Fred Ehrsam, I would frame the problem differently. The need is not just for creating most powerful artificial intelligences, it is about redesigning the marketplace that can nurture such an ecosystem. The marketplace cannot be taken for granted. A poorly designed marketplace means poor AI, even if we assume that the state of AI today is structurally stable.
As intelligence moves from arbitrary and erratic patterns of human discretionary knowledge-building toward a more systematic and organic AI, there is a need for a new market mechanism to validate, distribute, and reward intelligent processes. Such an intelligent market is built on a systematic, scientific, replicable (SSR) process that is objective, accountable and can be validated and used by the community. This general intelligence or “alpha” should be content-agnostic and context-focused - an alpha process reconfiguring the block of the blockchain into ‘AlphaBlock’, an intelligent market mechanism. Alpha prediction has conventionally been associated with domain-specific content and is known to be predictive systems that are non-replicable and are mostly non-scientific. The author defines a General AI predictive process that can be fused into the blockchain block, transforming the blockchain into a multi-purpose predictive tool which self-builds, self-protects, and self-validates. AlphaBlock becomes the essence of everything linked with data predictability, evolving into an intelligence layer on the blockchain and the web. It is a predictive ecosystem which blurs the distinction between financial and non-financial data - ultimately removing barriers between financial and services markets. The blockchain can achieve this evolved state and become an intelligent market state if it crosses three key hurdles: First, it securitizes blockchain assets and creates new alternative assets and asset classes. Second, it resolves the incapability of conventional finance to understand risk effectively and enhances return per unit of risk (outperform the market) using a General AI process. Third, it must offer a better mechanism to address currency risk than what is offered by the existing fiat currencies and cryptocurrencies.
While the world seems to have a solution for every problem, an app for everything, one simple problem about Bubbles and Crisis bother no one. How to make bubbles less bubbly and crisis less severe. We are so busy counting our crypto wealth, it does not bother us whether the wealth is here tomorrow and gone tomorrow. We write stories about how Google sentiment drives bitcoin prices or vice versa, unaware of the fact that a few decades ago we were wondering whether the sunspots used to lead the economic cycle or vice versa. The fragmented nature of our research and markets and focus on causality is the reason we are happy betting on alpha as alphabets rule the world and not focus on alphabots that allow disruption for the general good.
The recent paper “Why Indexing works”  gives a probabilistic explanation of the futility of the Active process and why Passive Indexing is hard to beat. For every 1000 people who read the Wall Street Journal, maybe 10 read the Bloomberg Markets (BM) magazine and for every 10 who read the last month’s issue of BM maybe 1 read this research paper cited in the article . And you don’t need a geologist to tell you that the chances to dig and find are small. This is why making a mathematical case against the underperformance of the USD 16 trillion plus active market using hypothetical probabilities is not easy.
AI is excited about jumping cats, How come AI can not solve the US 100 trillion investment management which can not beat the benchmark? The answers I got. The cat is important not the benchmark. AI needs to take small steps. Solving Cancer more important than beating the benchmark. Driverless cars more important focus. We don’t have another financial crisis to ask that question.
Finance is a key milestone for AI. Imagine coming back from vacation and talking to your virtual assistant about your investment portfolio and wondering how she does it, quarter after quarter, year after year. Managing money is the real test for human AI. It has to talk, it has to think, it has to have intuition and it has to make money. Despite the AI Game successes, there is no AI player with such capability today and it’s unclear whether brain emulation under Strong AI is the preferred direction for achieving human AI. This paper uses a historical context to explain why it maybe time to denounce social systems, embrace system thinking, and explore simple ideas like computational linguistics to explore technologies that can teach computers to talk, think, assimilate knowledge and hence also manage money. Such technologies should set up the foundation for Web 4.0.
Finance does not understand the physics of preferential attachment. According to Taleb, the intuitively appealing preferential attachment is incomplete. This is a tragedy for his ‘Black Swan’ because preferential attachment is the other name for ‘Rich Get Richer’. Taleb bases his philosophy of randomness on the non-normal power law behavior which is also another way of looking at ‘Rich Get Richer’ mathematics.
The two Nobel Prizes awarded in Economics in 1990  and 2013  define the boundaries of Modern Portfolio Theory (MPT). Size is the pillar for both the models. The 1990 winners assumed market to be driven by Market Capitalization (MCAP)  size, while the 2013 winner explained that factors like ‘Small Size’  can explain portfolio performance better than ‘Big Size’ . This conflict between the two ideas has bifurcated the industry into benchmark investing (MCAP)  and everything else not MCAP (Smart Beta) . The fact that benchmark investing and smart beta is expected to be 50% of the USD 100 trillion investment management industry in 2020  makes it imperative to seek a coherent argument and a conflict resolution.
When you make a big claim, you have to be careful. This is the lesson hardest to learn. I am still learning it. My stock market education helped me a lot. The one thing it always taught me was to be ready for a surprise. It happened again today, as markets got Trumped. The idea of frequent outliers is hard to grasp because humans herd. It gives us comfort to herd but that’s the only way the society can function. It has to form clusters and then burst them. The only way for life to continue is by surprises. The role of uncertainty is so critical when it comes to system functioning. This is why sticking your neck out is a dangerous way to live. Nobody can tell you this better than stock market forecasters. The pundit to disrepute journey is very uplifting and humbling. Anyway, a failed forecast is good for system building as it forces us to go back to the drawing board and look into our systems. Hence there is a positive flip side to every surprise.
Standing against the establishment, having a voice and speaking up needs courage. This is what you did. You spoke up against the industry which started the first Mutual Fund in 1775. Bloomberg calls it a revolution, you call it a revolution, Wall Street Journal is calling it differently, but that does not matter. Mutual Funds are in a descent. Stock Pickers might still continue to follow Graham and Dodd approach, but the facts are overwhelming. If an institution can’t beat the index then it is wasting resources.
Though ‘Size’ is the most important factor explaining stock market returns, the possibility of size being a proxy was first mentioned in Banz (1978). Even after forty years of factor investing the industry is still looking for answers. This paper chronologically lists the research on ‘Size’ and why the question regarding ‘The Size Proxy’ has never been so relevant.
Before we talk about Blockchain disruption, let us talk about the implosion happening on Wall Street. The Realization that stock picking is dead  after decades of Active underperformance  has made beating the market an adventure sport, where only a few succeed. There was only one Peter Lynch , the active management is set to become 65% of the overall market , the high fees are gone  and if this was all not enough we have the ‘Do Nothing Strategy’ from Nevada’s Pension Fund Manager, Steve Edmundson who slashed the fee for external managers by nearly a 10th from an average USD 120 million to USD 18 million . All this leaves little for the yachts and barely little for the golf club. Above all this, the technology is killing the incentive to make markets. It is the rise of the ‘Buy Side’  and Virtu’s of the world, which never lose . Basically, the party is over.
If you can explain your money management innovation (rule-based portfolios) to your mom, it is golden. My mom gets it. Succeeding in selling and marketing a new financial innovation just like any other innovation will be about design. If it is not intuitive, no overselling, branding and marketing money is going to stop the jumping from the ship.
Investment management which is worth USD 70 trillion can be seen like a fruit basket. The job of the fund manager was to select the fruits from the market and sell it to the investor. Global pensions are a part of this pool. Despite the important role fund managers play, there is a lot of confusion regarding financial theories and lack of standardization between investment management practices. Investment solutions are primarily for a rising market. There are limited solutions for a falling market. The investment solutions are primarily equity focussed. There are no standardized metrics to look at all asset classes together i.e. equities, commodities, bonds, currencies, alternatives. Academic thinking is also very equity focussed. ‘Size’ the most important factor explaining stock market returns is not understood well. The lack of standardization, solutions for an up trended market, equity skew is the ‘Fruit Basket Paradox’, a term that explains the fragmented nature of financial theories and the circular argument that rots the investment management business today.
The rich do not always get richer. Explaining this probabilistically resolves a 100-year-old puzzle, opening up opportunities for smart beta portfolios, an architecture of complexity and eventually an architecture of data that could become Web 4.0.
Ok! advisors might be inefficient but is it enough reason for the media wave against them. Agreed that evolution has to happen, and after the industry restructured broking, sized up researchers, and the equity sales, maybe it’s time to disrupt advising. One may argue that somebody has to take the responsibility for underperformance netted for a fee, so maybe it’s time for retribution for advisors. But before we choose the robots over the advisors, and see passive investing as a solution for all investing, maybe we need another perspective.
Despite its popularity, the power law has not been without its failures and has rather come under criticism. In the paper ‘Scale-dependent price fluctuations for the Indian stock market’, Matia K, Pal M, Salunkay H, Stanley HE (2004), the authors explained how Indian stock market may belong to a universality of class different than that observed in developed markets.
Adaptive Market Hypothesis (AMH) embraces Efficient Market Hypothesis (EMH) as an idealization that is economically unrealizable, but which serves as a useful benchmark for measuring relative efficiency. AMH’s adaptability to changing dynamics of the market suggests that investors are potentially capable of an optimal dynamic allocation. There is nothing wrong here in the direction pointed by Andrew Lo. However the assumption that human innovation driven adaptability is the way ahead is an open-ended solution. This leaves little room for system thinking and overrules the possibility that natural systems could explain human behavior rather than vice versa. AMH just like EMH is based on a set of assumptions, which are good for illustrating market idealizations but lack in terms of addressing contradictions. This makes both AMH and EMH a system philosophy rather than a system framework. Reversion Diversion Hypothesis (RDH) (Pal, 2015) reconciles the contradictory assumptions into a statistical framework that addresses the limitations of EMH, AMH and extends the idea of a natural system functioning to markets.
I was in Mumbai recently, meeting fund managers to understand where India was on the smart beta road. How fast was investor education evolving? What was the appetite for ETF’s? And what should be done from the regulation point of view to take the Indian markets to the next stage?
Jules Augustin Frédéric Regnault was a French stock broker's assistant who first suggested a modern theory of stock price changes in Calcul des Chances et Philosophie de la Bourse (1863) and used a random walk model. He is also one of the first authors who tried to create a "stock exchange science" based on a statistical and probabilistic analysis. His hypotheses were used by Louis Bachelier, who is considered a pioneer in the study of financial mathematics. Bachelier had anticipated many of the mathematical results developed in Albert Einstein’s 1905 paper.