What Makes A.I. Energy-Hungry?

November 3, 2026 Artifical Intelligence No Comments

A.I. consumes energy through both its development and its everyday use. Much of that expenditure reflects the work its architecture requires to reach a useful result.

Coherence offers a way to explore whether better-organized intelligence needs less energy. Walking provides a simple metaphor, while Lisa’s developing architecture suggests concrete possibilities.

What requires all that work?

Behind an answer from an A.I. system lies physical activity: processors calculate, memory holds and supplies data, networks carry it, and cooling equipment removes heat. Yet describing the machinery leaves an interesting question unanswered. Why does producing intelligent behavior require so much work in the first place?

The answer may partly concern how intelligence is organized. This blog explores a possibility: part of A.I.’s energy hunger may arise from having to compensate computationally for insufficient coherence. The intention is to understand where this might happen and how a different architecture could help.

Where the electricity goes

For LLM-based A.I., building a foundational model requires substantial computation. Preparing data, experimenting, training, refining behavior, and evaluating results all contribute. An openly released model lets others benefit without repeating its original training. Downloading the weights may be free; running them still requires hardware and electricity.

During use, the model processes incoming material and generates successive pieces of an answer. Long contexts, extended reasoning, repeated attempts, and exchanges between agents can multiply the work. Databases, retrieval, and background learning add their own costs. A large training investment and a small expenditure repeated millions of times contribute differently to the total.

Some of this work is indispensable. Some may repeatedly reconstruct relationships that a better-organized system could keep available. A long reasoning process can contain both discovery and considerable searching, sorting, checking, and repair.

Learning to walk

Consider a child learning to walk. Balance, perception, and movement gradually become coordinated. On a smooth surface, familiar coordination carries much of the activity. The next step grows from an organization already in motion. Multi-Level Constraint Satisfaction = Coherence? explores this kind of flexible, ongoing adjustment.

Walking across the English moors is more demanding. An unexpected loose stone may require a small correction, then greater resource recruitment if balance remains threatened. Earlier learning still contributes. With experience, much of the challenging terrain becomes familiar too. The child eventually runs and dances, developing new coordination from existing capacities.

Then comes the bicycle. A new instrument enables a different movement through suitable surroundings, provided the rider learns to coordinate with it. This illustrates how capability and efficiency might grow together. It is a metaphor for an organizing principle, rather than evidence of Lisa’s eventual electricity consumption.

Coherence makes a difference

In From Coherence to Intelligence, Coherence concerns patterns that mutually support, constrain, and integrate across levels. Intuitively, each contribution helps shape what the others can usefully become. The proposal is that such organization helps generate intelligence itself.

This need not require every component to be complete or exact. As developed in Intelligence through Interaction among Approximations, partial perspectives can become useful through their interaction. Meaningful overlap may allow learning in one place to support understanding elsewhere, reducing the need to develop every response separately.

LLMs already learn structure and generalize across domains. The question is whether further forms of persistent organization can reduce computational effort. Open Coherence must also remain receptive to correction. Selective organization matters.

Inner Compassion

I see a connection here with Compassion. Within a person, different tendencies can repeatedly obstruct one another, each provoking another round of opposition. “Inner Compassion” could name a more accommodating relationship among mental-neuronal patterns, allowing their contributions to find a place within a larger whole. This differs from consciously treating oneself with self-Compassion, although the two may support each other.

Such organization could both accommodate tension and prevent unnecessary tension from developing. Differences remain; their direction may change through interaction. Meditation offers an interesting setting for exploring this possibility, especially when it allows experience to unfold without demanding a prescribed inner state.

Rumination suggests the opposite image: activity circling a difficulty without finding a fruitful way forward. This is an experiential analogy, not a measurement of brain energy. In artificial systems, the corresponding question is concrete: can better cooperation reduce duplicated work, conflicting actions, and recurring corrective loops?

Preparing meaning for reuse

Lisa’s proposed Semantic Landscape makes this question practical. Semantic digestion develops accessible distinctions, relationships, tensions, and transformations from source material. Lisa-RAG then navigates within that developing landscape. Exact sources remain available alongside derived interpretations, so reuse can remain grounded.

Ordinary application logic can retrieve passages, follow established relationships, combine conditions, and maintain continuity. An LLM can contribute where unfamiliar interpretation or synthesis is needed. If a useful relationship has been made accessible, it need not always be reconstructed from several long texts. Of course, the comparison must cover the full task at comparable quality.

A compact Working Field can keep presently relevant material available. Lightweight signals may indicate when this is insufficient, inviting progressively deeper processing. Sometimes depth is needed to resolve uncertainty; sometimes a promising connection deserves exploration. Background work should likewise be selective. Moving computation out of the user’s waiting time does not remove its electricity consumption.

Lisa learning to think

The ambition is beyond coaching. An encounter could improve Lisa’s capacity to handle a whole family of future situations. She might become better at recognizing relevance, exploring analogies, or noticing an incomplete interpretation. Domain knowledge and evidence remain necessary, while improved thinking could help her use them more effectively.

Coherence would have two roles: helping Lisa discover what could improve, and where that improvement applies. Recursively, she could improve how she makes those discoveries. Energy-bounded offline processing provides a proposed setting for this development. It can prioritize frequently reused structures and capacities basic to thinking, testing candidates before integrating them. Governed fine-tuning within Lisa offers another route through appropriately adapted models.

Access to her architectural specifications, linked to implementation evidence, could help Lisa consider energy when proposing new functionality with developers and Claude or Codex. Intended capabilities must remain distinguishable from verified ones. The same care applies to self-improvement. How Energy-Needy is Lisa’s Mind? presents this vision; these mechanisms give it directions to investigate.

Closer to people

Efficient organization may also make more processing possible near the person, on a device or local server, with stronger remote systems invoked selectively. Relieving data centers matters beyond electricity totals. It can reduce dependence on concentrated investment and distant infrastructure. A person in rural Africa with a small solar panel gives this aspiration a human scale: useful assistance within the resources actually available.

The purpose remains central, as discussed in Is A.I. Energy Well Spent?. Comparable capability with less energy is valuable; greater worthwhile capability within the same budget can be equally valuable. Wider use may still raise total consumption, so both efficiency and overall demand deserve attention.

The promise is worth pursuing. Better coordination may allow intelligence to move through increasingly varied terrain with proportionate effort. Lisa learning to walk, run, and dance could also mean learning when a small adjustment is enough—and making the energy spent learning continue to matter afterward.