# ИИ глупый! (AI Is Stupid!)

#source

## Source Metadata

- **Date:** 2026-07-22 (ingestion date; publication date unknown)
- **Raw path:** `raw/sources/ИИ глупый!.md`
- **Source type:** Conclusions doc for a Russian-language YouTube Short (1:28) — https://www.youtube.com/shorts/P4eWd2jvz4k — titled «ИИ глупый! #ии #ai #бизнес»
- **Author:** unknown (business-facing Russian-speaking creator). Status: tentative.
- **Ingested:** 2026-07-22

## Core Claims

1. AI looks "stupid" not because the model is weak, but because it is systematically starved of two things: **context** (memory of *your* specific business) and a **harness** (the rules the model must reason by). Without them, even the strongest model answers like an outside expert, not like your employee.
2. **Intelligence without context loses to context without intelligence.** Analogy: ask "how will our sales month close?" — ten Nobel laureates can only cite industry averages ("~5% up/down across Russia"), while a rank-and-file employee of your company answers better, because they see your funnel, clients, seasonality, and deals.
3. The formula: **strong model + your business context + harness = employee-level answer.** Remove any component and you get "smart but generic," "specific but undisciplined," or "stupid AI."
4. Giving the model your company's context improves answer quality "by orders of magnitude" (author's hyperbole: "tens of times, maybe a million").
5. Investment advice: before swapping to a "smarter" model, invest in **context infrastructure** (data, memory, integrations — the video names RAG, long-term assistant memory, CRM/ERP/document ingestion) and in the **harness** (rules, checks, tooling). That is where the ×10…×1000 gains are, not in model version bumps.
6. Maturity metric for an AI rollout: *can the model answer a question about your business more accurately than an outside consultant?* If not, context or harness is missing.

## Key Evidence / Details

- The harness is defined here as more than a prompt: an "engineering wrapper" — what the model must verify, which tools to trust, how to shape the answer, what is forbidden — turning the LLM from "an encyclopedia of hospital averages" into a **procedural agent**.
- Target audience per the video: business owners and product managers disappointed by generic LLM answers; engineers building corporate assistants; anyone choosing between "get a bigger model" and "give the model the right data and rules."

## Connections

- [[harness]] — the video's second ingredient is exactly the vault's harness concept, restated for a business audience; independent convergence with [[konstantin]] and [[eugene]].
- [[context-as-scarce-resource]] — complements it from the *supply* side: the vault's page says context is the binding constraint inside the window; this source says the default failure is not providing business context at all.
- [[personal-ai-operating-system]] — the Nobel-vs-employee analogy is the business version of Allie's "feed the system who you are" (foundation docs).
- [[skills-as-memory]] — tension: this video names **RAG** as a practical context mechanism, while [[2026-07-14-skills-based-on-git]] argues skills-as-memory beats RAG (see Open Questions).
- [[levels-of-ai-usage]] — "stupid AI" is what the bottom rungs of the ladder feel like; the formula names what the upper rungs add.

## Open Questions

- Who is the author, and is the short connected to anyone already in the corpus (its context+harness framing matches the corpus suspiciously well)? Status: tentative.
- The video treats RAG and long-term memory as go-to context mechanisms; Konstantin's source argues skills beat RAG (harness loads on activation instead of pre-injecting). Is the difference audience-driven (business data vs procedures), or a real disagreement?
- The ×10…×1000 improvement claims are rhetorical, not measured.

## Change Impact on Wiki

- Created this source page.
- [[harness]] — added the business-facing definition ("engineering wrapper"), the three-part formula, and this source as independent convergent evidence.
- [[context-as-scarce-resource]] — added the supply-side facet ("intelligence without context loses to context without intelligence"), the Nobel-vs-employee analogy, and the RAG tension.
- [[overview]] — source count 7 → 8; convergence note (no change to the spine — this source *restates* it).
- `index.md`, `log.md` updated.
