MEXC to List KernelDAO ($KERNEL) With a 135,000 USDT Prize Pool

TheNewsCryptoPublicado a 2025-04-10Actualizado a 2025-04-10

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When LPs Teach Me Investment with Doubao: A Self-Narrative of a Private Equity GP Switching Careers

When LPs Use Doubao to Teach Investing: A Transition Story of a Private Equity GP AI is making life increasingly difficult for small private equity fund managers, as a former GP of an offshore dollar fund reveals. The fund, managing tens of millions in US stocks, outperformed the Nasdaq but struggled with fundraising. Its traditional Cayman SPC/BVI structure failed to attract major Asian LPs, who now prefer Hong Kong LPF or Singapore VCC frameworks. The rise of AI-powered quantitative strategies has further squeezed the space for funds like his, which relied on subjective, discretionary investing. AI tools have leveled the information playing field, empowering LPs—often high-net-worth individuals, entrepreneurs, or family offices—to analyze investments themselves using chatbots like Doubao. This has eroded trust in GPs' expertise, leading to more frequent challenges over investment decisions and even withdrawals, especially during market rallies when retail investors sometimes outperform funds. Friction arises not necessarily from AI's capabilities but from how LPs use it. Many rely on conversational AI for validation rather than rigorous analysis, sometimes receiving misleading or hallucinated advice. While AI democratizes research, effective investing still requires discerning real insight from plausible-sounding output. Ultimately, AI is unlikely to fully replace GPs. Asset management remains a trust-based service. However, the industry must adapt. The future may see "human私募" (private equity) learning from AI and focusing more on providing value beyond pure analysis—perhaps by mastering the emotional intelligence and trust-building that machines cannot replicate.

Odaily星球日报Hace 25 min(s)

When LPs Teach Me Investment with Doubao: A Self-Narrative of a Private Equity GP Switching Careers

Odaily星球日报Hace 25 min(s)

AI Leads to Layoffs? Research Shows AI Is More Expensive Than the People It Replaces

Title: AI Layoffs? Research Shows AI is More Expensive Than the Workers It Replaces. This year, nearly 50,000 employees have been laid off due to AI, with companies initially believing AI could replace human jobs. However, recent findings indicate that the actual operational costs of AI often exceed the expense of the human labor it was meant to replace. Examples include Uber exhausting its annual AI budget in just four months, Microsoft cutting Claude Code licenses due to high costs, and an Anthropic employee incurring $150,000 in API usage in a single month. A CloudZero survey reveals that 45% of enterprises spend over $100,000 monthly on AI, yet only 8% of S&P 500 companies report any AI-related revenue, and half struggle to measure ROI. Analyst Scott Galloway predicts a shift toward cheaper Chinese AI models, which are 10 to 30 times more affordable than American counterparts. Data shows Chinese models' usage among developers surged from 1% in 2024 to over 60% by mid-2026, with 80% of U.S. AI startups adopting them. This trend may prompt regulatory responses, such as potential restrictions from the Trump administration.

marsbitHace 39 min(s)

AI Leads to Layoffs? Research Shows AI Is More Expensive Than the People It Replaces

marsbitHace 39 min(s)

Wang Chuan: After Investing in Storage Stocks and Seeing a Thirty-Fold Return, How to Remain Unanxious (Part 7) - A Quarter-Century Cycle

Wang Chuan: Reflections on Investment Anxiety and Market Cycles After Observing a 30x Gain in a Storage Stock (Part 7) – A Quarter-Century Cycle This article examines the cyclical nature and inherent risks in technology hardware investments, using the storage and semiconductor sectors as examples. It criticizes the misleading practice of "annualized" Net Dollar Retention (NDR) rates, where short-term growth is extrapolated unrealistically. A key concept explored is "reflexivity" – demand driven by panic, exploration, and liquidity during market booms, which can vanish just as quickly when conditions reverse. This reflexivity exists both in product demand and among speculative stock buyers, creating powerful feedback loops that inflate prices during upturns and exacerbate crashes during downturns. The author highlights a major risk for hardware sectors: unlike assets with defined cycles (e.g., Bitcoin's halving), there's no guarantee of a swift recovery post-crash. Companies like Micron, Intel, and Cisco took roughly a quarter-century to surpass their 2000 highs, enduring drawdowns exceeding 80%. This is attributed to the "bullwhip effect" in supply chains, where demand collapses instantly but过剩产能 persists, and a migration of narrative-driven capital. High-valuation stories吸引 speculative funds during growth phases, but these funds quickly depart for the next hot narrative once growth slows, leaving behind stronger companies with much lower valuations. The piece warns of dangerous mental models formed during bull markets: 1) equating current strong demand with perpetual high growth, and 2) believing that making fast, large profits is easy. Citing巴菲特, the author notes that easy money undermines rationality, likening speculators to Cinderella at a ball with a clock that has no hands. The current phase presents an asymmetric risk-reward scenario: potential for further gains exists, but the downside risk is an 80%+ drawdown and a multi-decade wait for breakeven, which reflexive speculators cannot tolerate. The hypothetical investor "老王" (Lao Wang), who achieved a 30x return, is used to illustrate potential pitfalls. Leverage could lead to a wipeout during a sharp correction. Even without leverage, ingrained beliefs in easy money would likely lead him to double down after losses, expecting a quick rebound. Instead, he might face a protracted decline, depleting his resources through frantic trading as the high-growth narrative fades. The conclusion references Schopenhauer, comparing those who have seen multiple market cycles to an audience seeing the same magic trick repeatedly—once the illusion is understood, its power is gone.

marsbitHace 48 min(s)

Wang Chuan: After Investing in Storage Stocks and Seeing a Thirty-Fold Return, How to Remain Unanxious (Part 7) - A Quarter-Century Cycle

marsbitHace 48 min(s)

US Stocks Too Expensive? This Top CIO Scoured the Globe and Found 5 Stocks More Attractive Than NVIDIA

Summary: Main Street Research CIO James Demmert maintains his bullish 8,100 target for the S&P 500 but argues that greater opportunities now lie overseas. He identifies five international stocks with superior valuations poised to benefit from the AI revolution, suggesting international markets will outperform the US for years. Key Recommendations: 1. **ASML (Netherlands):** A foundational chip manufacturing technology provider, offering crucial AI exposure and geographic diversification. Demmert's top long-term pick. 2. **HSBC (UK/Asia):** A global bank with a 9x P/E ratio, better growth prospects than US peers like JPMorgan, and strong Asian presence. 3. **Siemens Energy (Germany):** A direct play on global power grid expansion driven by AI, crypto, and EV electricity demand. 4. **BHP Group (Australia):** A "hidden AI play" and "second derivative" of the trend due to massive copper demand for data centers. Trades at a 16x P/E. 5. **AstraZeneca (UK):** An undervalued healthcare stock with a strong pipeline (18x P/E, >20% growth), expected to benefit from AI's impact on medicine. Core Thesis: International outperformance is driven by both attractive valuations and a major policy shift. While the US tightens fiscal policy, Europe and Japan are launching unprecedented stimulus, reigniting growth. Demmert recommends allocating 45% of a portfolio internationally, citing excessive US investor conservatism as a key mistake.

marsbitHace 53 min(s)

US Stocks Too Expensive? This Top CIO Scoured the Globe and Found 5 Stocks More Attractive Than NVIDIA

marsbitHace 53 min(s)

a16z Partner: Three Paths for Crypto Projects to Find PMF

Author: Jason Rosenthal. Compiler: Shenchao TechFlow. Finding Product-Market Fit (PMF) is the most critical variable for a company's survival. In the crypto space, misaligned growth hacking and airdrops often mask the absence of true PMF. However, leading teams are now finding PMF faster. Here are three proven paths for crypto projects to achieve PMF: 1. **Co-build with Anchor Clients:** Partner with the most sophisticated potential clients in your field and develop the product based on their specific needs. Their adoption serves as the strongest validation, more valuable than media coverage or TVL metrics. This approach is shaping current product roadmaps, as seen in collaborations between crypto startups and traditional finance. 2. **Position Ahead of an Exponential Curve:** Identify and position yourself ahead of a major emerging trend before the market fully realizes it. The most evident current curve is the rise of AI Agents as autonomous economic actors. Projects like AgentCash by Merit Systems, which enables AI Agents to pay for API access with crypto, are building foundational payment rails for the impending Agent economy. 3. **Be Your Own First and Best Customer:** The most enduring infrastructure companies don't wait for external validation. They first build and prove their technology by using it to power their own applications at scale before offering it to others. Matter Labs exemplifies this by anchoring its ZKsync technology in a concrete application, Cari Network, which enables U.S. regional banks to conduct real-time, on-chain interbank transfers of tokenized deposits. The underlying logic is consistent: the fastest path to PMF involves choosing the right battlefield and executing with conviction—by co-building with clients whose validation compounds, positioning ahead of the curve before consensus forms, or becoming your own best case study.

marsbitHace 53 min(s)

a16z Partner: Three Paths for Crypto Projects to Find PMF

marsbitHace 53 min(s)

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Cómo comprar KERNEL

¡Bienvenido a HTX.com! Hemos hecho que comprar KernelDao (KERNEL) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar KernelDao (KERNEL) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu KernelDao (KERNEL)Después de comprar tu KernelDao (KERNEL), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear KernelDao (KERNEL)Tradear fácilmente con KernelDao (KERNEL) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

180 Vistas totalesPublicado en 2025.04.10Actualizado en 2026.06.02

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Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de KERNEL (KERNEL).