1. The Great Shift: From Software + Users → Infrastructure + Capital
For two decades, tech power was defined by software leverage and network effects. Small teams could write better code, attract users faster, and compound reach via viral distribution.
AI broke that equation. Training frontier models now costs billions, not millions. Power grids, data centers, and GPU supply—not creativity—set the ceiling for innovation velocity.
Metric of power: 1B users = unassailable moat. Mantra: Your friends are here, so you stay.
AI Infrastructure Era (2025+)
Moats are built on compute aggregation.
Training larger models requires exponential hardware scaling.
Hardware access is finite, permissioned, and capital-gated.
Each generation of chips compounds performance asymmetry.
Metric of power: 1M GPUs = unassailable moat. Mantra: We can train bigger, cheaper, faster than you can.
Where social platforms optimized for user time, AI platforms optimize for compute time. The constraint moved from attention scarcity to energy scarcity.
Software is now fungible — open-source models, shared architectures, and API parity flatten differentiation. The only sustainable advantage is how efficiently you can run intelligence at scale.
Code advantage → lasts months.
Compute advantage → lasts decades.
Even model weights are losing defensibility. But the cost of inference — power, cooling, bandwidth — embeds pricingcontrol in the infrastructure layer.
The code may be open, but the power plant isn’t.
7. The New Currency of Power: Energy, CapEx, and Geography
In the AI era, three assets matter more than software patents:
Energy Access:
Data centers now compete for megawatts, not MAUs.
Grid proximity defines latency and capacity.
Governments become de facto participants in AI competition.
CapEx Velocity:
The ability to deploy billions faster than rivals becomes a strategic weapon.
Construction, not iteration, is the new measure of speed.
Geographic Moats:
Locations with stable power, cooling, and regulation (U.S., Nordics, Japan) gain systemic leverage.
AI infrastructure will shape trade routes and alliances akin to oil in the 20th century.
8. Strategic Consequences
Investors: capital shifts from software multiples to infrastructure yield.
Startups: must specialize in tooling, orchestration, or vertical AI — not foundation models.
Governments: move from regulator to co-investor, financing national compute capacity.
Enterprises: treat compute as a budget line, not an assumption — competing on inference cost, not app design.
9. Meta-Implication: The End of the Software Century
From 1980 to 2020, software represented pure leverage over capital — ideas scaled faster than factories. AI inverts that equation. Inference now behaves like an industrial process, consuming resources at planetary scale.
The new industrialists are not coders but capacity builders. The next generation of trillion-dollar companies will own the datacenter floor, not the app store icon.
The 2010s crowned engineers. The 2030s will crown energy barons in hoodies.
10. Closing Thesis
The phrase “software eats the world” captured an age of deflationary innovation. The coming decade belongs to infrastructure inflation — an era where control over capital, energy, and compute defines the boundaries of progress.
Software no longer levels the field. It’s the field that’s tilted — and the winners are those who own it.
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.
