Musings on Token Offerings

There is absolutely an irrational exuberance happening right now in the market for cryptocurrencies. As I write this, the market capitalization for tokens and cryptocurrencies (for simplicity, I refer to the terminology "token" to represent both types of digital currencies) is roughly $111 billion, which for scale, lies somewhere between the market caps of Starbucks ($87 billion) and McDonald's ($125 billion). Whereas I can leave my Manhattan apartment now to pick up a coffee or burger from the latter two public companies, the derived value of tokens is largely propped up by speculation (greed), blind optimism (faith), and enticing storytelling (myth). There are, of course, legitimate companies using tokens as a mechanism to bring a decentralized network effect to their product, but even then, the valuation is not aligned to the value (current or expected). There is no better person to quote about value than Warren Buffet.

“For the investor, a too-high purchase price for the stock of an excellent company can undo the effects of a subsequent decade of favorable business developments.”

The message is simple: it's entirely possible to overpay for shares in a business, even if it's a spectacular business, because it will never be able to grow into the already sky-high valuation. While tokens don't represent equity ownership (at the moment), they are supposed to represent the future value of the underlying protocol or product. Right now, token holders seem to think that the future value of the protocols and products will be $111 billion. While I have no doubt that the market capitalization of decentralized protocols and applications will exceed that amount in the future, I do not believe this current iteration of tokens will be the generation to generate this kind of enormous value (to be explained in a future post).   

I will now admit to one other fact, and then give some miscellaneous and possibly confusingly organized list of thoughts on token offerings. Starting with the fact. I am an investor, and sometimes speculator, in tokens. This may confuse you, given what I mention in paragraphs above, but do not let it prevent you from missing the point of this piece: in this market, tokens are mispriced, misunderstood, and will inevitably crash at some point in time that I have no way of predicting, knowing, or attempting to explain. Having said that, the reason I partake in this highly inefficient and irrational market is threefold.

First -- you can start developing skills in token investing that will come in handy for second-generation tokens (after this bubble pops). Second -- having some sort of skin in the game makes you more likely to stay attuned to what is going on in this space. To quote the notorious Jesse Livermore, "the game taught me the game." The only way to play the game is to actually play it. Reading about tokens will only get you so far. The third and final reason -- purchasing tokens provides for good fun. You can buy and sell tokens seven days a week, twenty four hours a day. That's not always a good thing for an investor's psyche (friends may ask why you are suddenly anxious during a casual Saturday dinner), but it sure is entertaining. 

Now for the musings. Below are a collection of reflections I have gathered in the past few months after investing and speculating in various tokens. I do not aim for this to be comprehensive, fully correct (we are all figuring these things out as we go...anyone who thinks they have this stuff figured out is either delusional, lying, and possibly both), or any type of investment advice. 

  • With many newly minted token millionaires, it's easy to assume they have things figured out. As the recent Uber news showed us, success hides problems. Many token millionaires made their money not due to skill, a systematic process, or deep experience, but through the sheer luck of being at the right place at the right time. Don' be fooled by their randomness. Here, it's helpful to read the work of Michael Mauboussin on base rates. Just a short quote: "...when luck dominates the best prediction of the next outcome should stick closely to the base rate. For example, money management has a lot of luck, especially in the short run. So if a fund has a particularly good year, a reasonable forecast for the subsequent year would be a result closer to the average of all funds."
  • I believe the best investing analog for tokens is seed and angel investing. In seed and angel investments, the investor often has nothing to go by except a simple deck and an idea. Since these investors give founders money before product / market fit, they're really just betting on the founders' ability to execute. In short, they're betting on the team. Similarly, many token offerings raise money before any product is created. The only data points a token investor has before making the investing are the whitepaper, various code commit histories and community engagement, and the team (I do not recommend investing in undisclosed teams). The market, however, seems to over-index on the importance of whitepapers rather than on the importance of teams. The seed and angel analog for whitepapers would be pitch decks. I encourage you to look at the pitch decks for a few large companies / unicorns and compare their initial proposition to what the companies actually do now. You will find similarities in the originally stated goal of the company and the reality today, but most often, you will find many more differences. At all stages, but especially at early ones, a company needs to be flexible and course-correct when needed. Automobiles are course-corrected by the driver. Companies course-correct by the founders. My most profitable token investments have been those where I invested based on the strength of the team, not the whitepaper. How I evaluate teams will be a post somewhere down the line. 
  • It is my experience that the best investors often invest unemotionally. My biggest losers have been token investments that appealed to me emotionally. These investments would probably not have appealed to me in a rational state of mind. My biggest losers have also come from the investment choices I made when following the crowd. Academically, it is easy to digest that you should buy when others are fearful and sell when they are greedy. In practice, things are not always so simple, since you are fighting against the fear of missing out. You can read about these mental and emotional investing fallacies your whole life, but without actually riding the rollercoaster you won't fully absorb them. Charles Mackay knew this over 150 years ago: "Men, it has been well said, think in herds; it will be seen that they go mad in herds, while they only recover their senses slowly, and one by one." I am very happy I have the freedom to make these mistakes in this generation of tokens rather than the second, much more lucrative generation. 
  • Bifurcation of tokens is the first necessary step. There are two types of tokens: those you want to buy and hold for the long-term, and those you may want to trade for short-term profit opportunities. For the latter, token markets are inefficient. For the most part, you are not dealing with sophisticated traders, which can make the competition quite surmountable, if you do your reading. Although I am still developing the mental model I am about to describe, I think it works well enough to use in practice. There are two sides to every market: buyers and sellers. Buyers can be sellers, and sellers can be buyers, but the level of sophistication of each person varies. I have noticed that the markets for certain tokens skew either higher or lower as to the average level of buyer, holder, and seller sophistication. Specifically, I look for token markets where the average level of buyer and holder sophistication is low, which gives me the ability to sell back into the market at higher prices. Ether, for example, had a run up in price as a result of retail investors flooding in after reading about initial coin offerings and not wanting to miss out. While ether has some very sophisticated buyers and holders, this flood of new and unexperienced investors lowered the average level of sophistication of the buyer and holder. What this means in practice is that the average holder of ether will react more slowly to news, giving you an edge to trade against. On the contrary, certain tokens have very sophisticated holders, some of which are whales with inside information. In an effort to skew the probabilities of winning in your favor, I recommend you avoid competing with them. 

I am still learning about the business of token investing, and I have no plans to stop. All markets contain information asymmetry, but this is especially true for new markets such as the one we spoke about here. I find that writing about my investment strategies helps me better think about and improve on them, so you can say I write this out of selfishness. Feedback is welcome; feedback to refute the above points even more-so. 

Banal Things and Epistemology

One of my favorite things to do in Manhattan is to walk the streets and take in the daily bustle of New Yorkers. We're an anxious and rambunctious folk relative to other city-dwellers, so it is an interesting thing for me to do and stop to watch the commotion in slow motion. 

The Subway Echo Chamber

Every day, I take the subway to work from the Upper East Side to the office or client site. As I walk into the subway station every day, I see people rush down to the train tracks when they hear the sound of an incoming train, only to be disappointed that the train is on the opposite side of the tracks. I thought about why this happens, and came up with a few reasons. Firstly, people can hear the train coming but they cannot see it; their field of vision is restricted, but their hearing isn't. Thus, they decide to take a gamble and rush down the stairs with limited knowledge of the situation. Another reason people run down the stairs to an empty track is contagion. They see other people running down, who see other people who see others yet; this contagion escalates all the way up to people just entering the subway since they think those down below saw the train coming. Every day, I see the same thing happen. In fact, I used to be one of the suckers entering the subway station, noticing the contagion, and joining in the rush only to be disappointed the train is on the other side of the tracks. This banal daily occurrence reminds me of the things you see in the world of investing, such as a hot IPO that initially attracts lots of money (FOMO, contagion, call it whatever you like) only to deliver disappointing results down the line. 

The Pedestrian Light

We've all been taught to cross the street when we see the pedestrian light turn green. What we should have been taught, however, is to cross the street as soon as the car traffic light turns red. That is, after all, a leading indicator of the pedestrian traffic light turning green. I think a lot of leading indicators in the world of behavioral economics and investing. I had to introduce a startup panel the other week, and I thought the traffic light analogy was a very appropriate one. By spending your time learning about what startups in a certain sector are doing, you are looking at a leading indicator on some of the technology, products, and business models that will trickle down into enterprise five to ten years down the line. 

The Whole Foods Checkout Counter

There is a Whole Foods near my office, and once or twice a week I pick up a lunch there. For the unacquainted, Whole Foods is packed during all but a few hours of the day, at least in Manhattan. It's a supermarket, but it also has a great selection of salad bar food choices, sandwiches, and pizza. The salad bar is by far the most popular to-go option, so the checkout line is always packed. The sandwich and pizza checkout lines, while still busy, tend to move more quickly. What I started doing is getting hot food in the salad bar and instead of waiting in the long salad bar checkout line, I started paying for the hot food in the much faster pizza or sandwich checkout counters. There are a lot of people using this shortcut, but they are still the minority. I'm not sure what this means about human behavior, but I think we assign rules to our daily lives just because we assume they should exist. Whole Foods has no rule that you have to pay for salad bar food in the salad bar checkout counter or for pizza in the pizza counter, but we just assume it does. How many other rules do we follow just because we assume they exist?

My Favorite Books - 2016 Edition

I am deeply jealous, albeit slightly confused, of people who read over fifty books a year. I try to read around fifteen, and even then, I have trouble remembering what I read a few months after the fact. This is normal, of course, as the act of remembering information is a proxy of instituting that information in your daily thinking. If you don't think about it, you forget it. That's why the amalgam morning headlines you read with your first cup of morning coffee are forgotten by the time you have your third cup. The information comes in one ear and comes out the other. What comes in easy goes out just as easy. 

But some things stay with you. Out of the few thousand articles I read this year, I can recall maybe three or four that had a lasting impact on my thinking. That makes you wonder: was it even worth reading all of those articles in the first place? For me, the answer is an unequivocal "probably not." As I've publicly stated before, going forward in 2017, I will decrease my time spent reading news and increase my time spent reading books. Time is limited, knowledge and information are not. Your goal as an intellectually curious person, I believe, is to maximize the gathering of knowledge and information while minimizing the time spent garnering it all. 

With that preamble out of the way, let us look at some of the books that are worth remembering, just as we did in 2015

The True Believer

Most books are too long and cover too little. This book is the opposite of that. If you had to cram as much wisdom per sentence, you would have a hard time matching The True Believer. The book is all about mass movements; how they form, what keeps them in power, and finally, why they eventually fail. What makes it especially interesting is the author, who was a self-educated drifter and longshoreman. You will not find any pseudo-intellectualism in his writing. Here are some quotes that resonated with me:

When a mass movement begins to attract people who are interested in their individual careers, it is a sign that it has passed its vigorous stage; that it is no longer engaged in molding a new world but in possessing and preserving the present. I ceases then to be a movement and becomes an enterprise. 

When people revolt in a totalitarian society, they rise not against the wickedness of the regime but its weakness.

[On what makes a good leader] What are the talents requisite for such a performance? Exceptional intelligence, noble character and originality seem neither indispensable nor perhaps desirable. The main requirements seem to be: audacity and joy in defiance; an iron will; a fanatical conviction that he is in possession of the one and only truth; faith in his destiny and luck; a capacity for passionate hatred; contempt for the present; a cunning estimate of human nature; a delight in symbols (spectacles and ceremonials); unbounded brazenness which finds expression in a disregard of consistency and fairness; a recognition that the innermost craving of a following for communion and that there can never be too much of it; a capacity for winning and holding the utmost loyalty of a group of able lieutenants. 

The knowledge in this book can be applied almost anywhere - markets, technology, or your own leadership.

The Lessons of History 

History is a collection of stories that help you pattern match. When we live in a world filled with social media echo chambers and filter bubbles, it is important to be able to take a step back from the news and take a deep look at what's really going on. History lets you do that because the outcomes of each event are known. The patterns (stories) are there to be absorbed, and the matching (making links to the present) you must do on your own. More often than not, pattern matching results in a successful decision, but there are times it can hurt (a deep background in history and patterns can make you jaded and hesitant to act, whereas naivety encourages participation, even if through sheer inexperience). The Lessons of History is a course on the patterns our society goes through, and I use it to think about today. 

So the first biological lesson of history is that life is competition. Competition is not only the life of trade, it is the trade of life - peaceful when food abounds, violent when the mouths outrun the food. Animals eat one another without qualm; civilized men consume one another by due process of law. Co-operation is real, and increases with social development, but mostly because it is a tool and form of competition; we co-operate in our group - our family, community, club, church, party, "race", or nation - in order to strengthen our group in its competition with other groups. 

Intellect is therefore a vital force in history, but it can also be a dissolvent and destructive power. Out of every hundred new ideas, ninety-nine or more will probably be inferior to the traditional responses which they propose to replace. No one man, however brilliant or well-informed, can come in one lifetime to such fullness of understanding as to safely judge and dismiss the customs or institutions of his society, for these are the wisdom of generations after centuries of experiment in the laboratory of history. 

So the conservative who resists change is as valuable as the radical who proposes it - perhaps as much more valuable as roots are more vital than grafts. It is good that new ideas should be heard, for the sake of the few that can be used; but it also good that new ideas should be compelled to go through the mill of objection, opposition, and contumely; this is the trial heat which innovations must survive before being allowed to enter the human race. 

Before I tackle any business decision, I try first to find some sort of historical precedent as to the outcome. If the outcome is beneficial in my favor, I proceed to think deeper about the problem. If the outcome is negative, I reevaluate whether the historical apology is an apt one, and if it is, whether this is a decision that is worth pursuing. History doesn't repeat itself but it often rhymes.

Fooled by Randomness 

We are often assigned books to read at an early age, with the ultimate goal of having these books teach us something about life. I believe this can actually be detrimental. For a book to be impactful, you must not only understand it from an academic perspective, but also be "ready" for it. Reading a book you are not ready for is detrimental as you are less likely to pick it up sometime again in the future if you did not appreciate it the first time around. When I first read Fooled by Randomness a few years ago, I didn't fully appreciate the lessons it told. For this reason, I decided to re-read it this year, despite it not resonating with me years ago. And I'm very glad I did, for it has changed the way I approach certain situations in my life. 

My lesson from Soros is to start every meeting at my boutique by convincing everyone that we are a bunch of idiots who know nothing and are mistake-prone, but happen to be endowed with the rare privilege of knowing it.

People do not realize that the media is paid to get your attention. For a journalist, silence rarely surpasses any word.

Lucky fools do not bear the slightest suspicion that they may be lucky fools - by definition, they do not know that they belong to such a category.

The first lesson I took away from Fooled by Randomness is that the magnitude of an event is far more important than the frequency with which it occurs. This is simple to understand in the case of investing. In the first scenario, let's say you have $100 to invest, and you do so by investing $1 in 100 companies. Each of these investments returns 5x the initial capital invested. In the second scenario, you still have $100 to invest, but ninety-nine of your investments fail, and only one returns 1000x. In scenario one you make a total of $500 ($1 x 100 x 5). In scenario two you make $1000 ($1 x 1000 x 1). Frequency is overrated; magnitude is underrated. There is another application of this very same idea to networking. For work, I often have to attend conferences and various meetups. In the past, I usually opted to having short conversations with a large amount of people. These conversations tended to be chit-chatty in nature, and very rarely led to any sort of lasting relationship. More recently, however, I have pivoted to speaking with only one or two people at a conference, but giving them much more time and attention. This has been incredibly beneficial, both from a relationship-building perspective, but also for building friendships. Again, the magnitude of conversation mattered much more than the frequency of it.

The second lesson comes in the form of how I view luck vs skill. Taking advice from successful people is a popular pastime. But is it possible to separate how much of their success is attributed to luck vs skill? This is a question to the answer of which I am still trying to determine. This much I have learned, however: do not take advice from people who have have gotten rich based on the outcome of one event (more likely than not that advice is not reproducible and was the result of luck); take advice from those who have a consecutive record of success (it is more likely than not that skill was involved rather than luck); be wary of the advice from experts as soon as that advice enters a field they have not been successful in (skill does not often translate well to other topics); luck can be increased by rolling the die more times (take chances, fail often, learn, and move on). 

And with that, let's mark an end to 2016 and look forward to a memorable 2017. I know I haven't been great at keeping this blog updated, but thanks always for reading and keeping subscribed. 

How to Interpret Information in an Infinite Knowledge World

If you know me in person or follow me on Twitter, you know that I'm a pretty voracious reader. Having been like this for quite a while now, I have developed an approach on how I interpret information. This includes both news and books; temporary and permanent knowledge. 


Despite years of stagnation, Twitter remains by far my favorite social network. It gives you a glimpse into the head of another person; what they read and how they spend their time. A lot of hush hush watercooler conversations are actually public on Twitter, if you follow the right people. That said, there is also an incredible amount of noise, most of which can be safely ignored (e.g. politics Twitter, where facts go to die). 

When I browse my timeline, I try to remind myself of an old idea someone told me a while back (unfortunately I do not remember who that someone was). That idea is as follows. Imagine how much work goes into writing a book. Often times, the author has years of requisite knowledge (it's common to write your first book at 50 - 70 years old), puts in countless hours of research, and has a publisher fact check the results. Obviously not all books go through this rigorous process, but the best ones certainly do. Now think about the process of writing a tweet. Perhaps the person has years of knowledge, but it's unlikely she put in hours into thinking and fact checking the tweet. That wouldn't be practical. Think about that next time you retweet something you like. How much thought do you think went into it, and is it actually factually correct? I don't know about you, but my most popular tweets have been those I've posted after midnight, usually around my 3rd or 4th glass of wine. Twitter is fun, but don't treat every tweet as gospel. 


So we've established it takes a few seconds to post a tweet. What about to publish a news article or a blog post? Not every news outlet can be like The New Yorker and give the author months or years of investigative journalism before he hits publish. Or this post. I've thought about this topic for a while now, and even did some light research too, but who fact checked it except me? It would be hypocritical of me not to say proceed with caution, even with my own writing! 

Now, here is another trope I use, this time when I read news. A while back I was reading a Wall Street Journal article about some accounting standards a company misapplied. Now, it just so happened to be that I was just learning about that very same accounting standard in school, and my professor was a former partner at a big accounting firm - a subject matter expert. You might see where I'm going with this. The Wall Street Journal writer had the facts totally wrong! Not only did he apply the incorrect accounting standard, but he also misunderstood the standard he misapplied (if only two wrongs made a right?). The writer wasn't a bad guy; he was simply given a topic he hadn't much experience in. The WSJ is for the most part an excellent source of financial news, but even they make mistakes. Hiring a CPA to fact check every news article is impractical, and besides, even a CPA doesn't know about every new accounting standard.

The only reason I caught this mistake was because I just happened to be studying that exact same topic by an expert in the field. Unless you were also an expert in this topic, you probably took the whole thing as fact. And who could blame you, why should you know better about advanced accounting standards? Think about that next time you're reading about a topic you are otherwise clueless about. Is it possible the author is writing beyond his subject matter expertise? Probably. 


There were 304,912 books published and republished only in the United States in 2013. I will eat my shoe (Allen Edmonds uses good, tough leather, so I strike a fair dare) if each of these books were actually any good. What is a good book, anyway? For purposes of this post, a good book is one that is factually correct not today, but in the short to medium term future. On an infinite timescale, every knowledge book will be factually incorrect because we will discover new things that we did not know at the time of writing. The goal of reading a book today, then, is for the information contained within it to be useful in your life (20 - 80 years). 

What further complicates things is that out of the millions of books published every year, few will be great, many will be good, and the majority will be a waste of your time. How then, should you choose what to read when the constraining resource is time? In the past, I've used Google, GoodReads, and countless other book review websites to help me separate the good recent books from the bad. But what I've noticed is some good books became bad books as time went on. Reviews slowly went from four and a half stars to four, and then even to three stars in the span of a few years as the 'facts' presented in the books turned to actually be opinions.

Rather than trust reviews of modern day books, I've found another process that eliminates hype and filters for the best books: time. For the most part, I now read books that are still well-received at least ten years after they were published. What this tells me is the information contained in the book stood the test of time (Another fun exercise: take a look at your tweets from two months ago and cringe in absolute horror from all the things you got wrong). A book that was published fifty years ago and is still read today tells me it's a book with lasting content. If it's a business or investing book (which are notoriously trendy) that lasted that long, you can be sure it's got long lasting nuggets of wisdom. The last thing you want to spend your time on is reading ephemeral books - that's the definition of a waste of time. 

Books (and art, music, and all other knowledge content) are derivative instruments of prior work repackaged to the taste of modern times. I was watching a season of Dexter a few years ago, and I thought the episode finale was very well done, original, and downright chilling. A character that we were lead to believe is dead actually turned out to be alive, and not only that, but the true murderer in the case. A short time later I watched Psycho, a classic Alfred Hitchcock film from 1960, that essentially uses the same premise of assumed-dead-but-actually-isn't to even greater chilling effect. In short, Dexter copied Psycho, which I bet you copied something else from a time before that. What's old is new again; original wouldn't exist to a person who has seen all of history. 

Summing it all up

We live in a time where an almost infinite source of information is thrown at you. That makes it really hard to know what to spend your time on. The internet has also made it incredibly simple and free to publish ideas, lowering the standard of quality to the substandard. The above themes help me cope with the abundance of information, and I hope they will to you as well. 

Thinking through Artificial Intelligence

I’m really not a fan of the term ‘artificial intelligence’, or AI, for short. We tend to connote a negative meaning to the word artificial, implying that an artificial intelligence is unnatural, and possibly even evil. In fact, the term AI reminds me of another term — genetically modified organisms (GMOs) — which have also been the subject of vicious debates in recent years despite, well, science. I suppose AI could have a worse name, like maybe genetically modified intelligence, but we can leave that to be the villain of another sci-fi film.

As is often the case with new technology, there are camps of people who are incredibly paranoid about what such a technology can do to the stable world order. The cannonical example often used is one of the 19th century English textile workers who protested against the new technologies brought about by the Industrial Revolution — the Luddites. The term is now inscribed to mean a person who is anti-technology, even though the reality of the Luddite argument was quite a bit different. What we have now are AI Luddites who are afraid of artificial intelligence due to the potential catastrophic events an evil AI can cause.

My first encounter with an evil AI, as I imagine was most people’s, was the film The Terminator (1984). The main antagonist of the film, Skynet, was pure artistic genius on the part of the writers. From Wikipedia:

Skynet is a fictional conscious, gestalt, artificial general intelligence (see also Superintelligence) system that features centrally in the Terminator franchise and serves as the franchise’s main antagonist.
Rarely depicted visually in any of the Terminator media, Skynet gained self-awareness after it had spread into millions of computer servers all across the world; realizing the extent of its abilities, its creators tried to deactivate it. In the interest of self-preservation, Skynet concluded that all of humanity would attempt to destroy it and impede its capability in safeguarding the world. Its operations are almost exclusively performed by servers, mobile devices, drones, military satellites, war-machines, androids and cyborgs (usually a Terminator), and other computer systems. As a programming directive, Skynet’s manifestation is that of an overarching, global, artificial intelligence hierarchy (AI takeover), which seeks to exterminate the human race in order to fulfill the mandates of its original coding.

If Skynet doesn’t scare you, I don’t know what will. But let’s get back to a less evil artificial intelligence.

AI has a long, storied history, which you can read about here. But I’ll be picking up on the topic from even earlier, a 1957 movie and a favorite of mine, Desk Set.

Worrying about artificial intelligence, circa 1957

The film is classified as a romcom according to IDMB (or is it IMDB’s AI deciding what to tag it?), but it’s really much more than that. Taking place in the reference department of a library, we are introduced to a group of women whose job it is to pick up the phone, research facts, and answer questions on a wide array of topics. If that sounds inefficient, that is because it is, leading the president of the library to hire a methods engineer and efficiency expert to replace the reference department with an AI computer. A romantic hour later, the AI is programmed, installed, and production ready. Unfortunately, the AI ends up having trouble answering customer calls, and is later ‘upgraded’ back to the women who used to work in the reference department in the first place.

With the beautiful bias of hindsight, we know what actually killed the reference department were search engines like Google, not AI. The point of bringing this example up was to show that AI-ludditry is nothing new. What actually disrupts your job may not be what you think will disrupt your job. Outkast taught us that in the wonderfully deep lyrics of Ms. Jackson:

“You can plan a pretty picnic, but you can’t predict the weather”

Which leads me back to Skynet and evil AI. Why are so many people so paranoid about a strong AI breaking out of the box and taking over? My gut reaction to an evil strong AI is to ask if there has been any historical precedent for technology turning bad to hurt humans. Granted, there has never been such powerful AI tech as there is today, but nonetheless, the question stands. And besides, why does a strong AI have to be bad? It could turn out to be good just as it could evil. Innocent until proven guilty.

The next logical pattern to start pondering is as follows. Okay, so someone created an evil AI — what are the realities of such a situation? The human brain uses 20 Watts to operate, which is extremely efficient and so far non-reproducible in non-humans. Meanwhile, the Google computer (AlphaGo) that beat Lee Sedol in a game of G0 used approximately one Megawatt. That is 50,000 the energy consumption a human brain uses, and we’re only talking about a board game (a complex game, but still a game without the external factors of a real environment). Thus, the question becomes slightly different — is there enough computing power in the world for an evil AI to achieve world dominance?

By the way, I want to remind you that we’re speaking in hypotheticals here. A self-learning, cognitive, strong AI does not exist yet. The debate thus far has been around preventive measures that usually begin with “what if”. As you can probably tell now, I’m not very worried about a Skynet-esque AI. My strong suspicion is that people picture an evil AI because of all the science fiction films and novels that they read as children. But fine, let us embrace the possibility — at least for a second — that an evil AI does come into existence. Should we be spinning our wheels designing failsafes into the AI system to prevent such an outcome?

Obviously yes, we should be thinking about such remote possibilities in all system designs. But allow me to make a brief philosophical excursion on why instituting failsafes won’t rescue us from an evil AI. An artificial intelligence that turns evil is a low probability event. In other words, it’s a black swan event. And by definition, black swan events cannot be predicted in advance. It follows that designing a failsafe into the system will not prevent the evil AI from escaping, given that the definition of a black swan event is an unpredictable event. How can you take preventive measures against an unpredictable event? You can’t really.

I’ll leave off with an Alan Kay quote I’ve always enjoyed:

It’s easier to invent the future than to predict it

Go invent an AI instead of predicting the unpredictable!