Louis Miscioscia

Louis Miscioscia asked why traditional server growth wasn't higher, specifically questioning if inference workloads are primarily deployed on AI servers rather than traditional servers.

Answer

Jeff Clarke, Vice Chairman and Chief Operating Officer, Dell Technologies, explained that traditional server demand outpaced supply in Q4, showing double-digit growth across all use cases and geographies, driven by modernization and consolidation efforts. He noted increasing AI workloads on x86, particularly in enterprises for various use cases including inference. David Kennedy, Executive, Dell Technologies, added that Q1 guidance reflects strong double-digit growth, with moderation expected in the back half due to supply-demand dynamics.

Louis Miscioscia with Daiwa Capital Markets America questioned why traditional server growth wasn't higher, potentially in the solid double-digit range, given strong AI server performance. He also asked if inference deployment was primarily on AI servers rather than traditional servers.

Answer

Jeff Clarke explained that traditional server demand outpaced supply in the quarter, showing double-digit growth across all use cases and geographies, driven by modernization and consolidation. He noted increasing AI workloads on x86, particularly in enterprises for various use cases including inference. David Kennedy added that while Q1 guidance reflects strong double-digit growth, the outlook for the back half of the year is moderated due to supply-demand dynamics.

Louis Miscioscia with Daiwa Capital Markets America Inc. asked about the expected timing for inference applications to significantly drive new storage revenue, acknowledging that compute and network buildout for AI has preceded storage.

Answer

CEO George Kurian reiterated that compute and network buildout for AI would be bigger and earlier than storage, as training large language models doesn't require extensive storage. He explained that storage consumption will increase as inferencing, which accounts for 80% of storage use, becomes a larger part of the AI landscape. He noted current growth in data lake modernization and an uptick in AI proof-of-concepts in fiscal year 2026, with broader enterprise AI adoption being use-case dependent.

Louis Miscioscia asked when inference applications would generate enough data to drive material new storage revenue for NetApp, considering that compute and network buildouts typically precede storage.

Answer

George Kurian (CEO) reiterated that compute/network buildout is larger and earlier than storage, as training models requires less storage. He noted that 80% of storage use comes from inferencing, so storage consumption will increase as inferencing grows. He also mentioned data lake/modernization for large repositories and an expected uptick in proof-of-concepts for FY26, with enterprise AI adoption being use case-specific.

Louis Miscioscia of Daiwa Capital Markets America asked if a 'step function' of growth for storage could be anticipated in calendar 2026 as enterprise AI inference workloads begin to ramp up significantly.

Answer

CEO George Kurian agreed that the real business impact of AI will come from inferencing. He noted that enterprises are currently in the first phase of modernizing their data infrastructure to prepare for AI, a trend that is expanding. However, he stated that any 'step function' in growth is contingent on the business value customers derive from these AI use cases, the full extent of which is not yet clear, making it too early to predict such a sharp acceleration.

Louis Miscioscia of Daiwa Capital Markets America Inc. asked if a 'step function' of growth for storage could occur in calendar 2026 as enterprise AI inference adoption ramps up, building on management's previous long-term commentary.

Answer

CEO George Kurian reiterated his belief that the real value and growth in AI will come from business impact delivered through inferencing and reasoning models. He observed that enterprises are currently in the first phase of getting their data infrastructure ready for AI, a trend that is expanding quarter by quarter. He stated that the pace of a future growth step-up depends entirely on the business value customers derive from these AI use cases, which will become clearer over the next 12 to 18 months.

Louis Miscioscia from Daiwa Capital Markets America Inc. asked if NetApp anticipates a 'step function of growth' for storage in calendar 2026, driven by the expected ramp-up of enterprise AI inference workloads.

Answer

CEO George Kurian responded that while the enterprise is in the early phases of modernizing data infrastructure for AI, a 'step function' in growth is contingent on the business value customers derive from these new AI use cases. He believes the impact will become clearer over the next 12 to 18 months as these applications are deployed and their business impact is measured, suggesting it is too early to predict such a dramatic acceleration.

Louis Miscioscia asked for an updated timeline on when AI is expected to become a material revenue contributor for NetApp, suggesting it did not appear to be a significant driver in the current quarter's results.

Answer

CEO George Kurian reiterated that the transition of enterprise AI projects from proof-of-concept to full production is expected in the second half of calendar 2025 and into 2026. He noted that while larger clients are moving more quickly to build AI infrastructure, broader adoption will take time. He affirmed that NetApp is well-positioned and making good progress in this emerging market.

Louis Miscioscia asked for commentary on European demand versus the Americas and whether differing AI adoption rates were a factor.

Answer

CEO George Kurian acknowledged that the macro environment in Europe remains choppy but stated the European team has outperformed. He credited the strong performance in the Americas to excellent execution by the new leadership team across all segments, rather than a fundamental difference in AI adoption, though North America did see more AI wins.