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How DeepSeek R1 is Redefining AI

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How DeepSeek R1 is Redefining AI

The emergence of DeepSeek R1 has shaken the strategies of tech giants, sent shockwaves through financial markets, and ignited a new level of geopolitical competition between the United States and China. But beyond these immediate impacts, DeepSeek R1 represents a fundamental shift in how artificial intelligence (AI) is developed and deployed. Rather than following the traditional “bigger is better” approach, where massive models with trillions of parameters dominate, DeepSeek R1 champions a new paradigm: efficiency.

A Break from Tradition: The Efficiency Revolution

For years, the prevailing AI philosophy was simple: larger models, more GPUs, and higher energy consumption meant better performance. DeepSeek R1 challenges this notion. Trained at a fraction of the cost of its Western counterparts, just $5.6 million compared to the billions invested by OpenAI and Google, DeepSeek proves that scalability depends not solely on size but algorithmic intelligence.

The introduction of R1 raises critical questions about the future of Large Language Models (LLMs). Are these expansive models already on the verge of obsolescence? With rapid advancements in efficiency-driven AI, businesses and researchers must reconsider their dependence on resource-intensive models that leaner, more cost-effective alternatives may soon outpace.

The Geopolitical Battle Over AI

DeepSeek R1’s arrival is more than a technological breakthrough; it has geopolitical implications. The AI race is now a battleground for global influence, drawing comparisons to Huawei’s dominance in 5G technology. Just as the U.S. took extreme measures to curb Huawei’s expansion, it is now attempting to regulate AI development by restricting advanced GPUs and open-source AI.

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However, DeepSeek R1 demonstrates that such restrictions cannot slow China’s AI progress. By optimising efficiency and reducing dependency on high-end chips, DeepSeek has circumvented U.S. sanctions and emerged as a formidable competitor. This has raised concerns in the West about the control of AI-generated information. If AI models developed in China become globally dominant, the risk of information control and censorship increases, influencing public discourse on key issues.

Open-Source AI vs. Proprietary Models, A Coexisting Future

One of the most striking aspects of DeepSeek R1 is its open-source nature. Historically, open-source software has challenged proprietary solutions by dramatically reducing costs and increasing accessibility. We have seen this pattern with Linux in enterprise computing, Android in mobile operating systems, and MySQL in database management. AI is now following the same trajectory.

Yet, major Western AI labs, OpenAI, Google, and Anthropic, continue to lead in multimodal AI, safety protocols, and model security. DeepSeek R1 may be efficient, but concerns over its robustness and potential vulnerabilities remain. Microsoft’s immediate integration of DeepSeek R1 into Azure suggests a growing appetite for open models, particularly for businesses looking to balance cost and flexibility. However, proprietary models will continue to play a crucial role in ensuring security and regulatory compliance, leading to a hybrid AI ecosystem where both approaches coexist.

The Economic Implications of AI Cost Reduction

One of the most debated aspects of DeepSeek R1 is its development cost. While $5.6 million is a fraction of what leading AI firms spend, the figure likely only accounts for training, excluding infrastructure, engineering, and deployment costs. Nevertheless, the real game-changer is inference cost, the cost associated with using AI models in real-world applications. Lower inference costs mean broader adoption, much like declining semiconductor prices fueled the mass adoption of consumer electronics.

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This shift will have profound economic consequences. As AI becomes more affordable, startups and mid-sized enterprises can integrate advanced AI without requiring massive infrastructure investments. This democratisation of AI will disrupt industries traditionally dominated by a handful of tech giants.

The Role of Reinforcement Learning and AI Agents

DeepSeek R1 is not just another LLM but a shift toward reasoning-based AI. Historically, LLMs excelled at pattern recognition but struggled with logical reasoning and decision-making. DeepSeek R1 integrates reinforcement learning techniques, allowing it to solve complex problems methodically rather than simply predicting the next word in a sequence.

This evolution paves the way for autonomous AI agents capable of adapting to dynamic workflows. From customer service to administrative tasks and data analysis, AI is moving beyond predefined scripts to real-time decision-making. The business world must prepare for a future where AI-driven automation extends beyond simple chatbot interactions into comprehensive, intelligent task execution.

The Chip Shortage Driving Algorithmic Innovation

The U.S. imposed semiconductor export restrictions to limit China’s AI capabilities. However, these constraints have unintended consequences: they have pushed Chinese researchers to prioritise efficiency over brute computational power. As AI models become more optimised, the demand for high-end chips could decrease, fundamentally altering the AI hardware landscape.

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While Western AI firms continue to invest heavily in GPU-driven research, China’s focus on efficiency could prove to be a more sustainable long-term strategy. The balance between computational power and algorithmic efficiency will likely define the next phase of AI innovation.

What Comes Next? A Shifting AI Landscape

DeepSeek R1 is not the final chapter in AI development; it is the beginning of a broader shift. Here are three key takeaways for businesses, regulators, and AI researchers:

  1. Efficiency is the new frontier: The AI race will no longer be won by sheer computing power. Algorithmic advancements will drive the next wave of breakthroughs.
  2. Regulation must balance security with innovation: Overregulating AI could slow down Western progress while allowing China to take the lead in global adoption.
  3. Application matters more than model size: AI accessibility is increasing, but success will depend on how effectively companies integrate AI into their operations.

Conclusion: AI’s Future Lies in Strategic Deployment

The rise of DeepSeek R1 signals a transformation in AI development. Rather than investing solely in more extensive and expensive models, the industry must focus on efficiency, usability, and strategic deployment. Businesses that adapt to this shift will gain a competitive edge, while regulators must navigate the complex landscape of security, innovation, and geopolitical competition.



AI is no longer just about who builds the biggest model, it’s about who uses it most effectively. The future belongs to those who can harness AI’s power efficiently and strategically. DeepSeek R1 is just the beginning.

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Apollo private credit fund gives investors only 45% of requested withdrawals

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Apollo private credit fund gives investors only 45% of requested withdrawals

Marc Rowan, chief executive officer of Apollo Global Management LLC, during a Bloomberg Television interview in New York, US, on Tuesday, Dec. 5, 2023.

Jeenah Moon | Bloomberg | Getty Images

Apollo, the asset management giant, told investors in its flagship private credit fund that it will limit withdrawals this quarter to just under half of requests, the latest sign of stress in the asset class.

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In a filing with the Securities and Exchange Commission late Monday, Apollo Debt Solutions BDC said that it received redemption requests equal to 11.2% of shares outstanding in the first quarter, far exceeding the 5% quarterly cap the fund allows.

Unlike some other private credit players, Apollo is sticking with the 5% cap, an industry standard that rivals including Blackstone have recently relaxed to satisfy investor demands for their funds.

The vehicle — a non-traded business development company, or BDC — expects to return about $730 million to investors on a prorated basis, meaning redeeming shareholders will receive roughly 45% of the capital they requested. The fund has a net asset value of $15.1 billion, as of Feb. 28.

“Today’s decision reflects our ongoing commitment to long-term value creation for the Fund’s shareholders,” Apollo said. “As long-term stewards of capital, we have a fiduciary duty to act in the best interests of all Fund investors, balancing the interests of shareholders seeking liquidity with those who choose to remain invested.”

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Apollo said the fund’s net asset value per share declined 1.2% over the past three months through Feb. 28, but outperformed the U.S. Leveraged Loan Index, which fell 2.2% over the same period.

The withdrawals show that Apollo didn’t avoid the rush of investor redemptions plaguing rivals, driven by concern over private credit loans to software companies. Apollo executives have sought to distance themselves from other players recently, saying the firm typically made loans to larger, more stable companies.

Software is the single biggest sector at 12.3% of loans in the Apollo Debt Solutions BDC, according to the company.

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Polymarket Tightens Insider Trading Rules

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Polymarket Tightens Insider Trading Rules

The prediction market is updating insider trading and manipulation rules days after inking an exclusive partnership with Major League Baseball.

Polymarket on Monday announced updated market integrity rules across both its DeFi platform and its CFTC-regulated U.S. exchange, amplifying requirements governing insider trading and market manipulation. The new standards appear in the DeFi platform’s Terms of Use and the Polymarket US Rulebook.

“Markets thrive on clarity,” said Neal Kumar, Polymarket’s chief legal officer, in a release.

Prohibited Behavior

The rules spell out three categories of banned insider trading conduct. First, participants may not trade on any contract if they possess confidential information about the outcome of the underlying event, where using that information would violate a preexisting duty of trust or confidence.

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Second, participants may not trade on confidential information passed to them by someone who owed a preexisting duty of trust or confidence to someone else, if they know or have reason to know that the tipper would be prohibited from trading on it themselves.

Third, participants may not trade on any contract if they hold a position of authority or influence sufficient to affect the outcome of the underlying event.

Beyond insider trading, both platforms prohibit all types of fraud and market manipulation — including spoofing, wash trading, and fictitious transactions — as well as self-dealing, front-running, information misuse, attempted manipulation, and disruptive practices.

Enforcement

On the DeFi side, Polymarket maintains a multi-layered monitoring system and partners with surveillance and technology specialists, and all trades are executed on the Polygon blockchain, providing built-in on-chain transparency. When the platform or community flags unusual activity, Polymarket said it may ban wallet addresses or refer the matter to law enforcement.

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On Polymarket US, surveillance operates at three levels: partnerships with trade surveillance specialists, a control desk conducting real-time monitoring, and a Regulatory Services Agreement with the National Futures Association to detect rule violations and investigate offenders. Sanctions on the U.S. exchange can include suspension, termination, monetary penalties, or regulatory referrals.

The rule overhaul follows last week’s announcement that MLB named Polymarket its official and exclusive prediction market exchange. The deal centers on an integrity framework that restricts markets deemed to pose manipulation risk, including contracts on individual pitches, manager decisions, and umpire performance. MLB also signed an information-sharing agreement with the CFTC, the first such deal between the derivatives regulator and a professional sports body.

Polymarket received CFTC approval to operate in the U.S. in November 2025, following a $2 billion strategic investment from Intercontinental Exchange. The platform has since begun rolling out its U.S. app, starting with sports markets.

This article was written with the assistance of AI workflows. All our stories are curated, edited and fact-checked by a human.

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TRON Scales AI Fund to $1 Billion to Build the Financial Rails of the Agentic Economy

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Brian Armstrong's Bold Prediction: AI Agents Will Soon Dominate Global Financial

TLDR:

  • TRON DAO has expanded its AI Fund tenfold, growing from $100 million to a full $1 billion commitment.
  • The fund targets agent identity systems, stablecoin payment rails, and tokenized equity as core investment areas.
  • TRON’s network processes over $21 billion daily and holds $85 billion in USDT, supporting agent-scale payments.
  • Tokenized equity is positioned as the ownership layer for AI agents managing economic interests on behalf of users.

TRON DAO has expanded its AI Fund from $100 million to $1 billion. The fund targets early-stage companies building infrastructure for the agentic economy.

It focuses on agent identity systems, stablecoin payment rails, tokenized assets, and developer tooling. This move builds on a thesis formed in 2023, when TRON predicted AI and blockchain would converge.

TRON Doubles Down on AI and Blockchain Convergence

The TRON AI Fund first launched with a clear conviction: AI and blockchain technology would eventually merge. That prediction has gained enough traction to justify a tenfold increase in committed capital.

The fund now positions itself as a strategic vehicle, not just a financial one. Its expanded mandate reflects growing market demand for autonomous financial infrastructure.

Three core theses continue to drive the fund’s investment direction. As TRON stated, “AI agents will become active participants in the global economy, requiring new financial infrastructures that combine identity, payment, and asset ownership fully onchain.”

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This makes stablecoins the most practical payment layer for agent-to-agent commerce today. The fund views this as foundational, rather than experimental, infrastructure.

Stablecoins also serve individuals and small teams augmented by AI tools. A single person running AI-powered operations no longer needs a large team behind them.

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However, they still need payment systems that are simple, low-cost, and accessible. Traditional banking onboarding and intermediary fees make that difficult to achieve.

TRON noted that “AI-augmented people can run what once required entire teams from a single laptop.” That shift changes the demand for financial tools entirely.

Tokenized equity rounds out the fund’s framework as the ownership layer for this new economy. It is divisible, programmable, and transferable around the clock, supporting autonomous asset management.

TRON’s Network Scale Positions It for Agent-to-Agent Settlement

TRON’s blockchain currently supports over 370 million user accounts across its network. Daily transaction volume on the chain exceeds $21 billion, demonstrating its capacity at scale.

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The network also holds more than $85 billion in circulating USDT supply. These numbers place TRON among the largest stablecoin liquidity sources in the blockchain space.

TRON described agent-to-agent payments as systems expected to “rely on infrastructure that is already proven at scale.” Its network meets that standard through its user base, liquidity depth, and transaction throughput.

The fund intends to extend this infrastructure further into settlement and custody for tokenized assets. That expansion aligns with the broader goal of supporting autonomous financial systems.

The fund will also pursue acquisitions alongside traditional investments. Early-stage companies building core agentic tools are the primary target.

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Consolidation in this space is expected as the sector matures. TRON sees this as an opportunity to shape the foundational layer of the agentic economy.

As AI agents take on more economic roles, demand for on-chain financial rails will grow steadily. TRON’s expanded fund positions it to meet that demand directly and at scale.

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Bitcoin Bulls Fight To Hold $70K, Derivatives Data Signals Weakness

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Bitcoin Bulls Fight To Hold $70K, Derivatives Data Signals Weakness

Key takeaways:

  • Bearish Bitcoin futures premiums and low call option odds suggest traders remain skeptical despite BTC’s brief 4% relief rally.

  • High oil prices and cautious Fed policy continue to pressure risk assets, while Bitcoin derivatives metrics signal a lack of conviction.

Bitcoin (BTC) surged 4% within minutes of US President Donald Trump announcing his intention to temporarily de-escalate the conflict in Iran and pursue negotiations. While oil prices immediately tumbled 14% to $85 per WTI barrel and the S&P 500 climbed 3%, Bitcoin derivatives metrics continued to signal skepticism and a lack of confidence in the $68,000 support level.

Bitcoin 2-month futures annualized premium. Source: Laevitas.ch

Bitcoin futures traded at a 2% annualized premium relative to regular spot markets on Monday, indicating a lack of demand for bullish leverage. Under neutral conditions, this indicator typically ranges between 4% and 8% to compensate for the longer settlement period. This lack of conviction from bulls has been the norm for the past month, even during a recent rally toward $76,000 on Tuesday.

Short-term gains fail to offset five months of Bitcoin pain

Short-term positive updates regarding the US and Israel-Iran war are unlikely to reverse the pessimism following a five-month price decline. Because the specific causes of Bitcoin’s Oct. 10, 2025, flash crash and its subsequent failure to track traditional markets remain unconfirmed, traders treat any developments with high suspicion.

S&P 500 futures (left) vs. Bitcoin/USD (right). Source: TradingView

This major sell-off occurred alongside rising US import tariffs, including a 100% levy on Chinese goods after China restricted rare earth metal exports. However, the unprecedented $19 billion in liquidations caused the most significant damage, resulting in heavy losses for market makers and traders who utilized cross-margin positions.

Bitcoin options for April 24 at Deribit. Source: Deribit by Coinbase

At the Deribit exchange, the $80,000 Bitcoin call option for April 24 traded at 0.017 BTC ($1,207). With 31 days until expiry and an implied volatility of 48%, the market is pricing in only a 20% chance of Bitcoin reaching $80,000. This low expectation for a 13% monthly gain is rare in cryptocurrency markets, where participants are generally more optimistic.

USD stablecoin premium/discount relative to USD/CNY rate. Source: OKX

USD stablecoins traded at a 1.3% premium against the official US dollar to yuan exchange rate on Monday, indicating that there is not a particular imbalance between buying and selling demand in the region. Typically, high demand for cryptocurrency pushes this premium above the 1.5% neutral range, while panic selling causes stablecoins to trade at a discount.

Federal Reserve’s choice to pause rate cuts keeps investors in fixed-income

The data shows that there is modest resilience in Bitcoin derivative markets, especially since BTC retested the $67,500 level on Monday. Gold’s historic 21% price drop over ten days proved that no asset class is safe when traders fear an economic recession and inflationary risks, especially as fuel prices impact logistics and nearly every sector of the US economy.

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Related: Bitcoin spot volumes fall to 2023 lows as BTC rallies remain news-led

Monday’s 3% relief bounce in the S&P 500 is unlikely to cause investors to exit fixed-income positions, especially as the Fed gave little indication of continuing its monetary easing policy. High interest rates reduce incentives for consumer financing and create a burden for corporate capital costs.

There is undoubtedly a significant dependence on the duration of the war for risk assets, including Bitcoin. Until oil prices revert back to $75 or lower, odds are traders will act cautiously, but additional catalysts may need to emerge for Bitcoin traders to turn bullish, especially considering the persistent lack of conviction in onchain and derivatives metrics.