{"slug": "when-ai-gets-a-pass-the-rise-of-ai-exceptionalism", "title": "When AI gets a pass: the rise of 'AI Exceptionalism'", "summary": "A growing double standard known as 'AI Exceptionalism' allows people to apply different ethical rules to AI depending on whether it helps or threatens them, with journalists criticizing AI writing while embracing AI coding tools, and companies like OpenAI accusing rivals of theft while defending their own use of copyrighted data.", "body_md": "*“AI is unethical, unless it helps me.”*\n\nThat, increasingly, seems to be the guiding principle of the AI era. Not artificial intelligence. Not artificial general intelligence. Just… **artificial exceptions**.\n\nThere’s a growing tendency to apply one ethical standard when AI threatens our profession, our company or our livelihood, and a completely different standard when it happens to benefit us.\n\nCall it **AI Exceptionalism**.\n\nIt’s the belief that **AI should follow different rules depending on who is using it.**\n\nTo be clear, this isn’t an argument that all uses of AI are equally good. Nor is it an argument that copyright doesn’t matter, or that artists, writers, actors and educators don’t have legitimate concerns.\n\nThey absolutely do.\n\nBut if we’re going to have an honest conversation about AI, we should probably try applying the same principles consistently.\n\nHere are four examples where AI consistency disappears remarkably quickly.\n\n### On this page\n\n[AI shouldn’t write articles, but it can absolutely write code](#ai-writing-code)[Training on copyrighted books is fair use, but training on our AI is theft](#ai-training)[AI shouldn’t replace actors, but replacing everyone else seems negotiable](#ai-actors)[Students shouldn’t use AI, but universities definitely should](#ai-universities)[The Uncomfortable Question](#conclusion)\n\n## AI shouldn’t write articles, but it can absolutely write code\n\nSome of the strongest criticism of generative AI has come from journalists themselves.\n\nTake **Kara Swisher**. She has repeatedly warned about generative AI’s impact on journalism, describing concerns around misinformation, media quality and the erosion of trust. Yet she’s also spoken enthusiastically about AI’s potential in software development and has described AI coding tools as one of the genuinely transformative applications of the technology.\n\nOr consider **Kevin Roose** of *The New York Times*. Roose has written extensively about the dangers AI poses to journalism and the creative industries, while also documenting his own experiences using AI programming assistants and exploring how dramatically they can improve developer productivity.\n\nTechnology journalist **Casey Newton** has similarly argued that [AI presents profound challenges for writers and publishers](https://platformer.substack.com/p/what-should-newsrooms-do-with-ai), while frequently highlighting rapid advances in AI-assisted software engineering and discussing the remarkable capabilities of coding models.\n\nNone of these journalists are necessarily contradicting themselves. They may genuinely believe there are important differences between writing news articles and writing software. But those differences are rarely articulated.\n\nInstead, an interesting pattern emerges.\n\n- AI writing is often discussed as replacing skilled creative professionals.\n- AI coding is often discussed as augmenting skilled creative professionals.\n\nWhich raises an awkward question:\n\n**Why is writing code fundamentally different from writing prose?**\n\nBoth require creativity, experience, judgement and years of practice. Both are professional crafts. Both are capable of being partially automated. If AI assistance is acceptable because it makes programmers more productive, why isn’t the same argument valid for journalists?\n\nOr, if AI-generated writing undermines professional creativity, shouldn’t AI-generated code raise exactly the same concerns?\n\nPerhaps there *is* a meaningful distinction. If so, it’s a conversation worth having explicitly.\n\nBecause otherwise, to an outside observer,[ it can look suspiciously like AI is judged less by what it does and more by ]**whose profession it affects**.\n\n## Training on copyrighted books is fair use, but training on our AI is theft\n\nThis might be the clearest example of AI Exceptionalism currently playing out.\n\nIn early 2025, [OpenAI publicly accused DeepSeek](https://www.latimes.com/business/story/2026-02-13/openai-accuses-chinas-deepseek-of-stealing-ai-technology) of improperly distilling OpenAI models into competing systems.\n\nAnthropic has since [made similar allegations against DeepSeek, Alibaba and other Chinese AI labs](https://techcrunch.com/2026/02/23/anthropic-accuses-chinese-ai-labs-of-mining-claude-as-us-debates-ai-chip-exports/), claiming they created fake accounts to extract Claude’s behaviour at scale.\n\nBoth companies argue that model distillation unfairly copies years of research and billions of dollars of investment.\n\nThat’s a perfectly understandable position. Except…\n\nThose same companies continue to argue in court that training frontier AI models on enormous collections of copyrighted books, newspapers, websites, photographs and source code is legally permissible — often under the doctrine of fair use. Those arguments sit at the heart of ongoing copyright lawsuits brought by authors, publishers and creators.\n\nSo we end up with two remarkably similar statements.\n\n- Learning from millions of human-created works without permission?\n**Innovation**. - Learning from an AI model without permission?\n**Theft**.\n\nOf course, OpenAI and Anthropic argue there are important legal and technical differences. Model weights are proprietary. Distillation reproduces unique capabilities. Training data is transformed rather than copied.\n\nThose arguments may ultimately succeed in court. But to many observers, the optics are difficult to ignore.\n[\nWhen someone learns from ]*your* work, it’s innovation.\n\nWhen someone learns from *my* work, call the lawyers.\n\n## AI shouldn’t replace actors, but replacing everyone else seems negotiable\n\nThe 2023 Hollywood strikes brought AI into the mainstream. Actors raised genuine concerns about digital doubles, voice cloning and synthetic performances, eventually leading to new protections in the [SAG-AFTRA agreement](https://www.sagaftra.org/contracts-industry-resources/member-resources/artificial-intelligence) covering consent and compensation for AI replicas.\n\nThose concerns weren’t hypothetical. Studios were actively exploring them. That’s a good thing. But something else went largely unmentioned.\n\nHollywood has enthusiastically embraced AI everywhere else.\n\nAI-assisted rotoscoping, de-aging, background replacement, facial cleanup, animation workflows and visual effects are increasingly reducing work traditionally performed by VFX artists, compositors and junior production staff.\n\nThe ethical question becomes interesting.\n\nIf AI replacing actors is unacceptable because it threatens creative workers, why isn’t the same concern raised with equal force for everyone else working on the film?\n\nTo be fair, SAG-AFTRA exists to represent actors - not visual effects artists. That’s literally its job. But from the outside, the broader ethical principle can start to look selective.\n\n[\n“My profession deserves protection.”]\n\n“Yours is… more complicated.”\n\n## Students shouldn’t use AI, but universities definitely should\n\nThis one may feel familiar to anyone currently studying.\n\nMany universities now prohibit or heavily restrict students from using ChatGPT, Claude or Gemini in assessments while simultaneously publishing AI strategies encouraging staff to embrace generative AI for administration, teaching support and operational efficiency.\n\nUniversities including Oxford, Cambridge, Harvard and countless others have all published guidance drawing this distinction in different ways.\n\nThe reasoning is understandable. Universities exist to assess learning. If AI completes the assignment, what exactly is being evaluated?\n\nBut walk a little further across campus. Universities increasingly use AI themselves.\n\nAdmissions. Student support. Administrative automation. Research assistance. Marking support. Academic integrity systems. Email drafting. Scheduling. Operational planning.\n\nIn other words:\n\n- Students are told AI undermines learning.\n- Staff are told AI improves productivity.\n\nAgain, perhaps there really is a meaningful distinction. Assessment and administration aren’t the same thing.\n\nBut students naturally ask the obvious question.\n\n**If AI makes professors more productive, why shouldn’t it help students become more productive too?**\n\nThe answer might be “because learning matters.” That’s a perfectly reasonable position.\n\n[But it’s a conversation worth having openly — not one resolved by simply declaring one use virtuous and the other forbidden.]\n\n## The Uncomfortable Question\n\nNotice something? Every example follows the same pattern.\n\n- When AI threatens\n**my** profession, it’s unethical. - When AI benefits\n**my** profession, it’s innovation.\n\nIt’s remarkably human. We all do it.\n\nDoctors worry about AI diagnosing patients. Lawyers worry about AI drafting contracts. Programmers worry about AI replacing developers. Designers worry about AI generating artwork. Journalists worry about AI writing articles.\n\nEveryone becomes an AI ethicist the moment AI starts competing with *their* job.\n\n**Maybe the real conversation isn’t about AI.** Perhaps it’s about incentives. Humans have always been remarkably good at discovering ethical principles that just happen to align with their own interests.\n\nAI hasn’t changed that. It’s simply made it much easier to notice.\n\nBecause whether you’re a journalist, a Hollywood studio, an AI company or a university, the exceptions tend to appear at exactly the point where AI becomes personally useful.\n\nSo what should the rule be? Should AI be allowed everywhere? Restricted everywhere? Licensed? Compensated? Transparent?\n\nPerhaps.\n\nBut whatever principles we choose, they should probably apply consistently. Otherwise we aren’t really debating AI ethics.\n\nWe’re debating **who gets to benefit from AI — and who doesn’t.**\n\nThat’s not AI ethics. That’s AI Exceptionalism.\n\nAnd if there’s one thing humans have always excelled at, it’s believing that **the rules are different when they apply to us.**", "url": "https://wpnews.pro/news/when-ai-gets-a-pass-the-rise-of-ai-exceptionalism", "canonical_source": "https://www.magiclasso.co/insights/ai-exceptionalism/", "published_at": "2026-07-15 02:01:24+00:00", "updated_at": "2026-07-15 02:17:52.166187+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-ethics", "ai-policy"], "entities": ["Kara Swisher", "Kevin Roose", "Casey Newton", "The New York Times", "OpenAI", "DeepSeek"], "alternates": {"html": "https://wpnews.pro/news/when-ai-gets-a-pass-the-rise-of-ai-exceptionalism", "markdown": "https://wpnews.pro/news/when-ai-gets-a-pass-the-rise-of-ai-exceptionalism.md", "text": "https://wpnews.pro/news/when-ai-gets-a-pass-the-rise-of-ai-exceptionalism.txt", "jsonld": "https://wpnews.pro/news/when-ai-gets-a-pass-the-rise-of-ai-exceptionalism.jsonld"}}