No one expects the federal government to move quickly, least of all itself.
Built over decades as an expanding web of incremental upgrades and shifting mandates, the federal technology apparatus struggles under the weight of its own complexity.
"Vendor sprawl," as Randy Hayes, VP at Vast Data's Federal division, neatly puts it, has become so endemic it's almost part of the architecture itself.
But now, something is shifting. Agencies find themselves abruptly propelled into action by a new forcing function, symbolized in part by the "Doge" era of way leaner budgets, shrinking staff, and the non-negotiable imperative to integrate AI immediately or risk falling even further behind.
For a sector that historically favored cautious iteration over sweeping reform, this urgency is a shock to the system. Suddenly, long-ignored inefficiencies are intolerable. Hayes describes a kind of reckoning now happening across federal agencies, where leadership confronts an uncomfortable question daily: “What can we cut?”
While hyperscalers like AWS and Azure have long set the standard for flexibility at scale, they were never purpose-built for the kind of AI-centric workloads now becoming central to federal missions.
Here, Hayes points toward a wave of specialized players, the "neoclouds" such as CoreWeave, G42, and Lambda, companies born explicitly for GPU-driven, AI-native workloads. They're lean, fast, and ruthlessly specialized. Yet historically, they’ve been locked out by onerous compliance barriers like FedRAMP, which added millions in upfront investment and years of costly delay.
Fortunately, a much-needed simplification of FedRAMP may soon unlock these specialized AI clouds, streamlining the onboarding process from several painful years to mere weeks.
As Hayes notes, the economic logic for neoclouds changes entirely when FedRAMP becomes a sprint rather than a marathon. This promises a real shift, not just speeding adoption, but creating an entirely new set of competitive choices in federal AI infrastructure.
Yet even as the clouds shift overhead, another barrier becomes visible, the issue of talent scarcity.
To grasp just how dire the shortage of qualified AI experts has become, consider Meta’s eye-popping $500 million bonus budget aimed at poaching top AI minds from OpenAI and XAI.
With AI expertise rarer than ever, federal agencies have no realistic path to internalize all necessary expertise. The lesson Hayes insists federal customers must quickly learn is to look to commercial success stories. Trust companies who've navigated these waters before. In other words, lean heavily on demonstrated successes rather than trying to reinvent capabilities from scratch.
Hayes evokes a vivid example from NIH’s sprawling campus, constructed in the early 1950s, where now, a single rack of Nvidia’s Grace Blackwell GPU architecture consumes a staggering 125 kilowatts. Contrast that with legacy datacenter infrastructures in the DC metro area, many of which max out at a mere nine kilowatts per rack. This gulf between capability and capacity symbolizes an entire legacy infrastructure ill-prepared for AI’s power-hungry demands.
Retrofits might be necessary, but they're costly and slow. And that’s precisely why we may see federal integrators and cloud providers rapidly step up to fill these gaps with purpose-built, power-dense datacenters.
Concrete examples already illuminate the path forward. Hayes points to applications like cyber analytics, predictive maintenance, and especially the potential of "agentic AI" as game-changing capabilities. Imagine a federal worker aided by an intelligent agent capable of parsing IRS code in real-time, a state trooper instantly briefed on the risk profile of a stopped vehicle, or a VA specialist supported by an AI assistant that rapidly synthesizes critical patient information.
Each scenario vividly demonstrates how immediate and tangible AI’s federal potential really is, if and only if infrastructure can deliver on its promises.
At the core of this capability lies data, an asset federal agencies possess in abundance, yet have historically failed to leverage effectively due to pervasive siloing.
Hayes cites Vast Data’s NHL project as a model: the league spent years quietly contextualizing its enormous archives, positioning itself perfectly for immediate deployment when AI technology was ready. Agencies similarly sit atop data goldmines that must be integrated, contextualized, and made readily available.
Yet another dimension of AI’s rise within federal agencies is the inevitability of stringent regulation. Hayes emphasizes that auditability (traceable, accountable, transparent) is itslef mission-critical.
AI deployments will be intensely scrutinized, and agencies unable to produce rigorous, verifiable audit trails risk regulatory paralysis. Tesla’s comprehensive data capture during autonomous driving incidents offers a model here, the expectation of thorough, detailed accountability will soon permeate all federal AI implementations.
Trust, ultimately, emerges as the currency of federal AI modernization. The enormity of the challenge ahead demands partnerships grounded in proven security practices, certified through rigorous standards like the Department of Defense’s APL.
Hayes warns agencies that in an AI field crowded with companies operating under a shaky "fake it till you make it" ethos, federal customers must always interrogate prospective partners.
Have they done this before? Where have they succeeded? In an environment that cannot tolerate failure, past commercial and governmental successes become the best indicator of future performance.
For federal agencies AI is a forcing function, a moment in history demanding wholesale infrastructure reinvention.