Regulating Digital Businesses – Like Chasing Trains

chasing train

I don’t know if you’ve ever had the experience of running for a train that’s just started to move? I’ve had to do it a few times. Yes, I was younger and more foolish then. But it was usually within seconds of the train moving that I was on it. It’s only in old movies that you see the protagonists dashing down the platform as the train picks up speed. Usually, you just have the platform length and the problem is that the train is accelerating. There is a finite window of opportunity after which you’re just going to be left on the platform. This is my very long-winded analogy for regulators and technology. As technology accelerates – it’s getting harder for regulators to keep pace and in fact, in many areas they are just like the proverbial train chasers, running desperately after an accelerating train – often in a futile bid to control a business or industry that is on the verge of leaving the station of regulatory comfort. You can pick from a range of visual metaphors – a man trying to control seven unruly horses, or grabbing a tiger by the tail, but you get the idea. Regulators are in a fix.

The sight (and sounds) of the congressional hearing of Mark Zuckerberg did not bode well for regulators. They should have had Zuckerberg dead to rights over it’s (willing or unwilling) culpability in the Cambridge Analytica imbroglio. Yet he came out with barely a scar to show for 2 days of grilling. Many of the people asking him questions came across as the stereotypical grandparent trying to figure out the internet from their grandchild, even if these are very exaggerated caricatures. There is arguably a 40 year age gap between the average lawmaker and the average entrepreneur. But the age challenge is just a minor problem. Here are some bigger ones.

Technology businesses are shape-shifting enterprises invariably redefining industries. Platforms cannot be regulated like their industrial counterparts. Uber is not a taxi company. Facebook is not a media business. Airbnb is not a hotel. No matter how convenient it might be to classify and govern, or how often someone points out that the world’s biggest taxi company doesn’t have taxis. No, these are data and services platforms, and they need an entirely new definition. You could argue that the trouble with Facebook has come about because they were being treated like a media organisation, rather than a data platform. And let’s not forget that the only reason Facebook was in the dock is because of the success of Cambridge Analytica in actually influencing an election. Not for the misuse of customer data on a daily basis which may have gone on for months and years by Cambridge Analytica as well as other similar firms. While governments’ focus on Uber stems largely from incumbent and licensed taxi services, nobody seems to be worried that Uber knows the names, credit card details and the home and office residences of a majority of its users.

Tech businesses, even startups, are globally amorphous from a very early age. Even a 20 person startup barely out of its garage can be founded in California, have it’s key customers in Britain, its servers in Russia, its developers in Estonia and pay taxes in Ireland. Laws and governments are intrinsically country bound and struggle to keep up with this spread of jurisdiction. Just think of the number of torrent services that have survived by being beyond the reach of regulation.

These are known problems and have existed for a while. Here’s the next challenge which is a more fundamental and even an existential one for lawmakers. With the emergence of machine learning and AI, the speed of technology change is increasing. Metaphorically speaking, the train is about to leave the station. If regulators struggle with the speed and agility of technology companies today, imagine their challenge in dealing with the fast-evolving and non-determinate outcomes engendered by AI! And as technology accelerates, so do business models, and this impacts people, taxes, assets, and infrastructure. Imagine that a gig-economy firm that delivers food home builds an AI engine that routes its drivers and finds a routing mechanism that is faster but established as being riskier for the driver. Is there a framework under which this company would make this decision? How transparent would it need to be about the guidance it provides to its algorithms?

I read somewhere this wonderful and pithy expression for the challenge of regulation. A law is made only when it’s being broken. You make a law to officially outlaw a specific act or behaviour. Therefore the law can only follow the behaviour. Moreover, for most countries with a democratic process, a new law involves initial discussion with the public and with experts, crafting of terms, due debate across a number of forums and ultimately a voting process. This means we’re talking in months, not days and weeks. And if technology is to be effectively regulated and governed, a key challenge to address is the speed of law-making. Is it possible to create an ‘agile’ regulatory process? How much of the delay in regulation is because the key people are also involved with hundreds of other discussions. Would lawmaking work if a small group of people was tasked to focus on just one area and be empowered to move the process faster in an ‘agile’ manner? We are not talking about bypassing democratic processes, just moving through the steps as quickly as possible. A number of options are outlined in this piece from Nesta website – including anticipatory regulation (in direct contravention of the starting point of this paragraph), or iterative rather than definitive regulation. All of these have unintended consequences so we need to tread cautiously. But as with most businesses, continuing as present is not an option.

Then there’s the data challenge. The big technology platforms have endless access to data which allows them to analyse them and make smarter decisions. Why isn’t the same true of regulators and governments? What would true data-driven regulation look like? We currently have a commitment to evidence-driven policymaking in the UK (which has sometimes been unkindly called policy driven evidence making!) but it involves a manual hunt for supporting or contradicting data, which is again time-consuming. What if a government could analyse data at the speed of Facebook, and then present that to the experts, the public, and legislators in a transparent manner? The airline industry shares all the data about every incident, accident and near miss, across its ecosystem, competitors, and regulators, and this is a significant contributor to overall airline safety. (Outlined in the book Black Box Thinking, by Matthew Syed.) Why isn’t the same true for cybersecurity? Why isn’t there a common repository for all the significant cyber attacks, which can be accessed by regulators armed with data science tools and skills, so that they can spot trends, model the impact of initiatives and move faster to counter cyber attacks? If attacks seem to originate from a specific territory or impact a specific vulnerability of a product, pressure can be brought to bear on the relevant authorities to address those.

running after train
These are non-trivial challenges and we need to be aware of risks and unintended consequences. But there is no doubt that the time has come for us to think of regulation that can keep pace with the accelerating pace of change, or governments and regulators will start to feel like the protagonists of movies where people run after trains.

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Seven in 7 – Agile @ Scale, Maturing AI, The Ring of Success, Defending Democracy and More…

Agile at scale
As we head into the TCS UK Innovation Forum this week, I’m preparing myself to discuss big ideas and disruptive changes. With that in mind, this week’s Seven in 7 looks at scaling AI, a startup that was bought for a billion dollars, and hacking democracy. But also, as we’re committed to becoming agile as an organisation, where better to start, than at the great article in the new HBR about how to drive agile at scale!

(1) Doing Agile at Scale

This is a very timely look at Agile adoption at scale in the enterprise. It starts with enshrining Agile values in leadership roles, which requires a continuous approach to strategy. The next key thing is a clear taxonomy of initiatives which may be classified into 3 categories: customer experiences, business processes, and IT systems. The next step is sequencing the initiatives, with a clear understanding of timelines. It can take 5-7 years for real business impact, but there should be immediate customer value. Enterprise systems such as SAP can be delivered using agile as well. But it needs the organisation to create and move with a common rhythm. There are businesses working in agile who use hundreds of teams, solve large problems, and build sophisticated products. This can be made easier with modular products and operating architectures – which essentially mean the plug and play capabilities of individual components. It’s important to have shared priorities and financial empowerment of teams. Talent acquisition management needs to be reshaped to meet the new needs. And funding of projects and initiatives needs to be seen as ‘options for further discovery’. After all, at the heart of agile is the ability to proceed with a clear vision but without necessarily knowing all the steps to get there.

(2) Artificial Intelligence At Scale – for non-technology firms

It’s clear that Tech firms from Google, to Amazon and Twitter, have all been able to deploy AI at scale – in enabling recommendations, analysis and predictive behaviour. For non-tech firms too, the time may have come for delivering scaled AI. One of the key areas where AI seems to be ready to scale is around computer vision (image and video analysis) – relevant to insurance, security, or agribusinesses. The article below from the Economist also quotes TCS’s Gautam Shroff, who runs the NADIA chatbot project. A critical assertion the article makes is that implementing AI is not the same as installing a Microsoft program. This might be obvious, but what is less so, is that AI programs by design get better with age, and may be quite rudimentary at launch. Businesses looking to implement AI may need to play across multiple time horizons. And while the short-term opportunity and temptation is to focus on costs, there role of AI in creating new value is clearly much bigger.


(3) The Ring of Success:

What makes a new product successful? I met Jamie, the founder of Ring a couple of years ago in London and was struck by his directness and commitment. He even appears in his company’s ads. Ring.com was recently acquired by Amazon for $1bn. Here one of the backers of Ring talks about the factors which made Ring a success. In a nutshell, the list includes (1) the qualities of the founder, (2) execution focus and excellence, (3) continuous improvements, (4) having a single purpose, (5) pricing and customer value. (6) integration of hardware and software. (7) clarity about the role of the brand.


(4) Blockchain and ICO redux:

Do you know your Ethereum from your Eos or your MIATA from your Monero? This piece from the MIT Tech Review will sort you out. And for those of you who are still struggling to understand what exactly blockchain is, here’s a good primer. Of course, you could always go look at my earlier blog post on everything blockchain.

Links:


(5) X and Z – The Millennial Sandwich

X & Z: Or the millennial sandwich. All the talk in the digital revolves around millennial, but there is a generation on either side. The generation X – followed the baby boomers, and it turns out they have a better handle on traditional leadership values than millennial. This article talks about Generation X at work.

On the other side, there’s a generation after the millennials – the generation Z. They’re the ones who don’t have TV’s, don’t do facebook, and live their lives on mobile phones. This article talks about how Financial services are being shaped by Gen Z.


(6) Big tech validates Industry 4.0

This week, the large tech players disclosed significant earnings, beating expectations and seeing share prices surge. In a way it’s a validation of the industry 4.0 model – the abundance of capital, data, and infrastructure will enable businesses to create exponential value, despite the challenges of regulation, data stewardship issues and other problems.  Amazon still has headroom because when push comes to shove, Amazon Prime, which includes all you can consume music and movies can probably increase prices still more.


(7) Defending Democracy

The US elections meets the technology arms race – this article presents experiences from a hacking bootcamp., run for the teams who manage elections. While the details are interesting, there is a larger story here – more than influencing the elections either way, the greater harm this kind of election hacking wreaks is in its ability to shake people’s faith in democracy. As always, there’s no other answer than being prepared, but that’s easier said than done!

Reading List: 7 for 7 – April 23: Palantir > Facebook, Generative Design, Alexa With Eyes, and More…

The 7 most interesting things I’ve read over the past 7 days.
 future doors
(image credit: Pixabay)

(1) The One Thing You Should Read about AI this week: 
In March, we ran a TCS DEX event where we posed the question to our partners and clients, around whether every company should have an AI strategy. While there was general agreement about the need for an AI strategy, there was no clear starting point. This may be the challenge for most companies. And perhaps the first steps towards a strategy are gathering information and running experiments.
If you read one thing this week, read this AI paper by the McKinsey Global institute – they publish results from a comprehensive survey and analysis of AI across industries, functions, and use cases and by the relevance of the different techniques, such as Transfer Learning, Reinforcement Learning, and Deep Learning Neural Networks. If some of that sounds obscure to you, I suggest reading up a little bit as these will become common business parlance in the not-too-distant future, and clients will be asking about them. In any case, succinct explanations are provided in the paper. It will probably take you a couple of hours to read (not skim) the 40 odd pages. Here are some of the very high-level takeaways:
  1. Industries where the number of use cases are the highest, include (1) Insurance, (2) Banking, (3) Retail, and (4) Automotive & Assembly.
  2. Functions with the highest number of use cases include (1) Supply Chain management and manufacturing, and (2) Marketing and Sales
  3. Specific domains where the impact might be the highest include (1) customer service & management (2) risk modelling (3) predictive service / intervention (4) workforce productivity and efficiency (5) analytics-driven hiring and retention, and (6) yield optimisation.
Some other takeaways:
  • The highest absolute impact of AI is to be found in Retail, but Travel and transport & logistics can extract the highest incremental value over other analytics techniques.
  • Image data is the highest value, after structured and time series data, and ahead of text.
  • Challenges and limitations: (1) labelling training data (2) obtaining large enough data sets (3) explaining the outcomes and decisions in clear enough terms – e.g. for product classification or regulatory (4) transferring of findings to adjacent use cases, and (5) risk of bias in data/ algorithms

(2) Data: Facebook is a misguided amateur compared to Palantir 
Palantir is much more dangerous than FB. Why? (1) Because Peter Thiel, the founder is a man of metamorphosis – he has quixotic views of the world – such as ‘freedom is not compatible with democracy’; (2) because Palantir is a much more shadowy and secretive organisation but built specifically for next-generation analytics for powerful clients. (3) Because this kind of analytics power can be destructive if individuals go rogue – the article talks about Peter Caviccia who ended up running his own spying operation within JPMorgan in what is described in the article as Wall Street meets Apocalypse Now, and (4) because tools like this are being used by police forces such as LAPD to predict crime – but also to do that to build deep and intricate views of a lot of individuals and their lives. The article also provides a very good visual model of Peter Thiel’s incredible original Paypal team and network which includes Elon Musk, Reid Hoffman (LinkedIn), Steve Chen (Youtube) and many others.

(3) Design: Welcome to Generative 3D Design 
What do you do when you need to design and build a spinal implant that needs to be appropriately strong, light and pliant? You use an algorithm-driven design process called generative design with 3D printing. Algorithmic design takes in your specifications or requirements and generates a number of options, which are developed faster than humans and enables a lot more personalisation of complex materials. In future, these will probably be custom built to specs in a way that humans simply can’t. It also uses the least amount of material possible (it’s one of the constraints/ objectives). This story in the Wired magazine talks about how Nuvasive does this using AI and 3D.

(4) eCommerce and Retail – change of guard, and disruption for the economy
This week we had a direct comparison between M&S vs ASOS: M&S is a struggling brand – losing share in apparel, and under pressure on foods. Other brick and mortar retailers like New Look are also in trouble. ASOS sales, on the other hand, hit £1.9 bn 2017 which amounted to a 33% increase. It’s also instructive to note that eCommerce contributes some 25% of British clothes retail numbers. In fact, the UK has the highest amount of online commerce (as a % of overall retail numbers – almost 18%), but the retail industry also accounts for 10% people and 10% of the economy – so significant disruptions lie ahead.

(5) Asset-light business models 
We’ve seen them in telecoms (MVNOs), in retail, and also in utilities. Lightweight, direct to consumer competitors who don’t carry the baggage of their larger competitors. They have no legacy IT and are built ground up on digital platforms, for a start, and also have a much more nimble operating model. Companies like Asos and Ovo energy are successful because they attract a particular consumer niche, operate in an agile way and are not weighed down by the legacy business and IT challenges of their larger peers (zero inventory, for example). This trend goes all the way down to micro brands in the consumer goods space. Many of these businesses will die or stay micro, but once in a generation, they will lead to the next FB or Amazon.

(6) Alexa Fashion – a glimpse of the future 
What’s Alexa’s next trick? How about a camera that can give you fashion feedback? Amazon’s Echo Look (not yet launched to the public, but on invitation only basis) has a camera and lets you take selfies and gives you feedback on what you’re wearing. For those worried about whether Amazon was listening to all your conversations, this will definitely be a step too far! This piece is a good take on the social and psychological implication of a tool like this. Of course, if you want algorithmic advice but don’t want something that invasive, you can always turn to Miquela

(7) Battery Wars 
We all know that a move to electric cars is a ‘when’ and not an ‘if’ question by now. What that means, however, is a near insatiable demand for batteries and a huge spotlight on battery technology. Currently, the minerals that go into batteries such as Lithium and Magnesium are seeing a huge spurt in demand. It turns out that DR Congo is the worlds dominant source of Magnesium. In all of this, the UK is seeking to play a leadership role in battery technology. But is it either feasible or desirable? On the other hand, Williams has been working on safer batteries which are tough-tested in the Formula E competition – where electric-only cars race, collide and crash.

Blockchain, Bitcoin, Cryptocurrencies and ICOs – Everything I Wanted To Know

To understand blockchain and cryptocurrencies like Bitcoin, it needs us to understand stocks and markets, currency and it’s working, and a fair amount of technology, not to mention monetary policy. Consequently, very few people truly understand cryptocurrencies and bitcoin. Right now though there’s a feeding frenzy going on. Remember, when adding a .com to your company name increased your market value? Long Island Iced Tea Corp – which actually makes iced tea recently announced that they are changing their name to Blockchain Corp and their stock surged on the news. I started this post a while back and it kept growing. This is now, as the title suggests, everything I wanted to know about blockchain, Bitcoin, cryptocurrencies and ICOs.

What is Blockchain?

How do we know a transaction has been completed? Where do we keep these records? The history of accounting pre-dates the evolution of money – with the advent or writing and numeracy, records were kept way back in the Mesopotanian and Egyptian civilisations, but it was only much later in the 15th century, under the Mediccis that double entry accounting came into being. This dual entry system provided the bulwark of accounting and transaction records for over the next 500 years. The double entry system was reliable but not foolproof – you could go back and change the records and alter the ownership of assets, or erase records of transactions. Also usually the caretaker of financial transactions was the banking system, or another nominated institution. Which was both a blessing and a curse because it created a single point of both ownership and failure. Blockchain is an entirely new way of capturing transactions that goes from double entry to a ‘multiple entry’ system. How do we explain blockchain?

Here are a few excellent explanations of blockchain:

The Economist: a system that lets strangers transact using a dependable ledger

Colin Thompson has a great series explaining blockchain – here’s Part 1

And Part 2, Part 3, and Part 4

And this one from Kaspersky Labs explains Hashing in some detail

And finally from the FT

But if you’d like to skip reading the links, here’s a summary of blockchain in lay terms.

The first thing to remember is that blockchain is a network technology. Networks have certain features and properties which make them measurable, distinct and predictable. The way information travels on the Internet, and the way peer to peer streaming works also work off network capabilities. And just like peer-to-peer was created by Napster for music streaming, blockchain was created by the founders of Bitcoin. Both technologies have a life far beyond these initial cases. Peer-to-peer is used today for money transfers, loans and many other scenarios. Similarly, blockchain is being used for dozens of new and interesting use cases – from land registry to asset management.

How does it work? When a new transaction enters the network, it is added to an existing set of transactions to form a block, which is a predefined number of transactions. Let’s say a set of 5 transactions forms a block. This block has a lot of data including numbers, strings and connections. This set of instructions is then put through a ‘hash’ function which generates a long alphanumeric string. Which looks something like this. “7ae26e64679abd1e66cfe1e9b93a9e85”.

At this point, let’s do a quick exercise. If I give you a simple problem, say (3×4)+(5×6), you will quickly be able to work out the answer – which is 42. Note that we could get to 42 also by adding up all the numbers from 3 to 9, or multiplying 2,3 and 7. In fact the number of ways we can get to 42 (or any answer) is theoretically infinite. Now if I go all Douglas Adams on you and reverse the question – if the answer is 42, what is the question? You would have no way of logically ascertaining the original numbers. You would have to resort to guessing. This is what blockchain mining is about. Blockchain miners have the fiendishly difficult task of guessing a hash which they do by generating millions of options till some node on the network stumbles onto the right answer. The hash function can be tweaked to be harder or easier, which, in a network can define the time it takes to solve one block.

Once the ‘answer’ has been found (remember, verifying is easy, guessing is hard, just like our 42 problem above), a block is committed to the registry, which means that all the nodes in the network now will add this block to their registry of transactions. So the information lives not in a single place but in every computer on the network. This makes it exponentially harder to tamper with since you would have to change the data on every computer on the network, else there would be an immediate mismatch. This method of deriving the answer is done using an algorithm called ‘proof of work’ in the Bitcoin blockchain. As you can see it’s computationally very inefficient – which is one of the criticisms of blockchain. The Etherium network which is another blockchain network is proposing to switch to a different, more efficient algorithm called proof of stake, for this reason. In fact one of the criticisms of blockchain and bitcoin is the amount of energy and computation it uses. A switch to proof of stake will solve this problem.

So we understand the block, but what about the chain? Well, every time a new block is added, it’s added to the history of all previous blocks. And the header hash of of each block goes into the body of the next block and forms a part of the ‘hash’ of the next block. So now if you went back and changed a block, to keep it consistent, you would have to change the previous block and by extension the one before that and the one before that all the way to the first transaction. So not only are the transactions on every computer, they are also linked in a chain through to the first transaction.

This is why the blockchain is considered to be so superior – it relies on the network rather than an individual. And tampering with the transaction is fiendishly difficult because there is no single point of control, it’s near impossible to predict which computer will solve the hash problem, so you can’t hack the transaction itself.

To better understand the science of networks, read Albert-Laszlo Barabasi’s book Linked. And to understand the power and significance of networks, Niall Ferguson’s The Square and the Tower is a great starting point.

A Note on Ethereum

Ethereum is a “decentralised platform that runs smart contracts”, using a custom built blockchain, and accessible by developers across the world to build transaction applications on. It is built and run by Ethereum.org, a Swiss not-for-profit organisation. Note – Ethereum is a blockchain platform, and while it has its own cryptocurrency (Ether) it also allows developers and 3rd parties to create its own cryptocurrencies. You can use Ethereum to create a crowdfunding exercise with your own cryptocurrency, to build and sell an idea, platform or products. Needless to say, this opportunity has been seized by the ‘ICO’ market. More on that later. According to Ethereum, you can also build democratic autonomous organisations or decentralised applications.

Ethereum is the most mature blockchain platform available to developers and organisations across the world. For example, my colleagues have built a working prototype for a smart contract to enable electric vehicles and homeowners to create a market for charging EVs, with the management of contracts and settlement done intelligently in the background. Other use cases include clearing and settlement for banks, financial services firms, and many others.

Let’s talk about Bitcoin

If you’d like to get your head around bitcoin, I would suggest you read the book “History of Money” by Jack Weatherford. Among other things, it traces the evolution Fiat money which doesn’t have any intrinsic value, but is supported by a government decree, or a ‘promise to pay’. So a lot of currency today is already a result of what Yuval Harari calls ‘Intersubjective realities’ – i.e. something that has a meaning only because we collectively agree to the meaning – such as national borders, or the value of paper money. In this sense Bitcoin is just another level of abstraction – you also abstract away the role of a central bank or monetary authority, in favour of a collective, systemic governance, and a pre-fixed money supply (21 million).

Understanding Bitcoin

Many years ago, deep inside the bowels of the hype machine that was Silicon Valley in the late 90s, a few well-known entrepreneurs put together a spoof company. The only product of this make-believe company was its own stock. And the sales pitch went thus: the more you buy our product, the more valuable it gets. So please keep buying. The echoes of that satire have certainly been seen in the bitcoin mania (and by extension, the cryptocurrency craze) that is sweeping the world. The price of bitcoin is bungee jumping on a daily basis, confounding investors, economists and bankers alike.

bitcoin prices

 

At its core, there are 3 sources of confusion with Bitcoin: (1) is it a currency? (2) is it a stock and (3) how to value it? Let’s look at them one at a time.

Bitcoin as currency

Bitcoin is notionally a currency, but it fails a few key features of currencies. First, is not universally accepted at stores without workarounds, and by most creditors (you can’t pay your mortgage with bitcoin). Second, the ‘money supply’ while fixed, is not subject to any visible or discernible monetary policy. And finally, can it be taken seriously when it fluctuates as wildly as it has been? See chart 6 in this link. Stability is one of the key requirements for a currency. You don’t want to go to the market not knowing whether the money in your pocket will be enough for your monthly shopping or just a loaf of bread.

Sometime in the 1980s, the buses in the city of Kolkata printed coupons to solve the problem of change, on buses. Instead of giving coins and change back for tickets, they would give you printed coupons which could be used in lieu of coins on your next bus trips. Commuters accepted these with the odd grumble but got quite used to them. Then cornershops and other vendors started accepting them too and pretty soon, there was a parallel currency system flourishing. The government stepped in and banned the use of these coupons because the volume of these transactions had become significant, and it was creating a system of transactions which could neither be monitored nor controlled. After all, there was nothing stopping somebody from printing a bunch of fake coupons and using them at unsuspecting stores.

With any traditional currency, all the clearing is done by the banking system, for all ‘non-cash’ transactions such as checks, electronic transfers, etc. But no bank is involved in clearing bitcoin transactions. So in this aspect, it resembles cash as an extra-banking way of money, similar to the bus tokens of Kolkata.

The history of currency and payments is a story of layered abstraction. From barter systems to silver and gold coins, through to promissory notes and paper money, and ultimately through ledgers and information. (The book “History of Money” is a fascinating read, by the way). In a sense, cryptocurrencies such as Bitcoin are just the next step in this abstraction stack. What if we replaced ‘government’ with an abstract algorithm to control the amount of money and implement ‘monetary policy’. The problem is that with governments, we know or can ascertain the underlying objectives for the economy and for citizens, whereas with privately controlled cryptocurrency, the motives are opaque. We should, in fact, assume that a private enterprise wants to maximise profits, so in a sense we are playing the game of enabling somebody else’s profitability buy participating in a private cryptocurrency system. This is not by itself bad if the underlying decision making is transparent. After all we willingly participate in ecosystems governed by Uber, Google, or Amazon. But the complete lack of transparency for Bitcoin, is a real challenge.

Imagine what would happen if we all agreed to use black pebbles as currency. If we could magically all agree to value them at (say) £1 each. We would all go out and start gathering black pebbles from beaches, quarries, and wherever we could find them for all we were worth. But of course if we could find black pebbles for a cost that to us was less than £1, we would keep collecting them, and the supply of pebbles as currency would keep going up. If we wanted to buy something worth £10, it would be the effort of collecting 10 black pebbles. Perhaps the pebbles would start trading at a discount if they were really easy to get and people would start trading them in for other coins if they felt that the price might fall, thereby triggering, a sell off. Conversely, if black pebbles turned out to be in short supply, the price would rise to higher than £1. In this world, the value of the currency is connected with its supply and cost. With fiat money though, we have disconnected the cost of the currency from its value.

In the global economy of the 20th century and beyond, money has had to balance increasingly complex requirements of balance of payments, exchange rates and interest rates, acting often as a mirror of the goods and services being traded. There is no interest rate for Bitcoin, there are no balance of payments, and the currency value is driven primarily by speculative activity.

At a very practical level at present one transaction takes on average 10 minutes to conclude, which by itself disqualifies it from everyday purchases. In extreme cases one confirmation has taken up to 16 hours. You don’t want to be waiting with your bitcoin wallet at your coffee shop or your tube station waiting for your transaction to be authorised!

Bitcoin as an Investment Vehicle’

Of course bitcoin isn’t a stock, it’s not listed as a stock on any exchange. Yet, there are Bitcoin futures which have been launched by a number of investment banks, and fundamentally, the behaviours of bitcoin punters are similar to speculating on a stock. One that is fuelled by market rumours and short term spikes, but lacks any kind of underlying economic activity.

Any stock is valued on the basis of future earnings which pay out as dividends. As such Bitcoin doesn’t qualify. There is no interest and no dividends. So there is no future stream of income.

My co-panelist at a recent event pointed out, major banks are looking to set up bitcoin trading desks. Although for Goldman Sachsthis seems to have been an inadvertent step. But even if banks start trading in bitcoin, all it means is that that Bitcoin is similar to other arcane financial instruments and the average punters are likely to burn their fingers given that trading is a zero-sum game.

What Is The Value of Bitcoin?

The economist Robert Shiller says “Real understanding of the economic issues underlying the cryptocurrency is almost nonexistent”, and when a Nobel Prize winning economist can’t figure out the value, calls it ‘exceptionally ambiguous’,  and has to invoke ‘animal spirits’, what chance have the rest of us got?

One of the ways to value any asset is to look at the value of its underlying economic activity – for example, the activity of a firm. Clearly, that is not applicable here. There is no income stream – just pure speculative activity. The attractiveness of Bitcoin is its non-traceability and its popularity stems in no small part from its acceptance and use on the more nefarious parts of the internet – the Silk Road, and for contraband substances, for example, on the darknet.

Yuval Harari talks about our inter-subjective realities – the shared fiction that allows us to operate with conceptual constructs such as countries and currency. In this light, as long as people value bitcoin it has value. It’s a classic self-fulfilling prophecy.

Some people like to compare Bitcoin to gold, as a store of value. After all, they say, Gold is also only notionally valuable – if we stopped desiring it, it would lose value. But gold has specific metallurgical properties – it coruscates and is a malleable material which allows it to be turned into fine jewellery, and it has a history of demand dating back to the start of human history.

Is there a social value to Bitcoin? This is a far more interesting question. Going back to the beginning of this discussion, we said that blockchain is a decentralised and network-based technology. It eliminates the need for central banks and central authorities. In this sense, Bitcoin and other cryptocurrencies can be quite subversive and potentially act as disruptive agents in the face of repressive regimes, governments and act as an extra-national standard of transactions. On the other hand, as a currency that lacks any transparency of monetary policy, it remains a huge risk. The entire premise of bitcoin value is based on the principle of a finite supply. But there are scenarios where the Bitcoin community could fork and create more coins. And what happens if the faceless Satoshi Nakamoto sells his estimated 1 million coins?

In a lot of discussions around Bitcoin and blockchain, there is a tendency and a danger of mixing up the two faces of Bitcoin – as a store of value or an investment vehicle, albeit of a largely speculative nature, it definitely has a cachet, but as a currency for everyday transactions and for smoothening global transaction flows, it’s a different ask altogether, and one that bitcoin is a long way from delivering.

Bitcoin Hacks and Cybercrime

If you’ve followed so far, one of the questions that must have come to your mind is, if blockchain is so secure, how are there so many bitcoin hacks and heists in recent times?

Just to name a few, Coincheck a cryptocurrency exchange in Japan suffered a $530 million hack– for NEM coins, in 2017.

In 2014, Mt Gox, another Bitcoin exchange suffered a $480m hack and filed for bankruptcy.

In 2016, Bitfinex, a Hong Kong based Bitcoin exchange was hacked for $70m.

Here’s a longer list. There are some differences in the technicalities, but the point is that most thefts and hacks occur when the coins are stored in ‘wallets’ which are ready for spending. The point is, you are not hacking a transaction, which is still secure. You are hacking a store of coins. Typically done through copying a user’s cryptographic key which is used to unlock the wallet & transferring the coins to other pseudonymous addresses. Again, while blockchain can track the chain, the pseudonymity prevents actual tracking down of criminals. Further use of ‘tumblers’ or mixers, ensures that the stolen Bitcoin is mixed with others, creating new strings making it near impossible to track.

If you’re on the other side, the primary suggestion is don’t hold your coins in a hot wallet – i.e. one that is connected to the Internet. A cold wallet, by contrast is not connected to the Net, making it impossible for  hackers to access the coins.

And What about ICOs and other Cryptocurrencies?

Ah, this is where we’re in shark territory. At last count there are almost 1400 cryptocurrencies listed in Wikipedia. The first and obvious thing to say about this is that a currency is a standard of value and with standards, less is more. Imagine walking around with dozens of currencies in your pocket and not knowing which currency will be accepted when. Every transaction would be longer and more complex!

There are those who ponder whether governments could issue cryptocurrencies. While technically feasible, you would have to question the motive. As of today, it’s more expensive to manage, does not reach the entire population, and its adoption, use, value, and acceptance are still unclear. Besides, I don’t know of a government that willfully wants to give up control over its currency. Perhaps one for the future.

And what about ICOs? We have an absurd number of them now. Once again, it feels very much like the dotcom bubble. Then, a lot of Indian techies who had spent much time changing their names from Krishnamachari to Chris to fit into American culture were changing it back when it became fashionable to have Indian CIOs while wooing investors. In much the same vein, nobody seems to want to just raise money nowadays, without also attaching an ICO to it. The ICO or initial coin offering implies that the company will raise money to create its own cryptocurrency and investors will get these newly minted coins. The underlying promise is that the company will create an effective market for this currency, which is the difficult bit. In reality, there is no guarantee that these coins will be any use, but FOMO is driving investors in droves to the ICO market. Only 48% were successful last year but that yielded $5.6bn.

There have been ICOs from a very wide range of providers, including former lingerie tycoons, and online poker platforms. Although 90% of ICOs are expected to eventually crash, there are people who believe that future ICOs will be more tightly connected with the activity of the company, in what they call ICO 2.0.

One of the most eagerly anticipated ICOs in 2018 is from Telegram, the messaging app. On the plus side, having an existing network, user base, and value certainly gives Telegram a better shot and platform for making a success of a cryptocurrency. Telegram is looking to launch a new blockchain, potentially challenging Ethereum’s primacy. If Telegram can follow the path created by WeChat and integrate commerce into messaging, via it’s Gram coins, as it suggests, then we may have a winner. However, you do have to decipher terms like ‘Instant Hypercube Routing’ and ‘Byzantine Fault Tolerant’ protocol. Most importantly, it wants to make a million transactions per second. This is far ahead of the Bitcoin speeds we spoke about earlier, and even orders of magnitude faster than Visa and MasterCard, who collectively do 2000 transactions every second. Be warned, Telegram plans to keep 52% of its cryptocurrency – so the value of the currency will be significantly managed by the owners of Telegram.

In Sum

This has turned out to be a much, much longer post than I intended initially, but I think I can summarise my thoughts as follows:

Blockchain: a potentially massive new technology that can change the world, but still in its early stages of development and fine-tuning.

Bitcoin: a great option for speculative investment, but definitely not useful yet, as an alternative currency.

Cryptocurrencies: a minute fraction of them will be useful, finding the right one may be a matter of luck. But in 20 years we could all be using a extra-national cryptocurrency as legal tender.

ICOs – definitely a trap for FOMO investors looking to somehow get into the cryptocurrency game. Most will go nowhere.

When Technology Talks

Conversational Systems aka chatbots are starting to become mainstream – here’s why you should stay ahead of the game:

chatbots

The shape-shifting of the yin-yang between humans and technology is one of the hallmarks of digital technologies, but it is perhaps most pronounced and exploit in the area of Conversational Systems. But to truly appreciate conversational systems, we need to go back a few steps.

For the longest part of the evolution of information technology, the technology has been the unwieldy and intransigent partner requiring humans to contort in order to fit. Mainframe and ERP system were largely built to defend the single version of truth and cared little for the experience. Cue hours of training, anti-intuitive interfaces, clunky experiences, and flows designed by analysts, not designers. Most of us have lived through many ages of this type of IT will have experienced this first hand. If these systems were buildings they would be warehouses and fortresses, not homes or palaces. Too bad if you didn’t like it. What’s ‘like’ got to do with it! (As Tina Turner might have sung!)

Digital technology started to change this model. Because of its roots in consumer technology rather than enterprise, design and adoption were very much the problem of the providers. This story weaves it’s way through the emergence of web, social media and culminates with the launch of the iPhone. There is no doubt – the iPhone made technology sexy. To extend the oft-quoted NASA analogy, it was the rocket in your pocket! With the emergence of the app environment and broadband internet, which was key to Web 2.0, it suddenly introduced a whole new ingredient into the technology cookbook – emotion! Steve Jobs didn’t just want technology to be likable, he wanted it to be lickable.

The balance between humans and technology has since been redressed significantly – apps and websites focus on intuitiveness, and molding the process around the user. It means that to deal with a bank, you don’t have to follow the banks’ convenience, for time and place, and follow their processes of filling a lifetime’s worth of forms. Instead, banks work hard to make it work for you. And you want it 24/7, on the train, at bus stops, in the elevator and before you get out from under your blanket in the morning. And the banks have to make that happen. The mouse has given way to the finger. Humans and technology are ever closer. This was almost a meeting of equals.

But now the pendulum is swinging the other way. Technology wants to make it even easier for humans. Why should you learn to use an iPhone or figure out how to install and manage an app? You should just ask for it the way you would, in any other situation, and technology should do your bidding. Instead of downloading, installing and launching an app, you should simply ask the question in plain English (or a language of your choice) and the bank should respond. Welcome to the world of Conversational Systems. Ask Siri, ask Alexa, or Cortana, or Google or Bixby. But wait, we’ve gotten ahead of ourselves again.

The starting point for conversational systems is a chatbot. And a chatbot is an intelligent tool. Yes, we’re talking about AI and machine learning. Conversational systems are one of the early and universal applications of artificial intelligence. But it’s not so simple as just calling it AI. There are actually multiple points of intelligence in a conversational system. How does a chatbot work? Well for a user, you just type as though you were chatting with a human and you get human-like responses back in spoken language. Your experience is no different from talking on WhatsApp or Facebook Messenger for example, with another person. The point here is that you are able to ‘speak’ in a way that you are used to and the technology bend itself around you – your words, expressions, context, dialect, questions and even your mistakes.

Let’s look at that in a little more detail. This picture from Gartner does an excellent job of describing what goes into a chatbot:

The user interface is supported by a language processing and response generation engine. This means that the system needs to understand the users’ language. And it needs to generate responses that linguistically match the language of the user, and often the be cognizant of the mood. There are language engines like Microsoft’s LUIS, or Google’s language processing tool.

Behind this, the system needs to understand the user’s intent. Is this person trying to pay a bill? Change a password? Make a complaint? Ask a question? And to be able to qualify the question or issue, understand the urgency, etc. The third key area of intelligence is the contextual awareness. A customer talking to an insurance company in a flood-hit area has a fundamentally different context from a new prospect, though they may be asking the same question ‘does this policy cover xxx’. And of course, the context needs to be maintained through the conversation. An area which Amazon Alexa is just about fixing now. So when you say ‘Alexa who was the last president of the US’ and Alexa says ‘Barack Obama’ and you say ‘how tall is he?’ – Alexa doesn’t understand who ‘he’ is, because it hasn’t retained the context of the conversation.

And finally, the system needs to connect to a load of other systems to extract or enter data. And needless to say, when something goes wrong, it needs to ‘fail gracefully’: such as “Hmm… I don’t seem to know the answer to that. Let me check…” rather than “incorrect command” or “error, file not found”. These components are the building blocks of any conversational system. Just as with any AI application, we also need the data to train the chatbot, or allow it to learn ‘on the job’. One of the challenges in the latter approach is that the chatbot is prone to the biases of the data and real-time data may well have biases, as Microsoft discovered, with a Twitter-based chatbot.

We believe that chatbots are individually modular and very narrow in scope. You need to think of a network of chatbots, each doing a very small and focused task. One chatbot may just focus on verifying the customer’s information and authenticating her. Another may just do password changes. Although as far as the user is concerned, they may not know they’re communicating with many bots. The network of bots, therefore, acts as a single entity. We can even have humans and bots working in the same network with customers moving seamlessly between bots and human interactions depending on the state of the conversation. In fact, triaging the initial conversation and deciding whether a human or a bot needs to address the issue is also something a bot can be trained to do. My colleagues have built demos for bots which can walk a utility customer through a meter reading submission, for example, and also generate a bill for the customer.

Bots are by themselves individual micro-apps which are trained to perform certain tasks. You can have a meeting room bot which just helps you find and book the best available meeting room for your next meeting. Or a personal assistant bot that just manages your calendar, such as x.ai. We are building a number of these for our clients. Bots are excellent at handling multi-modal complexity – for example when the source of complexity is that there are many sources of information. The most classic case is 5 people trying to figure out the best time to meet, based on their calendars. As you well know, this is a repetitive, cyclical, time-consuming and often frustrating exercise, with dozens of emails and messages being exchanged. This is the kind of thing a bot can do very well, i.e. identify (say) the 3 best slots that fit everybody’s criteria on their calendars, keeping in mind travel and distances. Chatbots are just a special kind of bot that can also accept commands, and generate responses in natural language. Another kind of bot is a mailbot which can read an inbound email, contextualise it, and generate a response while capturing the relevant information in a data store. In our labs we have examples of mailbots which can respond to customers looking to change their address, for example.

Coming back to chatbots, if you also add a voice i.e. a speech to text engine to the interface, you get an Alexa or Siri kind of experience. Note that now we’re adding yet more intelligence that needs to recognise spoken words, often against background noises, and with a range of accents (yes, including Scottish ones). Of course, when it’s on the phone, there are many additional cues to the context of the user. The golden mean is in the space between recognising context and making appropriate suggestions, without making the user feel that their privacy is being compromised. Quite apart from the intelligence, one of the real benefits for users is often the design of the guided interface that allows a user to be walked step by step through what might be a daunting set of instructions or forms or a complex transaction – such as an insurance claim or a mortgage quote.

Gartner suggest that organisations will spend more on conversational systems in the next 3 years than they do on mobile applications. This would suggest a shift to a ‘conversation first’ interface model. There are already some excellent examples of early movers here. Babylon offers a conversational interface for providing initial medical inputs and is approved by the NHS. Quartz delivers news using a conversational model. You can also build conversational applications on Facebook to connect with customers and users. Chatbots are also being used to target online human trafficking. Needless to say, all those clunky corporate systems could well do with more conversational interfaces. Imagine just typing in “TravelBot – I need a ticket to Glasgow on Friday the 9th of February. Get me the first flight out from Heathrow and the last flight back to either Heathrow or Gatwick. The project code is 100153.” And sit back while the bot pulls up options for you, and also asks you whether you need to book conveyance.

Conversational systems will certainly make technology friendlier. It will humanise them in ways we have never experienced before. I often find myself saying please and thank you to Alexa and we will increasingly anthropomorphise technology via the nicknames we give these assistants. You may already have seen the movie “Her”. We should expect that this will bring many new great ideas, brilliant solutions and equally pose new social and psychological questions. Consider for example the chatbot that is desi§gned just for conversation – somebody to talk to when we need it. We often talk about how AI may take over the world and destroy us. But what if AI just wants to be our best friend?

My thanks to my colleagues and all the discussions which have sharpened my thinking about this – especially Anantha Sekar – who is my go-to person for all things Chatbots.

My book: Doing Digital – Connect, Quantify, Optimise – is available here, for the price of a coffee!

As with all my posts, all opinions here are my own – and not reflective of or necessarily shared by my employers.

CityMapper Does Connect, Quantify, Optimise

I know hundreds of people who know and love the Citymapper app, but they did something recently which really impressed me. As you know the app uses a number of public data streams to help you navigate your city – London being a good example. So you just have to say ‘get me to work’ or ‘get me home’ or any other destination and it tells you the best ways across buses, trains, walking, cycling, or driving. It also helpfully offers an Uber connection and for good measure includes a futuristic option such as ‘catapult’ or ‘teleportation’ to appeal to your quirky side.

They work across about 40 cities across 4 continents currently and base future cities expansion on a popular vote. Needless to say, they collect a ton of data about where people are travelling to and from. But the really interesting thing is what they do with all the data they collect.

In my recently published book Doing Digital, I proposed the model of Connect/ Quantify/ Optimise for digital. The model suggests these 3 stages for digital. Designing something that is easy and frictionless to use, allows you to get to Connect. Having thousands, or even millions of people use your app gives you the data which allows you to Quantify – for example, Citymapper can see where it’s most commonly visited areas are, where they have or lack coverage and market their app accordingly. They can build revenue models with Uber which allows commercialise the traffic they send to Uber. But the last step is where the magic often is – this is where you start to see new value and tweak your business or commercial model based on the opportunity that the Connect & Quantify stages throws up.

In the case of Citymapper, this is a bus service. According to CityMapper they can see based on their patterns, which areas and routes are underserved by public transport. Using this knowledge, they have launched a bus service in coordination with Transport for London to launch a small, green bus which runs on a fixed route. It’s called CMX1 and it’s a ‘pop-up route’ which presumably means that they will validate the route based on the data it generates over a trial period. What is even more fascinating is to consider some of the underlying assumptions that the CityMapper model is challenging. One of them being that bus routes are cast in stone and have to be long term commitments. But what if these routes could be intelligently introduced in response to shorter term needs and changes? The team are even trying to improve the experience of the bus journey by redesigning the bus outside and inside.

Photo 22-05-2017, 12 17 35 (1)
I love that their blog exhorts customers to ‘come and watch an app company fumble around with learning how to run a service with real vehicles and drivers’. This ticks the box of building learning organisations in the classic Eric Ries model. It is also an excellent example of ‘Connect / Quantify/ Optimise’. And I fully expect CityMapper to be thinking about Autonomous Vehicles in their R&D room – as they will probably be in a position to unleash an autonomous fleet in a few years based on their accumulated lessons from this exercise. Yet another Connect-Quantify-Optimise cycle at work.

Service Design Drives ‘Affordable Luxury’ Business Models

handcrafted shoes
One of the manifestations of digital business models built around good service design is the burgeoning of affordable luxury, which carves an entirely new aspirational category of of the sizeable middle class market.
But to illustrate, let me tell you a story, based on my experience of last week. I always have to buy trousers and get them altered because I don’t fit the shape that they come in, off the shelf. Or, as Garfield the cartoon cat once said, “I’m not overweight, I’m undertall”. So I was pleasantly surprised to find a high street retailer who offer an alteration service for their chinos (this is not common in London, btw). I bought a pair and took it to the counter to ask if they would measure and alter it for me. They said I would need to measure it myself and fold it to the point where I wanted the length reduced. Now, I don’t know if you’ve ever tried measuring your own trouser length. It’s about as easy as painting a smiley in the middle of your own back. So I said I’d take it home, measure it and bring it back. Next morning I was back with the trousers duly folded. Stood in the check out line and 10 mins later, I was told I needed to put a pin in to keep the fold. I asked for a pin, but of course, they didn’t have one. It took me another 10 mins of queuing at their alternation desk on another floor, and then a final wait in the original queue.  If you’re like me, at these moments you feel the life force seeping out of you.
For people who are time poor, which is most of us in most cities across the world, this ability to value the customers time is such a critical aspect of any service, that I’m always amazed when people don’t get it. In this case my joy at the finding the alteration service has definitely been tempered by the half an hour of my time I lost in the process. And based on my simple and one-off experience, you can immediately see how service design could be used to improve this dramatically – i.e. if somebody thought through the experience end to end, for the customer. Upfront information about the service terms is a simple idea. Just below the in-store poster announcing the service should be a simple list of what the shopper needs to do to use this wonderful service. Expectation setting often makes all the difference. Having a tape measure with a small weight that can be used like a plumb line in front of the mirror, to get an accurate length is another simple idea. These should be in the dressing room. On the basis that I might want to come back for more, why not let me store my measurements in the store app (they don’t). In my perfect world, I could sit at my desk at work at the end of day and order another couple of pairs, based on the new colours available, and they would have trousers ready in the store across from my work at a time that they could commit. The world is full of people like me who will repeat buy clothes from brands they trust and have had a good experience with. This is fundamentally the difference between a more traditional view of the business and an outside in view – driven by service design which puts the consumer in the centre and tries to remove all the friction in the entire buying cycle.
There are parts of the world, such as in most parts of India where this is an easy and people driven process. You buy a pair of trousers, and an in-house tailor takes your measurement and give you a time for delivery. This kind of people driven process, and infect the very idea of customisation is a luxury in the western world – especially when it comes to high street apparel brands. People are expensive. Factory made clothes cost less than half of tailor made ones. Thanks to improved stock management and product design, you can now get more options within the clothes you wear – a longer sleeve, a different collar, a slimmer cut, etc. But economics demands that any customisation at the point of delivery remains outside the purview of most products. Yet, digital models can significantly lower the bar for the accessibility of a luxury service. In my example of alteration – you can see how the app enablement and ordering based on my specific measurements could even be done in a centralised way and delivered to a store. I can live with a lead time of a week – as long as it’s a reliable one. At the core of this is the ability to take the customisation information off the universe of consumers and deliver the customisation at a much lower cost, at higher scale.
When you walk into your regular coffee shop, you don’t have to tell them each time that you want 2 shots of coffee, half a cup of foamed milk, with semi-skimmed extra hot milk (or as Niles Crane would say, “Double Cappuccino – half-caf, non-fat milk, with just enough foam to be aesthetically pleasing but not so much that it leaves a moustache”.) Instead, you can just say ‘the usual’. Starbucks can also increasingly do that via the app – because no matter which Starbucks you go to, if you order through the app, you can just do it with one click. And Starbucks can even analyse your choices, behaviours, and make suggestions for you.  Industrialisation in all its forms has historically created scale but lost customisation. Digitisation is allowing us to layer the customisation back over the industrial scale. This is why it’s so critical for consumer facing business to embrace this combination of service design and digital customisation.
Starbucks coffee options
We subscribe at home to a brand called Hello Fresh – they are one amongst a few who deliver ready to cook dinners. Each dinner is a dish that you’ve chosen from a menu via the site. It comes with a recipe and all ingredients pre measured and packed individually. If your tiger prawn recipe requires echalion shallot or samphire – don’t worry if you don’t have them in your fridge (let alone if like me you have to google them to learn what they are), they come in the box, in the right amounts. This too is like a luxury service but thanks to the underlying business model and the digital enablement of the ordering, menu and selection process, it can be delivered to a larger non-luxury audience.
If you look around there are dozens of places where this kind of customisation, once outside the purview of industrial models, is now back in vogue thanks to digital tools. Personal financial advisors, customised movie recommendations, configurable holidays, customised trainers – and many more. Remember though, this is not an efficiency play. It’s not enough to build a generic digital front end that will drive this mass customisation. It needs a commitment to service design to see the whole experience through the eyes of the consumer and to understand where her challenges, points of confusion, discomfort or dissatisfaction are and build the flexible digital model to address these.