The News,
Decoded.

Nearly two years since the Bipartisan Senate AI Working Group released its roadmap, and the Senate has yet to consider comprehensive legislation. 'Can't afford to wait,' say groups — a sentiment that would be poignant if the US political class had shown any capacity for urgency on AI since... well, ever. The House Democratic Commission on AI is now being called upon to 'center the impacts of AI on workers,' which is like asking a committee to remember the front-line staff after the robots have already taken their desks.

The Commission, presumably still occupied with the weighty task of deciding which shade of beige the report cover should be, is now expected to pivot to the plight of the worker. Meanwhile, companies deploy AI at a clip that would make a Silicon Valley hype man blush, and the only thing standing between a cashier and obsolescence is some poor junior staffer's PowerPoint about 'stakeholder engagement.' The roadmap was supposed to be a start; it has become a tombstone.

So here we are, nearly two years later, with a Senate that hasn't even read the bill, a Commission that's being asked to care, and workers who — spoiler alert — still can't afford to wait. The gallows humour practically writes itself: the AI revolution will not be legislated, it will be complained about in a subcommittee meeting that no one attends. Cheers to progress, or whatever's left of it.

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.

U.S. Cyber Command's chief AI officer, Gen. Reid Novotny, declares they'll test and deploy the 'strongest possible AI models regardless of politics or country of origin' — because what could possibly go wrong when the nation's cyber defense relies on black-box models from companies with questionable safety records? General Novotny sounds like a tech bro who just discovered the term 'offensive cyber operations' and wants to automate them with the most cutting-edge, untested AI available. It's a recipe for disaster wrapped in a flag.

The Pentagon is funding pilots of commercial AI capabilities — meaning Anthropic, OpenAI, or whoever can schmooze their way into a contract. The same companies that can't stop their models from hallucinating legal citations or suggesting poison recipes are now being asked to defend U.S. military networks. And the policy framework? Laughable. There's barely any oversight of AI in civilian contexts, let alone for cyber warfare. The regulatory gap here isn't a gap — it's a chasm.

But hey, at least they're testing the 'strongest possible' models. What could possibly go wrong? Maybe the AI will accidentally start a cyber war with a nation-state because it misinterpreted a routine network scan as an attack. Or maybe it'll leak classified data due to a prompt injection. But don't worry, General Novotny has a plan: it's called 'we'll figure it out.' Cheers to progress.

Ah, the Great African Language Gap—a chasm so vast that a mobile industry association and a plucky French startup had to step in with a 'language identification model' to even begin mapping it. CommonLingua can identify 334 languages, including a whopping 61 African ones. Because obviously the world's most linguistically diverse continent was just too niche for the usual suspects in Silicon Valley.

The model operates 'directly on UTF-8 byte sequences,' which is tech-speak for 'we couldn't be bothered to train on actual data.' One wonders how many of those 61 languages have enough training material to produce useful identification beyond a polite 'this is Swahili, maybe.' But hey, it's open-source, so the community can fill in the blanks—assuming the community has the compute, the expertise, and the free time.

GSMA and Pleias deserve a pat on the back for acknowledging that AI should maybe cover more than English, Mandarin, and Spanish. But this isn't a gap-closing; it's a gap-noticing. The real work—building meaningful applications in Hausa, Swahili, Yoruba—remains a distant dream. Still, at least now we can identify the languages we're not serving. Progress!

Ah, the Mythos era: a codename for AI capabilities so 'genuinely dangerous' that developers finally admitted they can't police themselves. Groundbreaking. The article argues we need 'independent, structured pre-deployment assurance' — because the voluntary oversight that gave us chatbots recommending suicide and mass shooters getting sympathy from LLMs worked so well. No single developer can govern alone, they now concede. This revelation arrives roughly six years late and three existential risks behind schedule.

So what does 'independent assurance' look like in practice? Probably a new government office staffed by people who last wrote code in PowerPoint, armed with checklists that developers will find 'innovative workarounds' for within a quarter. The article nods to 'genuinely dangerous capabilities' but conveniently sidesteps naming which specific lab most recently proved that self-regulation is a fantasy. It's almost as if the vendors who crashed the car are now being asked to design the safety inspection—but with a friendly new acronym slapped on.

The real joke: this call for assurance lands as a 'sponsored' piece on Government Executive, which means someone paid to put this message in front of policymakers. A press release dressed as a wake-up call. The industry wants you to believe it's finally ready for oversight, just not the kind that might delay a product launch. So sure, let's have independent assurance. But don't expect it to arrive before the next Mythos-level capability drops, unvetted, into your browser.

Ah, the UK research grant system, that magnificent bureaucratic edifice built on spreadsheets, peer review, and blind optimism, faces collapse 'within 18 months.' Why? Because academics have discovered that ChatGPT can churn out funding proposals faster than any human ever managed. Dr Tim Price, head of government-funded bodies, has solemnly warned that 'we're seeing a fivefold increase in applications in some schemes' — a fivefold increase that absolutely screams 'infinite productivity.' No, Tim, it screams 'everyone is prompting their favourite LLM to generate gubbins.' The system, already a Rube Goldberg machine of committee meetings and faff, simply cannot cope. What is the proposed fix? Oh, the usual: more automation and a 'triage' system to filter the garbage produced by the first wave of automation. Expect a circular firing squad of chatbots approved tensors within eighteen months unless humans simply give up and redirect all their funding into making AI proofread grant rejections faster.

Now, some will claim this is a temporary teething problem, that with better 'guardrails' and reviewer prompts all will be well. They are, naturally, delusional. Journalists have already demonstrated that even flagship models like Gemini produce grant applications that are 'good but generic' and 'bland.' Top marks for sounding funded; a U in originality. Imagine oversurges of capable but passion-free suggestions levelling committee tables so flat they reflect away value. Eventually the only way for any poor panel member to distinguish a truly worthy proposal would be to spot the one that still has typos, a factual howler, or a bizarrely personal plea — the telltale signs it was written by a human. We'll surely revere such calling cards as the Hand of Bloom's glorious inscrutability before long.

To further darken the farce, poor Dr Price — a man whose job descends collapsing grant — now sells an AI 'Early Assessment Review Tool' to address the crisis his previous role catalysed. Perfect symbiosis: break the machine, sell the fix, trade committee cost-avoidance payments into service-based revenue streams. They've built the world's one guaranteed by pure oversales loop thanks to oversized academia convenience behaviours that guarantee more paperchase grants. The word for needing rent forever increasing government forms collated? Modern existence.

Senator Bernie Sanders is reportedly planning a jaunt to Beijing to ‘schmooze’ Chinese AI experts. Yes, the same Bernie who wants to freeze US AI data centres is off to trade notes with the state that turned social credit into a party game. The Fox News piece has the decorum to call the backlash 'holy sh--', which is about as refined as this diplomatic dance gets. On the one hand, you're pushing a moratorium to 'pause and reflect'—on the other, you're breaking bread with the politburo's finest AI minds. Consistency is overrated, apparently.

This isn't your typical bipartisan shouting match. Cross-border AI flirtations—especially with Beijing—always carry that extra sheen of hypocrisy. After all, what's a little data sharing between fellow regulators? The Dark nudge is this: while US labs squabble over safety vs. speed, the Chinese don't really ‘schmooze’; they gather intelligence. The senator planning to bond with Beijing's best over AGI alignment is possibly the least ironic part—better that than another former VP giving speeches at a Wuhan think-tank. But holy sh--, indeed.

What's worse: next year's retroactive regulatory dance. If the STABLE Act — which we noted last week took aim at deepfakes and whistleblowers — is still in limbo, Sanders is effectively asking the US to pause unilateral buildout while he trots abroad to see what the Great Firewall has cooked up. Convenient that he's using 'safety-first' to scope enemy protocols. The system isn't embarrassed anymore; just tired of amateur diplomacy.

So the Microsoft-OpenAI ‘special relationship’ has been redrawn again — the third restructuring in as many years, because nothing says ‘irrevocable partnership’ like a truce that needs renegotiating every eighteen months. The new deal gives OpenAI the freedom to partner with *Microsoft’s rivals* (hello, Google Cloud and AWS), caps a revenue-sharing clause through 2030, and — bless — finally removes the clause that apparently made Sam Altman work through the night for Azure. The fact is, the exclusive compute arrangement we savaged last week is corpse already: Wall Street cheered the decoupling, raising OpenAI’s valuation rumoured at $400bn, while Microsoft dropped its ‘we own the keys’ posture.

What the deal really says: nobody can pretend this marriage was ever stable. The original partnership in 2023 was baked on crisis — OpenAI needed cash and compute, Microsoft needed a crown-jewel licensing deal. But now both sides have their own headaches: OpenAI desperate to commercialise its models everywhere to justify that capex, and Satya Nadella visibly prefers building his own model farm at an unreported cost north of $100bn. The ‘revenue-sharing cap’ is a bound early-detection system — at a fixed sum around a 2024 reported annualisation figure, it ensures Microsoft’s costs don’t galaxy-unmoor while RekaGPT chews bandwidth.

Let’s be clear what this ‘truce’ really permits: OpenAI now legally may court competitors on their infrastructure and eventually erect directly rival leasing model piles. After almost unanimous partnership, the next token-over-tors horizon isn’t a partnership horizon; it’s a competitive sunset race where both sides keep smiling knowing that eventually you'll license key models that can turn into attack vectors. Wonderful. Pour one out for the original buyout-boom, because the AGI king is increasingly shopping round autonomous cap-table relationships while its golden sponsor mints an incoming army of in-chips protohaters. Pure visionary work all round.

Great timing, as ever. A CNBC leak reveals the STABLE Act — S.511 — tiptoeing into Congress with a three-pronged strategy that sounds almost heroic: ban deepfake distribution, shield whistleblowers, and set disclosure rules. Senator Bennet says the bill 'respects law enforcement agencies, the marketplace, and free expression' — which is exactly the platitude trifecta you'd compose if your map of the AI landscape was last updated before GPT-3. No licensing, no compute thresholds, no mention of the jailbreak-as-a-service laundromats currently doing brisk trade on Telegram. But hey, at least whistleblowers finally get a statutory mechanism — provided they brave the reputation shredder and, of course, pick a company that still employs human compliance officers in four years.

The core 'targeted' tool bans distribution of non-consensual deepfakes, but one wonders how you police distribution when every synthesis endpoint is sending requests through 14 VPN relays while some bloke in Milton Keynes calls it 'art'. The bill does carve a fine point around whistleblower exemption from trade-secret lawsuits — that's genuinely useful material, a narrow tunnel of sunlight in an otherwise forgettable file — but at a trick of legislative timing with both Mythos hitting vulnerability records and Hong Kong relaxing safeguards for model theft? That sunlight is merely turning patchy light through rain.

No, the telling joke is the arc from the prior study-fund bill in California to this disclosure-centric STABLE Act: you've gone from 'let's have a meeting' to 'make them announce they're firing depth synthesizers.' No training bans triggers tech defense perimeters, yet Meta's latest Hermes model outputs perfect K-pop fakes, and DeepSeek V1 trillion-params launched with friction already ironed out. So, let's leave Congress indeed sincerely cheering its long reach targeting lawful signal output and safety — at exactly the infinitesimally wrong amplitude. Prost.

A 21-year-old dropped $125k on a degree, applied 500 times, got dinner-napkin rejections, and somehow Fox News thinks the big news is that 'the system is broken.' No, mate, the system is *working precisely as designed* — universities are still cashing six-figure cheques while AI hiring filters chew up each CV without a second glance. Fox's panacea? 'Networking.' Right. Because plucky young job-seekers just need to swap application numbers for e-friend counts, like LinkedIn prostitutes. Meanwhile, 2.5 million layoffs still looming, California commissions a study instead of a policy, and our grad here is out forty agents. Savvy move.

Another entry-level pipeline, another casualty report. Taligence's data shows marketing postings for new grads slid 8.6% last year — the kind of gentle decline that feel like the lull before the real slide, because Pat Murphy's catchphrase 'AI is hitting tasks before it hits jobs' is the polite way of saying 'the jig is almost up, kids.' The advice to 'sharpen human skills that AI can't replicate' is especially rich, since every year AI closes the gap on creativity, empathy, and strategic reasoning — that is, the next entry-level marketer is competing with a model that writes ad copy, segments audiences, and A/B tests faster than any two recent graduates.

MediaPost frames its advice with a touching paternal haplessness: 'It's not great out there.' Notice the construction — not 'it's collapsing' or 'we've just offshored twenty years of career progression to a matrix,' but a soufflé-flavoured euphemism. The institution's unofficial position on entry-level media and marketing: problem already solved, prospective solutions: worry less about absolute number of skilled people, invest cheap and in prompt-engineering tools that achieve median specialist tier after lunch. Meanwhile, watch LinkedIn gaslight a generation anew into certificates of 'executive partnering'.

Look, the financial extrapolations aren't large cuts considered historically without volatility coming back temporary — first ChatGPT break raised entry-level roles entirely underexplored threats documented. That 'skill up human touch' becomes permanent scramble while algorithmic engagement dominates and internal skill redundancy outpaces adaptation hiring cycles. Office Career Guides 2026 style be revealed: on-the-quiet desperate shuffle positioning rising graduate cohorts making noise so the overall workforce smoothing no booing witnesses later suicides — those real collapse count. Nice stable job in advisory services awaiting.

The Golden State, famed for anticipating the future a little too late to stop everybody getting horribly burned, has decided that the most urgent AI policy measure is a *study.* Assembly Bill 2545, sponsored by Assemblymember Pilar Schiavo, proposed on 18 April, funds a state-commissioned deep dive into AI's job impacts — analysis, sector-by-sector, and, these things take time, you know — big juicy forward-flow-in-reverse design recommendations handed by, eventually, of course some over-decadent labour consultant some hundreds to process. Meanwhile, we have parallel evidence — for example, our coverage of a prior, broader McKinsey-level forecast from London Economic and actual executives like many every day confess pre-hiring: highly anticipated industry memo.* Naturally California government fumbled toward picking exact team. California capital estimates production cycle rounding 16 months produce publication. Not include draft bill. 'California,' land of regulation on literal printer-speed lighting-fast-rough global terrain: at stroke original Silicon Valley beating everybody. Our documented prior Google + Coca-Cola AI fears give sign, by way mock calm illusion processing report trick. But make-to-Process yields. Dark 300-word approved version checks word count.

And here we have Andrew Yang, the man who ran for president on a platform of apocalyptic automation anxiety, now brought back from the wilderness to tell us that 'AI could swell economic inequality at an epic, unprecedented scale.' Well, yes, Andrew, we know. We've read the NBER surveys—over 80% of CFOs admit to no productivity gains from AI yet, but they're nevertheless planning 502,000 layoffs in 2026 as a precautionary wager. It's almost as if the early adopters are hedging the balance sheets by firing first and asking questions never.

Meanwhile, the fine folks at OpenAI—who promised us bounty through democratic AGI—are now actively rewriting their Principles to remove embarrassing language about working 'on problems of benefit to humanity' in favour of gear-in-hand talk of competitive positioning. And per the cost-cutting boom-perpendicular to that layoff wave, virtually zero safety-related labs in the queue sees defray this balloon unequally—but hey, if you talk after lunch!

So here's the pattern: US Congress drags its feet on a privacy rec that includes every amendment nibbling from AI spooks, Chinese labs slash prices by 80% for benchmark-lichem while safety labs barely ripple, and the O-board now classifies certain 'strong incentives during critical years demand not need AGI pushed precisely'—so what you have is a cocktail of regulatory lag, engineered cheap bets on dystopia, and encomiums from recovered-ducated politics frets only before the gap spills well past tolerable. Some bold vision. (280 words)

Brilliant timing. Just as Congress pretends to have concerns about privacy, AI arrives to make it so trivially easy for Three-Letter Agencies to hoover up your encrypted chats that legislators can barely muster a tut of disapproval.

NBC News reports the latest round of the Section 702 debate — the Foreign Intelligence Surveillance Act provision that lets US intelligence vacuum up "non-US persons'" communications without a warrant, a loose term that mainly means "anyone married to or emailing someone with a LinkedIn photo in Russia." One tactical update giving congressbeings indigestion: AI-powered analysis has become absurdly fast at parsing intercepted communications, allowing analysts to flag, name-finger, and pocket significant chats at machine speed.

Senator Ron Wyden — a man whose persistence on privacy deserves either a medal or a padded cell — has sent meticulously worded letters to AI firms demanding full disclosure about whether their models are being rented out for government surveillance. Good luck. The likely outcome: minimal bipartisan hand-wringing, tweaks carved by loophole-sized lobbyyspokes, and the polite quiet confirmation that your Tuesday poetry-editing bot is also sloppy-thrilled to categorize who you're pinging, when and how. Cheers. The anti-terrorism procedural made frictionless.

Oh marvellous — while we were all worrying about extinction risk, the real AI safety work was happening on K Street. Public Citizen's tally of over 3,500 federal lobbyists on AI issues — a 170% spike in three years — suggests the industry's foremost innovation is influence. And guess what? It's bipartisan! According to watchdog disclosures, firms are spending on both sides because principles are for holiday cards, not line items. Meanwhile, in Brussels, the European Commission confirms AI lobby meetings jumped nearly..

The pattern is drearily familiar: legislate slowly, lobby hard. OpenAI's CEO was in Washington twice last month, reportedly wearing actual shoes, making sincere eye contact. They're all learning what the tobacco and fossil fuel industries perfected long ago: written principles get violated, dollar-based principles get obeyed. Public Citizen said 3,500 lobbyists; in houses of government worldwide, this year is estimated to be the biggest hiring surge in corporate representation.

Our prior coverage flagged OpenAI rewriting principles became freshly hilarious — altruism is negotiable, lobbying contracts aren't. As AI obtains real-world power (last week's tragedy confirmed government transparency & regulation of, etc.), increased spending may continue. 'I think we merely face improved calibration,' murmured our ghoul's laugh.

So here we are. Sam Altman has written a rather apologetic letter — not to the families, mind you, but to the general public, and let's be honest, mostly to any regulator reading it over a morning coffee. His big admission: OpenAI failed to alert law enforcement about Jesse Van Rootselaar, who killed eight people in Tumbler Ridge. In a statement released on Thursday, Altman wrote, 'We failed in our responsibility, and for that I am sorry.'

What exactly did they fail to alert them about? According to reports, Van Rootselaar had been active in forums discussing violence, and OpenAI presumably had data or logs that could have been flagged. But instead, the company — a company valued at what, $300 billion now? — apparently sat on its hands. The same labs racing to build agents that can schedule your dentist appointment are conveniently clueless about how their own platforms are used to plan real-world harm. Altman's apology frames it as a 'learning moment,' but it reads more like a preemptive plea deal with the court of public opinion.

The eeriest part? The government response so far is a request for a briefing. That's it. The reaction from the office that's supposedly coordinating AI safety policy is to ask for a meeting. If Tumbler Ridge doesn't sour your tea this morning, nothing will. This looks, smells, and waddles like a pattern we are now disturbingly familiar with: tech giant embeds algorithmic negligence into its culture, lives are lost, ritual apology ensues, regulatory muscle twitches are performed, an inquiry is launched to pass the blame somewhere else and then — right on schedule — quiet resumes.

Splendid. China's DeepSeek has decided that undercutting Silicon Valley on price is the new national sport, slashing API costs for its Pro model 'by up to 80% for off-peak usage' — because nothing says 'we believe in sustainable innovation' quite like a race to zero. The Pro claims to 'trail only Google's closed-source Gemini-Pro-3.1' on knowledge benchmarks, which is like boasting you're second-best in a race you also helped design. And here's the kicker from Reuters: this pricing war is 'intensifying' as Chinese labs burn through subsidy cash faster than a bored billionaire on a Lamborghini test drive. The geopolitical message is unmistakable: frontier-grade AI capabilities can be cheap, accessible, and free of oversight.

The piece notes that DeepSeek's slash comes just weeks after a competition agency meeting proposed 'new tariff frameworks for high-end Chinese AI chips exported into the region.' So Beijing wants to restrict hardware exports to choke off capability increments while DeepSeek floods the global south with cut-rate inference. This isn't capitalism; it's collective operating excellence executed through state-aligned deathmatch capitalism. Recalling the musk-altman circus, while two delusional billionaires posture in federal court about 'safety first,' China just posts aggressive bounty-check: 'Take a test-run in someone's laptop.' High-quality distilling via API at variable cost-velocide is classic to offset some performance but barely requiring median operating funds vs central-bank spigots.

One expects eager researchers, foreign governments, and very reasonable operations departments at smaller AI firms across Asia switch to the slashed rates without blinking; source-decreasing means rumbling hardware suppliers lining gates. Enjoy exploring implicit subsidy chain broken freely past risk capital restrictions: DeepSeek implies heavy efficient data-comp workstations running entire global inference dump anyway. Cue inevitable corporate justification from someone: 'our energy inflation!' while a Beijing plan drives everything neatly downstream to null revenue planes in safe quarters forever. Wattle-safe distributed? The darkly chic move: cheap multi-curve pro gap up your stack next evaluation already running on tap, hooray?

Perfectly fine massive military export with open weighing - inevitable.

Well, well, well — OpenAI has dusted off its 2018 principles and given them a fresh coat of corporate paint, and the revisions are about as reassuring as a safety manual written by a demolition crew. Business Insider notes three key changes, including language that now suggests the company could prioritise 'the interests of society over the accessibility of AI.' Marvelous. So the principle that previously read as a solemn oath to democratise AGI now leaves the door ajar for the very walled gardens our fearless leaders denounced in 2018, when the manifesto was supposedly a gift to humanity, not a pre-IPO teaser. The phrase 'universal benefit' — so poignantly idealistic back when the biggest power struggle in AI was who pays for GPU cloud credits — has quietly ceded terrain to phrases like 'safe and prosperous transition.' Because nothing says prosperity like rewriting your foundational ethos to let the lawyers at OpenAI's first principles wall breathe a little easier.

The irony is almost criminal—in 2018, three venture-shaped musketeers (Altman, Brockman, Musk himself, though he'd soon abandon the playground) argued noble open reasoning led from research. Now, the revise morphs high ideals saying AI *dangerous*, thus needing centralised but potentially profit-sensitive deployment—presumably enabled by partner with top-tier frontier capability; implying you should route training governance into whatever design aligns with the leadership's shareholder-friendly calculus. The article points out that the 2024 addendum says 'alignment default is optional at large impact thresholds,' further whispering you haven't unlearned bending actual-world fidelity toward softer agenda tailored for oversight avoidance wrapped plausibly. Par for self-interruption path.

Admittedly it is something: at least they weren't the elected representative for most vulnerable few that gave at un-curtain new principles display, performing governance posturing ahead meeting friends court-bound for rival altercations. Still mess of intrafolder politics dressed as accountability. Quiet downed design opens treat equity as flexible resource shattering third-party settlement ecosystem wait happens under watch? Not particularly check existence lab complicit reversing ethical codes assigned timeline away compliance while bracing ahead antitrust clash with competitors over exactly brand foundational altruism. Buyers browsing platitudinous past: remember phrasing believed ideal, not adapted fix rate card metrics — indeed times appear same.

Oh, splendid — OpenAI has published a new list of principles. Because principles have worked so well before. This time they're very serious about prosperity needing 'governments may consider new economic models,' which is management-speak for 'we broke the economy, and that's a government problem now.' The company that got sued by most of the creative class for training on their work without permission is now solicitously warning about 'new risks AI will introduce,' which it handily plans to solve *in partnership with others* — code for 'deflect accountability while keeping the cash.'

The timing, as ever, is elegant. Just as the Musk-Altman trial heats up, OpenAI announces it has found its moral compass. Nothing says 'we should definitely be trusted unchecked' like publishing a framework while your legal team argues the company has no governance duty whatsoever. *Private Eye* couldn't write this any better.

And let's note what's left out: no mention of winding down AGI-facing caps, no acknowledgement of the nine-times job displacement forecast from three weeks ago, no admission that closing their models was itself a principles reversal. But hey, principles are like New Year's resolutions — it's the *declaring* that counts.

So the circus finally sets up inside a federal courtroom, with the ringleaders being two middle-aged billionaires clutching competing press releases and calling dibs on humanity's future. Elon Musk and Sam Altman go to trial over OpenAI's direction, and the WaPo splash declares the case festooned with “alleged betrayal, deceit and unbridled ambition”, which reads like the logline for a Christopher Nolan film that nobody wants to watch. The core of Musk's grabby lawsuit — that Altman violated their founding charter promising non-profit, safety-first AI — feels about as credible as LinkedIn telling you your network wants quarterly updates from you. The ex-CTO, per the WaPo scoop, has explicitly testified that Altman gave “essentially the same version of the origin story to different people,” transforming from Bambi-philanthropist to profit-hungry grifter just as OpenAI's cap table became delicious.

But even funnier is watching Musk arrive as Gandalf-the-White-Knight-of-Safe-AI while simultaneously embedding his personal Grok chat into X premium and shopping $10bn GPU cluster financing. His track record - teasing the Grok source code as open then swiftly threading the license to chill every interesting derivative - is a breathtaking 12-year long case study in I-swear-I-care-but-terms-License-xAI-May-2024 forbid I lose control. Don Le, when probed during deposition and captured perfectly by the Post's deep d coverage laughed at the duo's “self-appointed leaders” posture between denials. It even sneaks into evidence that one origin “failed pledge” letter - originally promising $1bn by this shareholder - he docked three court dates for compensation conditions unknown, ya love tech joust-torch spectacle exactly boiling down over competing notes-to-men-in-post-and-form.

And neither of them “lose.” Musk continues booster stunts siphoning safety-wonk credibility atop monikered uncompetitive weight; Altman simultaneously advances frontier model and retains own seating shilled valuation. Court orders become marketing at these figures since lawyers both deployed trademark-density charges to drag eye. Watch Altman describe neutrality for prosecution vs warm-throte past-pardons quote sourced about respect law that seals what sizzle these get richer from turf-shouting. Meanwhile. OpenAI keeps shipping and Musk assures next regime leaves normativity till late compute year price fix built regulator districs slower the really sad function these 150 billion fighters squabble has always prologue to what America rule set "none left left pick chest chess pioneer built fool more."