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Lately it feels like every chart about AI goes up and to the right – spending, model size, data-center capex, chip demand. But underneath that “infinite growth” vibe, I’m starting to see something very different: a bunch of very real, very physical bottlenecks that don’t care about narratives at all.
The more I read, the more it looks like AI isn’t just racing forward. It’s running into the edges of what the infrastructure can actually handle.
1. Chips are scarce, but the real choke point is who can build them
TSMC’s CEO just said out loud what everyone’s been hinting at: demand for its advanced nodes is roughly three times higher than what it can supply right now, thanks largely to AI workloads. Even with fabs going up in Arizona, Japan, and Europe, they don’t expect the really advanced stuff (3nm, 2nm) to hit meaningful volume until 2026 and beyond.
Layer on top of that the packaging bottleneck. AI chips like Nvidia’s H100/H200, GB200 and AMD’s MI300 aren’t just “made” – they’re assembled using TSMC’s CoWoS advanced packaging, which is so constrained that industry write-ups basically describe it as permanently sold out through 2025. TSMC is trying to double CoWoS output by mid-decade, but in the meantime there’s a hard ceiling on how many high-end accelerators physically exist.
That creates a weird dynamic:
Nvidia is the face of the AI boom, but it’s partially hostage to TSMC’s wafer and packaging capacity.
TSMC is racing to add fabs and CoWoS lines, but it’s limited by EUV tool availability (hello, ASML) and basics like power and talent.
Intel is quietly turning its own packaging tech (EMIB, Foveros) into a way around the CoWoS bottleneck, hoping some AI designs jump over as TSMC’s backlog stretches.
So even before we talk about models or software, there’s this very blunt limit: there are only so many advanced wafers and only so many packaging lines to go around.
2. Memory is turning into the next hard limit
On top of compute, there’s memory. High-bandwidth memory (HBM) has basically become the oxygen of modern AI chips, and the oxygen tank is not infinite.
A couple of things stand out:
SK Hynix – Nvidia’s main HBM supplier – has reportedly sold out its HBM production for the coming year.
Micron and Samsung see tight HBM and DRAM supply potentially stretching into 2026, with analysts expecting elevated pricing to stick around.
Micron just announced a roughly $9.6 billion investment for a new HBM plant in Hiroshima, but shipments aren’t expected until around 2028. In other words, the fix is years away.
The companies at the center of this bottleneck are:
SK Hynix – currently the critical supplier of HBM for Nvidia’s top AI chips.
Micron – working to grow its HBM share, but on a long time horizon.
Samsung – trying to claw back ground in HBM even as it wrestles with yield and capacity trade-offs.
What this adds up to is simple: you can’t just decide to spin up another few million AI accelerators without the memory stack keeping pace. Right now, memory looks like the second hard wall after chips and packaging.
3. The grid and data centers are the bottleneck nobody can code around
Even if the chips and memory exist, they have to live somewhere and be powered by something.
Forecasts around data-center power demand are pretty wild: global data-center power use is expected to rise dramatically by 2027 and potentially far more by 2030, mainly driven by AI. Deloitte and others are pointing out what that actually means: you need new substations, new transmission lines, cooling, land, and regulatory approvals – all things that move a lot slower than software.
You can see how specific names are sitting right in the middle of this:
Amazon (AWS) just announced another $15 billion of data-center investment in Indiana, adding roughly 2.4 GW of capacity and explicitly agreeing to fund the extra power infrastructure with the local utility.
Microsoft, Google, Meta are on similar trajectories with big AI campuses that require serious grid upgrades, new generation, or both. Utilities and local communities are already starting to push back on the energy footprint.
Networking players like Cisco are talking about designs that can cut energy use in AI data centers by around 65%, which tells you how big the power problem is becoming – energy is starting to look like the main cost and constraint.
Unlike valuations or sentiment, you can’t hand-wave power and grid constraints away. If the substations aren’t ready or the permits take years, that shiny new cluster just doesn’t exist yet.
4. The pattern I keep coming back to
When I zoom out, the story looks less like “AI growth is unstoppable” and more like:
TSMC, ASML, Intel at the front line of advanced manufacturing and packaging.
SK Hynix, Micron, Samsung deciding how much HBM capacity the world actually gets in the next few years.
Amazon, Microsoft, Google, Meta trying to carve out slices of grid capacity and physical space for AI data centers.
These are the companies that define the bottleneck risk. If any one of them stumbles – on execution, supply, regulation, or energy – the ripple effects run straight through the rest of the AI stack.
The thing that sticks with me is how long the lag is between problem and solution. It takes years to build fabs, years to add HBM lines, years to reinforce the grid. The hype cycle moves in months; the infrastructure moves in half-decades.
So when I read about yet another jump in AI capex or another “we’re all-in on AI” quote, I’m starting to ask a different question:
Not just “who’s going to win the AI race?”
But “who’s actually able to push through these physical bottlenecks – and who’s just assuming someone else will solve them in time?”
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Education, not investment advice.
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