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.