The Intelligence Age

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.
Resourcess:
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

Build AI That Works

Full Show Notes
https://www.theintelligenceagepodcast.com/840

Mark 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 topics
James 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

Full Show Notes
https://www.theintelligenceagepodcast.com/839
Mark Smth speaks with Ashish Bhatia about how his move from Microsoft to Audible changed the way he thinks about customers, AI, and product design. The conversation centers on personal AI, running OpenClaw locally, and why owning your memory, workflows, and data matters more as AI systems become more capable.

They also explore Audible’s role in learning, the future of interactive audio, and how agents are beginning to replace clunky app-based workflows with more direct, personalized experiences.

Key topics
Ashish explains the shift from B2B at Microsoft to B2C at Audible, and how that changes the way you learn from customers
He shares why personal AI matters to him more than ever, especially when it involves health data, finances, and memory
Mark describes building a large OpenClaw setup with 26 agents, including a nine-agent DevOps team and automated bug fixing
They discuss why running AI locally teaches real systems thinking through failure, debugging, and repeated iteration
Ashish talks about using OpenClaw to manage daily life, including lunch ordering through Grubhub as a mission-critical workflow
The conversation highlights the importance of owning your memory and being able to move your AI system across machines and platforms
They explore the idea of the death of UI, where agents increasingly bypass app interfaces and handle tasks directly
Mark raises concerns about AI companies ingesting books and content, leading to a discussion of copyright, hypocrisy, and cultural attitudes toward books
Ashish shares how Audible supports different learning styles, especially for commuters, slow readers, and people who learn better through audio plus text
He outlines Audible’s direction toward interactive, multilingual audiobooks with AI-powered recall, discovery, and personalization
Resource Recommendations:
The Infinity Machine - Demis Hassabis - Amazon.com: The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence: 9780593831847: Mallaby, Sebastian: Books
The Thinking Machine - Jensen Huang - The Thinking Machine: Jensen Huang, Nvidia, and the World's Most Coveted Microchip: Witt, Stephen: 9780593832691: Amazon.com: Books
Sapiens - Yual Noal Harari - Sapiens: A Brief History of Humankind eBook : Harari, Yuval Noah: Amazon.ca: Kindle Store
The Apple Podcast that I have curated - https://podcast.ashish-bhatia.com/feed.xml
Ashish Bhatia's website - ashish-bhatia.com
If you want to get in touch with me, you can message me here on Linkedin.

Thanks for listening 🚀 - Mark Smith