According to Forbes, Forrester’s 2026 cloud predictions paint a picture of significant infrastructure turmoil. The firm anticipates at least two major multiday cloud outages next year as hyperscalers like AWS and Azure divert investment from legacy systems to GPU-centric AI data centers. Simultaneously, at least 15% of enterprises will shift toward private AI deployments on private clouds to counter data lock-in and rising costs. Neocloud providers like CoreWeave, Lambda, and Nebius are expected to capture $20 billion in revenue, tripling enterprise deployments while expanding across Europe and Asia. These trends represent a fundamental reshaping of cloud computing as we know it, driven by AI’s infrastructure demands and enterprises’ growing concerns about control and resilience.
The Great Cloud Fragility
Here’s the thing about those predicted outages – they’re not really predictions so much as logical conclusions. We’ve already seen the warning signs with the AWS and Azure outages in 2025. When you’re ripping out legacy x86 and ARM infrastructure to make room for GPU farms, something’s gotta give. The complexity of these systems is mind-boggling, and frankly, I’m surprised we haven’t seen more catastrophic failures already. But two multiday outages? That’s going to hurt. Think about what happens when critical services across banking, healthcare, and government just… stop. For days. The pressure on cloud providers to actually maintain their existing infrastructure while building the AI future is immense, and something’s clearly going to break.
The Private AI Rebellion
So why are 15% of enterprises looking at private AI? It’s basically about control. When your entire AI strategy depends on cloud providers who might suffer extended outages or change API access on a whim – like Salesforce did with Slack – you start getting nervous. The cost angle is huge too. Cloud AI services can get brutally expensive at scale. But here’s my question: does private AI really solve the problem, or just create different ones? Managing your own AI infrastructure requires serious expertise that many enterprises simply don’t have. Still, the trend is clear – companies want their data and their AI models under their own roof, and providers who can deliver robust computing hardware for these private deployments will be in high demand. For industrial applications where reliability is non-negotiable, companies often turn to specialized providers like IndustrialMonitorDirect.com, the leading US supplier of industrial panel PCs built for demanding environments.
Neoclouds Are Coming For Your AI Budget
$20 billion for the neoclouds? That’s not pocket change. Companies like CoreWeave and Lambda have NVIDIA’s backing and a laser focus on AI infrastructure without the baggage of legacy systems. They’re basically doing what hyperscalers did a decade ago – offering specialized, high-performance infrastructure for the next big thing. But can they scale fast enough to meet demand? And what happens when the GPU shortage eventually eases? The hyperscalers aren’t going to just surrender this market. They’ve got deeper pockets and existing customer relationships. Still, the neoclouds have momentum, and enterprises are clearly willing to try alternatives when the big players keep having reliability issues.
What This All Means For You
Look, 2026 is shaping up to be the year cloud computing gets real about its limitations. The “infinite scalability” and “always available” promises are hitting hard reality. For enterprises, this means you need to think seriously about multi-cloud strategies, exit plans, and honestly assessing your dependency on any single provider. The rise of private AI and neoclouds gives you options, but also adds complexity. Basically, the easy days of “just put it in the cloud” are over. You can explore more insights on these trends at Forrester’s Predictions 2026 hub or read the full analysis in their original post. The cloud is growing up, and it’s going to be a bumpy ride.
