Tech Tokenization

Tech Tokenization is an effective way to distribute reward, maintain accounts, and fractionalize. The three core functions can be addressed by a general purpose technology (GPT) as accounting and fractionalizing should be service agnostic, where service is anything which adds tangible value. What is value may seem hard to quantify but in a tokenized world, value is dynamic, measurable, enhanceable and competitive. As dynamic as the periodicity of its input data. Measurable as an understandable metric. Enhanceable as its underlying algorithmic process learns. Competitive as it is operating in a level playing field. Any service which adds tangible value, aspires to be distributed globally and get its fair share of generated value.

Why General AI?

The beauty of AI is that it can enhance itself. This means that even if we are in the time of domain-specific AI growth, AI will generalize itself and evolve into a cross-domain interdisciplinary functionality, which means AI will power ultra-smart agents and become what we loosely refer to as Web 4.0.  Building a generalized AI is like building a master algo or in other words finding something that is very simple and hence powerful and all-encompassing. Such a general AI does not have to be invented, it has to be discovered. 

All policies and regulation should be thought around such a future. A future where AI processes can be easily measured, validated, secured, enhanced and distributed. In this future, data privacy is not a challenge as the AI processes are no more concerned with the content of the data and can operate in its context. Such an ecosystem will require data referencing as intelligent extracting bots will continue to cross-reference the dynamic data focussing on data as a group rather the data's source. This world of general AI will not have any privacy concerns but will seamless collaboration and objective reward systems. The convergence of many of these new technologies is the infrastructure where a general AI can thrive and live up to its potential.

This is why we should promote nascent convergence technologies that work in a domain agnostic environment, are universal in behavior, and work on data context (group) rather than data content (source).

Why were prediction markets destined to fail?

AlphaBots is any SSR process that is open to validation on the AlphaBlock. The process can be alpha agents like prediction markets or data scientists, or simply machines open to competing on the AlphaBlock. Competition is important for the accountability of an SSR process. Without the validation accountability any alpha process is simply a subjective claim of alpha without an objective proof.

Demystifying Incentives in the Consensus Computer

The verifier’s dilemma, which remains an open problem is another example of gaps in the current blockchain architecture and the consensus mechanism. There are of course workarounds with Ethereum’s smart contract while bitcoin suffers and continues to fork. However, the problem here is bigger and not just about Turing-complete scripts, increased DoS attacks and fiddling with gas limits. The bigger problem is still the unidirectional computation focus, disconnection of a transaction value from the miner’s fee and the currency which works more as an asset and will continue to oscillate in periods of up and down value. Though probabilistic thinking and approximate verifiability are steps in the right direction they don’t address the bigger problems mentioned above. The odd coincidence is the need of non-transparency.  Writing blocks in a sequence which involves non-transparency is a circuitous way of reaching the same goal which AlphaBlock’s predictive transaction offers. We understand that the change can’t happen overnight, but non-transparency and smart contract application are heartening steps.

Seminal paper on 'Verifier's dilemma'


My thoughts on "The Hedgie in Winter"

Modern finance is built on linear regression. Hence Asness’s textbook use of linear regression is brilliant because simplicity is powerful. He uses Aristotelian logic to elegantly bring out the information content buried in hedge fund performance.


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.

Why Indexing Fails

The recent paper “Why Indexing works” [1] 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 [2]. 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’s Jumping Cat Problem

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.

Human AI

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.

The Black Swan

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 Beta Maths

The two Nobel Prizes awarded in Economics in 1990 [1] and 2013 [2] 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) [3] size, while the 2013 winner explained that factors like ‘Small Size’ [4] can explain portfolio performance better than ‘Big Size’ [5]. This conflict between the two ideas has bifurcated the industry into benchmark investing (MCAP) [6] and everything else not MCAP (Smart Beta) [7]. The fact that benchmark investing and smart beta is expected to be 50% of the USD 100 trillion investment management industry in 2020 [8] makes it imperative to seek a coherent argument and a conflict resolution.

How The Noise Killed A Signal!

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.

Jack, Your Revolution is Over.

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.

The Size Proxy

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.

How Blockchain Could Disrupt Wall Street!

Before we talk about Blockchain disruption, let us talk about the implosion happening on Wall Street. The Realization that stock picking is dead [1] after decades of Active underperformance [2] has made beating the market an adventure sport, where only a few succeed. There was only one Peter Lynch [3], the active management is set to become 65% of the overall market [4], the high fees are gone [5] 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 [6]. 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’ [7] and Virtu’s of the world, which never lose [8]. Basically, the party is over.

Firefighter from Idaho

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.

Fruit Basket Paradox

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.