# Generative Models Erode Human Temporal Learning Through Market Selection

arXiv: https://arxiv.org/abs/2606.06572 · HTML: https://mlsanity.com/paper/2606.06572/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 78 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Fine-Tuning Language Models from Human Preferences](https://mlsanity.com/paper/1909.08593/) (2019)
- [Scaling Laws for Reward Model Overoptimization](https://mlsanity.com/paper/2210.10760/) (2022)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Provable Robust Watermarking for AI-Generated Text](https://mlsanity.com/paper/2306.17439/) (2023)
- [Self-Consuming Generative Models Go MAD](https://mlsanity.com/paper/2307.01850/) (2023)
- [Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development](https://mlsanity.com/paper/2501.16946/) (2025)
- [Position: Model Collapse Does Not Mean What You Think](https://mlsanity.com/paper/2503.03150/) (2025)
- [A Definition of AGI](https://mlsanity.com/paper/2510.18212/) (2025)
- [The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence](https://mlsanity.com/paper/2408.12622/) (2024)
- [Market Concentration Implications of Foundation Models](https://mlsanity.com/paper/2311.01550/) (2023)
- [Stop Automating Peer Review Without Rigorous Evaluation](https://mlsanity.com/paper/2605.03202/) (2026)
- [AI Safety Should Prioritize the Future of Work](https://mlsanity.com/paper/2504.13959/) (2025)
- [On Autopilot? An Empirical Study of Human-AI Teaming and Review Practices in Open Source](https://mlsanity.com/paper/2601.13754/) (2026)
- [Large Language Models at Work in China's Labor Market](https://mlsanity.com/paper/2308.08776/) (2023)
- [A Collectivist, Economic Perspective on AI](https://mlsanity.com/paper/2507.06268/) (2025)
- [Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents](https://mlsanity.com/paper/2605.09915/) (2026)
- [The Alignment Bottleneck](https://mlsanity.com/paper/2509.15932/) (2025)
- [Black Box Absorption: LLMs Undermining Innovative Ideas](https://mlsanity.com/paper/2510.20612/) (2025)

## Abstract (arXiv)

We argue that modern generative models create structural risks for knowledge and cultural production at current, sub-AGI capability levels. We define Human Temporal Learning (HTL) as path-dependent knowledge accumulation through sustained engagement with problems over time. Generative outputs increasingly resemble HTL-intensive work in surface features, so verifying whether a given output reflects genuine human learning grows costly relative to its expected benefit. Once verification loses economic justification, evaluators reward outputs regardless of production mode, and producers who invested years of learning compete on price against outputs that cost almost nothing to generate. We call this pathway value collapse and formalize it through a costly-inspection framework. Cross-domain evidence from academic publishing, legal practice, content platforms, and software security maps onto four stages of verification erosion. Alignment success is orthogonal. Better-aligned models narrow observable gaps between human and AI outputs, making source verification harder and intensifying competitive pressure against HTL-intensive work even when individual AI outputs improve.