AI 101: The 2026 Foundation

20 slides. Zero jargon debt. What AI actually is, what changed in 2026, and what it means if you run a business, a brand, or a one-person empire.

20 lessons · ~4 min · free, no signup.

Lessons

Full course text

1. What is AI, really?

Artificial Intelligence is software that performs tasks that normally require human judgment — understanding language, recognizing patterns, making decisions. In 2026, when people say ‘AI,’ they usually mean generative AI powered by large language models.

Source: Stanford HAI — AI Index Report

2. 88% of organizations use AI

88% of organizations now use AI in at least one business function — up from 78% a year earlier and 55% in 2023. Adoption is no longer the question. Doing it well is.

Source: McKinsey — The State of AI (2025)

3. LLM (Large Language Model)

A neural network trained on massive text datasets to predict the next word — which turns out to be enough to write, code, reason, and converse. ChatGPT, Claude, and Gemini are all LLMs.

Source: Google — Introduction to LLMs

4. Prompt, token, context window

A prompt is your input. A token is a chunk of text (~¾ of a word) — models read and bill in tokens. The context window is the model’s working memory: how much it can consider at once, now often 200K+ tokens (a whole book).

Source: Anthropic Docs — Glossary

5. ChatGPT weekly users

ChatGPT reached ~900 million weekly active users by February 2026 — roughly double the year before — processing about 2.5 billion prompts per day. Your customers are already there, asking about your category.

Source: OpenAI via Search Engine Land (2026)

6. Generative vs. traditional AI

Traditional AI classifies and predicts (spam filters, fraud scores). Generative AI creates — text, images, code, video. The business shift of the decade: machines went from sorting information to producing work.

Source: McKinsey Explainers

7. Hallucination

When a model states something false with total confidence. It’s not lying — it’s pattern-completion without a fact-checker. Rule: AI drafts, humans verify. Anything customer-facing or legal gets human review.

Source: IBM — What are AI hallucinations?

8. Annual value from gen AI

McKinsey estimates generative AI could add $2.6–4.4 trillion in value annually across 63 business use cases — comparable to adding an economy the size of the UK, every year.

Source: McKinsey — Economic potential of gen AI

9. Myth: Adopting AI = getting value from AI

Nearly two-thirds of companies haven’t scaled AI beyond pilots, and only ~39% report any bottom-line (EBIT) impact. The gap between using AI and profiting from it is the defining business story of 2026.

Source: McKinsey — The State of AI (2025)

10. AI high-performers

Only about 5.5% of organizations qualify as AI ‘high performers’ (attributing 5%+ of profit to AI). What they do differently: redesign workflows end-to-end, set growth goals (not just cost-cutting), and put senior leaders in charge.

Source: McKinsey — The State of AI (2025)

11. Multimodal

Models that handle more than text — images, audio, video, documents — in and out. Practical upshot: you can photograph a contract, a spreadsheet, or a broken part and ask AI about it.

Source: Google DeepMind — Gemini

12. Worldwide AI spending, 2026

Worldwide AI spending is forecast to reach roughly $2.5 trillion in 2026, up ~44% year over year — with more than half going to infrastructure (chips, data centers, energy).

Source: Gartner forecast (2026)

13. Fine-tuning vs. RAG

Fine-tuning re-trains a model on your examples (changes how it behaves). RAG — retrieval-augmented generation — feeds a model your documents at question time (changes what it knows). Most businesses need RAG first; it’s cheaper and always current.

Source: AWS — What is RAG?

14. “AI is literally going to change every job.”

— Doug McMillon, CEO of Walmart (2025). Not eliminate every job — change every job. The competitive edge shifts to people who direct AI well.

Source: CNBC (Sept 2025)

15. For business managers

Start with one painful, repetitive workflow — reporting, first-draft proposals, support triage. Measure hours saved. Redesign the workflow around AI rather than bolting AI onto the old process. Workflow redesign is the #1 predictor of profit impact.

Source: McKinsey — The State of AI (2025)

16. For creators

Use AI for volume and versioning (outlines, cuts, captions, repurposing) — keep voice, taste, and firsthand experience human. AI engines increasingly reward original data and lived expertise, because that’s what they can’t generate.

Source: Google Search Central — Helpful content

17. For entrepreneurs

The stack is cheap now: an LLM API, a no-code builder (like Lovable), and automation glue (n8n/Zapier) replace what took a dev team in 2022. Ship a working prototype in a weekend; validate before you build.

Source: Y Combinator — Library

18. Agents: 2026’s defining trend

62% of organizations are at least experimenting with AI agents — software that doesn’t just answer, but acts: booking, buying, filing, fixing. (That’s the entire next course.)

Source: McKinsey — The State of AI (2025)

19. Discovery has moved

About 1 in 3 U.S. consumers now start product research in AI tools instead of a search engine. If AI models don’t know your business exists, a growing share of buyers never will either.

Source: Similarweb Market Research Panel (Jan 2026)

20. You now know more than most executives.

You’ve got the vocabulary, the real adoption numbers, and the playbook starters. Next up: the technology every analyst calls the story of 2026.

Source: Continue → AI Agents

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