Crypto ETP Boom Set To Go Into Overdrive In 2026, Bitwise Says

bitcoinistPubblicato 2025-12-17Pubblicato ultima volta 2025-12-17

Introduzione

More than 100 new crypto exchange-traded products (ETPs) are expected to launch in 2026 due to a recent US regulatory change, according to Bitwise researcher Ryan Rasmussen. The SEC's new generic listing standards eliminate the need for lengthy individual approvals, streamlining the process. This shift allows issuers to file a wider variety of products, including spot crypto, index funds, and equity-linked ETPs. While this will significantly expand investor choice, analysts caution that success will depend on real demand, liquidity, and clear use cases. The market will likely see a wave of new listings, but assets are expected to concentrate in a few large funds, mirroring the broader ETF market.

More than 100 new crypto exchange-traded products could hit the market in 2026 after a recent rule change by the US securities regulator, a researcher at Bitwise said. According to Ryan Rasmussen, that drop in red tape will let firms file many more ETPs without the long, individual approval process that slowed launches in the past.

Regulatory Shift Lowers Bar

The SEC issued generic listing standards in October that remove the need for separate 19(b) approvals for qualifying crypto ETPs. That step cuts out a process some issuers had to wait through — a delay that could stretch to a 240-day clock under earlier practice.

Reports have disclosed that the number of crypto ETPs already sits above 300, based on data from Fineqia International, which shows the market is no longer limited to just a few funds.

Institutional Appetite

Market watchers say new listings make it easier for issuers. But easier access is not the same thing as strong buying. Bitfinex analysts warned in August that altcoins are unlikely to enjoy a major rally until ETFs that track assets beyond the largest coins are available and attract real money. Liquidity, investor interest, and clear use cases still matter a lot. An ETF wrapper does not fix those basic needs by itself.

Total crypto market cap currently at $2.91 trillion. Chart: TradingView

Issuers Race To Expand Menus

Rasmussen said issuers can now plan a variety of products — spot crypto, index funds, equity-linked ETPs, smart beta strategies and momentum plays.

He compared the change to moving from a tiny menu to a much larger one, saying investors will have more choices about where to put money. He also noted it has been about 15 years since the Winklevoss twins first filed for a Bitcoin ETF, and yet only a handful of crypto ETPs are widely held today.

Many New Products, Few Big Winners

Expect a wave of filings. But expect concentration too. In the wider ETF market, most assets gather in a few large funds while many other listings see thin trading.

That pattern is likely to repeat in crypto: dozens of niche products may be launched, while a smaller group gathers most assets under management. Issuers get to plant flags quickly. Investors will sort the winners from the rest over time.

Market Reaction Hinges On Demand

On Sept. 17, Bloomberg ETF analyst James Seyffart said the rule change could trigger a “wave of spot crypto ETP launches.”

Crypto di tendenza

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.

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

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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.

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

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

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