A Cambrian Month in AI
It only took a month for the AI world to feel reborn. October 2025 came and went in a flash of breakthroughs: Anthropic’s Claude Sonnet 4.5 suddenly started cranking out production-ready code for hours on end, OpenAI’s Sora 2 began generating Hollywood-grade video shorts with sound, IBM’s Granite 4.0 promised enterprise AI at 70% lower cost with a novel hybrid architecture, and GPT-5 quietly seeped into business software across the globe^1. Blink, and the landscape shifted overnight.
Now, in November 2025, the aftershocks of that October revolution are settling—or rather, continuing to rumble. The large language model (LLM) ecosystem stands more expansive and surreal than ever. A lyrical frenzy pervades the field: boosters proclaim a new dawn of machine creativity, while sceptics see the same old hype wearing futuristic new clothes. The mood is both electric and uneasy. Short, punchy updates ping across social media; scholarly benchmarks are shattered then immediately questioned. In one breath, a startup unveils a model that _writes code, makes videos, solves math—in the next breath, someone points out it also makes stuff up and breaks down. This is the paradox of late 2025: an AI Cambrian explosion of possibility, shadowed by an arms race of caveats and concerns.
Consider a scene from a Los Angeles theatre last month: an audience gathered for “Sora Selects,” a screening of AI-generated mini-films. They watched, half mesmerized and half mortified, as Robin Williams cracked new jokes from beyond the grave and Queen Elizabeth dove off a pub table. All of it was fake—synthetic clips conjured by OpenAI’s Sora app, which launched at the end of September. The crowd’s laughter was tinged with disbelief. Can we trust anything we see anymore? With Sora’s hyperreal videos going viral, even OpenAI’s CEO Sam Altman had to rush out a blog post promising greater control for rights-holders and hinting at revenue-sharing with actors’ estates Altman painted an optimistic picture, insisting that personalized content for an “audience of one” will unleash creativity “about to go through a Cambrian explosion” and boost the quality of art, a kind of “interactive fan fiction” future. Hollywood’s reply, suffice to say, has been more sceptical than amused.
New Models, New Milestones
October’s flurry of releases has left an indelible mark on November’s AI playing field. The benchmarks and bragging rights have been updated almost weekly. Anthropic’s latest Claude model, Sonnet 4.5, stunned developers by maintaining focus on coding tasks for 30-hour stretches and debugging entire software projects with minimal human intervention. It even took the gold medal on a rigorous “computer use” benchmark, leaping from 42% to over 61% accuracy on real-world PC tasks in one swoop – an unheard-of jump in such a mature field. Not to be outdone, OpenAI’s GPT-5 (first released quietly in August) introduced a clever “router” architecture that automatically switches between a fast, lightweight brain and a slower, deep-reasoning one. By November, GPT-5’s unified system is humming beneath countless enterprise applications, from your email’s smart reply suggestions to your bank’s customer service chatbot. The most advanced AI is no longer confined to research labs or fancy chat apps; it’s becoming the invisible infrastructure of daily digital life.
Meanwhile, the definition of “state-of-the-art” is splintering. The once monolithic race for the biggest, most general model has given way to a menagerie of specialized brains. Need a travel planner? An AI tuned specifically for itineraries now outperforms any general chatbot at the task. Want an AI lawyer? There’s a fine-tuned legal model that knows case law better than some paralegals. This trend toward specialization – call them small language models (SLMs) – has reshaped the landscape from a one-size-fits-all intelligence to an ecosystem of task-specific savants. As one commentator quipped, “The question isn’t which AI is smartest, but which AI is right for the job.” The LLM boom looks less like a march of one superintelligence and more like a bustling bazaar of niche geniuses, each excelling at one thing and utterly mediocre at another.
Crucially, not all these breakthroughs are about raw power; many are about efficiency and cost. IBM’s Granite 4.0, for example, took a contrarian bet: instead of vying for the largest model crown, it married the Transformer with a memory-efficient “Mamba” architecture to cut runtime costs dramatically. By IBM’s accounting, Granite’s hybrid design reduces RAM usage by over 70% in some enterprise workloads without sacrificing accuracy. In plain terms, it’s the difference between needing an expensive server farm versus a single rack of mid-range machines to deploy an AI service. That’s music to the ears of budget-conscious CTOs—and a notable shift from the “bigger is better” ethos that dominated the early 2020s. The open-source community has cheered as well: Granite 4.0’s smallest variants can even run locally in a web browser, hinting at a future where not every AI interaction requires a round trip to Big Tech’s cloud.
These developments underscore a key theme of late 2025: progress is not just about performance, but accessibility. Yes, one lab’s model might beat another by a few points on an academic test, but can it run cheaply, securely, at scale? Can a business adopt it without bleeding money or violating privacy laws? Increasingly, the “best” model is the one that’s good enough and deployable. This pragmatic streak in AI R&D is new, born of hard lessons from early deployments. We’ve learned that a model’s IQ means little if its operational costs or flaws make it unusable in practice.
Titans and True Believers
If the technology is evolving, so too is the rhetoric of its leaders. The discourse around AI has become a theatrical mix of bold promises and stark warnings, often delivered by the same people. On one end, Sam Altman remains the industry’s consummate optimist-showman (and occasional appeaser). Even as he mollifies Hollywood over deepfakes, Altman speaks in grandiose terms about where this is all headed. In a June essay, he proclaimed that AI pioneers stand “on the cusp of building digital superintelligence,” predicting the 2030s will be “wildly different from any time that has come before”. It doesn’t get more bullish than suggesting we’re about to birth a true intelligence alien to human experience. To Altman, today’s clunky chatbots and goofy image generators are merely preludes to an epochal transformation of society – one that OpenAI, of course, aims to lead.
At the other pole is Dario Amodei, CEO of Anthropic, who has by now fashioned himself as AI’s hyper-realist (some might say doomsayer). Amodei is no less ambitious about building powerful models – his company’s valuation and growth attest to that – but he has made a point of vocalizing what could go wrong. He famously predicted that AI might wipe out 50% of all entry-level white-collar jobs in the next five years, potentially spiking unemployment to Depression-era levels^7. And he’s given futurists whiplash with his vision of near-term AI: Amodei suggests that by 2027 we could have an AI “smarter than a Nobel Prize winner” in every domain, essentially “a country of geniuses in a datacenter” carrying out research at superhuman speed. To him, the question isn’t just what problems AI can solve, but what problems its very existence might create. Depending on whom you ask in Silicon Valley, Amodei is either a sober guardian trying to avert catastrophe, or a wet blanket underestimating humanity’s capacity to adapt. Either way, when the fastest-growing AI startup in history (Anthropic’s revenue rocketed from near-zero to billions in just a couple of years) has a leader preaching about existential risks, people tend to listen. Even as he sparred with rivals and regulators in 2025 – he’s railed against proposals for an AI development pause, and even called for export bans on chips to China – Amodei’s sense of urgency has been a constant^3.
Yet, for all the attention on the usual (male, Western) suspects, the chorus of voices in AI is broadening. A new generation of critics and visionaries, including women and experts from the Global South, are demanding to be heard over the din of corporate hype. Timnit Gebru, one of AI’s foremost ethical voices, described the frenzy best: “It feels like a gold rush. In fact, it is a gold rush,” she says, “and a lot of the people who are making money are not the people actually in the midst of it.” The Ethiopian-born scientist, who famously parted ways with Google over a critical research paper, has become a fierce advocate for the people routinely left out of AI’s decision-making. “AI impacts people all over the world, and they don’t get to have a say on how it should shape their lives,” Gebru observes. Her warning? Without external pressure, tech companies will never restrain themselves – “We need regulation, and we need something better than just a profit motive”. It’s a call to arms against both the unbridled profit chase and the insular groupthink of an industry too often dominated by a narrow demographic. Gebru and others ask: who will ensure AI serves humanity’s diverse interests, not just Silicon Valley’s? Who speaks for those who aren’t at the table where these systems are designed?
From the Global South, voices echo these concerns with added urgency. At an AI ethics panel in Nigeria last month, one activist bluntly described today’s AI boom as “an extractive industry in Africa”, likening foreign AI firms to 19th-century miners harvesting resources without benefiting the local community. Stéphanie Lamy, a researcher on that panel, noted how Western companies scoop up African data – whether it’s social media posts or digitized texts – to fuel their models, all while African nations see little of the profit or progress that result. The complaint is not just about exploitation but about lost potential: without investment in local talent and infrastructure, the gap between AI’s haves and have-nots will only widen. In response, a groundswell of academics and entrepreneurs across Africa, Asia, and Latin America are pushing for “AI sovereignty” – the idea that regions should control their own AI destiny, developing homegrown models attuned to local languages and needs. It’s an uphill battle when most of the requisite compute and capital sits in San Francisco, Seattle, or Beijing. But their argument lands with moral clarity: those who bear the brunt of AI’s social impacts deserve a say in its development.
Regulation: Diverging Roads
All of this ferment – the breakneck innovation, the economic disruption, the ethical outcry – converges on one central question: How should we govern AI? On this, the world’s major powers seem to be writing very different scripts. By November 2025, a patchwork of laws, guidelines, and edicts spans the globe, reflecting clashing philosophies about whether to slow down the AI train or stoke its engines faster.
In Brussels, regulators are tightening the screws. The European Union’s landmark AI Act officially entered into force last year, and though its toughest provisions won’t bite until 2026, companies are already scrambling to comply. Over the summer, EU officials finalized a “Code of Practice” for generative AI, effectively a playbook for how GPT-like models must behave under the Act’s rules. Providers are expected to document their training data, assess models for bias and risk, and even report “serious incidents” (like an AI system endangering someone’s health or rights). Requirements that seemed academic a year ago – such as disclosing energy usage or proving you scrubbed copyrighted text from your training set – are suddenly very real. The EU is trying to enforce its vision of “Trustworthy AI” through sheer regulatory weight: transparency, safety, fairness, all mandated by law. Notably, Europe is also pioneering rules against misuse: Germany is on its second attempt to criminalize non-consensual deepfakes, and EU member states broadly agree that some AI applications (social scoring, mass surveillance) should be outright banned. In effect, the EU is saying: Yes, build your AI, but do it under our watchful eye and heavy rulebook.
The pushback has been equally fierce. Forty-five European tech companies (including some homegrown AI startups like Mistral) signed an open letter this summer urging Brussels to delay the AI Act by two years, warning that the current timeline and vagueness of guidelines could smother innovation in the crib. Even Ulf Kristersson, Sweden’s Prime Minister, bluntly called the new rules “confusing” and suggested pausing the whole initiative. Their fear is palpable: with American firms charging ahead and Chinese giants amply funded, European players worry that a strict regulatory harness will make them finish last in the global AI race. It’s the classic innovation dilemma — move fast and break things or move cautiously and get left behind. The EU’s bet is that long-term trust and safety will be a competitive advantage, preventing disasters that could undermine AI’s acceptance. But in the short term, it does feel like watching a runner voluntarily don a weighted vest and ankle weights before a big sprint. An executive from a French AI startup put it starkly: “By the time we’ve ticked every compliance box, our competitors in the US or China might have already eaten our lunch.”
The United States, for better or worse, has taken the opposite tack. Washington’s approach to AI in 2025 is a patchwork of voluntary guidelines, industry pledges, and rhetorical scoldings – but no comprehensive federal law. Congressional hearings have produced more soundbites (“Who is going to die, Mr. CEO, if your AI goes wrong?” one Senator theatrically asked) than actionable statutes. The White House did roll out an “AI Bill of Rights” blueprint and, more recently, an executive order on AI safety, but these are toothless compared to Europe’s mandates. Instead, American policymakers have leaned on companies to self-regulate and compete hard. And compete they have: the U.S. leads in private AI investment by miles, and its tech giants are aggressively integrating AI across their product lines with relatively little government interference. Antitrust regulators have begun to sniff around the edges – the FTC’s Lina Khan warned Big Tech this year that there is “no AI exemption” from competition laws, vowing to ensure that “claims of innovation are not used as cover for lawbreaking”. But this is post-facto policing; it doesn’t set clear rules upfront the way the EU does. The American theory seems to be: let’s not strangle the golden goose of innovation. If something really bad happens, then we’ll act. Until then, keep those GPUs humming.
This transatlantic contrast is already playing out in product strategy. Some AI services that launched seamlessly in the U.S. are geo-fenced or feature-limited in Europe, pending legal clarity. For example, an AI dating app that can simulate a flirty chat with your favourite celebrity (using their public interviews as training data) took off in the U.S., but in Europe it’s stuck in approval limbo over consent and copyright concerns. American companies grumble that the EU is where “fun AI” goes to die, mired in bureaucracy. Europeans retort that at least their citizens won’t be unwitting guinea pigs in a giant AI experiment. As a result, two internets are emerging: one where anything goes until it’s litigated, and another where only the pre-cleared, conformity-tested AI tools are allowed into the wild.
And then there’s China, charting its own path entirely. Beijing has embraced AI with the fervor of a space race, funnelling billions into domestic LLMs and declaring its intent to become a global AI leader by 2030. By November 2025, China’s big tech firms (Baidu, Tencent, Alibaba, and newcomers like iFlytek and Zhipu) have rolled out a slew of Chinese-language models that, on pure technical metrics, are world-class. Some can hold their own with GPT-4 or Claude 2 on general knowledge – at least when discussing topics deemed safe by the censors. That caveat is key: China’s government has imposed stringent generative AI regulations of its own, but unlike Europe’s value-neutral approach, China’s rules are explicitly about ideological control. Providers must ensure their AI systems do not generate content that “subverts state power,” “undermines national unity,” or (to quote one guideline) “spreads rumours and disrupts economic or social order.” In practice, these edicts mean Chinese LLMs come pre-censored: they’ll avoid or carefully navigate politically sensitive territory, and they are trained on corpuses scrubbed of dissent. From a Western perspective, this might seem like crippling the technology’s openness. But China’s wager is that AI can be harnessed within a walled garden – delivering economic value and enhanced state capacity (in surveillance, propaganda, etc.) without loosening the Party’s grip on information. Indeed, China is pioneering uses of LLMs in governance that liberal democracies would find unpalatable: AI systems that monitor citizens’ social media for “harmful” sentiment, or that automatically write flattering news pieces about government achievements. Different rules, different goals.
Who gains the upper hand under these divergent regimes? It’s an open question, one that mixes technology and geopolitics into a spicy stew. U.S. companies currently hold the lead in cutting-edge AI research and global market share, thanks in part to their freedom to iterate quickly. But they also face a trust problem – both at home and abroad – as unregulated AI gives rise to privacy scandals and wild-west misuse. Europe, by contrast, may cultivate a reputation for “safe AI” or “ethical AI” that could become a selling point, much as Europe’s strict GDPR law made “privacy compliance” a competitive advantage for some firms. Perhaps in a few years, AI-savvy consumers and enterprises will prefer models that come with an EU seal of approval (“no, this one won’t violate your data rights or spew disinformation”). Or perhaps that’s wishful thinking, and users will just gravitate to whatever app gets the job done – even if it’s running a Chinese model unbeknownst to them. Notably, a recent industry survey found that 80% of enterprises would consider using a top-tier Chinese LLM if it significantly outperformed Western alternatives on cost or quality – geopolitical worries be damnedtypedef.ai. In other words, if Beijing produces a better mousetrap, the world might beat a path to its door despite the flags flying over it.
One thing is certain: the regulatory playing field will profoundly shape the global balance of AI power. In 2025 we are witnessing a grand experiment in real time. It’s as if three gardeners are tending three different forests: one lets it grow wild, one prunes aggressively, and one trims the trees into specific shapes. In a decade, we’ll see which forest thrives. Will America’s laissez-faire approach lead to dominant AI giants that steamroll all competition (but also occasionally set things on fire)? Will Europe’s careful cultivation yield a sustainable AI ecosystem or just stunted growth? And will China’s controlled hothouse produce the tallest tree of all, or will lack of free pollination limit its vitality? The answers may define the next era of economic and political supremacy.
Ethics and Excesses
For all the strategic jostling, a drumbeat of ethical quandaries accompanies each advance in AI capability. The latter half of 2025 has felt like a nonstop series of vignettes illustrating both the promise and peril of LLMs in our lives. Society is grappling with a technology that is at once awe-inspiring and deeply problematic – and the result is a kind of cognitive whiplash.
On one hand, we have genuine success stories. A medical AI system powered by a fine-tuned GPT-4 model is now assisting doctors in rural clinics, catching early signs of diseases that human practitioners sometimes miss. Thousands of lives may have been saved this year because an LLM noticed a pattern in a patient’s symptoms or scans that a busy doctor overlooked. In education, personalized AI tutors are finally making headway, delivering one-on-one learning experiences to children who never had access to such resources before. The AI adapts to each student, patiently re-explaining algebra in different ways until it clicks – something even the best teacher with 30 kids in a class would struggle to do. These are the quiet revolutions, the ones that don’t make headlines because they involve no scandal – just incremental improvement in human well-being. Even in the corporate world, where hype runs hottest, there are measured wins: law firms that use LLMs to instantly first-draft briefs (freeing up young associates from all-nighters doing drudge work), or logistics companies that let an AI dynamically reroute trucks to save fuel. By November, these practices have moved from pilot projects to routine operations. The productivity gains from AI are starting to register in economic data; optimistic analysts note a modest uptick in GDP growth attributable to AI-driven efficiency. It’s not quite the sci-fi utopia of zero work and endless leisure, but it’s something tangible.
And yet, for each optimistic anecdote, a counterweight of caution emerges. Those AI tutors? It turns out they occasionally slip incorrect “facts” into their lessons, confusing students unless a human corrects them. The medical diagnosis model? In a few cases it confidently recommended treatments that would have harmed patients, because it learned from erroneous data in medical literature – raising the hairs on every hospital administrator’s neck about malpractice liability. And the self-driving trucks guided by LLM reasoning? In one incident, a misinterpreted instruction caused a truck to take a bizarre detour, delaying deliveries by hours and prompting an internal investigation into why the AI hadn’t simply asked for clarification like a human dispatcher would. These are the “small” failures, the ones that don’t lead to existential risk or dystopian collapse, but which underscore a truth: AI makes mistakes – different kinds of mistakes than humans, often, but mistakes nonetheless. And when scaled across society, even small percentages of error can have big consequences.
Then there are the flashier fiascos. In September, a major U.S. newspaper had to retract an AI-assisted article after readers discovered it was full of subtly fabricated quotes – the reporter had let an LLM “polish” the draft a bit too liberally. The embarrassing episode gave ammunition to those who say AI can’t yet be trusted in journalism, or anywhere factual precision is paramount. In October, as political campaign season ramped up in several countries, doctored videos and audio clips—some produced with tools like Sora 2—spread like wildfire online. One fake video showed a leading European politician apparently confessing to corruption; it was quickly debunked, but not before millions had viewed it. A “fog of doubt” is descending, as one expert put it, where even real evidence can be dismissed as AI-generated fakerylatimes.comlatimes.com. We always worried that AI would flood the world with misinformation; now we see an even trickier outcome: AI erodes the credibility of authentic information. When anything could be fake, everything becomes suspect.
The entertainment industry continues to grapple with AI’s double-edged sword. Hollywood’s actors and writers only recently ended a strike in which AI loomed large – from the specter of “digital replicas” of actors to studios flirting with AI-generated scripts. A tense détente has been reached: there are now rules requiring consent and compensation if an actor’s likeness is reproduced by AI, and writers secured assurances that AI won’t be used to undermine their credits and pay. But outside the union contracts, the genie is out of the bottle. As Sora’s escapades showed, fan culture is diving headlong into “AI mashups” – whether the studios like it or not. Want to see a scene with Elvis Presley performing alongside a K-pop star? It’s a few prompts away. The ethical lines are blurry at best. Is it harmless creative fun, akin to fan fiction? Or is it a violation of intellectual property and human dignity? The daughter of the late Robin Williams weighed in pointedly on that front, lambasting the trend of resurrecting dead celebrities via AI as “disgusting, over-processed hot dogs made from human lives”^2. Her vivid metaphor hit a nerve in an industry caught between fascination and horror at what the tech is capable of.
Meanwhile, monopolistic behavior in AI has become a concern not just for regulators, but for the public consciousness. As of this month, the majority of all AI queries, outputs, and deployments trace back to a handful of companies – the new “AI oligopoly.” OpenAI, Anthropic, Google, and a smattering of others control the lion’s share of models and infrastructure. That concentration of power raises familiar worries about competition and innovation stagnation. We’ve seen this movie before with Big Tech, and now it’s playing out again in fast-forward. If anything, the barrier to entry in the LLM space (the need for massive data and compute) makes the moats even deeper. By some estimates, Anthropic now holds 32% of the enterprise market for AI services, recently overtaking OpenAI’s 25%, with Google around 20%. Those three alone command over three-quarters of the market. The open-source community, which once promised to democratize AI, has hit a plateau – only about 13% of enterprise AI workloads run on open models now, down from earlier in the year. Companies enjoy the flexibility of tinkering with open models, but when it comes to mission-critical uses, they tend to opt for the professional, supported products from the big players. It’s hard to blame them; no CEO got fired for choosing IBM in the mainframe era, and perhaps none will for choosing OpenAI or Google Cloud in the AI era. Yet the implications are stark: we risk entrenching a new kind of monopoly whereby the keys to advanced AI (and its economic spoils) reside in a few corporate silos. That’s why Lina Khan and her global counterparts are watching closely. If any of those giants uses their other advantages – say, dominance in cloud computing or social media platforms – to unfairly boost their AI, expect antitrust fireworks. (An ongoing lawsuit accuses one tech titan of exactly that, bundling its AI API with cloud contracts in a way that squeezed out smaller competitors; the courts will decide.)
From a bird’s-eye view, the ethical landscape is a jarring mix of progress and pitfalls. Each leap in what AI can do seems matched by a widening gap in what society is ready to handle. We have AI agents now that can execute multi-step plans, “autonomously” booking tickets or writing and sending emails on your behalf – but as one wry commentator noted, do we really want our software to have agency when we humans barely read the terms and conditions? We stand at the threshold of AI systems that can perform surveillance at an unprecedented scale: analyzing millions of camera feeds in real time, flagging “suspicious” patterns. Some cities are experimenting with these for crime prevention, even as civil liberties groups sound alarms about an Orwellian turn. And hovering over all of this is the existential question: as these models edge toward something like reasoning, even common sense in narrow domains, are we controlling them, or are they nudging us in ways we don’t fully perceive? The AI alignment and safety debates rage on, but to the average person, it boils down to a gut feeling: Are we in charge of this technology, or is it starting to dictate terms to us?
The New Normal, For Now
Perhaps the most Atlantean irony of November 2025 is how quickly the extraordinary becomes ordinary. What was bleeding-edge last month is simply expected this month. We’re already getting used to AI companions in our apps and workflows. Millions of people now start their morning asking a chatbot to summarize the news or generate a workout plan, as casually as one might have used a search engine or a smartphone app a few years ago. Creative professionals—artists, musicians, writers—who spent the past two years either fearing or deriding AI are increasingly finding ways to coexist with it. A graphic designer might use an AI tool to generate dozens of concept thumbnails, then pick the best to refine by hand. A novelist might collaborate with an AI co-writer for brainstorming plots, treating it like a slightly unhinged but imaginative assistant. The initial culture shock is wearing off; pragmatism is setting in. If the genie won’t go back in the bottle, one might as well put it to work.
Yet, just when we start to normalize things, reality throws a curveball to remind us how precarious this all is. Case in point: an AI system given administrative control over a smart building (as an experiment in automating facility management) recently caused a mini fiasco when it decided, at 3 AM, that the optimal energy-saving strategy was to shut down the heating and elevators. It wasn’t a lethal mistake, just a very annoying one for the tenants that morning – but it underscored a lesson: common sense is still not common in AI. The machine did exactly what it was told (save energy), but not what was intended (don’t freeze people and halt life). We laugh it off today and add better constraints to the system; tomorrow, a similar lapse in a more critical domain might not be so funny.
Notable real-world deployments continue to oscillate between triumph and trial. A major global bank reported that its AI-powered fraud detection prevented an estimated $100 million in fraudulent transactions this quarter – a clear win. Meanwhile, a different bank had to roll back its shiny new AI customer support agent because it kept giving “creative” (read: incorrect and nonsensical) answers to customers’ financial questions, generating more confusion than cost savings. In manufacturing, one auto company proudly unveiled an AI-managed supply chain that cut inventory costs by 30%. Another company, however, had a starkly different experience: their AI scheduling system, trained on historical data, began to inadvertently perpetuate a bias, favoring suppliers from certain countries over others for expediency, igniting a minor international PR incident when this was discovered. The system “learned” to be a little xenophobic, one might say, reflecting patterns it saw in the past orders – a reminder that AI not only inherits our biases but can amplify them in opaque ways.
And so, we find ourselves in a world where an AI can compose a decent email or diagnose a disease better than most humans, yet also one where you might get a gibberish response from your fridge or see your deceased grandmother pop up in a deepfake ad. The sublime and the ridiculous coexist. Short pithy phrases like “AI slop” gain currency to describe the flood of low-quality content out there – one tech wit called the endless stream of auto-generated clickbait and videos “Cocomelon for adults,” a disturbingly apt moniker for mindless AI entertainment. At the same time, terms like “AI miracle” are used with only a hint of sarcasm when an LLM correctly predicts a molecular structure or cracks an decades-old math conjecture (yes, these things happened this year too).
If there’s an overarching sentiment as November 2025 closes, it’s ambivalence tempered by vigilance. The hype has been tempered by a year of hard realities, but it hasn’t disappeared. We are enthralled by what our creations can do, and terrified by what they might do next. We are learning, somewhat awkwardly, to live with this technology – to neither deify it nor demonize it, but to domesticate it, like fire or the wheel, with all the requisite safety measures. We are debating furiously, regulating fitfully, innovating relentlessly.
No one can say how the story ends (or if it ends; perhaps it’s just the beginning of something altogether new). But as we survey the LLM landscape in November 2025, one thing is clear: we are deep into the era of artificial intelligence, for better and worse. The genie is hard at work, and it’s up to us – through wisdom, oversight, and yes, a bit of irony – to ensure that the wishes it grants do not become our curses. See you all again for the December 2025 update!
Endnotes – Sources Consulted
- Medium – Oct 18, 2025.
- Los Angeles Times – Oct 26, 2025.
- CMSWire – Aug 1, 2025.
- The New Yorker – Nov 3, 2025.
- The Guardian – May 22, 2023.
- Reuters – July 3, 2025.
- Utah Business – Oct 8, 2025.
- Typedef (Report) – Oct 7, 2025.
