Last gasps of the rent seeking class?
Claims that open-source AI and cheap local inference will topple “rent-seeking” tech and finance elites meet widespread skepticism. Commenters argue that AI tools will likely amplify existing power asymmetries, as corporations can deploy far more compute, data, and regulatory influence than individuals, turning “your bot vs their bot” into another arms race. The thread also underscores confusion between economic concepts like rent-seeking, moats, and free markets, with many expecting AI to change the form—not the existence—of monopoly power and friction-based business models.
Rent Seeking, Free Markets, and Enclosure
- Many argue “rent seeking” in the article is misused; it should mean extracting economic rent via regulation and enclosures, not just “business models I dislike.”
- Others use a broader, more colloquial sense: any pursuit of monopoly‑like rents or moats, especially via friction, subscriptions, and platform lock‑in.
- Several comments link modern rent extraction to historical enclosure (turning commons into private property), with IP framed as a new enclosure of ideas.
- Disagreement over whether what we have is a “free market,” a “capitalist market,” or a heavily captured system shaped by lobbying and policy.
AI, LLMs, and Local vs Cloud Inference
- Some see open‑weight models and cheap local hardware as a serious challenge to centralized, token‑priced AI APIs.
- Others think centralization will continue: big players will train larger, proprietary models tied to their chips and clouds; the rent‑seeking just moves to LLM access.
- Skeptics note that running powerful models at home requires money, hardware, and electricity many people lack; most will end up on paid subscriptions.
Agentic Commerce and Marketplaces
- Optimistic view: AI “agentic commerce” could bypass rent‑taking marketplaces by going directly to sellers, doing comparison shopping and due‑diligence.
- Counter‑view: you just swap Amazon’s 15–20% cut for an AI platform’s cut, affiliate fees, or hidden “marketplaces” inside the model.
- Concerns that LLMs are bad at judging trust; trust and logistics (warehouses, delivery) remain strong moats for incumbents.
Consumers vs Corporations in an AI World
- One camp: AI equalizes time; if both sides use AI, it becomes too expensive for companies to weaponize friction and call centers.
- Opposing camp: corporations will deploy more and better AI, tuned at scale on millions of interactions; it becomes “your bots vs theirs,” and they still win.
- Expectation that AI will often exacerbate asymmetries, not flatten them.
Self‑Driving Cars and Broader Automation
- Some argue self‑driving is necessary given aging populations, labor shortages, and safety gains.
- Others see it as a way to replace workers so value shifts from drivers to tech firms, mirroring broader automation‑driven concentration of wealth.
Moats, Platforms, and SaaS
- View that the durable moats will be at the application layer: distribution, network effects, proprietary data, “systems of record,” and perceived stability.
- Even with democratized model access, large platforms can still dominate via scale, marketing, and integration.
Optimism vs Pessimism about Collapse and Democracy of Tech
- Optimists cite past tech waves (web, YouTube, smartphones) reducing barriers and creating more creators and builders.
- Pessimists reply that power merely re‑concentrates in new gatekeepers, and that calls for a US economic “collapse” are reckless given global interdependence.