3 posts tagged with "Systems Design"

In April 2021 I wrote a piece on why modern software engineering can't do without project management. In September 2022, one asking whether your team is practising agile correctly. That December, another on why complexity deserves a seat in software design. At the time they felt like three different topics: process, methodology, architecture. Rereading them recently, I realised all three had been circling the same question: why do more people make a team slower? The complexity piece even opens with the claim that "as project scale grows, complexity increases exponentially — left uncontrolled, it will eventually sink the project."

Years of running teams moved that question off the page and into my own schedule. A project slips; the reflex is to add people. The people arrive and the slip gets worse — newcomers need context, veterans stop to provide it, the meeting count doubles, and the cost of staying aligned grows faster than the output. Fred Brooks nailed this fifty years ago in The Mythical Man-Month (1975): "Adding manpower to a late software project makes it later." Every programmer has heard the line and nodded along. But strictly speaking it has always been an aphorism, not a law — no company can run a controlled experiment on its own team to check how right it is.

Here is the interesting turn: the check finally arrived, and it came from AI rather than management science. In late 2025, researchers at MIT and Google Research tested 180 configurations of agent systems, and the results read like a re-enactment of Brooks: on tasks that must proceed step by step, every multi-agent variant they tried did worse than a single agent — by 39 to 70% — and past three or four agents, adding more only deepened the hole. Fifty years after publication, the man-month got its first controlled experiment. The subjects just happened to be agents, not people.

That is this article in one sentence: AI agent teams are faithfully reproducing the oldest disease of human organisations — and, the more interesting half, they are the first form of organisation in history where the bill can be fully itemised.

The AWS Summit in Shanghai, 23–24 June 2026.

In June 2026, I gave what was almost certainly the least glamorous talk at the AWS Summit in Shanghai. I opened with a disclaimer: our Nova is not the Nova from Amazon. The Nova on my slides is an internal platform my team builds at HP; the Nova everyone else kept name-dropping is Amazon's frontier model. The room laughed at the collision — and then I spent the session on something no keynote would touch: how we moved a reporting stack off Power BI and onto Amazon Athena and Apache Iceberg. It was a thirty-minute, 300-level breakout, upstairs on the sixth floor, well away from the crowds.

The floor guide: breakout talks — mine among them — on the sixth floor, the keynote on the fifth, the expo downstairs.

Down on the ground floor, the expo hall was selling the opposite of unglamorous. Unitree humanoids reached for objects under hot lights. A dexterous robotic hand, priced at ¥9,999, flashed a peace sign for the cameras. On one screen, a swarm of coding agents shipped software with no human in the loop; on another, an AI turned footage of a football match into tactics and player metrics. The spotlight was on intelligence learning to perceive the world, to make things, and to act in it.

Down on the ground floor: the expo, where the crowds were.

That contrast is the argument of this piece. For two centuries, output scaled with headcount: more work meant more hands. AI is breaking that link — output is starting to scale with infrastructure (models, compute, and data) rather than people. This is the industrialization of intelligence, and like the first industrial revolution, it will be won not by whoever owns the flashiest machine but by whoever builds the floor those machines stand on. My boring migration is the proof in miniature: what made our reports better was not a smarter model but a new foundation underneath them — a refresh that once took four to six hours now finishes in one, and a report you could once only read became data anyone can now question in plain language.

So this article works from the floor up. First, the three frontiers the show floor was celebrating — machines that perceive, create, and act. Then the layer beneath all three, the one I went to Shanghai to talk about: the data foundation that decides how high any of them can climb.

Every AI keynote in 2026 opens with the same three slides: a bigger model, a faster chip, a smarter agent. The fourth slide — the one about how any of that actually reaches a user in production — is usually missing. That missing slide is where the next decade of value will be created, and it will not be created by another round of model fine-tuning. It will be created by the most unglamorous layer in our stack: infrastructure.

The numbers back the hunch. MIT's 2025 "State of AI in Business" report found that 95% of generative-AI pilots fail to reach production. Gartner found that only 15% of IT application leaders are even piloting fully autonomous agents, despite the agent market projected to grow from $7.8B in 2025 to $52.6B by 2030. The bottleneck is not intelligence. Frontier models cluster around 70–75% on SWE-bench Verified. The bottleneck is everything between a model that can write code and an organization that can ship it — and that everything is infrastructure.

Here is the hot take, stated plainly: as coding gets cheap, infrastructure gets scarce. The DevOps, CI/CD, container, Kubernetes, and cloud-architecture knowledge that the AI narrative treats as "solved plumbing" is about to become the single biggest lever for turning AI capability into shipped product. The reason is simple. Agents can now write code. They cannot, by themselves, run a build, own a deploy, route a rollback, or provision a region. They need a substrate that does those things for them — and that substrate is the accumulated, low-cost, battle-tested output of two decades of DevOps work.