AI is reversing 30 years of dematerialization — shifting value from software to infrastructure, energy, and geopolitics.
The bottlenecks are physical, not computational — gigawatt power, fabs, rare earths, HBM, and heat define the speed of AI progress.
The new competitive frontier is mastering constraints, not escaping them — the opposite of the software era advantage. (See: Atoms Strike Back, BusinessEngineer.ai)
For three decades, the defining logic of technology was simple: Bits beat atoms.
Software ate the world because it escaped the physical world entirely. It scaled at zero marginal cost, reached billions instantly, and required no factories, supply chains, or gigawatts of electricity. This logic produced Facebook, Google, Netflix, Salesforce — the entire cloud economy — and justified the core mantra of the internet era: “Own the IP, outsource the physical.” (See: When Software Ate the World, BusinessEngineer.ai)
AI breaks this logic.
Foundation models are not “just software” — they are machines whose performance is gated by power, memory, bandwidth, cooling, fabs, and rare earth minerals. Instead of escaping physics, AI slams directly into it. The result is a structural reversal: Value is re-materializing. Atoms are striking back. (See: The Manufacturing Paradox, BusinessEngineer.ai)
Transformation: From Infinite to Finite
The software era was defined by infinite scalability. The AI era is defined by finite constraints.
Software Era: Infinite
Weightless
Virtual
Copyable
Unconstrained
AI Era: Finite
Physical
Constrained
Resource-intensive
Bound by thermodynamics
The contrast is not philosophical — it is mechanical. AI progress is limited by bottlenecks that software never had to think about.
The primary bottlenecks are three interlocking chokepoints — each derived directly from physical limitations.
The Three Physical Chokepoints
(See: The Six Chokepoints, BusinessEngineer.ai)
1. Gigawatt Power: The Thermodynamic Wall
Large-scale AI compute clusters require 1–5 gigawatts per site, which is nuclear-reactor scale. Power is the single most important constraint on AI scaling, because:
Every doubling of model size increases energy draw.
Cooling systems face thermodynamic limits.
Substation upgrades take 10–15 years.
Renewable intermittency cannot support 24/7 training runs.
This is why OpenAI, Microsoft, Meta, Google, and Amazon are all exploring nuclear partnerships for AI compute — a physical constraint software cannot negotiate around.
Only ASML produces EUV lithography machines (~40–50/year).
New fabs require 5–7 years and tens of billions of dollars.
No fast substitutes exist in the United States or Europe.
This introduces unprecedented strategic fragility: Taiwan = Single Point of Failure for AI civilization. (Citation: The Six Chokepoints — Semiconductor Fabs, BusinessEngineer.ai)
If Taiwan is disrupted, AI progress pauses globally. No amount of cloud infrastructure, GPUs, or modelinnovation compensates for a broken chip supply chain.
3. Rare Earths: The Geopolitical Wall
AI requires magnets, motors, batteries, and diversified power infrastructure — all of which depend on rare earth minerals. China controls:
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
