Around two years ago, almost to the day, I spent an absolutely frantic evening in Berkeley, going through enough coffee to power a mid-sized city, hammering away at my laptop on trying to figure out what comes after LLMs. What to most people was still barely on the horizon at the time has been a subject I have been working on in various capacities for the best part of the past decade, on and off.

There’s a cave in Ethiopia, in an area called Dikika. At some point, around 3.4 million years ago, an early hominin made some incisions on an animal carcass, leaving some notches on a bone as the makeshift knife cut past the muscle and sinew into the bone, tell-tale kerf marks that speak of the first time one of our ancestors used a tool. ¹ What happened in that cave changed everything for our species. ¹ McPherron, S.

When Pygmalion carved Galatea from ivory, he fell so deeply in love with his creation that he begged Aphrodite to bring her to life. The goddess, moved by his devotion, granted his wish. ¹ It’s a beautiful myth about art, obsession and the blurring lines between creation and creator. What the myth doesn’t tell us is whether Galatea charged a monthly subscription fee. ¹ Ovid, Metamorphoses , Book 10, 243-297.

I’ve spent the better part of this weekend putting OpenAI’s latest offerings through their paces - both the newly released open-weight models and GPT-5 itself. Armed with a selection of coding challenges, mathematical problems, and the sort of esoteric research queries that usually separate the wheat from the chaff, I’ve been conducting what amounts to a weekend-long torture test of these systems.

The Greeks loved oracles. The average temple of Apollo, who among others was in charge of soothsaying and predictery, was adorned to the gills with gifts from grateful worshippers whose inscrutable questions got equally inscrutable answers from Apollo’s oracles. None of these were more famous than the Pythia, the young ladies high as a kite on volcanic fumes at Apollo’s temple in Delphi.

There’s a curious terminological schism that nobody talks about at what passes for dinner parties for the AI crowd.

When I read Andrej Karpathy’s endorsement of “context engineering” in a Twitter exchange with Shopify’s Tobi Lutke, I felt he tapped into something we all felt to some degree: tweet={"url":"https:\/\/twitter.com\/karpathy\/status\/1937902205765607626","author_name":"Andrej Karpathy","author_url":"https:\/\/twitter.com\/karpathy","html":"\u003Cblockquote class=\"twitter-tweet\" align=\"center\"\u003E\u003Cp lang=\"en\" dir=\"ltr\"\u003E+1 for

There’s a pervasive problem with semantics in artificial intelligence. It’s present at the creation – the term itself characterises the subject as a man-made simulacrum of something ‘natural’ the way we speak of artificial flavourings and artificial rubber.

If you spend any time on LinkedIn, it’s almost a certainty that you have come across a bevy of alleged ‘agentic AI architectures’. They all look something like this: All very neat, but the audience might be forgiven for asking what exactly is agentic about this, except for relabeling subprocesses in what is a run-of-the-mill RPA workflow as ‘agents’. And the audience is, this once, perfectly right.

DeepSeek has been grabbing headlines in AI circles lately, showing up everywhere from Discord servers full of ML enthusiasts to LinkedIn posts where “thought leaders” tag each other in endless threads.

The Department for Science, Innovation and Technology has just dropped its long-awaited AI Opportunities Action Plan, a 50-page vision of how the UK government plans to guide us into an AI-powered future.