There is a significant gap between storage companies and their ability to effectively support AI infrastructure. In this episode of the Tech Field Day podcast, recorded during the AI Data Infrastructure Field Day 1 in Santa Clara, host Stephen Foskett and guests Kurtis Kemple, Brian Booden, and Rohan Puri explore the evolving relationship between storage and AI. The discussion highlights a significant gap between storage companies’ current capabilities and the demands of AI applications. While storage vendors are pivoting to support AI, many lack deep AI expertise, often focusing on cost and efficiency rather than offering integrated, AI-specific solutions. The panel emphasizes the need for storage companies to move beyond being mere data repositories and instead develop end-to-end solutions that address AI workflows, data preparation, and metadata management. They also stress the importance of education, partnerships, and hiring AI specialists to bridge the knowledge gap and drive innovation. The conversation underscores the early stage of this convergence, with a call for clearer strategies, open standards, and more cohesive integration between storage and AI to meet the growing demands of data-driven applications.
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Learn more about AI Data Infrastructure Field Day 1 and watch videos from these presentations on the Tech Field Day website.
How Can Storage Support AI?
The intersection of storage and AI infrastructure presents a complex and evolving challenge. While storage companies are increasingly pivoting toward AI solutions, there remains a significant gap in understanding and integration. Storage has traditionally been viewed as a low-level, technical domain focused on hardware like disks and file systems. On the other hand, AI, particularly in the context of large language models (LLMs) and data analytics, operates at a higher level, requiring nuanced data management and application-specific insights. This disconnect highlights the need for storage companies to move beyond simply offering cost-effective and high-performance infrastructure. Instead, they must develop a deeper understanding of AI workflows and provide solutions that address the specific needs of AI applications, such as data preparation, metadata management, and seamless integration with AI training pipelines.
One of the key challenges is the lack of “solutioning” in the storage industry. Many storage vendors focus on infrastructure performance and efficiency but fail to address how their products fit into the broader AI ecosystem. For instance, while some companies are integrating with GPU technologies to support AI workloads, this approach often stops at the infrastructure level. True integration requires a more comprehensive understanding of AI applications, extending beyond hardware to include data management, insights, and application-level affordances. Without this, storage solutions risk being perceived as generic and interchangeable, reducing their value proposition in the AI space.
Another critical issue is the fragmentation of data sources and the absence of standardized frameworks for integration. Data in AI workflows often comes from diverse sources, including databases, data warehouses, file systems, and cloud storage. These sources are frequently siloed, making it challenging to consolidate and analyze data effectively. While some progress has been made in the database world with open formats and decoupled layers, similar advancements are lacking in the storage domain. The industry needs open standards and protocols that enable seamless data integration across vendors and platforms, facilitating the development of unified AI solutions.
The role of storage companies in AI could evolve in two distinct directions: becoming specialized storage solutions for AI or serving as connectors that enable AI applications to access existing data seamlessly. Both approaches have merit, but they require a clear strategy and a deep understanding of AI workflows. Companies that choose to specialize in AI storage must offer features like automated data preparation, efficient data movement, and real-time insights. Conversely, those opting to act as connectors must focus on breaking down data silos and providing tools that simplify data access and integration for AI applications.
Education and leadership are crucial for bridging the gap between storage and AI. Storage companies need to hire AI specialists and empower them to influence product development and strategy. This requires a top-down approach, with leadership roles dedicated to understanding and addressing the unique challenges of AI. Without this internal expertise, companies risk creating a disconnect between their AI-focused messaging and the actual capabilities of their products. Moreover, fostering collaboration between storage and AI teams within organizations can lead to more innovative and effective solutions.
Finally, the industry is still in the early stages of addressing the intersection of storage and AI. While the rapid growth of data and the increasing complexity of AI workloads present significant challenges, they also offer opportunities for innovation. Storage companies that can adapt to these demands by developing specialized products, embracing open standards, and fostering cross-disciplinary expertise will be better positioned to succeed. As the market matures, we can expect to see a blending of technologies and a shift toward more integrated and user-friendly solutions that cater to the unique needs of AI applications.
Podcast Information:
Stephen Foskett is the President of the Tech Field Day Business Unit and Organizer of the Tech Field Day Event Series, now part of The Futurum Group. Connect with Stephen on LinkedIn or on X/Twitter.
Brian Booden is the Managing Director at DataGlow IT. You can connect with Brian on X/Twitter and on LinkedIn. Learn more about DataGlow IT on their website.
Rohan Puri is an Storage Infrastructure Engineer. You can connect with Rohan on LinkedIn or on X/Twitter. Learn more about him on his personal website.
Kurtis Kemple is the Director of Developer Relations as Slack. You can connect with Kurtis on LinkedIn or on X/Twitter. Learn more about Kurtis on his website.
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