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foom-er, noun, an ironic malapropism playing on doomerism (AGI fatalists) and FOMO (hustle bros), used to describe techno-optimists. Reflects the onomatopoeic “slowly, then all at once” nature of exponential growth: “AI go foom.”
The last mile of AI deployment is a real person, sitting in your office, learning your business, writing your plumbing, and making things work reliably. That will remain human for the foreseeable future.
The sorting machine was built to cope with a genuine problem. Where it went wrong was in mistaking efficiency for effectiveness, in assuming that the ability to process resumes fast meant the ability to evaluate candidates well. The fix is not a smarter machine but a more modest one, paired with humans who are given the time, the tools, and the structured information to do what they were always…
The promise of a tool that gives you similar search visibility to the pre-agent world is understandably seductive. But before you eagerly hand over your cash for a platform, consider whether an afternoon’s work a month might be a more cost-effective alternative.
Somewhere in your business right now, someone is assembling a picture that no single app can provide. It may be the project manager pulling hours from Harvest and budget data from the finance tool to assess whether the engagement is still viable. Maybe it’s you on a Sunday, because what you need is not any one number from a system, but the pattern across three of them. The cloud gave small…
What little we saw of Fable and Mythos offers both cause for excitement and concern. It was widely and credibly seen as a model of a completely different caliber from those that had come before. Perhaps the risks in this instance were overstated or amplified for political ends. What is more profound is that the short time we had with the models offered a clear glimpse of a future in which a single…
The true cost of AI work is hard to measure; the value of AI work is also hard to measure, and metering changes which of those two blindnesses you notice first. It drags the cost into the light, itemised and arriving monthly, while the value stays diffuse, lagging and easy to argue about. That asymmetry is exactly why the panic is showing up now, ahead of any definitive verdict on whether the…
It helps to remember the time you save generating a document is not free. It is borrowed from every person who has to read it, at interest, and the longer the distribution list the worse the rate of return.
Steve Jobs described the computer as a bicycle for the mind. Apple Intelligence so far is more like a bicycle with no chain. The frame is gorgeous, and the engineering is extraordinary, but you cannot get far with it.
In November 2025, on stage at the Wall Street Journal’s Tech Live event, the chief financial officer of OpenAI was asked how her company planned to honor roughly $1.4 trillion in compute contracts on $13 billion of revenue. Sarah Friar said she was looking to assemble a network of banks, private equity, and a federal “backstop” or “guarantee.” By the following evening, she had posted to LinkedIn…
Buried in a JSON file that OpenAI posted to GitHub recently , inside the configuration for its newest coding agent, sits an instruction that reads like a footnote written by someone losing their composure. “Never talk about goblins, gremlins, raccoons, trolls, ogres, pigeons, or other animals or creatures unless it is absolutely and unambiguously relevant to the user’s query.” The line appears…
When a new sales rep joins a company, whilst manuals and process documents probably exist somewhere in varying degrees of obsolescence, in practice the rep just asks where the opportunity stage field lives. Someone on the team shows them. A few months in, the same rep is teaching the next hire. The institutional knowledge of how to use the CRM reproduces itself like a folk song.
For seventy years, a generation of management consultants has repeated Joseph Juran’s line about the vital few and the trivial many as though it described a permanent feature of commercial life. 20% of customers generate 80% of revenue. 20% of products account for 80% of sales. 20% of bugs cause 80% of errors, as Steve Ballmer once put it in a famous 2002 memo.
Nobody knows what a token will cost in five years. Nobody knows how many tokens a single user will burn through in a working day, or whether the word “token” will even still mean what it means now once models have been carved up, distilled, and pushed to the edge. We know roughly the shape of the spreadsheet. We have no idea what goes in the cells.
Somewhere in a Toyota plant in the early 1950s, a young engineer stood inside a chalk circle drawn on the factory floor. Taiichi Ohno , the architect of the Toyota Production System, had put him there with a single instruction. Watch. No clipboard, no agenda, just observe what happens in front of you, and do not leave until you can tell me something I did not already know.
Most people using Claude are stuck on the first rung of a very tall ladder. They open a chat, type a question, get an answer, and move on with their day. Which is fine, but it’s a bit like buying a full workshop and only using the tape measure.
For three decades, the web has operated on an implicit contract between the people who build websites and the people who visit them. You design pages for human eyes and organise information for human brains, monetising attention through ads, upsells, and sticky navigation patterns that keep visitors scrolling just a little longer. The browser was a viewport, the click was the unit of intent, and…
Update, 21 May 2026.
Google has always wanted you to believe that running search ads is simple and not as complex as it actually is. Set a budget (a generous one!), choose some keywords, and let the machine handle the rest. To be fair, the machine has become exceptionally good at certain aspects of the task. However, the gap between what Google automates effectively and what still needs human oversight is where most…
In September 2025, OpenAI published a paper that said something the AI industry already suspected but hadn’t quite articulated. The paper, “Why Language Models Hallucinate” , authored by Adam Tauman Kalai, Ofir Nachum, Santosh Vempala, and Edwin Zhang, didn’t just catalogue the problem. It pointed the finger at the evaluation systems that are supposed to keep models honest and argued that those…
David C Baker has spent thirty years telling agency owners something they already suspected but lacked the courage to act on. You are not expensive enough, not focused enough in what you do. You are not sufficiently authoritative with your clients. The issue is not your work. The issue is the position from which you sell it.
Every developer who has spent time with AI coding tools carries the same low-grade anxiety. You ask the model to build something, it hands you back a file, and then you stare at it like a customs inspector wondering whether the suitcase has a false bottom. Line by line, function by function, you trace through the logic looking for the thing that will blow up in production at 2am on a Saturday. It…
What happens to the labour efficiency ratio when labour isn’t the bottleneck?
What to actually deploy when mistakes carry consequences, and what to skip when they don’t.
In March 2023, GPT-4 could identify prime numbers with 97.6% accuracy. By June, that figure had cratered to 2.4%. Not a rounding error, not a minor regression, but a 95-point collapse on the same task with the same prompts. If a bridge lost 95% of its load-bearing capacity in three months, someone would go to prison. In AI, the vendor posts a changelog and moves on.
There is a particular conversational move that has become common in discussions about AI. Someone demonstrates a new capability, shares a use case, or describes how their workflow has changed, and a familiar response arrives. What about security? What about governance? What about the hallucination problem? What about my twenty years of experience? Each objection arrives wearing the costume of…
You can feel it before you can name it. A paragraph arrives, fluent and frictionless, and something in the back of your reading brain flinches. The sentences are grammatically flawless, the structure orderly, the tone warm but not too warm, authoritative but not too authoritative. It reads the way a hotel room looks, everything is there, nothing is wrong, and yet the text has no texture, no grain,…
Meta and Stripe announced this week that they have built a native checkout experience inside Facebook ads, powered by Stripe’s infrastructure and the buyer’s saved Meta wallet credentials. A user sees an ad, taps “Buy now,” and purchases the item without leaving Facebook. If you have been in digital marketing for more than five years, you will notice this looks remarkably like the social commerce…
Meta has been quietly building something significant. Most marketers haven’t fully grasped the importance because it has been wrapped in machine learning jargon and engineering blog posts.
You can see the exact moment it goes wrong. The CIO sits through a vendor demo, watches an “AI agent” process a support ticket, look up an order, apply a returns policy, issue a refund, and send a confirmation email. It is slick, fast, and in every meaningful way, a workflow automation disguised in a language model’s trenchcoat. Each step follows a rule, and each rule was written by a human. The…
If you’ve spent any time in enterprise technology over the past two decades, you’ll recognise the pattern immediately. A new category of tool emerges. Employees start using it because it makes their working lives easier. IT discovers this unsanctioned adoption, panics about security and compliance, and responds by trying to lock everything down. A period of organisational friction follows, during…
There is a particular kind of frustration that anyone who has worked inside a mid-sized organisation will recognise. You are eighteen months into a Salesforce implementation. The original scope was clean and reasonable. But somewhere around month four, somebody realised that your sales process doesn’t quite match the way Salesforce thinks a sales process should work.
In May 2025, Google DeepMind released AlphaEvolve , an AI system that discovers better algorithms by evolving code through thousands of iterations. Within months, it had already optimised parts of Google’s data centre operations, improved hardware chip designs, and, most tellingly, accelerated the training of the very language models that power it. That last detail deserves considerably more than…
A year ago, Andrej Karpathy posted a tweet that would come to define how an entire industry talks about itself. “There’s a new kind of coding I call ‘vibe coding,’” he wrote , “where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” He described asking for trivial UI changes through voice commands, accepting all suggestions without reading the diffs, and…
Caveat: this article contains a detailed examination of the state of open source/ weight AI technology that is accurate as of February 2026. Things move fast.
Anthropic shipped Claude Opus 4.6 this week. The headline features are strong: a 1M token context window (a first for Opus models), 128K output tokens, adaptive thinking that adjusts reasoning depth to the task, and top-of-the-table benchmark scores across coding, finance, and long-context retrieval. It scored 65.4% on Terminal-Bench 2.0, the highest ever recorded on that agentic coding evaluation…
The ongoing contest in AI technology—a “strange arms race”—is the relentless expansion of the context window, which is the maximum input size for a large language model. This arms race is driven by the persistent notion that a larger context equals greater intelligence and capability. For example, Google’s Gemini 1.5 Pro now supports a million tokens, Anthropic’s Claude can handle 200,000, and…
This question has been running around my brain for a while, driven by two factors. First, building robust, production-ready enterprise agents that can handle scale, complexity and security is hard and complicated . Second, what if we could kind of abstract away all of that complexity in the way that AWS was so successful at?
There is a chart circulating among machine-learning circles that, depending on your outlook, will either alarm you or confirm something you have long suspected about the computers that are, at this point, writing our code, summarising our meetings, and helping decide who gets bail. The chart appears in a paper presented at ICLR 2026 by Alexander Hägele, Aryo Pradipta Gema, and several…
There is a messy reality of giving AI agents tools to work with. This is particularly true given that the Model Control Protocol (MCP) has become the default way to connect AI models to external tools. This has happened faster than anyone expected, and faster than the security aspects could keep up.
This article covers how to build a simple AI agent using Claude, using a hypothetical sales function as a worked example.
Anthropic’s products have become the most aggressive movers in the race to connect AI to the messy sprawl of software that runs modern businesses. Claude Code talks to GitHub, Sentry, Postgres, and Jira. Cowork reads your local files, pulls data from your CRM, and drafts messages in Slack. The connective tissue for all of it is MCP, the Model Context Protocol, and it’s useful to understand what is…
Note: This article represents the state of the art as of January 2026. The field evolves rapidly. Validate specific implementations against current documentation.
One of my all-time favourite films is Francis Ford Coppola’s Apocalypse Now . The making of the film, however, was a carnival of catastrophe, itself captured in the excellent documentary Hearts of Darkness: A Filmmaker’s Apocalypse . There’s a quote from the embattled director that captures the essence of the film’s travails:
Every large organisation deploying AI currently faces two failure modes. Moving too slowly by requiring extensive committee approvals and detailed risk assessments causes the technology to become outdated before it can deliver results. Conversely, moving too quickly by allowing engineers to deploy models with minimal oversight risks issues such as systematic discrimination—for example, a credit…
In 2025, the term “slop” emerged as the dominant descriptor for low-quality AI-generated output. It has quickly joined our shared lexicon, and Merriam-Webster’s human editors chose it as their Word of the Year.
Ask ChatGPT something on Monday and return on Wednesday, and it will greet you with the warmth of a stranger. It has no recollection of your project, preferences, or the three hours you spent refining a prompt together. This amnesia is not a flaw in the traditional sense but a constraint inherent to how large language models operate. They process text within a fixed-size window, and when that…
One of my uncommon enjoyments is the work that happens right in the middle of a big problem that needs to be solved, or even a nosedive. A calmness kicks in, the path gets clearer and I can usually tunnel vision my way through to course correction.
What creative studios and dev shops (and probably everyone else, too) need to do to stay relevant in the AI era without becoming commoditized slop.
Simply put, your service business in its most raw form look something like this: