Summary of "4 типа задач, которые нужно немедленно передать ИИ"

High-level idea

Two economic return curves determine what you should delegate to AI versus keep for humans:

• Diminishing returns (Alfred Marshall): early effort yields big gains, later effort yields little incremental value — delegate these tasks to AI.
• Increasing returns (Brian Arthur): additional effort compounds and can produce disproportionate outcomes — keep these tasks in human hands (strategy, negotiations, relationship-building, high‑judgment decisions).

Decision framework (quick test to classify a task)

Ask two questions:

1. If I make this task 10% better, will anything fundamentally change?
   • Yes → increasing-returns zone (keep for humans).
   • No → diminishing-returns zone (delegate).
2. Does this task have an objective quality ceiling?
   • If yes → likely delegate.

Heuristics:

• Repeatability / routine → delegate.
• Unique context, judgment, people/strategy → keep.

Primary playbook — 4R framework (tasks to delegate to AI)

4R = Acceleration, Research, Ranking, Routine. These are the categories of first-curve tasks to hand to AI/agents.

1) Acceleration (start / ideation)

Purpose: break “blank page” procrastination and produce drafts, outlines, slide logic, product concepts, activity drafts.

• Role of AI: jump-start creative processes and present unexpected directions.
• Operational tip: iterate across models (some are better at quick idea generation; others are deeper). Expect many initial outputs to be “bad” — their job is to trigger human insight.
• Example: used AI to draft a workshop idea, moved prompts across Claude and GPT variants, then synthesized the best result.

2) Research (information collection)

Use case: deep background, forums, social listening, quotes, linked sources; preparing for decisions or testing ideas.

• Use of agents: autonomous web-surfing agents can open pages, follow links, collect and return structured reports with quotes and links.
• Example/result: a Dipagent collected forum/Twitter/Reddit quotes and produced a structured report in ~10 minutes instead of hours.
• Caveat: AI is a search accelerator, not an authoritative source — always verify links and sources returned by agents.

3) Ranking (structuring, prioritizing)

Use case: turn scattered information into structure — summaries, bullet points, comparisons, prioritization, grouping, and contradiction detection.

• Technique: “Cronalization” — load multiple documents and ask the model to find overlaps, conflicts, and synthesis.
• Multimodal capability: combine transcripts, PDFs, audio, tables into one analytical dashboard.
• Operational prompt approach: load multiple inputs, ask for themes, contradictions, and prioritized actions.

4) Routine (automation / repeatable execution)

Use case: repetitive tasks requiring execution but not judgment (formatting, translation, data cleaning, content repurposing).

Automation playbook (recommended sequence):

1. Do the task manually to understand the process.
2. Do it together with AI (human + AI hybrid) to tune prompts/steps.
3. Automate via an agent/e-agent once the prompt/process is reliable.
4. Deploy the agent/app and share it with the team.

• Example automation prompt (paraphrased): “Create a mini-app that accepts a Telegram post, extracts 5–7 key ideas, produces separate cards with a title (≤5 words) and main text for each card.” Iterate prompts, refine visuals, deploy.
• Operational caution: do not automate processes you don’t understand — you will “automate nonsense.” First map and validate the process.

Tools, cost & implementation notes

• Tools referenced: ChatGPT / GPT chat aggregator (called “ChatM” in the video), Claude, various image/video generators (e.g., Nani Banana), Dipagent (agent platform).
• Pricing notes:
  • Chat/agent aggregators have lower tiers (~$10) but limited; $20 tier recommended.
  • ChatGPT Plus / higher tiers include agent tasks and access to broader models; tokens are consumed differently per underlying model.
  • Agent usage and cost depend on which model or media generator you call — monitor token/model costs.
• Agent strengths: autonomous web browsing, linkable sources, multimodal ingestion, deployable apps (publish via deploy button).

Concrete examples & case studies

• Research case: Dipagent gathered Reddit/Twitter/forum quotes and compiled a structured report with links in ~10 minutes vs hours manually. Actionable pattern: use agents for social listening and objection mapping in product/marketing.
• Content repurposing automation: manual → AI-assisted → agent that converts a long Telegram post into an Instagram carousel (titles + card text + visuals).
• Cronalization example: combined forecasts from universities and analytics firms to produce a unified list of relevant skills for content and episode planning.

Actionable recommendations (how to adopt immediately)

• Audit your weekly tasks:
  • Classify each as Acceleration / Research / Ranking / Routine or “keep (strategy/people).”
  • Use the two-question test for hard cases.
• Quick pilots:
  • Pick 1–3 high-frequency routine tasks and run manual → hybrid → agent automation workflow.
  • Pilot agent-based research for one decision (e.g., competitor sentiment, product objections) and measure time saved and actionable insights.
• Prompting and model strategy:
  • Test multiple models for the same prompt (some are iterative/fast, others deeper).
  • Improve prompts and store best prompts in a shared document.
• Team enablement:
  • Build simple agent/apps for employees (e.g., content repurposing) and share links.
  • Train staff to verify AI outputs and to understand the process before automating.
• Controls:
  • Always verify sources returned by agents (risk of hallucination).
  • Track token & model costs; set budgets for agent usage.

Suggested KPIs / metrics to track

• Time saved per task (e.g., research reduced from hours to ~10 minutes).
• Number of tasks automated (weekly / monthly).
• Employee hours reallocated to second-curve work (strategy, sales).
• Error / quality rate of automated outputs (post-deployment).
• Agent usage cost (tokens / $ per month) vs productivity gain / ROI.
• Number of agent / app deployments shared internally.

Risks & guardrails

• Don’t accept AI outputs as fact — verify citations.
• Don’t automate processes you cannot map and validate yourself.
• Watch token/model costs when using multimodel aggregators or heavy media generators.
• Keep human ownership for tasks with high leverage, people dimension, or ambiguous context.

One-sentence executive summary

Use the 2-curve mental model to triage tasks: delegate Acceleration, Research, Ranking, Routine (4R) to AI/agents following a manual→hybrid→automate playbook, and keep strategy, negotiation, people‑centered, high-judgment work for humans; verify sources, control costs, and iterate prompts across models.

Presenters / sources

• Presenter referenced as “M.”
• Economists cited: Alfred Marshall (diminishing returns), Brian Arthur (increasing returns).
• Tools/platforms mentioned: ChatGPT / LLM chat aggregator, Claude, Dipagent, various image/video generators (e.g., Nani Banana).

Category

Business

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