48 lines
4.4 KiB
Markdown
48 lines
4.4 KiB
Markdown
# ИИ глупый! (AI Is Stupid!)
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#source
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## Source Metadata
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- **Date:** 2026-07-22 (ingestion date; publication date unknown)
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- **Raw path:** `raw/sources/ИИ глупый!.md`
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- **Source type:** Conclusions doc for a Russian-language YouTube Short (1:28) — https://www.youtube.com/shorts/P4eWd2jvz4k — titled «ИИ глупый! #ии #ai #бизнес»
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- **Author:** unknown (business-facing Russian-speaking creator). Status: tentative.
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- **Ingested:** 2026-07-22
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## Core Claims
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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.
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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.
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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."
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4. Giving the model your company's context improves answer quality "by orders of magnitude" (author's hyperbole: "tens of times, maybe a million").
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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.
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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.
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## Key Evidence / Details
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- 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**.
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- 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."
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## Connections
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- [[harness]] — the video's second ingredient is exactly the vault's harness concept, restated for a business audience; independent convergence with [[konstantin]] and [[eugene]].
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- [[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.
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- [[personal-ai-operating-system]] — the Nobel-vs-employee analogy is the business version of Allie's "feed the system who you are" (foundation docs).
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- [[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).
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- [[levels-of-ai-usage]] — "stupid AI" is what the bottom rungs of the ladder feel like; the formula names what the upper rungs add.
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## Open Questions
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- 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.
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- 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?
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- The ×10…×1000 improvement claims are rhetorical, not measured.
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## Change Impact on Wiki
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- Created this source page.
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- [[harness]] — added the business-facing definition ("engineering wrapper"), the three-part formula, and this source as independent convergent evidence.
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- [[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.
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- [[overview]] — source count 7 → 8; convergence note (no change to the spine — this source *restates* it).
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- `index.md`, `log.md` updated.
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