Data Disruption: A Massive Shift to Hyperscale Data
AI and quantum technologies are propelling us into a new era of hyperscale data which will overwhelm today’s existing architecture.
This is the second in our series about Massive’s focus in 2025. To see more of our thoughts and follow along, please subscribe.
The Data Tidal Wave
The scale of data being created and consumed is accelerating beyond prior expectations:
Global data creation is projected to reach 463 exabytes per day by the end of 2025, up from just 2.5 exabytes per day in 2010 (World Economic Forum). That is the equivalent of more than 200 million DVDs per day!
AI training datasets have grown more than 300,000× in the last decade, from millions of tokens in early models to trillions today (Epoch AI).
By 2030, the total amount of data generated worldwide could exceed 610 zettabytes annually, nearly 10× today’s levels (Statista).
This isn’t “more of the same” data. It’s hyperscale data that breaks existing infrastructure by sheer size, speed, and complexity.
From AI Hype to Industry-Specific Gold
While headlines spotlight foundation models from the big tech players, real enterprise value is emerging from industry- and task-specialized AI-first companies paired with hyperscale data processors.
Traditional enterprise tools—SaaS platforms, BI dashboards, and generic data lakes—have left organizations drowning in petabytes of complex data they can’t easily action. That’s why domain-focused LLMs and agentic AI services are so compelling:
Contextual understanding of industry jargon, workflows, and nuance
Targeted insights distilled from raw, domain-specific data
Task automation via agents coordinating across enterprise systems
This is data gold mining: faster decisions, productivity unlocked, new innovation pathways, and durable competitive advantage.
Quantum Meets Hyperscale Data
Quantum is accelerating the disruption. The data it produces and requires is compounding the AI-driven surge:
Qubits are doubling roughly every 18 months, following Rose’s Law, the quantum analog to Moore’s Law.
Research intensity is exploding: publications on quantum computing increased by 200% between 2016 and 2022 (WorldMetrics).
The economic impact of quantum could reach $700 billion globally by 2030, driven by applications in finance, logistics, and pharmaceuticals (WorldMetrics).
Quantum doesn’t just scale existing workloads—it creates new categories of data and computational demand.
Why the Old Stack Doesn’t Work
Legacy infrastructure can’t keep up with hyperscale data:
Volume: Petabyte-to-exabyte workloads are quickly becoming standard.
Velocity: Real-time data pipelines are essential for AI and quantum systems.
Variety: Structured, unstructured, synthetic, and probabilistic data collide.
Veracity: At scale, provenance and trust in data are mission-critical.
Incremental fixes to yesterday’s stack won’t suffice. We need new architectures.
The Next Wave of Infrastructure
At Massive, we see opportunities for founders building:
Infrastructure and compute optimized for AI + quantum workloads in the environments where they are needed most (in the real world, not in a lab)
High-bandwidth, low-latency networks to fluidly move massive datasets
Reimagined storage and memory architectures for hyperscale data
DataOps at scale, embedding provenance, trust, and efficiency by design
These solutions won’t just enable AI and quantum; they’ll define how these technologies realize their potential.
Why It Matters
Data Disruption isn’t a vertical - it’s the connective tissue of innovation across Massive’s other focus areas of cybersecurity, energy, and space. Enterprises that unlock their data through specialized AI and next-gen processing will 10× their decision-making velocity and insight quality, cascading into exponential gains.
The startups building the infrastructure to make this possible will become some of the most valuable companies of the next decade.
At Massive, we’re backing the architects of this new data frontier.
Key Portfolio Investments:
I have been integrating LLMs into my daily workflow. I do a lot of creative development that benefits greatly from a swift iterative approach. In my world, versioning fully developed concepts is key to understanding the overall picture and LLMs do in fact 10x or more the speed of these cycles.
With this type of efficiency comes a new issue... sure from the top level down this tool empowers me, the creative executive, that knows what I'm looking for, and can make experience based judgments to know that what is being presented to me, by the LLMs, is useful or correct. But... How did I become that person that knows things and can apply judgment? I was the assistant to brilliant minds before me and had the institutional knowledge, beat into me... er, bestowed upon me to get to where I am today.
Now, what happens to the next generation of creative executives, the current assistants, that fall victim to these new efficient LLM workflow approaches. How do I replace myself? How do I make new creatives? How do I pass down institutional knowledge?
Society prospers when the old timers sow seeds they will never reap… I’ve been thinking about this a lot lately and I am working on developing a more apprentice type of learning structure…
We are in exciting times folks!