Originally published on Medium on 3 June 2026 Part 1 showed how the AI dividend is already being allocated unevenly, with early-career white-collar workers absorbing much of the adjustment. Part 2 asks what kind of technological wave produces that pattern and why …
The AI Disruption Part 2: Inside the Sixth Technology Wave Read more »
Originally published on Medium on 27 May 2026 AI is not just another productivity tool. It is changing who gets paid for productivity. In this first part of the series, I look past the hype and examine what the data actually says …
The AI Disruption Part 1: How AI is Reshaping Work Before Our Institutions are Ready Read more »
Originally published on Medium on 19 February 2026 In the 1980s, the big bet was chips. Singapore set out to become a serious player in semiconductors and electronics, starting from low‑cost assembly and eventually becoming a key node in the global chip …
Chips, Cells and Code: How Singapore is Applying its 40-Year Industrial Playbook to AI Read more »
In the 12th and final post of the series, I release the open-source repository that implements the LinkedIn analytics pipelines, and discuss future plans.
In the 11th and penultimate post of the series, I look back on what has been achieved, what can be done better and what has been learned.
In the 10th post of the series, I show how to set up observability on our data pipeline to monitor its condition and act as necessary.
In the 9th post of the series, I use a combination of Git and Databricks Asset Bundles to make the data pipeline easily deployable and maintainable.
In the 8th post of the series, I convert the scattered pieces of data ingestion, processing and dashboarding into an orchestrated and automated data pipeline.
In the 7th post of the series, I build a dashbord on Databricks for the ingested LinkedIn data.
In the 6h post of the series, I explore the approaches to modelling the LinkedIn data in the gold layer.