Convergence is a clash – a tug-of-war between different forces. While geopolitical war takes center stage, AI, crypto and other emerging technologies are rapidly developing new frontiers to challenge traditional ways of living. Per The Myth of Social Cost by Steven N.S. Cheung, “Any analysis which predicts the outcomes of a given policy will throw light on its desirability . . . Harm is reciprocal.”
Which arrangement would yield the highest net value, avoid greater harm, and minimize frictions/transaction costs for society? What are the early indicators? How to directionally predict and evaluate different available options to consider not only the BestEx (best execution) but the most advantageous course of action if no agreement is reached (best alternative to a negotiated agreement, or BATNA)?
The pragmatic U.S. approach is geared toward survival of the fittest. Self-interest prevails over altruism when people are asked to contribute resources and take risks for the greater good.
Many worry about AI taking away jobs. It is true that private investments that previously poured into SaaS is being disrupted by AI’s code-generation ability. But the piping to tokenize securities, perfecting related valuations, and democratizing access to alternative investments is underway. The SEC and CFTC have proposed reducing private fund reporting requirements. In turn, more resources could be allocated to entrepreneurs to build toward this shared future.
Reskilling is uncomfortable, yet countless professional athletes, capital market traders, and those who were overstressed and forced into early retirement have experienced it. Now, this challenge is spreading to wider populations across sectors and government. The only way to revive one’s career is to learn, unlearn, and relearn – embracing the surprising usefulness of useless knowledge. While many are shortsightedly looking to profit from the commercialization of AI applications, the greater opportunity lies in plugging into the ecosystem that contributes to the advancement of AI and quantum computing.
The SEC proposal to ban volume-based pricing tiers has been withdrawn. The voice of critics was ultimately heard. Not only did the SEC approve another, modified version of the Consolidated Audit Trail (CAT) NMS Plan in March to further slash wastage by $50-$70 million, but the concept release also incorporated our ideas – such as analyzing data directly at the sources – to overhaul the outdated design, address civil liberties and privacy concerns, and petition for changes to the funding model.
Kelvin To
A tug-of-war is where U.S. banks ask for relief in the Basel III endgame. In the U.K., the Financial Conduct Authority is weighing a move away from existing EU-derived rules for investment firms’ market risk capital requirements, aligning instead to the U.S. net risk approach. Stablecoin issuers have no deals with policymakers unless they permit yield-bearing tokens.
Meanwhile, the higher-for-longer interest rate environment raises stagflation risks, and fractured bond-equity correlations are prompting investors to rethink classic hedges. Many are now actively seeking to insulate their portfolios from intensifying geopolitical shocks. The line between trading and gambling is blurring.
The ability to predict the future drives productivity and economic growth. Zero-days-to-expiry (0DTE) options drive 59% of S&P 500 Index options volume. When choosing between a 0DTE Protective Put and an Iron Condor, the decision depends on whether one is hedging against a directional crash or seeking to offset the cost of waiting in a stagnant market. The complexity may not be suitable for retail. Prediction markets are cheaper and easier to navigate because of wide coverage of real-world outcomes and efficiency in aggregating information.
Prediction markets are not new. Intellectual groundowrk was laid decades ago by economists who viewed markets as information processors, and research has validated their forecasting effectiveness. The markets can gauge and/or project how innovative products, services, or business models would be perceived in a live environment.
Unlike securities markets defined by continuous price discovery, prediction trading is in short-lived exposures to a “cause,” while 0DTE options provide exposure to the “effect.” The beauty of simultaneous hedging strategies is the decoupling of Event and Price Risk.
Standard options disclosures are insufficient to explain the all-or-nothing nature, illiquidity, expiration timing, susceptibility to influence by a small group, etc. Event contracts are not designed to track the underlying asset’s price movements. Rather, contract pricing reflects market-implied probabilities of specific outcomes, which may result in a basis risk or a lack of direct correlation with a participant’s underlying financial exposure.
Opposers cry foul on speculative trading in prediction markets. According to Karl Whelan and co-authors, favorite-longshot bias is the core problem that harms retail. Market friction starves out “healthy” speculators. Liquidity is a Catch-22; makers widen spreads to protect against being “picked off.” High fee-to-contract-value ratio prevents arbitragers from engaging in price improvement, and an “overpriced lottery ticket effect” is not corrected. It is a volume-versus-Integrity paradox.
We advocate a licensing mechanism to rebalance the “Information tax.” This structure turns the zero-sum game into a sovereign information hedging utility (SIHU) aligning “selfish” interests toward a collective public good. We hope more focus can be channeled toward the constructive fix of market designs (e.g. natural versus toxic liquidity, adverse selection, anti-masquerading, resolution mechanism, surveillance challenges, governance, stress and claw-back, etc.) to make prediction markets a positive sum for risk hedging, market efficiency, growth and financial stability.
Integrating intelligence about real-world probabilities enhances performance to manage uncertainty. It allows AI to move beyond deterministic yes/no outputs to a more nuanced “shades of uncertainty.” The model requires less data to reach accuracy. Also, it is the backbone of quantum computing. AI models are now used to predict and correct qubit noise.
Consider what insurance actuaries do, dealing with probabilities of event outcomes. Insurance contracts and prediction markets/0DTE options compete with and complement each other. 98% to 99.9% incremental improvement is better than 85% to 90% because it is 95% error reduction vs just 33%.
It is a race to reduce unknown unknowns. The availability of superior insurance products will be driven by a shift from reactive to preventive models, where risks are mitigated before they occur. While insurance is expected to remain a necessity, quantum and pre-cognitive AI will fundamentally transform the industry – actuarial roles are evolving from number crunchers to strategic risk managers.
Kelvin To (kelvin.to@databoiler.com) is a big data and financial technology platform innovator and founder and president of Data Boiler Technologies.