AI didn’t level the playing field. It raised the floor while making the ceiling harder to reach for most people.
For years, the story was simple: learn valuable skills, get ahead. Then the internet made information abundant. Then no-code tools made building easier. Now AI has made output abundant. You can generate writing, code, designs, strategies, and research at a level that would have been impressive just a few years ago.
The problem is that abundance changes the game. When something becomes easy to produce, it loses its value. What used to be an edge becomes table stakes. And the people who treat AI as the end of the leverage game are going to be disappointed. — Read More
Daily Archives: July 10, 2026
The Death of Moore’s Law Just Kicked Off a Three-Front War — and Nobody Told You About It
TSMC achieved the impossible. Then, Huawei, IBM, and OpenAI each announced they no longer need it. Something fundamental broke in the chip industry last week, and it changes everything from your next.
The Rule That Ran the World for 50 Years Is Dying.
For half a century, the logic was almost offensively simple. Shrink the transistor, get twice the performance. No strategy needed. Physics handled it. The rule is called Moore’s Law — the empirical observation that transistor counts would roughly double every two years — and it’s what took computers from filling an entire room to fitting in your pocket.
But we are in mid-2026. And three major announcements in the space of about a week — from Huawei, IBM, and OpenAI — are all saying the same thing, from three different directions: the era of shrinking transistors as the primary lever of progress is closing. — Read More
Tools vs. Subagents: Building Effective AI Agents Without Over-Engineering
Every AI agent you build reaches the same decision point eventually. You have a task that needs to be done — call an API, search a database, run a calculation — and you need to decide: should this be a tool the agent calls directly, or should it be a separate agent that handles the work independently?
Get this wrong in one direction and you end up with a bloated agent that tries to do too much in a single context window. Get it wrong in the other direction and you’ve added coordination overhead, extra LLM calls, and debugging complexity to a problem that a simple function would have solved.
This article explains what tools and subagents are, where each fits, and how to make the choice every time. — Read More
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