A new survey of 1,125 senior technology leaders reveals a concern for every developer building AI-powered systems: the database layer is emerging as a critical point of failure.
According to Cockroach Labs' "The State of AI Infrastructure 2026" report, 30% of respondents identified the database as the first component that would fail when AI workloads exceed system capacity. That's second only to cloud infrastructure itself at 36%.
But databases have been "solved" for decades. So what changed?
From Human-Paced to Machine-Driven
Spencer Kimball, CEO of Cockroach Labs, explains the shift: "For decades, databases were designed around human-driven workloads with predictable traffic and low concurrency. But AI obliterates this expectation. Unlike serving millions of human users, enterprises now serve billions or trillions of autonomous agents that operate around the clock. This creates failure modes that developers didn't even have to consider for the past 40 years."
The research backs this up. Traditional databases were built for traffic patterns shaped by human behavior—Black Friday spikes, lunchtime surges, predictable daily cycles. AI agents don't follow those patterns. They generate continuous queries, writes, and coordination tasks around the clock.
The Timeline Is Compressed
The findings show that AI-scale infrastructure failures are not distant hypotheticals. They're imminent:
83% of technology leaders expect their data infrastructure to fail without major upgrades within the next two years. One-third expect failure within 11 months.
For companies with over 20 years in business, that urgency is even higher: 40% believe their infrastructure won't survive the next year under AI load.
And the cost of failure is substantial. Nearly two-thirds of companies say one hour of AI-related downtime would cost more than $100,000.
Why AI Workloads Are Different
The report identifies three characteristics that distinguish AI workloads from traditional application traffic:
Continuous load. AI agents don't sleep. Unlike human-initiated traffic that has natural peaks and valleys, AI workloads create an always-on demand that intensifies over time.
High concurrency under stress. Real-time systems such as recommendation engines and AI copilots issue simultaneous reads and writes, requiring database engines that can handle high throughput with minimal contention.
Coordination at scale. Agentic AI chains actions across systems and services, introducing interdependent transactions that strain consistency models, especially across regions.
The result is what Kimball describes as "an entirely new and unpredictable type of load that can stress systems at a moment's notice."
The Investment Response
Developers aren't ignoring this problem. The survey found that 99.6% of companies plan to invest in improving AI scalability and database performance over the next year. But the investments are spread across multiple approaches: 26% favor horizontal scaling, 22% favor vertical scaling, and 51% pursue hybrid strategies.
That dispersed focus suggests organizations are still figuring out the right architectural approach. And there's a concerning gap in executive awareness: 63% of respondents say their leadership teams underestimate how quickly AI demands will outpace existing infrastructure capacity.
What Developers Should Consider
The report emphasizes that surviving AI scale requires more than adding capacity. It requires a different architecture:
- Global distribution by default
- Multi-active availability, where any node can serve reads and writes
- Built-in fault isolation to prevent cascading failures
- Transactional consistency under load with strong guarantees at global scale
- Automated rerouting and recovery without human intervention
- Elastic scaling that adapts in real time to unpredictable demand
Traditional databases weren't designed for these requirements. They were built for single-region deployments with predictable traffic patterns and human-controlled system interaction.
As Kimball notes: "Reliability comes from designing for failure, not success. It's about exposing the gap between yesterday's systems and the next generation of autonomous computing power."
Looking Ahead
The research makes clear that AI workloads will continue to grow. All survey respondents—100%—expect AI workloads to increase in the next 12 months, with 63% predicting increases of 20% or more.
More significantly, 52% expect agentic AI and automation to be a critical driver of data-infrastructure strategy over the next two years. As AI shifts from answering individual queries to orchestrating complex, autonomous workflows, the stress on underlying systems will only intensify.
The database layer has been reliable for decades, but it was designed for a world that no longer exists. Developers building AI-powered systems need to understand that the assumptions underlying most enterprise architectures are breaking down—and the timeline to address them is shorter than most organizations realize.
The full "State of AI Infrastructure 2026" report is available at https://www.cockroachlabs.com/state-of-ai-infrastructure-2026/
