# Сопутствующие статьи по теме AI

Новостной центр HTX предлагает последние статьи и углубленный анализ по "AI", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

Metrics Ventures Market Watch: The Brewing Storm

In the past month, the market has been actively trading contrasting expectations, balancing global supply chain disruptions fueling re-inflation against both actual and anticipated (Walsh) interest rate hikes. This volatility has impacted commodities and most equities, though tech has temporarily benefited from concentrated short-term liquidity. Fundamentally, as previously analyzed regarding the Strait of Hormuz situation, the US faces deep-seated balance sheet issues beyond what any single Fed chair can resolve. Hypotheses around a figure like Walsh could only materialize if AI fundamentally reshapes production relations. Until then, most non-AI-leading nations (effectively all except the US and China) risk fiscal and monetary policy collapse, rendering the identity of the Fed chair ultimately irrelevant. For crypto assets, there is currently no clear role in these dominant narratives. The market remains strongly capped by the 200-day moving average. While trends may shift from "anything but AI" to "anything but mines," this phase is dominated by the silicon vs. carbon (AI vs. traditional) dichotomy, leaving little room for crypto—though its time will come. **Market Overview & Commentary** The crypto market lacks significant catalysts beyond hype, plagued by low volume and scarce innovation, with clear technical resistance. Currently, crypto struggles for attention as global focus lies elsewhere. Assets like gold, oil, and grains are more direct hedges against supply-chain-driven inflation/stagflation. Bitcoin needs more time for capitulation and consolidation; this reset is expected to last until at least Q4 2026. Looking ahead, three factors will likely drive future market volatility: 1. Whether Walsh repeats the patterns of predecessors like Bassant or Musk, shifting stance into a new policy cycle. 2. The market underestimates the severity of global supply chain damage and the prolonged time needed for repair, which will eventually lead to recognition of acute resource shortages and price swings. 3. AI non-beneficiary, high-inflation nations (e.g., UK, Japan) will face severe fiscal and monetary crises. Rapid AI-driven displacement could trigger a collapse of existing credit and welfare systems. Ultimately, the market may realize that an AI bubble burst could spark contagious sovereign credit crises. The monetary and fiscal responses to such a scenario could serve as the ultimate catalyst for Bitcoin's next major bull run.

marsbit05/26 07:43

Metrics Ventures Market Watch: The Brewing Storm

marsbit05/26 07:43

Ethereum Reduced to a Chinese Concept Stock

The article titled "Ethereum Becomes a Chinese Concept Stock" presents a critical analysis of Ethereum's perceived decline in market confidence and its structural parallels to Chinese companies listed on US stock exchanges. It begins by noting significant sell-offs by early investors like Wanxiang and key figures like Bankless's Hoffman in 2026, despite Ethereum's strong fundamental activity. The piece questions the erosion of trust in Vitalik Buterin and the Ethereum Foundation (EF), arguing that while other ecosystems have faced founder controversies, Ethereum's issues stem from its internal governance model. The author draws a direct comparison to "China concept stocks," which are Chinese businesses operating globally but reliant on foreign capital and listings. Similarly, Ethereum, funded early by Chinese capital like Wanxiang, developed a strong institutional framework from its IXO to its PoS transition. The core problem, according to the article, is a leadership vacuum regarding price and direction. Vitalik's move to make the EF smaller and less active is framed as a mistake. While he advocates for ETH as a "commodity," the ecosystem lacks a clear entity to steward its price stability, creating tension within the PoS system, as seen with Lido's challenges. The narrative suggests that excessive abstraction and a hands-off approach from the EF have left the community adrift, contrasting with more proactive foundations like Solana's. The article then examines emerging technical narratives for Ethereum: privacy (ZK-proofs), AI integration, and a refocus on Layer-1. However, it observes a shift from Ethereum leading as a "world computer" to merely adapting to trends like AI, where crypto-native projects are finding success independently of Ethereum. The piece posits that Ethereum's unique value in an increasingly fragmented world may be as a permissionless, global financial testing ground—a neutral platform amid geopolitical tensions. In conclusion, it asserts that Ethereum's fate mirrors that of China concept stocks: an asset born from one region (conceptually "A"), funded by another ("B"), and dependent on "B" for exit liquidity. While Ethereum's "golden age" may be over, and selling pressure from early backers will continue, it remains positioned as a critical linkage point in a divided global landscape, standing at a new, albeit uncertain, starting point.

marsbit05/26 07:17

Ethereum Reduced to a Chinese Concept Stock

marsbit05/26 07:17

From Power Infrastructure to Token Economy: The 'Seven-Layer Cake' of the AI Industry Chain

From Power Grid to Token Economy: The AI Industry's "Seven-Layer Cake" The AI industry is shifting from a "model-centric" paradigm focused on massive training to a "token-centric" industrial era driven by inference demand. This new phase revolves around the production, distribution, scheduling, and consumption of tokens—the units of computation used by AI agents for every interaction and task. The article proposes a "seven-layer cake" framework for the AI economy: 1. **Power**: The foundational energy source, with competition shifting to securing stable, low-cost electricity. 2. **AIDC (AI Data Centers)**: Large-scale "Token factories." A trend toward smaller, modular, and regionally deployed AI Factories is emerging for efficiency and proximity to users. 3. **GPU**: The core production hardware for tokens. While NVIDIA dominates, competition exists from AMD, ASIC makers, and Chinese chipmakers, with a growing focus on inference efficiency. 4. **LLMs**: The "engines" that generate tokens. The competition is evolving beyond model size to prioritize factors like token cost, inference efficiency, and operational synergy with infrastructure. 5. **Token Distribution**: The "grid" that allocates and rents out compute resources, led by cloud giants and specialized AI-native platforms. 6. **Token Optimization & Intelligent Scheduling**: The critical "brain" layer that intelligently routes tasks (e.g., to local, cloud, or edge models) for optimal cost, latency, and privacy—maximizing the value of each token. 7. **AI Agents & Models**: The end consumers of tokens. The vision involves billions of AI agents working and interacting concurrently, consuming vast amounts of tokens. Currently, the industry faces fragmentation and inefficiencies between these layers. The true "mass adoption era" of AI will begin only when this seven-layer infrastructure is fully integrated and operates as a cohesive, intelligent network—transforming AI from a software tool into a global industrial system spanning energy, hardware, and compute logistics.

marsbit05/26 05:43

From Power Infrastructure to Token Economy: The 'Seven-Layer Cake' of the AI Industry Chain

marsbit05/26 05:43

Semiconductors up 78% annually, software down 12% annually: The 'Liquidity Siphon' is playing out within tech stocks

Semiconductor ETFs like SOXX have surged 78.5% year-to-date, while software ETFs like IGV have dropped 12.5%, creating a record performance gap exceeding 90 percentage points. This reflects a major "liquidity suction" within tech stocks, with capital flooding into semiconductors as software faces selling pressure. Driving the semiconductor boom are staggering capital expenditure plans from hyperscalers like Microsoft, Alphabet, Amazon, and Meta, whose combined 2026 capex is projected near $700 billion. This fuels demand for chips, with companies like SanDisk (up 426%), Intel (up 222%), and Micron (up 154%) leading the S&P 500. In contrast, major software firms like Microsoft, Adobe, and Salesforce are all down over 17% year-to-date. The software sector faces a dual challenge: capital is being redirected to semiconductors, and the rise of AI agents like Claude Code threatens traditional SaaS business models, triggering a narrative of AI displacement. Key unanswered questions remain: How long can hyperscalers sustain their massive capex, given potential free cash flow pressures? And will capital eventually rotate back into the deeply oversold software sector? While some analysts warn of a potential semiconductor bubble akin to the dot-com era, the sector's powerful momentum continues, making market timing exceptionally difficult.

marsbit05/26 05:43

Semiconductors up 78% annually, software down 12% annually: The 'Liquidity Siphon' is playing out within tech stocks

marsbit05/26 05:43

Notion CEO: AI companies should be a 'Jazz Band,' and I am a 'Refounder'

Notion CEO Ivan Zhao, in a recent podcast, shared his journey of twice rebuilding the company from near-collapse and now applying the same "Refounder" mindset to reshape the 1000-person organization in the AI era. He argues that AI has commoditized technical capability (Capability). True talent now hinges on Taste (judgment/values) and Agency (proactive drive), necessitating a shift in hiring—e.g., hiring more juniors for curiosity and having sales candidates demonstrate work upfront. Zhao envisions the company as a "Jazz Band"—agile and improvisational—versus a rigid "Marching Band." This is reflected in an engineering "dumbbell" structure (super juniors + top-tier seniors, with middle layers compressed), dissolving the CMO role to let teams operate directly, and integrating entrepreneurs via acquisitions to lead their expertise areas. Notion has abandoned traditional long-term product roadmaps, planning only conservatively for finances while adopting a week-by-week, improvisational approach to product strategy, as longer plans proved futile during rapid AI shifts. He concludes that while human nature and roles remain constants, companies must rewrite their approaches to hiring (valuing Taste/Agency over Capability), organizational design (reducing roles focused on coordination/execution), and planning (embracing flexibility). Modern knowledge work, being only ~150 years old, is ripe for reinvention.

marsbit05/26 04:43

Notion CEO: AI companies should be a 'Jazz Band,' and I am a 'Refounder'

marsbit05/26 04:43

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