2026-05-31 Domingo

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Bit Digital CEO: Why I Bought More ETH

Bit Digital CEO Sam Tabar explains his recent decision to purchase more Ethereum (ETH). He emphasizes that his investment is not based on market cycles or popular narratives, but on data-driven analysis identifying a pricing discrepancy in the asset. The article critiques the "ETH as money" framework, arguing it is an incorrect lens for evaluating Ethereum. Unlike Bitcoin's singular focus on becoming a monetary asset, Ethereum prioritizes utility by serving as a programmable settlement layer for global development. This practical focus, while precluding it from winning a "money" coordination game, has created unique value. Tabar counters criticism about Ethereum's fragmented ecosystem by highlighting that substantial, real-world value is already being settled on-chain. This includes the issuance of stablecoins, tokenization of assets like U.S. Treasuries, and transactions for AI agents. He sees Ethereum, combined with computational layers, as providing the core primitives—computation and settlement—necessary for institutional finance to migrate on-chain. He believes the catalyst for ETH's value re-rating will come from this institutional demand, which follows its own, slower regulatory and operational timeline, not retail narrative cycles. Tabar concludes by stating his purchase fulfills a fiduciary duty to make sound capital allocation decisions. He views ETH as a yield-generating asset (citing 94.7% gross margins from staking in Q1) that secures the world's dominant smart contract platform, which processes trillions in transactions annually. He does not need ETH to become a global reserve currency; its current utility and discounted valuation relative to the infrastructure it powers are sufficient reasons to buy and hold.

marsbit2 dias atrás 02:54

Bit Digital CEO: Why I Bought More ETH

marsbit2 dias atrás 02:54

Jensen Huang Joins Tsinghua, But Did Musk Actually Arrive Ten Years Ago?

Jensen Huang, founder of NVIDIA, is set to join the Advisory Board of Tsinghua University's School of Economics and Management. This marks his first appointment to an advisory body at a mainland Chinese university, following similar roles at institutions like National Taiwan University, Stanford, and Harvard. The article explores why his entry comes now, a decade after Elon Musk joined the same prestigious committee in 2015. The Tsinghua advisory board, established in 2000, is a high-level strategic body comprising global business elites like Apple's Tim Cook (Chair), Tesla's Elon Musk, Microsoft's Satya Nadella, and Meta's Mark Zuckerberg, alongside financial giants and leading Chinese entrepreneurs. The timing is attributed to a confluence of factors: Huang's current eligibility driven by NVIDIA's dominant role in AI, a recent vacancy on the board, the rising challenge from domestic Chinese chips necessitating stronger local ties, and a recent thaw in U.S.-China relations following high-level diplomatic visits. In contrast, Musk's 2015 entry occurred during a period of warmer bilateral ties, where his disruptive innovation profile aligned well with the board's needs without significant political friction. Huang is noted for his active engagement with academia, holding several honorary doctorates and advisory roles at other universities. His appointment is framed as a reflection of shifting geopolitics, market dynamics, and strategic recalculations over the past decade, underscoring the enduring importance of the Chinese market for NVIDIA.

marsbit2 dias atrás 02:51

Jensen Huang Joins Tsinghua, But Did Musk Actually Arrive Ten Years Ago?

marsbit2 dias atrás 02:51

The Free Era of the Internet Has Come to an End

The free era of the internet is ending. On May 27th, Meta officially announced a global paid subscription rollout, including Instagram Plus ($3.99/month), Facebook Plus ($3.99/month), and WhatsApp Plus ($2.99/month). This follows a major company shift towards AI, marked by recent layoffs and a massive $125-145 billion investment in AI infrastructure. The move aims to create a predictable revenue stream for investors, moving beyond reliance on fluctuating ad income. Unlike the earlier European "pay for no ads" model, these new tiers focus on offering enhanced features—like anonymous Story viewing on Instagram or privacy tools on WhatsApp—to provide "a bit more control." However, a Forrester survey indicates 70% of users are reluctant to pay, questioning the value. The core of Meta's strategy lies in its upcoming AI subscriptions, priced at $7.99 and $19.99, offering advanced reasoning and higher usage limits, mirroring the freemium models of OpenAI and Anthropic. With Meta's billions of users, even a small conversion rate could generate significant revenue. Analysts are optimistic, with some projecting WhatsApp alone could bring in $40 billion annually by 2030. This shift reflects a broader industry trend where the old bargain of "free services for user data" is under pressure from rising privacy regulations and the immense costs of AI development. The success of Meta's subscriptions hinges on whether users find enough value in these premium features to open their wallets, signaling a fundamental change in how the internet is funded.

marsbit2 dias atrás 02:15

The Free Era of the Internet Has Come to an End

marsbit2 dias atrás 02:15

Kelp DAO Vulnerability Triggers Exodus of Hundreds of Billions; Two Major DeFi Lending Pathologies Clash Head-On

Title: Kelp DAO Exploit Triggers $15 Billion Exodus, Exposing a Clash Between Two DeFi Lending Models. In April 2026, a hacker exploited a LayerZero bridge vulnerability in the Kelp DAO project, minting $292 million in fake rsETH tokens. These were deposited into Aave as collateral to borrow real Ethereum, draining the protocol's liquidity. Within three and a half days, Aave saw $15 billion in deposits flee, forcing a costly $160 million bailout. The root cause was identified as Aave's governance, which had previously voted to set rsETH's loan-to-value ratio to a risky 93%, leaving minimal safety margin. This incident starkly contrasts with the experience of Morpho, the second-largest DeFi lending protocol. Some fake rsETH also flowed into Morpho, but the exposure was limited to $1 million across isolated, pre-configured markets, preventing systemic contagion. The event highlights a fundamental divergence in DeFi lending architectures. Aave employs a shared liquidity pool model, where all deposits back all approved collateral assets, governed by DAO vote. This creates systemic risk, as seen when even users who never interacted with rsETH faced frozen funds. Furthermore, Aave's governance, influenced by leveraged borrowers, prioritized their interests during the crisis, even lowering borrowing rates for frozen markets at the expense of safer depositors. Its supplemental insurance mechanism, Umbrella, also failed as providers withdrew capital when needed. Morpho operates on an isolated market model. Anyone can create a separate lending market with fixed parameters (collateral, loan asset, oracle, rates). Independent risk managers (curators) allocate capital to these markets, bearing losses within their own vaults if they occur. This structure prevents risk from spreading and removes governance conflicts, as curators' decisions are not subject to community override. Beyond crisis management, the shared pool model carries a hidden cost: idle capital. In Aave's core markets, the spread between borrowing and deposit rates represents unusable funds, costing an estimated $52 million annually in lost value. Morpho's model targets a higher utilization rate (90% vs. Aave's 60-80%) because it eliminates rehypothecation risk, dynamically adjusting rates to balance supply and demand without governance delays. Consequently, Morpho often offers higher net yields to depositors. Institutional adoption underscores this difference. Major players like Coinbase (powering its lending for over 100M users), Apollo Global Management, Anchorage Digital, and SG-FORGE (Societe Generale) have chosen to build on Morpho. They require compliant, self-controlled risk parameters that Aave's community-governed model cannot provide. This trend is amplified by regulations like the proposed US GENIUS Act, which will push stablecoin issuers to seek neutral, controllable infrastructure like Morpho to manage trillions in reserve assets.

marsbit2 dias atrás 01:44

Kelp DAO Vulnerability Triggers Exodus of Hundreds of Billions; Two Major DeFi Lending Pathologies Clash Head-On

marsbit2 dias atrás 01:44

When Tokens Cost More Than People, 'AI Narrative' Runs Into Trouble

Title: When Tokens Cost More Than People, the "AI Narrative" Hits Trouble The economic sustainability of corporate AI adoption is under scrutiny as token consumption soars while measurable business value remains elusive. Major companies like Uber and Microsoft report struggling to justify rising AI costs, with executives coining terms like "tokenmaxxing" to describe wasteful usage. Data reveals a stark picture: for every dollar spent on AI tokens, only 18 cents translates to user-facing value, with the rest consumed by bug fixes, rework, and friction. The debate splits into bullish and bearish camps. Bulls, like Goldman Sachs analysts, see current inefficiencies as growing pains, predicting a 24-fold increase in token demand by 2030 and a shift towards healthier metrics like "cost per effective action." They point to indicators of real productivity gains and argue current tech valuations are not in bubble territory. Bears, however, highlight an unsustainable model where value is heavily concentrated in semiconductor companies like Nvidia, funded by cloud giants taking on massive debt. Studies show 95% of firms investing in generative AI see zero return. A deeper concern is the circular financial structure between cloud providers (hyperscalers) and AI labs like OpenAI and Anthropic. Billions in cloud service commitments are tied to these labs, which are partly funded by the hyperscalers' own investment. This creates a loop where cloud revenue depends on labs securing continuous external funding to pay their compute bills, which in turn relies on end-corporates willing to pay ever-higher token costs. The sustainability of this cycle is now in question. While not a classic bubble—AI technology is real and delivers productivity for power users—the central issue has shifted. The focus is no longer just on technological capability but on economics: whether the savings AI generates for businesses can outpace the soaring costs and justify the valuations of labs and cloud providers. The era of equating rising token usage with successful AI transformation is over. The bill for AI has arrived, but who ultimately pays remains uncertain.

marsbit2 dias atrás 01:44

When Tokens Cost More Than People, 'AI Narrative' Runs Into Trouble

marsbit2 dias atrás 01:44

Li Kaifu and Wang Xiaochuan Pivot: The First Half of the Large Model Entrepreneurship Era Ends

Li Kaifu and Wang Xiaochuan, leading figures in China's AI industry, are signaling a strategic shift, marking the end of the first phase of the large language model (LLM) startup boom. Li's 01.AI, once seen as a potential "Chinese OpenAI," is now pivoting towards enterprise applications and Agent technology, explicitly modeling itself after the低调但 profitable Palantir with a goal of profitability by 2026. Wang's Baichuan Intelligence is fully转战ing the vertical field of healthcare, launching a medical LLM and AI doctor product. This reflects a broader industry清醒. The initial狂热 of 2023, with its focus on chasing参数, benchmarks, and the "Chinese OpenAI" narrative, has collided with the harsh reality of an AI "heavy industry" war dominated by immense capital expenditure from US tech giants (微软, Google, etc.) and Chinese互联网大厂. The cost of competing in foundational模型 has become prohibitively high for most startups. The paths of the original "Six Tigers" have diverged: some like智谱 and MiniMax achieved high valuations via IPOs, effectively closing the capital window for new通用模型 players. Others, like 01.AI and Baichuan, are retreating from the通用模型 race to focus on商业化 and垂直场景. The deeper change is China's AI sector accepting that its comparative advantage may not lie in foundational model突破 but in applications, engineering, commercialization speed, and integrating AI into real-world industrial and user scenarios—turning AI into a viable industry. Li and Wang, veterans from the互联网 era, represent a generation that entered with理想主义 but is now pragmatically adjusting to reality. Their strategic转身 signifies a交棒 from the狂热造神 phase to a more mature stage focused on sustainable business,合同, and现金流. This isn't a story of failure, but a体面告别 to unrealistic expectations, with the long-term battle ahead passed to a new generation of AI-native builders.

marsbit2 dias atrás 01:30

Li Kaifu and Wang Xiaochuan Pivot: The First Half of the Large Model Entrepreneurship Era Ends

marsbit2 dias atrás 01:30

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