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🧠 BREAKING: U.S. AI safety firm Anthropic says multiple Chinese AI companies, including DeepSeek, Moonshot AI, and MiniMax, ran industrial-scale “distillation” campaigns on its Claude model — generating millions of interactions via ~24,000 fraudulent accounts to extract capabilities for their own models.

🔎 What Anthropic Alleges

The operations involved generating over 16 million exchanges with Claude to illicitly “distill” its advanced reasoning, coding, and tool-use capabilities.

These were unauthorized and violated Anthropic’s terms, according to the company.

Anthropic says it traced the campaigns with “high confidence” using IP, metadata, and infrastructure signals.

The three labs are accused of using proxy services and fake accounts to evade access restrictions.

🧩 What “Distillation” Means Here

Distillation is a legitimate technique where a smaller model is trained on outputs from a larger one. But Anthropic claims the campaigns weren’t benign — instead seeking to shortcut years of research.

It’s a growing flashpoint in the AI race, where access controls and IP protection are increasingly strained.

🛰️ Geopolitical & Security Context

Anthropic does not commercially offer Claude in China and says it restricts access globally for Chinese-owned firms for national security reasons.

Beyond commercial rivalry, the company warns that distilled models lacking U.S. safety guardrails could be repurposed for surveillance, cyber operations, or disinformation tools.

🪪 Reactions So Far
None of the named Chinese firms have publicly responded to the allegations.

This follows similar claims by other U.S. AI labs that Chinese players have sought to replicate capabilities by training on Western model outputs.

#Anthropic #DeepSeek #ClaudeAI #AIRace #ArtificialIntelligence

🚨 The Efficiency Trap: Why "Better" AI Might Be the Economy’s Worst Enemy 📉

​The biggest threat isn't AI failing—it’s AI working too well. Here’s the "Citrini" thesis on how AI success triggers a 2008-scale crisis:

​The "Ghost GDP" Trap: AI drives massive productivity, but wages collapse. Machines produce; humans lose the income to consume.

​The Consumption Crater: AI hits high-earners (coding, legal, finance). When the top 20% stop spending, the consumer economy stalls. $BAS

​The SaaS Death Spiral: Companies use AI to build internal tools, firing vendors and cutting software seats. SaaS firms then fire staff to survive, accelerating the loop. $BULLA

​The Agent Economy: AI agents don't have "brand loyalty." They hunt for the absolute lowest prices and cheapest payment rails (Stablecoins), erasing corporate margins.

​The Credit Crunch: If human income structurally weakens, mortgages default and private credit markets buckle. $PIPPIN

​The Bottom Line: Rate cuts can't fix a world where a bot costs $0.05 and a human costs $50. We are trading long-term economic stability for short-term corporate efficiency.

#AIRace #EconomicStability #WhenWillCLARITYActPass

🇺🇸 AI Race: Energy Gap Threatens US Dominance

​Donald J. Trump has challenged a WSJ report on China's AI power advantage, insisting the US is "leading the World in AI, BY FAR." While the US holds a massive lead in AI models and computing capacity, the global race is constrained by the "electron gap"—the fundamental power capacity needed for next-generation models.

​🇨🇳 China's Power Edge

​Massive Buildout: China added 8x more new power capacity last year (429 GW) than the US (51 GW).

​Total Capacity: China's total annual electricity production is nearly double that of the US.

​Low Cost: Wholesale electricity rates for Chinese data centers are often as low as 3 cents per kWh, significantly cheaper than US rates (7-9 cents).

​Future Capacity: Projected to have over 400 GW of spare capacity by 2030, fueling long-term scaling.

​🇺🇸 US Challenges

​Grid Constraint: The national grid is struggling to handle surging AI data center demand, which is projected to require 44 GW of new capacity by 2028—a supply shortfall of almost 20 GW.

​Infrastructure Delays:

​Permitting: Building new transmission lines and power sources can take years, hindering quick-scale projects.

​Transformer Shortage: The US relies heavily on foreign-made components, and supply chain issues have caused wait times for crucial transformers to stretch from months to multiple years (in some cases, up to 5 years).

​Self-Powering vs. Scaling: While new US facilities are generating their own power (as Trump noted), this is often a strategy to bypass the strained public grid, not an indicator of readily scalable national infrastructure.

​In Short: The US leads in AI innovation (the software), but China is rapidly building the necessary physical energy infrastructure (the fuel) at a scale and speed the US cannot currently match, raising concerns about America's long-term competitive edge in the global AI race.

#AIRace
#PowerConsumption
#BinanceSquareFamily

$GAIX $GAIB $BAS

The AI Crown Shifts: Google Surpasses Apple in Market Value

​The global tech hierarchy has been rewritten. Driven by the massive success of its Gemini 3 ecosystem, Alphabet (Google) has officially overtaken Apple to become the world’s second most valuable company by market cap.

​The Core Shift

​AI Integration: While Apple’s "Apple Intelligence" faced rollout delays, Google successfully integrated Gemini 3 across Search, Cloud, and Android, proving it can monetize generative AI at scale.

​Silicon Independence: Google’s internal AI chips (TPUs) have reduced its reliance on Nvidia, allowing for higher profit margins and faster innovation than hardware-centric rivals.

​Future Bets: Momentum in Waymo’s global expansion has given Google a "moonshot" edge that Apple lacks following the cancellation of its automotive projects.

​The New "Big Three" (Jan 2026)
​Nvidia: The infrastructure king ($4.6T)
​Alphabet: The AI software leader ($3.9T)
​Apple: The hardware giant ($3.8T)

Wall Street is now betting on AI infrastructure and software over traditional consumer hardware cycles.

#AIRace
#Token2049Singapore
#BinanceSquareFamily

$PARTI $JASMY $GALA