---
title: "💢💢💢"
type: "Topics"
locale: "en"
url: "https://longbridge.com/en/topics/39266531.md"
description: "🔥⚡Morgan Stanley reminds the market: The real bottleneck for AI may not be algorithms, but electricity. Over the past two years, the entire AI industry has been discussing one question: where will the next breakthrough come from? Many thought the answer lay in model architecture. But Morgan Stanley's assessment is completely different. They believe the key variable for AI is actually the scale of computing power. If the hardware scale used to train models increases tenfold, the intelligence level of the models will likely see a significant leap. The logic behind this isn't complicated. The capabilities of large models..."
datetime: "2026-03-15T08:27:41.000Z"
locales:
  - [en](https://longbridge.com/en/topics/39266531.md)
  - [zh-CN](https://longbridge.com/zh-CN/topics/39266531.md)
  - [zh-HK](https://longbridge.com/zh-HK/topics/39266531.md)
author: "[辰逸](https://longbridge.com/en/profiles/16318663.md)"
---

# 💢💢💢

🔥⚡Morgan Stanley warns the market: The real bottleneck for AI may not be algorithms, but electricity.

Over the past two years, the entire AI industry has been discussing one question:  
Where will the next breakthrough come from?

Many thought the answer lay in model architecture.  
But Morgan Stanley's assessment is completely different.

They believe the key variable for AI is actually the scale of computing power.

If the hardware scale used to train models increases tenfold, the intelligence level of models is likely to see a significant leap.

The logic behind this is not complicated.

The capabilities of large models largely come from three variables:

Data scale  
Model parameters  
Training compute

When computing power increases substantially, models can undergo longer and more complex training, leading to clear progress in reasoning, logic, and specialized tasks.

The recently released GPT-5.4 reasoning model has already shown this trend.

In professional capability tests, it achieved an 83% score on the GDPVal benchmark, beginning to approach human expert levels.

This means AI is gradually shifting from a "tool" towards a professional execution system.

But Morgan Stanley points out a new problem is emerging:

Energy.

If computing power continues to grow exponentially, electricity demand will surge in tandem.

According to their estimates, the U.S. power grid may face a shortfall of about 18 gigawatts on December 28th.

What does this mean for the AI industry?

It means the expansion of computing power is no longer just a chip problem, but an energy problem.

Some AI companies have already started bypassing the traditional power grid.

They directly take over power facilities originally used for cryptocurrency mining, or deploy natural gas turbines to provide independent energy for data centers.

This change is bringing about a new investment cycle.

Large AI data centers are signing power lease contracts lasting up to 15 years.

The reason is simple:  
As long as AI can continuously generate value, every watt of electricity can be converted into profit.

In other words, electricity itself is becoming a new factor of production for AI.

At the same time, AI's capability improvements are also starting to change corporate structures.

As a new generation of AI tools can complete specialized tasks at extremely low cost, some large companies have already begun cutting certain positions.

This is not a short-term phenomenon, but more like a structural change within a technology cycle.

When production efficiency sees an order-of-magnitude improvement, organizational structures are often redesigned.

More noteworthy is a viewpoint raised by researchers:

In the future, AI may enter a recursive self-improvement stage.

Once software can optimize its own code without human intervention, its development speed may accelerate further.

If this model holds, then the foundational resources of the future economy may change.

The core resources of the past industrial era were:

Oil  
Steel  
Land

In the AI era, a new resource is emerging:

Raw intelligence.

This intelligence does not come from individuals, but from vast computing and energy clusters.

These clusters continuously train, upgrade, and optimize models, and output intelligence as a service to the entire society.

If this trend continues to develop, the future economic system may revolve around a new core:

Whoever possesses computing power and energy possesses the ability to produce intelligence.

And intelligence itself may gradually become a commodity that can be produced, distributed, and traded.

As AI begins to mass-produce intelligence, one question is becoming increasingly worth pondering:

In the future, will the truly scarce resource be computing power, energy,  
or humanity's own creativity?

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- [OpenAI.NA](https://longbridge.com/en/quote/OpenAI.NA.md)