a16z partner Tim Sullivan stated that after AI reduces content production costs, the surge of low-quality "slop" content and the ethical panic surrounding AI creation are not new phenomena; similar controversies have arisen from the decline in media costs throughout history, from 18th-century London Grub Street, cheap newspapers, popular novels, television, blogs to social media.

He believes that an oversupply of content does not automatically mean a decline in cultural quality. Historically, low-cost production has expanded both the scale of creators and audiences, generating a large amount of derivative content, and providing space for experimentation for a few new forms, new authors, and new distribution mechanisms; AI has simply accelerated this cost compression process to text, images, videos, and code.

The article cites social science perspectives indicating that "taste" is not a single aesthetic talent but may consist of various mechanisms: professional judgment in recognizing quality, the ability to access cross-domain information, experience in filtering potential successes amid uncertainty, and evaluation frameworks formed through long-term feedback.

Sullivan mentioned that research from Columbia University shows that whether a work becomes a hit is highly influenced by social factors and path dependence, making it difficult even for experts to reliably predict the final winners. Experts may be better at assessing certain clear quality dimensions but cannot fully foresee whether a work will gain network effects among specific groups, platforms, and time points.

He referenced sociologist Ron Burt's "structural hole" theory: the most innovative employees are usually not deeply embedded in a single tight-knit community but are positioned between previously disconnected groups. This "intermediary" position allows individuals to access different information, languages, and problem frameworks, thereby forming new combinations that others may not see.

AI can quickly search, summarize, and reorganize information across fields, but it is difficult to fully replicate the tacit knowledge, trust, shared experiences, and long-term relational responsibilities that humans accumulate in multiple social networks. Therefore, while AI can expand the breadth of information, it cannot automatically generate the judgment that comes from real community participation.

Sullivan believes that after the widespread adoption of AI, the high-value roles of humans will lean more towards verification, selection, and judgment; these abilities rely on apprenticeship, repeated feedback, and professional practice. If companies reduce entry-level positions and training processes to save costs with AI, there may be a future talent gap in mature judgment capable of reviewing AI outputs.

In market mechanisms, AI will lower the prices of initial drafts, basic designs, and routine analyses, but will raise the value of credible filtering, editorial responsibility, domain validation, community connections, and final decision-making. Beneficiaries include organizations with high-quality feedback systems, real user networks, professional review mechanisms, and apprenticeship training capabilities; the pressured parties are content producers relying solely on bulk generation, lacking verification standards and brand trust.

Source: Public Information