This is a profound “connect-the-dots” analysis. You are describing the industrialization of cognition: the transition from biological thinking (constrained by calories) to synthetic thinking (constrained by gigawatts).
Here is how these concepts assemble into a single, cohesive evolutionary narrative.
1. The Biological Constraint: The PFC as a Metabolic Chokepoint
The human brain is an energy hog, consuming ~20% of our metabolic output despite being only ~2% of our mass. The “expensive” part is the Prefrontal Cortex (PFC)—the seat of executive function, logic, and complex reasoning.
Kahneman’s System 2: This is the PFC in overdrive. It is slow, serial, logical, and metabolically expensive. When you solve a complex math problem, you are burning glucose at a rate the body essentially “resent.”
The Chokepoint: We cannot run System 2 for long. We suffer from “decision fatigue” because the PFC is a metabolic chokepoint. It is a biological bottleneck that limits how much heavy cognitive lifting a human can do in a day before performance collapses.
2. The Design Solution: Steve Jobs & The System 1 Interface
Steve Jobs’ genius wasn’t just aesthetic; it was an intuitive understanding of human metabolic limits. He designed for System 1.
System 1: Fast, automatic, intuitive, and low energy.
The UI/UX Philosophy: A perfect UI “don’t make me think” (to quote Steve Krug). When a device is “intuitive,” it means you can operate it using System 1, bypassing the expensive PFC.
Friction Reduction: Jobs removed the “cognitive friction” of file directories, command lines, and styluses. He allowed users to stay in a low-energy, metabolic “flow state” while the machine handled the logic.
3. The Industrial Shift: Outsourcing to 10 GW AI Plants
We are now taking the Jobsian principle to its extreme conclusion. We are not just offloading interface tasks; we are offloading reasoning itself.
The New “System 2”: If the biological PFC is too expensive and limited to run 24/7, we build a synthetic one.
10 GW AI Plants: Current “Stargate” class projects (like those planned by Microsoft/OpenAI) are discussing 5 to 10 Gigawatt data centers. To put this in perspective, a 10 GW plant consumes roughly the same power as New York City.
The Trade: We are outsourcing the metabolic cost of thinking from the human 100-watt brain (fueled by sandwiches) to a 10-gigawatt cluster (fueled by nuclear and gas).
The Synthesis: The Cognitive Power Grid
The narrative arc is clear:
Biology: We hit a hard limit on System 2 thinking due to the PFC’s metabolic chokepoint.
Design: Steve Jobs showed us that technology succeeds when it saves us from spending that energy.
Future: We are building “Cognitive Power Plants” (10 GW AI clusters) to perform massive System 2 tasks (coding, diagnosis, strategy) and deliver the results to us via simple System 1 interfaces.
In this future, humans provide the intent (low energy), and the 10 GW plant provides the inference (high energy).
10 GW AI Data Center Analysis
This video breaks down the physics and logistics of the specific “10 Gigawatt” figure you mentioned, analyzing whether such a massive concentration of compute is even physically possible given current power grid constraints.
This video is relevant because it specifically investigates the “10 GW” figure cited in recent OpenAI/Microsoft “Stargate” reports, treating it as a tangible engineering challenge rather than just a buzzword.
Summary of the Video’s Analysis:
The host (DJ Ware) and his AI co-host (HAL) break down the sheer absurdity of the proposed 10 Gigawatt (GW) figure for a single AI data center (referencing reports about OpenAI/Microsoft’s “Stargate” project).
The Core Problem: 10 GW is not just a “big data center”; it is a grid-scale event equivalent to the entire energy consumption of Switzerland (approx. 87 TWh/year).
Coal: You would need to burn 65,000–90,000 tons of coal per day. That requires 600–1,000 train cars of coal arriving daily, non-stop.
Nuclear: You would need 10 full-scale nuclear reactors running at maximum capacity on a single site (essentially impossible given safety margins and regulatory hurdles). The US hasn’t built a new reactor in 30 years.
Solar: You would need 200 square miles of solar panels. But since the sun sets, you’d need massive battery storage, which drastically increases the physical footprint and cost.
Hydro: A 10 GW load would require five Hoover Dams running at peak capacity (or 20 running at average capacity). There are no undammed rivers left in the US to support this.
Cooling & Reliability: 10 GW of power in means 10 GW of heat out. The cooling infrastructure alone would consume massive amounts of power (compressors, fans, pumps). Furthermore, there is no UPS (Uninterruptible Power Supply) on Earth capable of instantly backing up a 10 GW load if the grid flickers; if the power cuts, the system crashes hard.
Conclusion: The host argues this “10 GW AI plant” is currently “engineering theater” rather than a feasible reality. The logistics of moving that much energy (and removing that much heat) at a single point on the planet are currently unsolved.