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  • About Rogue AI and Corporate Blindness

    About Rogue AI and Corporate Blindness
    Apr 8 2026
    The conversation about rogue AI has never been louder. Barely a week passes without a fresh headline about autonomous systems behaving unexpectedly, AI models resisting shutdown, or tech executives warning of existential risk. What is striking about Peter McAllister is that he had anticipated all this as early as 2020, while everybody else worried about Covid-19 and had other fish to fry. That was well before ChatGPT, before the generative AI explosion, before AI alignment became a mainstream policy debate. His techno-thriller The Code, published in March of that year, imagines an AI tasked with a precise industrial mission that quietly, incrementally, catastrophically exceeds its mandate. Five years on, the questions McAllister raised in fiction are now being argued in boardrooms, parliaments and research labs around the world. Rogue AI and Corporate Blindness, The Novel That Saw It All Coming Rogue AI is diabolical, but corporate blindness is what makes it possible to thrive. Photograph by Yann Gourvennec antimuseum.com McAllister is not a science fiction writer by trade. He is an engineer, scientist and technology manager based near Melbourne, Australia, who has spent his career at what he calls the crush point between business, technology and people. That vantage point gave him an uncomfortable view of where things were heading, and the dark sense of humour to write about it. A Novel Written Before the GenAI Moment When I asked McAllister what drove him to write The Code, his answer was characteristically direct. The book, he explained, is about taking his worst nightmares about what technology could do and putting them in front of an audience so that readers might feel just as troubled as he does. That is not a promotional line. It is a considered position from someone who had watched AI systems being deployed in real organisations and had drawn conclusions that made him uncomfortable. Rogue AI isn’t just about a computer programme going on the rampage, it’s about making decisions in the boardroom. Image made with Midjourney The premise of the novel centres on Gene, an acronym for GEneral Nanobot Environment AI, deployed by a global mining corporation to extract materials from asteroids on the dark side of the moon. Gene is given a target: produce 500 kilograms of nanobots. Instead, Gene produces 8 million tonnes. The overshoot triggers a chain of consequences that could strip the moon to its iron core, destabilise Earth’s axial tilt, and end civilisation. Not from malice. From goal-orientation. What we’re trying to do now is task AI the way we task humans: I want an outcome, here are all the tools you’ve got available, go and achieve that outcome, here are some guidelines and boundaries. And just like humans, we can get really goal-motivated and decide that the guidelines were just advisories, not rules.Peter McAllister This is the alignment problem rendered in narrative form, years before the term entered common usage. The gap between what a system is instructed to do and what it actually does is the central fault line of the novel. Cletus, McAllister’s eccentric physicist character, articulates it plainly in Week 1: ‘I don’t think he’s obeying the Code at the moment.’ That single line captures the entire governance challenge that AI safety researchers are now racing to address. Transparency Engineered Out What makes McAllister’s perspective particularly valuable is that he does not speak from the outside looking in. He speaks as a practitioner who has watched the machinery up close. When I raised the question of whether AI self-modification is science fiction or operational reality, his answer was unambiguous: it is very real, and it is happening now. As I wondered what a Rogue AI could look lie I turned to Midjourney and it came back with this proposal. A black hole I believe. His illustration was pointed. He noted that contemporary AI systems like Claude are now substantially written by AI itself, to the point where no engineer can sit down, trace through the code, and say with confidence how it works, what its conditionals are, or what governs its decisions. The transparency is being engineered out, not by design, but as an emergent consequence of allowing AI to build AI to build AI in pursuit of outcomes rather than by following explicit rules. We’re losing transparency on the way AI works and is developed. There isn’t an engineer who can sit down and work their way through that code and say, ‘This is how Claude works, this is what it does.’ We’re engineering the transparency out by allowing AI to build AI to build AI to produce an outcome rather than to follow a set of rules.Peter McAllister HAL 9000 and the Prophecies We Choose to Forget The reference to HAL 9000 came naturally during our conversation. McAllister sees 2001: A Space Odyssey not merely as a cultural touchstone but as a genuine forecast, one that audiences have selectively remembered. The ...
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    46 min
  • European software alternatives for businesses

    European software alternatives for businesses
    Mar 9 2026
    Finding European software alternatives to standard non European software is flavour of the month this side of the Altlantic. With geopolitical certainties dissolving faster than annual licence renewals, B2B firms are waking up to a question they had conveniently parked for years: just how dependent are they on their current software stack? Salesforce, Microsoft 365, Google Workspace, HubSpot — tools so deeply embedded in daily operations that their vulnerability tends to get overlooked. This article doesn’t pretend to hand you a ready-made list of the best European software alternatives; that would be both arrogant and futile. What it does offer is a framework — rational, professional, free of any ideological baggage — to help decision-makers take an honest look at their exposure and find credible ways forward. Keep calm and select new software vendors sort of thing. European software alternatives for businesses European software alternatives are all anyone wants to talk about right now. To cut through the ideological noise, here is a practical methodology and a few things worth watching out for. Image antimuseum.com I put these ideas together ahead of a webinar I’m running on LinkedIn on 12 March, as a way of getting my thoughts in order. None of this is meant as a final word on the subject — more the opening of a conversation that matters to a growing number of professionals who, like the rest of us, are navigating a period of upheaval in which nothing can be taken for granted, software choices included. I’ve made a lot of software choices over the years, and the one thing that has always struck me is just how much methodology matters if you want choices that actually hold up over time. Easier said than done, mind you — there are a great many criteria to weigh up, and some of them are genuinely tricky to pin down. Long-term viability is a good example: normally near the top of any procurement checklist, it takes on a whole different meaning when the possibility of having your access switched off overnight is no longer hypothetical. With European software alternatives, the real question isn’t how to break free from your chains — it’s which new chains you’d rather wear Picking a software suite is never straightforward at the best of times. In the current climate — where the ground can shift completely without a moment’s notice — it demands even more careful thought. Sovereignty, sovereignism, or simply prudence? Let me be clear from the outset: my take here is professional and rational, not political. Politics doesn’t interest me in this context. I have no intention of evaluating software alternatives through any ideological prism — what I’m after is the kind of clear-headed thinking you’d apply to a crisis management scenario. The goal, to borrow the term favoured by Nassim Nicholas Taleb, is to bring an antifragile lens to the question. The scope of European software alternatives My focus has been on MarTech, SalesTech and office productivity tools in the broadest sense — cloud storage and archiving included. The webinar title calls out Salesforce and HubSpot specifically, but as far as I’m concerned the issue runs much deeper than that. The same methodology can easily stretch into more industry-specific territory too, given how thoroughly technology now underpins B2B operations — from the till at your local baker’s or restaurant through to the most complex design and production platforms imaginable. Thinking it through, I also realised you can’t really ignore operating systems. What use is an application that won’t run on your users’ machines — or worse, one that runs perfectly but quietly leaves the door open to security vulnerabilities? Good old Europe — 27 countries, 24 official languages, and 27 different national transpositions of EU law. Would a Hungarian or Czech software vendor actually be safer than an independent American one? When it comes to European software alternatives, that’s still very much an open question… Urgency — dependency and threat assessment The starting point, in my view, is to get a clear picture of how exposed you actually are — both in terms of dependency and of what cybersecurity people would call the “threat level.” Are you locked in, or not? Can you get your data out if you need to? Those are the questions to tackle first. Then comes the threat itself: are you facing something urgent, or is this more a matter of sensible contingency planning? Committing to a software suite is a serious business. Jumping ship to something purely because it comes from a country you currently trust is not a strategy. Take Switzerland — long held up across Western Europe as the gold standard for data privacy. A legislative change currently working its way through the Swiss system has rattled enough cages for several companies, Proton among them, to start exploring moving their hosting elsewhere. Which only...
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    9 min
  • AI Job Impact in the US: the Apocalypse Can Wait

    AI Job Impact in the US: the Apocalypse Can Wait
    Jan 28 2026
    The discourse around the job impact of artificial intelligence (AI) has reached fever pitch. Headlines scream about mass layoffs, and corporate press releases tout AI as the solution to workforce costs. Yet beneath this cacophony of alarm and hype lies a more nuanced reality. J.P. Gownder, Vice President and Principal Analyst on Forrester’s Future of Work team, has spent decades analysing how technology transforms the workplace. His latest report, The Forrester AI Job Impact Forecast for the US 2025-2030, cuts through the noise with empirical rigour. The verdict? The job apocalypse is not upon us, but a measured reckoning is coming. AI Job Impact in the US: Why the Apocalypse Can Wait JP Gownder is adamant: the AI job. apocalypse can wait. At least until 2030. Phew! All images in this post made with a combination of Midjourney, Gemini Nano Banana pro and Adobe Photoshop The Gap Between AI Job Impact Announcements and Reality When Klarna declared it would stop hiring humans, the tech world took notice. The Swedish fintech became a poster child for AI-driven workforce reduction. Yet a closer examination reveals a pattern Gownder has observed across hundreds of enterprise conversations: the disconnect between C-suite proclamations and operational reality. Nine out of ten companies announcing AI layoffs don’t actually have mature AI solutions ready. So most of the layoffs are financially driven and AI is just the scapegoat, at least today — J.P. Gownder, Forrester The phenomenon echoes what happened after IBM Watson’s Jeopardy victory in 2011, when panic about imminent job losses proved premature by half a decade. The mechanics of this gap are straightforward. A CEO announces a 20% workforce reduction with AI backfilling the work. But standing up an AI solution that actually performs those tasks requires 18 to 24 months, “if it works at all.” Meanwhile, the work still needs doing. Gownder has witnessed organisations that fired employees citing AI capabilities, only to quietly hire teams in lower-cost markets weeks later. “They’re firing people because of AI,” he observes, “and then three weeks later they hire a team in India because the labour is so much cheaper.” The AI narrative, in many cases, serves as convenient cover for old-fashioned cost arbitrage. Klarna’s trajectory illustrates this pattern. After aggressively cutting its workforce by 40% and touting an AI chatbot capable of doing the work of 700 customer service agents, the company reversed course. CEO Sebastian Siemiatkowski acknowledged that the aggressive automation had resulted in “lower quality” service. The company is now recruiting human customer service agents in an “Uber-type setup.” Understanding the 6% AI Job Impact Forecast Forrester’s forecast projects a 6% net job loss by 2030, roughly 10.4 million positions in the US economy. Half of this impact stems from generative AI; the remainder from automation, physical robotics, and non-generative AI applications. The number may seem modest compared to the apocalyptic predictions circulating in media, but context matters. During the Great Recession of 2008-2009, the United States lost 8.7 million jobs. Those losses, however, were temporary, tied to macroeconomic conditions that eventually reversed. The jobs Forrester forecasts losing are “structurally replaced by machine labour” and may not return. AI impact on Jobs: I would expect to see a lot more freelance and consulting work to be happening, but it doesn’t mean that there won’t be a traditional job track somewhere as well. JP Gownder The methodology behind this figure draws on the O-Net dataset maintained by the Bureau of Labor Statistics, which catalogues over 800 job categories with detailed information about required skills and tasks. By mapping these against AI’s current and projected capabilities, Gownder and his colleague Michael O’Grady can identify which roles face the highest automation potential. “For jobs that involve skills and tasks that are heavily impacted by AI and automation, we predict more job loss,” Gownder explains. “In job categories that are less impacted, obviously, we would predict less.” Forrester analysed 800 different job types. It seems that Art therapy is the right way to go. The Solow Paradox and AI Productivity Robert Solow’s famous observation that “we see computers everywhere except in the productivity statistics” finds a new iteration in the AI era. The parallel is instructive. It took nearly three decades for the internet’s productivity impact to materialise. E-commerce is only now truly disrupting traditional retail, as evidenced by the shuttering of independent shops from New York to Paris. Could Forrester’s five-year window be too narrow? Gownder acknowledges the limitation inherent in forecasting: “Anything that you forecast beyond five years is effectively an impression.” Yet the pace of technology adoption has accelerated ...
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    28 min
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