How Scarcity Is Steering AI Adoption

Oliver Jack Dean


From mid-2024 through late 2025, a more precise AI thesis has emerged across leading analysts and recent disclosures from the largest cloud providers: the industry has been modeling the wrong bottleneck.

The early narrative priced growth off GPU orders and accelerator counts. Mainly focused on the enterprise of course, but still relevant for large caps.

However, the new reality is harsher and clearer though:

  • Chip, memory, and storage tightness still matter. But the binding constraint is increasingly energized capacity - power, sites, and grid connections
  • Inference remains the core profit engine, but only for those who can light their hardware up fast enough

Looking at this top-down, this shifts how AI capex, competitive moats, and adoption curves need to be understood.

From GPU Scarcity to Power Scarcity

Initial tightness in GPUs, HBM and NAND acted as a forcing function: it delayed some projects, but it also pushed hyperscalers to prioritize high-ROI inference workloads, lock in long-term supply, and pre-build AI-capable devices at the edge.

By 2025, the constraint set had clearly broadened: long lead times for memory and storage, limited rack space, and critically - insufficient power and “warm” data center shells. Recent comments from major operators confirm what the numbers implied: GPUs are now sitting idle because they cannot be powered or housed, not because chips are missing.

This has three direct implications:

1. GPU-count-based valuation models are breaking down. A chip that can’t be energized is not productive capacity, it’s sunk cost plus depreciation

2. The bottleneck has migrated up the stack to permitting, substations, transmission, and on-site generation - things that move on multi-year timelines

3. Scarcity is now physical and temporal. You can secure GPUs in months; securing hundreds of megawatts can take years

This deepens, rather than erases, the earlier point I made: tightness still acts as a discipline mechanism, but the discipline now comes from concrete, copper and grid interconnects, not only from HBM wafers.

Inference Economics Under a Shorter Hardware Clock


Earlier, inference economics looked straightforward: high margins, long useful lives, token-based revenue, and resale value turned inference clusters into “profit engines”.

Two developments refine that story:

1. Annualized or accelerated GPU refresh cycles. Nvidia and others are moving toward a one-year rhythm for major datacenter GPU upgrades, compressing the performance leadership window for each generation

2. Deployment lag now destroys more value. If power or construction delays mean a GPU sits dark for 12–18 months, that unused period overlaps with a large fraction of its performance edge

Result: the ROI equation for inference is now brutally sensitive to speed-to-energize:

  • Inference margins and 5-6 year asset life assumptions only hold if capacity is online quickly and "run hot"
  • Every quarter of delay is: zero revenue, continued capex drag plus effective obsolescence risk as the next generation approaches

Again, this doesn’t invalidate inference as the profit center; it adds a condition: only skilled and dicplined operators who can match hardware purchasing with timely power and facility readiness fully realize the economics.


Power, Deployment Speed, and the New Moats


The strategic map shifts accordingly.

Previously: Moats were framed around model quality, data, and software ecosystems. Workarounds centered on leasing, vendor financing, and custom silicon.

Now, a clearer hierarchy is visible: Energized Capacity as Primary Moat

The durable advantage lies with players who:

  • Locked in large-scale power purchase agreements and grid capacity years ago
  • Control or own their data center sites, substations, and (in some cases) generation
  • Can move from GPUs-in-boxes to live clusters in months, not years

Because:

  • You can copy a model
  • You can rent GPUs
  • You cannot clone 500MW of fully permitted, interconnected, low-latency power on a 6-12 month timeline

Speed-to-Deployment as ROI Multiplier

With faster refresh cycles, whoever energizes capacity first:

  • Captures a disproportionate share of high-priced inference demand while capacity is scarce
  • Defends higher utilization and better unit economics over the life of that generation
  • Forces slower peers into a structurally worse game: buying late, deploying late, and competing with newer silicon sooner


Financial & Operational Workarounds Repriced

Leasing, vendor financing, circular revenue deals, custom accelerators - all still matter. But they are now second-order to a simpler question:

Can you power and fill what you’ve ordered?

For operators who can’t, these tools risk amplifying misallocation: funding GPUs that sit idle while still accruing depreciation and interest.


What This Means for Adoption and Valuation


In practical terms, the story now is becoming more "simple": AI adoption will keep growing, but the advantage goes to those who can turn expensive hardware into powered, high-utilization inference capacity quickly.

Scarcity is no longer just about chips; it’s about having the energy, data centers, and grid access to actually use them, which naturally favors the hyperscalers and operators who locked that in early.

Inference still looks like the sustainable profit engine of this cycle, but only when deployment lags are short enough that GPUs earn before they’re leapfrogged by the next generation.

Everyone else faces a harsher reality: over-ordered accelerators sitting idle, shrinking performance windows, and rising capex and financing commitments that don’t match real revenue.

So, that's my spicy take: the market narrative built on “GPU count = AI leadership” seems to be increasingly misses the point; the real constraint, and the real valuation driver, is energized capacity and the speed at which it’s brought online.