🚨 UPDATE: Strategy is now sitting on its largest-ever $BTC unrealized loss at $10.8B after six years of accumulation.

🚨 UPDATE: Virtuals protocol to move $700M in $VIRTUAL to Chainlink CCIP for secure cross-chain AI agent payments.

🚨 UPDATE: Figure AI scales production 24x to 1 robot per hour, set to build 55 humanoids this week.

🇨🇳 LATEST: China's KAI Robotics launches multi-purpose humanoid robot that can learn tasks without updates.

🐋 WHALE ALERT: OG Shiba whale who turned $13K into $8.9B at peak, sells 800B Shiba Inu, still holding $625M worth.

🚨 ALERT: Wasabi Protocol exploited for $5M across multiple chains, including Ethereum and Base, per PeckShield.

🚨 LATEST: Elon Musk said in court that while some cryptocurrencies have merit, most are scams, during testimony in his lawsuit against OpenAI.

🇧🇷 NEW: Brazil central bank bans crypto use in regulated cross border payment settlements.

Before judging @StringMetaverse, I’d rather hear the team explain it directly. Gaming revenue structure, Hong Kong/Singapore/Dubai subsidiaries, the reported $920M activity, and post-bonus roadmap. The upcoming session could separate real signals from timeline noise.

Most FUD around @StringMetaverse seems to come from people treating it like a typical Web3 hype play. But the company has a different structure. It is listed, reports financials, has public disclosures, and has already reported ₹278Cr Q3 revenue with ₹27Cr profit. The retail-focused bonus move also stands out because promoter entitlement was waived for public shareholders. That is not a small signal. The fair debate should be around execution, roadmap, and how the company scales from here. Hopefully the upcoming session gives people a clearer picture instead of leaving the timeline to guess.

📅 Worst month for DeFi, 25 protocols hacked in past 30 days ($624,000,000 total) KelpDAO — $293,000,000 Drift — $285,000,000 Rhea Lend — $18,400,000 Grinex — $15,000,000 Volo Vault — $3,500,000 Hyperbridge — $2,500,000 BSC TMM/USDT — $1,665,000 Giddy — $1,300,000 Purrlend — $1,500,000 Aftermath Finance — $1,140,000 LML/USDT Staking — $950,000 Aethir — $423,000 Singularity Finance — $413,000 Dango — $410,000 Silo V2 — $392,000 ZetaChain — $300,000 Judao — $228,000 Scallop Lend — $150,000 Zerion Wallet — $100,000 Kipseli — $80,000 MONA — $60,950 SubQuery Network — $60,000 Juicebox V3 — $52,000 Thetanuts Finance — $50,000 Someone needs to stop this 🙏

The AI Market Is Not One Market Anymore The confusion around AI startups in 2026 comes from treating them as if they belong to the same category. They do not. The same label now covers companies with revenue, companies with strategic leverage, and companies funded on belief alone. Valuations look inconsistent because they are measuring different things. This becomes clear once you separate the market into three parts. The first group is anchored in revenue and usage. Databricks (@databricks) is the cleanest example. The company reported a $5.4 billion revenue run rate, growing more than 65 percent year over year, with net retention above 140 percent and hundreds of customers spending more than $1 million annually. Its AI products alone crossed a $1 billion run rate. These numbers resemble enterprise infrastructure scaling, not speculative growth. Cursor, built by Anysphere (@cursor_ai), shows a different version of the same pattern. The company raised billions at a valuation near $30 billion after rapid enterprise adoption. The important detail is not the exact growth multiple. It is where the product sits. If code editors become the environment where software is written, reviewed, and deployed, ownership of that surface creates durable leverage. Harvey AI (@harvey_ai) operates inside legal workflows where risk tolerance is low and generic tools fail quickly. The company reports thousands of deployed agents across customers and reached an $11 billion valuation. The significance lies in workflow integration. Once embedded, replacement becomes costly in both time and risk. Cohere (@cohere) operates at another layer. Its valuation, combined with strategic moves like acquiring Aleph Alpha, reflects something enterprise buyers care about but retail narratives ignore. Control over where data lives, how models are deployed, and who has access to them. In regulated environments, that matters more than marginal model improvements. These companies share one trait. Their valuations are at least partially anchored to usage, customers, or infrastructure that enterprises rely on. Now look at the second market. OpenAI (@OpenAI), Anthropic (@AnthropicAI), and xAI (@xai) fall into this category. Their valuations, ranging from tens to hundreds of billions, reflect their position as providers of frontier intelligence. These companies are not selling features. They are selling access to capability. Mistral AI (@MistralAI) represents another dimension of the same logic. Its valuation is not just about model quality. It reflects Europe’s need for a credible domestic AI provider. Sovereignty is now a pricing factor. Scale AI (@scale_ai) occupies a different but equally important position. Training data, labeling, and evaluation pipelines determine how models improve. Control over this layer gives influence over model performance across multiple providers. In all these cases, revenue matters less than position. The question investors are asking is whether the company controls something others depend on. Then there is a third category. Companies like Safe Superintelligence and Thinking Machines Lab. They have minimal public product. Limited disclosed metrics. Enormous valuations. The only way to make sense of them is to understand what is being priced. It is not current performance. It is the probability that a small number of teams can still shape frontier AI outcomes. It is talent concentration, research direction, and timing. Calling this irrational is tempting but incomplete. The more accurate view is that valuation has become a proxy for different kinds of leverage. Revenue leverage in Databricks. Workflow leverage in Harvey. Developer leverage in Cursor. Infrastructure leverage in OpenAI and Anthropic. Data pipeline leverage in Scale AI. Sovereign leverage in Mistral. Once you see that, the market stops looking inconsistent. It starts looking segmented. This matters because most companies will not win in this environment. Enterprise adoption data shows a clear pattern. Many organizations experiment with AI, but far fewer deploy it into core workflows. The gap between experimentation and production is where budgets tighten and decisions become practical. This is where most startups fail. The filter is not model quality or demo performance. It is whether the product becomes difficult to remove. Databricks is difficult to remove because it sits on enterprise data infrastructure. Cursor becomes difficult to remove if development workflows adapt around it. Harvey becomes difficult to remove if legal processes depend on its outputs. The same logic applies at the infrastructure layer. If access to frontier models or training pipelines becomes standardized through a small number of providers, those providers gain durable leverage. Most companies in the current cycle will not reach that position. The ones that do will define how AI is actually used, not just how it is built. Watching valuation tables will not tell you which ones those are. Understanding what each company controls will.

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🔥 INSIGHT: Bitcoin tends to outperform traditional assets like gold and stocks during geopolitical shocks, per BlackRock research.

🚨 ALERT: CZ shared an article urging caution against fake photos and impersonation scams circulating online.

⚡️ INSIGHT: Chinese AI models DeepSeek and Qwen just outperformed ChatGPT and Grok in crypto trading. Is AI changing the game?

🇺🇸 TRUMP: "New tariffs on China will rise to 155% effective November 1."