Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
The edge in 2026 goes to operators who tame AI’s sub-second bursts, preserve density with modular power and cooling, and turn small efficiency gains into big savings by collaborating earlier across the stack.
By Brandon Smith, VP of Global Sales and Product, ZincFive
If 2024-2025 was the beginning of the AI build-out, 2026 is when scale and physics collide: grids under strain, racks pushing practical limits, and operators demanding capacity that doesn’t triple their footprint.
Here are five trends I expect to shape how we design and run data centers next year.
1. AI Dynamic Workloads Become a Grid-Level Problem – and Need Pulse-Ready Power
String enough GPUs together and you don’t get a steady power draw – you get rapid, sub-second pulses. That’s not theoretical; it’s production reality across AI clusters today. What changes in 2026 is the magnitude: as campuses step from megawatts toward gigawatt scale, those pulses stop being a local power-quality nuisance and start becoming a grid-level constraint. Utilities and interconnect providers are already asking for mitigation plans up front because total harmonic distortion and flicker-style effects, once tolerable inside a data hall, compound to city-scale problems when you multiply them across pods and buildings.
ZincFive‘s new BC 2 AI nickel-zinc battery cabinet – purpose-built for AI workloads – sits in line with your UPS/inverter to deliver sub-second discharge during GPU bursts and recover in the troughs. That flattens the profile the grid and upstream gear “see,” so you don’t have to overbuild breakers, UPS, PDUs, or server power stages just to survive millisecond spikes. The same controls extend to the rack level: BC 2 AI can coordinate with rack- or server-level energy storage to follow the workload rhythm. The battery cabinet operates in dual-mode, handling fast pulse cycling and providing traditional outage runtime in a single system.
2. Power Density Depends on How You Tame the Spikes
Raw AI demand suggests more gear, more space, more everything. If you design for peak without mitigation, you can easily see 30-70% increases in footprint and capex – and in some scenarios, plan for 3� equipment to support what would otherwise be a 100 MW compute target. That’s not sustainable in space-constrained markets or on balance sheets. ZincFive’s nickel-zinc approach delivers duty-cycle durability while preserving power density, avoiding the footprint creep typical of systems sized to survive millions of rapid cycles.
The better path is intelligent density: pair smarter controls with energy storage that actually handles the dynamic profile. Not all storage choices respond the same way. Lithium systems sized to endure millions of short cycles tend to grow materially in footprint to survive that duty cycle. Nickel-zinc maintains a compact form factor under the same pulse demands, preserving power density while solving the AI spike problem. With BC 2 AI providing the peak-shaving at the electrical edge, you keep racks dense and avoid oversizing the entire chain. NiZn’s transient response and cycling profile are a match for AI’s sub-second pulses, preserving rack density while flattening the upstream profile.
3. Collaboration Becomes a Design Requirement
Roadmap sharing across the ecosystem is no longer optional. GPU vendors have set an aggressive cadence and, notably, persuaded the entire supply chain – from switchgear to thermal – to design toward that cadence. You can see it in public CEO-level conversations and on industry stages where competitors compare notes because the scale problem is bigger than any single firm.
This cuts both ways. Early engagement helps the right products show up at the right time. It also guards against the risk of everyone optimizing around a single assumption that later changes. In practice, the teams that are winning time-to-capacity are the ones convening partners early, exposing realistic load and duty-cycle data, and pressure-testing designs together. For our part, we’ve been validating nickel-zinc chemistry and controls against real GPU duty cycles, culminating in the BC 2 AI cabinet built for AI dynamic loads. The faster the feedback loops between operators, silicon vendors, power and thermal suppliers, the faster we move from pilot to production – without overbuilding.
4. Modularity Becomes the Default, Not the Exception
Speed is the new currency. Prefabricated, modular blocks – power, cooling, IT – shorten time to capacity, simplify commissioning, and let operators scale in predictable increments. On the power side, ZincFive’s BC 2 AI modular battery cabinet drops in pulse-ready peak-shaving capability via factory-sealed, plug-and-play blocks – first at the UPS, then closer to the load as clusters grow, so AI campuses can add capacity without tripping grid or room constraints.
From where we sit, nickel-zinc brings two clear wins for modular power. First, BC 2 AI cabinets stay compact under AI pulse duty, so they’re easier to place near the load and typically require less additional cooling and fire-safety accommodation than comparable lithium installs. BC 2 AI is designed for high-intensity discharge cycles while maintaining a compact footprint near IT load. Second, factory-sealed, plug-and-play units cut install time, labor, and shipping/handling costs. Replicate that block across a campus and the gains compound. Standardize on a modular power building block that preserves density and natively mitigates AI dynamic workloads, and your architecture gets simpler while your deployment tempo speeds up.
5. Efficiency Matters More at Gigawatt Scale
For years, a 1% efficiency improvement in a UPS or conversion stage was nice to have. At gigawatt campuses, 1% is now a grid-connection differentiator. Expect relentless attention on conversion losses, cooling parasitics, and idle overhead throughout the stack.
Batteries play a quieter role here, but it adds up. Nickel-zinc does not require continuous float charging. We bring the system to full, electrically isolate it from the charger, rest for roughly 30 days, then top up for about an hour. At a single site, that difference might not move the needle; across a fleet of very large facilities, reduced trickle losses become real money and real carbon avoided. Couple that with higher-efficiency power electronics and you start clawing back meaningful megawatts without touching the compute.
What This Means for 2026 Execution
If you take these trends together, a pattern emerges. The unit of design is shifting from boxes to blocks; density is gated by your ability to tame milliseconds, not just manage megawatts; and efficiency gains matter because the denominator got huge. The practical playbook looks like this:
- Treat AI dynamic workloads as a first-class design constraint, not an afterthought
- Use energy storage to absorb sub-second peaks so you don’t oversize everything upstream
- Standardize on modular power and cooling blocks you can copy-paste across a campus
- Audit efficiency at every conversion step; the 1% wins finally matter
- Pull partners into the room early and iterate against real duty-cycle data
Do those things well and you protect your grid interconnects, preserve density, and accelerate deployments – while keeping GPUs fully utilized and stakeholders happier. That’s what 2026 is going to demand.
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ABOUT THE AUTHOR
Brandon Smith is the Vice President of Global Sales and Product Management at ZincFive, where he brings more than a decade of leadership experience across battery engineering, product development, and mission-critical power systems. Having worked with global energy storage manufacturers on VRLA, Li-ion, and advanced monitoring technologies, he has a proven track record of shaping product strategy and scaling solutions in high-stakes infrastructure environments. At ZincFive, he leads global commercialization and product direction, helping data centers worldwide deploy power systems that are safer, more reliable, and built for the demands of modern compute.
