The State of AI Infrastructure 2026
Report by: Cockroach Labs & Wakefield Research Methodology: Survey of 1,125 senior cloud architects, engineering & technology executives (Director+) across 11 markets (US, Canada, Germany, Italy, France, UK, Israel, India, Australia, Singapore, Japan) Field dates: December 5–16, 2025 Pages: 28
Executive Summary — 7 Key Findings
1. AI growth is guaranteed
100% of respondents expect AI workloads to grow in the next year. 60%+ predict increases of 20% or more.
2. Infrastructure failure is expected very soon
83% of leaders believe their data infrastructure will fail without major upgrades in the next 24 months.
3. Breaking point < 1 year for 1/3 of companies
34% expect infrastructure failure within the next 11 months.
4. Database layer is a critical failure point
30% identified the database as the first point of failure in an AI-overload scenario (2nd only to cloud infrastructure itself at 36%).
5. AI will drive significant outages
77% expect AI to drive at least 10% of all service disruptions in the year ahead. 29% predict >25%.
6. Financial impact is substantial
98% say 1 hour of AI-related downtime costs at least 10,000. Nearly 2/3 say over 100,000.
7. Leadership misalignment accelerates risk
63% say their leadership teams underestimate how quickly AI demands will outpace existing infrastructure.
Part 1: Scaling to Meet AI Demands
- AI adoption: 81% of all companies scaled AI adoption in the past 12 months, with 26% scaling fast with major new deployments
- Pilot to production: 98% report at least one AI project moved from pilot to production. 57% moved 3+ projects
- Infrastructure scaling: 87% scaled data infrastructure aggressively to support AI
- IT spend: 85% spending 10%+ of total IT budget on AI infrastructure. 24% spending >25%
Future workloads driving strategy:
- Predictive/analytical AI: 62%
- AI agents & automation: 52%
- Generative AI: 52%
- Embedded/transactional AI: 51%
- Real-time/streaming AI: 44%
Part 2: State of Enterprise Infrastructure
Cloud deployment: 87% using CSPs — 32% single-region single CSP, 32% multi-region single CSP, 22% multi-region multi-cloud, 13% on-prem
Top challenges:
- Balancing cost, storage & performance at AI scale: 50%
- Integrating & maintaining data quality across types: 46%
- Sustaining performance across transactional & analytical workloads: 44%
- Scaling for unpredictable AI workloads: 46%
- Maintaining uptime during traffic spikes: 42%
- Supporting new formats (vectors, embeddings): 40%
Failure timeline:
- 83% expect infrastructure to hit limit within 2 years
- 34% expect failure in <11 months
- For companies 20+ years old: 40% expect failure in <11 months
What fails first:
- Cloud infrastructure/CSP: 36%
- Database layer: 30%
- Data pipeline/orchestration: 14%
- Application layer: 0%
Scaling approach:
- Hybrid/dynamic (both): 51%
- Vertical: 22%
- Horizontal: 26%
Part 3: Cost of Downtime
| Cost per hour of AI downtime | % of companies |
|---|---|
10,000–49,999 | 33% |
50,000–99,999 | 24% |
100,000–249,999 | 23% |
250,000–499,999 | 5% |
500,000–999,999 | 1% |
| $1,000,000+ | 0% (2% don’t know) |
Preparedness:
- 64% noted at least some gaps in current strategy
- 41% “moderately proactive” — know of gaps in testing
- 23% reactive or unprepared
- 98% have modeled costs and/or stress-tested systems
Part 4: Distributed SQL for the AI Era
The report makes the case for distributed SQL (specifically CockroachDB) as the solution:
New failure modes from AI:
- Continuous load — AI agents don’t sleep (no human-paced traffic patterns)
- Concurrency under stress — simultaneous reads/writes from real-time systems
- Coordination at scale — agentic AI chains actions across systems
- Degraded performance — latency, contention, deadlocks before total collapse
Requirements for resilience:
- Global distribution by default
- Multi-active availability (any node serves reads/writes)
- Built-in fault isolation
- Transactional consistency at global scale
- Automated rerouting without human intervention
- Elastic scaling in real time
What distributed SQL enables for AI:
- Unify structured + semantic data (transactional + vectors in one system)
- Persist agent memory as first-class data
- Operate at machine pace (strong consistency with thousands of parallel agents)
- Eliminate fragmentation (no more PostgreSQL + vector store sprawl)
- Full AI lifecycle from prototype to autonomous agents
Part 5: Meeting the Future of Agentic AI
Strategic priorities for 2026:
- Scaling AI infrastructure: 55%
- Exploring new AI use cases: 51%
- Ensuring compliance & governance: 51%
- Strengthening resilience & uptime: 50%
- Containing AI-related costs: 50%
Key insight: AI is scaling faster than any platform shift before it (including the internet). Machine-generated traffic will vastly exceed human-driven workloads. Infrastructure not designed for autonomous, persistent, machine-driven activity will fail.