The Intelligence Age

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

Data sovereignty, governance debt, and AI for Māori startups
Full Show Notes
https://www.theintelligenceagepodcast.com/842 
Amber Taylor joins Mark Smith to unpack Māori data sovereignty at the point of AI system design, with a sharp focus on what startups need to think about before they build. The conversation gets practical fast, moving from governance debt and investor pressure to the risks of using third party AI platforms with sensitive indigenous knowledge.

We discuss why governance has to be designed in upfront, how sovereignty changes when data touches external AI systems, and what an indigenous layer for AI could look like in practice.

Key topics
Amber shares why the paper started with a startup lens, not just a sovereignty lens, because small teams do not have the same governance or architecture resources as large organisations.
She introduces the idea of governance debt, arguing that AI governance added after a system is built creates the same kind of accumulated risk as technical debt.
Amber explains why many AI governance frameworks miss the realities of founders and small teams who need workable, early-stage decision support.
The discussion covers the limits of simply keeping data on Aotearoa soil if it still passes through external AI systems that the organisation does not control.
Amber and Mark dig into the misconception that platforms are not training on user data, and why ticking a consent box is not the same as governance.
They explore how investors can pressure startups to monetise data in ways that conflict with the original agreements made with communities and data holders.
Amber shares how her own work with indigenous storytelling set clear protocols upfront, including ownership, attribution, and what happens if a company is sold.
The conversation uses 23andMe as a cautionary example of what happens when DNA data becomes an acquired asset under new ownership.
Amber outlines a future direction for an indigenous AI layer that could sit on top of any model, govern knowledge use, improve accuracy, and return reciprocity to source communities.
The episode closes on the idea that New Zealand often builds first and regulation catches up later, creating opportunity for smaller AI companies to move quickly and responsibly.
Resources:
Native Sentient Website: https://www.nativesentient.com/
Link to paper we talked about: https://zenodo.org/records/21219022
Marae TV interview; https://www.tvnz.co.nz/tvseries/marae
If you want to get in touch with me, you can message me here on Linkedin.

Thanks for listening 🚀 - Mark Smith

AI, Shipping Faster, and the Future of Distribution

Full Show Notes
https://www.theintelligenceagepodcast.com/841

Mark Smith talks with Steve Mordue about how AI has changed the pace of building, shipping, and distributing software over the last six months. Steve shares what it looks like to move from being a seller and product visionary to building almost everything himself, why the last 5 percent is often the hardest part, and why distribution is now as important as the product itself.

They also get into Microsoft’s role in AI, the rise of open weight models, model routing, enterprise trust, and how sovereign data concerns are reshaping what companies can safely adopt.

Key topics
In this episode, Steve Mordue describes the last six months in AI as a mix of speed, exhaustion, and concentration
He says work is moving so fast that things built just months ago already feel outdated
AI has not reduced the workload so much as multiplied the number of possible ideas
Steve reflects on building Rapid Start CRM and what that taught him about scale
He says the platform reached about 170,000 users
Looking back, he feels it should have reached far more if marketing had been stronger
We discuss the “last 5 percent” problem
Mark raises the challenge of turning a near-finished product into something shippable
Steve admits he is deeply affected by perfectionism and often obsesses over details others do not care about
Steve explains why he now launches before perfection
He says he recently forced himself to ship an AI app at 95 percent complete
His view is that waiting for 100 percent means never launching
We talk about how AI has changed his role from seller to builder
Steve says his core strength has always been selling
He now uses AI to write code, prototype, and build without depending on a large dev team
He says the old dependency on teams and contractors has largely disappeared
Every future Rapid Start product, he says, will be built by him alone
His main limitation is no longer engineering access, but time
They explore the value of domain expertise in the AI era
Steve argues that people with deep knowledge in a narrow business area are best positioned to apply AI well
He believes AI works best when paired with years of real industry experience
Distribution becomes the central strategic problem
Mark and Steve discuss how social platforms, email, and newsletters are losing effectiveness
Steve says LinkedIn followers are usually peers, not buyers, which limits direct revenue impact
They compare Microsoft’s strengths and weaknesses in AI
Steve acknowledges Microsoft’s global distribution, infrastructure, and governance strengths
If you want to get in touch with me, you can message me here on Linkedin.

Thanks for listening 🚀 - Mark Smith