Vamos a pagar por IA igual que pagamos por el coche

Vamos a pagar por IA igual que pagamos por el coche

We are going to pay for AI just like we pay for the car

by Jon Hernández · View the channel on PSI

Humanities, Social Sciences & Thought Economics & Finance KCVEconomics of specific sectorsKCVMDigital or internet economics
🎙 Jon Hernández 👥 808K 📅 September 25, 2026 ⏱ 11 min 👁 42 📄 opinion expert 🧭 2026-09-25
Available in: English (current) Français

Keywords

AI pricingJevons paradoxlocal AItokenscompetitive advantage

Summary

The video discusses the evolving cost landscape of artificial intelligence, arguing that although the per-task cost of frontier AI has plummeted dramatically (citing a specific benchmark example from $4,500 to less than $0.02), personal and enterprise expenditures are rising due to increased usage driven by the Jevons paradox. The creator, Jon Hernández, explains that cheaper AI enables more applications, leading to higher token consumption and larger total bills. He highlights new models from OpenAI (e.g., GPT-6 Luna and Sol) that are cheaper but not superior, allowing providers to offer more usage within subscriptions. He also explores the potential of free local AI models, noting that current mobile phones can run models equivalent to GPT-4, and predicts that within two years, today’s cutting-edge models will run locally at no cost. However, he questions whether local AI can keep pace with competitive pressures from entities using frontier models. He concludes that AI costs will become a regular monthly expense similar to car payments or mortgages, and warns that companies will likely pay based on value derived.

174 words

Critical Evaluation

Value of the Information & Strength of the Argument

The value lies in clearly articulating the economic paradox of falling unit costs but rising total spend, supported by a striking numerical example. The argumentation is coherent and accessible, using the Jevons paradox analogy (gasoline) effectively. However, the reasoning is largely anecdotal and relies on unverified data from memory, without referencing specific industry reports or scientific studies. The discussion of local AI feasibility is plausible but speculative, lacking technical depth on hardware constraints or model efficiency.

Scientific Rigor, Source Quality, Title Accuracy

The video cites no direct scientific sources; the description contains only promotional links to the author’s products and services. The claims about model pricing and benchmark costs are presented as facts but without citations, undermining scientific rigor. The title aligns well with the content, which consistently frames AI as a future household expense comparable to a car payment. No comments were provided, so no audience trends can be assessed.

160 words

Title / Content Match

The title is metaphorical and directly relates to the central thesis that AI will become a recurring personal expense, consistent with the video's content.

Quality & Reliability

6/10

The video presents specific quantitative claims (e.g., cost drop from $4500 to $0.018 for the ARC-AGI task) without citing verifiable sources, relying on personal analysis and anecdotal evidence. The economic reasoning about Jevons' paradox is sound but framed as opinion.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

  • Club de la IA — Referenced as a community for AI news and learning
  • IA Perfecta — mentioned as a web with the best AIs
  • Jon Hernández Education — Author's website with AI training and projects
  • Guía para usar ChatGPT como un PRO — Promotional link for a guide
  • NotebookLM Guide — Promotional link
  • Guía VibeCoding — Promotional link
  • Guía imágenes con ChatGPT y Nano Banana — Promotional link
  • La hostIA que viene — Author's book
  • Newsletter de IA — Newsletter subscription
  • WhatsApp Channel — Channel for AI news

Concurring Sources

  • Jevons paradox — Supports the explanation of why lower costs lead to higher total consumption.

Dissenting Sources

  • No specific discordant sources mentioned — The video does not discuss contradictory sources.

Contribution & Novelties

The video offers a personal perspective on AI cost dynamics, combining economic theory with current industry examples. Its original contribution is framing the falling token costs against rising total expenditure through the Jevons paradox, applicable to both consumers and enterprises. It also raises the prospect of free local AI, which is a significant trend for accessibility.

Pour aller plus loin :

  • Jevons paradox — Directly relevant to the central economic argument; explains why efficiency gains increase consumption.
  • ARC-AGI benchmark — The benchmark cited for the cost comparison; provides context on AI task difficulty.
  • Local large language models — Relevant to the discussion of running AI locally on devices.
  • OpenAI pricing — Not explicitly cited but useful for verifying token costs.
  • Moore’s law analog for AI — Often applied to AI compute cost trends; helpful for future projections.

137 words

Radar Profile

The radar profile shows moderate scores across information quantity, quality, technical depth, and reliability, reflecting a content that is informative but not rigorously sourced. The high emphasis on economic interpretation contrasts with lower technical verification.

Reliability 4/10