CFTC vs. Binance

Do Kwon arrested in Montenegro, Binance CEO sued by CFTC over ‘Willful Evasion’ of U.S. laws, and yet within the madness, firms are still raising $ and building the future tech stack of global finance

Author

Ryan Allis
March 29, 2023

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💵 Weekly Crypto Fundraises & Deals

Here are all the crypto fundraises we heard about this week, ranked by size…

🗞️ Crypto News Recap: The Top 5 Stories

Welcome back to This Week in Crypto… everything you need to know in one scannable format. Here are the top 5 stories of the week…

1. 🔗‍ Do Kwon Arrested in Montenegro, Says Country’s Minister of Interior- After being on the run from INTERPOL, Terraform Labs co-founder and CEO Do Kwon was arrested Thursday at an airport in Montenegro’s capital city of Podgorica, according to the country’s Minister of Interior, Filip Adzic. He was caught attempting to board a private plane to Dubai using a fake passport.

2.⚖️ Binance, CEO Zhao Sued by CFTC Over ‘Willful Evasion’ of U.S. Laws, Unregistered Crypto Derivatives Products- The U.S. Commodity Futures Trading Commission (CFTC) sued crypto exchange Binance and founder Changpeng Zhao Monday on allegations the company knowingly offered unregistered crypto derivatives products in the U.S. against federal law.

3. 😑 ‘Unexpected and Disappointing’: Binance Reacts to CFTC Lawsuit- In response to a federal regulatory lawsuit targeting Binance that has rocked the crypto industry, the firm said the best path forward is to continue protecting its users while still working with regulators to develop a clear regulatory framework.

4.🚀 USDT Stablecoin Payments Launch on Telegram- Telegram users can now send each other Tether’s stablecoin. In a Wednesday notification, the messaging app informed its customers that the wallet function now supports USDT-TRON, better known as TRC20.The wallet bot is a function available on Telegram but was developed by independent developers—not Telegram.

5.🚀 Polygon Labs Unveils zkEVM Layer 2 To Help Scale Ethereum- Scaling project Polygon has released a highly anticipated zero-knowledge-based rollup network, with Ethereum founder Vitalik Buterin set to perform the first symbolic transaction in a YouTube broadcast scheduled for 10:30 a.m. EDT.

💬 Tweet of the Week

📊 Key Stats of the Week

Here are the most important and interesting stats in crypto this week...

1. 📈 Synthetix Perps Surpass $490M in Daily Trading Volume

Source: @OurNetwork

2. Bitcoin Ordinals Has Crossed Over 600,000 Inscriptions 📈

Source: @OurNetwork

3. Perennial Grows to $10M+ in Liquidity

Source: @OurNetwork

4. Binance’s Market Share Has Fallen More Than 10% Since They Removed Zero-Fee Trading for BTC

Source: @KaikoData

5. GMX Crosses $113B Total Volume

Source: @OurNetwork

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Here are the top highlights from the best crypto research reports this week…

Intro

The last two weeks have been an absolute whirlwind in the world of generative artificial intelligence (AI), with groundbreaking new releases and cutting-edge integrations taking place. OpenAI released its highly anticipated GPT-4 model, Midjourney unveiled its latest V5 model, and Stanford released their Alpaca 7B language model. Meanwhile, Google launched generative AI across its entire Workspace suite, while Anthropic introduced its AI assistant Claude, and Microsoft integrated its powerful generative AI tool Copilot into the Microsoft 365 suite.

The pace of AI development and adoption is showing no signs of slowing down as businesses have begun to realize the value of AI and automation and the need to adopt these technologies to remain competitive in the market.

Despite the seemingly smooth progress of AI development, there are underlying challenges and bottlenecks that must be addressed. As more businesses and consumers embrace AI, a bottleneck is emerging around computing power. The computations required for AI systems is doubling every few months, while the supply of computing resources struggles to keep pace. Furthermore, the cost of training large-scale AI models continues to soar, with an increase of approximately 3100% per year over the past decade.

This trend towards rising costs and increasing resource demands needed to develop and train cutting-edge AI systems is resulting in centralization, where only entities with massive budgets are able to conduct research and produce models. However, several crypto-based projects are building decentralized solutions to address these issues using open compute and machine intelligence networks.

Primer on AI and ML

The advancement of AI is driven by three main factors:

  • Algorithmic innovation: Researchers are constantly developing new algorithms and techniques that allow AI models to process and analyze data more efficiently and accurately.

  • Data: AI models rely on large datasets as the fuel for their training, enabling them to learn from patterns and relationships within the data.

  • Compute: The complex calculations necessary for training AI models require large amounts of computational processing power.

However, there are two main problems that are hindering the development of AI. Back in 2021, obtaining data was the top challenge facing AI companies in their pursuit of AI development. Last year, compute-related issues surpassed data as a challenge, specifically due to the lack of on-demand access to compute resources, driven by high levels of demand.

The second problem is related to inefficiencies in algorithmic innovation. While researchers continue to make incremental improvements to models by building on previous ones, the intelligence, or patterns, extracted by these models is always lost.

Let's dig deeper into these problems.

Compute Bottleneck

Training foundational machine learning models requires extensive resources, often involving the use of large numbers of GPUs over extended periods of time. For instance, Stablility.AI needed 4,000 Nvidia A100 GPUs running in AWS’ cloud to train their AI models, which cost them more than $50 million in a month. OpenAI’s GPT-3, on the other hand, was trained using 1,000 Nvidia V100 GPUs, costing $12 million.

AI companies are often faced with two choices: investing in their own hardware and sacrificing scalability, or opting for cloud providers and paying inflated prices. While larger companies can afford to choose the latter, smaller companies may not have that luxury. With the rising cost of capital, startups are being forced to cut back on cloud spending, even as the cost of expanding infrastructure for large cloud providers remains largely unchanged.

The high cost of computing for AI creates significant obstacles for researchers and organizations pursuing advancements in the field. Currently, there is a pressing need for an affordable, on-demand serverless compute platform for ML work, which is absent in the traditional computing landscape. Fortunately, several crypto projects are working to develop decentralized machine learning compute networks that can address this need.

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Tracking the most important blockchain stories of the 2020s, including a decentralized internet and the creation of a new open global monetary system that works for everyone. As always, published for informational purposes only. Please do your own research. Just our opinions. Not intended as financial advice as we are not financial advisors. We may own some of the digital assets we write about as we believe strongly in the sector. Please do your own research. Published and written weekly by Ryan Allis and Mike Gavela.

Coinstack is a news and analysis newsletter for the digital asset industry. None of the information here is a recommendation to invest in any securities or other types of investments. Past performance is no guarantee of future results. Any historical returns, expected returns, or probability projections may not reflect actual future performance. All investments involve risk and may result in loss.

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