterprise decision-makers will need to keep this in mind. In subsequent posts I will lay out in more detail how I think these new models will look, and how in my experience some industries seem to be doing a better job than others in preparing for the decentralized future.

 

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2017: the year we get real?

There is no doubt that 2016 has been a tumultuous year.

From Brexit to Trump and the Ukraine to Syria, we have seen many upheavals on the geopolitical front. A lot has happened in Fintech, too, although here the upheaval has in my opinion been almost all positive. Today FinTech is firmly established as one of the biggest sectors in all technology.

What will next year bring?

We can look forward to more tumult I think. If there is one overarching FinTech trend, I would say that several things that were only “potentials” in 2016 will become much more concrete. That could make 2017 the “year of getting real” on a number of fronts.

Here are some of my thoughts.

The year ahead: Predictions for 2017

  • The year of the pilot. 2017 will be the “year of the pilot” for blockchain in financial services, as it moves from proof-of-concept into production. We should see this in particular in cross-border payments and trade finance. Overall however blockchain will still be restricted to the “low hanging fruit” in banking. I remain convinced that broad-based application of DLTs will happen more quickly outside of financial services.
  • The year of the standard. We may see significant progress in blockchain standards during the year. If so, it will be driven by small groups working on specific use cases as opposed to large, complex consortia. Indeed, I expect we will see consolidation in the blockchain consortia area.
  • The year of the platform. On the back of increased standards and interoperability, we should see broad-based platforms and ecosystems continue to emerge, driving banking as a service and the creation of new business models. Look for this particularly in the robo-advisory and lending businesses.
  • The year of the attack. The number of cyber-attacks on organizations will increase significantly, and we can expect a steady stream of revelations about hacks. Denial of service is becoming much more threatening and dangerous for banks and in 2017 banks and others will be called on to toughen their defenses. This will be reflected in cyber-security spends, which among wholesale banks will increase from 5% of total tech budgets to 7-8%.

Eye on the prizes: Trends to watch in 2017

Along with the above “predictions”, here are some of the trends I think worth keeping an eye on in the coming year.

  • PSD2 pushing partnerships between banks and FinTechs. Banks and other financial services players will have to spend 2017 preparing for the implementation of the revised EU payment services directive PSD2 in 2018. With the creation of open banking platforms, there will be opportunities for FinTechs to partner with banks to create more exciting customer experiences and provide increased transparency on performance and fee structures.
  • Competition among financial centers for FinTech innovation. 2016 was the year of regulatory sandboxes with the FCA and MAS Singapore leading the change by establishing themselves as business developers with a mandate to attract business to their respective jurisdictions. In 2017, leading regulators will strengthen their position with global collaboration and implementation of new policies and laws based on learnings from their “sandbox” environments in order to reduce uncertainty in the FinTech ecosystem.
  • The continued rise of smart machines. It’s no secret that there are great strides happening right now in artificial intelligence. Advances in machine learning and robotics will I think continue to sweep the business world. Startups will continue to get funding in the areas of risk assessment, research, investment management, trading and back office automation.
  • An intensified war for talent. Banks and FinTechs will be competing for people with the right skills. The key expertise in financial services will be in artificial intelligence, in particular robotics and machine learning, where the game will be to attract scientists with Masters Degrees and PhDs. There will also be a battle for domain and technicaly expertise in finance, distributed ledger technology, and cyber security.

A new road

Finally, 2016 was a very big year for me personally.

2016 was also the year of the launch of Bussmann Advisory, with the goal of helping companies stay ahead of the digital disruption curve.

The company has gotten off to excellent start, better than I could have imagined. For that I am grateful, to my new clients and all those who have collaborated with me and supported this move.

With that, I would like to wish everyone the best of the season and a happy and healthy new year. It promises to be an interesting one.

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Mastering the 4th Industrial Revolution

UBS published a White Paper for the WEF Annual Meeting 2016 about extreme automation and connectivity: The global, regional, and investment implications of the 4th Industrial Revolution.

See the Executive Summary below and read the UBS White Paper now:

A brief history of industrial revolutions

  • Prior industrial revolutions have centered around improvements in automation and connectivity.
  • The First Industrial Revolution introduced early automation through machinery, and boosted intra-national connections through the building of bridges and railways.
  • The Second Industrial Revolution began when automation enabled mass production and fostered more efficient, productive connectivity via the division of labor.
  • The Third Industrial Revolution was propelled by the rise of the digital age, of moresophisticated automation, and of increasing connectivity between and within humanity and the natural world.
  • The Fourth Industrial Revolution is being driven by extreme automation and connectivity. A special feature of the Fourth Industrial Revolution will be the wider implementation of artificial intelligence.

 

 

What are the potential global economic consequences?

  • Polarization of the labor force as low-skill jobs continue to be automated an this trend increasingly spreads to middle-skill jobs. This implies higher potential levels of inequality in the short-run, and a need for labor market flexibility to harness Fourth Industrial Revolution benefits in the long-run.
  • Greater returns accruing to those with already-high savings rates. In the short run, this could exacerbate inequality via relatively lower borrowing costs and higher asset valuations.
  • As the issuer of the world’s reserve currency, the US’ competitive advantages, sitting at the heart of the Fourth Industrial Revolution, could tighten effective monetary conditions among US dollar-linked economies.
  • The Fourth Industrial Revolution increases the magnitude and probability of tail risks related to cybersecurity and geopolitics, but may spur regional action to invest and embrace Fourth Industrial Revolution benefits.

 

 

Who will be the regional winners and losers?

  • “Flexibility” will be key to success in the Fourth Industrial Revolution; economies with the most flexible labor markets, educational systems, infrastructure, and legal systems are likely to be relative beneficiaries.
  • Developed economies are likely to be relative winners at this stage, whereas developing economies face greater challenges as their abundance of low-skill labor ceases to be an advantage and becomes more of a headwind.
  • Emerging markets in their demographic prime may find that extreme automation displaces low-skill workers, but that their limited technology infrastructures do not allow them to reap the full benefits of extreme connectivity.

 

What are the investment consequences?

  • Given current assessments of relative competitiveness, emerging markets maybe less well placed to profit from Fourth Industrial Revolution benefits, relative to developed markets.
  • We expect further disruption to traditional industries from extreme automation and connectivity.
  • Big data beneficiaries include firms that harness big data to cut costs or target sales; firms that automate big data analysis, and firms that keep big data secure.
  • Blockchain applications could benefit firms that use them to automate processes securely, to cut out costly intermediaries, and to protect intellectual property.