Over 70 Crypto ETFs Still in Limbo as SEC Leadership Transition Drags On

ccn.comDipublikasikan tanggal 2025-04-22Terakhir diperbarui pada 2025-04-22

Key Takeaways

  • Paul Atkins was confirmed as SEC chair.
  • Atkins secured a narrow Senate confirmation amid concerns about his Wall Street ties.
  • Over 70 pending crypto ETF applications, including those for SOL, XRP, and DOGE, await the new chair’s direction.

After a months-long delay, Paul Atkins officially took over as the chair of the Securities and Exchange Commission (SEC).

His confirmation comes as over 70 crypto exchange-traded fund (ETF) applications gather dust, awaiting a decision that could reshape how digital assets are regulated in the U.S.

Atkins, a former commissioner with deep ties to Wall Street, was confirmed after a contentious Senate floor vote earlier this month.

Democrats pushed back on his nomination, citing concerns over his deregulatory stance and close industry connections.

Nonetheless, the vote went through, and he was sworn in on April 21, ending a leadership vacuum that had stretched into President Donald Trump’s second term.

ETF Gridlock Breaks, Maybe

With Atkins now officially in place, the fate of dozens of crypto ETF proposals is back in play.

During the leadership gap, the SEC had effectively paused decisions on a wide range of applications, including those tied to XRP, Solana (SOL), Litecoin (LTC), and even memecoins like Dogecoin (DOGE).

The most closely watched filings involve spot ETFs for XRP and SOL, submitted by over a dozen asset managers.

Acting chair Mark Uyeda had overseen the SEC during the transition, but industry observers noted a reluctance to proceed with major crypto decisions without a confirmed leader.

The crypto industry is now looking to Atkins to either open the floodgates or keep them firmly shut.

A Hard Pivot on Crypto Regulation

Atkins has positioned himself as a crypto-friendly reformer, promising to ease what he describes as excessive red tape imposed during Joe Biden’s administration.

He has echoed Trump’s broader deregulatory vision and called for a more “innovation-forward” approach that prioritizes competitiveness in financial technology.

That outlook has drawn criticism from Democratic lawmakers, especially Sen. Elizabeth Warren, who argued Atkins could weaken investor protections and create space for illicit activity. She led a vocal opposition during his confirmation, warning that crypto deregulation could backfire.

Still, the administration has wasted no time charting a different course.

Since January, the SEC has dropped several high-profile enforcement actions against crypto companies.

The Department of Justice (DOJ) also quietly shut down its crypto-focused task force, signaling a broader pullback from the aggressive enforcement strategies of previous years.

Whether Atkins will fast-track ETF approvals or take a slower, more methodical approach remains to be seen. However, with investor interest growing and institutions waiting on regulatory clarity, the pressure is mounting.

Was this Article helpful? Yes No

Bacaan Terkait

AI Pembuatan Gambar Tanpa Pelatihan Dipercepat 1000%, Caranya: 'Pipa Tiga Tahap' Paling Sederhana

Kemampuan gambar AI semakin kuat, namun pengguna masih merasakannya lambat. Metode akselerasi model difusi tradisional seperti kuantisasi atau distilasi langkah sering kali bergantung pada perangkat keras atau fine-tuning yang mahal. Tim peneliti dari Beihang University, NTU, dan ETH memperkenalkan **MrFlow (Multi-Resolution Flow Matching)**, sebuah pipeline tiga tahap sederhana dan bebas pelatihan untuk mempercepat pembuatan gambar secara signifikan: 1. **Pembuatan Kerangka Beresolusi Rendah:** Model asli menghasilkan gambar struktur global (subjek, tata letak, semantik) di ruang latens beresolusi rendah. Token gambar jauh lebih sedikit, sehingga setiap langkah lebih murah dan konvergensinya lebih cepat. 2. **Super-Resolution di Ruang Pixel:** Hasil beresolusi rendah didekode ke gambar, lalu ditingkatkan resolusinya di ruang pixel menggunakan model super-resolution yang telah dilatih sebelumnya (seperti Real-ESRGAN). Pendekatan ini mempertahankan struktur dengan lebih baik daripada upsampling di ruang latens. 3. **Pemurnian Satu Langkah Beresolusi Tinggi:** Gambar super-resolution dienkode ulang ke ruang latens, ditambahkan sedikit noise intensitas rendah (~0.12), lalu dimurnikan oleh model flow-matching asli hanya dalam **satu langkah** inferensi resolusi tinggi. Noise rendah memungkinkan titik awal dekat dengan gambar bersih. Dengan konfigurasi default "12+1" (12 langkah rendah-res, 1 langkah tinggi-res), MrFlow mencapai **percepatan 10.35x** (dari 49.32s menjadi 4.77s) pada model seperti Qwen-Image, dengan penurunan kualitas minimal (~1%). Metode ini unggul dalam kurva trade-off kecepatan-kualitas dibanding metode akselerasi bebas pelatihan lainnya, dapat digabungkan dengan model distilasi untuk akselerasi lebih lanjut, dan sudah tersedia sebagai kode open-source beserta plugin ComfyUI.

marsbit1j yang lalu

AI Pembuatan Gambar Tanpa Pelatihan Dipercepat 1000%, Caranya: 'Pipa Tiga Tahap' Paling Sederhana

marsbit1j yang lalu

Trading

Spot
活动图片