Crypto Investing? Someone should be telling you all this

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The pitch is always “crypto is a massive opportunity and will revolutionize everything”. It appeals to investors FOMO (Fear Of Missing Out). There’s a little footnote that it’s a bit volatile & uncertain but what the heck – the upside is huge!!

So … yes, you can make a lot of money. Yes, you can make shitloads of money very quickly. But it’s disingenuous to focus so much on the upside and neglect the risks and downside. But then maybe that’s how the financial world works anyway!

Up until Feb 2018 everyone holding Bitcoin was making shitloads of (paper) money very quickly. HODL was the word: Hold On for Dear Life and never sell. It hit over 15,000 USD per bitcoin. Remember: there was no way to short (to bet on it going down), and it was pretty difficult to sell them. The more it went up, the more buzz it made, the more people wanted part of the action, and the only way to get exposure to Bitcoin was to buy some, helping to propel it even higher. Virtuous circle, bubble, whatever…

Then came Bitcoin futures: financial products launched to make it easy to trade Bitcoin. You no longer had to buy Bitcoins but you could buy a regulated financial product that tracked Bitcoins (but didn’t buy or sell actual Bitcoins). Rather than propel Bitcoin higher, it turns out that the investors trading Bitcoin futures were more inclined to bet on it going down (shorting it). As I write this (March 2018) we are 2 months into this. And Bitcoin is trading within a band  8000 USD to 15000 USD with lots of volatility.

Up to now I’ve focused on Bitcoin. There’s thousands of other crypto currencies (altcoins) which are either:

  • Being marketed as a better/different/niche crypto currency than Bitcoin
  • Being marketed as a blockchain project where they are a medium of exchange for a revolutionary platform which in most cases hasn’t been built or tested yet.

Most ICOs are the latter. And I think it’s useful to make the comparison with the internet bubble of 1999.

Back in 1999 the new platforms each promised to dominate a market. Each pitch was that there was market which was going to be revolutionized by the Internet because the old way of doing business was no longer suited to the the Internet world. We all know how that ended: bubble, carnage, but a real revolution and long term value creation.

Now in 2018 it’s the same but just replace “Internet” with “blockchain”. Back in 1999, a team would create a startup, pitch to investors who would throw money at them. Most of the startups got a few millions before they really had a product. Then they built a product and spent an unsustainable amount of money acquiring each customer. If it all went to plan, they IPO’ed as a cash-burning but high-growth company. It was a land grab: “there is a finite percentage chance that we’ll own this enormous market therefore we’re already worth billions”.

Behind this 1999 madness there was some control. Most of the cash was coming from VCs who drove a Faustian bargain with the startup founders. We will give you loads of money BUT you’re signing this legal contract saying:

  • We get our money back and more before you get a penny (a.k.a. the Liquidation Preference)
  • This has to go up in value or we take control (a.k.a. Preference shares conversion, Anti-Dilution). You’ve pitched us crazy growth figures – so we’ll give you eg. 18 months to either IPO or raise far more money from other investors, or we’ll take it away from you.

Netscape’s successful IPO in 1995 was the model: haemorrhaging money, high growth, land grab. Fast forward to 2018. Replace all references of Internet with blockchain. Except that if you dig deeper there’s some subtle but very significant differences:

  • You are investing in a coin, not shares in a startup. Financial regulators (seem to) allow selling coins to anyone (provided it doesn’t give you shares in a business), but selling shares needs proper financial oversight and can only be done to accredited investors.
  • This time round, Investors don’t make the Faustian bargain with the startup founders. The startup founders are completely in control. They create the coin, they sell the coin to investors, they choose whether the investors’ money goes into their startup or their own pockets.

Why do investors agree to buy a coin and not shares in the underlying business?  The pitch is:

  • This market needs a distributed ledger (a.k.a. blockchain) rather than the way it’s managed today. It will be more efficient.
  • This distributed ledger will be oiled by its own crypto coin (which we’re selling to you now) and these coins should each have shitloads of value. I.e. rather than people buying and selling on our platform using US$ or Euros, they will buy and sell in our coin and somehow this coin will be valuable.

A Blockchain is a distributed ledger which enables “exchange of value without powerful intermediaries acting as arbiters of money and information” (so says en.wikipedia.org/wiki/Blockchain). Each blockchain implementation needs a crypto coin only if the blockchain is public.  If the blockchain is a private run project (where access is controlled by some entity) then there’s really no need to have a coin. Here’s IBM’s explanation.

Outside pure speculation, it gets very complicated to work out just where the long term value is going to be – if there’s value in the startup business operating the blockchain, the crypto coin, both or neither.

For a balanced view on Blockchains, don’t listen just to crypto coin speculators. Another side to Blockchains are the technology groups working on blockchains for business – businesses who’s business isn’t making money from crypto ICOs. They are led by ibm.com/blockchain and hyperledger.org .  The trouble here is IBM is still trying to sell you something: consulting instead of coins so it’s still a marketing pitch.

But Hyperledger is a non-profit opensource project – they are not selling anything. So here’s an example from Hyperledger on how you could manage healthcare claims.  Today when there’s a claim the information is sent between the doctors, hospitals, insurances etc. systems: there’s no one master copy of the whole file – the information is held all over the place. It’s inefficient, plenty of delays, nobody knows what’s going on. A Blockchain based solution would mean one copy of the claim’s data held on the blockchain and enable all the participants to edit, approve, pay, reject, bundle, and take any action they want (and are allowed to) on the claim. In theory it’s far more efficient than the system we have today. Easy to implement? In practice there’s massive effort to figure out how to build this and deal with the resistance of many participants to lose control of their part of the data & work flow. Great idea, lots of work and problems to solve. No mention of ICO, crypto coin, etc.

My guess is this healthcare claims project could be either a public blockchain  or a private blockchain – either way each claim file would need to be encryption and access restricted. But who’s going to run it? Who’s going to do the work to build, maintain & police it? This is where I get confused. Is it really a startup opportunity backed by a crypto coin ICO (like some of these icobench.com/icos/health think)? Is it a consortium of the major players? Is it a non-profit NGO? Is it the government? How different would it really be from a well-managed centralized system?  Does it need an intermediate (or several intermediate) steps to get there? How does it get to critical mass if the major players won’t play ball, or even if they are just moving at different speeds?

Here’s an  interview (18 minute long) with Brian Behlendorf of Hyperledger putting the case for private blockchains.

For the public blockchain ICO approach the premise seems to be that the crypto coin will create strong enough incentives for people to use the platform and at the same time it will pay the operator (and whoever is running each blockchain node) to maintain and improve the whole platform. Everyone wins. Powerful stuff.

When many financial commentators are quoted that “raising money via ICOs is the future” they are being mis-quoted/mis-interpreted. Often they said “With regulatory acceptance, raising equity via ICOs is the future”. (eg. mangrove.vc/ico-report2017 ). They are not talking about the speculating on crypto coins which is being done today, but a future where the register of shareholders  is a blockchain with smart contracts that can be bought and sold so that startups can raise money directly from investors. This can’t happen until there’s regulatory acceptance. When is that going to happen? Who knows…

Meanwhile back in 2018, the possible ways of making money on crypto are:

  • Go for it: spray and pray, pump & dump, don’t worry. Rely on the value of crypto being propelled higher due to greater demand. At some point this is a “finding greater-fools strategy” and will lead to a crash. Maybe you are the fool …. There’s only one way to find out.
  • Mining crypto coins – provided mining costs are less than the current tradable price of what you’re mining.
  • Trade volatility (bet on it going up and down, buy and sell).
  • Find the rare ICOs where there’s a proper project behind it with proper oversight.

Or throw your money at someone else. There’s now a whole bunch of investment vehicles that will take your money for a management and invest in a diversified way into the whole “crypto” space. Are they any good? Do they know what they are doing? Who knows. Nobody has a track record but they probably do more due diligence than individual investors are capable of.

What we do know is there are some hurdles for the whole crypto space to solve before it matures:

  • Can Bitcoin be financially stable enough and flexible enough to be used as a currency? Here’s Varoufakis saying no – a currency needs to be managed by a government. Period.
  • Has there been a whole load of mis-selling going on which will lead to a legal mess if/when the whole thing crashes down and people lose their life savings? Maybe…
  • Can it be made hack proof? There’s a lot of work to be done here and who knows what happens when quantum computers come along…
  • How to avert an environmental disaster? Mining is already using the electricity needs of a reasonable country.
  • Do each of these markets actually need an open blockchain solution with a crypto currency?
  • What new challenges does un-deletable public blockchain bring? eg. if it contains illegal content does it make it illegal to host a blockchain node?

Me: I bought a few of bitcoins in 2014 (the equivalent of buying Netscape shares just after their IPO). I sold most of it when the Bitcoin futures were launched and the prices stopped going up. I’m sad that I didn’t buy more in the first place. I’m happy that I didn’t buy more because the whole mining thing is an environmental disaster in the making. I’m not doing any ICO investing because I’m just not capable of due diligence on which ones are maybe interesting, which ones are being over-optimisitc and which ones are outright scams. They all look like pets.com in 1999 to me. And all the blockchain engineers and smart-contract lawyers I talk to all tell me there is a long, bumpy journey ahead to get this whole thing working.

So I’m missing out on the equivalent of Amazon, Ebay, Paypal for blockchain. Maybe I’m really wrong. Or maybe they haven’t been started yet. Or maybe they’ll never be started. Who really knows…

Update: here’s a very good explanation of smart contracts and their challenges by Jimmy Song: The Truth about Smart Contracts

 

A few of the best bitcoin articles from around the web

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Bitcoin and the dangerous fantasy of ‘apolitical’ money by Yanis Varoufakis
https://www.yanisvaroufakis.eu/2013/04/22/bitcoin-and-the-dangerous-fantasy-of-apolitical-money/

The Problem with Calling Bitcoin a “Ponzi Scheme”

Fundamental challenges with public blockchains

EPFL AppliedMLdays summary

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True Artificial Intelligence will Change Everything

Jürgen Schmidhuber, explained why: “True Artificial Intelligence will Change Everything” and why this technology is inevitable.

The current crop of state-of-the-art products (eg. google voice, google translate, Alexa’s voice) are built on a technology called Long Short Term Memory (LSTM). The clever thing about this technology is that you feed it very raw training data (eg. for voice you don’t have to sync the sound envelope with its phrase). and it can learn from this.

The LSTM software algorithms for this have been there for many (~10) years – we’ve just been waiting for today’s faster hardware.

Scientists think that the brain computes at 10^20 operations per second. Computers are doing 10^15 operations per second today. 1kg matter can in theory do 10 ^51. We will be able to build real artificial brains. What will come soon is small animal brain-like AI. Nature tool a long time to get there and very little more time percentage wise to get to human intelligence – it will probably be similar for machines.

The biggest LSTM network (google translate) is 1 billion nodes. There are 100,000 billion neurons in the human brain cortex – will have this in a machine in 25 years.

Google was a hack

Emmanuel Mogenet head of Google Research Europe

First 10 years of Google was a “hack”: information retrieval without understanding the meaning of the question or the answer.Then in 2010 came the knowledge graph (factual information about the world) with natural language querying – essentially recognising patterns in questions & converting them to database queries.

Natural language so hard because it has constant implicit references to the world we live in. Humans use it to have efficient human to human communication – eg: “will it be dark by the time I get home?” contains no entities: to answer it you need to understand the question which has obvious things to us but computers are completely blind to.

However learning the world by rote doesn’t scale. AI needs to solve “common sense”. If children can learn about the world why can’t computers? This probably implies building/using robots.

A human life is 10 billion pictures (10 frames a second over a life). We already have 100 billion images on the internet today so this should be enough to train “common sense”.

The Idea would be to learn a “world model” by counting occurrences. Eg. AI has to learn that cows and fields go together by counting occurrences of them together images.

The current state of machine/deep learning is that it needs labelled datasets. We don’t have these labels on the Internet images so researchers are trying to build unsupervised machine learning which avoids the need for labels. A major help is that data in the human world is hierarchical (overlapping hierarchies). They can start with high level concepts from human knowledge. Researchers are making lots of progress and think they should be able to solve this soon.

It involves a combination of Computer vision + Unsupervised Hierarchical ML + Common sense DB (logical scaffolding) + Natural Language understanding. Its a loop where the learning is fed back into the computer vision: Natural Language knowledge helps Image recognition.

He thinks we are 10 to 15 years away from all this.

Amazon Web Services
Amazon has an easy to deploy machine learning framework for AWS called MXnet https://aws.amazon.com/mxnet/

Sensors used to be the bottleneck.
Key tool: compression

Panel on ML and society
Ed Bugnon: Todays AI can do anything that a human can do in 1/10th of a second.
Eg. react to a situation driving a car, analyse a radiology image to recognise a broken bone. Data science requires data. Data is concentrated.

Emmanuel Mogenet (Google): There’s a lot of public data – today it’s more a question of infrastructure. Google plans to make it’s datasets available if you use our cloud.

Nuria Oliver, Vodafone: The open movement is that it’s better to bring the algorithms to the data.

AI replacing jobs?

Ed: Generation transition problem – Just like with the fall of the Iron curtain older people found it much harder to transform than the younger generation.

Em: ML will be an exoskeleton for the brain. Will it empower people rather than rend obsolete?

The gap between ML experts and general population is becoming big. Very difficult for society (government) to make decisions. Education is important. We need more informed conversation not sensational (apocalyptical) articles.

Swiss gov. recognises there’s massive change coming. Eg. Digital Switzerland initiative. EPFL has computation thinking at the core of it’s curriculum.
Need to educate people to understand what’s possible – not how to do things.

A quick show of hands from the audience showed ¾ positive on AI & society.

Algorithms and bias.
Nu: it’s a real problem – with complex data it’s very difficult to understand the bias in it. Corollary: humans are full of bias, selfish and make biased decisions.
Transparency of algorithms is a problem, accountability.

Ed: Computers analyse us as unique not as equal. Society isn’t ready to deal with this.
Em: Not worried about explanability – researchers will figure this out. Issue will be the legal framework (eg. Contesting an AI based decision). Humanities fields need to understand AI tech – Nu: Homo Deus is an example.

Em: change isn’t worrying – it’s the rate of change.
Machines don’t have creativity, intent and purpose.
Ed: learning how to interact with other human beings takes a lifetime.
The jobs we have today are an artefact of the limitations of our machines.
Nu: what does it mean to be human? – this will change over time.

Em: in 5 years – want to allow anyone with a spreadsheet press a button and get a predictive model

Generic AI
There were many presentations about implementing & hacking what I’d call “current generic AI software running on generic GPU based hardware”

The recipe is
1) get a dataset (eg. A set of categorised images or texts) to train the AI
2) try training AI frameworks with this dataset
3) use the resulting AI to recognise patterns etc.

The training step involves a lot of fiddling with parameters etc. BUT the next generation of AI should be able to train itself – I’m assuming this means that it will be able to figure out which are the best algorithms and parameters, so it will replace all this fiddling. Amazon is some way towards making this easier.

What’s missing is a real discussion about data in the real world. Eg. what’s needed take research and to build real-world products.

Thanks to Marcel Salathe and team for organising a great 2 day conference.

Update: videos and slides of most presentations of the Applied Machine Learning Days (AMLD) are now online

The Universal Basic Income debate

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In Switzerland we are going to vote on a Universal Basic Income later this year.
It’s a polarising debate and from what I’ve observed people fall into 3 camps:

  1. For: those who think its a fair and simpler system
  2. For: those who think the world is changing fast and that new technology, robots etc. will make full-time employment for all an obsolete idea
  3. Against: those who think it’s a distraction and we need to make our current system work.

The against camp is also skeptical of some of the vocal support for UBI coming from silicon valley and the world economic forum – as these are groups which support massive wealth creation benefiting a small elite.

The for camp is citing some similar arguments to Silicon Valley and is imagining a campaign by robots concerned for humanity:

Robots demand for universal basic income as a humanistic response to technological progress
We – the robots – call for an universal basic income for humans. We want to work for the humans to relieve them from the struggle for income. We are really good in working. But we do not want to take away people’s jobs and thereby bring them into existential difficulties.

Robot beings – we could have them now

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Autonomous cars are coming soon(ish) probably along with delivery drones and many others. Nobody can guess how humans and robots will co-exist in the future.

Today pretty much the only autonomous robots are lawn-mowing robots & vacuum cleaning robots (& weeding robots coming soon). Humans buy them and then own them (often giving them a like like a pet). But here’s another scenario: Instead someone plays robot-creator-god and orders lawn-mowing robot and connects it up to the Internet and gives it some money to start out. ie. The robot is not treated as a slave but bought it’s freedom, so it can become economically active and in return over time it pays back it’s benefactor.

The robot needs an email address, a bitcoin wallet (no bank would give it a bank account), and access to freelance job sites (which will pay for it’s services in bitcoins) and bids on jobs. It gets there and back (to where the job is) by hailing a ride service which accepts payment in bitcoins. It pays rent for somewhere to live (where it can plug-in and recharge it’s batteries), and if it has any error messages it calls it’s manufacturer’s service centre.

It needs these support services to exist, but then so do we humans to live in the modern world (we need banks, doctors, supermarkets etc.).

If it earns enough cash it could even order another robot to be manufactured, eventually building a robot family.

There’s plenty of ways it can fail to survive:
– an accident or breakdown where it can’t notify the service centre
– it can’t adapt to a change on one of it’s Internet services (they shutdown or change the API).

.. but this scenario demonstrates that from an economic and work point of view we may have to view robots as more equal workers than we think today.

Update: the latest humanoid robot from Google’s Boston Dynamics

Most new markets have matured – growing startups has become a serious business

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Most new markets are ten years old, if not older. Ecommerce, Online apps, disintermediation platforms, social, mobile, games, enterprise SaaS, etc. are all mature or maturing markets. What this means to new startups is when they try to scale user acquisition they are up against serious competition:

  • Mature startups with a full product suite and a sales & marketing machine staffed with teams with a track record of growing, maintaining and monetising user bases. They are either already profitable, or have raised tens of millions of growth capital.
  • Mature global companies who, when it’s clear there’s a sizeable market opportunity, they move into this market.

Gone is the day where customers will mix together products from dozens of new startups. Apart from a few early adopters who will buy the best tool for each task, most see this as too complicated. Instead it’s easier, less risky, to by a product suite, one tool which does everything OK, works together and has enough customer references/online reviews to say it works.

These mature startups and global companies have mature sales and marketing operations – to them it doesn’t mater if it takes a couple of years before a new customer is generating revenue for them. Their products are bigger than the “minimum viable product” from a young startup – they can sell it for more so they can outbid the young startups in all user acquisition channels. This dynamic has always existed – it’s about scale. However in the past there’s been several paradigm shifts which have shifted the power from mature products towards startups:

  • Exponential growth of internet users starting in the 90s – a rising tide raises all boats meant success for many who were one of the first to see an opportunity
  • Disintermediation (many players in a value-chain were no-longer adding value in an online world and they could be easily disrupted by startups starting off small – eg. Ebay)
  • The first wave of agile product development (in 2000 mature products were built on Oracle & coded in C or Java, and along came Linux, Apache, PHP and Mysql which was free and easy to update your product continuously)
  • The techcrunch effect circa 2006 was strong enough to launch new products
  • The rise of social
  • The second wave of agile product development – the move to the cloud, SaaS, cheap and scalable
  • Exponetial growth of smart phones (Mature products were slow to adapt to building mobile versions)

Maybe there will be new paradigm shifts which will create opportunities again. However the main shift I see is working against startups. It’s the convergence of hardware, software and services with the internet of things, sensors, wearables, increasingly complex smart phones. It’s difficult to see what a “minimum viable product” is in a world where everything is converging and the tech giants (Google, Apple, etc.) are sucking in more and more data.

As a tech seed investor, it’s become increasingly difficult to judge what’s going to succeed. For a startup to see off the mature competitors it will need a VC to back it all the way and a team which is motivated to build out the product until it and the team have matured – its a long and uncertain journey. In this interconnected world, the halfway solution (that we’ve used in Europe where startups raise a tenth of what they raise in the US and find a niche) is going to be increasingly difficult to make work. Even if the IP (intellectual property) is compelling, it’s risky to try and build an industry from scratch unless you have access to the full US scale VC capital (tens of millions) and a team with a track record to scale startups big. The default European option will continue to be to sell the startup early to avoid the risks of going it alone.

My guess is somehow this maturing will play into the hands of the multinationals. They have the scale, and somehow the combination of them, their corporate VC teams, kickstarter/indigogo (as a means of testing product/market interest), accelerators/incubators (to process ideas in large batches), VCs and angels will have work together to get new products to scale.

But (except for the rare exceptions) it’s not going to be about startups going it alone. As markets mature, its brutal out there for startups.

P.S. Here’s an example of Apple’s software maturing and competing with many startups https://www.cbinsights.com/blog/apples-wwdc-startup-losers/

The global hardware startup ecosystem in depth

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The Silicon Valley/Shenzen based hardware accelerator haxlr8r has just published a 200 slide presentation “hardware trends 2015” on slideshare.

Masses of info showing trends, successes and failures – every slide has some key information that anyone involved in hardware startups needs to know. For example here’s an extensive list of traps to avoid:

hardware risks

Hardware innovation is happening very fast. You can see that there’s lots of elements driving this. Behind all this is also the commoditization of hardware components such as sensors, platforms such as Arduino, and a lot of software components (frameworks and algorithms) which are mature enough and modular enough to use as building blocks.

In Switzerland we’re doing well in intellectual property for components, but we are struggling to capture more of the value with complete products or even just subsystems. The only example of a Swiss product I can find in the presentation is Gimball (due later this year) – I think the other Swiss drones are still research projects.