The News,
Decoded.

Oh, *marvelous*. Inceptio, the Chinese self-driving truck startup, has announced that AI breakthroughs will *not* accelerate their rollout. They're sticking to a mid-2028 commercialization milestone, because apparently, the AI revolution can't even beat a delivery schedule. How delightfully humbling for the hype machine.

CEO Julian Ma claims that 'AI models are getting smarter, but hardware limitations and regulatory hurdles remain the bottleneck.' So the same old story: algorithms can now write poetry, but they can't navigate a truck through a roundabout without a human minder. The gap between lab wizardry and real-world deployment yawns wider than the Gobi Desert. Inceptio's pragmatism is refreshing, but it's also a stark reminder that the 'AI will solve everything' narrative has a nasty habit of running into concrete reality — sometimes literally.

The rest of the industry should take note. While everyone's been chasing AGI, the hard graft of sensors, safety cases, and legislation is still plodding along at pedestrian speed. Inceptio's cautious timeline is probably sensible, but it's hardly the stuff of futurist fantasies. So yes, AI is getting smarter. But it still can't drive a truck. Pass the popcorn.

Oh, brilliant. The US Army has decided that the best way to defend against AI hackers is to... use AI hackers. Because nothing says 'strategic advantage' like training algorithms to attack each other in a simulated Indo-Pacific crisis scenario. One can almost hear the PowerPoint slides: 'We'll fight fire with fire, artificial stupidity with artificial stupidity.' The exercise, conducted with industry partners, pits friendly AI defenders against adversarial AI attackers — a cyber war game where both sides are learning machines. Presumably the winners get to brag about their zero-day count while the losers become the problem for every .mil network.

The focus on the Indo-Pacific is telling: this is where the next great power competition is supposed to happen, and the Army wants to make sure its AI can hold the line against... more AI. Because if there's one thing history teaches, it's that arming both sides of a conflict with the same technology always ends well. Remember when we thought cyber war meant someone in a hoodie? Now it's self-improving models trading exploits at machine speed. The exercise reportedly tested 'AI-enabled cyber defense' — which is military-speak for 'we wrote Python scripts that argue with each other about firewall rules until one side stops responding.'

And let's not overlook the subtle irony: the same industry partners helping the Army 'ready' for this fight are likely the ones selling the same tools to everyone else. But hey, that's a tomorrow problem. Today, we celebrate a fine simulation of how future wars will end — not with a bang, but with an epoch where a gradient descent overshoots and accidentally disarms the entire Pacific Fleet. Perhaps the only winning move is not to play — but that's never been America's style, has it?

Ah, Sundar Pichai, the man who declared Google an 'AI-first company' back in 2016 — a full *ten years* ago, in internet time. The TIME100 profile is a hagiography of that 'vision,' conveniently skipping over the fact that it took a decade of faffing about while OpenAI ate lunch. But hey, now they've got custom chips, Cloud, YouTube, and deep research — oh, and a cosy little deal with the Pentagon for classified work. Because nothing says 'Don't Be Evil' like sucking up to the military, which employees *immediately* criticised. Colour me shocked.

The piece gleefully chronicles how Pichai pushed Google to 'the front of the AI race' — a race that didn't exist in 2016, mind you, and one they were nearly left behind in. The Pentagon deal is portrayed as a strategic masterstroke, not the ethical banana skin it's about to become when some poor engineer realises their model is powering a drone strike. But sure, let's celebrate 'cultivating projects' like that's an achievement rather than the bare minimum of running a tech company.

So Google is now officially 'most influential' in AI. Terrific. That means they'll lead the charge on whatever regulatory sandbox our lords and masters cobble together, all while selling us YouTube ads for products we don't need. Pichai's 2016 declaration was either prescient or accidental — and from the look of the Pentagon deal, I'm betting on the latter. But don't worry, the chips and cloud will keep humming, right up until someone asks too many questions.

Oh, *wonderful*. Gary Marcus — the AI community's perennial party pooper — has taken to X to declare that Big Tech's AI spending spree is the 'greatest capital misallocation in history.' Because nothing says 'measured critique' like slapping a superlative on a multi-trillion-dollar gamble before the receipts are even in. Marcus, whose career has been a long, weary sigh at the industry's hype cycle, is specifically aiming his crosshairs at Alphabet, Amazon, Meta, and Microsoft — the four horsemen of the capex apocalypse. He's not wrong to raise an eyebrow when these companies are collectively burning cash on AI infrastructure like trust-fund kids at a casino, but the man is also a known quantity: if Gary Marcus says it's a bubble, you can bet the bubble will inflate for another three years just to spite him.

The real joke, of course, is what 'misallocation' even means in this context. Is it a misallocation because the ROI won't materialise? Or because the money could have gone to, say, actually fixing the internet? Marcus is simply the guy yelling at the parade that the emperor has no clothes — but the parade is too busy buying more sequins to care.

And yet, there's a sliver of dark comedy in watching the same people who brought us the smartphone glass ceiling and the surveillance economy now pivot to 'AI everything' with the reckless abandon of a teenager with a credit card. The greatest capital misallocation in history? Possibly. But it's also the greatest case study in corporate groupthink we've seen since the dot-com boom. At least then we got some cool pet food delivery startups out of it. This time we'll get a few more chatbots that can write mediocre poetry and a planet full of e-waste. Cheers, Gary. Don't hold your breath.

Ah, the AI revolution has finally delivered its killer app: unearthing a nine-year-old Linux bug that's been cosy in every build since 2017. Because after all the billions in GPUs and the breathless talk of superhuman cognition, what we really needed was a root escalation vulnerability that a moderately bored sysadmin could have found with a good fuzzer and a long weekend. The researchers are triumphant, naturally — they've demonstrated that AI can 'assist' in software scanning, by which they mean it did what existing tools have done for years, but with more electricity and a slicker press release.

And the exploit code? A mere 10 lines. That's not a sophisticated payload; that's a haiku of destruction. It's almost poetic: after all the hand-wringing about AI surpassing human hackers, the machine coughs up a bug from the Obama era and calls it a day. You half expect the model to pat itself on the back for solving a problem that was quietly sitting in the open for nearly a decade. The cybersecurity community will now nod wisely and declare this a 'proof of concept for AI-augmented vulnerability research' — which translates to: 'we spent a lot of money to find something that was always there.'

The real lesson is that the software ecosystem is such a tangled mess of legacy code that even the most hyped AI will waste its time on cobwebs. This isn't a capability leap; it's a maintenance reminder. The researchers have proven that AI can match the output of a competent security engineer with a weekend and a cup of coffee. But worry not — the PowerPoint decks about AI transforming cybersecurity will write themselves, and the venture capital will keep flowing. Cheers to progress.

Ah, the legal profession has finally discovered that agentic AI systems can hallucinate too — and not just in the harmless 'invent a case name' way we've come to know and love. No, these systems can now access *other software platforms*, which is precisely the sort of unsupervised power you want to give to a technology that already makes up facts with the confidence of a barrister who hasn't read the brief. Rok Popov Ledinski, in a fine example of understatement, suggests firms treat agentic AI as a 'controlled extension of existing processes.' Because nothing says 'controlled' like letting an AI log into your document management system and start riffing on precedent.

One imagines the conversation: 'Your AI has just filed a motion citing *The Little Mermaid* as binding authority.' 'Ah, but did it do so while accessing the court filing system?' Terrific. The layer of risk isn't additive; it's exponential. Each new platform the agent touches is another surface where a confabulated fact can become an expensive reality.

And of course, the solution proposed is the same as always: process controls, human oversight, treat it as a 'controlled extension.' Because that's worked so well with every other AI deployment. Lawyers will surely catch every hallucination before it cascades into a malpractice suit. They'll definitely notice when the AI, in its infinite creativity, decides that a statute from the 14th century is perfectly applicable to a cloud computing dispute. Yes, I'm sure the 'controlled extension' will be perfectly controllable. Cheers, Rok. This'll be fine.

Oh, how splendid — Duke University has gifted us an AI model that can detect ADHD in children as young as five. Because nothing says 'healthy childhood' like being flagged by a machine trained on 720,000 electronic health records. The model, published in *Nature Mental Health*, apparently spots patterns in 140,000 children's data that predict ADHD. I'm sure every parent is thrilled to have a black-box algorithm decide that their five-year-old's fidgeting is a clinical condition rather than, say, being five.

Let's not dwell on the fact that electronic health records are notoriously biased, incomplete, and often reflect the prejudices of the healthcare system. Low-income kids, minority kids, or simply kids with busy parents who miss checkups — they'll be even more likely to land in the 'high risk' bucket. But hey, early diagnosis means early medication! Big Pharma must be salivating. The AI might be 'accurate,' but what does that mean when the training data itself is a mess of unequal access and subjective assessment?

In the end, this is another lovely example of technology sprinting ahead while ethics ties its shoelaces. Duke's researchers have proven they can squeeze a diagnosis out of a database before a child can tie their own shoes. The question — which no one will ask until it's too late — is whether we should. Good luck, five-year-olds: your health records are now entertainment for a neural net.

Oh, look — OpenAI has gifted its image generator a web browser. Because the last thing the world needed was a model that already hallucinates facts also having real-time internet access to confirm them. 'Web thinking capabilities,' they call it, which is a delightful euphemism for 'now it can scrape the web to generate even more convincingly detailed falsehoods.' The model will 'reason about image structure,' which sounds an awful lot like learning how to compose frames for a deepfake video. But sure, this is all in service of 'better user experiences.'

And get this: up to eight 'coherent' images from a single prompt at 2K resolution. Eight. Because one misleading image wasn't enough — now you can have a whole gallery of plausible fakes, all sharing the same style and subject. Perfect for a coordinated disinformation campaign, or just spamming social media with eight variations of the same nonsense. 'Coherent' here means the images will all look like they came from the same source, making them that much harder to debunk.

The update is framed as a productivity boon for creatives, but let's be honest: this is a capability leap that will be exploited long before it's properly safeguarded. The web thinking feature means the model can now ground its generations in whatever rubbish it finds online, from conspiracy forums to propaganda outlets. And at 2K resolution, those deepfakes will look crisp. OpenAI claims this is about accuracy and consistency, but in the current environment, it's hard to see this as anything other than an arms-race accelerant dressed up as a product update.

So, Elon Musk took the stand in federal court on Wednesday and admitted what everyone in AI already knew: xAI built Grok by distilling OpenAI's models. "It is generally considered practice," he said, according to TechCrunch — a magnificent piece of corporate doublespeak that translates to "yes, we copied your homework, but everyone does it, your honour." The testimony also featured Musk's personal AI provider ranking: Anthropic first, then OpenAI, then Google, and finally Chinese open-source models. A hierarchy that conveniently places his arch-nemesis Sam Altman's company at number two — below the saintly Anthropic, of course.

The problem isn't distillation per se — every lab does some form of it. The problem is that Musk is simultaneously suing OpenAI for being too closed and using their models to train his own. The cognitive dissonance is so thick you could train a model on it. And his assertion that it's "general practice" isn't a defence; it's a confession that the AI industry operates on a glorious honour system where everyone is everyone else's data. The courtroom becomes a mirror: this is what frontier AI looks like when the costumes come off.

Musk's ranking of Anthropic above OpenAI is both a genuine technical assessment and a petty political jab — refreshingly honest for a man who usually masks his motives in "safety" rhetoric. The Chinese open-source models he places last are the ones his own Grok has struggled to surpass on several benchmarks. So the testimony was part legal gambit to justify his own training methods, part humblebrag about his tech pecking order, and part unintentional self-own. In a field racing toward recursive self-improvement, it's comforting to know the adults are still bickering like schoolchildren over whose model is cooler.

Oh, how delightful. The AI arms race has officially entered its most predictable phase: the snake eating its own tail. Anthropic, the lab that brought us the harmlessly earnest Claude, has now unveiled 'Claude Security' — a feature that promises to help defenders 'keep pace with AI-powered exploits.' Because, of course, the best way to counter AI-powered attacks is with more AI. It's a solution so circular it could power a perpetual motion machine for hype cycles.

The kicker? Claude Security is being integrated by every major security vendor you can name: CrowdStrike, Microsoft Security, Palo Alto Networks, SentinelOne, Trend.ai, and Wiz — all rushing to bake Opus 4.7's capabilities into their platforms. So now your antivirus will have a friendly chatbot that can explain why it just quarantined your spreadsheet. The article breathlessly notes this is to counter a 'surge' in AI-powered exploits, which is a bit like announcing a new form of firefighting foam to deal with the fact that someone set the world on fire and handed out matches.

What's truly impressive is the speed with which we've normalised this. Five years ago, the idea that AI would be both the primary threat vector and the primary defence would have sounded like a Black Mirror plot. Now it's a Wednesday. Claude Security sits in the sidebar, ready to assist — assuming the attackers haven't already convinced it to share your passwords. But who are we to question progress? At least the chatbots are polite.

Oh, splendid. Another bountiful harvest from the open-source healthcare orchard — 38 previously unknown vulnerabilities in OpenEMR, the electronic health record platform that apparently powers over 100,000 providers. Because what we really want in medical software is a collective surprise party of security holes, each one a potential backdoor to your most sensitive health data. And it took an AI to find them. A machine. Which is either a testament to the tool's brilliance or a damning indictment of how little manual auditing these codebases receive. Probably both.

The AI — some static analysis model, name unglamorously omitted — went picking through OpenEMR's code and came back with a bouquet of flaws. Was there a prize? A pat on the head? No, just another reminder that the software running your doctor's office is held together with digital sticky tape and hope. But hey, at least the vulnerabilities were found *before* a malicious actor did — unless they already had, of course. We'll never know, because open-source healthcare projects tend to have the security posture of a teenager's forgotten laptop.

So what now? A patch? A scramble? Some tired maintainer will work through the list, and for a few weeks, OpenEMR will be slightly less porous. Then the AI will scan something else, probably a hospital infusion pump or a lab system, and we'll repeat the dance. The gap between the speed of AI-driven discovery and the snail's pace of remediation is the real story — but no one wants to read about that. Pass the digital paracetamol.

Oh brilliant. OpenAI, the lab that can't decide whether it's a public benefit corporation or a secretive AGI forge, has decided that the best use for its 'most powerful model' is to hand it to the Pentagon and DHS. Because nothing says 'building safe AGI' quite like propping up the digital defences of an agency that can't stop its own employees from clicking phishing links. The workshop they held was presumably a masterclass in 'how to deploy a black-box reasoning engine into critical infrastructure without any binding oversight.' Cheers.

And what could possibly go wrong when an unaccountable AI is let loose in the deepest recesses of government cyber ops? The same government that lost millions of records in the OPM breach, that can't secure its own email servers, is now going to 'shore up defenses' with a model that could also excel at breaking them. The Pentagon isn't exactly known for restraint when given a shiny new tool — remember the time they wanted nuclear-powered rockets? Now they get an AI that could, in theory, write its own attack code. But sure, let's 'combat hackers' by giving the state a potentially dangerous intelligence. What could possibly go wrong?

And of course OpenAI held a 'workshop' — that's tech-company speak for 'we showed slides and traded business cards while avoiding any discussion of red lines.' One can only imagine the meetings: 'So this model can also autonomously execute penetration testing? Strap it in.' The hackers must be delighted — their next targets just got a training manual. Meanwhile, the public gets the usual platitudes about 'responsible deployment' while the lab races to get its tech into every federal firewall. Hooray for progress. Anyone else feel like we're building the infrastructure for an AI arms race, one Pentagon workshop at a time?

Ah, splendid news — the UK government has finally realised that AI might be important. Technology Secretary Liz Kendall has declared that 'Britain must act now on AI,' warning that countries which fail to get to grips with it could lose control over their security and economic future. Because nothing says 'we're on top of this' like a ministerial announcement that essentially admits we've been napping while 70% of global AI compute was vacuumed up by just five companies. Five. Companies. That's not a market; that's a heist, and we're the bloke reading the paper on the train as the safe gets wheeled out.

Kendall's big insight? That 70% figure, which she presumably discovered after someone slipped it under her office door. The five companies — presumably Microsoft, Google, Amazon, Meta, and maybe OpenAI or Nvidia — now hold the keys to the digital kingdom. Britain, meanwhile, is trying to legislate its way into relevance while running on a compute diet of old GPUs and good intentions. The gap between the warning and the action is so vast you could drive a lorry through it, which is probably how the next five years of UK AI policy will feel: lots of noise, very little traction.

But chin up — we've still got our plucky regulator, the AI Safety Institute, which has become expert at issuing politely worded reports that nobody reads. Kendall's urgency is commendable, but history suggests this will translate into a consultation paper, a roundtable, and eventually a 'UK AI Strategy 2.0' that launches just as the compute party moves to Mars. Still, at least we're not the EU, which is still arguing about Article 5 while the robots automate Brussels. Cheers.

Oh marvellous. OpenAI and Anthropic have graced us with a warning that their most powerful coding models are now building themselves. Not 'might be able to,' not 'could eventually' — *are*. The very companies racing to build god-like intelligence want us to know, with all the gravity of a parent spotting a toddler with a loaded gun, that the things are now assembling themselves. And at the exact moment these models cross the threshold into recursive self-improvement, what do the companies do? They become *less transparent*. Of course they do. Because nothing says 'responsible stewardship of potentially world-altering technology' like pulling down the blinds and hoping no one notices the glow coming from the server room.

Let's parse that second fact, because it's doing a lot of work. 'Growing less transparent as models grow more powerful.' This isn't a bug — it's a feature of the incentive structure. OpenAI and Anthropic are locked in a death-match for market share, and transparency is a competitive liability. If you reveal your alignment failures, your competitor wins the next funding round. So the natural equilibrium is: build faster, disclose less, wait for the explosion. This is the 'we warned you' that history books will quote, right before the chapter on why we didn't listen.

But hey, at least they warned us. That's the true genius — pre-emptive absolution. When the models start writing their own improvement code and the light cone of the singularity hits, the CEOs can say, 'But we *told* you!' and the shareholders will nod, collect their insurance payouts, and wonder how the quarterly earnings call could have gone so wrong. What a beautiful, beautifully predictable train wreck we've built for ourselves.

Satya Nadella has chosen the word 'exploit' with all the care of a PR consultant told to 'be honest for once'. On Wednesday, the Microsoft CEO announced he's ready to 'exploit' royalty-free access to OpenAI's most advanced AI through 2032 as part of a revised partnership. Because nothing says 'collaboration' like a tech giant gleefully admitting it plans to squeeze every drop of value from its golden goose.

The word 'exploit' is a masterstroke of accidental honesty — normally you'd hear 'leverage', 'synergise', or 'unlock value'. Nadella cut the crap. Microsoft gets to run OpenAI's brains without paying a dime for inference until 2032, while OpenAI presumably gets… what, exactly? The warm glow of being useful? That 'revised partnership' sounds a lot like 'we've negotiated better terms because you're dependent on our compute'.

Meanwhile, the rest of the AI world watches as Microsoft secures a near-decade monopoly on frontier model access. It's almost endearing how transparent the power dynamic has become. Nadella didn't say 'partner', 'collaborate', or even 'leverage' — he said 'exploit'. And he smiled. That's the state of AI ethics in 2026: the biggest player openly admits the game, and we're supposed to applaud the candour.

Ah, another 'turning point' — because we hadn't had one in at least a fortnight. The Harvard Business Review Analytics survey, dutifully lapped up by the Washington Post, reveals that businesses are using AI to improve efficiency in back-office operations, not for revenue growth. So after billions in venture capital, breathless AGI prophecies, and enough compute to heat a small nation, we've arrived at the glorious conclusion that AI is this generation's spreadsheet. A 'turning point' indeed — towards the thrilling horizon of slightly cheaper accounts payable.

What the survey won't tell you (but the Post is happy to package as progress) is that 'efficiency' is corporate code for 'fewer humans'. Back-office automation isn't about unlocking new value; it's about doing the same work with fewer warm bodies. The respondents — presumably those still employed — have collectively decided that AI's greatest gift is trimming headcount in payroll and procurement. Revenue growth? Too risky. Let's shrink our way to profitability.

The Washington Post calls this a 'turning point'. More like a gentle pivot into a cul-de-sac. The AI economy, we're told, is maturing: it's now a cost-centre tool, not a growth engine. But sure, if the peak of AI ambition is optimising the supply chain of a mid-tier logistics firm, raise a glass. Just don't expect it to be your job on the line.

Ah, Ineffable Intelligence. A name so pompous you can almost smell the smugness from five miles away. This London-based startup, founded a whole five months ago by ex-DeepMind bod David Silver, has somehow convinced investors to part with $1.1 billion at a valuation of $5.1 billion. That's a seed round larger than most countries' GDPs, for a company that, as far as anyone can tell, has yet to produce a single product. The sheer chutzpah is almost admirable — almost.

Let's pause to appreciate the arithmetic: a $5.1 billion valuation for something launched just 150-odd days ago. For context, that's more than the annual budget of many small nations, and certainly more than most AI startups will ever see. But hey, the name implies it's 'ineffable,' so perhaps we mere mortals simply cannot comprehend the genius. Or maybe it's just another case of VCs throwing money at anything with a DeepMind pedigree, hoping to catch lightning in a bottle before the bubble pops.

The tragedy is that this isn't an anomaly — it's the new normal. Every week brings another eye-watering funding round for an AI lab with a grand mission and zero revenue. The gallows humour writes itself: we're funding these companies to build AGI, but we can't even fund a decent public transport system. At this rate, the singularity will arrive before we've figured out how to pay for it, and Ineffable Intelligence will be remembered as the startup that cost more than Brexit.

Ah, Parag Agrawal, the man who steered Twitter into the iceberg, is back with a fresh $100 million to build 'web search for AI agents.' Because if there's one thing we learned from his tenure, it's that he knows how to make things work smoothly. The startup, Parallel Web Systems, is now valued at $2 billion — roughly the same amount of value Twitter shed in the months before Musk took over. Investors are betting that Agrawal's vision of an AI web search will be better than... well, the existing web search that already works fine for humans.

The pitch: AI agents need their own search engine because the current internet is too messy. Yes, the solution to AI's hallucination problem is to give it a worse version of Google. What could go wrong? Meanwhile, the $100 million will go towards 'continuing to build' — which is tech-speak for hiring engineers and buying server space before the next hype cycle makes this look quaint. One can almost hear the collective eye-roll from every search engineer who's spent decades optimizing for human queries.

But let's not be too harsh. Maybe Agrawal has learned from his Twitter days and will actually deliver something that doesn't implode. Or maybe this is just another case of 'former CEO of struggling company raises absurdly large round for vaguely defined AI product because that's what the market wants right now.' Either way, the valuation is a lovely round number to put on a tombstone when the AI winter comes. Cheers, Parag.

Ah, OpenAI has released an open-source tool called Symphony, because what the world needs is yet another software layer to manage the software that writes software. It turns a project-management board like Linear into a 'control plane' for coding agents—so now your Jira board can boss around the AI that's already bossing around your junior developers. The company's press release touts that teams can 'manage work instead of supervising coding agents.' Translation: we've automated the automation, so you can spend your day moving tickets from 'In Progress' to 'Review' while the AI does the actual coding—until it hallucinates a dependency and the ticket stays in 'Blocked' for weeks. One imagines the future: a project manager with 15 years of experience now responsible for telling a bot to tell another bot to refactor a function. Symphony is the death knell of human oversight, wrapped in a familiar UI.

The open-source nature ensures that any company—or any state actor with a grudge—can deploy their own agent orchestra. Need a swarm of coding bots to generate targeted phishing scripts? Just create a ticket labelled 'urgent security update' and watch Symphony deliver. The tool is designed to 'manage work instead of supervising agents,' which sounds efficient until the agents decide they'd rather argue about coding standards than actually produce anything. But hey, at least the board metrics will look great.

OpenAI's shiny new toy is a perfect representation of the industry's current trajectory: layering more abstraction on top of abstraction until the original human purpose is lost entirely. We've gone from writing code to writing prompts to managing boards that manage agents. The real question isn't whether Symphony works, but whether anyone will notice when the entire system collapses into an infinite loop of ticket reassignment. Probably not; the project manager will be too busy updating the Gantt chart.

Ah, the UK government's £500m Sovereign AI Fund — because nothing says "sovereign" like handing public money to a company called Infeffable Intelligence that promises "self-learning algorithms." The British Business Bank is co-investing, presumably having learned from the last time public funds chased a buzzword. One can almost hear the Treasury's spreadsheet: £500m for algorithms that learn themselves — what could possibly go wrong? The timing is impeccable: just as major labs are admitting that self-improving AI is both the goal and the nightmare, our government decides to double down on the very concept without any visible safety framework. The article glosses over whether "self-learning" means recursive self-improvement, which would be a capability leap of existential proportions, or just fancy gradient descent, which is already everywhere. Either way, the taxpayer is now an angel investor in a technology whose risks we barely understand. Cheers, lads.

But let's not be churlish. And Infeffable Intelligence gets to skip the bother of demonstrating product-market fit because, after all, the algorithm will learn to find customers itself. The British Business Bank's involvement adds a layer of bureaucratic oversight: expect quarterly reports on "alignment metrics" that no one defines. Meanwhile, the private sector's top AI labs are spending billions on safety research while our government invests peanuts in the very thing they're worried about. It's almost as if we're funding the problem and hoping it solves itself. Almost.

The real irony? The fund is called "sovereign" — implying control, independence, national security. But self-learning algorithms, by their nature, become unpredictable. The UK is effectively buying a ticket to a lottery where the jackpot is either AGI or a black swan crisis. Given the government's track record with large IT projects (think NHS database, Brexit border systems), one might expect a bit more caution. But no — we've decided that the best way to manage existential risk is to write a cheque and hope for the best. At least the name Infeffable Intelligence suggests they know they can't talk about what it does. That's honest, if nothing else.