Jigar Shah Profile picture
Apr 19 9 tweets 3 min read Read on X
Everyone's talking about the AI data center buildout like it's just a money problem. It's not. There are 5 distinct hard constraints stacked on top of each other — and solving one just reveals the next.

Why there is no way the US can unlock 100GW of AI compute by 2030.
First, the scale of the gap.

The US has ~50 GW of data center capacity online today. Bain and McKinsey both put demand at ~100 GW by 2030.

That means adding ~10 GW per year for 5 years straight. The record year so far? About 2.5 GW actually delivered. We need 4x that. Every year.

Constraint #1: Grid interconnection.
Lets assume this gets solved with an interuptible tariff.
Data centers get a "qualified" yes which requires them to curtail 100-200 hours a year. Match with VPPs and you could see 10% rate decreases across the country.
energyempirepodcast.substack.com/p/the-ai-power…
Constraint #2: Transformers and Electrical Equipment
Every data center — grid-tied OR behind-the-meter — needs large power transformers to distribute electricity internally. No substitute.

Lets assume this also gets solved by importing 100GVA of transformers from India.

techcrunch.com/2026/04/15/thi…
Constraint #3: GPU chips.
Blackwell (NVIDIA's current AI GPU) is sold out through mid-2026 with a 3.6 million unit backlog. TSMC's advanced fabs are running above 95% utilization. NVIDIA has locked up ~70% of next year's CoWoS packaging capacity. Enterprise buyers wait 36-52 weeks.
New fabs take 5 years to build.
Constraint #4: HBM memory.
Every AI GPU requires High Bandwidth Memory. SK Hynix has "already sold out our entire 2026 HBM supply." OpenAI's Stargate project alone is targeting 900,000 DRAM wafers per month — roughly 40% of all global DRAM output.
New fabs don't open until 2027-28.
Constraint #5: Skilled labor.
Electricians, plumbers, and data center mechanical trades are in severe national shortage.
They're also being competed for by: semiconductor fab construction, grid hardening, EV charging rollout, and factory reshoring.
So what's the realistic number?
Lets assume we solve the grid/transformer challenges. The other constraints limit us to:
2026: ~3 GW added
2027: ~5 GW
2028: ~7 GW
2029: ~9 GW
2030: ~10 GW
Cumulative: ~34 GW added
Nowhere close to the numbers being thrown around right now.
energyempirepodcast.substack.com/p/the-ai-power…
The meta-point:
This isn't a funding problem.
$650B+ in hyperscaler capex is committed for 2026 alone.
It's a physical supply chain problem — transformers, chips, memory, skilled workers — where every layer has multi-year lead times that can't be money'd away.
The buildings are the easy part.

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More from @JigarShahDC

May 21
The US electric grid hasn’t grown meaningfully in 20 years. Now data centers want to add the equivalent of three Californias to Texas alone by 2030. You can’t just plug that into existing utility ratemaking math. A new paradigm is needed. 🧵
@TKavulla describes the core problem: utility pricing recovers average embedded costs. But every new data center megawatt today costs more than average to serve — materials up 152% since 2019, capital costs rising fast. Under current rules, legacy ratepayers automatically subsidize new data center load. That’s the trap.
The Trump admin’s Ratepayer Protection Pledge (March 2026) names the right goal: AI companies pay for their own infrastructure, full stop. But a pledge isn’t a mechanism. Three mechanisms actually work: open season auctions for grid access, customer-prepaid capital, and Bring Your Own Generation madness.
Read 5 tweets
May 18
76 countries are now in “emergency measures” for the oil crisis, up from 55 just six weeks ago. That’s not a statistic. That’s the fastest spreading energy emergency in history. And the number is still climbing. 🧵
What does “emergency measures” actually mean? It’s a huge range. Lithuania is cutting train fares. Australia made public transport free. Myanmar and South Korea are telling people they can only drive on certain days. That’s the mild end.
The middle: Philippines declared a full state of national emergency. Indonesia, Japan, South Korea and India are spending billions on fuel subsidies which should be spent on solar instead. Bangladesh asked businesses to turn off unnecessary lights. Pakistan is rationing fuel.
Read 5 tweets
May 18
Bain & Standard Chartered’s 2026 SE Asia Green Economy Report: data centres, EVs & green industrial parks will drive 100+ TWh of new power demand in 3-4 years, requiring $200B+ in investment. $540B in announced green spending is on a credible path to deployment. 🧵
@JavierBlas on Odd Lots today: everyone expected oil at $200+ with 60+ days of Hormuz closed. We’re not there because bypass pipelines, SPR drawdowns, and inventory burns have cushioned the shock. But the biggest surprise? ~5% demand destruction that nobody saw coming.
@JavierBlas Where did that 5% oil demand destruction come from? Not just EVs — it’s price-driven behavioural change, Asia absorbing the sharpest hit, and crucially: countries with existing clean energy infrastructure simply felt less pain. The energy transition was quietly doing work.
Read 5 tweets
May 17
America cannot lead the AI race without building the power and computing infrastructure AI requires. On that I agree completely. But the piece gets the diagnosis wrong — and a wrong diagnosis produces the wrong cure. 🧵
The Gallup poll it cites shows a majority of Americans oppose data centers near their home, including 63% of Republicans. The real enemies of AI are electric utilities that use data centers to jack up rates and GW+ developers that fail to engage communities.
The electricity price comparison also misses the structural problem. Investor owned utilities don’t make money when you use what we have already paid for more efficiently. They only make money when they invest new money even if they don’t need to.
Read 6 tweets
May 11
.@EpochAIResearch has quietly assembled the most rigorous data set on AI infrastructure in existence. Here's what it tells us about how much compute we need by 2030, how many giant campuses are actually required, and where the real distributed inference opportunity lies. 🧵
The baseline: AI compute stock is growing at 3.4× per year, doubling every 7 months. Training compute for frontier models grows at 5× per year. US AI data center capacity will exceed 50 GW by 2030 — approaching 5% of total US generation capacity.
This is not scary for the grid.
But not all of that 50 GW needs to be concentrated. Final frontier training runs — the kind that require extreme GPU synchrony — represent only ~10% of total R&D compute spend. The other 90% is experiments, fine-tuning, inference, and synthetic data. Distributable.
epoch.ai/gradient-updat…
Read 12 tweets
May 10
Everyone's asking: what actually powers AI data centers?

The answer is more complicated — and more interesting — than "natural gas vs renewables."

A 🧵 on what's really happening, and where it's going.
Start with the obvious: data centers need firm power, not really 24/7 as they run at 50% capcity factors.
Solar/Wind are fuel, batteries are really providing the capacity.

So the assumption is gas & nuclear carry the load.

Big Tech energy portfolios tell a more nuanced story.
Amazon & Microsoft: 40+ GW wind/solar each
Google & Meta: ~15 GW wind/solar each
All four signed nuclear deals as well
The nuclear numbers are smaller
In short they are sticking to their commitments to clean energy and buying NG units for "capacity" for speed to power.
Read 13 tweets

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