AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

Rob Reid
Cockroach Labs

AI workloads require an enormous amount of computing power. So much so that discussions around putting additional data centers in space are heating up (it's actually very interesting and involves arranging them in helio-synchronous orbits, but I digress). What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure.

According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report, which is based on a global survey of 1,125 senior cloud architects, engineers, and technology executives, suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

This is one "pulse of the industry" that IT leaders can't afford to miss, because its implications are both far-reaching and immediate. Several storylines jump off the page.

AI Workloads Are Growing Faster Than Infrastructure Can Handle

AI doesn't follow normal business hours, sleep, or take breaks to eat or watch the kids like humans do. It doesn't follow predictable usage patterns. And it doesn't show signs of slowing anytime soon.

A full 100% of the report's respondents expect AI workloads at their organization to grow in the next year. More than 60% expect workloads to increase by at least 20%. So, we know AI deployments will only grow larger, but what does this mean for the underlying infrastructure these systems rely upon?

For years, IT leaders have relied upon trusted historical patterns to determine how much computing power they'll require to support the organization. This strategy is no longer feasible. Architects must assume that AI-driven load will exceed previously set forecasts exponentially and design for volatility rather than averages.

The One-Year Outage Countdown Is On

Perhaps the most troubling takeaway from the report is just how close most organizations are to experiencing systems failure related to AI scale. 83% of respondents expect AI-driven demand to push their data infrastructure to failure within just two years. One-third believe it'll occur within the next 11 months.

There are a number of factors at play. AI innovations in recent years have made it possible for agents to operate continuously, completing transactions in real time, personalizing responses for consumers, and just about anything else you can imagine. Much of the enterprise infrastructure deployed today was engineered for an entirely different era and is set to woefully underserve their organizations.

For many organizations, systems that worked well in 2022 are in grave danger of being overwhelmed without significant upgrades. IT leaders need to treat AI-driven systems failure as an immediate operational risk, not something to worry about tomorrow.

The Enormous Cost of Outages

One of the most frequently discussed (and rarely agreed upon) aspects of an outage is the financial cost to the organization. Calculations must factor in the length of the outage, how many customers were impacted, how damaging it was to customer satisfaction, the list goes on. In many ways, it's incalculable and case-by-case dependent.

It's startling to discover that 98% of global tech leaders expect one hour of AI-related downtime would cost their business at least $10,000 and nearly two-thirds believe losses would exceed $100,000 per hour. There's no metric that's more urgent to understand and be on top of.

As AI workloads continue to grow and accelerate, the timeline before an outage occurs becomes shorter and significant financial risks present themselves. And with many outages caused by random spikes in demand, leaders need to build systems to withstand both scale and unpredictability.

Leadership Misalignment

These factors present a golden opportunity for technology leaders to justify a strong business case for modernizing and updating their data architectures. There's just one more problem … Most leadership teams aren't aware of the risks yet.

According to the survey, 63% of tech leaders say their leadership teams underestimate how quickly AI demands will outpace existing data infrastructure. This gap in knowledge is occurring at a time when nearly every single respondent (99.6%) acknowledges that investment in AI scalability is a priority in the coming year.

The big takeaway is that while companies have been investing heavily in AI, their spending has skewed towards reactive product upgrades instead of essential infrastructure needs. If a significant portion of an organization's AI investment is not dedicated to modernizing architecture for continuous, agent-driven scale, it's in for a rude awakening.

The Opportunity Ahead

While stark, these findings are ultimately not too surprising. Database infrastructures have been approaching end-of-life for many years now, and the past few years' explosion in AI-driven demand only speeds up that timeline.

The findings also highlight several key priorities as enterprises approach the 1-2 year failure countdown.

First, IT leaders must re-architect their systems for continuous, machine-driven load. Do not make assumptions about peaks and troughs; rather, assume every time of day could be a peak.

When designing this modernized architecture, another critical consideration is that resilience is just as important as performance. AI exacerbates failures that may have already been disastrous for organizations, so reliability must come first. Add to this the stampeding herd effect of not only humans but also agents returning to a recovered system and the risk of immediate and repeated failures cannot be ignored.

Finally, given the significant gap between tech leaders and the C-suite, achieving executive buy-in from the outset is crucial. Future infrastructure will only be as resilient as executives allow it to be, so they must be on board from the get-go.

The countdown is on. How will your business respond?

Rob Reid is Technical Evangelist at Cockroach Labs

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AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

Rob Reid
Cockroach Labs

AI workloads require an enormous amount of computing power. So much so that discussions around putting additional data centers in space are heating up (it's actually very interesting and involves arranging them in helio-synchronous orbits, but I digress). What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure.

According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report, which is based on a global survey of 1,125 senior cloud architects, engineers, and technology executives, suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

This is one "pulse of the industry" that IT leaders can't afford to miss, because its implications are both far-reaching and immediate. Several storylines jump off the page.

AI Workloads Are Growing Faster Than Infrastructure Can Handle

AI doesn't follow normal business hours, sleep, or take breaks to eat or watch the kids like humans do. It doesn't follow predictable usage patterns. And it doesn't show signs of slowing anytime soon.

A full 100% of the report's respondents expect AI workloads at their organization to grow in the next year. More than 60% expect workloads to increase by at least 20%. So, we know AI deployments will only grow larger, but what does this mean for the underlying infrastructure these systems rely upon?

For years, IT leaders have relied upon trusted historical patterns to determine how much computing power they'll require to support the organization. This strategy is no longer feasible. Architects must assume that AI-driven load will exceed previously set forecasts exponentially and design for volatility rather than averages.

The One-Year Outage Countdown Is On

Perhaps the most troubling takeaway from the report is just how close most organizations are to experiencing systems failure related to AI scale. 83% of respondents expect AI-driven demand to push their data infrastructure to failure within just two years. One-third believe it'll occur within the next 11 months.

There are a number of factors at play. AI innovations in recent years have made it possible for agents to operate continuously, completing transactions in real time, personalizing responses for consumers, and just about anything else you can imagine. Much of the enterprise infrastructure deployed today was engineered for an entirely different era and is set to woefully underserve their organizations.

For many organizations, systems that worked well in 2022 are in grave danger of being overwhelmed without significant upgrades. IT leaders need to treat AI-driven systems failure as an immediate operational risk, not something to worry about tomorrow.

The Enormous Cost of Outages

One of the most frequently discussed (and rarely agreed upon) aspects of an outage is the financial cost to the organization. Calculations must factor in the length of the outage, how many customers were impacted, how damaging it was to customer satisfaction, the list goes on. In many ways, it's incalculable and case-by-case dependent.

It's startling to discover that 98% of global tech leaders expect one hour of AI-related downtime would cost their business at least $10,000 and nearly two-thirds believe losses would exceed $100,000 per hour. There's no metric that's more urgent to understand and be on top of.

As AI workloads continue to grow and accelerate, the timeline before an outage occurs becomes shorter and significant financial risks present themselves. And with many outages caused by random spikes in demand, leaders need to build systems to withstand both scale and unpredictability.

Leadership Misalignment

These factors present a golden opportunity for technology leaders to justify a strong business case for modernizing and updating their data architectures. There's just one more problem … Most leadership teams aren't aware of the risks yet.

According to the survey, 63% of tech leaders say their leadership teams underestimate how quickly AI demands will outpace existing data infrastructure. This gap in knowledge is occurring at a time when nearly every single respondent (99.6%) acknowledges that investment in AI scalability is a priority in the coming year.

The big takeaway is that while companies have been investing heavily in AI, their spending has skewed towards reactive product upgrades instead of essential infrastructure needs. If a significant portion of an organization's AI investment is not dedicated to modernizing architecture for continuous, agent-driven scale, it's in for a rude awakening.

The Opportunity Ahead

While stark, these findings are ultimately not too surprising. Database infrastructures have been approaching end-of-life for many years now, and the past few years' explosion in AI-driven demand only speeds up that timeline.

The findings also highlight several key priorities as enterprises approach the 1-2 year failure countdown.

First, IT leaders must re-architect their systems for continuous, machine-driven load. Do not make assumptions about peaks and troughs; rather, assume every time of day could be a peak.

When designing this modernized architecture, another critical consideration is that resilience is just as important as performance. AI exacerbates failures that may have already been disastrous for organizations, so reliability must come first. Add to this the stampeding herd effect of not only humans but also agents returning to a recovered system and the risk of immediate and repeated failures cannot be ignored.

Finally, given the significant gap between tech leaders and the C-suite, achieving executive buy-in from the outset is crucial. Future infrastructure will only be as resilient as executives allow it to be, so they must be on board from the get-go.

The countdown is on. How will your business respond?

Rob Reid is Technical Evangelist at Cockroach Labs

Related Links

Hot Topics

The Latest

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Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

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Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

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For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...