Pantera合伙人：基于Base的L3游戏链B3有哪些创新之处？

长文源：区块律动Publicado a 2016-08-24Actualizado a 2024-08-16

Lecturas Relacionadas

Microsoft CEO Satya Nadella's Latest Warning: Betting Entirely on a Single AI Model Hands Over a Company's Lifeblood

Microsoft CEO Satya Nadella warns that companies relying solely on a single AI model could jeopardize their survival. He argues that over-dependence leads to "vendor lock-in," where businesses risk ceding control over their core data, memory, contextual history, and AI usage patterns. This dependence essentially outsources a company's critical thinking and operational know-how to an external provider. The deeper a company integrates with one AI system—feeding it prompts, internal data, and workflows—the more it reveals its unique business methods and competitive edge. This accumulated knowledge could become accessible to the AI supplier. Furthermore, switching providers becomes extremely costly and complex, as companies would need to rebuild their entire AI-augmented workflow, memory, and tool integrations from scratch. Nadella's solution is "decoupling." Companies should separate their proprietary data, memory, and control layer (or "harness") from the underlying AI models. By retaining metadata from every AI interaction, businesses can preserve their operational "brain" or institutional knowledge. This allows them to flexibly use different AI models (e.g., from OpenAI, Anthropic, Microsoft) for specific tasks without losing their accumulated expertise. The core idea: companies can rent the smartest models available, but they must keep their own "brain" and operational control firmly in-house.

marsbitHace 41 min(s)

Microsoft CEO Satya Nadella's Latest Warning: Betting Entirely on a Single AI Model Hands Over a Company's Lifeblood

marsbitHace 41 min(s)

KOSPI Index Halts Trading Due to Eighth Price Limit Up Circuit Breaker Trigger of 2026

The South Korean KOSPI index has triggered a market-wide trading halt for the eighth time in 2026, falling another 8% and losing over 28% in the past month. This latest plunge is driven by a severe sell-off in semiconductor and AI-related stocks. Key contributors include a sharp decline in shares of SK Hynix (down 50% from its June high) and heavyweights like Samsung, which comprises 50% of the index. The drop followed a sector-wide weakness triggered by the IPO of Changxin Memory Technology and falling US chip stocks like Nvidia (-4.4%). Analysts point to massive AI financing deals (like Nvidia's $250B support for OpenAI) raising debt cost concerns and questioning AI scalability viability. The article notes Bitcoin's correlation with AI stocks has weakened, but KOSPI's volatility could redirect some liquidity to crypto markets. Blockchain-based exchanges are exploring KOSPI-linked perpetual futures to allow speculation on further declines or a short squeeze, as traditional altcoin trading in South Korea diminishes amid the index's dramatic swings.

cryptonews.ruHace 41 min(s)

KOSPI Index Halts Trading Due to Eighth Price Limit Up Circuit Breaker Trigger of 2026

cryptonews.ruHace 41 min(s)

Miners Advised Not to Buy GPUs for AI and to Focus on Infrastructure

A founder at an energy investment forum advises bitcoin miners not to purchase GPUs for AI themselves, but to instead focus on infrastructure like power and data center space. Mike Alfred of Alpine Fox stated that while AI infrastructure demand is a long-term, 20-30 year trend, it presents a key choice for miners. The first, riskier model involves owning and operating GPUs, which requires financing expensive hardware that quickly becomes obsolete. The second, more conservative model is akin to real estate: providing colocation services where clients bring their own servers, and the miner sells space, power, cooling, and water. Alfred noted this model is easier to finance. Most existing bitcoin mining sites are difficult and expensive to convert for AI, as AI data centers require far higher construction costs, redundant fiber connections, backup power, complex cooling, and near 100% uptime. A hybrid model, where mining acts as a flexible load to use excess power during AI data center construction or from generation facilities, was discussed. However, participants concluded this is only viable with very cheap power; otherwise, developers are better off focusing solely on AI. Miners are increasingly being evaluated for their available power capacity and project portfolios rather than just bitcoin output. Panelists also warned of risks in the AI sector, predicting at least one major default or contract breach among AI tenants, lenders, or landlords before bitcoin's next halving in 2028.

cryptonews.ruHace 51 min(s)

Miners Advised Not to Buy GPUs for AI and to Focus on Infrastructure

cryptonews.ruHace 51 min(s)

Millisecond 'Pay-to-Cut': How Did Hyperliquid's Priority Fee Turn into a Multi-Million Dollar Business?

"Millisecond 'Paid Queue-Jumping': How Hyperliquid's Priority Fee Became a Multi-Million Dollar Annual Business" In traditional finance, high-frequency trading firms spend millions on infrastructure for millisecond advantages. Hyperliquid has translated this race onto the blockchain with its "Priority Fee" system, creating an open economic game for speed. This system auctions two types of priority: **Gossip Priority** for faster data feeds (via a Dutch auction every 3 minutes), and **Order Priority** for front-of-queue trade execution (users bid a fee for lower latency). This converts a hardware race into a transparent, market-priced mechanism. Since launch, this feature has generated over $5M in protocol revenue. Projected annualized buybacks from this income exceed $30M, accounting for ~7% of total protocol revenue. The demand stems from large traders and market makers on Hyperliquid, for whom milliseconds can mean the difference between profit/loss or avoiding liquidation. Market makers pay these fees as "protection" to ensure their orders execute first, which in turn improves liquidity for all users. Crucially, Hyperliquid internalizes Maximum Extractable Value (MEV) that typically leaks to external validators or searchers, creating a new revenue stream beyond trading fees. The mechanism also strengthens HYPE's tokenomics. While 97% of trading fees fund secondary market buybacks (via the Assistance Fund), Priority Fees are **directly burned**, adding a second deflationary engine. Furthermore, fees for order priority are deducted from users' undelegated HYPE balances, encouraging large traders to hold and lock up tokens, reducing circulating supply. However, a key challenge remains: balancing the speed needs of institutional players with fair market access for retail users, as those who cannot pay high fees may suffer worse slippage during volatility. In summary, Hyperliquid's Priority Fee is a novel model that monetizes latency, captures MEV for the protocol, and enhances its native token's value through burning and lock-ups.

marsbitHace 56 min(s)

Millisecond 'Pay-to-Cut': How Did Hyperliquid's Priority Fee Turn into a Multi-Million Dollar Business?

marsbitHace 56 min(s)

Jito Seizes User Traffic with JTX, Why Is It Still Undervalued by the Market?

Jito, a Solana infrastructure provider, has launched a new product called JTX, which is shifting its business model from being a price-taker on Solana block space to becoming a price-setter for user transaction flow. Despite this strategic shift aimed at capturing and monetizing user traffic, the market continues to undervalue its native token, JTO. Analysts present a valuation case for JTO, arguing the market still prices it as backend infrastructure. Their base case scenario assumes JTX captures 15% of Solana DEX volume by Q2 2027, generating significant annualized revenue. Applying a conservative 30x P/S multiple to this revenue implies a JTO price of $1.18, representing ~57% upside from current levels (~$0.75). A probability-weighted target, including bullish (40% probability, 25% market share) and bearish (10% probability, 5% share) scenarios, points to a target of $2.75, suggesting ~267% potential upside. The thesis hinges on JTX's early positive performance data and its potential to benefit from the expected growth of on-chain trading, particularly from tokenized stocks and real-world assets. Furthermore, an estimated 80% of JTX's net revenue is slated for JTO buybacks, creating a potential value-accrual flywheel. The report concludes that Jito's transformation via JTX is not yet reflected in its valuation.

marsbitHace 1 hora(s)

Jito Seizes User Traffic with JTX, Why Is It Still Undervalued by the Market?

marsbitHace 1 hora(s)

Trading

Spot