The Intelligence Age

From Copilot Adoption to Copilot FluencyFull Show Notes: https://www.theintelligenceagepodcast.com/847Mark 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 takeawaysFrom 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 TechnologyFull 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 topicsNatasha 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/storyMila: https://mila.quebec/en/news/mila-and-cifar-launch-the-indigenous-ai-advisory-circleIf 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 workflowsFull Show Notes: https://www.theintelligenceagepodcast.com/845Mark 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 topicsJoris 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 meaningFull Show Notes: https://www.theintelligenceagepodcast.com/844Mark 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 topicsTane 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.ResourcesAnyone looking for a translator can source one from here: https://en.tetaurawhiri.govt.nz/translatorsFor those wanting to up their te reo game can find some really cool resources from here: https://www.reomaori.co.nz/resourcesBooksAncestral 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 BiggsTe 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 MāoriIf 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/843Mark 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 topicsIn 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 startupsFull Show Noteshttps://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 topicsAmber 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/21219022Marae TV interview; https://www.tvnz.co.nz/tvseries/maraeIf 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 Noteshttps://www.theintelligenceagepodcast.com/841Mark 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 topicsIn this episode, Steve Mordue describes the last six months in AI as a mix of speed, exhaustion, and concentrationHe says work is moving so fast that things built just months ago already feel outdatedAI has not reduced the workload so much as multiplied the number of possible ideasSteve reflects on building Rapid Start CRM and what that taught him about scaleHe says the platform reached about 170,000 usersLooking back, he feels it should have reached far more if marketing had been strongerWe discuss the “last 5 percent” problemMark raises the challenge of turning a near-finished product into something shippableSteve admits he is deeply affected by perfectionism and often obsesses over details others do not care aboutSteve explains why he now launches before perfectionHe says he recently forced himself to ship an AI app at 95 percent completeHis view is that waiting for 100 percent means never launchingWe talk about how AI has changed his role from seller to builderSteve says his core strength has always been sellingHe now uses AI to write code, prototype, and build without depending on a large dev teamHe says the old dependency on teams and contractors has largely disappearedEvery future Rapid Start product, he says, will be built by him aloneHis main limitation is no longer engineering access, but timeThey explore the value of domain expertise in the AI eraSteve argues that people with deep knowledge in a narrow business area are best positioned to apply AI wellHe believes AI works best when paired with years of real industry experienceDistribution becomes the central strategic problemMark and Steve discuss how social platforms, email, and newsletters are losing effectivenessSteve says LinkedIn followers are usually peers, not buyers, which limits direct revenue impactThey compare Microsoft’s strengths and weaknesses in AISteve acknowledges Microsoft’s global distribution, infrastructure, and governance strengthsIf you want to get in touch with me, you can message me here on Linkedin.Thanks for listening 🚀 - Mark Smith

Build AI That WorksFull Show Noteshttps://www.theintelligenceagepodcast.com/840Mark Smth talks with James Diekman about how Accelerate Tech has scaled, where AI is actually creating value in government workflows, and why most successful projects are targeted workflow changes rather than broad rollout tools. They also dig into Australia’s growing caution around AI governance, data residency, and the practical push toward local models and sovereign infrastructure.We discuss how AI is being used inside engineering and marketing, why many Copilot-style deployments lose momentum, and how project management itself may evolve as agents take on governance, reporting, and coordination tasks. James shares what’s working in production, what keeps failing before launch, and where the next 6 to 12 months of AI adoption is heading.Key topicsJames Diekman shares that Accelerate Tech has grown to about 32 staff in the last six months, driven by demand in government projects and AI-enabled solutions.The conversation contrasts AI use in engineering and marketing versus more passive consumption in functions like HR and finance.James explains that the strongest results come from embedding AI into specific workflows, rather than treating it as a standalone chatbot or a broad deployment.They discuss why many Copilot rollouts see mixed adoption and usage drop-off when the tool sits outside the daily workflow.James describes the most successful approach as identifying a business process, pulling apart a sub-workflow, and then applying AI, automation, or judgment-based reasoning to that narrow area first.He notes that many AI projects never reach production, and says that out of 30-plus AI projects delivered, only five or six have made it fully into production.The discussion turns to local government systems, with James outlining how his team often acts as the integration layer, or “the plumbers,” between older council platforms and newer software.They cover Australia’s increasing AI governance maturity, including state frameworks, federal requirements, and the rise of dedicated AI roles inside agencies.Mark and James debate public concerns around data centers, water use, and energy, with James emphasizing the need for better policy and the practical constraints of local compute.They explore the move toward local models, onshore hosting, and reserving compute capacity as organizations seek more control, lower risk, and better throughput.James makes the case that teams do not always need frontier models like Opus for every task and that model choice should match the job, cost, and risk.The episode closes on project management, where James outlines a “project brain” concept using agents, shared knowledge, registers, ticketing, and workflow automation to support or partially replace manual PM effort.If you want to get in touch with me, you can message me here on Linkedin.Thanks for listening 🚀 - Mark Smith

Ashish Bhatia, a former Microsoft technology executive now at Audible, explores personal AI, local agents, and the case for owning your data and memory. They discuss 26-agent OpenClaw systems, app-free workflows, and the possible death of traditional interfaces. The conversation also examines AI, copyright, accessible learning, and Audible’s future of interactive, multilingual audio.

Full Show Noteshttps://www.theintelligenceagepodcast.com/838AI is about to replace bloated SaaS, and most businesses are still only playing with chatbotsSean G Muller says the next wave of AI is not about better prompts or prettier copilots. It is about rebuilding business around context, agents, and what actually creates value - before your software stack becomes the expensive middleman.Mark Smth and Sean unpack why the last six months have been a genuine shift: agentic loops are now good enough to handle real business work, not just experiments. Sean explains how he moved from traditional technical architecture into building full application pipelines, MCP servers, and background agents that review email, track social signals, draft responses, and keep business moving without adding more human overhead.You'll discover why context is the missing ingredient in almost every failed AI project, how Sean uses a simple meal-planning example to explain it, and why companies that scatter knowledge across laptops, SharePoint, Google Cloud, and people's heads are sitting on hidden risk. Sean also breaks down the difference between AI as a feature and AI as a business transformation engine, including the mistake many firms make when they bolt chat onto old workflows and call it progress.We also get into the coming SaaS pocalypse - the idea that tools like HubSpot, Salesforce, Xero, Slack, and Atlassian may face a serious reckoning as businesses realize they can build leaner, custom, agent-first systems for less than the cost of endless licenses and modules. Sean shares how he built a headless, agent-driven CRM and why he thinks greenfield builds will replace expensive transformation projects much sooner than most executives expect. This conversation matters if you lead a business, run operations, own a small or mid-sized company, or simply suspect your current software is forcing you to work the wrong way. If you want to understand where AI is actually delivering leverage right now - and how to avoid wasting money on shallow pilots - this episode is essential listening.Mark Smth hosts the conversation and brings the enterprise and product lens, pushing Sean to get specific about what success looks like for real businesses in New Zealand.Sean G Muller is an AI and enterprise architecture specialist based in New Zealand, known for helping organizations build practical AI systems, implement agentic workflows, and rethink business process from the ground up.Resources1. The Cuckoo's Egg: Tracking a Spy Through the Maze of Computer Espionage - https://www.amazon.com.au/dp/0385249462?ref_=mr_referred_us_au_nz2. Gemini: A Family of Highly Capable Multimodal Models — 2312.11805.pdf ⁠https://arxiv.org/abs/2312.118053. On the Measure of Intelligence — 1911.01547.pdf - https://arxiv.org/pdf/1911.01547If you want to get in touch with me, you can message me here on Linkedin.Thanks for listening 🚀 - Mark Smith