Bloomberg: Prediction Markets Are Gambling, and Congress Should Regulate Them

marsbitPubblicato 2026-03-05Pubblicato ultima volta 2026-03-05

Introduzione

Bloomberg Opinion argues that prediction markets like Kalshi and Polymarket are effectively unregulated gambling operations that circumvent state-level betting regulations. These platforms, which allow users to bet on sports, politics, and other events, claim to be "event contract" markets regulated under the Commodity Exchange Act (CEA) rather than gambling businesses. This allows them to avoid age restrictions (serving users as young as 18), consumer protections, and licensing requirements that apply to traditional sportsbooks. With nearly 90% of Kalshi’s revenue coming from sports betting, the editorial warns of significant risks including addiction, debt, insider trading, and market manipulation—citing suspicious trading activity ahead of geopolitical events. It calls on Congress to amend the CEA to clearly distinguish legitimate prediction markets from gambling, enforce consistent consumer safeguards, and restrict political event betting. The piece urges regulatory action before further harm occurs.

Author: The Editorial Board, Bloomberg Opinion

Translated by: Deep Tide TechFlow

Deep Tide Introduction: Bloomberg's editorial board has rarely called out Kalshi and Polymarket, directly stating that these two companies are gambling operations circumventing regulation—90% of their revenue comes from sports betting, their user age threshold is three years lower than that of legal casinos, and there may even be insider trading.

As the monthly trading volume of prediction markets surpasses ten billion dollars and Nasdaq begins to enter the arena, the regulatory pressure represented by this article deserves serious attention.

Full text as follows:

There's an old saying about ducks: if it looks like a duck, swims like a duck, and quacks like a duck, then it probably is a duck. U.S. regulators are currently ignoring this logic—they are accepting the argument from certain betting companies that "we are not betting companies, we are prediction markets." Congress should intervene before this charade causes more harm.

Over the past year, platforms like Kalshi and Polymarket have flourished, allowing users to predict the outcomes of sports events, political developments, and various other events. These platforms have a preferred euphemism: they are not betting brokers but "prediction markets"; placing money on the outcome of a football match is not called a bet but an "event contract."

This is not just a semantic debate; it involves real regulatory arbitrage.

Traditional sports betting companies like FanDuel and DraftKings obtain licenses state by state and must comply with age restrictions, geographic limitations, and responsible gambling safeguards. In contrast, prediction market platforms claim their products fall under the federal Commodity Exchange Act (CEA) and should not be subject to these state-level rules. They register with the Commodity Futures Trading Commission (CFTC) and seek regulatory endorsement for their betting operations.

The CFTC has shown a willingness to cooperate—last month, it withdrew a proposal that would have banned sports and political contracts. Several states have filed lawsuits to defend their gambling regulatory authority, and the dispute over who has the right to regulate these companies will likely end up in the Supreme Court. But the core facts are clear.

First, these companies are engaged in sports betting and other gambling activities, with almost nothing in common with traditional commodity market trading.

Second, they hold a significant advantage over competitors who comply with state laws. Approximately 90% of Kalshi's fee income comes from sports events. Meanwhile, the stock prices of traditional sports betting companies have been hit hard.

Third, Kalshi and Polymarket allow users as young as 18 to participate, while the legal age in most states is 21. This exposes younger users to risks such as debt, financial instability, addiction, and crime. Some applications are also applying to offer margin trading (i.e., trading on credit), which could further exacerbate the problem.

Fourth, Kalshi has partnered with Robinhood, meaning the line between brokerages and bookmakers is blurring, which could have catastrophic consequences for a large number of investment accounts.

Beyond these issues, there is also the risk of corruption. Just before Iran's Supreme Leader Khamenei was killed in an Israeli airstrike on February 28, an account on Polymarket heavily bought contracts predicting "he will lose power soon"—suggesting that some traders may have had insider information. The total trading volume surrounding this airstrike exceeded $500 million.

When Congress enacted the Commodity Exchange Act in 1936, it clearly did not anticipate that it would give rise to large-scale national betting companies or create prediction markets for public affairs that are so easily manipulated. Congress should take the initiative to intervene rather than wait for years of litigation to drag on, leaving countless users to lose their money in the meantime.

As a starting point, Congress should amend the CEA to clearly define "event contracts"—distinguishing contracts with reasonable market logic from purely gambling in nature (such as sports bets), while restricting gambling on political events; ensure prediction markets follow clear rules rather than the CFTC's arbitrary discretion; and, referencing the SAFE Betting Act, establish basic consumer protection standards for all betting companies while allowing states to impose stricter regulations.

Ideally, lawmakers should perhaps take this opportunity to reexamine America's experiment with "ubiquitous, anytime gambling" in the smartphone era—an experiment that has led to rising debt, defaults, and other social problems. For now, merely bringing order and common-sense restrictions to these chaotic markets would be a significant step forward.

Domande pertinenti

QWhat is the main argument Bloomberg's editorial board makes about prediction markets like Kalshi and Polymarket?

AThe editorial board argues that prediction markets like Kalshi and Polymarket are essentially gambling operations engaging in regulatory arbitrage by misclassifying themselves as 'prediction markets' under the Commodity Exchange Act (CEA) to avoid state-level gambling regulations that apply to traditional sportsbooks.

QWhat regulatory advantage do Kalshi and Polymarket have over traditional sports betting companies according to the article?

AThey operate under federal CFTC registration as 'prediction markets' instead of obtaining state-by-state gambling licenses, allowing them to bypass state age restrictions (serving users as young as 18 vs. 21), geographic limits, and responsible gambling safeguards that apply to competitors like FanDuel and DraftKings.

QWhat specific risks to younger users does the article associate with these prediction markets?

AThe article highlights that these platforms expose younger users (aged 18+) to risks of debt, financial instability, gambling addiction, and criminal activity, which are normally mitigated by the 21+ age restriction enforced for traditional gambling platforms in most states.

QWhat example of potential insider trading or manipulation is cited in the article involving Polymarket?

AThe article notes that shortly before Iran's Supreme Leader Khamenei was killed in an Israeli airstrike on February 28, accounts on Polymarket heavily bought contracts predicting 'he would lose power,' suggesting traders might have had insider information. Total trading volume around this event exceeded $500 million.

QWhat legislative actions does the article recommend Congress take to address these issues?

AIt recommends Congress amend the Commodity Exchange Act to clearly define 'event contracts,' distinguishing legitimate market-based contracts from pure gambling (e.g., sports bets), restrict political event gambling; establish clear rules instead of CFTC discretion; and enact basic consumer protection standards similar to the SAFE Betting Act while allowing states to impose stricter regulations.

Letture associate

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit1 h fa

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit1 h fa

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit1 h fa

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit1 h fa

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit1 h fa

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit1 h fa

Trading

Spot
活动图片