Wasabi Establishes AI Business in Silicon Valley, Names Pinaki Mukherjee SVP & GM

Semiconductor and storage veteran to lead dedicated AI business to accelerate the independent storage foundation for AI labs, startups, and neocloud push as data demand grows to unprecedented scale

Wasabi Technologies, the hot cloud storage company, announced the formation of a dedicated AI business, and the appointment of semiconductor and storage veteran Pinaki Mukherjee as senior vice president and general manager to lead it.

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Wasabi stores hundreds of petabytes of AI data for frontier model labs, generative AI startups, and neocloud compute providers worldwide. This new AI business brings dedicated strategy, partnerships and go-to-market focus for this demand as AI infrastructure matures.

“Mukherjee has a track record of driving the kind of high-value partnerships that move markets, and that’s exactly what this moment requires,” said Marty Falaro, president and COO of Wasabi. “AI workloads are pushing storage demand to a scale we’ve never seen, and Mukherjee is the right person to build the partnerships that extend Wasabi’s position as the industry’s choice for cloud storage, at the exact moment inference is reshaping what that storage needs to do.”

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Proven leadership in the heart of Silicon Valley

Mukherjee brings more than 20 years of experience building technology ecosystems across semiconductors, storage, and AI infrastructure. He will lead Wasabi’s AI strategy, partnerships, and go-to-market efforts from Silicon Valley. His track record includes:

• Over $2 billion in partnership-driven revenue and more than $10 billion in strategic M&A and investment outcomes
• Senior business strategy and corporate development roles at Fungible, Druva, and Western Digital
• Leading semiconductor strategy and AI infrastructure engagements at Alvarez & Marsal, a global consulting firm

Why AI demand is remaking the storage market

Storage has become one of the most strategic line items in AI infrastructure budgets. AI training reads the same datasets tens or hundreds of times over, and inference pipelines move data continuously between storage and GPU compute. Hyperscaler cloud pricing models haven’t kept pace with how AI actually operates: data that needs to move freely, repeatedly, and unpredictably across environments. Avoiding vendor lock-in has become a deliberate strategy in its own right as companies want the freedom to adopt new infrastructure as the market shifts, without their own data becoming the obstacle.

Wasabi’s framework optimizes storage across the entire AI lifecycle, so data moves freely to wherever compute lives, at a flat, predictable cost with no egress or API fees, and no vendor lock-in. Under Mukherjee’s leadership, Wasabi will bring this framework to the AI industry at scale.

“AI customers don’t need another hyperscaler, they need the freedom to move their data wherever their workloads take them, without egress fees, API fees, or lock-in dictating their architecture,” said Mukherjee. “Wasabi is already the independent choice that innovative enterprises, neoclouds, and data platforms turn to as they scale beyond what any single hyperscaler ecosystem can offer. I’m looking forward to building the ecosystem that scales that freedom across the AI ecosystem.”

Built over a decade, delivering ROI at petabyte scale

Wasabi has spent a decade building the infrastructure that now stores petabytes of AI data for customers globally, supported by 16 storage regions worldwide, 18,000 channel partners, and over $700 million in funding to date. Together with its Megaport partnership and growing portfolio of developer and agentic AI capabilities, including Wasabi MCP, Wasabi continues to expand its role in AI infrastructure.

As autonomous AI agents take on more of the data lifecycle, including retrieving training sets, checkpointing intermediate results, and moving data between compute environments without human intervention, having a predictable cost structure is even more critical. Storing AI data with Wasabi ensures agents can read, write, and move data as often as a task requires without every API call becoming a hidden cost. Recent deployments show the ROI:

• An image- and video-generation lab migrated 175 petabytes off a hyperscaler within months
• A robotics data consortium has stored multiple petabytes while projecting annual savings of over JPY 100 million.

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