KKR Shares Drop 1.33% as $450M Volume Surges 51.89% to 227th Rank Strategic AI Advisor Appointment Targets AI-Driven Infrastructure

. 5, , . The decline followed the firm’s strategic appointment of , former Amazon Web Services CEO, as a senior advisor for technology and AI strategy. Selipsky brings over two decades of experience in scaling cloud infrastructure and leading data analytics firms, including his tenure at AWS and Tableau. , particularly in aligning digital infrastructure with AI-driven demand for compute, energy, and connectivity.

KKR’s strategy underlines the growing importance of AI in reshaping infrastructure requirements. . , . Recent moves, such as investments in Gulf Data Hub and partnerships with CyrusOne, highlight KKR’s focus on consolidating assets to meet hyperscaler and AI developer needs. Selipsky’s expertise in managing cloud infrastructure transitions and sustainability efforts at AWS is expected to accelerate KKR’s integration of power, data centers, and connectivity into a unified AI infrastructure platform.

KKR’s leadership emphasized the transformative potential of AI in creating bottlenecks and opportunities in infrastructure. , head of digital infrastructure, noted the need for coordinated strategies to address power demands, cooling, and network capacity challenges posed by AI workloads. Selipsky’s appointment is seen as a pivotal step in positioning KKR as a leading investor in AI infrastructure, leveraging his experience in scaling AWS and navigating the technical complexities of machine learning applications.

I’d like to clarify a few points to make sure the back-test accurately reflects what you have in mind and stays technically feasible: 1. Universe • Should we consider all U.S. common stocks (NYSE + NASDAQ) or a narrower universe (e.g., S&P 1500 constituents, Russell 3000, etc.)? 2. “Top 500 by daily trading volume” • Do you want the portfolio re-selected every trading day (i.e., dynamically pick that day’s highest-volume 500 names), or is it acceptable to define a static list (e.g., the 500 tickers with the highest average volume over a recent look-back period) and rebalance daily? 3. Execution price • Buy at the day’s close and sell at the next day’s close? 4. Transaction costs / slippage • Any commission or slippage assumptions to include? 5. Equal-weighting • Should each of the 500 positions be equally weighted at the time of entry? With these details confirmed, I can set up the data retrieval and run the back-test from 2022-01-03 through today.