Context is everything

Submissions to the Joint Select Committee on Artificial Intelligence closed on 14 September and Parliament has published 540 of them: 5,839 pages.

Part II of three.

This is the first post in a three-part series.

The broader market narrative, driven by frontier labs, suggests that foundational models are the sole capability differentiator.

Running a bootstrapped business is a hard slog - to say the least - so it's worth celebrating a little when you get the chance.

My thoughts on why enterprise knowledge systems have failed for sixty years, and what might finally replace them.

By every measure of the conventional startup playbook, I should be spending my evenings refining pitch decks and my mornings cold-emailing VCs.

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.

Two years ago, I wrote about managing twenty microservices at Qantas with a small team.

I’ve read a lot of government reports in my time.

I’ve just finished reading Dario Amodei’s latest essay, "The Adolescence of Technology."

I've just had a look at the Australian Government’s "National AI Plan 2025" and I'm not surprised.

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.

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.

I remember sitting in a Gartner Conference workshop around 2013, somewhere in the Sydney.

I’ve just read the latest Australian Public Service Data Maturity Report... and honestly, it feels like déjà vu.