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.
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 […]
Nobel Prize Lecture of Eric Maskin Followed by talk of blockchains and mechanism design by Jeff Coleman
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.
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 […]
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.
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.