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Stand on a hilltop in the Peak District outside Macclesfield and look west, and you see the watermark of a culture whose flood-tide re-invented engineering, music, painting, writing and sport across the world, over a span of two centuries. You can see North-West autodidactism.

GenAI has permeated everyday life. The go-to answer for millions of people faced with any number of different tasks. So why does it have such a basic monochrome interface? Open a browser or app for any of the mainstream GenAIs, and you’ll be faced with some variation of a black page with a box for entering white text into. You can turn the mic on, upload a file or image, but those are ultimately context-driven sideshows. The text box is the start-point for almost all GenAI w

"Praxis is the idea that you do something cos you want to do it. And after you've done it, you find out all the reasons why you did it." Tony Wilson said that in 1984, sitting in a bathtub in south Manchester, trying to explain Factory Records to anyone listening. It sounds romantic. It sounds like permission to follow your gut and figure it out later. But that's a misreading.

Under the EU AI Act (2024), AI companies are obliged to mark text generated by their LLMs as of August 2nd 2026. This means that companies who signed the EU Code of Practice on Transparency are introducing ways to do so. Failure to comply with text-marking could result in fines of up to €15 million, or 3% of worldwide annual turnover. Much coverage has been given to Claude’s announced rollout of watermarking. In reality there’s actually very little to say.

On Side 1 of Television’s 1977 LP ‘Marquee Moon’, you’ll hear Tom Verlaine sneer: “How does a snake get out its skin? Here's a depiction…” The answer is of course ‘Friction’, as the song title has already given away. Use that F word in a tech environment though and people recoil in horror. There are very few concepts more repulsive to a UX designer or Product manager than that of friction. Metrics are dedicated to the elimination of friction in all its unseemly forms.

Fleet operators running electric vehicles face an asymmetry of information that costs them money every week. The vehicles generate enormous quantities of data - battery state, voltage, current draw, motor temperature, GPS, speed - every few seconds. Most of it goes nowhere. When something fails, it fails in the field: recovery time, roadside assistance, cargo delays.

Lots of rhetoric aimed at leadership teams positions the question about AI and software development as primarily whether it will replace engineers. It's the wrong question. Asking it means missing something more useful. The better question is what a single engineer can build in an hour, starting from scratch, and what that tells you about how technical work is changing.

There's a particular kind of strategic paralysis that sets in when someone from the board - or a consultant, or a competitor's press release - tells you that digital transformation is coming for your category. You need to create a Luxury Digital strategy. The room gets serious. Roadmaps get built. Budget conversations start. And at some point, probably within eighteen months, you're affixing a fitness tracker to the Milanese-mesh bracelet of a 6-grand watch and calling it inn

Non-terrestrial networks are a recurring subject I've been asked about over the last year or two. Interest amongst Automotive and IoT teams has been piqued by Starlink, and high-return space stocks like RKLB and ASTS have pulled NTNs into the public consciousness. So to answer these questions I'll skip the full telco perspective here and focus on practical deployment.

Engineering, at its best, is repeatable, testable, transferable between people because it's written down as a rule. That's exactly what the razor unit's makers had. What they didn't have was design judgement - the thing rooted in observation rather than specification, that knows when the rule has stopped serving the purpose it was built for. They applied technical rationality to a problem that needed reflection instead, and the result was an object with exact tolerances and n

The gap between a written product concept and working, tested, version-controlled code can now be closed without a developer writing a line of application code. From PRD to PR without code, automation is happening today.

"Systems thinking" has become the kind of phrase people add to their profile the way a small business adds "artisan" to a jar of jam. It's been emptied of meaning by people using it as a shield. So it's worth asking what it would actually mean, precisely, if the word still had edges.

Once you've played around with AI for any length of time and got under the skin of LLMs, you inevitably start to uncover the quirks, weaknesses and vulnerabilities inherent to the fundamental design concepts, and the execution itself. As seen in my other AI projects on the site, it's not hard to trigger those weaknesses once you get beyond basic GenAI and into genuine design and build scenarios.

Somewhere this quarter, in a talent review, a manager will describe an engineer as T-shaped. Heads will nod and a box will be ticked. The engineer, who is not in the room, will spend the next year receiving work shaped like the letter rather than shaped like their ability. Nobody will check whether the label was accurate, because it does not feel like a claim. It feels like an observation, but it’s neither.

Every collecting market eventually produces the same short list of things everyone agrees are worth owning. That list is not arbitrary. It is the output of mimetic desire, corporate portfolio management, and an algorithm that rewards thumbnail legibility over depth. Understanding how the list forms does not mean rejecting it. It does mean you can decide whether you are on the list because you chose to be, or because nobody told you there was a choice.

Rubens designed the major works, established the compositional framework, and reviewed his apprentices' execution. Van Dyck eventually forked the codebase, established his own studio, and surpassed Rubens in the field he chose. Git, the version control system underpinning most of the world's software, formalises exactly this: a master repository, branches, independent lines of work, the ability to merge or to diverge permanently.

Most organisations now have an AI strategy. Very few have asked what it is doing to the planet. That is not an abstract concern. It is a capital allocation question with a measurable environmental consequence attached to it. Boards are declining to ask it. The omission will not age well.

For decades the businesses of expertise, craftsmanship and luxury have run on a simple exchange: you paid for authority because authority was grounded in the living presence of a specific person. A marketer. A strategist. A lawyer. Someone who could sit across from you, who had walked through the problem themselves, whose judgement was embodied in a living mind that you could hire and pay. Presence became the marker of truth. Not the accuracy of what they said, but the fact t

Just about every senior leader has already decided to adopt AI. The all understand the risks of not doing, and a large proportion of them understand the risks of doing so. The question some aren't asking is what happens when one of the risks becomes real, and whether they can recover from it.

The board asked for an AI strategy. What they got was a vendor presentation dressed in strategy's clothes. The vocabulary is being set by people with a commercial interest in your confusion. AI is not complicated. The industry around it is. What follows is the version of this conversation that the hyperbolic presentations skip. Here's the AI landscape for executives.