AI Grid Bottleneck: How Electricity Delivery Constrains Data Centers and AI Video

By VEONIB | 2026-08-02

Quick Answer

The AI grid bottleneck is a delivery problem, not an energy shortage. The United States has enough electricity generation, but grid interconnection wait times grew from under 20 months in 2005 to 55 months by 2023, and this backlog now delays data center projects such as OpenAI's Stargate campus in Texas. Until grid processes are modernized, power delivery — not chips or capital — will constrain AI expansion.

TL;DR

Table of Contents

Introduction

According to "What's really slowing down the AI buildout" published by Works in Progress, the most expensive constraint on artificial intelligence today is not a shortage of electricity but a shortage of grid connections. The report, written by Chris Gillett, argues that America has enough power generation; the problem is delivering that power through an interconnection process designed for an earlier era. Median wait times for connecting new power plants have grown from less than 20 months in 2005 to 55 months by 2023, leaving data centers and energy suppliers stuck in backlogged queues. For ecommerce and AI video teams, these delays matter because every generated video frame depends on inference infrastructure that must be powered, cooled, and connected. This article explains the AI grid bottleneck, compares regional pressures, and translates the energy story into practical recommendations for merchants, creators, and developers.

Hero Image Alt Text: AI grid bottleneck — transmission lines and data center cooling towers under pressure Caption: America's grid, not electricity supply, is the key constraint on AI data center growth. OG Image Title: AI Grid Bottleneck: Why Electricity Delivery Limits AI Buildout Suggested Visual: A split image showing a dense data center campus on one side and congested high-voltage transmission lines converging on the other, with a warm sunset horizon.

The AI Data Center Electricity Demand Surge

Original Fact

AI infrastructure is becoming one of the largest electricity consumers in history. According to Works in Progress, Stargate, the flagship joint venture led by OpenAI and SoftBank, is under construction in Abilene, Texas, and is expected to cost well over $40 billion. At peak load, the campus will draw 1.2 gigawatts — roughly the consumption of 313,000 median American homes. A report by EpochAI and an energy research institute projects that total AI computing power could reach 100 gigawatts worldwide by 2030 if 2025 growth rates continue. Data centers are not the only energy-hungry infrastructure: the largest US battery plants draw about 115 megawatts, and the first phase of TSMC's Arizona semiconductor facility will draw 200 megawatts.

Industry leaders agree on the scale of the problem. Nvidia CEO Jensen Huang has said every future data center will be power-limited. Meta's Mark Zuckerberg said his company would build larger training clusters if it could secure the energy, and OpenAI CEO Sam Altman told Congress that the abundance of AI will be limited by the abundance of energy.

VEONIB Insight

This demand surge changes the economics of every AI service, including AI video generation. Training frontier video models requires thousands of GPUs running for weeks, and each inference request for a video-generation model consumes far more compute than a text response. If new data centers cannot connect to the grid, model providers face higher costs for existing capacity, longer waits for new capacity, and pressure to raise prices or ration access. Ecommerce teams that rely on AI-generated product videos should treat compute availability as a strategic risk, not just a technical detail. It influences which models launch, how fast they roll out, and what they cost per video.

Why the Grid, Not Electricity Supply, Is the Real Bottleneck

The central claim of the Works in Progress analysis is that the United States is not short of electricity; it is short of the transmission and interconnection capacity required to move electricity to where AI infrastructure is being built. Before any new power plant or data center can connect, grid operators must study how it will change power flows and determine whether system upgrades are required. That study process has become severely backlogged. The median power plant in 2005 waited less than 20 months for interconnection; by 2023, the median wait had jumped to 55 months.

The distinction matters because generation and delivery are different markets facing different problems. Solar, wind, gas, and nuclear projects can all secure financing and permits, but none of them can sell power until the grid says they can connect. For AI data centers, the implication is direct: even when chips are available and capital is plentiful, a project cannot begin operating until interconnection studies and transmission upgrades are complete.

VEONIB Insight

For AI video platforms, the grid bottleneck is effectively a supply-side constraint on inference capacity. Most commercial video-generation services run on cloud GPUs inside large data centers, and those data centers are exactly the projects caught in interconnection queues. When power delivery is delayed, video-model providers cannot add serving capacity where demand is growing fastest. That creates regional price differences and availability gaps — a factor ecommerce merchants should monitor when choosing AI video tools and planning production calendars.

The Interconnection Queue: Built for a Different Era

Original Fact

Works in Progress argues that the interconnection process was not created for today's world. Grids use an inflexible first-come, first-served queue that leaves valuable projects stuck behind less important ones. The evaluation criteria also do not reward plants that are willing to cover their own power needs for short periods, which discourages flexible generation and on-site storage. In the United States, 88 percent of large-scale power projects currently in development are privately organized and funded, meaning most investment risk is already private — yet the queue is still managed with rules designed for vertically integrated utilities.

Works in Progress concludes that grid processes need to change to prepare for the AI age. The article's core market argument is that the question of which generation technology to build should be answered in the marketplace for electricity, not in the marketplace of ideas.

VEONIB Insight

The shortage of flexible interconnection rules has a parallel in AI video production: teams that rely on a single provider inherit that provider's infrastructure constraints. A merchant generating TikTok ads through one video API may see slower rendering or higher prices if that vendor cannot expand capacity. Diversifying across multiple video-generation providers and building a workflow that can switch models — the VEONIB approach of generating storyboards, image prompts, and video prompts in a model-agnostic way — reduces exposure to any single provider's grid problems. The same principle applies to energy: grid reform that rewards flexible, self-sufficient projects will unlock capacity faster than debates about the "best" generation technology.

Grid Congestion and Regional Pressure Points

Original Fact

The demand surge is already straining grids, and the costs are measurable. In the United States, additional costs caused by grid inefficiencies such as congestion reached $11.5 billion in 2023, up 45 percent from the previous year. Congestion occurs when cheaper plants sit on the wrong side of transmission bottlenecks and there are not enough cables to move the electricity through.

Regional operators show different levels of stress:

Grid Operator Coverage Area Key Challenge Status
ERCOT About 90% of Texas, including Abilene Forecasts insufficient power to meet summer 2028 demand Capacity deficit expected
PJM Chicago to New Jersey and North Carolina Could not buy enough future generating capacity in 2025 Active capacity shortage
MISO Louisiana to Minnesota Resource adequacy risks could grow without new capacity additions Tight resource outlook

PJM's CEO was direct: "We need capacity — a lot of capacity." Works in Progress notes that debates about gas versus nuclear versus solar are a distraction; the fundamental issue is ensuring power can be delivered where needed, since diverse generation technologies combined can produce a cheaper, more reliable, and less polluting grid than any single source alone.

VEONIB Insight

Regional grid stress should influence where AI video infrastructure is built and how merchants choose providers. Texas data centers depend on ERCOT, which faces a projected shortfall by summer 2028; that timeline is within the planning horizon of any serious ecommerce operation. If a video-generation vendor hosts inference capacity in a stressed region, customers may experience latency spikes or price increases during peak periods. When evaluating AI video tools, ask where the vendor's compute is located and whether they have contracted power in multiple regions. For SaaS founders, this is also a site-selection issue: new AI products should be designed to run on the most energy-secure clouds available.

The Electrified Economy Beyond Data Centers

Original Fact

The AI buildout is only one part of a long-term shift toward electrification across the entire economy. Electricity can be converted into work instantly and with little energy loss, while fuels must first be combusted in an engine. This explains why electric vehicles cost about half as much to fuel and have roughly half the lifetime maintenance costs of gas-powered cars. Between 1990 and 2024, the price of electric motors declined by 97.5 percent, power electronics by 99.5 percent, processors by nearly 99.9 percent, and batteries by 98.8 percent. At the same time, battery energy density has increased five-fold, enabling everything from the Walkman to the iPhone to electric delivery vans, autonomous trucks, robotic vacuum cleaners, and early humanoid robots.

The implication is that electricity demand growth is structural, not cyclical. Even if AI investment slows, electrification of transport, manufacturing, and logistics will continue to pressure grids.

VEONIB Insight

Electrification trends reinforce the case for treating compute as a premium resource. As more of the economy runs on electricity, the marginal cost of AI inference may rise before new generation and transmission catch up. For ecommerce teams, the practical response is efficiency: use smaller models where quality permits, batch video generation during off-peak hours, and avoid regenerating assets unnecessarily. Platforms like VEONIB already reduce waste by turning a product URL into a structured plan — script, storyboard, image prompts, video prompts — so creative teams generate once and reuse assets instead of iterating directly on expensive video renders.

What the Grid Bottleneck Means for AI Video Generation and Ecommerce

AI video generation is among the most compute-intensive applications of modern AI. Every frame of a generated product commercial requires a diffusion or transformer model to run thousands of operations, and longer videos multiply that cost. When grid constraints delay data center expansion, three consequences follow for ecommerce video production.

First, model availability: frontier video models — from OpenAI, Google AI, Runway, or other providers — will launch and scale according to compute availability, not just research progress. Second, pricing: inference-heavy products are more exposed to electricity and hardware costs, so video-generation pricing may be less stable than text-based AI services. Third, innovation direction: energy constraints encourage efficiency research, including smaller models, distillation, and faster inference hardware, which historically benefits users through lower costs over time.

For ecommerce teams, the workflow matters. The VEONIB pipeline — product URL to product analysis, script, storyboard, image prompt, video prompt, AI video, voice, subtitle, and publishing — is designed to be provider-agnostic. Our analysis of Google Gemini 3.5 for ecommerce AI video shows how quickly the model landscape is shifting, and social norms research for AI video avatars demonstrates that quality gains come from prompt and workflow design as much as raw compute. A portable workflow is a direct hedge against grid-driven volatility: if one video model becomes expensive or unavailable, the same storyboard and prompts can move to another model without restarting creative work.

VEONIB Insight

Ecommerce businesses should adopt AI video generation now, but design for portability. The long-term trend favors cheaper, more efficient video models as energy pressure drives optimization, so the winning strategy is to build a reusable production pipeline today that can swap models as the market evolves. Waiting is preferable only for organizations that cannot tolerate short-term price volatility in AI services; for everyone else, the cost of delayed adoption is worse than the risk of paying slightly more per video during the transition.

Recommendations

Shopify Merchants

Amazon Sellers

AI Developers

SaaS Founders

Content Marketers

Video Creators

FAQ

Is the United States actually running out of electricity?

No. According to Works in Progress, the country has enough electricity generation; the problem is delivering it. Grid interconnection wait times have grown from under 20 months in 2005 to 55 months by 2023, so new demand cannot connect quickly enough.

Why does grid interconnection take so long?

Grid operators must study how each new project changes power flows and whether upgrades are needed. The process uses a first-come, first-served queue that leaves high-value projects stuck behind less important ones, and it does not reward plants that can cover their own power needs for short periods.

How does the grid bottleneck affect AI video generation?

AI video generation requires massive compute for both training and inference. When data centers cannot connect to the grid, video model providers face constrained capacity, which can delay model rollouts, increase prices, and create regional availability gaps.

Will AI video production costs rise because of energy constraints?

In the short to medium term, costs may rise in stressed regions such as ERCOT, which forecasts a shortfall by summer 2028. Over time, energy pressure usually drives efficiency improvements that reduce costs, but the transition period favors teams with efficient, portable workflows.

Which US regions face the most severe grid risks?

ERCOT (Texas) forecasts insufficient capacity by summer 2028, PJM could not buy enough future capacity in 2025, and MISO reports growing resource adequacy risks. AI infrastructure in these regions faces the greatest uncertainty.

What should ecommerce businesses do to prepare?

Diversify across AI video providers, store prompts and storyboards independently of any single model, and generate high-value product assets during off-peak periods. This reduces exposure to energy-driven price and availability swings.

References

Sources

Try VEONIB

VEONIB automatically turns a product URL into product analysis, video scripts, storyboards, image prompts, video prompts, and AI marketing videos. Try the VEONIB AI video generator to build a portable, model-agnostic video production pipeline that adapts as energy and compute markets evolve.

Credibility Assessment

The factual claims — Stargate's cost and power draw, interconnection wait times, congestion costs, regional grid forecasts, price declines for electrification technologies, and industry leader quotes — come directly from the Works in Progress article and its cited sources. The projection of 100 gigawatts of AI computing power by 2030 depends on growth-rate assumptions and remains uncertain. VEONIB's analysis covers the implications for AI video generation, ecommerce workflows, and provider diversification; these conclusions are our interpretations, not claims from the source. Specific vendor pricing and capacity decisions are not specified in the original source and should be verified directly with providers.