Is Washington About To Kill Crypto Prediction Markets For Good? — Why Congress Suddenly Cares

bitcoinistPublicado a 2026-03-26Actualizado a 2026-03-26

Resumen

On March 25, two separate acts were introduced to ban U.S. congressional staff, members of Congress, and federal officials from trading on prediction markets. Representative Seth Moulton of Massachusetts issued an immediate office-wide ban, calling these markets “playgrounds for corrupt insiders” that create perverse incentives. Separately, Representatives Adrian Smith and Nikki Budzinski introduced the bipartisan PREDICT Act, which would prohibit elected officials, senior appointees, and their families from trading on political and policy outcome markets. Violators would face a 10% fine on the value of the trade and forfeiture of profits. The moves reflect growing concern in Washington over insider trading risks in prediction markets, especially following instances of traders making large profits on geopolitical events. These actions may lead to stricter regulations and increased scrutiny on crypto-based prediction platforms.

Two different acts banning congressional staff, members of congress and federal officials from trading on prediction markets were introduced on Wednesday, March 25, one of them being effective immediately.

Massachusetts Bans Crypto Prediction Market

Washington’s battle against prediction markets rages on. Following a bipartisan Senate bill introduced on Monday that targets sports‐style bets on platforms like Polymarket and Kalshi, democratic representative Seth Moulton of Massachusetts (MA-06) formally banned all of his staff from “participating in prediction markets”, such as the aforementioned, “to trade or hold positions on political, legislative, regulatory, geopolitical outcomes, or any information that is learned in an official capacity”. The press release frames it as the first such explicit office-wide ban in Congress.

Moulton’s rationale is clear: staff are meant to serve constituents, not profit from policy choices and global events. As he views it, prediction markets have become ethically questionable “playgrounds for corrupt insiders”:

Prediction markets have become a playground for corrupt insiders who are able to place bets on things like election outcomes, wars, and even the deaths of public figures. This is creating a perverse incentive structure that poses a genuine threat to American society today.

Congressional staff and the Members they work for exist to serve the constituents of the districts they represent, not to profit off of the very policy decisions and world events that we are here to respond to.

Nebraska Bans Crypto Prediction Market Too

On Nebraska’s side, Congressman Adrian Smith (R-NE-03) and Congresswoman Nikki Budzinski (D-IL-13) introduced the Preventing Real-time Exploitation and Deceptive Insider Congressional Trading Act (PREDICT Act), another bipartisan effort that aims to ban members of Congress, their spouses and children, the president and vice president, and senior appointees from trading on political and policy outcome markets.

Their core argument and statement are very similar to Moulton’s. Recent episodes of little‐known traders making massive profits on contracts tied to war with Iran or the length of government shutdowns have sharpened fears about insider information leaking into these markets. Smith said:

Serving the American people is a privilege, not a pathway to profit. Our commonsense, bipartisan bill will give Americans confidence that the decisions of their elected officials are guided by merit, not personal profit.

Budzinski added:

The American people are tired of politicians using their influence for personal gain, and the rise of prediction markets has made those concerns even more relevant. In recent months, we’ve seen instances of little-known traders making massive profits on events ranging from war with Iran to how long a government shutdown will last, raising necessary questions about the use of inside information.

Breaking the PREDICT Act would trigger a civil fine equal to 10% of the value of the banned trade, plus a requirement to hand over all profits from it to the U.S. Treasury, the announcement states.

A Growing Concern For Washington?

These new episodes come on top of earlier efforts like Rep. Ritchie Torres’s Financial Prediction Markets Public Integrity Act, following the capture of Venezuela’s former dictator Nicolás Maduro, which also targeted insider trading on platforms such as Polymarket.

For on‐chain and offshore prediction markets, a hard ban on US officials could actually de‐risk the space by reducing headline “insider” scandals, but it also raises the odds of stricter KYC and monitoring requirements in the US.

As it becomes increasingly clear that Washington has its attention set on ethically questionable crypto ventures, it is not too far-fetched to think that similar logic could be extended to other high‐beta crypto venues where policy and profit visibly collide (e.g., tokens tightly linked to election or war outcomes). Traders would do well pricing in regulatory overhang alongside usual market risk.

BTC’s price drops slightly after reaching $71k yesterday, trading for around $69k today. Source: BTCUSD on Tradingview

Cover image from Perplexity, BTCUSD chart from Tradingview

Preguntas relacionadas

QWhat is the main reason cited by Representative Seth Moulton for banning his staff from participating in prediction markets?

AMoulton believes staff should serve constituents, not profit from policy choices and global events, and views prediction markets as ethically questionable 'playgrounds for corrupt insiders'.

QWhat is the name of the bipartisan act introduced by Congressman Adrian Smith and Congresswoman Nikki Budzinski to ban trading on political prediction markets?

AThe Preventing Real-time Exploitation and Deceptive Insider Congressional Trading Act (PREDICT Act).

QWhat are the penalties for violating the PREDICT Act according to the announcement?

AA civil fine equal to 10% of the value of the banned trade, plus a requirement to hand over all profits from it to the U.S. Treasury.

QWhich earlier legislative effort targeted insider trading on platforms like Polymarket, as mentioned in the article?

ARep. Ritchie Torres's Financial Prediction Markets Public Integrity Act.

QHow might a hard ban on US officials using prediction markets affect the crypto prediction market space?

AIt could de-risk the space by reducing headline 'insider' scandals but also increase the likelihood of stricter KYC and monitoring requirements in the US.

Lecturas Relacionadas

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.

marsbitHace 44 min(s)

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

marsbitHace 44 min(s)

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.

marsbitHace 49 min(s)

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

marsbitHace 49 min(s)

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.

marsbitHace 49 min(s)

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

marsbitHace 49 min(s)

Trading

Spot
活动图片