9 AI-навыков, которые должен освоить каждый в 2026 году

Overall thesis

A forecast‑based practical checklist: nine AI skills to master in 2026 so you stay ahead of most AI users. Recommendations are grounded in recent forecasts and research (subtitles reference UC Berkeley and a research firm). The skills respond to trends such as demand for verifiable AI outputs, multi‑model workflows, multimodal I/O, low‑code/no‑code generation, exploding AI content, AI‑powered fraud, and new checks that require human‑only performance.

Key trends driving these skills

• Higher demand for verifiable, source‑referenced AI outputs (reduce hallucinations).
• No single “best” model — multi‑model workflows and cross‑checking are becoming standard.
• Multimodal input/output (text, audio, image, video) is increasingly important.
• Low‑code and no‑code generation tools are ubiquitous and maturing.
• Explosion of AI content increases noise; human curation matters more.
• Growth of AI‑powered fraud (deepfakes, personalized scams) requires digital awareness.
• Some checks and gatekeeping will require human‑only performance; alternate work modes preserve cognitive skills.

The nine AI skills (with techniques, tools, examples)

1. Context & source management (reduce hallucinations)

   • Technique: provide the model with your own documents (PDFs, transcripts); require answers “based only on this source”; add confidence labels (high/medium/low); list uncertainties or unverifiable claims.
   • Tools & tips: text expanders for standardized prompt templates; Google’s LM Notebook / “LM laptop” to build a dataset and connect it to Gemini so answers cite sources.

2. Building an AI council (multi‑model cross‑checking)

   • Technique: send the same task to multiple models, compare answers, ask models to critique each other, or have one model synthesize a final answer as a “chairperson.”
   • Benefit: more balanced outputs, less risk of missing important points or accepting hallucinations.

3. Orchestration (connecting and sequencing tools)

   • Process: map repetitive tasks into discrete steps, assign the best tool/model to each step, and connect them into a pipeline.
   • Example pipeline (video/content production): GPT chat for initial research → LM Notebook for deep source work → human writer for scripts → Gemini for SEO → Nanoban for visuals/covers.
   • Emphasis: practical experimentation to discover which models handle which steps best.

4. Automation & AI agents

   • Goal: convert orchestrated steps into automated agents so you don’t repeat prompts manually.
   • Platforms cited: Make (MA), n8n, ManyChat (constructors for bots/automation).
   • Training: hands‑on courses and YouTube tutorials available for building and deploying agents.

5. Multimodal fluency (working across text, audio, image, video)

   • Input: pick the most efficient input format (e.g., film a room instead of describing it). Gemini reportedly accepts video/audio and can analyze frame‑by‑frame.
   • Output: convert results into the most effective medium (audio → short text summary; idea → infographic or podcast).
   • Tools: Gemini, Nanoban (visual generation).

6. “Wipe‑coding” / vibecoding (AI‑driven no‑code / prompt‑based app creation)

   • Definition: describe the desired functionality to AI and get working mini‑apps (forms, trackers, bots) without writing traditional code.
   • Example platforms: Lovable (accessible, award‑winning), Google AI Studio (dialogue with Gemini to build tools), and more advanced environments like Cursor.
   • Advice: start with simple builders, then progress to flexible tools; the core skill is structuring requests effectively.

7. Curatorial taste / critical evaluation of AI output

   • Problem: massive volume of AI content increases noise; human taste and judgment determine quality and differentiation.
   • Practice: train visual and editorial acuity through study (design analyses, guided museum visits, critique practice).
   • Outcome: ability to select, edit, and elevate AI outputs will be a high‑value skill.

8. Critical thinking & digital awareness (defense against AI‑enabled fraud)

   • Threats: more convincing phishing, voice/video deepfakes, and AI‑written personalized scams with higher engagement rates.
   • Defensive habits: question urgency, tone, and context; verify via alternate channels; maintain skepticism and consistent verification routines.

9. Alternating work modes to preserve cognitive skills

   • Trend: employers may require AI‑free checks; overreliance on AI erodes reasoning.
   • Practice: alternate between AI‑assisted tasks and fully independent tasks (write without prompts, form arguments manually) to keep reasoning “muscles” active.

Product & platform features called out

• Google Gemini (Gemini 3): multimodal (video/audio input); can connect to LM Notebook datasets for source‑referenced answers; used for SEO tasks.
• Google LM Notebook / “LM laptop”: assemble personal datasets (articles, audio, transcripts) so chat models answer based only on those docs and provide references.
• Text expanders: insert standardized prompt templates quickly.
• Nanoban (Nanobanana / variations in subtitles): generate visuals, covers, and infographics.
• Make (MA), n8n, ManyChat: platforms for building automations and agents.
• Lovable: accessible no‑code/vibecoding tool mentioned as award‑winning.
• Google AI Studio: build mini‑apps through dialogue with Gemini.
• Cursor and other advanced tools: for deeper no‑code development.
• BL VPN (promoted in subtitles): claimed features include discounts, unlimited devices/traffic, up to 10 Gbps speeds, 50+ locations, 30‑day money‑back.

Guides, tutorials and reviews referenced

• Detailed guide on using the LM Notebook (the presenter’s video).
• Previous video covering text expanders and AI techniques.
• Ongoing YouTube series and training programs for automation, agents, and “wipe coding.”
• Multi‑month paid trainings (offered by the channel/author) for AI agents and orchestrations.
• Recommendation: experiment with free resources before buying orchestration courses — many skills are learnable through practice.

Practical templates & recommended habits

• End each AI task with a standard template:

  Answer based only on this source. If unsure, say “I don’t know.” For each main statement add confidence: high/medium/low. List what you couldn’t verify.

• Build and maintain a personal knowledge base (LM Notebook) and connect it to chat models.

• Map complex tasks into steps, test multiple models per step, then automate with agents where appropriate.
• Regularly perform tasks without AI to preserve reasoning ability.
• Use multimodal inputs when they save time or communicate meaning better (e.g., video for spatial tasks, audio for nuance).

Notes about subtitle accuracy

• Several proper names in the subtitles may be mistranscribed (examples: “LM laptop,” “GMI/GN,” “Nanoban,” “Lovable,” “Andriy Karpatyy / Andrew Ying,” “Gther”). The summary preserves subtitle names as they appear but they may correspond to:
  • Google’s LM Notebook, Gemini, ManyChat / Runway / Nanobase, Lovelace / Vev / Bubble, Andrew Ng, etc.

Main speakers & sources (as listed in subtitles)

• Primary presenter / channel author (first‑person speaker and course creator).
• Cited researchers/projects in subtitles: Andriy Karpatyy, Andrew Ying (possibly variant spellings); Andrey Karpatov (credited with the “AI council” concept/service).
• Institutions mentioned: University of California, Berkeley; a research firm listed as “Gther” in subtitles.

Optional next outputs available

• One‑page printable checklist (prompts, templates, tools organized by skill).
• Concrete how‑to mapping of the example orchestration pipeline with step‑by‑step connector recommendations.
• Verification and cleanup of ambiguous product names from the transcript (cross‑referencing likely real‑world names).