What if I can just do it faster than talking to you?

10 hours ago

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.
Jobs evolve as technology does; AI may not replace jobs outright but can render old methods irrelevant by enabling faster alternatives.
The line between code and non-code tasks is blurring; AI makes coding accessible for diverse tasks, from reports to complex programs.
Ignoring AI tools is a mistake; practical usefulness matters regardless of debates on consciousness or hype.
The key competition is between those using fast, cheap AI and those relying on traditional methods, not between AI and humans directly.
Quantity, quality, and cost improvements in AI, along with expanding code-expressible tasks, make staying curious essential.

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.

AI tools can rapidly create working prototypes, but the distance from a prototype to a production-grade system remains significant.
Building production-grade software requires judgment, system design, error handling, security, scalability, and observability—areas AI does not automate.
Learning computer science is more valuable than ever because it provides a mental model to evaluate AI-generated code for flaws like performance issues or race conditions.

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.

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.

AI can be useful as a data distiller, condensing information retrieval and refinement steps that were previously manual.
Using AI to generate code is inefficient; it often produces unverified, opaque outputs that lack proper abstraction and increase repetition.
AI's opacity makes 'prompt engineering' questionable, as manipulating a black-box system without understanding its source code is unstable and unreliable.

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.

AI tools can rapidly create working prototypes, but the distance from a prototype to a production-grade system remains significant.
Building production-grade software requires judgment, system design, error handling, security, scalability, and observability—areas AI does not automate.
Learning computer science is more valuable than ever because it provides a mental model to evaluate AI-generated code for flaws like performance issues or race conditions.

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.

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.

AI can be useful as a data distiller, condensing information retrieval and refinement steps that were previously manual.
Using AI to generate code is inefficient; it often produces unverified, opaque outputs that lack proper abstraction and increase repetition.
AI's opacity makes 'prompt engineering' questionable, as manipulating a black-box system without understanding its source code is unstable and unreliable.

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