AI is too expensive

AI pricing, subsidies, and the coming “switch”

• Many see current prices as artificially low, with a later “switch” to higher pricing once dependence grows.
• Others argue token prices have already fallen dramatically and will keep dropping due to hardware and efficiency gains, even if flagship models stay pricey.
• Some predict per-user costs stabilizing around a mobile-phone-bill level; others think that would be too expensive for broad use.

Profitability, bubble risk, and hyperscaler capex

• Strong disagreement on whether current AI spend is sustainable.
• One side argues no major pure-play AI firm is truly profitable, capex is enormous, and returns won’t justify the trillions being invested.
• The other side notes AI revenue growth is “exploding,” capacity is constrained, and big cloud providers are pouring nearly all profits into AI infrastructure, suggesting substantial demand.
• Several posters separate “AI is valuable” from “AI justifies today’s investment levels.”

Open, local, and Chinese model competition

• Cheaper Chinese and open-weight models are highlighted as serious competition; some claim healthy margins even at much lower prices.
• Skeptics question whether those providers will keep prices low and whether state backing really removes profit pressure.
• Many expect a shift from frontier models to smaller/cheaper ones once customers see real bills and investors demand profit.

Use cases and real-world impact

• Clear value reported for coding help, boilerplate writing, KYC/data entry, and other narrow business processes.
• Opinions diverge on “agents”: some see them as mostly chatbots plus APIs; others think they already do meaningful work in specific workflows.
• Some users gladly pay personally for API access; others refuse to subsidize employers or feel AI removes the joy of programming.

Lock-in, advertising, and influence

• Widespread fear of future lock-in: once workflows depend on AI, providers can hike prices or inject undisclosed ads and influence into outputs.
• Some argue this can be mitigated if people are willing to pay directly for transparent, ad-free services.

Analogies and macro risk

• Uber is used both as a cautionary and optimistic analogy: subsidized early, later profitable at higher but acceptable prices—though critics say AI’s economics are very different.
• Others compare AI build-out to dotcom dark fiber or early mainframe games: even if a bubble bursts, the infrastructure and techniques may yield long-term value.