AI, Data & Computing
The layer everything else now runs on: software, models, data and the infrastructure holding them up.
Training catalog: AI & Computing →
The Technology Held Up Better Than the Use Cases
Distributed consensus is a genuine computer science achievement. Most enterprise blockchain projects failed because a shared database with governance was the actual requirement.
Somebody Still Has to Know Where the Packet Went
Cloud abstractions hide infrastructure, they do not remove it. Why network and systems knowledge keeps being the thing that resolves outages nobody else can explain.
The Cloud Bill Is a Design Document
What an organisation spends on infrastructure describes how its software was built. Why cost, reliability and delivery speed are the same engineering problem.
There Is No Entry-Level Cybersecurity Shortage
Junior applicants are plentiful and experienced defenders are not. Why the security gap is a progression problem, and what to build instead of another certification budget.
The Fastest-Growing Job in Tech Is Mostly Cleaning Data
BLS projects 33.5 percent growth for data scientists to 2034, the highest of any technology occupation. Most of the role is data quality, definitions and asking the right question.
Writing the Code Was Never the Bottleneck
Most of a software engineer's life is spent reading, reviewing, integrating and maintaining. BLS projects 267,700 more software developer jobs by 2034, and the job is changing shape.
Fundamentals Are What You Reach For When the Tool Fails
Frameworks change every few years and the underlying ideas do not. Why computer science fundamentals are the part of technical capability that does not go stale.
The AI Skills Gap Is Mostly a Cybersecurity Gap
Eighty-six percent of employers expect AI to transform their business by 2030, and the role the labour data actually shows growing fastest is information security analyst. Where the demand really sits, and who can be trained into it.