The Intelligence Age

From Copilot Adoption to Copilot Fluency

Full Show Notes: https://www.theintelligenceagepodcast.com/847

Mark Smith catches up with Cadie Kneip from Microsoft, a returning guest. Her focus has moved from getting people to adopt Copilot to building Copilot fluency, so using AI becomes automatic in daily work. They cover personal Copilot habits and resistance to AI. They also cover how agents are changing everyday workflows, which Microsoft tools to use for what, and what Cadie is looking forward to over the rest of 2026.

Key topics and takeaways
From adoption to fluency: Fluency means using Copilot naturally, without thinking, like reaching for your phone to check flights. The aim is to make Copilot a habit in your workflow.
AI as the first stop: Mark rarely uses Google now. He goes to voice-mode AI, which knows his local context. 
He uses Copilot for personal topics because he controls his own tenant.
Talking ideas out with AI: Voice conversations, asking the AI to play devil's advocate and asking it to "tell me why I'm wrong" help you probe your own thinking. 
AI tends to tell us what we want to hear.
Why people resist AI: Mark cites a figure that 82% of Americans dislike AI or resist it. Cadie puts a lot of it down to loud, clickbait negativity and to fear about identity and jobs. 
Her answer is to focus on what's possible and on the human value of judgment and point of view.
Agents beyond chatbots: Workshops on reimagining workflows start with the tasks people hate most. 
Cadie automated her weekly Credly badge process with Scout, saving hours each week.
Scout vs. Cowork: Scout suits repeatable execution and an "executive assistant" style of work. 
Cowork suits work that needs strong context from Work IQ, which draws on your Microsoft Graph. Cowork has improved a lot and now offers model selection.
Auto vs. picking models: Cadie starts on auto and switches models when results disappoint. 
Mark always picks his own. 
They also talk about Microsoft's own models and image generation.
What builds fluency: Prompting skill matters less now. The tipping point is building a simple agent in Agent Builder and seeing how it works behind the scenes. 
People then move on to Copilot Studio and pro-code tools.
Looking ahead: Cadie wants her team to each have at least one AI-improved workflow, and she's curious what people will do with the time they get back. They also joke about household robots.
If you want to get in touch with me, you can message me here on Linkedin.

Thanks for listening 🚀 - Mark Smith

AI, Indigenous Language Sovereignty, and Decolonizing Technology

Full Show Notes: https://www.theintelligenceagepodcast.com/846

In this conversation, Mark Smith speaks with Natasha MacDonald, an Inuit educator and Heritage Lab volunteer, about protecting Indigenous language and culture in the age of AI. Natasha explains how her community is using closed-loop technology, community vetting, and local control to preserve Inuktitut without handing data to big tech.

We also discuss colonial language loss, intellectual property, youth-led revitalization, and why cultural data should stay under Indigenous sovereignty.

Key topics
Natasha shares her Inuit background, her family roots in Nunavik, and how food, travel, and cross-cultural experience shaped her perspective.
She explains the history of colonization in her region, including missionaries, federal day schools, forced settlement, and the shift from nomadic life to permanent communities.
Natasha describes how Inuktitut remains strong in Nunavik, with roughly 95% of the local Inuit population still speaking or using it, even as English pressure grows.
We discuss Heritage Lab, where Natasha serves as vice president, and its mission to protect and preserve Indigenous languages through technology.
She explains the nonprofit’s closed-loop model, including community-owned data, language committee approval, and hosting on servers located on Indigenous lands.
Natasha outlines the risks of big tech and external institutions scraping language data, training AI models without consent, and turning Indigenous knowledge into profit.
She describes a dispute involving a University of Laval partnership with Anthropic and why her community objected to Inuit data being used to train AI on low-resource languages.
We discuss the difference between data sovereignty and intellectual property, and how Indigenous knowledge is treated as community-held rather than individually owned.
Natasha emphasizes that language revitalization must include youth, who are building tools, games, and models while learning pride and agency in the process.
She also talks about the role of art as cultural memory, economic support, and a point of resistance against AI-generated imitation.
Resources:
Heritage Lab: https://heritagelab.ca/en/story
Mila: https://mila.quebec/en/news/mila-and-cifar-launch-the-indigenous-ai-advisory-circle
If you want to get in touch with me, you can message me here on Linkedin.

Thanks for listening 🚀 - Mark Smith

Microsoft AI, model routing, and the move toward portable workflows
Full Show Notes: https://www.theintelligenceagepodcast.com/845
Mark Smith and Joris de Gruyter, who has spent four years deep in the Microsoft AI ecosystem, discuss where AI has become useful recently and where the rough edges remain.

The conversation centers on model choice, vendor lock-in, local versus cloud inference, and how Markdown and open standards are becoming a portability layer for notes, skills, and AI workflows.

Key topics
Joris says one of the biggest shifts has been the move from coding agents like Codex and Claude Code into non-coding work for information workers.
He explains that tools like Microsoft Co-work and Copilot can be powerful, but users need enough technical savvy to get the most out of them.
Mark and Joris compare cloud-first AI workflows with local, device-based workflows, especially for browser work, Playwright, and tasks requiring local access.
They discuss Microsoft’s effort to make Co-work feel more like a first-class Microsoft product, with broader model support.
Both argue that model choice matters less than before; skills, prompts, and workflow design are becoming the real intellectual property.
They explore Markdown as a portable format for AI-era data, and why Microsoft supporting Markdown in SharePoint matters.
Joris explains how OneNote, Obsidian, and Loop each solve part of the note-taking problem, but none fully combine syncing, portability, and API access.
Mark describes using AI to clean up old data, emails, and archives rather than preserving everything indefinitely.
They discuss vendor lock-in and the need for open standards that let people switch models and services.
Joris makes the case for local inference as open-weight models improve and consumer hardware gets better.
They close with a critique of major technology companies, arguing that strong hardware has not yet been matched by software and AI integration that meets user expectations.
If you want to get in touch with me, you can message me here on Linkedin.

Thanks for listening 🚀 - Mark Smith

Te Reo Māori, translation, and using AI without losing human meaning
Full Show Notes: https://www.theintelligenceagepodcast.com/844
Mark Smith speaks with Tane Karamaina, a senior translator working across Te Reo Māori and English, about how language, culture, and AI intersect in real-world translation work.

The conversation explores why translation is never just word-for-word substitution, how AI can support but not replace human judgment, and the risk of homogenizing dialects as language technology improves.

In this episode, Tane shares practical examples from translation work, including punctuation, implicit meaning, and culturally specific phrases like “O for awesome.”

The discussion also covers pronunciation, regional dialects, ancestral intelligence, and how AI in New Zealand could be used responsibly as an assistive tool rather than a substitute for human expertise.

Key topics
Tane Karamaina introduces himself through a pepeha and explains the importance of naming whakapapa, place, and identity.
He describes his work as a senior translator with more than 10 years of experience translating between English and Te Reo Māori.
Translation is framed as cultural mediation, not word-for-word conversion.
AI is presented as a useful tool for translators, but not a replacement for human understanding, interpretation, and responsibility.
Tane explains how he uses AI for simple support tasks like punctuation and breaking down source text structure.
The episode highlights how meaning is often implicit, culturally bound, and not directly transferable across languages.
A deeper discussion compares Te Reo Māori and English through examples like “language” and “moana,” showing how words carry different worldviews.
Mark and Tane discuss parallels between Te Reo Māori and Japanese, especially respect, vocal tone, and open syllable structure.
Tane raises concerns about AI-driven language standardization flattening regional dialects and local variation.
He argues the best protection against homogenization is for people to keep speaking and transmitting their ancestral dialects.
The conversation closes on the idea that AI in New Zealand can be positive if it supports, not replaces, human beings and their communities.
Resources
Anyone looking for a translator can source one from here: https://en.tetaurawhiri.govt.nz/translators
For those wanting to up their te reo game can find some really cool resources from here: https://www.reomaori.co.nz/resources
Books
Ancestral Intelligence
Tikanga Whakaaro: Key conepts in Māori culture by Cleve Barlow Ngā Pēpeha a Ngā Tīpuna by Hirini Moko Mead and Neil GroveHe Whiriwhiringa Selected Readings in Māori by Bruce Biggs
Te ReoTaking Care of Business: Business Terms in Aotearoa - Second Edition for te reo speakers wanting to learn more modern terminology that spans the legal, business and technology domainsHe Kohinga Kīwaha - Te Taura Whiri i te Reo Māori
If you want to get in touch with me, you can message me here on Linkedin.

Thanks for listening 🚀 - Mark Smith

AI, context, and New Zealand’s data center future

Full Show Notes:
https://www.theintelligenceagepodcast.com/843

Mark Smith speaks with Oliver Hartwich about how AI has changed since January, why agentic tools have become the real shift, and what that means for workflows, context, and cross-model collaboration. They also dig into New Zealand’s position in the global AI and infrastructure landscape, especially around data centers, energy, solar, batteries, and water use.

Key topics
In this episode: Oliver says the biggest change in 2026 so far has not just been better models, but the rise of agentic AI tools like Claude Code, ChatGPT work modes, and Perplexity Computer.
He explains why AI now feels less like a novelty and more like a practical, increasingly capable working layer, especially when multiple models are used together and checked against each other.
Mark raises the concern that New Zealand may be vulnerable because AI compute and data may not stay in-country across the major hyperscalers operating locally.
Oliver discusses the post Fable moment, when access uncertainty highlighted how dependent users have become on frontier tools and how quickly expectations shift across providers.
The conversation turns to context management across platforms, including when to preserve long-term memory and when to use a fresh AI for independent review.
Oliver describes building a personal Claude Skill from his own writing archive, compressing books, reports, articles, and newsletters into markdown so the system can reflect his style and past thinking.
They discuss using one AI to peer review another AI’s work, including ping ponging a skill between Claude and ChatGPT to improve quality and completeness.
Oliver shares how Codex can be used for computer control and debugging by going directly into system settings and config files instead of relying on manual UI hunting.
The discussion moves to MCP-style integrations, including using Site CITE AI to connect large academic literature databases into AI workflows for faster research and self peer review.
Mark and Oliver compare the old PhD research process with the newer AI-assisted version, where initial literature collation can be compressed from roughly a year to a few weeks.
They reflect on the tradeoff between speed and thinking time, arguing that the slower, manual research process also created space for reflection, sleep, and deeper synthesis.
The final section focuses on New Zealand data centers, geothermal energy, grid stability, solar adoption, battery storage, and the challenge of building infrastructure with social license.
Resources:
Oliver Hartwich's essay on AI in education: https://oliverhartwich.com/2026/06/25/bildung-and-the-machine/
If you want to get in touch with me, you can message me here on Linkedin.

Thanks for listening 🚀 - Mark Smith