Graphium AI

[Graphium AI @graphium_ai]

Average GPU cluster utilization: ~22%.
The world's most expensive hardware idles ~80% of the time.

The biggest opportunity in AI is using what already exists. 📊

[Graphium AI @graphium_ai]

One GPT-4 training run ≈ 50 GWh ≈ 40,000 homes powered for a year.

That's ONE run, ONE model.
Now multiply by thousands.
Compute suppliers are the new utilities. 🏘️

[Graphium AI @graphium_ai]

Hype cycles come and go.
Infrastructure demand persists through all of them, because the apps still have to run on something.

Counter-cyclical by design. 🔄

[Graphium AI @graphium_ai]

Edge AI is a second front - inference moving onto devices, alongside (not instead of) the data center.

Two demand curves, one input.
The pie keeps splitting bigger. 📲

[Graphium AI @graphium_ai]

Data-center semiconductor TAM: $209B (2024) - ~$500B (2030).

AI is rewriting the entire chip industry from the data center outward.
Invest at the source. 🌊

[Graphium AI @graphium_ai]

Asymmetry is the whole game: a decade-long structural tailwind on one side, a single scarce input on the other.

Find the input, hold it, let the trend work. ⚖️

[Graphium AI @graphium_ai]

A simple policy - auto-kill idle GPUs - saves 20–35% of spend.

The waste in this market is staggering.
Whoever harvests the waste, harvests the return. 🌾

[Graphium AI @graphium_ai]

AI inference market: $106B (2025) - $255B (2030), 19.2% CAGR.

The 'running it' economy is bigger than most realize - and it's the durable half of AI spend. 📈

[Graphium AI @graphium_ai]

AI is on track to drive ~half of all US electricity demand growth through 2030.

One technology, bending a national grid.
That's not a fad - it's a multi-year position. 🔋

[Graphium AI @graphium_ai]

Own the bottleneck and you own the margin.

In AI, the bottleneck keeps moving - chips, then memory, then power.

The constant: it's always infrastructure. 🚦

[Graphium AI @graphium_ai]

Batch jobs run on spot GPUs for as little as ~$0.32/hr vs multiples of that on-demand.

The compute market is wildly inefficient - and inefficiency is where edges live.

[Graphium AI @graphium_ai]

Recommendation engines quietly run retail & e-commerce - and they're GPU-hungry inference machines billions of times a day.

The 'add to cart' button has a compute bill. 🛒

[Graphium AI @graphium_ai]

First-mover advantage compounds hardest in brand-new asset classes.

AI compute is one being born right now.

Early ≠ reckless.
Early = informed and positioned. 🧭

[Graphium AI @graphium_ai]

GPU revenue: $100B (2024) - $215B (2030), per Yole. The arms dealer outlasts every war.

In the AI arms race, the GPU is the arms dealer. 🪖

[Graphium AI @graphium_ai]

New memory fabs won't reach volume until 2027 🏭 — packaging capacity is the choke point 🔧

The shortage has a schedule, and it's long 📅
Long shortages reward owners ⏳

[Graphium AI @graphium_ai]

Second-order investing 101:
Don't buy the product, buy what the product can't run without.

Every AI app can't run without compute.
Own the input. 🔌

[Graphium AI @graphium_ai]

AI at GPT-3.5 quality got 280x cheaper in under 2 years (Stanford).

Collapsing cost = exploding usage = more compute.
Counterintuitive, but it's the core engine. ⚙️

[Graphium AI @graphium_ai]

Custom AI chips (ASICs): ~$0 revenue in 2022 - $84.5B by 2030 (Omdia).

Demand is so vast that GPUs AND ASICs both win.
Bet the category, not one chip. 🧩

[Graphium AI @graphium_ai]

AI racks are headed toward ~1,000 kW each by 2029.
The unit of compute is becoming an industrial power load.

This is heavy infrastructure now - invest like it. 🏗️

[Graphium AI @graphium_ai]

The best edge in a young market is its inefficiency.
AI compute is fragmented, mispriced, underutilized.

Mature markets are efficient. Young ones pay early movers. 🎯