Every CIO surveyed says AI workloads will grow this year. Not most. Not 95%. Every single one. That is the headline finding from the Cockroach Labs State of AI Infrastructure 2026 report, based on a Wakefield Research survey of 1,125 cloud architects, engineers, and technology executives across North America, EMEA, and APAC. The more alarming number is what comes next: 83% of those same leaders expect their data infrastructure to fail without major upgrades within 24 months. A third expect failure within 11 months.
This is not a hypothetical. The breaking point is a calendar event, and most enterprises are staring at it with infrastructure that was built for a world where humans typed queries, not machines firing thousands of API calls per second.
What 1,125 Technology Leaders Actually Reported
The Cockroach Labs/Wakefield Research survey, conducted between December 5 and 16, 2025, is one of the most comprehensive snapshots of enterprise AI infrastructure readiness to date. The results are consistent across geographies and company sizes, which makes the findings harder to dismiss as regional or sector-specific.
The Growth Numbers Are Unanimous
100% of respondents expect AI workloads to increase. Over 60% forecast growth of 20% or more in the next year alone. That unanimity is rare in enterprise surveys, where you typically find at least 10-15% of respondents bucking any trend. On AI workload growth, there are zero dissenters.
The reason: AI agents. Unlike a chatbot that waits for a human to type, agents operate autonomously and continuously. As Cockroach Labs CEO Spencer Kimball told SiliconANGLE: “When a Python script is accessing your API, you’re not talking about an action every two seconds; you’re talking about 5,000 actions in a second.” Traditional enterprise systems were designed around human interaction patterns, sessions that start, pause, and end. Agents never pause.
The 24-Month Countdown
83% of respondents say their infrastructure will fail without significant upgrades within two years. That is not a prediction about a future technology shift. It is a statement about current systems running current workloads. The gap between what AI demands and what infrastructure delivers is already measurable.
34% put the timeline at 11 months or less. These are not pessimists; these are the teams that have already deployed production agents and watched their database connection pools exhaust, their network latency spike, and their cloud bills double quarter over quarter.
The survey also found that 63% of respondents say their leadership teams underestimate how quickly AI demands will outpace existing infrastructure. The C-suite sees AI as a software investment. The infrastructure teams see it as a physics problem: compute, bandwidth, storage, and power are finite, and AI agents consume all four without pause.
What One Hour of Downtime Costs
98% of respondents report that one hour of AI-related downtime costs at least $10,000. Nearly two-thirds estimate losses exceeding $100,000 per hour. That figure includes direct revenue impact, SLA penalties, productivity losses, and the cascading effect of automated workflows going offline.
When a human-driven process goes down, workers switch to manual fallbacks. When an AI agent process goes down, there is no fallback. The 200 tasks that agent was handling per hour simply stop. The Broadcom 2026 State of Network Operations Report puts it bluntly: machines generate 100x more requests than humans with zero off-hours. A single AI feature deployment can trigger millions of additional requests per hour. When that system fails, the blast radius is proportionally larger.
Where Systems Break Under AI Agent Load
The breaking points are not uniformly distributed. The survey identified a clear hierarchy of failure.
Databases Hit First
30% of respondents identified the database layer as the first or second failure point in AI-overload scenarios. 36% pointed to cloud infrastructure more broadly. The database finding is significant because it is often invisible until catastrophic.
AI agents do not create the kind of load that traditional monitoring tools are built to detect. A traffic spike from a product launch looks like a sharp peak on a dashboard. AI agent load looks like a permanently elevated plateau. As Cockroach Labs explains in their blog post on the findings: “AI doesn’t break systems by causing dramatic spikes; it breaks them by never stopping. The architecture designed for predictable human behavior cannot sustain machines that operate continuously without recovery windows.”
The specific failure mode is coordination overhead. When multiple agents query a database simultaneously across regions, the system spends increasing resources on retries, contention resolution, partial failure recovery, and cross-region coordination. These costs do not appear on cloud invoices. They manifest as slowly degrading response times until a threshold is crossed and cascading failures begin.
Networks Were Not Built for Machines
Only 49% of organizations say their networks can support the bandwidth and low-latency requirements that AI workloads demand, according to the Broadcom report. The gap is architectural. Enterprise networks were designed for bursty human traffic: high at 10 AM when everyone logs in, low at 3 AM when nobody is working.
AI agents work at 3 AM. They work at 10 AM. They work during lunch. The traffic pattern is flat and relentless, which means networks sized for peak human usage are actually undersized for sustained AI usage. Transformer lead times for data center power infrastructure now stretch past two years, so capacity additions that should have started in 2024 will not come online until 2026 or 2027.
Power Became the Gating Factor
US data centers are on track to require 22% more grid power by end of 2026 than a year earlier. In many regions, power availability, not budget, is the constraint. An enterprise can approve a $50 million infrastructure expansion and still wait 18 months for the utility company to provision the necessary electrical capacity.
This is why 77% of survey respondents expect AI to drive at least 10% of all service disruptions annually. The failures will not be software bugs. They will be physical limits: insufficient power, inadequate cooling, exhausted network capacity.
CIOs Know the Problem. Most Are Not Fixing It Fast Enough.
The Cockroach Labs data paints a picture of informed paralysis. CIOs understand the trajectory. They are not moving fast enough to change the outcome.
The Budget Gap Is Real
85% of companies spend at least 10% of their IT budget on AI initiatives. 24% allocate over 25%. But 99.6% say they need to prioritize investment in AI scalability and database performance, which means current spending levels are insufficient.
The Dataiku/Harris Poll survey of 600 CIOs adds pressure from above: 71% say AI budgets face cuts or freezes if targets are not achieved by mid-2026. CIOs are caught between needing more infrastructure investment and facing budget cuts if current AI projects do not show returns. That squeeze explains why 74% of CIOs say their role is at risk without measurable AI business gains within two years.
Shadow AI Makes It Worse
82% of enterprises report employees creating AI agents and apps faster than IT can govern them. 54% have discovered unsanctioned “shadow AI” already operating internally. Each ungoverned agent adds load to infrastructure that nobody budgeted for. Only 25% of organizations have full real-time visibility into the AI agents embedded in their critical operations, even though 87% have agents running in production.
The infrastructure team cannot scale what it cannot see. Shadow AI turns the infrastructure problem from a planning challenge into a discovery challenge.
What the Survivors Are Doing Differently
Not every enterprise is sleepwalking into failure. The survey data and adjacent research point to three approaches that separate the organizations adapting from those waiting.
Distributed-First Architecture
About half of survey respondents are adopting hybrid or dynamic scaling strategies. 26% focus on horizontal scaling (adding more nodes) rather than vertical scaling (bigger machines). The logic: AI agent workloads are inherently distributed. Agents operate across regions, time zones, and cloud providers simultaneously. Infrastructure that can add capacity by adding nodes, rather than replacing existing hardware with larger hardware, matches the shape of the problem.
Distributed SQL databases like CockroachDB (which, yes, is Cockroach Labs’ product, so factor in the bias) address one specific failure mode: multi-region consistency under high concurrency. When 500 agents across three continents query the same database simultaneously, a distributed database handles coordination natively instead of routing everything through a single primary.
The Three-Tier Hybrid Model
Deloitte’s 2026 Tech Trends recommends splitting AI workloads across three tiers: public cloud for variable training and burst capacity, private infrastructure for high-volume inference at predictable costs, and edge computing for time-critical decisions requiring minimal latency.
This is not theoretical. Deloitte reports that when cloud costs reach 60-70% of equivalent on-premises hardware acquisition costs, organizations should evaluate alternatives. With inference costs making up two-thirds of all AI compute by 2026, many enterprises have already crossed that threshold. The Gartner forecast of $2.52 trillion in global AI spending for 2026 puts infrastructure as the largest single category at $1.37 trillion.
Observability Before Optimization
You cannot fix what you cannot measure. The CIOs who are ahead have invested in AI-specific observability before attempting to optimize infrastructure. That means semantic telemetry (machine-readable logging so agents can self-diagnose), stateless API design for self-correcting workflows, and metadata layers with knowledge graphs and vector metadata for context-rich monitoring.
The Cockroach Labs survey found that the actual strain from AI workloads manifests as coordination overhead: retries, contention, partial failures, and recovery demands that do not show on standard cloud dashboards but degrade performance until cascading failures begin. Traditional APM tools miss this entirely because they were built to monitor request/response patterns, not sustained concurrent load from autonomous software.
Frequently Asked Questions
What percentage of CIOs expect AI infrastructure to fail?
According to the Cockroach Labs/Wakefield Research survey of 1,125 technology leaders, 83% expect their data infrastructure to fail without major upgrades within 24 months. 34% expect failure within 11 months. 100% of respondents expect AI workloads to grow in the coming year.
How much does AI infrastructure downtime cost enterprises?
98% of survey respondents report that one hour of AI-related downtime costs at least $10,000. Nearly two-thirds estimate losses exceeding $100,000 per hour, including direct revenue impact, SLA penalties, and cascading workflow failures.
Where does enterprise AI infrastructure break first?
36% of respondents identified cloud infrastructure as the first failure point in AI-overload scenarios, and 30% cited the database layer as the second most likely failure point. AI agents create sustained, continuous load rather than temporary spikes, which exhausts database connection pools and overwhelms networks designed for bursty human traffic patterns.
Why do AI agents put more strain on infrastructure than traditional software?
AI agents operate autonomously and continuously, generating 100x more requests than human users with zero off-hours. A single AI feature deployment can trigger millions of additional requests per hour. Unlike human users who pause, log off, and sleep, agents run 24/7 across all time zones, creating a permanently elevated load that traditional infrastructure was never designed to sustain.
How are enterprises adapting their infrastructure for AI agents?
Leading enterprises are adopting three approaches: distributed-first architecture with horizontal scaling (adding nodes rather than bigger machines), a three-tier hybrid model splitting workloads across public cloud, private infrastructure, and edge computing, and AI-specific observability tools that can detect coordination overhead and cascading failures before they become outages.