Accelerating Value with AI

We hear consistently from the leadership teams at our companies that while they understand the imperative to be investing in more advanced technologies, knowing where to begin is a real challenge. At our CEO Conference in early March, where we convened over 200 CEOs from the Blackstone ecosystem, almost 60% told us that access to talent and understanding the highest value applications of AI for their business were the primary barriers holding them back.

As investors, we believe the capabilities that we have built around data science and AI give us a meaningful advantage, from driving real value in our businesses to seeking returns for our limited partners. Our 50-plus data scientists are deeply integrated into our investing process for our private market equity-oriented strategies, and they have worked directly with leadership at over 70 of our companies to help deploy advanced analytics and AI.

The scale of our platform as the world’s largest alternative asset manager provides further benefits. It allows us to derive insights from over 230 portfolio companies and over 12,500 real estate assets, and to develop technologies and applications that we can deploy across our portfolio. Our model’s been successful, with applications like pricing, opportunity prioritization, labor staffing, and generative AI-based content creation and customer engagement driving meaningful impact. We aim to effectively leverage the central infrastructure and application layers to accelerate the deployment of these technologies into our companies, driving faster time-to-value and meaningfully de-risking the investments.

Our strategy in helping our companies drive value creation through AI considers a dual-pronged approach. On the one hand, we are seeking to broadly enable all our companies by providing the tools, resources, and preferred partnerships they need to be successful. This includes access to a 300+ strong community of advanced analytics leaders, team members, and C-level executives across our companies. This community is powered by thought leadership, successful case studies of applied AI and data science, and opportunities to connect with peers.

We also engage on a more hands-on basis with some of our larger controlled portfolio companies where we help define their AI strategy, build and deploy models, and hire the leadership they need to take their initiatives forward. Further, we selectively deploy data scientists from our team to help companies build capabilities on the ground via our secondment program, which has been a central feature of many of our most impactful engagements.

But AI and data science aren’t just tools that we apply to strengthen our companies; they’re often a part of the way we determine which companies to invest in. Our decision-making process frequently includes using more robust quantitative techniques to underwrite the financial and operating performance of potential targets and providing a qualitative assessment of a target’s claims about their AI capabilities. We recently evaluated a company that made significant claims about the data moat that they built being hard for competitors to replicate. In just a few hours, we were able to replicate the performance of their models by rapidly prototyping a competitive technology using the latest large language models. The insights we gained significantly changed our perception of the disruption risk for the business and contributed to our decision to pass on the deal.

We’re also proactively pursuing companies that we view as poised to directly benefit from or central to enabling the AI megatrend. Investments like Vectra, which uses AI to interrupt cyberattacks in the cloud, exemplify the former, more direct approach; our investments in data centers, like QTS, exemplify the latter, more derivative approach. Our 2021 acquisition of QTS, the largest data center provider by leased megawatt in the US, was the largest transaction in data center history at the time.