Part II of three. Part I argued that as machines make analysis cheap, the value migrates upstream into standards and downstream into accountable judgement. This part is about what is needed to make an organisation's use of AI safe. When I was younger, I drove a Mazda 3
This is the first post in a three-part series. I want you to try something - drop the word intelligence into the conversation at a Saturday lunch, and see what looks you get from people. Something like: "I got good intelligence from talking to the neighbours before the auction.
The broader market narrative, driven by frontier labs, suggests that foundational models are the sole capability differentiator. I compared twenty models across 5 domains (thousands of extractions) to measure how much it actually matters: less than the prompt and far less than the problem domain. For 48 hours earlier in
It feels like we are at an inflection point; the capabilities of Large Language Models (LLMs) are extraordinary, offering new ways to synthesize information, automate complex cognitive tasks, and extract insights from vast unstructured datasets. But integrating these stochastic components - these probabilistic black boxes - into systems that demand reliability, traceability,
[This post originally appeared on the Qantas Engineering Blog] Whilst you might not know “what” Polylith is, I’ll bet that the factors that influenced its adoption as our source architecture — the “why” — will be very familiar. This story explores the driving forces of code changeability, consistency and complexity and
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Running a bootstrapped business is a hard slog - to say the least - so it's worth celebrating a little when you get the chance. Well, today is one of those days, with a nice article in the Canberra Times about my journey from Public Servant to running a technology
My thoughts on why enterprise knowledge systems have failed for sixty years, and what might finally replace them. A couple of weeks ago I demoed one part of what I have been building to a senior exec at a global enterprise - someone who had been asked to lead and guide
By every measure of the conventional startup playbook, I should be spending my evenings refining pitch decks and my mornings cold-emailing VCs. I should be optimising for a Series A. Instead, I'm trying to understand a structural shift in the economics of building software - one that changes
What the data already tells us about how the Australian Public Service is adopting AI, what it isn't telling us, and why the gap between the two matters. Why this guide exists The usual question about AI in the Australian Public Service is prescriptive: what should the APS
Two years ago, I wrote about managing twenty microservices at Qantas with a small team. The problem was keeping services in sync, coordinating changes across system boundaries, fighting the entropy of a codebase that grew faster than our ability to reason about it. Many years before my time, someone had
I’ve read a lot of government reports in my time. Most of them are dry, dusty things that serve as excellent door stops or cures for insomnia. But every now and then, you come across one that tries so hard to polish a specific narrative that the cracks in
I’ve just finished reading Dario Amodei’s latest essay, "The Adolescence of Technology." I must admit, reading it gave me a distinct sense of déjà vu - specifically, the feeling I get when I’m sitting in a strategy workshop with a room full of highly paid executives
I've just had a look at the Australian Government’s "National AI Plan 2025" and I'm not surprised. It certainly looks like a strategy. It has vision statements, goals, and nine distinct actions. But look closer, and the familiar emptiness yawns back at you.
There’s a persistent belief in the public sector that if the existing machinery is slow, risk-averse, and fragmented, the solution is to build a new, smaller machine next to it. A "Centre of Excellence," a "Transformation Office," an "Innovation Hub." It’s
It feels like we are at an inflection point; the capabilities of Large Language Models (LLMs) are extraordinary, offering new ways to synthesize information, automate complex cognitive tasks, and extract insights from vast unstructured datasets. But integrating these stochastic components - these probabilistic black boxes - into systems that demand reliability, traceability,
I remember sitting in a Gartner Conference workshop around 2013, somewhere in the Sydney. We were talking about "Big Data" and the Australian Public Service. The consultancy partner leading the session, all suit and enthusiasm, declared that data was the "new oil" and that if organisations
I’ve just read the latest Australian Public Service Data Maturity Report... and honestly, it feels like déjà vu. The headline? A system-wide score of 2 out of 5 which Finance optimistically labels as “Developing”. Key areas like Data Architecture, Analytics, and crucially Data Quality & Metadata are stuck
