If you run a data center, or you’re trying to build one, or you’re trying to buy electronic components for one — you already know things are bad. But you might not know why they’re bad, or how bad, or how long they’ll stay bad. So let me walk you through the whole thing.
The short version is: everyone wants to build AI data centers at the same time, the factories that make the parts can’t keep up, the factories that make the machines that make the parts also can’t keep up, and — this is the fun part — you can’t even plug the data centers into the electrical grid because there aren’t enough transformers, and the wait for a new transformer is now four years.
Four years! For a transformer! That’s longer than most presidential terms.
The Basic Problem
Here’s what happened. In November 2022, OpenAI released ChatGPT. It became the fastest-growing consumer app ever. Every big tech company looked at it and said “oh no, we need to build a lot more AI infrastructure, immediately.” And then they all tried to buy the same stuff from the same handful of factories at the same time.
The companies spending the money are the “hyperscalers” — Amazon, Google, Meta, Microsoft, and a few others. In 2023, they spent about $155 billion combined on infrastructure. In 2025, they spent about $443 billion. For 2026, they’ve announced plans to spend somewhere around $650 to $700 billion.
That’s a lot of money. Amazon alone said it’ll spend $200 billion in 2026. Google doubled to $175 billion. These are numbers that would have seemed insane three years ago, and frankly they still seem insane, but here we are.
The problem is that money doesn’t turn into data centers instantly. You need chips, memory, packaging, servers, power supplies, transformers, cooling systems, optical cables, and construction workers. And right now, almost every single one of those things is in short supply.
The Seven Bottlenecks
I think of the AI supply chain as a series of choke points. Each one can independently slow everything down, and they all make each other worse. A GPU can’t ship without memory. Memory can’t be assembled without packaging. None of it works without power and cooling. It’s bottlenecks all the way down.
Let’s walk through each one.
1. GPUs: The Chip Everyone Wants
NVIDIA makes about 80% of AI chips. Their latest “Blackwell” chips are manufactured by TSMC in Taiwan. They are sold out through mid-2026, with a backlog of roughly 3.6 million units. If you want GPU servers, you need to put down a non-refundable deposit 9–12 months in advance.
AMD is the main alternative. They’ve got a credible product now (the MI350), and they scored a huge deal with OpenAI worth potentially $100 billion over four years. AMD’s share of the AI chip market is climbing toward 15–20%. Intel, meanwhile, has basically given up — they cancelled their big AI chip (Falcon Shores) and publicly admitted they’re not meaningfully competing in this market yet.
But even if you can get chips from AMD, you still need everything else on this list.
2. HBM Memory: The Real Bottleneck
This one is wild. High Bandwidth Memory (HBM) is the special memory that sits right next to the GPU on the chip. It’s what lets AI chips process huge models quickly. Three companies make it: SK Hynix (62% market share), Micron (21%), and Samsung (17%).
All three have confirmed their entire production for 2025 and 2026 is already sold. SK Hynix’s CFO literally said they’ve sold out all of 2026. There is no more to buy.
And the problem gets worse with each new GPU generation, because each one needs more memory:
The H100 uses 80 GB. The new Rubin chip will use 288 GB. That’s a 3.6× increase per chip. So even if memory production grows, each chip eats more of it. Memory prices went up 246% in 2025, and Bloomberg Intelligence says oversupply won’t happen until 2033.
I’ll say that again: 2033. We’re looking at a structural shortage for the next 7+ years.
3. Advanced Packaging: The Quiet Chokepoint
So you’ve got a GPU design, and you’ve got HBM chips. Now you need to stick them together. This is called “advanced packaging,” and it’s done through a process called CoWoS (Chip-on-Wafer-on-Substrate). TSMC basically has a monopoly on this for AI chips.
The situation here is: demand for 2026 is ~1 million wafers. Available capacity is maybe 120,000–130,000 wafers per month. TSMC has pre-booked 85%+ of its capacity to its biggest customers. If you’re not NVIDIA, AMD, or a major hyperscaler, good luck getting a slot.
Here’s a detail that captures how tight this is: NVIDIA alone needs about 595,000 CoWoS wafers in 2026 — roughly 60% of total global capacity. One company. One product.
4. Power Transformers: The Show-Stopper
This is the bottleneck that surprises people the most, and honestly it’s the scariest one, because semiconductor supply chains can scale in 1–2 years, but power infrastructure takes 5–10 years.
A large power transformer — the kind you need to connect a data center to the grid — now has a lead time of 128 to 210 weeks. That’s 2.5 to 4 years. Before the AI boom, it was 6–8 months.
Why? A few reasons. First, these transformers can’t be mass-produced — each one is custom-designed, individually tested, and weighs 100 to 400 tons. The US has about 10 railcars capable of transporting them. Second, only 20% of large US transformers are made domestically. Third, demand has grown 274% since 2019 while capacity hasn’t remotely kept up. Manufacturers have invested $2 billion in new factories, but most won’t open until 2027–2028.
Wood Mackenzie estimates a 30% supply shortfall for power transformers in 2025. Prices have gone up 4–6× since 2022.
5. Cooling: Air Won’t Cut It Anymore
Old data center racks use 5–10 kilowatts and can be cooled with air conditioning. An NVIDIA GB200 rack uses 120–130 kilowatts. The next generation (Rubin) will hit 200+ kilowatts. NVIDIA has already shown reference designs at 1 megawatt per rack.
You can’t air-condition your way out of a megawatt rack. You need liquid cooling — pipes running coolant directly to the chips. Demand for liquid cooling surged 156% year-over-year. Vertiv, a major cooling equipment maker, has a $9.5 billion backlog. CDU (cooling distribution unit) lead times are now 6–9 months.
6. Networking: 864 Fibers Per Rack
Every AI rack needs to talk to every other AI rack, very fast. Modern AI clusters use 800G optical transceivers. Each rack requires 864 individual fiber connections, rising to 1,526 for next-gen systems. McKinsey estimates $150 billion in fiber needs — enough cable to circle Earth 120 times.
China-based manufacturers make about 60% of these transceivers (mainly Innolight and Eoptolink). They’re moving production to Thailand and Vietnam to work around US tariffs.
7. Critical Materials: The Ones You’ve Never Heard Of
There are some really obscure bottlenecks that matter a lot. The best example is T-Glass — a specialized glass cloth made almost exclusively by one Japanese company (Nittobo). It’s used in the circuit boards inside AI servers. Nikkei Asia called it “one of the biggest bottlenecks for the electronics-making and AI industry for 2026.” Apple, NVIDIA, AMD, and Google have all sent people to Japan to try to secure supply. New capacity isn’t expected until late 2027.
Where Everything Is Made (And Why That’s Scary)
The AI supply chain is the most geographically concentrated critical infrastructure in the world. Here’s a simplified picture:
I want to stress how unusual this is. Ninety percent of the world’s most advanced chips are made on one island. One Dutch company makes 100% of the machines required to produce them. One Korean company makes 62% of the memory. If any single link in this chain breaks — whether from a natural disaster, a geopolitical crisis, or just a factory fire — the entire global AI buildout stops.
This is why there’s so much government money flowing into “chip sovereignty.” The US CHIPS Act has put $30.9 billion into domestic semiconductor manufacturing. TSMC is building a $165 billion campus in Arizona — the largest foreign investment in US history. The EU has its own Chips Act that’s attracted €69 billion. But new fabs take 3–5 years to build and cost $15–20 billion each, so relief is years away.
The Geopolitics: Trade War Meets Chip War
The US and China have been in an escalating chip war since October 2022, when the Biden administration first blocked exports of advanced AI chips to China. Here’s the short timeline:
- Oct 2022: US blocks A100/H100 exports to China
- Oct 2023: Rules expanded to close the H800 loophole
- Jan 2025: Biden’s “AI Diffusion Rule” creates a 3-tier global licensing framework
- Apr 2025: Trump bans even the “compliant” H20 chip; NVIDIA takes a $5.5 billion write-off
- Jul 2025: Trump rescinds the Diffusion Rule, creates a new AI Action Plan
- Aug 2025: H20 and AMD MI308 approved for China with a novel 15% revenue-sharing deal
Meanwhile, China has hit back with export controls on critical minerals — germanium, gallium, and rare earths that are essential for chip manufacturing. They’ve also poured $138 billion into their own state-backed semiconductor fund. But they’re still far behind: China’s AI chip output is estimated at only 1–4% of US production.
Sovereign AI is also creating new demand centers. The UAE and Saudi Arabia are planning $100+ billion in AI infrastructure. The US Commerce Department recently authorized 70,000 NVIDIA GB300 chips for the Middle East. Basically, everyone wants AI chips, and there aren’t enough for everyone.
The Power Problem Is the Worst One
I’ve saved the most important section for last, because I think this is the part most people underestimate.
You can speed up chip manufacturing. TSMC is expanding CoWoS capacity. Samsung is boosting HBM production 50% for 2026. The semiconductor industry is really, really good at scaling up when there’s demand.
But power infrastructure? That runs on a different clock entirely.
Goldman Sachs projects data center power demand will grow 165% by 2030. The IEA says data center electricity consumption will double to ~945 TWh by 2030 — about 3% of all electricity on Earth.
Northern Virginia, the world’s biggest data center market, already consumes over 25% of the state’s electricity. Dublin crossed 10% and imposed a moratorium. Amsterdam capped new data center growth until 2030. Over 100 US counties and cities have passed temporary freezes on new data centers.
The hyperscalers are desperate enough to go nuclear. Microsoft signed a 20-year deal to restart Three Mile Island (yes, that Three Mile Island). Amazon secured 1.92 GW of nuclear capacity. Meta and Google have their own nuclear deals. But nuclear restarts take years, and small modular reactors won’t deliver meaningful power until the 2030s.
Here’s the bottom line for procurement people: you now need to plan power infrastructure 2–4 years in advance. If you haven’t ordered your transformers yet, you’re already behind.
So When Does It Get Better?
Honestly? Not soon. Here’s my rough outlook:
CoWoS packaging is the most likely to ease first. TSMC is aggressively expanding capacity and expects to roughly triple output by end-2026. The acute shortage will ease, though it’ll remain tight.
GPU availability should improve in late 2026 as AMD scales up and TSMC brings more capacity online. But demand is also accelerating, so “improve” means “slightly less impossible,” not “easy.”
HBM memory stays structurally tight through at least 2028. All three manufacturers are expanding, but demand is growing just as fast.
Power infrastructure is the long pole. Transformer manufacturing capacity won’t meaningfully expand until 2027–2028. Grid connections in major markets are measured in years. This is the constraint that will define the AI era.
What Does This Mean for You?
If you’re in data center procurement, here’s the actionable version:
Plan 2–4 years ahead for power. If you need a transformer or a new grid connection, you should have ordered it yesterday. The lead times are not going to shorten meaningfully before 2028.
Plan 12–18 months ahead for GPU/server allocations. Put down deposits early. The companies that secure supply first will be the ones that can actually build.
Expect elevated pricing through at least 2027. Memory up 246%. Transformers up 4–6×. Cooling equipment backlogs growing. This is the new normal, not a temporary blip.
Diversify suppliers where possible. AMD’s MI350/MI450 are real alternatives to NVIDIA now. Ethernet networking is overtaking InfiniBand. Multiple cooling vendors are scaling. Don’t be single-sourced on anything.
Watch the geopolitics. US-China chip export rules change every few months. Tariffs on Chinese-made optical transceivers may force supply chain reshuffling. CHIPS Act subsidies are available but time-limited.
The Big Picture
Previous chip shortages — the 2020-2021 COVID crunch, the 2011 Thailand floods — resolved within 12–18 months as demand normalized. This one is different, for three reasons:
- Demand is exponential. AI compute is growing ~2.25× per year. It’s not going to normalize — it’s accelerating.
- Manufacturing is maximally concentrated. One foundry, one lithography company, three memory makers. There’s no slack in the system.
- Infrastructure runs on decade timescales. You can scale chip production in 2 years. You can’t scale the power grid in 2 years.
The combination of exponential demand growth, extreme geographic concentration, and decade-long infrastructure timescales is what makes this a structural crisis rather than a cyclical one. The industry is spending more money than ever, building faster than ever, and it’s still not enough. It won’t be enough for a while.
The companies that figured this out early — that locked in transformer orders in 2023, secured HBM allocations in 2024, and started building relationships with cooling equipment makers before everyone else realized they’d need liquid cooling — those are the companies that will have working data centers in 2026 and 2027. Everyone else is in the queue.
Welcome to the queue.
Data sourced from Bloomberg Intelligence, Goldman Sachs Research, McKinsey, CNBC, Fusion Worldwide, POWER Magazine, Dell’Oro Group, Deloitte, Congressional Research Service, TrendForce, NVIDIA, SK Hynix, and other industry sources cited throughout. Data current as of February 2026.