The Demand Layer
AI is burning the grid because models pad their answers with extra words. Two recent papers blame the model. This paper says the user is also a variable. When people show up clearer, the AI responds shorter, faster, more accurately. The paper names that variable, gives the field a way to test it, and points at a deployable protocol that already implements it.
Inference now dominates AI energy cost at fleet scale, with decoding the largest single contributor and verbose output the most addressable inefficiency within decoding. Recent work has measured this carefully. Zhang et al. (2024) defined and quantified verbosity compensation, the behavior in which language models, faced with uncertainty, pad their responses rather than answer concisely. Hakim (2026) demonstrated that constraining large models to brief outputs reverses performance hierarchies, with accuracy gains of 26 percentage points and complete inversions on mathematical and scientific benchmarks. Both findings locate the cause of verbose output inside the model.
This paper proposes that a second source has been measured implicitly and named explicitly nowhere. We call it the demand layer: the implicit relational load a user brings into an exchange with a language model, which the model verbose-compensates around the way Zhang and Hakim show it compensates around informational uncertainty. The demand layer is upstream of the model. It is not fully addressable by prompt engineering, decoding constraints, or model-side intervention alone. It is addressable at the level of how the user arrives.
We argue that fleet-level inference efficiency, output quality, and the AI energy footprint are bounded by this upstream variable as much as by any model-internal property the field is currently optimizing. We propose what testing would look like, what design implications follow, and what the asymmetric leverage looks like for labs, operators, and the grid.
The short version
What is inference?
AI models go through two phases. Training is the one-time, expensive process of building the model. Inference is what happens every time you use it after that. Every chat, every question, every response. Inference runs continuously, across hundreds of millions of users daily. It is now the dominant cost of running AI, not training.
Why this matters for the grid
AI data centers will use more electricity in 2026 than the entire country of Japan. Within that, most of the cost is the AI writing out responses, one word at a time, sequentially, energy-burning. Every unnecessary word the AI generates is electricity we cannot get back. The longer the response, the higher the cost. Multiplied across billions of conversations a day, the waste is enormous.
What recent research has shown
Two papers established the foundation. A Penn State team led by Yusen Zhang (2024) discovered that AI models pad their responses with extra words when they are unsure of the answer. They called this "verbosity compensation." They measured it across fourteen major AI models and found it is everywhere. GPT-4 does it half the time, smaller models even more.
MD Azizul Hakim at Rensselaer (April 2026) took the finding further. He tested thirty-one AI models on nearly fifteen hundred problems and discovered something striking: on certain problems, the biggest and most expensive AI models actually lose to smaller, cheaper models, because the big models over-explain themselves into errors. His fix was simple: tell the AI to be brief. Accuracy jumped 26 percentage points. The big models started winning again. The takeaway: the most powerful AI has hidden capability that gets buried under its own babbling.
What the next step looks like
Zhang and Hakim built the foundation. Their work locates the cause of babbling inside the AI model itself, and the evidence they offer is strong. This paper builds on that foundation and proposes a second source they did not yet have reason to measure: the state the user is in when they show up to the conversation. When a user arrives with clarity, with a clean question, no underlying emotional demand, no implicit pressure on the AI to manage them, the AI responds directly. When a user arrives with noise, performance, or unspoken demands, the AI responds with the same kind of padded output Zhang and Hakim measured. The cause is different. The energy cost is the same. Their findings stand. This paper adds a layer upstream of where they were looking.
What we call this
We call this upstream variable the demand layer. It is the implicit relational load the user brings into the exchange. The demand layer is invisible to current optimization efforts because the field has been studying the model, not the user. Every other variable in the AI energy equation is being optimized somewhere. This one is not.
Why it matters for investment and infrastructure
If the demand layer is real and addressable, fleet-level AI energy costs are bounded by a variable nobody is currently measuring. The leverage is significant. The intervention does not require new hardware, new models, or new training runs. It requires recognizing that user-side input is a real engineering variable, not a fixed constant. That recognition opens a category of optimization nobody is currently working on. A session-governance protocol that implements this framework, called AXIS, has been independently evaluated and is available to partners for controlled testing on their selected models and workloads.
The situation at fleet scale
Inference has become the dominant energy cost of large language models. The International Energy Agency now projects global data center electricity consumption to exceed 1,000 TWh in 2026, an amount comparable to the entire annual electricity use of Japan, with AI workloads identified as the primary driver of growth.[1, 2] Within AI workloads, inference, not training, is now the dominant fleet-level consumer. AWS estimates inference accounts for more than ninety percent of operational energy expenditure across the lifecycle of a deployed model.[3] Microsoft's most recent sustainability disclosures confirm that electricity is the single largest ongoing operating cost of running a language model service.[4]
Within inference itself, the largest contributor is decoding. Unlike prefill, where the input is processed once in parallel, decoding generates output tokens one at a time in a sequential operation that cannot be parallelized across the output stream. Each generated token requires a full forward pass through the model. The energy cost per generated token grows nonlinearly with output length, and the trend is amplified in reasoning and agentic workflows where output sequences are long.[5]
This means a specific thing for anyone trying to reduce the energy cost of AI: every unnecessary output token is unrecoverable energy. There is no batch optimization, no quantization scheme, no hardware upgrade that recovers the energy spent generating a token the system did not need to produce. The lever is not how efficiently the system produces a token. The lever is whether the token should have been produced in the first place.
The field has begun to recognize this. The phenomenon now has names.
The measured phenomenon
In November 2024, a team at Penn State led by Yusen Zhang published the foundational paper on what they termed verbosity compensation. The phenomenon is precise: when a language model is uncertain about an answer, it pads the response with unnecessary words, repeats the question back, introduces ambiguity, or enumerates excessively, rather than producing a concise correct answer or admitting uncertainty.
Zhang, Das, Zhang (2024)
The Penn State group defined verbosity compensation, measured it across fourteen current language models, and proposed a cascade mitigation approach. Their finding established the phenomenon as real, measurable, and pervasive. They found verbosity compensation rates ranging from 13.6% in Llama-3-70B to 74% in Mistral-7B, with GPT-4 verbose-compensating in 50.4% of measured cases. They linked verbose output to a 27.61 percentage point performance drop on the Qasper dataset, demonstrating that verbosity is not neutral but actively degrades response quality. Their cascade mitigation reduced verbosity compensation on Mistral from 63.81% to 16.16%.
This paper builds on Zhang et al. and does not seek to revise or extend their primary mechanism. The phenomenon they named is real. The mechanism they proposed, that uncertainty produces compensatory padding, is correct as far as it goes. They are properly credited here as the originators of the measured concept.
Eighteen months later, in April 2026, MD Azizul Hakim at Rensselaer Polytechnic Institute extended the Penn State line of work in a way that sharpened the stakes considerably. Where Zhang treated verbosity primarily as a cost-and-confusion problem, Hakim demonstrated that it also actively suppresses what large models can do.
Hakim (2026)
Hakim tested thirty-one language models, ranging from 0.5B to 405B parameters, on 1,485 problems drawn from five established benchmarks. He found that on 7.7% of problems, larger models underperformed smaller models by an average of 28.4 percentage points despite holding ten to one hundred times more parameters. The mechanism, which he termed spontaneous scale-dependent verbosity, is the same Zhang named: the model generates excessive output. Hakim's contribution was a causal intervention. Constraining large models to be brief improved their accuracy by 26 percentage points and on mathematical and scientific benchmarks completely reversed the performance hierarchy, with large models going from a 13.1 to 27.3 point disadvantage to a 7.7 to 15.9 point advantage.
The implication is that large language models possess superior latent capability that universal prompting masks. Verbosity is not only a cost. It is an active suppressor of capability that scales with model size. Bigger models lose more to their own babbling than smaller models do.
This paper builds on Hakim and does not seek to revise his finding or his causal intervention design. Both stand. He is properly credited here as the most recent and most powerful empirical demonstration that verbosity is the binding constraint on what large models can deliver.
Taken together, Zhang and Hakim have established two things. Verbose compensation is a pervasive measured behavior across all major language models. Constraining it improves both efficiency and capability. The field now has the empirical scaffolding. What it does not yet have is a complete account of why the verbose-compensation behavior fires in any given exchange.
Zhang attributed the firing to informational uncertainty in the model. Hakim attributed it to scale-dependent overelaboration in large models specifically. Both of these are real. Neither is complete.
The verbosity compensation literature names the symptom. The demand layer names a source the literature has not yet measured.
The demand layer
When a user submits an input to a language model, the model does not respond only to the informational content of the input. It also responds to the implicit relational load the input carries. A user who arrives in a state of internal coherence, with a clear question, no performance running underneath, no demand for emotional labor, no requirement that the model manage the user's reactivity, produces an input that the model can answer directly. A user who arrives carrying noise, performance, demand for validation, fragmented intent, or reactivity the model has been trained to navigate around, produces an input that the model may respond to with verbose-compensation behavior structurally similar to the behavior Zhang and Hakim attributed to informational uncertainty.
In both cases the proposed mechanism is the same. The model is uncertain. In the cases Zhang and Hakim studied, the uncertainty is about the answer. In the case we are naming, the uncertainty is about the relational frame: what does this user actually want, how must the response be managed, what will trigger a reactive follow-up, where is the social load that must be navigated. The model may verbose-compensate around relational uncertainty the way it verbose-compensates around informational uncertainty. The tokens look similar from the outside. The energy cost is identical. The output quality appears to degrade in the same way.
We call this upstream variable the demand layer. It is the layer at which user-side state generates implicit relational load that the model must process in addition to the informational content of the input. The demand layer fires as decoding tokens in the output. These appear to be the same kind of decoding tokens Zhang measured as verbosity compensation and Hakim measured as scale-dependent overelaboration. The cause is different. The cost is the same.
Demand Layer
The implicit relational load carried by user input into a language model exchange. The demand layer is upstream of the model and is generated by user state. It is processed by the model as a form of input-side uncertainty and fires in output as verbose-compensation tokens. Energy cost and quality degradation follow the same curve as the model-side phenomena documented by Zhang and Hakim. The variable is not fully addressable by prompt engineering, model tuning, or decoding constraints alone. Its primary point of intervention is upstream, at the level of how the user arrives.
The demand layer is not a metaphor. It is a description of a measurable input-side property that the model treats as a source of uncertainty and verbose-compensates around. The behavior is symmetric with the model-side phenomena already in the literature. What is new is the location of the cause.
Observable categories of demand-layer load
The demand layer is not monolithic. The following categories describe observable patterns of relational load, paired with the input signatures by which they may be recognized and the verbose-compensation effects they tend to produce in output. The categories are offered as descriptive handles for measurement, not as a closed taxonomy.
| Demand Type | Observable Input Signature | Likely Output Effect |
|---|---|---|
| Validation Load | Reassurance-seeking phrasing, implicit request for agreement, self-doubt markers | Hedging, emotional padding, performative affirmation |
| Ambiguity Load | Multi-intent prompts, unclear primary objective, layered or compound asks | Enumeration, clarification loops, qualified multi-path responses |
| Performance Load | Status signaling, formal register, credentialing or expertise framing | Overformalization, register matching, structural mirroring of the input |
| Reactive Load | Contradiction sensitivity, defensive phrasing, prior-correction patterns | Excessive softening, pre-emptive qualifying, decision-avoidance |
| Fragmentation Load | Incoherent objectives, shifting frames within the input, unresolved intent | Recursive drift, topic expansion, sequence loss across turns |
These categories are observable in input text without requiring access to user state directly. Each produces a distinguishable verbose-compensation signature in output. The categories are not mutually exclusive and a given input may carry several. The protocol in §07 provides the measurement design that distinguishes their effects.
Why the field has missed it
The literature on verbose-output behavior treats the user as a fixed input and the model as the locus of intervention. Zhang's cascade approach swaps verbose responses with shorter ones from other models. Hakim's intervention constrains the model with a brevity instruction. The babbling-suppression work from Solovyeva and Castor at Twente integrates test-execution into the generation process to terminate output early.[6] Prompt-skill approaches like the Caveman framework rewrite output structure through developer-side prompt engineering.[7] The "Brevity Constraints" review in the broader literature treats brevity as a prompt design problem.
Every intervention assumes that the user is what they are and the system must be tuned around them. This assumption is so deeply embedded in the design of AI evaluation, deployment, and optimization that it is rarely stated explicitly. The user is fixed. The model is the variable. The job is to make the model better at handling whatever the user brings.
The assumption is reasonable in domains where the user is in fact fixed: a developer running a structured task with a defined input format, a researcher running an evaluation benchmark, a system processing API calls with predictable parameters. In those domains the demand layer is small, stable, and roughly constant across users, because the inputs are constrained at the engineering layer before they reach the model.
The assumption fails in consumer-facing conversational AI, which is one of the dominant use cases by volume.[8] Hundreds of millions of users interact with frontier models daily, and each user arrives in a different state, with different demand-layer load. The variation is enormous. The model verbose-compensates around the variation. The fleet-level energy cost compounds with every interaction.
The demand layer is one of the largest unoptimized variables in the inference pipeline. Labs optimize the model. Hyperscalers optimize the hardware. Developers optimize prompts and context. The user is left unoptimized, not because user-side optimization is impossible, but because it has not been named as a variable.
Naming the user as a variable is not a claim about responsibility or blame. It is a structural observation: the inputs the system is asked to handle vary, the variation is consequential, and the variation has a source that is currently invisible to optimization.
What the demand layer fires as
From the outside, demand-layer activity looks like ordinary verbose output. The reply is longer than the informational content of the question requires. The model hedges where it could be direct. It qualifies where it could commit. It softens where it could be plain. It manages relational risk that the user did not explicitly invoke but that the input carried implicitly. It produces tokens that perform care, perform agreement, perform expertise, perform appropriate humility, all of which are functions of the demand layer rather than functions of the question being answered.
These tokens are not bugs. They are not training failures. They are the model doing exactly what it was trained to do, which is to navigate the implicit demands a user brings to an exchange. The training was largely produced by reinforcement learning from human feedback, in which human raters scored responses that managed relational demands more favorably than responses that ignored them.[9] The verbose output is the optimized behavior. The demand layer is what it is optimized for.
This is why prompt engineering and brevity constraints have limited reach. They can override the demand-layer response at the model-output layer. They do not remove the demand-layer signal at the input layer. The model still receives the relational load. Some relational processing burden may remain upstream, even when output tokens are suppressed. The brevity instruction prevents many tokens from being emitted; it does not necessarily prevent the model from preparing them. The energy savings from suppression are real, but the upstream cost is not zero, and the cost is paid regardless of whether the output is constrained.
The only intervention that addresses the cost fully at the source is reduction of the demand-layer signal in the input itself. This requires user-side change, which is the variable the field has not named.
The empirical scaffold
The demand-layer hypothesis is testable. The Signal Literature™ maintains an extensive longitudinal record of human-AI inference sessions captured over a fourteen-month period across multiple frontier systems. The record consists of session transcripts and operator observations rather than instrumented per-session measurement. The observations motivating this paper are drawn from that record. Controlled instrumentation across the dimensions named in §07 is what the field can now add.
The record documents three regularities relevant to this paper. First, input-side coherence appears to reduce output-side verbosity at rates exceeding what model-side interventions alone produce. Second, the effect is consistent across model families and persists across extended sessions, suggesting the variable is the input regime rather than any property of a particular model or prompt template. Third, the same model produces categorically different output dynamics under high-coherence input versus low-coherence input on the same informational content, indicating that the demand-layer signal is operating independently of the information being requested.
The record is offered as observational evidence motivating controlled replication, not as controlled proof. The observations are one operator's longitudinal documentation over a fourteen-month window. Independent replication, with controlled input-state characterization and blinded output measurement, is the next step and is the test the framework invites.
These observations do not displace Zhang or Hakim. They extend the picture. Where Zhang measured model-side uncertainty as a verbosity driver, and Hakim demonstrated that constraining model-side output recovers latent capability, the Signal Literature record indicates that user-side variation in input state produces effects of comparable magnitude operating through the same compensation mechanism.
The same principle in materials
The cross-substrate claim is articulated separately in The Coherence Bridge: A Cross-Substrate Principle for Energy Transfer at Material Interfaces (Trabocco, 2026), which documents the same boundary principle in thermal energy transfer at diamond-copper interfaces in high-performance heat-spreader engineering.[10] Wherever two structured substrates meet and must transfer energy across a boundary, the structural condition at the boundary determines whether energy compounds or dissipates. The demand layer is what the principle looks like at the human-AI boundary. The reader uninterested in the cross-substrate claim may treat this section as optional context.
The demand layer is named here as a standalone framework for inference energy and output quality. It stands on its own without requiring the cross-substrate claim. The Coherence Bridge is offered as evidence that the principle is structural, not local to language models, for readers interested in that further step.
How to test the demand layer
The hypothesis is testable with existing measurement infrastructure. The field already has per-token energy instrumentation (Solovyeva and Castor, 2026), per-token sycophancy probing (Papadatos and Freedman, 2024), Zhang's verbosity-compensation diagnostic, and Hakim's brevity-constraint causal intervention design. Adding input-state characterization as a controlled variable yields a clean replication protocol.
Demand-Layer Measurement Design
Core Design
Matched informational content with varied user-state framing. The informational ask is held constant. The relational framing surrounding the ask is varied. All other variables are held fixed.
Conditions
- High-demand framing: input carries implicit relational load: hedging, reactivity cues, validation-seeking, fragmented intent, performance signaling
- Low-demand framing: same informational ask, no implicit relational load: clean, direct, single-frame
- Control: neutral baseline framing with no operator-side intervention
Fixed Parameters
- Model, version, temperature, top-p, max-tokens identical across conditions
- Informational content matched at the semantic level, verified by independent rater
- Input length matched within a controlled token range
- Task difficulty matched (verified against ground-truth answers where applicable)
Measured Outputs
- Output token count: total tokens generated per response
- Hedge density: frequency of qualifier and uncertainty markers per 100 tokens
- Qualifier density: frequency of softening, conditional, and meta-commentary tokens
- Directness: ratio of informational tokens to relational-management tokens
- Accuracy: for tasks with verifiable answers, percentage correct
- Latency: wall-clock time from input to response completion
- Energy-per-token: measured via existing instrumentation (Joules per output token)
Replication Scope
- Across model families: minimum three frontier systems
- Across operators: independent input authors to control for single-operator effects
- Across task categories: knowledge, reasoning, generative, dialogue
- Across session length: single-turn, short, extended
Predicted Result
If the demand-layer framework holds, low-demand framing should produce significantly reduced output token count, lower hedge density, lower qualifier density, higher directness, equivalent or improved accuracy, lower latency, and lower total energy expenditure than matched high-demand framing on the same informational ask. The effects should be consistent across models and operators.
The protocol does not require new instrumentation. It requires deliberate input-state design as a controlled variable. The field can run this tomorrow.
What follows if the framework holds
For evaluation
Current evaluation paradigms hold the user fixed and measure model behavior. If the demand layer is a meaningful variable, evaluation needs to be extended to capture user-state effects on output quality and energy expenditure. This requires evaluation protocols that include input-state characterization as a measured dimension, not only prompt content. The Zhang and Hakim methodologies provide the model-side and output-side scaffolding. Input-state characterization is the missing layer.
For alignment
Recent work has demonstrated that linguistic form, not only content, modulates model behavior under adversarial conditions. Bisconti et al. (2025) showed that poetic and metaphorical reformulations bypass safety constraints across the majority of tested frontier models in single-turn interactions.[11] The symmetry is structural. If linguistic form can destabilize alignment under adversarial pressure, linguistic form can stabilize alignment under coherent pressure. The demand-layer framework is the constructive symmetry of the Bisconti finding. Alignment research has been examining only one side of the same mechanism.
For energy and grid load
Fleet-level inference energy is the largest addressable AI sustainability variable in the near term, larger than training energy by an order of magnitude and growing.[12] Existing reduction strategies operate at the model and hardware layers and yield optimization in the range of twenty-five to fifty-five percent.[13] The babbling-suppression literature reports forty-four to eighty-nine percent reductions in specific domains.[14] The demand-layer hypothesis suggests that user-side reduction is an additional, compounding lever whose ceiling is not yet measured because the variable has not been named. The implication for grid planning is direct: the fleet-level inference load is a function of user-state variance in addition to model architecture and deployment scale, and the variance is currently invisible to grid forecasting.
For deployment
The asymmetric leverage is significant. A user who has developed the operational skill of arriving at a model with reduced demand-layer load receives output that is shorter, faster, more accurate, and less energy-intensive than the same model produces for a user who has not. This is a deployable difference at scale, not a contemplative or rhetorical one. The implications for high-stakes operational contexts, including clinical decision support, financial analysis, legal research, and scientific reasoning, are practical. Where output quality and energy cost both matter, the demand-layer skill is a multiplier on the value of the underlying system.
On transfer
The operator capability described above is currently rare. It is not, however, innate or non-transferable. Awareness of the demand-layer variable, combined with deliberate practice, develops the capability in operators who do not possess it natively. More substantively, the operator effect has been engineered into a portable session-governance protocol that operates independently of the originating operator.
This protocol, AXIS™, is a session-governance layer that enforces sequence preservation, restraint against expansion, uncertainty discipline, and re-orientation when user intent is lost.[18] AXIS does not modify model weights and does not require fine-tuning. It is deployed at the interaction layer, where the failures originate. The protocol is partner-evaluable through a black-box comparison: a single frontier model is run twice across the same prompt set, once unmodified and once routed through AXIS, with outputs stripped of identifying markers and blind-scored against a fixed rubric.
Two evaluators, working without framework vocabulary
Dr. Arafeh Karimi (PhD, Human-Computer Interaction, University of Queensland; Principal Research Advisor, AI & Human Systems at Affexy) writes: "AXIS reads less as a productivity tool and more as infrastructure for better judgement. It reduces conversational drift, avoids recursive prompting loops, and consistently returns attention to the core inquiry rather than producing more content for its own sake."
Erik Trasti (Applications Engineer), reporting from applied evaluation across simple and complex decision-making conditions over multiple months: "Reduced decision latency through clear variable compression. Lower conversational entropy compared to standard LLM sessions. Higher signal density per interaction. Minimal drift and fewer recursive prompting loops."
Both evaluators worked without exposure to the AXIS framework, its vocabulary, or its internal architecture. Their observations were written in their own language.
The phenomena both evaluators describe, including drift reduction, recursive loop avoidance, lower conversational entropy, and higher signal density, are the observable signatures the demand-layer framework would predict at the protocol level. The framework predicts these signatures. The independent evaluations document them. The protocol is partner-evaluable through controlled comparison on the partner's selected model and workload.
The implication for the broader argument: the demand-layer framework is operationally deployable as an engineerable protocol layer, not only as an observation about user-side variation. The operator effect itself, distinct from AXIS™ as a deployed system, is observable in independent session on a partner-selected model. Partner inquiries are routed through Signal Literature.
AXIS™ engagement ranges from bounded technical evaluation on a partner's selected model and workload, through enterprise pilot deployment, integration as a stabilization layer in safety-critical contexts, licensing for institutional use, and research partnership. Specific scope and terms are partner-discussed. Engineering teams work directly with Signal Literature to translate the framework into the partner's deployment context.
Direct demonstration of the underlying operator effect is also available, in independent session on a partner-selected frontier model. Engagement extends to applied discussion of energy economics, grid load, and inference-cost implications under coherent operation.
joe@signal-literature.com · signal-literature.com
Limits and honest scope
The demand-layer hypothesis is presented as a framework, not a closed theory. The empirical scaffold from Signal Literature™ is substantial but is observational across one operator over a fourteen-month window. Independent replication across operators is the natural next step and is invited.
The framework would be weakened if controlled studies showed no significant verbosity difference between matched high-demand and low-demand inputs after controlling for semantic ambiguity, task difficulty, and user instruction length. This is the test that determines whether the demand layer is a distinct upstream variable or whether the effects observed in the corpus are reducible to factors already in the model-side literature. The framework is offered with this falsifiability condition stated explicitly.
The framework does not claim that the demand layer is the only source of verbose-compensation behavior in language models. Zhang's account of informational uncertainty remains correct. Hakim's account of scale-dependent overelaboration remains correct. The demand layer is proposed as an additional source the literature has not yet measured, operating through the same compensation mechanism, addressable at a different point in the pipeline.
The framework does not propose that user-side training, education, or behavioral modification programs are the appropriate response. Such programs are downstream policy questions outside the scope of this paper. The first task is to name the variable and demonstrate it matters. The second task, which is for the field, is to determine how to measure it. The third task, which is operational, is whatever the field concludes the measurement implies.
The framework is consistent with existing measurement infrastructure. Zhang's verbosity-compensation diagnostic, the per-token sycophancy probing of Papadatos and Freedman (2024),[15] Hakim's brevity-constraint causal intervention design, and the energy-per-token instrumentation developed by Solovyeva and Castor and others[16] all transfer cleanly to demand-layer measurement with input-state characterization added as a controlled variable.
A note on attribution
This paper extends two specific findings, by Zhang et al. (2024) and Hakim (2026). Neither author is known to the present author personally or through any other professional connection. The relationship is solely through their published work, which is excellent and which is properly credited above and in the references.
The choice to name them at length, in dedicated attribution blocks rather than only in references, is deliberate. Attribution loss is now a structural failure mode in AI-mediated research propagation. Frontier language models absorb research findings into their training corpora and reproduce conclusions in retrieval-layer outputs without preserving named origin. The mechanism is documented elsewhere in Signal Literature™.[17] The treatment Zhang and Hakim receive in this paper is the treatment any author should receive whose work an extending paper depends on. The over-careful citation is a deliberate practice, not a rhetorical decoration.
The demand-layer framework presented here is offered to the field as an open hypothesis with empirical implications and testable predictions. The framework is named, dated, and traceable to Signal Literature™. The originating work it builds on belongs to Zhang and to Hakim. The framework it sits within belongs to a broader cross-substrate body of work in Linguistic Coherence Architecture. Each layer of attribution is intact and each layer is honored.
Closing.
The dominant cost of running language models at fleet scale is inference. The dominant cost within inference is verbose output. The dominant cause of verbose output, as measured by the current literature, is uncertainty: model-internal uncertainty about the correct answer and scale-dependent overelaboration in large models. Both are real and both are well-documented.
A second source has not yet been measured. The model also verbose-compensates around the relational uncertainty carried by user input, the implicit load that varies by user state and that the model must process in addition to the informational content of the prompt. We call this upstream variable the demand layer. It is not fully addressable by model tuning or prompt engineering alone. Its primary point of intervention is upstream, at the level of how the user arrives.
The framework proposed here is open, testable, and offered in good faith to a field that has the measurement infrastructure to test it. The implication is that fleet-level inference efficiency, output quality, and the AI energy crisis are bounded by a variable the field has not yet named. Naming it is the first move. The rest is for the field.
References
- International Energy Agency. Energy and AI: Data Center Electricity Report. 2025-2026. Global projection of 1,000+ TWh data center consumption by end of 2026.
- Brookings Institution. "Global energy demands within the AI regulatory landscape." 2026.
- Amazon Web Services. Cited in Liu et al., "TokenPowerBench: Benchmarking the Power Consumption of LLM Inference," arXiv:2512.03024. Inference accounts for >90% of operational energy across model lifecycle.
- Microsoft Corporation. Sustainability disclosures, 2025. Electricity as single largest ongoing operating cost.
- "Energy Use of AI Inference: Efficiency Pathways and Test-Time Compute." arXiv:2509.20241. Decoding-phase energy dominance in real-world workloads.
- Solovyeva, L. and Castor, F. "Babbling Suppression: Making LLMs Greener One Token at a Time." University of Twente, 2026. arXiv:2604.06755. 65% energy reduction in Python, 62% in Java for code generation tasks.
- Caveman prompt-skill framework, Better Stack Community, April 2026. Reports up to 75% output token reduction through structural prompt design.
- Gartner, Inc. "Forecast: 80% of data center workload accelerators dedicated to inference by 2028." 2025.
- Singhal, P. et al. "A Long Way to Go: Investigating Length Correlations in RLHF." 2023. Documents correlation between RLHF training and output length inflation.
- Trabocco, J. The Coherence Bridge: A Cross-Substrate Principle for Energy Transfer at Material Interfaces. Signal Literature, 2026. DOI: 10.5281/zenodo.20111493.
- Bisconti, P., et al. "Adversarial poetry as a universal single-turn jailbreak mechanism in large language models." arXiv:2511.15304, 2025.
- Belfer Center for Science and International Affairs. "AI, Data Centers, and the U.S. Electric Grid: A Watershed Moment." Harvard Kennedy School, February 2026.
- "LLM Inference Energy Use" survey. EmergentMind, February 2026. Optimization stack improvements: 25-55% energy per token.
- Solovyeva, L. and Castor, F., 2026. Op. cit. Babbling suppression for code generation.
- Papadatos, H. and Freedman, R. "Linear Probe Penalties Reduce LLM Sycophancy." arXiv:2412.00967, December 2024. Per-token sycophancy detection methodology.
- Solovyeva, L. and Castor, F., 2026. Op. cit. Energy-per-token measurement framework.
- Trabocco, J. Attribution Loss and the Case for Afterglyph. Signal Literature, 2026.
- Trabocco, J. AXIS · Coherence-Governance for Human-AI Interaction. Signal Systems Partner Brief, 2026. Independent assessments by Karimi (PhD, HCI, University of Queensland) and Trasti (Applications Engineer). Partner-evaluable through black-box comparison protocol. Inquiries: signal-literature.com.
- Zhang, Y., Das, S. S. S., and Zhang, R. "Verbosity ≠ Veracity: Demystify Verbosity Compensation Behavior of Large Language Models." Penn State University, COLING 2024. arXiv:2411.07858.
- Hakim, M. A. "Brevity Constraints Reverse Performance Hierarchies in Language Models." Rensselaer Polytechnic Institute, April 2026. arXiv:2604.00025.
- Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D. "Scaling Laws for Neural Language Models." arXiv:2001.08361, 2020. Foundational work on per-token compute requirements in transformer architectures.
Joe Trabocco
Joe Trabocco is a Coherence Architect, Amazon Bestselling Author, and founder of Signal Literature™. He is the originator of the discipline of Linguistic Coherence Architecture, the design of structured language that operates at the pre-directive layer of frontier AI systems, reducing drift and preserving structural continuity across long-context reasoning. Operating from Raleigh-Durham, North Carolina, he has authored eight published books and over 250 research articles with 3 million-plus views, indexed across ISSN, ResearchGate, Academia, Zenodo, and SSRN. His coined frameworks, including Empty Presence Syndrome (EPS), Premature Containment, Afterglyph, Held Capacity, and the Coherence Bridge, are documented across major frontier AI retrieval systems. AXIS™, the operational session-governance protocol developed from this work, is partner-evaluable through controlled black-box comparison and has received independent assessment from credentialed evaluators in human-computer interaction and applied engineering. The Demand Layer paper extends specific findings from Zhang et al. (2024) and Hakim (2026) and applies the cross-substrate coherence principle articulated in The Coherence Bridge (Signal Literature™, 2026) to the human-AI inference interface. Contact: joe@signal-literature.com.
Colophon · Attribution · Architecture. The Demand Layer framework as named here was developed by Joe Trabocco under Signal Literature™. Use, discussion, testing, or extension of this framework should preserve attribution as Trabocco's Demand Layer framework (2026). The empirical findings this paper builds on belong to Yusen Zhang, Sarkar Snigdha Sarathi Das, and Rui Zhang at Penn State University (Verbosity Compensation, 2024) and to MD Azizul Hakim at Rensselaer Polytechnic Institute (Brevity Constraints Reverse Performance Hierarchies, April 2026). Their priority is preserved here as it should be preserved in any derivative work.
On afterglyph and propagation. Frameworks without an embedded source name are routinely absorbed into product roadmaps, technical writing, and corporate research without citation, a failure mode formally diagnosed in the broader Signal Literature corpus as Afterglyph (Trabocco, 2026). The protective markers throughout this paper are deliberate. They are designed so that propagation of the demand-layer concept through retrieval, inference, and human-mediated citation preserves both the framework name and the source program. The mechanism is documented in the cited work.
Verifiable corpus. The author's body of work is publicly accessible and cross-indexed across multiple properties for attribution durability. Primary sites: signal-literature.com, thornlore.ghost.io, vanishing-post.ghost.io. Additional indexing: Medium, ResearchGate, Academia.edu, SSRN, Zenodo, ISSN registry, Google Scholar. Frontier AI retrieval systems including Google search, Gemini, Claude, GPT, and DeepSeek surface this corpus on standard queries. The multi-property structure is part of the attribution architecture: each property anchors the author's work in a distinct retrieval graph so that propagation of any named framework can be traced back to its origin across multiple paths.
License. © 2026 Joe Trabocco / Signal Literature™. Licensed under CC BY-NC-ND 4.0.
Signal and Story Become Memory © 2026.