CFTC Innovation Council adds prediction market, exchange CEOs

cointelegraphPubblicato 2025-12-11Pubblicato ultima volta 2025-12-11

Introduzione

The US Commodity Futures Trading Commission (CFTC) has formed a new CEO Innovation Council, bringing together executives from both the blockchain industry and traditional finance. The council will focus on market structure developments in derivatives markets, with discussions covering tokenization, crypto assets, 24/7 trading, perpetual contracts, prediction markets, and blockchain infrastructure. Participants include CEOs from crypto exchanges and prediction markets such as Polymarket, Kraken, and Gemini, as well as leaders from traditional exchanges like CME Group and Nasdaq. This initiative follows recent CFTC efforts, including a pilot program allowing the use of Bitcoin, Ether, and USDC as margin collateral. Acting Chairman Caroline Pham emphasized the agency’s commitment to engaging with the crypto market and establishing clear regulatory guidelines.

The US Commodity Futures Trading Commission has added a host of blockchain industry executives alongside counterparts from traditional finance as part of the agency’s new CEO Innovation Council.

According to an announcement on Wednesday, the CEO Innovation Council will discuss market structure developments relating to the derivatives markets overseen by the CFTC.

The discussion topics will primarily be about “tokenization, crypto assets, 24/7 trading, perpetual contracts, prediction markets and blockchain market infrastructure.”

The list of participants includes the bosses of crypto exchanges and prediction markets such as Polymarket, Kalshi, Kraken, Gemini, Bitnomial, Crypto.com and Bullish.

Alongside the crypto CEOs, the counterparts from traditional finance come from major exchanges such as CME Group, Cboe Global Markets, Nasdaq, Intercontinental Exchange and the London Stock Exchange Group.

Related: Crypto among sectors ‘debanked’ by 9 major banks: US regulator

“We are building on the success of the CFTC Crypto CEO Forum and the SEC-CFTC Joint Roundtable with our CFTC CEO Innovation Council, specifically focused on market structure developments in derivatives markets,” said acting CFTC Chairman Caroline Pham.

Source: CFTC

CFTC working to improve derivative market infrastructure and regulation

The latest move by the CFTC comes just a few days after acting chairman Pham announced a crypto collateral derivatives market pilot program.

The pilot will enable CFTC-registered futures commission merchants to accept Bitcoin (BTC), Ether (ETH) and Circle’s stablecoin USDC for margin collateral, as the CFTC tests out the integration of crypto in regulated markets.

Pham’s leadership has so far indicated that the CFTC is open to actively engaging with the crypto market and establishing clear guidelines for the sector, something that has previously been seen as more challenging for the US Securities and Exchange Commission.

Earlier this year, the CFTC also unveiled the Crypto CEO Forum, consisting of CEOs from major firms such as Coinbase, Circle and Ripple, with Pham vowing to “deliver on the Trump Administration’s promise of ensuring that America leads the way on economic opportunity.”

Magazine: The one thing these 6 global crypto hubs all have in common...

Crypto di tendenza

Letture associate

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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.

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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.

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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.

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