SBI Holdings is investing ¥500 million in Anthropic's Claude to build AI agents for enterprise automation.
The company expects this to deliver ¥2.7 billion in new revenue, a 5.4× return.
SBI chairman Kitao Yoshitaka distinguished his Anthropic strategy from SoftBank's OpenAI alignment, emphasizing customer data integration and security over general-purpose AI.
What happened
SBI Holdings is deploying ¥500 million to develop AI agents (autonomous software that performs tasks) using Anthropic's Claude language model and establishing an "AI transformer" — a system that converts and processes customer data across SBI's businesses. The company projects this will generate ¥2.7 billion in incremental revenue, a 5.4× return on the investment.
Why it matters
SBI chairman Kitao Yoshitaka publicly contrasted his Anthropic bet against SoftBank founder Son Masayoshi's OpenAI focus, signaling a distinct strategic choice. The AI transformer approach targets enterprise customers by automating task chains across different data systems, reducing manual work and speeding up service delivery — a concrete business application beyond general-purpose AI chatbots.
What to watch
SBI is implementing security innovations including a password-free authentication system and technology partner EVERSPIN, with a system-wide defensive architecture expected to be in place soon. The company frames the ¥2.7 billion revenue forecast as an "objective" milestone, contingent on full deployment of the AI agent system across customer touchpoints.
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SBI's ¥500 million commitment to Anthropic reflects a deliberate divergence from the OpenAI-dominated narrative in Japanese technology leadership. Kitao Yoshitaka's public framing — "Son is OpenAI, I am Anthropic" — signals both a strategic bet on a rival AI platform and a claim to a distinct enterprise-automation use case. The real substance lies not in the LLM vendor choice alone, but in SBI's stated goal to build an "AI transformer" that orchestrates customer data across disparate business systems and automates multi-step workflows. This is a harder engineering and organizational problem than deploying a single chatbot, and SBI's ¥2.7 billion revenue projection (a 5.4× multiple on investment) is contingent on successfully integrating these agents into live customer-facing operations. The company is also laying groundwork on security — passwordless authentication, partnerships with EVERSPIN, and a system-wide defensive architecture — which suggests SBI views this as mission-critical enterprise infrastructure, not an experimental AI feature. Whether the ¥2.7 billion target materializes depends on SBI's ability to automate tasks at scale across its customer base without operational friction.
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