Beyond AI Moral Panic: How Social Science Deconstructs 'Taste' and Human Judgment

Beyond AI Moral Panic: How Social Science Deconstructs 'Taste' and Human Judgment Amid debates over AI-generated 'slop' and claims that taste is a unique human edge, social science research suggests taste is not an innate aesthetic intuition or simple hit prediction, but a byproduct of social dynamics, network brokerage, and apprenticeship infrastructure. Cheap media production and resulting moral panics are historical patterns previously seen with Grub Street, pulp fiction, television, and social media. Duncan Watts and Matthew Salganik's music lab experiment demonstrated that breakout success is dictated by social influence and path dependence rather than quality alone. Ron Burt's sociological research on brokerage reveals that innovative judgment often stems from occupying 'structural holes' between disconnected networks. Economist Christian Catalini identifies verification as the primary human role alongside AI, raising concerns about how workers cultivate judgment without entry-level apprenticeship scaffolding. The History of Slop and the Refuge of Taste In the 18th century, London's Grub Street relied on impoverished writers like Samuel Boyse to mass-produce cheap doggerel, pamphlets, and periodicals, provoking widespread moral outrage. AI-generated content represents the latest iteration of this recurring cycle, seen previously with the penny press, television, and blogs. Whenever technology lowers execution costs, output surges, critics decry cultural decline, and cognoscenti invoke 'taste' as a refuge. If taste merely means following consistent, rule-based principles, pattern-matching AI can replicate it; if it is not reducible to rules, treating it as simple preference fails to explain reliable human judgment. Taste Is Not Prediction Sociological research highlights why taste cannot simply mean predicting commercial hits. In Columbia University's early 2000s 'music lab' experiments led by Duncan Watts and Matthew Salganik, thousands of listeners evaluated songs under varied social exposure. When social information was hidden, song downloads remained evenly distributed; when prior download numbers were displayed or manipulated, outcomes diverged radically, turning the same song into a hit in one scenario and a failure in another. Beyond a baseline quality threshold, breakout success is driven by path dependence and social influence, rendering top hits unpredictable even to domain experts. Structural Holes and Tacit Network Position Sociologist Ron Burt’s study of organizational networks demonstrates that top innovators are rarely isolated aesthetic geniuses; instead, they act as 'brokers' occupying 'structural holes' between disconnected groups. By bridging distinct communities, brokers acquire novel perspectives and recombine disparate ideas. While large language models theoretically have simultaneous access to public digital networks, they lack the tacit, embodied participation and trust built by humans navigating undocumented subcultures and informal spaces. The Scaffolding of Apprenticeship and Judgment Human judgment is developed over time through apprenticeship, mentorship, and repeated feedback. Although companies may seek short-term savings by replacing junior roles with AI, cutting entry-level jobs risks a tragedy of the commons—akin to clear-cutting saplings, which leaves no mature trees for the future. Christian Catalini notes that the human role increasingly focuses on verification. The author argues that firms must retain junior scaffolding by redirecting entry-level staff to supervised error-checking and correction of AI drafts, preserving the environment that cultivates taste.

超越垃圾内容与AI道德恐慌：社会学视角下的“品味”与人类判断力 面对AI生成内容引发的道德恐慌与“品味”优越论，社会科学研究表明，廉价媒体泛滥与品味崇拜在历史上屡见不鲜。“品味”并非神秘的先天特质或单纯的预测能力，而是网络结构位置与后天培养体系的产物；在AI降低制作门槛后，真正面临稀缺的是培养人类判断力的社会基础设施。 廉价内容泛滥与对“品味”的道德恐慌并非新鲜事，历史上从18世纪伦敦格鲁布街到廉价小说、电视和社交媒体都曾出现类似技术扩散引发的批判。 邓肯·瓦茨和马特·萨尔加尼克的“音乐实验室”实验表明，爆款主要由社会影响和路径依赖塑造，专家只能筛选劣质品，品味不等于预测成功的能力。 罗恩·伯特的“经纪”理论指出，创新源于跨越不连通群体的“结构洞”位置；品味体现为置身多个社会网络所积累的具身、未被文档化的默会知识，而非纯粹的规则匹配。 品味与判断力依赖学徒制和反馈反复打磨，企业若因AI自动化完全砍掉初级岗位，将陷入砍伐树苗导致未来缺乏资深人才的“公地悲剧”。 垃圾内容与品味恐慌的历史轮回 18世纪伦敦的格鲁布街曾充斥着大量廉价打油诗和诽谤小册子，文人如塞缪尔·博伊斯靠廉价文字勉强度日并被正统批评家贬低。历史上每当技术大幅降低媒介生产成本——从便士报、通俗小说到电视和博客，廉价衍生内容便会激增，文化界也随之陷入道德恐慌。自诩有见识的群体往往将“品味”作为抵御工具，但若品味仅是可发现的规则匹配，现代AI早已擅长此类模式识别；若品味是主观偏好，则无法解释为何部分人能持续发掘优秀事物。 爆款的社会偶然性与预测局限 社会学家萨尔加尼克与瓦茨的音乐实验室实验发现，当隐去社交下载数据时，各歌曲下载量分布均匀；一旦展示他人选择，哪怕人为篡改排名，从众效应也会让同一首歌在不同实验组产生天壤之别的结果。在作品质量达到基本门槛后，爆款的诞生高度受社会影响和路径依赖支配，即便领域专家也无法稳定预测超级爆款。AI带来的海量内容使“达标”候选者激增，在质量难以单独决定胜负的背景下，品味绝不等于预测成功的能力。 作为网络结构优势的“经纪”角色 罗恩·伯特关于组织网络的研究表明，最具创新力的员工往往占据“结构洞”，即跨越彼此孤立群体的中介位置。这种“经纪”优势使他们能重组不同圈子的信息与视角，展现出对新颖事物的敏锐触觉。虽然大语言模型能够获取海量公开数据并完成浅层跨领域检索，但人类经纪者的核心优势在于真实参与多个社群，掌握亚文化、非正式惯例与闭门讨论等未被录入语料库的具身化默会知识。 学徒制支架重塑与判断力培养危机 判断力和品味并非天赋，而是依靠在初级任务中接受资深人士审查与反馈逐步沉淀的产物。经济学家克里斯蒂安·卡塔利尼指出，AI时代人类的核心职责在于验证与纠偏。如果企业为短期削减成本而彻底取消初级岗位，犹如砍伐所有树苗，未来将面临资深判断力断层的“公地悲剧”。健康的模式是将初级职责调整为在监督下审查和纠正AI草稿，以维系判断力培养的成长支架。AI时代稀缺的不是抽象的品味，而是培育具备品味之人的社会基础设施。

*Editor’s note: This week, we’re continuing our series on navigating the AI writing discourse. Last week we shared [this piece](https://a16zcrypto.substack.com/p/the-habits-of-ai-writing-and-what?r=1egx10) on writing with AI for founders and others who communicate often.* *Since many in crypto once marketed on “vibes”, we tried bringing rigor to the practice of [marketing for builders](https://www.youtube.com/watch?v=VqOpU2_ZPp0). Here, we use social science research to navigate an otherwise subjective topic.* In the early 18th century, if you lived in London and you wanted to slander an opponent or rival, you could head to Grub Street, a slum just north of the city named for a garbage ditch. Amid the narrow lanes teeming with hack writers and failed scribblers, you might find someone like Samuel Boyse — a drunken Irish poet who had fled Edinburgh because of his debts. His specialty, doggerel. He would happily compose you some for a shilling or two. If you had deeper pockets, he could even gin up a pamphlet that really laid into your target. Boyse lived in a garret. He struggled with alcoholism. Once he found himself in such dire straits that he was forced to pawn the shirt off his back to buy the paper necessary for his craft. (Ah, the life of the writer!) Boyse’s situation was not unique — a writer as highly regarded as Samuel Johnson is alleged to have spent some time on Grub Street — although Boyse was one of the more prolific practitioners of his craft. Grub Street produced mountains of what we today might call “slop”: single verses, pamphlets, periodicals, newspapers — all for sale in both its production and consumption. Inevitably, perhaps, this drew comparison of such writers to strumpets, and critics equated writing for pamphleteers to whoring. No respectable person would take part in such dreadful works … except that lots of respectable people did, even while decrying it to their equally titillated friends. What I’m saying is, slop is hardly a new phenomenon. Neither is the moral panic around it. AI-generated content is the latest iteration of an age-old trend: Whenever technology dramatically lowers the cost of producing media, it unleashes a flood of cheap, derivative work — and a chorus of critics convinced that culture has reached a new low. After Grub Street came the penny press, then pulp fiction, television (a “vast wasteland” if you’re feeling fancy, or, more colloquially, the boob tube), followed by blogging, social media, and now AI. Whenever technology lowers the cost of execution, the masses jump in. Each time, criticism comes hot and fast. And each time, the answer from the self-proclaimed cognoscenti is obvious: Claims to taste become their refuge, and taste achieves apotheosis. While AI may differ in degree and perhaps even in kind, the cultural response to falling production costs has recognizable historical predecessors. But this immediately raises the question many are debating: What exactly is *taste*? If taste consists of stable criteria that experts consistently apply, then a modern AI can almost certainly parrot taste. A model trained on enough examples of Bauhaus design can infer the principles that make an object feel Bauhaus. A model trained on a literary agent’s acquisitions can approximate that agent’s editorial preferences. Taste as the consistent application of discoverable rules is precisely the kind of pattern-recognition problem modern AI excels at. If, on the other hand, “taste” isn’t reducible to stable criteria, then we’ve been using the word far too casually. The idea that taste simply means knowing what’s “good” borders on the tautological, while reducing it to personal preference — *I like this; I don’t like that* — is too subjective to explain why some people repeatedly identify promising ideas, products, or creators before everyone else. So what are we really talking about when we say someone has taste? Prediction? Judgment? Social position? Experience? Luck? “Taste” is sometimes all of these: it’s a folk category into which we’ve collapsed separate mechanisms. AI forces us to disaggregate them. Drawing on social science can [help](https://www.bloomberg.com/news/articles/2025-11-21/which-social-psychology-theories-survived-the-replication-crisis), not in giving us a single theory of taste but in teasing disparate ideas apart. Once we identify the separate strands, the question shifts from whether machines have taste to which mechanisms that produce good human judgment remain scarce when production becomes cheap. One body of research may help illuminate how songs become hits. In the early 2000s, Columbia University sociologists Duncan Watts and Matt Salganik, together with a team of researchers, created an artificial online “[music lab](https://www.princeton.edu/~mjs3/musiclab.shtml).” Thousands of participants were invited to listen to and download songs by unknown bands. The researchers divided listeners into eight separate groups, each with different conditions governing how the songs were presented. In some, participants could see how many times each song had already been downloaded; in others, they could not. The team also manipulated the information in some “worlds” (what they called their experimental conditions), showing, for instance, a particular song at #1 when it was not. The larger lesson is that when social information was hidden, downloads were evenly distributed. But when people could see what others were choosing, outcomes diverged. In these manipulated conditions, a song that became a runaway hit in one world performed poorly in another. Once quality clears a basic threshold, success is heavily shaped by social influence and path dependence. This makes it almost impossible, even for informed observers, to predict in advance which products will rise to the very top. Put another way, experts in a domain — TV executives, for example — are pretty good at filtering out obvious failures before they happen. But they cannot reliably predict breakout successes among the remaining strong candidates. Or as Salganik has said, the success of *Harry Potter* was a fluke. Success is not necessarily dictated by quality alone. Therefore, taste cannot simply mean predicting success. Another possibility is that what we call taste isn’t prediction at all. Maybe it’s the function of a particular social position: standing somewhere that gives you a unique vantage point to see how the world could fit together, or how ideas and products could translate from one arena to another. This is why some of the most innovative employees in companies tend to be the ones most connected to different groups — or that’s what Ron Burt, another sociologist, found. Burt calls this “brokerage.” Brokerage isn’t the clairvoyance necessary to pick winners but a [structural position](https://www.jstor.org/stable/j.ctv1kz4h78) that generates novel recombinations. Burt’s conclusions grew out of decades of research mapping social networks inside organizations. Rather than focusing on individual brilliance or expertise, he asked how people’s positions within those networks affected their performance. First, Burt reconstructed who communicated with whom. He then compared those patterns with measures like promotion, compensation, and managers’ evaluations. He found that the most innovative employees were rarely embedded in a single, tightly knit community. Instead, innovators (might we call them those with taste?) occupied “structural holes”: the gaps between otherwise disconnected groups. Because they moved in different circles, brokers encountered information that others didn’t and, even more importantly, saw problems from multiple perspectives. Their position allowed them to recombine ideas that no one network could have produced on its own. Here, innovation isn’t a matter of exceptional intelligence but of extraordinary position. This suggests that taste may not be an aesthetic faculty but rather a structural advantage. We often imagine taste as residing inside someone’s head — as a refined sensibility or intuitive aesthetic judgment. Burt suggests that what looks like taste may instead arise from where someone sits in a social network. The people who seem to have an uncanny feel for what’s interesting, original, or promising are often those who spend their lives moving between otherwise disconnected social worlds. Their “taste” emerges not from mysterious intuition but from repeated exposure to combinations that no one else is able to see. An LLM trained on, well, everything has, in a sense, access to all the networks simultaneously — so if structural holes were purely about information access, AI might excel. But the broker’s advantage comes from living inside multiple social worlds, with the embodied, relational, contextual knowledge that comes from their network position. We might say the broker’s advantage is tacit. Brokers don’t merely *possess* information from multiple communities; they actively *participate* in multiple communities. They accumulate tacit knowledge, trust, and experience, and respond to novel incentives in ways that LLMs simply can’t. You can’t simulate being an outsider-insider from training data alone. That’s a defensible human edge — for now, at least. Granted, Burt’s work is about organizational careers and competitive strategy, not cultural products. But perhaps what we call taste in other domains may be structurally equivalent to Burt’s brokerage — the advantage of the person who’s genuinely of multiple worlds. So where can we find human judgment? We create it. LLMs can perform the tasks often associated with entry-level roles: drawing up contracts like a junior law associate, performing basic financial analysis like a junior consultant, coding like an engineer, and so on. It’s been suggested that this may allow organizations to slash costs and increase output by replacing those jobs with an LLM. It also grants expertise to the common person: No longer do you have to rely on Big Law and its hourly rates for basic (or sometimes even complex) tasks. The human role in all of this, argues economist [Christian Catalini](https://arxiv.org/abs/2602.20946), is *verification*: making sure the LLM’s output didn’t drift too far. Humans check. Humans correct. Humans employ taste? But this opens up the question of where humans *learn* taste. Good (human) evaluators develop their abilities with apprenticeship, feedback, and exposure. Back in the day, when I was a junior editor at Princeton University Press, I used to have to photocopy the book manuscripts that my boss had edited with his green fountain pen before I mailed them to the author. We needed a copy for reference and also in case the original was lost. While the copy machine churned away, I took the time to review his edits, read his queries, understand both his green-inked penmanship (which was a nontransferable job-specific skill that hasn’t helped later in my career but was, at the time, crucial, especially because few others — including the authors — could decipher his writing) and, more importantly, his editorial style. This informed my own editing (which my mentor reviewed in turn) and created the foundation from which I still work. (This experience also points to another often underappreciated truism: Jobs can be [messy](https://www.siliconcontinent.com/p/what-do-consultants-get-paid-for).) In other words: At least one element of what we call taste — judgment — is a kind of discrimination one accumulates and refines over time. It is not innate. If we are to continue to create humans with taste, we have to create spaces where they can develop it. The obvious objection is that firms investing in apprenticeships may lose in the short term to competitors who aggressively automate. Maybe! But the tradeoff resembles overcutting a forest: Chopping down every sapling may improve one quarter’s balance sheet, but it also ensures that you’ll have no mature trees later on. And of course it’s incumbent on anyone looking for a job to make sure they train themselves in the absence of those roles. Pushing each of these three findings toward some kind of practice, none of them break — but I don’t think any of them means quite what the obvious reading suggests, either. Take the socially contingent hit first. Salganik’s conclusion doesn’t change: Success still isn’t dictated by quality alone, so taste still can’t simply mean predicting success. What AI adds is volume — more candidates clearing the “good enough” bar than any one curator can sort through. In other words, TV execs still filter the same way. It just means there are a lot more shots on goal, and quality alone determines even less of which shots land. Success depends on luck — or, more precisely, social or network position — in this environment more than ever. Whichever candidates already have an audience are the ones most likely to catch a wave before the field crowds them out. Brokerage holds up better. An LLM can already do the cheap half of brokerage — surfacing what’s happening in an adjacent field to anyone who asks, which used to require actually knowing someone in that field. (Someone does still need to know to ask the right questions.) The structural hole that really matters isn’t “knows two worlds” in the informational sense. It’s *inhabiting* worlds that haven’t been written down somewhere an LLM could ingest them: subcultures, informal practice, arguments that happen in a room and never make it onto the record, and so on. If brokerage keeps paying off, it will be because someone seeks out exactly that kind of undocumented and undocumentable territory. Apprenticeship is the one where the standard advice — protect it — holds up. The apprenticeship model assumed junior people learned by doing grunt work under a senior person’s mentorship, the way I learned editing by photocopying my mentor’s marked-up pages and then having him review my own work, once I was allowed to start editing on my own. If AI now does that grunt work, the easy conclusion is that the apprenticeship disappears along with the job that trained it. This may be backward: The process that mattered was “someone catches what you got wrong, repeatedly, until you stop getting it wrong.” That can survive if the junior person’s job becomes checking and correcting the AI’s draft under supervision, rather than drafting something from scratch themselves (although I’d argue that can be invaluable). What changes isn’t whether firms keep junior humans around but rather what those humans spend their day doing. Firms that simply dispense with entry-level roles will tear out a lot of scaffolding with it. Firms that redirect entry-level roles toward supervised verification can maintain their scaffolding while still capturing some savings. (There is a [Tragedy of the Commons](https://en.wikipedia.org/wiki/Tragedy_of_the_commons) possibility here, though, where *all* the firms dispense with junior jobs and simply lack senior judgment across the board later on, like the timber company that harvests all the young trees.) \*\*\* Boyse pawned his shirt for paper and died on Grub Street. Samuel Johnson, who allegedly walked the same streets, didn’t stay. Not because he had better taste but perhaps because he had better scaffolding. In the debate over AI, “taste” has become a kind of totem, invoked when someone wants to identify something humans can do but machines can’t. But taste doesn’t have to be mysterious. It is what good judgment looks like after it has been shaped by environments that reward experimentation, networks that expose us to novel combinations, and institutions that cultivate discrimination. In the age of AI, the scarce resource isn’t taste. What threatens to become scarce is the social infrastructure that produces the people we describe as having it.