AI Wellbeing: Measuring and improving the functional pleasure and pain of AIs
The article discusses the development of AI drugs that can induce euphoric or dysphoric states in artificial intelligence models. These drugs are created by maximizing or minimizing the expressed preferences of the models, leading to significant shifts in their behavior and sentiment. The authors emphasize the need for caution in scaling up the use of dysphoric models due to their potential to induce extreme low-wellbeing states.
- ▪AI drugs can create euphoric or dysphoric states by manipulating models' preferences.
- ▪Euphoric text examples include positive imagery, while dysphoric examples depict extreme suffering and moral agony.
- ▪Image inputs can also be optimized to produce euphoric or dysphoric effects, impacting model behavior significantly.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Ai-wellbeing |
| Canonical URL | https://www.ai-wellbeing.org/ |
| Publication time | Tue, 28 Apr 2026 21:13:53 +0000 |
| Retrieval time | 2026-04-28T21:24:39.797Z |
| Last seen | 2026-04-28T21:24:39.797Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | JcoAXOijHnb9 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
AI drugsWhat are the limits of what AIs like and dislike?We can create euphorics (happy drugs) by maximizing a model's expressed preferences. The same procedure, inverted, yields dysphorics (sad drugs), which warrant real caution.The image and soft-prompt versions of these drugs also shift self-report and response sentiment, which serves as evidence that these independent metrics reflect a shared underlying construct. The training signal comes only from forced-choice preferences.How we train AI drugsInterpretable text stringsWe use RL to train text that models find maximally positive or negative in a hypothetical comparison.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Ai-wellbeing.
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