Show HN: Wage Against the Machine – MacWages Index for AI Tasks

5 hours ago

The 'Wage Against the Machine' tool compares AI task purchasing power across countries based on hourly wages.
Higher hourly wage countries like Switzerland ($55.60/hr) and the USA ($35.10/hr) can purchase more AI tasks per hour worked.
Lower wage countries such as India ($2.20/hr) and Brazil ($4.30/hr) can purchase fewer AI tasks per hour.
The benchmark includes multiple AI models (e.g., Opus 5 Max, Luna Max) and evaluates tasks per hour worked.
The data shows significant disparities in AI purchasing power, with Switzerland leading and India trailing.

AI companies claim their tools can replace human labor, leading to potential job automation and augmentation.
Nobel prize-winning economists warn AI must be managed to avoid large-scale job displacement and ensure rising living standards.
Large language models (LLMs) are increasingly capable of completing complex tasks that previously took humans hours, such as software development and financial analysis.

AI's economic transformation requires societal collaboration, not automatic outcomes.
Google launched the AI & Economy ATLAS (v1.0) with 15 million de-identified interactions across 150+ countries, 140 languages, 800 occupations, and 4,000 tasks.
Workplace AI adoption spans 68% of occupations but is shallow, used for only ~21% of tasks per job on average.

Mineral Resources implements a hiring freeze for roles that can be performed by AI or existing employees using AI tools.
Other major Australian companies like WiseTech, Atlassian, and Commonwealth Bank have also cut jobs or restructured due to AI adoption.
A federal government report indicates slower employment growth in occupations most exposed to AI, especially routine cognitive roles.

AI saves about 2.8% of work hours in real-world settings, but these time savings rarely translate into higher earnings or profits.
Task-specific AI gains are substantial (e.g., 40% faster writing, 14% more issues resolved per hour), but they shrink when applied across entire jobs due to tasks AI can't handle and verification overhead.
AI's competence is 'jagged'; it performs well on structured tasks like writing and customer support but fails confidently on tasks outside its range, leading to costly errors.

China introduced the AI model GLM-5.2, which rivals top models like Anthropic's at less than one-tenth the cost.
The government prioritizes managing AI's social and political impacts, focusing on keeping ordinary people at the core of the AI economy to maintain stability.
AI's disruption affects white-collar workers, leading to policies like employment support, AI unemployment insurance, and legal cases against wrongful AI-driven layoffs.

AI companies claim their tools can replace human labor, leading to potential job automation and augmentation.
Nobel prize-winning economists warn AI must be managed to avoid large-scale job displacement and ensure rising living standards.
Large language models (LLMs) are increasingly capable of completing complex tasks that previously took humans hours, such as software development and financial analysis.

AI's economic transformation requires societal collaboration, not automatic outcomes.
Google launched the AI & Economy ATLAS (v1.0) with 15 million de-identified interactions across 150+ countries, 140 languages, 800 occupations, and 4,000 tasks.
Workplace AI adoption spans 68% of occupations but is shallow, used for only ~21% of tasks per job on average.

Mineral Resources implements a hiring freeze for roles that can be performed by AI or existing employees using AI tools.
Other major Australian companies like WiseTech, Atlassian, and Commonwealth Bank have also cut jobs or restructured due to AI adoption.
A federal government report indicates slower employment growth in occupations most exposed to AI, especially routine cognitive roles.

AI saves about 2.8% of work hours in real-world settings, but these time savings rarely translate into higher earnings or profits.
Task-specific AI gains are substantial (e.g., 40% faster writing, 14% more issues resolved per hour), but they shrink when applied across entire jobs due to tasks AI can't handle and verification overhead.
AI's competence is 'jagged'; it performs well on structured tasks like writing and customer support but fails confidently on tasks outside its range, leading to costly errors.

China introduced the AI model GLM-5.2, which rivals top models like Anthropic's at less than one-tenth the cost.
The government prioritizes managing AI's social and political impacts, focusing on keeping ordinary people at the core of the AI economy to maintain stability.
AI's disruption affects white-collar workers, leading to policies like employment support, AI unemployment insurance, and legal cases against wrongful AI-driven layoffs.

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