For more than a decade, the industry has been preparing for a data explosion.
Zettabytes. Exponential curves. Hockey sticks on slides. Whether it was IDC’s DataSphere forecasts or countless vendor keynotes, the message was consistent: the amount of data created and stored worldwide was about to grow very, very fast.
And to be fair, that part largely went to plan.
Enterprises adapted. Storage scaled out. Cloud elasticity became normal. Analytical workloads were pushed away from systems of record. The industry did the work required to survive — and even thrive — in a world of exploding data volumes.
What almost nobody questioned, however, was a much quieter assumption baked into all of that planning.
The Assumption Nobody Revisited
All of those forecasts — explicit or implicit — assumed that the users of enterprise systems would remain human.
Humans are slow. Humans are bursty. Humans sleep.
Even power users have natural limits, predictable working patterns and an instinct for self-preservation when systems start pushing back. Entire generations of database design, connection management and capacity planning quietly depend on those characteristics.
It wasn’t a bad assumption. It was a reasonable one. Until it wasn’t.
A Step-Change, Not a Trend
What has changed is not just how much data exists, but who — or what — is accessing it.
AI agents introduce a new class of user into enterprise computing: non-human, machine-speed actors operating directly against application logic and data sources. This isn’t a continuation of an existing trend. It’s a step-change.
You’re not adding more users along the same curve. You’re changing the curve itself.
The data explosion was predicted. The user explosion — at least in this form — was not.
Why AI Agents Break Old Rules
AI agents don’t just behave like very enthusiastic humans.
They are fundamentally different:
- Speed: they operate at machine speed, turning milliseconds into meaningful units of work
- Relentlessness: they don’t pause, sleep or slow down unless explicitly forced to
- Unpredictability: agentic workflows fan out, retry, amplify and cascade in ways humans never could
These aren’t “power users”. They’re closer to autonomous load generators.
When Agents Hit Systems of Record
Critically, AI agents don’t want last night’s report.
They want now.
That pulls them towards operational systems of record — the RDBMS platforms that were carefully protected for the last twenty years from exactly this kind of access pattern. Read replicas help, until they don’t. Caches help, until coherence matters. Copy lag becomes a business problem, not a technical detail.
The long-standing truce between OLTP and everything else is under strain.
Capacity Planning Enters the Chaos Zone
Traditional infrastructure planning assumes that tomorrow looks broadly like yesterday, just a bit bigger.
AI agents break that assumption.
Sudden workload spikes. Non-linear fan-out. Cost curves that move faster than budgeting cycles. Organisations are forced into an uncomfortable choice: over-provision aggressively and accept unpredictable cloud bills, or under-provision and risk outages in systems that now sit directly on critical decision paths.
Capacity planning stops being optimisation. It becomes risk management.
This Is Already Happening
None of this is theoretical.
Organisations are already talking openly about AI agents as part of their workforce — not as tools, but as actors performing work at scale.
Enterprises are comfortable counting tens of thousands of AI agents as “workers”, but it shouldn’t be surprising when those workers behave very differently to humans — and place very different demands on the systems beneath them.
The Equation Has Changed
The data explosion followed the forecast.
The explosion in users did not.
Databases were built for humans — slow, bursty, predictable ones — and that assumption shaped everything from architecture to cost models. AI agents don’t fit that mould… and pretending they do is how organisations drift into outages, runaway costs or both.
Databases were built for humans. AI agents didn’t get the memo — and they’re already in production.
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