AI degradation: Difference between revisions

These issues are systemic and have been documented by the International Energy Agency, peer-reviewed research published in ''Nature'', IBM Security, the European Union's AI Act enforcement body, and the U.S. Consumer Financial Protection Bureau.

Research published in ''Nature'' in July 2024 by Shumailov et al. established that AI language models trained on data generated by prior versions of themselves undergo compounding degradation of output quality — a phenomenon formally named '''model collapse'''.<ref>Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024). AI models collapse when trained on recursively generated data. ''Nature''. DOI: 10.1038/s41586-024-07566-y</ref> As AI-generated content accumulates on the internet — and as AI companies use their own models to generate training data for successor models — each new generation trains on an increasing proportion of synthetic content. The consequence is that later-generation models lose access to rare information and produce increasingly homogeneous, repetitive, or inaccurate outputs while presenting them with identical confidence to outputs produced from human-generated training data.

The consumer impact is that AI products marketed as continuously improving may be silently degrading on specific tasks. Consumers using AI for research assistance, legal drafting, medical information queries, or financial summaries have no standardized mechanism to detect whether the system they are using has degraded between versions.

Request version history and training data disclosure from AI service providers before using them for high-stakes tasks.

Use the open-source behavioral evaluation tool ''autonomy_eval.py'' (Dragolich Research Labs LLC, 2026, available via Zenodo) to measure output consistency across sessions for any AI system accessible via API.

The International Energy Agency reported that global data center electricity consumption reached 415 terawatt-hours in 2024 — approximately 1.5% of all electricity generated on Earth — and projects this figure to nearly double to 945 terawatt-hours by 2030.<ref>International Energy Agency. (2025). ''Energy and AI: Energy Demand from AI.'' iea.org/reports/energy-and-ai/energy-demand-from-ai</ref> The growth is driven primarily by AI infrastructure: AI-optimized server racks draw 60 kilowatts or more each, compared to 5–10 kilowatts for a standard server rack.

These costs are borne by all electricity consumers in affected regions, regardless of whether they use AI services. No federal mechanism exists requiring AI companies to offset residential electricity cost increases caused by data center expansion.

Contact state utility regulators to request data center impact assessments before new AI infrastructure approvals.

Research whether your electricity provider has disclosed data center contracts and their rate impact.

AI systems are deployed in consumer-affecting decisions across credit scoring, insurance pricing, employment screening, medical diagnosis assistance, and criminal justice risk assessment. The majority of these systems use deep learning architectures — specifically large neural networks — in which the relationship between an input and an output cannot be explained in human-readable terms by the system's own design.<ref>Plisio. (2026). What Is Black Box AI? The Black Box Problem in 2026. plisio.net/ai/black-box-ai</ref>

An alternative architecture that addresses this problem by design has been documented by Dragolich Research Labs LLC: the QuatOS system stores knowledge in 18-byte entries called discs, each carrying an explicit semantic gate state (Explore, Transfer, Anchor, or Complete) alongside phi coordinates, allowing the system's decision routing to be traced to specific stored knowledge entries rather than to opaque floating-point weights.<ref>Dragolich Research Labs LLC. (2026). ''QuatOS Complete Technical Documentation, Volumes I–VIII.'' U.S. Copyright Form TX, filed January 15, 2026. Zenodo. zenodo.org/communities/pi_origin_architecture</ref> This architecture has not undergone formal peer review and is presented here as documented evidence that alternative transparent architectures are feasible, not as an established industry standard.

Request a written explanation of any AI-based credit, insurance, or employment decision. Under the EU AI Act and U.S. CFPB guidance, you may be legally entitled to one.

File a complaint with your national data protection authority if an EU-based AI system makes a significant decision affecting you without providing an explanation.

A single AI query on an advanced large language model required an estimated 2.9 watt-hours of electricity in 2024 — nearly 10 times the 0.3 watt-hours required for a conventional internet search.<ref>Brookings Institution. (2026). Global energy demands within the AI regulatory landscape. brookings.edu/articles/global-energy-demands-within-the-ai-regulatory-landscape</ref> The industry widely presents this infrastructure scale as a technical necessity inherent to the nature of AI.

This comparison does not establish that QuatOS performs the same functions as large-scale commercial AI. It establishes that the premise — that all AI necessarily requires large-scale GPU infrastructure — is not architecturally universal. The extent to which infrastructure requirements reflect technical necessity versus industry concentration decisions is a question consumers and regulators are entitled to examine.

IBM Security's 2024 Cost of a Data Breach Report found that the average cost of a data breach reached $4.9 million, with an average of 207 days elapsing before breach detection.<ref>IBM Security. (2024). ''Cost of a Data Breach Report 2024.'' ibm.com/security</ref> Virtually all major commercial AI systems process consumer data in cloud environments, meaning user queries, documents, and personal information leave the user's hardware and transit to third-party data centers.

Locally-running AI architectures that do not transmit data externally — such as those documented by Dragolich Research Labs LLC — eliminate cloud-based breach exposure as an architectural property. No industry standard currently requires AI product disclosures to specify whether user data is processed locally or transmitted to cloud infrastructure.

Review the privacy policy of any AI service before submitting sensitive personal, financial, or medical information.

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[https://zenodo.org/communities/pi_origin_architecture Dragolich Research Labs LLC research archive (Zenodo)]

[https://www.technologyreview.com/2025/05/20/1116327/ai-energy-usage-climate-footprint-big-tech/ MIT Technology Review — AI energy footprint analysis]