Blackstone is embedding roughly 50 full-time AI engineers across its deal teams to prototype and deploy AI tools that solve specific business problems. Rather than building products separately, these engineers work alongside investors and analysts day-to-day, shadowing workflows and rapidly testing solutions. The strategy reflects Blackstone's broader $150 billion（約24兆円） data center commitment and ambition to become "McKinsey for AI," spreading LLM-based transformation across the economy.
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Blackstone has deployed roughly 50 full-time applied AI engineers across its investing and operating teams to prototype AI tools. These engineers, like Sophia Oguri on the private equity team, work embedded with deal teams to understand workflows, build prototypes (often in Python), and identify time-consuming manual tasks that AI can address. Some prototypes have grown into firm-wide tools such as Secure Chat and Document AI.
Why it matters
Blackstone has committed more than $150 billion（約24兆円） to data center bets and plans to create "McKinsey for AI" to spread LLM-based transformation across the economy. The embedded model—where technologists sit alongside business teams day-to-day—ensures AI solutions solve real problems rather than building solutions in isolation. This approach grew from the firm's existing data analytics model, now expanded to generative AI.
What to watch
Success depends on cross-team alignment, which requires extensive meetings between business and tech teams. Oguri notes that mornings are often heavy with meetings to align needs, while afternoons are reserved for building prototypes. The approach extends beyond internal tools: Oguri also supports portfolio companies on AI and data strategy, speaking at the firm's late 2025 portfolio company technology conference.
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