Why Flash Models, Not Frontier Models, Will Win in 2026

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What's really happening with AI in 2026 as the hype era ends?
The common story is that AI will keep dazzling us with flashy demos and benchmarks — but the reality is more complicated.
In this video, I share the inside scoop on why 2026 marks AI's shift from clever releases to systems that actually work:
Why protocols and constraints now matter more than prompting alone
How agentic workflows reduce entropy when LLMs are narrowly scoped
What dual fluency means for AI careers and talent repricing
Where robotics and generative UI unlock post-ChatGPT software futures

Chapters:
0:00 - Introduction: AI moving from hype to reality
0:45 - The burst of the AI hype bubble in 2025
1:30 - Seeing AI capabilities in high definition
2:26 - The emerging AI talent ecosystem
3:30 - Bet 1: Protocols matter more than prompting
4:47 - Bet 2: Taking constraints seriously
6:17 - Bet 3: Understanding where AI fits in workflows
8:10 - Bet 4: LLMs as entropy reducers
10:20 - Bet 5: The post-ChatGPT software future
12:10 - Bet 6: Graphical AI becomes normal
13:10 - Bet 7: Careers repricing around dual fluency
14:45 - Bet 8: Robotics breakthrough year ahead
15:30 - Closing thoughts

The teams that win in 2026 will be the ones who stop treating LLMs as magic content generators and start building composable, constrained systems with real verification loops — but only if they can hold technical depth and customer outcomes in one head.

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