# What You Can Realistically Expect from AI in 2026
**Runtime:** ~18 minutes
**Tenet:** AI + Clarity
## Cold Open
"Everyone's talking about what AI *will* do. Today I'm going to tell you what it *can't* do — and that's where the actual opportunity is."
## Segments
### 1. The Setup (3 min): The Hype vs Reality Gap
AI is everywhere. CEOs are demanding "AI strategies." But here's the dirty secret: most AI projects fail because people don't understand the hard limits.
Today we're going to look at the actual research — not the marketing — on what AI can and cannot do. This isn't pessimism. It's clarity. And clarity is where you find the edge.
### 2. The Deep Dive — What AI Can't Do (8 min)
**The Reasoning Illusion**
Apple researchers proved this in 2024. Take a simple maths problem — "Sophie has 5 apples, buys 3 more." The AI gets it right. Now add: "Sophie also has a red shirt." Suddenly the AI fails 65% of the time. Why? It's pattern-matching, not reasoning. It can't tell what's relevant.
Implication for you: Don't trust AI for novel situations. Use it for known patterns.
**Model Collapse**
Here's the thermodynamic truth: AI trained on AI output dies. Within a few generations, the output becomes gibberish. Human creativity is the irreplaceable fuel.
Implication: Your people matter more, not less.
**The Creativity Trap**
Wharton study: AI-assisted groups produced ideas that were only 6% unique. Everyone converged on "Build-a-Breeze Castle." The AI is a normalisation engine, not an innovation engine.
Implication: Use AI to raise the floor, not the ceiling. Breakthroughs still require humans.
### 3. How to Actually Build (5 min)
If you're building with AI — or hiring people who are — here's what separates the amateurs from the professionals:
**Research → Plan → Implement**
Never let AI start coding without research first. The biggest failure mode is "vibe coding" — just asking AI to build something and hoping it works.
**Context Engineering**
The AI's memory is precious. Don't dump everything in. Select, compress, isolate.
**Test-Driven Development**
Write the test first. Make the AI pass the test. This is how you get reliable outputs from probabilistic systems.
### 4. Outro (2 min)
The opportunity isn't in pretending AI can do everything. It's in knowing exactly where it fails — and building humans and processes around those gaps.
That's the Clarity play. That's where we win.
Next episode: How to orchestrate multiple AI agents without losing your mind.
## Quotable Moments
- "AI doesn't ask for clarification. It amplifies the ambiguity at scale."
- "Model collapse is thermodynamic. You can't train AI on AI forever — the entropy wins."
- "The AI is a normalisation engine, not an innovation engine. Use it to raise the floor, not the ceiling."
## Links for Show Notes
- Apple GSM-Symbolic research: arxiv.org/abs/2410.05229
- Model Collapse study: Nature 2024
- Wharton Creativity study: knowledge.wharton.upenn.edu