Anthropic Hits 380 Billion

In this exclusive report from AI Secret, we bring you the latest developments in artificial intelligence. AI Secret is the world’s #1 AI newsletter, providing daily insights into OpenAI, Anthropic, Google, Microsoft, and the broader AI ecosystem.

🛎️ Anthropic Hits 380 Billion

Good Morning, AI Enthusiasts!

AI is becoming labor infrastructure, capital magnet, and geopolitical asset — all at once.

Anthropic Hits 380 Billion

👀What’s happening:Anthropic just raised $30 billion, pushing its valuation to $380 billion,more than double its last round. Claude Code revenue is running above $2.5 billion, with total annualized revenue at $14 billion. Enterprise subscriptions have quadrupled. Separately, AISecret interviewedMyClaw.aion compute usage, and over 90 percent of users are independently choosing Claude Opus 4.6 to run OpenClaw.

🌍How this hits reality:Software multiples were already under pressure. Now capital and user behavior are aligning around foundation models that execute workflows directly. Claude Cowork plugins triggered aglobal software selloff. When agents users freely select Opus 4.6 at that scale, it signals perceived performance advantage, not contractual bundling. Model preference becomes a market signal, not marketing.

🛎️Key takeaway:This is voluntary consolidation around capability. If users keep routing serious workloads to the same model tier, pricing power and enterprise gravity concentrate at the model layer, and traditional SaaS loses negotiating leverage.

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OpenAI Accuses DeepSeek of Model Theft

👀What’s happening:OpenAI has submitted a formal memo to US lawmakers accusing DeepSeek ofsystematically distilling outputsfrom top American models to train its own systems. The allegation is not casual. It claims structured extraction at scale. This shifts the issue fromonline debateinto regulatory territory.

🌍How this hits reality:Large labs are spending tens of billions on compute, data pipelines, and energy contracts. If a competitor can query those models, compress the answers, and replicate performance at lower cost, the economics invert. API access becomes a data refinery. Training advantage shifts from raw capability to harvesting efficiency.

🛎️Key takeaway:If this path is normalized and proven true, frontier AI competition stops being about who builds better models. It becomes about who can extract, filter, and distill faster. The moat moves from intelligence to capture infrastructure.

IBM Reverses AI Hiring Freeze

👀 What’s happening:IBM istripling entry level hiringafter years of signaling AI would replace thousands of back office roles. The company had paused recruitment and targeted roughly 7,800 administrative jobs for automation. Now HR leadership says many junior roles are being redesigned, not eliminated, because AI tools failed to fully replace human oversight and coordination.

🌍 How this hits reality:For two years the assumption was that generative AI would hollow out entry pipelines first. CFO models were built around 20 to 30 percent labor efficiency gains in support and operations. IBM’s pivot suggests the bottleneck issupervision and judgment. If AI output requires constant review, labor costs do not disappear. They shift.

🛎️Key takeaway:If early adopters cannot remove junior layers, enterprise AI becomes augmentation, not substitution. Expect slower workforce contraction, higher oversight staffing, and renewed competition for entry talent trained to manage automated systems.

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