Bank of America 2026 Global Technology Conference
Logotype for Nebius Group N.V.

Nebius (NBIS) Bank of America 2026 Global Technology Conference summary

Event summary combining transcript, slides, and related documents.

Logotype for Nebius Group N.V.

Bank of America 2026 Global Technology Conference summary

3 Jun, 2026

Market positioning and strategy

  • Focuses on providing essential infrastructure for AI, evolving from cloud backup to infrastructure-as-a-service with a strong emphasis on high-performance, low-cost, and independent data storage.

  • Addresses pain points of hyperscalers by offering free egress, better connectivity, and avoiding data lock-in, enabling customers to use multiple neoclouds and AI providers.

  • Serves both AI builders and neoclouds, offering a white-label storage solution that integrates into AI workflows and supports innovation.

  • Sees significant opportunity in the AI-driven re-platforming of the industry, aiming to be the preferred storage provider for both neoclouds and AI builders.

Customer traction and growth

  • Reports 76% growth in AI customers and states that one in three new bookings now comes from AI-related clients.

  • Closed a $1 million ARR deal in just 11 days, reflecting urgency and demand in the AI sector.

  • Signed contracts ranging from six-figure to eight-figure deals, with AI storage needs expected to grow as inferencing data volumes outpace training data.

  • Highlights a shift from small business clients to large enterprise contracts, including a recent $15 million deal.

Product innovation and differentiation

  • Developed Shard Stash for rapid data ingress and B2 Overdrive for high-throughput data transfer, addressing AI workflow bottlenecks.

  • Launched B2 Neo, a dedicated white-label storage solution for neoclouds, providing manageability and scalability.

  • Focuses on optimizing the technology stack for efficiency and performance, with a patent-pending approach to file upload speed.

  • Differentiates from flash storage providers by targeting the large-scale, high-performance data lake layer critical for AI workloads.

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