Things I learned about how people use AI after 1800 people
An AI fluency assessment of 1,800 professionals revealed significant gaps in self‑assessment and actual competence. Product managers scored higher than engineers, while HR staff had the lowest fluency scores. The data shows wide variation in AI skills within teams, challenging assumptions of uniform training needs.
- ▪The least AI‑fluent participants overestimated their scores by about 40 points, whereas the most fluent underestimated by 27, creating a 67‑point Dunning‑Kruger gap.
- ▪Product managers outperformed engineers in AI fluency, scoring 59.2 versus 53.7, indicating applied judgement may outweigh technical knowledge.
- ▪HR professionals recorded the lowest AI fluency scores despite being involved in AI‑driven hiring decisions.
- ▪Overall, the average AI fluency score was 48, placing most respondents in the Developing tier despite regular AI usage.
- ▪Within a single company, the disparity between the least and most AI‑fluent employee in one team spanned 82 points, from 15 to 97.
2 outlets in our directory ran this story, first to last over 32 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ How are people using AI agents to buy things? — Authoryze
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| Original publisher | Ycombinator |
| Canonical URL | https://news.ycombinator.com/item?id=49226913 |
| Publication time | Sat, 08 Aug 2026 23:35:27 +0000 |
| Retrieval time | 2026-08-08T23:40:43.475Z |
| Last seen | 2026-08-08T23:40:44.016Z |
| 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 | R7pmxPehPJju · 2 stories |
| 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 |
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| 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
I'm running a an AI fluency assessment tool and after 1800 real users who interact with our chat bot 20-40 minutes; here are some weird things we found out.1. The least AI-fluent professionals overestimated their score by 40 points. The most fluent underestimated by 27. 67 point Dunning-Kruger gap.2. Product managers outscore engineers on AI fluency (59.2 vs 53.7). Applied judgement beats technical knowledge.3. HR people (ironically who make hiring decisions using AI) understand AI the least, with the lowest AI fluency score.4. People consistently say they are good with AI, but 2 out 3 fail to reach even proficient level.5. The average AI fluency score across 1,800 professionals is 48 — squarely in the Developing tier. Most people use AI regularly but without systematic practice.6.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Ycombinator.