The 2028 Prediction: What I'm Betting My AI Company Future On
By 2028, more than 40% of new enterprise AI compute capacity will deploy on modular, energy-sovereign infrastructure.
Every prediction I make goes in writing with timestamps and falsification conditions.
Here’s what I’m betting DCXPS on:
By 2028, more than 40% of new enterprise AI compute capacity will deploy on modular, energy-sovereign infrastructure.
Not hyperscale data centers waiting for grid connections.
Modular containers with integrated renewable power that deploy in weeks rather than years.
This prediction is specific enough to be wrong.
That’s intentional.
The Structural Forces
Five irreversible trends support this prediction. Each trend has observable markers and falsification conditions.
Force One: Grid Infrastructure Exhaustion
PJM’s interconnection queue contains 2,300 GW of requests.
ERCOT’s large load requests exploded from 56 GW to 205 GW in thirteen months.
Grid infrastructure investment required to serve projected AI demand exceeds $30 billion in Texas alone.
The queue isn’t clearing.
Grid investment isn’t accelerating fast enough.
Traditional infrastructure cannot deploy at the rate AI demand is growing.
Falsification condition: If grid interconnection queues drop below 1,000 GW aggregate by 2027, the supply-side constraint loosens faster than projected.
Force Two: Power Density Transformation
AI racks are moving from 7-10 kW to 50-150 kW today, with projections reaching 1 MW per rack by 2028. Traditional data center infrastructure cannot support these densities without fundamental redesign.
The building stock that houses current data centers cannot be upgraded to support next-generation AI hardware. New purpose-built facilities are required.
Falsification condition: If average AI rack density stabilizes below 200 kW by 2027, existing infrastructure adaptation extends viability longer than projected.
Force Three: Deployment Velocity Requirements
AI capabilities are improving 10x annually.
Infrastructure that takes 5 years to deploy serves technology that’s been obsolete for 4 years by the time it’s operational.
The timing mismatch between AI development velocity and traditional infrastructure deployment creates structural demand for faster alternatives.
Falsification condition: If AI capability improvement slows to 2x annually by 2027, longer deployment timelines become acceptable.
Force Four: Community Opposition Scale
$64 billion in data center projects blocked or delayed by local opposition.
188 grassroots organizations actively fighting AI infrastructure across 28 states.
Bipartisan political alignment against hyperscale development.
The locations that can build traditional data centers are shrinking. The political economy increasingly favors infrastructure that communities want.
Falsification condition: If blocked/delayed projects drop below $20 billion annually by 2027, community opposition moderates faster than projected.
Force Five: Regulatory Sovereignty Momentum
EU AI Act requirements favor auditable infrastructure. GDPR enforcement favors local data processing. Singapore, Dubai, and emerging AI hubs are implementing their own frameworks.
Centralized offshore compute becomes harder as regulatory fragmentation increases.
Falsification condition: If regulatory harmonization creates unified global AI governance by 2027, sovereignty premium disappears.
The Prediction Mechanics
For 40% of new enterprise AI compute capacity to deploy on modular energy-sovereign infrastructure by 2028, several things must happen:
Modular infrastructure must prove operational reliability at scale. We’re deploying 2 MW by June 2026, with 20 MW contracted for follow-on in Q1/2027. Plans and capacity secured stands right now at 2 GW for 2028.
The operational track record must validate the architecture.
Energy integration must demonstrate cost competitiveness. Our unit economics show 42% cost advantage over CoreWeave, 70% advantage over hyperscalers.
These numbers must prove out in production.
Enterprise procurement must accept modular deployment models. The sales cycle for our compute is 30 days versus 12-24 months for traditional infrastructure.
Procurement processes must adapt.
Community opposition must continue making traditional development difficult.
The trends support this, but political dynamics can shift.
The Bet
I’m not predicting modular infrastructure wins because I want it to. I’m building modular infrastructure because the structural forces make traditional alternatives increasingly non-viable.
The hyperscalers will adapt. They’ll offer modular products, energy-sovereign options, faster deployment models. Their resources are immense. Their engineering capabilities are excellent.
But their architecture assumes centralized infrastructure at scale. Distributed modular deployment requires different operational models, different unit economics, different market approaches. Those adaptations take time.
The window between when traditional infrastructure becomes insufficient and when hyperscalers adapt is the opportunity window for companies like DCXPS.
The Personal Stakes
I’ve founded 110+ startups. I’ve seen predictions fail. I’ve bet on trends that reversed.
This prediction carries my reputation. If I’m wrong, it will be visible. The timestamps exist. The falsification conditions are specific.
That visibility is intentional. Predictions without accountability are marketing. Predictions with accountability are strategy.
By 2028, we’ll know whether the structural forces I’ve identified produce the outcomes I’m predicting.
If they do, DCXPS will have built significant market position during the adaptation window. If they don’t, the falsification conditions will reveal which assumptions failed.
Either way, the prediction is useful.
Correct predictions guide resource allocation.
Incorrect predictions with clear falsification conditions reveal strategic blind spots.
What This Means For You
If you’re evaluating AI infrastructure options, consider the structural forces regardless of what you think about my prediction.
Grid constraints are real.
Community opposition is real.
Regulatory fragmentation is real.
Power density requirements are real.
Deployment velocity mismatches are real.
How you respond to those forces depends on your use case, your risk tolerance, and your strategic priorities.
But ignoring them isn’t an option.
The infrastructure decisions you make in 2026 will determine what AI capabilities you can deploy in 2028. Traditional paths are closing. Alternative paths are opening.
Where are you placing your bet?
JF is a C-level executive and serial entrepreneur who has founded 110+ startups. He runs the AI Executive Transformation Program in Prague and writes about uncomfortable truths in AI implementation at AI Off the Coast (
).