
Marc Alcantara
912 posts











🔴 ÚLTIMA HORA | Muere una niña de dos años en Brión (A Coruña) tras quedarse durante varias horas olvidada en un coche social.elpais.com/30rbl







Estudio nuevo: 73% de los devs juniors no pueden programar sin IA. Martín tiene 23 años, vive en Córdoba, cobra USD 1.200/mes como junior en una fintech. Nunca escribió un loop sin Copilot. No sabe qué es un puntero. No entiende el código que pushea. Pero sus PRs pasan review porque funcionan. Su tech lead lo sabe. No dice nada porque él tampoco entiende el 30% del código que genera Claude. El sistema entero funciona sobre algo que nadie comprende del todo. Y acá la pregunta que nadie quiere hacerse: ¿Martín es developer o es prompter con sueldo de developer? Porque si es lo segundo, hay alguien en Mendoza que ya hizo los números. Y no contrató a Martín.




ponerte un curro de fin de semana para que no te tengan que pagar tus mierdas tus padres con 25 años ni se lo plantean….





🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products. My Take The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested. This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown. Hedgie🤗












