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DeepSeek hit 100 million users with zero budget. Claude Code ships a version every day. DingTalk, Feishu, and WeCom copied the same feature within three days. The underlying logic of product competition has changed—money and volume are no longer decisive weapons.

A tool I couldn't build in two days was done in half an hour by switching models. When I couldn't log into my computer, an AI agent fixed it in half an hour. Later, I built Aima Service—so every device can have an AI ready to help when things go wrong.

The sudden death of Zhang Xuefeng, the OpenClaw founder's lost years after selling his company, young people flocking to AI fortune-telling. Technology is sprinting ahead, but when it comes to how we should live in the future, we barely have a decent answer.

AI has driven software production costs down to the level of a few thousand dollars and a few days. The bidding system, built on information asymmetry and commoditized comparison, is losing its foundation. It's the same playbook as the LLM market collapse last year.

When production and distribution costs both approach zero, platforms explode. The short video industry proved this once; now the software industry is replaying the same story. The OpenClaw phenomenon is worth watching.

Nine days without a release and people start asking questions; one week of silence and no one pays attention anymore. The four real bottlenecks stalling AI startup teams—passion, focus, process, and stamina—none of them are technical.

Zero-friction onboarding plus extremely powerful AI intelligence—two pillars can't hold up a product. When users see an agent executing incomprehensible commands in the terminal, their first reaction isn't awe, it's fear. The missing pillar is progressive trust.

Prompts, RAG, fine-tuning, knowledge graphs, context engineering—five paradigm shifts in three years. Models are getting stronger, but we still don't know how to make agents perform well in products.

Coding agents have driven implementation costs down to a few hundred dollars per day. The traditional ideation funnel logic no longer holds. The bottleneck has shifted from execution to hypothesis generation. People with ideas have become the scarce resource.

Mac's permission model is designed for humans; Linux is designed for programs. An Agent is not an app—it is infrastructure. Running an Agent on a Mac is like stuffing a server into a laptop chassis—it works, but everything feels off.

I switch between models every day. Opus is reckless but gets the job done, GPT 5.4 is comprehensive but easily drifts off-course, Gemini has taste but can't fix bugs, and the three domestic models each have their own quirks. Use them enough, and you'll realize swapping models works better than tweaking parameters.

I previously wrote about cost and power. This piece fills in the legal gap. The cloud is rented to you; the edge is sold to you. When AI Agents start making autonomous decisions, this distinction is far more than a business choice.