a16z's key bet: Kalshi's weekly trading volume approaches $3 billion, transitioning from "prediction games" to financial infrastructure, the market begins to price "uncertainty."

In the traditional financial system, "price" typically only belongs to assets.

Stocks, interest rates, commodities—these can be traded because there exists a unified measurement method and a consensus pricing mechanism. In contrast, those variables that truly affect market fluctuations—policy directions, macro data, political events—have long remained in a more primitive state: discussed, predicted, but rarely directly priced.

These variables have always existed but lack standardized expression. The emergence of Kalshi fundamentally changes this. It does not create new information but provides a tradable pricing system for "the event itself."

In a recent research conference, a noteworthy piece of data was that the weekly trading volume for sports-related trades has approached $3 billion, but its proportion of the overall trading volume is declining. In other words, the most visible part is growing, but the underlying structure is changing.

At the same time, institutions, including a16z, have begun to pay continuous attention to this sector. This is not because the prediction market "has become hotter," but because it has begun to exhibit characteristics of infrastructure. The prediction market is transitioning from a fringe product to a "pricing for uncertainty" infrastructure.

01 Wall Street's Focus: From "Discussable" to "Priced"

The operation of financial markets relies on one premise: there must be a tradable benchmark price.

• S&P 500 is the core anchor of the stock market

• Interest rate curve defines the cost of capital

• Commodity futures provide forward expectations for supply and demand

However, in many key decisions, the variables that truly affect outcomes are not among these assets, especially "event-type variables," which have long lacked standardized pricing methods. For example:

1. Whether a certain policy is implemented

2. Whether inflation data exceeds expectations

3. Whether regulatory changes occur

These factors can affect the market but cannot be directly traded. The past solution was to express them indirectly through "related assets" (e.g., hedging election risks with stock indices). The problem is that this method implies two layers of risk assumptions:

The second layer is often more uncontrollable. The core significance of the prediction market is to eliminate this structural bias: to turn "the event itself" into a tradable object. When "the probability of a certain policy passing" is priced at 40% by the market, this number is no longer just an opinion but a variable that can be traded, hedged, and modeled.

02 The Misunderstood Starting Point: Why "Sports" is Not the Focus, but Just an Entry Point

The earliest scaling of prediction markets came from sports and elections, which is a natural result:

• Clear event boundaries

• Discrete outcomes

• Low user participation threshold

These scenarios are naturally suitable for early market initiation but also bring a misleading notion: people treat "the most visible demand" as "all demand." However, from the data disclosed by Kalshi, the structure is reversing:

This indicates a key issue: high-traffic scenarios do not equate to high-value scenarios.

Sports are more like a "cold start mechanism," providing users and liquidity; but those that truly possess financial attributes are the variables that institutions can use for hedging and pricing. Participants from Goldman Sachs and Tradeweb mentioned in the conference that macro events (such as CPI, interest rate paths) are becoming the most noteworthy categories in prediction markets.

These variables share a common characteristic: they are not assets themselves but determine asset prices.

03 The Real Path of Institutional Adoption: From "Reference Indicator" to "Trading Tool"

Despite the rising discussion, prediction markets are still in the early stages of institutionalization. According to Kalshi's classification, the institutional adoption path can be divided into three stages:

Currently, most institutions remain in the first two stages. A key constraint comes from the trading structure itself: current prediction markets require 100% margin to establish a position.

For institutions that rely on leverage and capital efficiency, this means a higher opportunity cost. This is also why Kalshi is working with the CFTC to promote the introduction of a margin mechanism. Once this constraint is lifted, the growth of the trading layer may undergo structural changes.

04 From Asset Pricing to "Probability Pricing": An Extension of the Financial System

If we view prediction markets in the context of a longer financial history, they are not an isolated innovation but rather an expansion of the pricing system.

• Traditional markets price: assets, cash flows, risk premiums.

• Prediction markets price: events, probabilities, expected paths.

The difference between the two is: the former is outcome-oriented, while the latter is process-oriented. An important change brought about by this is that information begins to be expressed in the form of "prices," rather than remaining at the level of analysis and narrative. For example, when the market gives a "60% probability of a certain policy passing," this number can be embedded in quantitative models, used for risk hedging, or serve as input for decision-making. This is closer to the way the financial system utilizes information than traditional expert judgments or polling data.

05 The Intersection with Agent / AI: From "Prediction Tool" to "Decision Input Layer"

Another layer of significance for prediction markets lies in their potential integration with AI systems. Currently, most agents face a common problem: they can generate conclusions but struggle to quantify uncertainty.

Prediction markets offer a different path:

1. Constrain predictions with real capital

2. Aggregate information using market mechanisms

3. Express probabilities with prices

As agents begin to participate in financial decision-making, risk management, or strategy generation, these "probability prices" will become key inputs.

06 The Endgame is Not Complicated: Becoming a "Default Existence" Infrastructure

In the conference, a viewpoint was repeatedly mentioned: it is truly successful when it becomes boring.

This is not a devaluation but a typical path of financial infrastructure:

• The options market was similarly controversial in the 1970s.

• ETFs were seen as fringe tools in their early days.

But once they become standard configurations, they are no longer discussed. Prediction markets may be entering a similar phase: transitioning from academic experiments to tools for elections and sports, then to macro and institutional applications, ultimately becoming a "default existence" pricing layer. At that point, it will no longer be referred to as "prediction markets," but simply as part of the financial system.

07 When "Uncertainty" is Incorporated into the Pricing System

Returning to the initial question, the core of this change lies not in trading volume or user scale, but in a more fundamental transformation: uncertainty begins to be expressed in standardized terms.

When events can be priced, and probabilities can be traded, the future is no longer just a subject of discussion but becomes a variable that can be computed and configured. In this process, the prediction market is not just a new product but a new layer of financial language. Once this language is widely accepted, what it changes is not just the way of trading but the entire structure of the decision-making system.