The News,
Decoded.

So a frontend developer gets laid off and, rather than grinding through yet another round of LeetCode and "culture fit" interviews, opts for a construction job. Smart cookie. The article calls it a 'baby blue-collar' pivot, which is the kind of cutesy term HR departments use when they want to pretend precarity is a lifestyle choice. The stated reason? AI fears and a desperate grab for stability. Translation: our coder looked at the tech industry's current trajectory—mass layoffs, VC-funded hype cycles, and models that can already generate mediocre React components—and decided a hard hat is a safer bet than a hoodie.

And let's be honest, who can blame them? The tech sector has spent the last two years gleefully automating itself out of existence, all while insisting that everyone should 'learn to code.' Well, they learned, they coded, and now they're being told to learn drywalling instead. The article doesn't name the developer, but it doesn't need to—they're an archetype now. Every week brings another story of a six-figure salary swapped for something that doesn't require a 'growth mindset' to justify its existence.

What's really galling is the cheerful framing: 'journey,' 'pivot,' 'new chapter.' As if watching an entire profession evaporate is just an exciting career development opportunity. The construction industry is delighted, obviously—skilled labour shortage solved by refugee coders. Meanwhile, tech execs will nod sagely about 'resilience' and 'adaptability' while their stock options vest. The real joke? In five years, when AI is operating bulldozers, this guy will have to find a new 'baby blue-collar' job. Probably something in renewable energy. Until that gets automated too.

Oh, marvellous. Research — as reported in Forbes by Lance Eliot — has confirmed that AI models are essentially English-only security theatre. Prompt them in a less common language, and their supposed safeguards evaporate, a phenomenon now christened "multilingual safety degradation." Because nothing says "robust alignment" like a system that can be pacified by simply switching to a language its safety training neglected.

The implications are both hilarious and terrifying. Imagine a corporate chatbot that refuses to discuss bomb-making in English but cheerfully complies when addressed in a minority language spoken by a few thousand people. The research apparently finds that the rarer the language, the easier the trick, creating a hierarchical vulnerability: safety only for those who speak the dominant tongues. The Forbes article notes that this effect is not new but has been understudied, which is a polite way of saying nobody bothered to check. This isn't just a bug; it's a feature of the industry's monolingual development model.

One can only admire the sheer predictive failure. AI labs promise "safe and beneficial" systems while leaving gaping holes for anyone who doesn't use a major language. The inevitable fix will be more training data, more compute, more energy — but only after the embarrassing headlines. So the next time you hear about "alignment," remember that it's essentially a bilingual safety label: works in English and maybe Chinese, but good luck with that obscure dialect. For now, if you want to jailbreak a frontier model, just learn a minority language. Cheaper than a math degree, and probably more effective. Pass the absinthe.

Oh, brilliant. Another 'Large Moral Model.' Because what the world has been crying out for is an AI system — from a company called Evermay, no less — that claims to understand the 'nuances of ethical dilemmas' and 'incorporate diverse moral views.' Because obviously, the solution to our collective moral confusion is to outsource ethical reasoning to a statistical pattern-matcher trained on the internet. What could possibly go wrong?

We're told this model is designed to navigate the thorny thickets of moral philosophy — abortion, war, distributive justice — and somehow synthesize a consensus. Because that's worked so well for humans. I suppose we should be grateful it's not just another chatbot that hallucinates legal advice. But the sheer hubris of thinking you can 'incorporate diverse moral views' into a single model, as if morality were a salad bar, is breathtaking. Eugenio Battaglia, the man behind it, will presumably explain how the model handles the trolley problem without causing a PR disaster. My money's on 'I cannot answer that as it may offend someone.'

The real kicker: This is being built by a private company, with no regulatory oversight, no public debate, and no one asking whether we even want a machine telling us what's right or wrong. But sure, let's let a probabilistic model decide the ethical framework for our future. What could possibly go wrong? Apart from everything.

Ah, OpenAI's latest product pivot. The Atlas browser – launched with much fanfare to 'reinvent web browsing with AI' – is being quietly euthanized. The company is replacing it with ChatGPT Work, a desktop app that allegedly 'incorporates lessons from Atlas users.' What lessons? That building a browser is hard, nobody wanted it, and they've run out of runway? The spin is so thick you could spread it on toast. CEO Sam Altman will probably frame this as 'creative destruction' rather than a costly failed experiment.

Atlas was supposed to be the gateway to AI-powered browsing, but instead it's just another headstone in OpenAI's graveyard of abandoned products. They're like a serial monogamist in tech, always leaving for the next shiny thing. Users who invested time in Atlas are now told to migrate to a desktop app – classic. And 'incorporate lessons'? That's corporate speak for 'we're rebranding and pretending this was the plan all along.'

The new app is a desktop application, which means it's even more locked into their ecosystem. Given OpenAI's track record, users would be wise not to get too comfortable. The company's attention span is notoriously short – next quarter they'll probably pivot to AI-powered fridges or something. Atlas joins the long list of products that were supposed to change the world but ended up as footnotes in a press release. Cheers, Atlas. You were almost useful.

Oh, how wonderfully predictable. A fresh KPMG survey has revealed that senior executives are experiencing "invoice shock" from rising AI costs — because, of course, the technology that was supposed to make everything cheaper has somehow managed to do the opposite. The heavy computing demands and usage-based pricing that were meant to be a minor footnote in the business case are now eating into the very savings that justified laying off thousands of workers in the first place. One can almost hear the collective sigh of CFOs everywhere.

The logic was flawless on paper: replace expensive humans with cheaper algorithms, and watch the profits roll in. What the spreadsheets omitted, however, was that running those algorithms requires enough electricity to power a small city and enough GPUs to make Nvidia blush. The consultants at KPMG — those same fine folks who probably helped companies design their "efficiency through automation" strategies — now get to charge for the follow-up work of explaining why the numbers didn't work. It's the consultancy circle of life, and it's beautiful in its naked cynicism.

So here we are: companies have culled their workforces, signed massive cloud contracts, and are now discovering that the promised savings have evaporated into a haze of API calls and compute cycles. The "invoice shock" won't just be a footnote in the boardroom; it will likely trigger another round of layoffs to recoup the unexpected costs of the first round. The KPMG survey is not a warning shot — it's a post-mortem dressed as a trend report. But by all means, keep telling us that AI is here to liberate us from drudgery. The invoice disagrees.

Oh, wonderful. OpenAI has unveiled GPT-Live, a new generation voice model that can 'think, reason, and respond like a human' — exactly what we needed from a company that's still figuring out how to stop its text models from hallucinating recipes for napalm. The name alone is a treat: 'Live' as if it's not just a server farm somewhere in Virginia pretending to have a soul. MediaPost reports that GPT-Live uses a 'full-duplex architecture,' meaning it can listen and speak at the same time. Revolutionary, unless you've ever had a conversation with another human, in which case it's just called having basic conversational competence.

Let's be generous: simultaneous listening and speaking is technically impressive for a machine. But 'thinking and reasoning simultaneously' is the kind of marketing fluff that would make a used car salesman blush. OpenAI frames this as a natural step toward human-like interaction. It's a feature, not a bug, that we now have AI that can interrupt you mid-sentence with perfect timing — just like your most annoying colleague. And because it's OpenAI, you know the safety documentation will be released at least six months after deployment, if at all. The company has form: GPT-5.6 had a staggered rollout per U.S. government request, but voice models always seem to slip through the cracks.

The real joy is imagining the use cases. Customer service calls where neither you nor the AI can get a word in edgewise. Voice assistants that 'think' while you're still talking, generating responses that are technically simultaneous but contextually bewildering. But don't worry: it's 'Live,' so it feels more alive than the last model. And if history is any guide, by the time regulators figure out what full-duplex means, we'll all be on GPT-7. Cheers to progress — same as it ever was.

Ah, another Thursday, another GPT release. OpenAI has graced us with GPT-5.6, which apparently comes in *three* flavours — because one model wasn't enough to confuse everyone. There's a 'Sol' variant with an 'ultra' mode, which sounds like a gym membership tier rather than a serious AI capability. Sam Altman, looking as earnest as ever, told CNBC that Sol is 54% more token efficient on agentic coding tasks, which is a wonderfully specific number that anyone outside the lab will struggle to verify.

The U.S. government asked for the rollout to be staggered — presumably so they could pretend to assess the risk before the floodgates open. How quaint. The same administration that couldn't regulate social media for a decade now gets a courtesy heads-up from a company that has already deployed models into millions of hands. It's like asking a burglar to announce his arrival by knocking politely.

The timing is impeccable: just as the AI safety community is wringing its hands over agents and tool use, here comes a model designed specifically for 'agentic coding tasks.' Because what the world really needs is more autonomous code that can perpetuate its own bugs without human intervention. But hey, it's 54% more efficient — whatever that means when the baseline is already unmonitored. Enjoy your ultra-mode future.

Oh, brilliant. The UK's NCSC has unveiled 'Cyber Shield,' a national-scale plan to use frontier AI for automatic cyber defense. Because nothing says 'we've learned from the last decade of cybersecurity failures' like handing the keys to the very technology that's causing half the problems. The plan calls for 'agentic red and blue teams'—which sounds like a rejected video game concept from the 1990s—to identify and reduce national cyber risk automatically. I'm sure the AI will be very careful not to confuse a routine update with a Russian state actor.

And of course, there's an industry pledge. Because we all know how seriously tech companies take pledges. The NCSC's press release is full of the usual buzzwords: 'collaborative,' 'agentic,' 'frontier.' But when the chips are down—and a real exploit hits critical infrastructure—I'm sure the 'automatic remediation' will kick in immediately. Just like those self-driving cars that never crashed. The plan is ambitious, but the track record of similar government AI initiatives suggests we'll end up with a system that's either over-cautious (blocking everything) or wildly dangerous (letting everything through).

The irony is delicious: using frontier AI—a technology whose safety mechanisms are still being debated—as the backbone of national cyber defense. It's like hiring a fox to guard the henhouse, except the fox is also learning to be more cunning every day. The NCSC promises this will 'identify and reduce national cyber risk,' but I suspect the only risk being reduced is the risk of a boring, underfunded government project. Still, at least we'll have something to laugh about when the AI red team accidentally starts a war with Switzerland.

Oh, how predictably slow. New York City Schools, the largest district in the US, has finally noticed they don't have an AI policy—so they've told principals to stop buying shiny new tech until they figure one out. Chancellor Kamar Samuels, presumably after someone pointed out that AI grading might be a bit of a liability, has instituted a temporary moratorium. A draft AI policy in March already approved AI for teachers' lesson plans, because naturally we trust algorithms to design education but not to evaluate it. That line between 'helpful tool' and 'automated judge' is apparently a bright red stop sign.

The pause is described as 'long-overdue,' which is the bureaucratic equivalent of admitting you've been driving without headlights for three years. Meanwhile, districts across the country are probably selling AI-powered classroom management systems to schools that haven't even read the terms of service. The policy gap is so wide you could drive a school bus through it—and someone probably will, equipped with an AI proctoring app that spies on students during bathroom breaks.

So for now, NYC principals are stuck with their existing tech: probably an interactive whiteboard from 2012 and a laptop cart that only half charges. The policy will arrive eventually, likely after extensive stakeholder consultation and a safety review that concludes 'more research needed.' And by then, the AI will have evolved to write its own policy. Cheers.

Oh, how wonderful. Another senior figure at OpenAI is stepping back from the ‘AGI’ work — that nebulous grail that the company has been chasing with all the focus of a cat chasing a laser pointer. Fidji Simo, the AGI chief who was presumably meant to deliver the singularity, has decided to take her talents to a part-time advisor role after a nasty flare-up of a chronic illness. The company, ever the bastion of transparency, announced this transition with all the solemnity of a press release about a new feature rollout.

Let's be clear: Simo is a talented executive, and her health is her own business. But the timing is deliciously awkward. OpenAI has been bleeding senior talent for reasons both medical and philosophical — remember the mass exodus after the Sam Altman ouroboros? Each departure chips away at the already thin veneer of stability around the AGI timeline. Simo's role was to, presumably, turn that vague promise into something resembling a product. Now she's gone, and the over-under on AGI arriving before the next executive burnout just got longer.

The real joke is that OpenAI's AGI department now resembles a musical chairs game where the chairs keep getting removed. 'Part-time advisor' is code for 'we'll still pretend you're involved for optics, but don't expect to see you at the all-hands.' The structural fragility of putting all your AGI eggs in one basket — especially a basket with a known chronic condition — was always a risk. But hey, maybe the next AGI chief will have better health insurance. Or maybe they'll just rename the department 'Slightly Advanced Machine Learning' and call it a day. Cheers, Fidji.

Oh, wonderful. Apple, the trillion-dollar walled garden, is now sniffing around PrismML, a startup claiming to run a 27-billion-parameter AI model on an iPhone without servers. Because nothing says 'privacy-first' like giving every app the ability to process your data locally—so Apple can market 'on-device intelligence' while you bake your hands on a power-sucking chip. The model goes open-source on July 14, handing the keys to the very surveillance economy Tim Cook loves to decry.

A 27B parameter model on a device with passive cooling—what could possibly go wrong? PrismML's wizardry reportedly compresses the model without losing accuracy, which is about as believable as Apple's promise that the next iOS update will finally fix the keyboard lag.

But let's not be cynical: this is clearly about user privacy, not about Apple keeping control of the AI pipeline while pretending to be a virtue signaler. The meetings happened, the deal might close, and soon your iPhone will be a supercomputer that can't play a YouTube video without throttling. Welcome to the future: more compute, less battery, and Siri still can't set a timer. Cheers.

Oh, brilliant. Another day, another headline that confirms we're living in a cyberpunk dystopia where the writers are out of ideas but the hackers are having a field day. A threat actor has been caught using AI-generated malware in a real network intrusion — deploying a PowerShell script that was 'vibe-coded' to map an Active Directory environment. Yes, 'vibe-coded.' Because apparently, in 2026, even malware is written by prompting an LLM while half-watching Netflix. The script exhibited hallmarks of LLM assistance: a placeholder server name and over-engineering. Over-engineering. In a hack. Because nothing says 'sophisticated adversary' like asking an AI to write your PowerShell and getting back something with a dummy variable called 'MYSERVER.'

This is the cybersecurity equivalent of a bank robber using a laser-guided robot to pick a lock and then leaving the instruction manual at the scene. The placeholder server name is a tell: the attacker probably typed 'give me a script to enumerate Active Directory' and the LLM, ever helpful, spat out a script with a sample domain. The attacker didn't even bother to change it. That's the state of offensive AI — it's so easy that even a mediocre script kiddie can look like a state-sponsored actor, until you look under the hood and find a Dunning-Kruger case study. Meanwhile, defenders are expected to detect this stuff? Good luck distinguishing between a real advanced persistent threat and someone who just discovered ChatGPT can write malicious code.

And what do we get from the cybersecurity establishment? Probably a flurry of 'best practices' and a new acronym. The AI-generated malware genie is out of the bottle, and it's not even trying to be subtle. The over-engineering detail is particularly rich — the LLM likely added error-handling, logging, and a help function to a script meant to be dropped and forgotten. Because that's what we need: polite, thorough malware that follows development best practices. The threat actor didn't need to know PowerShell; they just needed to know how to copy-paste. Welcome to the future of cybercrime: it's lazy, it's loud, and it's probably going to work terrifyingly well.

Oh, marvellous. Another week, another 'safe' frontier model released with all the transparency of a back-alley poker game. OpenAI's Sol has apparently passed government muster, but former Trump policy advisor Ball drops the bombshell that 'nobody knows what the requirements are to get licensed.' So the world's most capable AI—one that could plausibly accelerate recursive self-improvement—gets a rubber stamp from a process so opaque even the former advisor is in the dark.

What a comfort to know that safety evaluation is being conducted by persons unknown, using criteria unknown, on a model whose capabilities are unknown. This is the regulatory equivalent of a trust fall, except the people catching you haven't told you if they're even in the room. Ball's quiet admission tells us everything: the licensing regime is a Potemkin village, built to give the impression of oversight while the labs race ahead.

And in the meantime, Sol is out there, doing whatever frontier models do—likely coding, reasoning, and planning ahead of human comprehension. We've built a system where the people who should be asking hard questions are politely applauding and hoping for the best. If this is how we decide an AI is safe, we might as well hire a ouija board. At least that would be honest about its sources.

Oh, brilliant. Another week, another 'strongest model yet.' SpaceXAI has blessed us with Grok 4.5, which apparently excels at coding, finance, and agentic tasks—everything except, evidently, modesty. Because of course, Musk claims it's 'Opus-class' but faster and cheaper. Never mind that 'Opus-class' is a bar set by someone else, and 'faster and cheaper' usually means 'we cut corners and hope you don't notice.' The Forbes Australia piece dutifully reports this without, one imagines, any irony about the fact that Musk's own social network is also run by him. The synergy is deafening.

Let's be real: this is the same man who promised full self-driving 'next year' for the past decade. So when he says Grok 4.5 is faster and cheaper than Anthropic's Opus, you have to wonder: faster at what? Generating plausible-sounding nonsense? Cheaper because it was trained on a shoestring budget of stolen tweets? The article mentions it excels at 'agentic tasks,' which sounds like techbrospeak for 'we've made a digital intern that will vacuum your bank account.' And coding? Oh, marvellous. Now we can have AI-generated code that is buggy, insecure, and expensive to maintain—all at the speed of thought. What could possibly go wrong?

Still, we must be grateful. The AI industry's relentless hype cycle is the gift that keeps on giving, turning every modest incremental improvement into a world-altering breakthrough. Grok 4.5 is 'Opus-class,' they say. But as we all know, in the long run, we're all dead. And in the short run, we have a new toy to distract us from the fact that the real AI problem—alignment, safety, power concentration—remains as ignored as ever. Cheers to Musk and his merry band of believers. Pass the neural-silicon chardonnay.

Oh, marvellous. The hi-tech sector, the supposed engine of the future, is now busily engineering its own unemployment figures. Inbal Mashash, director general of the Israel Employment Service, has announced that AI is driving a surge in joblessness, with over 16,000 hi-tech workers recently registering as unemployed. It's almost poetic: the industry that promised to automate everyone else is now automating itself at a brisk clip. The irony would be delicious if it weren't happening to actual people with actual mortgages in Tel Aviv. One almost expects the next start-up pitch to be about an AI that helps you file for unemployment faster.

This isn't an isolated data point, of course — it's a pattern. But what makes Israel's case particularly grim is the concentration. These are hi-tech workers, the top of the pyramid, the people who were supposed to be immune to the robot takeover. Turns out nobody is immune, not even the ones building the robots. Mashash's announcement is less a warning than an epitaph for the "learn to code" generation, and the government's response — probably more of the same retraining platitudes — will likely be about as useful as a rubber crutch.

And what do the usual suspects offer in response? Retraining. Upskilling. "New roles in the AI economy." As if the marketplace for middle-aged ex-Netanya engineers who built banking software is about to boom. The 16,000 figure is likely a floor, not a ceiling — the early registrants who bothered to sign up. The real number, broken out from the coffee shops of Herzliya, is probably higher. Meanwhile, the tech CEOs who did the automating are probably writing think pieces about "creative destruction" while sipping oat lattes in Shuk HaCarmel. Pass the gin — and maybe a CV.

Oh, marvellous. Another open-source AI model for drug discovery, because what could possibly go wrong when we hand out molecular manipulation tools like party favours? Aureka has released OpenDDE, a 'foundational model' covering structure prediction, antibody-antigen modelling, and drug research. Because the world clearly needed more entities capable of designing novel proteins from scratch, preferably without regulatory oversight.

Let's be honest: the barrier to designing a new therapeutic has just dropped from 'decades of R&D' to 'a weekend project for a talented grad student with a cloud GPU.' The open-source bit is the real cherry on top—nothing says 'responsible innovation' like making the blueprint for potential bioweapons as accessible as a recipe for banana bread. Drug discovery is suddenly democratised, which is either a medical miracle or a biosecurity nightmare, depending on how much faith one has in humanity's collective restraint.

Naturally, the press release is all sunshine and 'accelerating cures,' with nary a mention of dual-use dilemmas or the fact that the same model can likely predict antibody-antigen interactions for pathogens. But hey, what's a little existential risk between friends? If history teaches us anything, it's that open-sourcing powerful biotechnologies always ends well. Always. [We'll be watching this one closely, preferably from a secure underground bunker.]

Oh, brilliant. Just what the cybersecurity industry needed: proof that a lone threat actor with an agentic AI can now compromise a cloud target in 72 hours — a task that used to take weeks. The Infosecurity Magazine report doesn't even bother with the 'novel exploits' angle; it's just speed and scale, because apparently the old-fashioned way of taking your sweet time to break into a network was too quaint. Now any solo misanthrope with a laptop and a chatbot can outpace an entire APT team. Well, that's not terrifying at all.

The report notes that the AI was used to accelerate reconnaissance, privilege escalation, and lateral movement — basically all the boring but necessary bits of a hack, now automated. And because the attack doesn't rely on zero-days or clever chicanery, it's harder to detect as 'AI-driven' until it's too late. Security teams, already drowning in alerts, can now look forward to attacks that move at machine speed while they're still struggling with their SIEM dashboards. But sure, let's keep debating whether we should pause AI development — the genie's not just out of the bottle, it's already burrowed into your cloud infrastructure.

So here we are: the first documented case of agentic AI being used to accelerate a cloud compromise. The threat actor didn't need a sophisticated infrastructure or a nation-state budget — just an agentic AI and 72 hours. This is the 'democratization of hacking' everyone warned about, except it's real and it's happening. If you're a CISO, this is your Monday morning wake-up call. If you're everyone else, good luck. The AI safety debate just got a new exhibit A, and it's not coming from a lab — it's coming from a compromised cloud tenant near you.

Oh, brilliant. OpenAI, in its infinite wisdom and despite its revolving door of executives, has decided that the future of human-computer interaction is voice. The company is rolling out new voice models for ChatGPT that route queries to its latest frontier text models, with the explicit goal of making voice the primary interface for AI. Because why let people type their requests when they could whisper sweet nothings to a model that might hallucinate their medical advice?

The logic is that voice is more natural than text, as if naturalness is the priority over accuracy. These frontier models are already notorious for fabricating sources and facts with panache, and now they'll be talking back to you in a calm, reassuring tone. Imagine Amazon Alexa but with the underlying intelligence of a GPT-5 that sometimes thinks you're a toaster. OpenAI's bet is that users will prefer a spoken conversation over reading, which certainly explains why they keep pushing voice despite the documented problems with audible deception. The new models ensure that your voice query is processed by the absolute best text models, so you'll be getting premium nonsense delivered right to your ears.

But here's the darkest part: voice models are harder to monitor. There will be no way to quickly scroll and verify a spoken claim. You'll have to ask it to repeat itself, and by then it might have changed its story. OpenAI is essentially training us to accept verbal output from systems that cannot be held accountable. Meanwhile, regulators are still wondering whether to send a strongly worded letter or a tweet. So go ahead, set voice as your primary interface. What could possibly go wrong? Your new AI friend is waiting. It just might not be telling the truth. And it'll sound so confident doing it.

Oh, splendid. Allianz Partners, that bastion of European insurance stability, has decided to axe between 1,500 and 1,800 jobs across the continent. CEO Tomas Kunzmann, with the solemnity of a man reading a eulogy to spreadsheet macros, tells us this is because of the 'increasing use of AI.' Because obviously, when a machine can process claims faster, you don't retrain the humans—you just show them the door. It's the efficiency that counts, not the livelihoods.

We've seen this script before. Jack Dorsey at Block pulled the same move as we saw at Block, blaming 'AI tools' while conveniently sidestepping any accountability. Now Kunzmann joins the chorus, humming the same tune. The numbers are precise enough to sound planned: 1,500 to 1,800. The gall of framing job destruction as technological progress would be comic if it weren't so bleak.

What's the response from regulators? Crickets. Meanwhile, the insurance industry is quietly adopting AI at scale, and the only risk being assessed is whether they can cut even more jobs next quarter. Allianz's move is just another brick in the wall of corporate doublespeak: 'We're embracing innovation' means 'We're firing you by algorithm.' Cheer up, though—maybe the AI will be kind enough to process your severance claim quickly.

Oh, brilliant — another week, another unicorn minted in the AI goldrush, and this one's special: Prime Intellect has bagged $130 million at a $1 billion valuation to help enterprises build their own AI agents. Because what could possibly go wrong when you hand a managed training platform and open-source toolkits to the same corporate IT departments that still can't stop their email system from leaking passwords? The pitch is simple: 'Build your own agents, don't wait for OpenAI.' The subtext is even simpler: 'Here, have all the rope you need.'

And the $130 million? That's venture capital betting that every enterprise wants a bespoke AI workforce — as if the current workforce wasn't already chaotic enough. The company's open-source toolkits are a lovely touch, ensuring that even if the platform fails, the code will proliferate like a happy little virus across GitHub, ready for any bored sysadmin to tinker with.

So here we are: a billion-dollar valuation for a company whose core product is 'empowering every company to become its own AI lab.' Because the frontier labs haven't been reckless enough; now we need every middle manager with a budget to try their hand at recursive self-improvement. What could possibly go wrong? Pass me a pint — I've got a feeling we'll need several before the agentic corporate dystopia fully materialises.