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Trump Moves to Override State AI Laws, Triggering Fierce Federalism Clash and Backlash From Both Parties

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President Trump on Thursday signed an executive order that seeks to sharply curtail the power of U.S. states to regulate artificial intelligence, marking one of the most aggressive federal interventions yet in the rapidly expanding AI sector.

The order authorizes the U.S. attorney general to challenge and potentially overturn state laws deemed inconsistent with what the administration calls “the United States’ global A.I. dominance,” placing dozens of existing safety, consumer protection, and transparency measures in legal jeopardy.

Under the order, states that refuse to roll back targeted AI laws could also face financial pressure. Trump directed federal agencies to withhold funds tied to broadband expansion and other infrastructure programs from states that maintain regulations viewed as obstructive. The threat adds a fiscal lever to what is already shaping up as a major constitutional confrontation between federal authority and state police powers.

Trump framed the move as a necessary step to eliminate what he described as a confusing and burdensome regulatory landscape. Speaking in the Oval Office alongside senior officials, including David Sacks, the administration’s AI and crypto czar, Trump argued that innovation could not thrive under a fragmented system of state rules.

“It’s got to be one source,” he said. “You can’t go to 50 different sources.”

He also tied the order directly to geopolitical competition, repeatedly citing the need for the United States to stay ahead of China in artificial intelligence.

The executive action reflects Trump’s broader realignment toward Silicon Valley and the AI industry. Over the past year, his administration has issued multiple orders designed to ease regulatory scrutiny, expand private-sector access to federal data, streamline permitting for data centers and power infrastructure, and loosen restrictions on exporting advanced AI chips. Trump has also publicly praised leading technology executives and elevated Sacks — a venture capitalist with deep ties to the tech sector — into a central policy role with significant influence over AI governance.

However, the order has already sparked widespread bipartisan resistance, with legal experts warning that it may exceed the president’s constitutional authority. States and consumer advocacy groups are expected to challenge the measure in court, arguing that only Congress can preempt state laws on this scale.

Several legal scholars have noted that while federal agencies can set standards in specific domains, a blanket attempt to invalidate state statutes through executive action is likely to face serious judicial scrutiny.

Even some voices aligned with Trump’s ideological camp expressed concern. Wes Hodges, acting director of the Center for Technology and the Human Person at the Heritage Foundation, said that if the administration succeeds in undermining state rules, it has a responsibility to replace them with a robust national framework.

“Doing so before establishing commensurate national protections is a carve-out for Big Tech,” Hodges said, underscoring fears that the order prioritizes speed and scale over public safeguards.

The stakes are high because generative AI systems have moved rapidly from experimental tools to mass-market products. Technologies capable of generating realistic text, voices, images, and video are now embedded across finance, education, healthcare, marketing, and social media. At the same time, documented harms have multiplied, including deepfake political content, financial scams, data misuse, and cases in which chatbots have provided harmful advice to minors.

In the absence of comprehensive federal legislation, states have stepped in aggressively. According to the National Conference of State Legislatures, all 50 states and U.S. territories introduced AI-related bills this year, and 38 states enacted roughly 100 new laws. These measures vary widely but generally aim to impose transparency requirements, restrict certain uses of AI, and hold companies accountable for foreseeable harms.

California adopted one of the most consequential laws, requiring developers of the largest AI models — including OpenAI’s ChatGPT and Google’s Gemini — to conduct safety testing and disclose the results. South Dakota moved to curb election-related manipulation by banning AI-generated deepfake videos in political ads within months of an election. Utah, Illinois, and Nevada passed laws governing AI chatbots used in mental health contexts, mandating user disclosures and limiting how sensitive data can be collected and used.

Child safety has emerged as a particularly active area of state regulation. Several states have passed laws aimed at protecting minors from AI-powered chatbots and algorithm-driven platforms, especially where AI tools simulate emotional support or companionship. Trump’s executive order states that it will not pre-empt child-safety laws, but it does not define how that exemption will be applied, leaving advocates concerned that protections could still be weakened through litigation or narrow interpretations.

“Blocking state laws regulating A.I. is an unacceptable nightmare for parents and anyone who cares about protecting children online,” said Sarah Gardner, chief executive of Heat Initiative, a child-safety advocacy group.

She warned that states have become the primary line of defense as federal action has lagged.

The AI industry, for its part, has mounted an intense lobbying campaign against state-level regulation. Companies argue that complying with dozens of different legal regimes raises costs, slows product development, and discourages startups. Earlier this year, lawmakers attempted to include a ten-year moratorium on state AI laws in a major domestic policy bill, but the proposal was abandoned after strong bipartisan opposition. Venture capitalist Marc Andreessen captured industry sentiment in a social media post last month, calling the state-by-state approach “a startup killer.”

Trump’s order effectively revives that fight through executive authority, raising the prospect of prolonged legal battles that could inject uncertainty into the AI market. While the administration argues that centralization will accelerate innovation and strengthen U.S. competitiveness, critics counter that the absence of binding federal standards leaves consumers, workers, and children exposed at a time when AI systems are becoming more powerful and less transparent.

Beyond domestic policy, the order also signals how Trump views AI as a strategic asset. By tying deregulation to competition with China, the administration is framing AI governance not as a consumer-protection issue but as a national power contest. That framing may resonate with parts of Congress, but it also heightens tensions with states that see immediate local risks from unchecked AI deployment.

As the order moves toward inevitable court challenges, the outcome could reshape the balance of power over technology regulation in the United States. If Trump prevails, states may find their ability to respond quickly to emerging AI harms sharply reduced. If the courts strike the order down, pressure will mount on Congress to finally craft a national AI framework that balances innovation with enforceable safeguards.

Either way, the executive order marks a turning point. It crystallizes a growing divide between federal ambitions to dominate AI globally and state-level efforts to manage its risks locally.

Predictive Oncology Becomes Axe Compute, Expanding Into High-Performance AI Infrastructure

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Predictive Oncology (NASDAQ: POAI) announced that it has changed its name to Axe Compute Inc., with its common stock to begin trading on Nasdaq under the ticker symbol AGPU on December 12, 2025.

Axe Compute will continue to operate its AI-driven drug discovery business and expand its business into high-performance enterprise AI infrastructure, addressing rising global demand for predictable, scalable compute capacity across enterprise AI workloads.

The decision reflects Axe Compute’s fundamental observation about the current AI landscape: the bottleneck to AI progress is increasingly infrastructure, not algorithms. While attention concentrates on model capabilities and benchmark performance, Axe Compute believes the enterprises building AI applications face a more immediate problem—access to the compute required to train and run those models at all.

The Infrastructure Gap

Axe Compute believes it will be able to utilize its ATH strategic compute reserve and its agreement with Aethir to secure GPU capacity and services on the Aethir network to support compute demand.

GPU procurement timelines have extended to 40-52 weeks for high-end hardware as centralized cloud providers face capacity constraints that create multi-month deployment queues. Meanwhile, global enterprise spending on AI cloud services is projected to exceed $400 billion in 2025, with demand continuing to outpace supply.

Axe Compute will operate as an active infrastructure company rather than a passive treasury. The distinction matters: Axe Compute will acquire digital assets tied to AI infrastructure—beginning with capacity on the Aethir network—and deploy those assets to serve enterprise clients under service contracts.

Axe Compute believes it will be able to derive revenue from token rewards and the margin captured between infrastructure acquisition cost and enterprise billing rates. Axe Compute is not Aethir; Aethir operates the underlying network.

Axe Compute will monetize access to that network for enterprise buyers who require guaranteed capacity, service-level agreements, and a counterparty that operates within traditional corporate and regulatory structures.

Infrastructure as the Enabling Layer

Axe Compute’s thesis rests on a structural view of AI development: breakthroughs in models depend on the infrastructure that makes experimentation possible.

Transformers require the compute to train them. Scaling laws require the hardware to test them. Production AI requires the capacity to run it. This positions infrastructure operators differently than the hyperscalers or the model developers.

Axe Compute does not compete with AWS on breadth of services or with OpenAI on model capabilities. It operates in the space between: utilizing the Aethir network to provide the specific, dedicated GPU capacity that AI-native companies require when cloud queues are too long and building internal infrastructure is too slow.

Axe Compute believes it will be able to introduce the flexibility of a resource pool that can be allocated to support varied project requirements as they arise. Collectively, Axe Compute believes these early workloads will show that Axe Compute can function as a stable backbone for a wide range of production AI systems, reinforcing the need for decentralized compute as a foundational layer of enterprise AI infrastructure.

Axe Compute anticipates sourcing infrastructure at competitive rates and providing reliable, predictable access to high-capacity compute through the Aethir network.

Axe Compute believes it is positioned to demonstrate the scalability and effectiveness of its model as initial deployments come online and its enterprise client base is expanded.

Axe Compute will continue to operate its AI-driven drug discovery business and may explore potential expansion into other digital asset categories beyond compute infrastructure as its operating model matures.

Axe Compute (NASDAQ: AGPU) plans to make world-class AI compute accessible to all through its access to the Aethir network. By delivering Aethir-provided decentralized global infrastructure.

Axe Compute endeavors to deliver instant access to bare-metal GPUs at scale to innovators and established businesses alike. Axe Compute is where decentralized choice meets enterprise trust.

Tesla U.S. Sales Plunge to Four-Year Low Despite Affordable EV Rollout

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U.S. sales for Tesla plunged to a near four-year low in November, dropping to approximately 39,800 vehicles, according to a Reuters report.

This represents a sharp decline of nearly 23% from the 51,513 units sold in November 2024 and marks the company’s lowest monthly sales volume in the U.S. since January 2022, according to exclusive estimates from Cox Automotive.

The slump occurred despite Tesla’s proactive launch of new, cheaper “Standard” versions of its best-selling Model Y SUV and Model 3 compact sedan in October, which were priced approximately $5,000 below the previous base models.

The Impact of the Tax Credit Expiration

The primary headwind for the entire U.S. Electric Vehicle (EV) market was the expiration of the $7,500 federal tax credit at the end of September, which was rescinded by the Trump administration. This incentive’s end triggered a rush of purchases in Q3 2025 and an anticipated “hangover period” in the following months.

The overall U.S. EV market was hit severely, with sales plummeting by more than 41% in November. However, while Tesla’s own sales declined, the relatively smaller percentage drop allowed the company to significantly increase its market share to 56.7%, up from 43.1% a year earlier. This suggests that despite its struggles, Tesla is weathering the market contraction better than most of its competitors.

Cannibalization and Weak Demand for Standard Variants

The core strategic challenge for Tesla is that the introduction of the cheaper Standard variants has failed to generate sufficient incremental demand to offset the loss of the tax credit.

“The drop certainly shows there is not enough demand for the Standard variants that were supposed to boost sales after the tax credit expiry,” said Stephanie Valdez Streaty, Cox’s director of industry insights.

She added that an additional concern is that sales of the lower-cost Standard versions are cannibalizing demand for the higher-margin Premium versions, particularly the Model 3.

To combat what analysts view as weak demand, Tesla is resorting to aggressive incentives, including offering 0% financing on the Standard Model Y—an unusually steep promotion for a variant that only began deliveries a month prior. The availability of both Standard models in inventory with reduced pricing further supports the view that the market is not absorbing the new supply as quickly as anticipated. Shawn Campbell, an adviser at Camelthorn Investments, noted, “I think the bottom line is, if the demand was there they wouldn’t be offering 0% financing.”

Old Models Meet New Competition

The sales slowdown extends a broader trend for Tesla. Deliveries fell for the first time in years during 2024 and are expected to drop again this year, pressured by high borrowing costs and intensifying global competition.

Tesla’s current lineup is aging, with minor refreshes, and the company has not introduced a completely new volume vehicle since its struggle with the Cybertruck pickup. The long-term $1.4 trillion valuation of the company is tied less to current vehicle sales and more to the successful transition to robotaxis and humanoid robots. However, without compelling new vehicles, the near-term revenue stream is under threat.

Cox’s Streaty was direct in her assessment of the competitive threat: “Tesla has a serious challenge on its hands next year when several other automakers are planning to roll out cheaper vehicles that are also full of fun features. So the answer is that Tesla needs a completely new vehicle in its fleet. Period.”

Adding to the demand issue, CEO Elon Musk’s political rhetoric and associations have reportedly sparked protests and hurt Tesla’s brand image, further complicating the sales environment in a market where brand perception plays a significant role. The solution, analysts conclude, must ultimately come in the form of “new, fresh models” to reinvigorate demand and sustain momentum.

Solana Ecosystem Expands, but L.xyz Is Where Early Positioning Is Quietly Happening

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The Solana ecosystem continues to expand at a rapid pace. Trading volumes are increasing, new applications are launching, and capital is flowing toward platforms that can handle scale without sacrificing speed. As this growth accelerates, traders are becoming more selective about where they deploy capital, especially when it comes to decentralized exchanges.

Rather than chasing every new launch, many experienced traders focus on identifying platforms that address real structural gaps. L.xyz is increasingly appearing in this category. Built on Solana, the project is developing a decentralized exchange designed around execution quality, liquidity depth, and professional-grade trading tools.

Why Solana’s Growth Is Creating New Demands

As more activity moves on-chain, limitations in existing trading infrastructure become more visible. Many decentralized exchanges rely entirely on AMM models, which can struggle during periods of high volatility or large order flow. Slippage increases, pricing becomes less predictable, and execution quality suffers.

L.xyz aims to solve this by integrating a hybrid AMM and order book architecture. Automated liquidity pools provide continuous access to markets, while the order book allows traders to place limit orders, stop orders, and structured entries. This combination is designed to improve price discovery and reduce execution inefficiencies.

Solana’s high throughput and low transaction costs enable this architecture to function efficiently, allowing traders to react quickly to changing market conditions.

Early Positioning in Infrastructure Follows a Pattern

Infrastructure adoption rarely happens overnight. Early participants tend to focus on architecture, security, and long-term utility. Broader adoption follows once platforms demonstrate reliability under real trading conditions.

L.xyz is currently in this early positioning phase. Its roadmap outlines spot trading, futures markets, leverage up to 100x on select pairs, advanced risk management tools, and future cross-chain capabilities. These features are being built as core components rather than experimental add-ons.

For traders who understand how infrastructure adoption unfolds, early positioning often means engaging before platforms become widely recognized.

Transparency and Audits Support Quiet Accumulation

Trust plays a central role in early-stage participation. L.xyz has reinforced transparency through multiple independent audits conducted by SpyWolf and QuillAudits, with public verification available via SolidProof’s TrustNet.

These audits confirm that the LXYZ token supply is permanently fixed at 500 million units. Mint authority has been revoked, preventing inflation. Freeze authority is disabled, ensuring user balances cannot be restricted. The audits also verify the absence of hidden taxes, transfer fees, or blacklist mechanisms.

This level of verification removes many of the uncertainties that typically surround presale projects and allows traders to focus on platform fundamentals.

Why L.xyz Keeps Appearing in Trader Research

The LXYZ token supports governance participation, staking rewards, and liquidity incentives that align long-term participation with platform growth. Early participants are not simply acquiring exposure, but positioning themselves within an ecosystem designed to support active trading.

As the Solana ecosystem continues expanding, early positioning in performance-focused infrastructure platforms like L.xyz is becoming a recurring theme among traders who prioritize structure over noise.

 

Telegram: T.me/ldotxyz

X: X.com/ldotxyz

Peter Schiff Dismisses Claims of Jamie Dimon Softening His Bitcoin Stance

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JP Morgan Chase puts contents through its CEO account, it goes viral. But the same content via JPMC account, no one cares (WSJ)

Peter Schiff, a long-time critic of Bitcoin and an outspoken supporter of gold, has dismissed claims that JPMorgan CEO Jamie Dimon is changing his stance on cryptocurrencies.

The misunderstanding followed Dimon’s December 11, 2025, interview on Fox Business, in which he discussed blockchain’s practical uses in traditional finance.

During the interview, Dimon reiterated that blockchain technology is “real” and increasingly effective for moving money. He emphasized that JPMorgan uses blockchain to settle trillions of dollars daily in faster and cheaper ways, particularly through stablecoins pegged to the U.S. dollar.

He also highlighted the bank’s ongoing development of a stablecoin built on the Base network and the tokenization of real-world assets. Despite this, Dimon maintained his skepticism toward Bitcoin, describing it as speculative and separate from enterprise blockchain applications.

JPMorgan’s broader blockchain adoption is reflected in its Onyx platform, which processes more than $1 trillion annually through services like intraday repos and cross-border payments. These systems rely on permissioned blockchain networks—far removed from Bitcoin’s decentralized nature.

Some observers had interpreted Dimon’s praise of blockchain as a softening of his long-standing criticism of Bitcoin. However, Schiff refuted this interpretation, insisting that Dimon’s views on Bitcoin remain unchanged.

He wrote,

“He is talking about stablecoins not Bitcoin. His opinion on Bitcoin has not changed. He still knows it’s a Ponzi.”

According to Schiff, those celebrating Dimon’s remarks as bullish for Bitcoin are misunderstanding the distinction between blockchain as a technological tool and Bitcoin as a decentralized monetary system.

In his response, Schiff stressed that Dimon’s comments were strictly about blockchain infrastructure and stablecoins not Bitcoin. He bluntly stated that Dimon “still knows it’s a Ponzi,” reaffirming the JPMorgan chief’s historical position.

Schiff’s clarification underscores a broader reality in the financial sector: institutional adoption of blockchain technology does not necessarily translate into endorsement of Bitcoin.

Peter Schiff, a prominent economist, gold advocate, and long-time critic of unconventional assets, has remained one of Bitcoin’s most vocal skeptics. Even as cryptocurrencies gain wider acceptance among institutional investors and traditional financial players, Schiff’s opposition has stayed firm.

His criticism is rooted not just in market movements, but in deep-seated economic beliefs that shape how he views money, value, and financial stability. At the core of Schiff’s argument is his belief that Bitcoin lacks intrinsic value. He maintains that, unlike productive assets such as businesses or tangible commodities, Bitcoin does not generate income or serve a fundamental economic function.

According to Schiff, Bitcoin’s price is largely sustained by speculation and the expectation that someone else will be willing to buy it at a higher price in the future, a dynamic he believes is unsustainable over the long term.

Schiff’s long-standing support for gold also plays a central role in his critique. He views gold as “real money,” pointing to its thousands of years of history as a store of value, its physical properties, and its widespread acceptance across cultures and economies. In contrast, he argues that Bitcoin, as a purely digital asset, has not been tested across multiple economic cycles and lacks the historical credibility that gold enjoys.

Outlook

The exchange between Schiff and Bitcoin proponents highlights a broader divide shaping the future of finance. As banks and financial institutions increasingly adopt blockchain infrastructure, stablecoins, and tokenized assets, skepticism toward Bitcoin itself persists among many traditional economists and executives.

For critics like Schiff, institutional blockchain adoption reinforces not weakens the argument that the technology’s value lies in regulated, centralized applications rather than decentralized cryptocurrencies.

Meanwhile, Bitcoin supporters continue to argue that fixed supply, decentralization, and growing global awareness will ultimately validate its role as digital gold.