Industry News

Tracks company news, strategic changes, funding activities, and personnel adjustments across the blockchain and crypto industries, delivering a full-spectrum industry overview for our users.

1 Billion DOT Minted Out of Thin Air, Yet Hacker Only Made $230,000

On April 13, a security breach occurred involving the Polkadot bridge on the Ethereum network, where an attacker exploited a replay vulnerability in the MMR proof mechanism of Hyperbridge’s ISMP protocol. By reusing a historically valid proof and pairing it with a malicious request, the attacker bypassed verification and gained admin and minting rights over the wrapped DOT contract on Ethereum. They then minted 1 billion wrapped DOT tokens—2,805 times the existing supply—and attempted to liquidate them. However, due to extremely low liquidity in the wrapped DOT market, the massive sell-off crashed the token’s price by 99.98%, from $1.22 to approximately $0.000128. The attacker ultimately exchanged the tokens for only about 108.2 ETH (worth roughly $237,000), with gas costs as low as $0.74. The same exploit had been used previously in attacks on MANTA and CERE tokens, resulting in a total loss of around $242,000. Polkadot confirmed that the incident only affected DOT bridged via Hyperbridge to Ethereum and did not impact the native Polkadot network or DOT on other bridges. Exchanges including Upbit and Bithumb temporarily suspended DOT deposits and withdrawals as a precaution. The event highlights ongoing vulnerabilities in cross-chain infrastructure and the critical role of liquidity in limiting actual damages during large-scale exploits. It also reflects a broader trend of increasing DeFi security incidents in early 2026.

marsbit04/13 10:10

1 Billion DOT Minted Out of Thin Air, Yet Hacker Only Made $230,000

marsbit04/13 10:10

The Creator of Kling Returns to Alibaba and Builds Another Dark Horse

The article discusses the rise of HappyHorse-1.0, an AI video generation model developed by Alibaba, which topped the Artificial Analysis leaderboard in both text-to-video and image-to-video categories in April 2026. The model was created under the leadership of Zhang Di, who returned to Alibaba in November 2025 after working at Kuaishou, where he led the development of the Kling model. HappyHorse is open-source and commercially available, similar to Alibaba's Qwen model. Zhang Di's background includes extensive experience in large-scale data systems and machine learning at Alibaba and Kuaishou, which contributed to the rapid development of HappyHorse within just five months. The model uses a 15-billion-parameter transformer architecture with native multimodal training, supporting multiple languages and lip-sync capabilities. It also focuses on reducing inference time and cost, making it practical for commercial use. The primary application of HappyHorse is in e-commerce, where it can generate product videos to enhance user engagement and conversion rates by creating contextual and personalized content. This aligns with Alibaba's strengths in commerce, advertising, and data feedback loops. The model's success with open-source approach contrasts with challenges faced by closed-source models like OpenAI's Sora (shut down due to high costs) and ByteDance's Seedance 2.0 (paused over copyright issues). HappyHorse represents a strategic move for Alibaba to integrate AI video generation into its core business ecosystems.

marsbit04/13 05:10

The Creator of Kling Returns to Alibaba and Builds Another Dark Horse

marsbit04/13 05:10

Giants Collectively Raise Prices, Is the AI Price Hike Wave Coming? Can We Still Afford Lobster Employees?

Major AI companies, including Alibaba Cloud, Baidu Intelligent Cloud, Tencent Cloud, and Zhipu, have recently announced significant price increases for AI computing and storage services, with hikes ranging from 5% to over 460% in some models. This trend follows similar moves by global giants like Amazon AWS and Google Cloud earlier this year. The price surge is driven by explosive demand for computing power, fueled by the rapid adoption of AI agents like OpenClaw (referred to as "Lobster" in the article), which consume tokens at rates dozens or even hundreds of times higher than traditional AI applications. This has created a severe supply-demand imbalance. Additionally, shortages in high-end hardware—such as AI chips and high-bandwidth memory (HBM)—have constrained computing capacity and raised operational costs. The industry is shifting away from loss-leading pricing strategies toward value-based models, prioritizing sustainable development over market-share competition. A new "token economy" is emerging, where pricing is increasingly based on token usage, complexity, and speed rather than flat fees. This reflects AI computing's evolution from a generic service to a specialized, high-value resource. Some companies are even considering token allowances as part of employee benefits, highlighting its growing role as both a production tool and a cost factor. The article concludes by questioning whether AI services will remain affordable as compute costs continue to rise.

marsbit04/13 04:20

Giants Collectively Raise Prices, Is the AI Price Hike Wave Coming? Can We Still Afford Lobster Employees?

marsbit04/13 04:20

How Should Crypto VCs Survive? When Top Projects No Longer Need Institutional Funding

Cryptocurrency venture capital is at a watershed moment. Token exits, once the primary driver of outsized returns, are undergoing a major reset. The definition of token value is being rewritten in real-time, yet no standard valuation framework has emerged. Key market shifts include the rise of tokens with real, on-chain revenue (like HYPE), which exposed the weakness of governance tokens with no fundamentals; a supply shock from meme coins (e.g., PUMP) fragmenting liquidity; and competition from prediction markets, stock perps, and leveraged ETFs diverting retail speculative capital. This has compressed token lifecycles and cratered holding periods. VCs now face critical questions: Are they underwriting equity, tokens, or a hybrid? What is the best practice for on-chain value accrual beyond potentially toxic buybacks? Will the "crypto premium" vanish entirely, forcing valuations to align with public equities and crashing many Layer 1 tokens? The result is a divergence: early-stage investors are becoming more price-sensitive on token projects, while appetite for equity deals is growing. Later-stage crypto VCs are increasingly competing with traditional funds in "Web2.5" deals. To survive, crypto VCs must find their product-market fit with founders. Capital alone is no longer sufficient. Winning the best deals—from projects that may not even need institutional funding—requires providing unmatched brand value and non-capital advantages.

marsbit04/13 04:08

How Should Crypto VCs Survive? When Top Projects No Longer Need Institutional Funding

marsbit04/13 04:08

Tens of Millions of Errors Per Hour: Investigation Reveals the 'Accuracy Illusion' of Google AI Search

A New York Times investigation, in collaboration with AI startup Oumi, reveals significant accuracy and reliability issues with Google's AI Overviews search feature. Testing over 4,300 queries showed the accuracy rate improved from 85% (powered by Gemini 2) to 91% (Gemini 3). However, given Google's scale of ~5 trillion annual searches, this 9% error rate translates to nearly 57 million incorrect answers generated hourly. A critical finding is the prevalence of "unsubstantiated citations." For correct answers, the rate of citations that do not support the AI's summary surged from 37% to 56% with the Gemini 3 upgrade, making it difficult for users to verify information. The AI heavily relies on low-quality sources, with Facebook and Reddit being among its top-cited websites. Furthermore, the system is highly manipulable. A BBC journalist successfully "poisoned" it by publishing a fabricated article; Google's AI began presenting the false information as fact within 24 hours. Google disputed the study's methodology, criticizing its use of the SimpleQA benchmark and an AI model (Oumi's own) to evaluate another AI. The company maintains its AI Overviews, combined with its search ranking systems, perform better than the underlying model alone. Critics note this defense does little to bolster user confidence in the feature's reliability.

marsbit04/13 02:09

Tens of Millions of Errors Per Hour: Investigation Reveals the 'Accuracy Illusion' of Google AI Search

marsbit04/13 02:09

From Wall Street to Silicon Valley, Anthropic Steals All the Spotlight from OpenAI

From Wall Street to Silicon Valley, Anthropic is seizing the spotlight from OpenAI. In just one year, the power dynamics in the AI have shifted significantly. Anthropic is now challenging OpenAI across multiple fronts: market share, secondary market valuation, venture capital sentiment, and public perception. At the recent HumanX AI conference, the consensus was clear—Anthropic is the new darling of Silicon Valley. Its annualized recurring revenue (ARR) has reportedly reached $300 billion, surpassing OpenAI's $250 billion. In the secondary market, Anthropic's valuation has overtaken OpenAI's, with strong investor preference for its shares. Anthropic dominates the enterprise sector, holding 42-54% of the code generation market and 40% of the enterprise agent market, compared to OpenAI's 21% and 27%, respectively. It also leads in new enterprise adoption and cost efficiency. While OpenAI retains a strong consumer user base with ChatGPT, it faces challenges inization and high operational expenses. A leaked internal memo from OpenAI identified Anthropic as its biggest threat, emphasizing its compute infrastructure advantage, but the very need for such a memo highlights its defensive position. Despite OpenAI's strong backing from Amazon and NVIDIA, the market is now valuing efficiency, cost-effectiveness, and precise market fit—areas where Anthropic currently leads. However, experts caution that the AI race is far from over and the landscape remains highly fluid.

marsbit04/13 01:07

From Wall Street to Silicon Valley, Anthropic Steals All the Spotlight from OpenAI

marsbit04/13 01:07

Stop Staring at GPUs: CPUs Are Becoming the 'New Bottleneck' in the AI Era

In the AI era, while GPUs have long been the focus for computational power, the narrative is shifting as CPUs are increasingly becoming the new bottleneck. By 2026, system performance is more dependent on execution and scheduling capabilities, with CPUs playing a critical role in enabling AI operations. A supply crisis is emerging, with server CPU prices rising about 30% in Q4 2025 due to high demand and production constraints, as GPU orders compete for limited semiconductor capacity. Companies like Google and Intel have deepened collaborations, and Elon Musk is investing in custom CPU solutions for his ventures, highlighting the strategic importance of CPU infrastructure. The shift is driven by the rise of agentic AI, where CPUs handle tasks such as multi-step reasoning, API calls, and data I/O, accounting for 50–90.6% of total latency in intelligent workloads. Expanding context windows in AI models further strain GPU memory, necessitating CPU offloading for key-value cache management. Major players are adopting varied strategies: Intel is strengthening its Xeon processor line and partnerships; AMD is benefiting from increased demand, with server CPU revenue surpassing 40%; and NVIDIA is designing CPUs like Grace to optimize GPU-CPU synergy through high-speed interconnects. The industry is witnessing a rebalancing of compute infrastructure, with CPUs gaining prominence as essential enablers of scalable AI agent systems. By 2030, the CPU market is projected to double to $60 billion, driven largely by AI demands. The focus is now on overcoming system-level bottlenecks to maximize the efficiency and economic viability of AI deployments.

marsbit04/13 00:57

Stop Staring at GPUs: CPUs Are Becoming the 'New Bottleneck' in the AI Era

marsbit04/13 00:57

Ethereum Leads The Tokenization Race With Billions In Assets

Ethereum is leading the tokenization of real-world assets, hosting over $22.5 billion in tokenized treasury products—nearly 72% of the total market share. Major institutions like JPMorgan Chase, BlackRock, and Franklin Templeton are using Ethereum for institutional-grade products such as tokenized bonds and funds. The network is becoming the preferred infrastructure for autonomous agents and DeFi due to its predictable yields, deep liquidity, and reduced smart contract vulnerabilities. Additionally, firms like Broadridge and Galaxy Digital are advancing on-chain governance and institutional participation, signaling a shift toward a new financial layer built by established Wall Street entities rather than crypto-native startups.

bitcoinist04/12 14:01

Ethereum Leads The Tokenization Race With Billions In Assets

bitcoinist04/12 14:01

Anthropic's 'Cry' Sparks Wall Street Panic! 27-Year-Old, Mythos Instantly Defeated by 8 AIs

Anthropic's announcement of Claude Mythos, an AI tool claiming to discover thousands of zero-day vulnerabilities—including a 27-year-old bug in OpenBSD—triggered alarm on Wall Street and prompted emergency meetings among financial regulators fearing systemic cyberattacks. However, independent tests revealed significant exaggerations in these claims. Researchers found that many reported vulnerabilities were in obsolete software or were impractical to exploit, and the findings relied on only 198 manual reviews. Furthermore, multiple smaller, open-source AI models (some with as few as 3 billion parameters) successfully identified the same critical flaws at a fraction of the cost, demonstrating that AI cybersecurity capability does not linearly scale with model size. Meanwhile, users reported severe performance degradation in Claude Opus 4.6, with reduced reasoning depth and increased API costs. Critics, including prominent hacker George Hotz, accused Anthropic of overstating risks for marketing purposes, creating a "wolf cry" scenario where hype overshadows reality.

marsbit04/12 08:03

Anthropic's 'Cry' Sparks Wall Street Panic! 27-Year-Old, Mythos Instantly Defeated by 8 AIs

marsbit04/12 08:03

Edge AI Daily Morning Report (April 12)

Edge AI Daily Brief (April 12) **Silicon Valley Front:** CoreWeave expanded partnerships with Meta and Anthropic, reflecting surging AI compute demand. Major cloud providers in China raised prices by 5%-30% due to soaring GPU costs and a 1000x increase in daily token usage since 2024. Anthropic, with annualized revenue exceeding $30B, is exploring in-house chip development to address shortages and signed a 3.5GW TPU deal with Google and Broadcom. The U.S. MATCH Act tightened semiconductor export controls, lowering technology thresholds and threatening global supply chains. ASML and Tokyo Electron saw stock declines. OpenAI addressed a third-party Axios library security issue, requiring macOS app updates. Microsoft restructured Windows Insider channels to simplify testing. Meta, Amazon, and Google invested in small modular nuclear reactors (SMRs) to power energy-intensive AI data centers. Mozilla criticized Microsoft for forcing Copilot integration in Windows 11, highlighting broader concerns about user choice and DMA compliance. Microsoft paused new carbon credit purchases due to quality concerns. **Domestic Progress:** MUJI’s Q2 revenue grew 14.8%, while Amazon launched a global smart hub in Shenzhen to streamline cross-border logistics for Chinese sellers, cutting delivery times by up to 7 days. **Open Source Trends:** Meta AI and KAIST proposed "Neural Computers" (NCs), merging computation and memory into learning runtime states. Agent AI is shifting from prediction to world-state modeling, driving edge infrastructure redesign. Quantum computing demonstrated exponential advantages in classical data processing, using under 60 logical qubits to outperform classical machines. France began migrating government systems to Linux to enhance digital sovereignty and reduce U.S. tech reliance. (Source: Edge AI Daily, Guangjiao Guancha)

marsbit04/12 00:52