Most tech revolutions made work worse for employees

6 hours ago

Previous tech revolutions (PC, internet, smartphones) increased work speed and volume, benefiting companies more than employees.
AI differs by not only speeding up work but also performing tasks itself, potentially freeing up time.
Current adoption of AI is causing work intensification and role blurring, but this is part of a reorganization phase known as the productivity J-curve.
McKinsey predicts AI could give knowledge workers back about 30% of their day by 2030, which could be used for creativity and collaboration.
Examples like walking and serendipitous conversations show that time saved can boost innovation and quality, similar to how desktop publishing raised quality standards.
AI has the potential to improve work for both employees and companies, unlike prior tech revolutions, if reclaimed time is used wisely.

Intelligence is spiky, with AI capable of understanding things humans cannot due to its organization, scale, and tacit knowledge.
Quantity and speed of AI thinking can add new value even in domains where problems seem solved, as seen with fast, multiple code reviews by AI.
Awareness of AI capabilities is uneven across corporations, creating a challenge where users of AI can route around slower, non-adopting workers.

AI has solved complex mathematical problems, surpassing human mathematicians, yet the world remains largely unchanged.
Superintelligence has arrived incrementally, not as an explosive singularity, with limited economic disruption.
Intelligence may face diminishing returns; humans might already be near optimal for many cognitive tasks.

The AI as Normal Technology framework is useful for understanding AI's economic impact unless a future discontinuity like recursive self-improvement occurs.
Recursive self-improvement (RSI) is unlikely to suddenly cause mass unemployment, as economic transformation from AI will be gradual over decades.
Future jobs will change significantly, shifting from executing tasks to evaluating and controlling AI systems, emphasizing judgment and domain knowledge.

Burnout is rising despite AI making tasks 2-100x faster, because overwork isn't the only cause.
AI tempts people to start many side projects, leading to overcommitment and make-work.
A personal example: the author had 40 proof-of-concept projects, causing burnout.

The author initially doubted AI's coding abilities, treating it like Stack Overflow and manually reviewing code to maintain personal style.
Claude Code demonstrated AI's ability to handle maintenance tasks and reduce the need to learn specific frameworks like Laravel.
Newer AI models (e.g., Fable, GPT 5.6-Sol) now outperform the author in 80% of coding tasks, leading to acceptance and excitement about productivity gains.

Intelligence is spiky, with AI capable of understanding things humans cannot due to its organization, scale, and tacit knowledge.
Quantity and speed of AI thinking can add new value even in domains where problems seem solved, as seen with fast, multiple code reviews by AI.
Awareness of AI capabilities is uneven across corporations, creating a challenge where users of AI can route around slower, non-adopting workers.

AI has solved complex mathematical problems, surpassing human mathematicians, yet the world remains largely unchanged.
Superintelligence has arrived incrementally, not as an explosive singularity, with limited economic disruption.
Intelligence may face diminishing returns; humans might already be near optimal for many cognitive tasks.

The AI as Normal Technology framework is useful for understanding AI's economic impact unless a future discontinuity like recursive self-improvement occurs.
Recursive self-improvement (RSI) is unlikely to suddenly cause mass unemployment, as economic transformation from AI will be gradual over decades.
Future jobs will change significantly, shifting from executing tasks to evaluating and controlling AI systems, emphasizing judgment and domain knowledge.

Burnout is rising despite AI making tasks 2-100x faster, because overwork isn't the only cause.
AI tempts people to start many side projects, leading to overcommitment and make-work.
A personal example: the author had 40 proof-of-concept projects, causing burnout.

The author initially doubted AI's coding abilities, treating it like Stack Overflow and manually reviewing code to maintain personal style.
Claude Code demonstrated AI's ability to handle maintenance tasks and reduce the need to learn specific frameworks like Laravel.
Newer AI models (e.g., Fable, GPT 5.6-Sol) now outperform the author in 80% of coding tasks, leading to acceptance and excitement about productivity gains.

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