Category: Business

The U.S. Commodity Futures Trading Commission launched a two-pronged regulatory push into prediction markets Thursday, moves Chairman Michael Selig framed as the agency finally stepping up after years of inaction.

The CFTC’s Division of Market Oversight’s Letter No. 26-08, published Thursday, directs registered exchanges on compliance and product listing requirements for event contracts, derivatives whose payouts hinge on real-world outcomes, from sports results to political elections.

The commission also published an Advanced Notice of Proposed Rulemaking, or ANPRM, inviting public comment on whether it needs to write new rules or amend existing ones for prediction market oversight, with comments due within 45 days of Federal Register publication.

“Prediction markets are here to stay, and under my leadership, I’ll protect the agency’s jurisdiction over these markets and allow them to flourish in the U.S.,” Selig posted on X.

The twin actions come as the CFTC scrambles to assert control over a sector it claims falls squarely within its mandate, but which states increasingly view as unlicensed sports gambling operating behind a financial-instrument fig leaf. 

Peter Hammon, an attorney and advisor in the online gaming and sports betting industry, told Decrypt that the overall picture is less dramatic than it appears.

“Selig/CFTC mostly restated current regulations without offering any opinions or new ideas and then asked for input from stakeholders,” he said.

Hammon said two takeaways stood out: that Selig appears to see responsible gambling as “a serious PR problem,” and that the remarks acknowledge prediction markets are “not a novel idea,” noting similar platforms have operated under regulation in the U.S. and overseas for decades.

“There is mostly no dispute over CFTC’s regulatory authority over prediction markets that don’t involve sporting events,” he said. “The dispute is whether or not CFTC should be allowed to classify sports prediction markets as a financial asset class, instead of as sports betting.” 

He noted that every other Western country with regulated gambling and financial markets opts to classify the activity as gambling. 

“Maybe there is something unique to the American system or American financialization psyche,” he said, “but I’ve yet to hear that argument articulated by stakeholders.”

Nominated by President Donald Trump to the Chair post, Selig has spent the past month publicly warning states that the CFTC will defend its turf in court. The agency has already filed an amicus brief in the Ninth U.S. Circuit Court of Appeals in support of Crypto.com. 

In announcing the rulemaking last week at the FIA Global Cleared Markets Conference in Florida, Selig said the agency was “no longer going to sit idly while these markets develop within our framework” and that prediction markets are “now viewed by the public as more accurate than political polls.”

The advisory reminds exchanges that insider trading and manipulation rules apply to event contracts, warning that it is unlawful to “defraud” or manipulate prices, including through the misuse of confidential information.

It also flags risks in sports contracts tied to injuries or single-player actions, urging exchanges to coordinate with leagues and warning the CFTC can halt listings if contracts fail compliance standards.

“The only genuine threat to sports prediction markets is a negative Supreme Court ruling,” Hammon noted. 

State-level licensing has already been tried and failed, he added, “largely due to high gaming excise taxes, lack of liquidity, and cumbersome rules regarding liquidity pooling across state lines,” meaning a Supreme Court loss would likely kill the business model outright.

Bitcoin has been locked in a relatively tight trading range for weeks. But that hasn’t stopped a few altcoins from staging double-digit rallies fueled by specific catalysts and a broad return of risk appetite.

The leading cryptocurrency has traded between roughly $73,000 and $62,000 for the past five weeks. Over the last 24 hours, Bitcoin has shown renewed resilience, climbing nearly 3% to trade at $72,300, according to crypto price aggregator CoinGecko. This stabilization comes as exchange-traded fund inflows have continued to stabilize over the past two weeks, Decrypt previously reported.

The Official Trump token has surged 48% over 24 hours, coinciding with an announcement for a “Crypto and Business Conference” with President Donald Trump at Mar-a-Lago.

Other altcoins, such as Pi Network and Render, are up nearly 15% over 24 hours. The gains in Pi Network follow U.S. exchange Kraken’s confirmation of a token listing. Pi Network is a mobile-first cryptocurrency ecosystem founded by Stanford PhDs that has transitioned from a social experiment into a live blockchain. Its popularity stems from a unique “mobile mining” mechanism where over 60 million users participate by checking in daily.

Render, a token in the artificial intelligence category, has soared 14% amid ongoing AI developments, extending a rally that began on March 10 and pushing its monthly gains to 45.5%.

“Altcoins like Trump memecoins, Render, and Pi Network are ripping higher on their own stories: political hype and policy teases fuel $TRUMP, AI/GPU momentum and burns lift Render, while Pi rides pre-Pi Day upgrades, Kraken listing buzz, and retail FOMO into +20-30% moves,” Andri Fauzan Adziima, research lead at Singapore-based crypto exchange Bitrue, told Decrypt.

This selective altcoin activity, alongside Bitcoin’s stabilization, signals capital rotating into specific narratives as broader market sentiment improves. It suggests a targeted play rather than a universal altseason, with fresh catalysts driving individual token performance.

“Bitcoin meanwhile keeps carving higher highs and lows around $70,000-$72,000, backed by steady-to-strong ETF inflows (hundreds of millions daily, BlackRock dominating) and shrinking exchange supply, giving this recovery real legs for $80,000+ if the bid holds,” Adziima added.

The bigger picture points to a “classic risk-on relief rally,” according to Adziima.

Meanwhile, easing geopolitical tensions in the Middle East—with President Trump reportedly signaling a quick Iran wind-down and oil prices sliding—could encourage capital to flow back into crypto markets.

Nvidia just shipped Nemotron 3 Super, a 120-billion-parameter open-weight model built to do one thing well: run autonomous AI agents without bleeding your compute budget dry.

That’s not a small problem. Multi-agent systems generate a lot more tokens than a normal chat—every tool call, reasoning step, and slice of context gets re-sent from scratch. As a result, costs explode, models tend to drift, and the agents slowly forget what they were supposed to be doing in the first place… or at least decrease in accuracy.

Nemotron 3 Super is Nvidia’s answer to all of that. The model runs 12 billion active parameters out of 120 billion total, using a mixture-of-experts (MoE) design that keeps inference cheap while retaining the reasoning depth complex workflows need. It packs a 1-million-token context window, so agents can hold an entire codebase, or nearly 750,000 words in memory before collapsing.

To build its model, Nvidia combined three components that rarely appear together in the same architecture: Mamba-2 state-space layers—a faster, memory-efficient alternative to attention for handling long token streams—along with Transformer attention layers for precise recall, and a new “Latent MoE” design that compresses token embeddings before routing them to experts. That allows the model to activate four times as many specialists at the same compute cost.

The model was also pretrained natively in NVFP4, Nvidia’s 4-bit floating-point format. In practice, that means the system learned to operate accurately within 4-bit arithmetic from the very first gradient update, rather than being trained at high precision and compressed afterward, which often causes models to lose accuracy.

For context, a model’s precision is measured in bits. Full precision, known as FP32, is the gold standard—but it is also extremely expensive to run at scale. Developers often reduce precision to save compute while trying to preserve useful performance.

Think of it like shrinking a 4K image down to 1080p: The picture still looks the same at a glance, just with less detail. Normally, dropping from 32-bit precision all the way to 4-bit would cripple a model’s reasoning ability. Nemotron avoids that problem by learning to operate at low precision from the start, instead of being squeezed into it later.

Compared to its own predecessor, Nemotron 3 Super delivers more than five times the throughput. Against external rivals, it’s 2.2x faster than OpenAI’s GPT-OSS 120B on inference throughput, and 7.5x faster than Alibaba’s Qwen3.5-122B.

We ran our own quick test. The reasoning held up well, including on prompts that were deliberately vague, badly worded, or based on wrong information. The model caught small errors in context without being asked to, handled math and logic problems cleanly, and didn’t fall apart when the question itself was slightly off.

The full training pipeline is public: weights on Hugging Face, 10 trillion curated pretraining tokens seen over 25 trillion total during training, 40 million post-training samples, and reinforcement learning recipes across 21 environment configurations. Perplexity, Palantir, Cadence, and Siemens are already integrating the model in their workflows.

The $26 billion bet

The model may be one piece of a larger strategy. A 2025 financial filing shows Nvidia plans to spend $26 billion over the next five years building open-weight AI models. Executives confirmed it, too.

Bryan Catanzaro, VP of applied deep learning research, told Wired the company recently finished pretraining a 550-billion-parameter model. Nvidia released its first Nemotron model back in November 2023, but that filing makes clear this is no longer a side project.

The investment is strategic considering Nvidia’s chips are still the default infrastructure for training and running frontier models. Models tuned to its hardware give customers a built-in reason to stay on Nvidia despite efforts from competitors to use other hardware. But there’s a more urgent pressure behind the move: America is losing the open-source AI race, and losing it fast.

Chinese open models went from barely 1.2% of global open-model usage in late 2024 to roughly 30% by the end of 2025, according to research by OpenRouter and Andreessen Horowitz. Alibaba’s Qwen overtook Meta’s Llama as the most-used self-hosted open-source model, according to Runpod. American companies including Airbnb adopted it for customer service. Startups worldwide are building on top of it. Beyond market share, that kind of adoption creates infrastructure dependencies that are hard to reverse.

While U.S. giants like OpenAI, Anthropic, and Google keep their best models locked behind APIs, Chinese labs from DeepSeek to Alibaba have been flooding the open ecosystem. Meta was the one major American player competing in open source with Llama, but Zuckerberg recently signaled the company might not make future models fully open.

The gap between “best proprietary model” and “best open model” used to be massive—and in America’s favor. That gap is now very small, and the open side of the ledger is increasingly Chinese.

There’s also a hardware threat underneath all of this. A new DeepSeek model is widely expected to drop soon, and it’s rumored to have been trained entirely on chips made by Huawei—a sanctioned Chinese company. If that’s confirmed, then it would give developers around the world, particularly in China, a concrete reason to start testing Huawei’s hardware. China’s Ziphu AI is already doing that.

That’s the scenario Nvidia most needs to prevent: Chinese open models and Chinese chips building an ecosystem that doesn’t need Nvidia at all.

The crypto industry is becoming increasingly aware that quantum computing could eventually challenge the cryptographic systems that secure Bitcoin and other prominent networks. However, the threat is likely years or decades away, according to a new report by Ark Invest—the investment management firm of tech investor Cathie Wood—and Bitcoin-focused financial services firm Unchained.

The report published on Wednesday examines whether advances in quantum computing could enable Shor’s algorithm to break the elliptic curve cryptography used to secure Bitcoin wallets. The authors say current quantum machines remain far below the capability required to compromise Bitcoin’s security, echoing comments from quantum computing experts.

“Today’s quantum systems lack the capabilities required to compromise Bitcoin. Meaningful breakthroughs would disrupt internet security first, triggering coordinated responses well beyond Bitcoin,” the researchers wrote. “In our view, quantum development will be a gradual technological progression—not a sudden ‘Q-day‘ event—giving markets and the Bitcoin network time to adapt.”

The report comes as the conversation around quantum computing and cryptocurrency has steadily increased over the last year, with prominent figures including Coinbase CEO Brian Armstrong, Ethereum co-founder Vitalik Buterin, and Cardano founder Charles Hoskinson addressing the risk.

“Commentators often parse two distinct eras in the development of quantum computing in relation to Bitcoin, one era in which quantum computing cannot affect Bitcoin and another in which it has broken Bitcoin’s underlying cryptography completely,” the report said.

Bitcoin’s security relies on hash functions that protect mining and block structure, and elliptic curve cryptography that proves wallet ownership. However, future quantum computers could potentially reverse public keys to recover private keys, raising concerns about “harvest now, decrypt later” attacks in which blockchain data is collected today to exploit it once quantum computers become powerful enough.

The report, however, says today’s quantum computers operate in the “Noisy Intermediate-Scale Quantum,” era, typically using around 100 logical qubits. Breaking a Bitcoin key with a quantum computer would require thousands of high‑quality, error‑corrected qubits and an enormous number of reliable quantum operations—far beyond what today’s quantum machines can do.

Because of those limits, the report says any quantum threat to Bitcoin would likely emerge in stages rather than all at once.

“In our view, within 10-20 years, the [practical quantum computing] research community will make enough progress on algorithms to give the Bitcoin developer community time to adapt and optimize them for the Bitcoin blockchain, virtual machine, and ecosystem of tools, devices, and companies,” the researchers wrote.

Researchers estimate that quantum computers would first become useful in fields such as chemistry before advancing enough to break weaker cryptographic systems. Later, they would become capable of attacking the elliptic curve cryptography used in Bitcoin wallets, initially taking significant time to break individual keys. In its final stage, quantum computers would be able to break keys faster than Bitcoin’s roughly 10-minute block interval.

Even if the threat is gradual instead of instant, the report notes a substantial share of Bitcoin’s supply could face exposure if quantum computers eventually break elliptic curve cryptography.

“About 1.7 million Bitcoin are held in vulnerable P2PK addresses that are believed to be lost, while another roughly 5.2 million BTC sit in reused or Taproot addresses that could be migrated—together accounting for about 35% of the total Bitcoin supply,” the researchers wrote.

The report says Bitcoin developers may eventually need to adopt post-quantum cryptography, a class of cryptographic systems designed to remain secure against quantum computers.

In February, developers merged BIP 360 into Bitcoin’s GitHub improvement repository, advancing a potential post-quantum framework for the network. BIP 360 introduces a new output type called Pay-to-Merkle-Root, or P2MR, that would disable a technical feature called key-path spending, which exposes public keys when coins are spent.

Integrating those protections into the Bitcoin network would require changes to its consensus rules, however, a process that depends on agreement across the decentralized community of developers, miners, and users.

“Bitcoin isn’t just one piece of software. There’s an entire ecosystem of wallets, hardware devices, and exchanges, and migrating all of that will take time,” BIP 360 co-author and cryptographer Ethan Heilman told Decrypt. “There are still open questions about which algorithms to use and what the right approach is, so discussions about post-quantum upgrades could take five to 10 years.”

Bitcoin’s design makes major changes difficult, a feature the report says protects the network but can slow the process of adopting and enacting upgrades.

“From that perspective, Bitcoin’s caution represents a tradeoff between adaptability and assurance, which will continue to shape its long-term evolution,” the report said.

That dynamic, Heilman said, could also shape how developers prioritize upgrades: “If the threat isn’t urgent, things move slowly. Once it becomes real, development tends to accelerate.”

Ark Invest and Unchained did not immediately respond to requests for comment by Decrypt.