AI Empowered

About AI Empowered

Everyone has access to the same AI tools. A few people get dramatically better results. AI Empowered (https://www.aiempoweredbook.com) isn't about technology; it's a mental model you bring to it. AI Empowered explores the psychology of extraordinary human-AI collaboration — the ideas from the book of the same name, taken further. Each episode works a concept from the book: where it came from, how it plays out in practice, and what the latest research and news do to it. Solo episodes run about ten minutes. Interviews with researchers, builders, and leaders go deeper. Hosted by Aaron Douglas, a marketing strategist trained as an organizational psychologist and family therapist. He runs Auspicious (https://auspicious.llc), a fractional CMO practice, and wrote AI Empowered: The Psychology of Extraordinary Human-AI Collaboration (https://aiempoweredbook.com), available on Amazon (https://a.co/d/0hAq2yXu).

Publisher

Aaron Douglas

Category

business · education · technology

Language

en

Explicit

No

First episode

4 Aug 2025

Latest episode

1 Oct 2026

Latest episodes

21 episodes in the feed.

1. 

   1 Oct 2026

   Scaffolding AI in Education (and beyond) as Condition to Enhance Critical Thinking

   A Harvard AI tutor more than doubled what an active-learning classroom taught. A chatbot handed to high schoolers left them seventeen percent behind kids who never had it. Same class of tool. The difference was what each design made the human do before the answer arrived. This episode returns to chapter six of AI Empowered, the collaboration demands more of you, not less, and pays off the half of the argument that "The Deskilling Trap" (July) left standing alone. That episode used Bastani et al.'s PNAS field experiment (about 1,000 Turkish math students) to show what unscaffolded AI costs when the tool goes quiet. This one adds Kestin et al.'s crossover RCT in Scientific Reports (194 Harvard introductory-physics students), where a GPT-4 tutor built on the course's own pedagogy, and told to guide rather than answer, produced more than double the median learning gains of the in-class version, in less time, with higher reported engagement. Set side by side, the two studies isolate design, not model capability, as the variable. Aaron carries both studies' boundaries in the script: Kestin measured immediate gains only and says nothing about retention or deskilling; it compared two scaffolded designs, not AI against human, and no plain chatbot arm was tested; the at-home, self-paced delivery is confounded with the tutor; the material sat at the understand-apply-analyze level, not synthesis or professional judgment; and both samples are students, so the extension to knowledge work is the book's inference and is labeled as such. The work anchor is a design rule in the spec for the AIRAA (pronounced "era") app's coaching feature: ask what the user thinks first, ask for evidence, refuse to write the work product, end every exchange with a decision the user makes. Dependency on the system is treated as a failure mode. The turn to the listener is one audit question for one workflow: what does it make the human do before the answer arrives? Studies:Kestin, G., Miller, K., Klales, A., Milbourne, T., & Ponti, G. (2025). AI tutoring outperforms in-class active learning. Scientific Reports, 15, 17458. Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning. PNAS, 122(26). Earlier episode: The Deskilling Trap (July 2026). --- This show extends the book AI Empowered: The Psychology of Extraordinary Human-AI Collaboration. Read the first chapter free at https://www.aiempoweredbook.com, or get the book on Amazon. Aaron Douglas runs Auspicious, a fractional marketing practice. Blog: https://auspicious.llc/insights/ X: https://x.com/aaron_douglas LinkedIn: https://www.linkedin.com/in/aaronddouglas/

2. 

   10 Sept 2026

   Every Metric Was Green

   Nine months into an AI rollout, the dashboard says eighty-something percent weekly usage, training completion in the nineties, time-to-first-draft down forty percent. Every number is accurate. Then somebody pulls three client proposals from before and three from after and reads a paragraph from each out loud, and you could swap the client names without anyone noticing. This episode draws on chapter ten of AI Empowered, the chapter about organizations, and pairs it with a working paper posted in August called "How Organizations Use AI: Evidence from ChatGPT" — the largest look yet at enterprise AI telemetry. Three of its five authors work at OpenAI, two are academics at Columbia and Wharton who did the work as paid contractors for OpenAI, and it studies OpenAI's own product. Seventeen hundred and sixty-four companies, seventeen and a half million messages, measured six months after adoption. It documents how fast usage is growing, what kinds of firms adopt early, who inside a company actually uses the tool, and what they use it for. And then, in its own limitations paragraph, it says the thing worth sitting with: the task results "do not measure downstream work products, productivity effects, or changes in organizational routines." A dataset that size can tell you exactly how much people typed and nothing about whether anything got better. The paper's own summary line is the one to keep: adoption is only the beginning of deployment. This show extends the book AI Empowered: The Psychology of Extraordinary Human-AI Collaboration*. Read the first chapter free at https://www.aiempoweredbook.com or get the book on Amazon https://a.co/d/082wePlH Aaron Douglas runs Auspicious a fractional marketing practice.

3. 

   11 Aug 2026

   Why AI Training Doesn't Fix the Actual Problem

   Four out of five organizations are running AI training. Three out of five still report an AI skills gap. The training is happening; the gap isn't moving. That isn't a quality failure — it's a targeting failure. This episode works the argument from chapter two of AI Empowered: the three dominant responses to the AI collaboration gap — education, prompting technique, and change management — each address something real, and each aims at the wrong layer. The live element is a Microsoft field experiment with 388 employees at a Fortune 500 retailer, published this spring, where everyone got the same AI tool and only the structure around its use was varied. The structured behavioral protocol was associated with *lower* document quality and substantially less work finished. Partnership training that reframed AI as a thought partner was associated with higher quality — but only at the top of the distribution, and the authors are candid that their belief-change result likely reflects carry-over rather than the training itself. Also covered: why the social cost of AI adoption (a measured nine percent competence penalty from peers, twenty-six percent for women engineers evaluated by male non-adopters) is a barrier no communication plan reaches, and what turns up when you go looking for a product that teaches the layer underneath — trust calibration, knowing when to override, what you personally bring that the model can't generate. The question to stop asking is "have our people been trained." The question to start asking: when someone on your team opens an AI tool, what do they believe their job is in that exchange? --- This show extends the book AI Empowered: The Psychology of Extraordinary Human-AI Collaboration. Read the first chapter free at https://aiempoweredbook.com, or get the book on Amazon. Aaron Douglas runs Auspicious, a fractional marketing and AI empowerment practice.

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Apple Podcasts (US)

4.3 / 5

6 ratings

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