AI
GenAI
The Skill of Not Building in the AI Agent Era (5) — Six Checks to Run Before You Start
Will that code still be needed once the next model ships? Six things worth checking before you start.
Posts tagged#Career
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AI
GenAI
The Skill of Not Building in the AI Agent Era (4) — Implementations That Don't Repay the Effort, and the Case for Waiting
Homegrown long-term memory, a workflow DSL, a model router. Each one works, and each is genuinely interesting to build. But what if three months of waiting made them unnecessary?
AI
GenAI
The Skill of Not Building in the AI Agent Era (3) — Run the Loop Instead of Writing by Hand
Boris Cherny, who built Claude Code, says he hasn't hand-written a line of code in eight months. Is the craft of polishing prompts giving way to the craft of designing loops?
AI
GenAI
The Skill of Not Building in the AI Agent Era (2) — Implementations at the Platform's Mercy
The code runs and the tests pass. Only the value of using it has vanished. A look at this failure mode as a stranded asset rather than technical debt.
AI
GenAI
The Skill of Not Building in the AI Agent Era (1) — Third Parties Swallowed by the Platform
PDF chat, AI slides, ChatGPT plugins: all standard features of ChatGPT and Gemini now. Where did the implementations that once powered them go?
AI
GenAI
US vs. Japan IT Industries — "Product" and "Service," and the AI Reshuffle
In the US, an "IT company" means Microsoft or NVIDIA; in Japan, it means NTT Data or Fujitsu. The same words point to different things. One side mass-produces products for the world; the other supports each customer's bespoke operations. That structural gap has split revenue, talent, and competitiveness. How does generative AI reshape it? A look from Japan's weaknesses and strengths.
DATA
Data & DB
The Skill AI Can't Beat: Data Engineering
Coding agents are strong at logic and tests. But they can't tell what a piece of data means, who owns it, how fresh it flows, or which copy is authoritative. It follows from the fact that today's AI has no embodiment: the people who rise in value are those who can design the meaning, quality, lineage, and responsibility of data. A look at the trend with the latest data-engineering discussion.
AI
GenAI
Part 5: Those Who Grow AI Win — From "Users" to "Growers"
The finale. The core skill common to every part is turning experience into a form both people and AI can act on. Why it does not commoditize and instead compounds, the daily practices, and even "what if AI gains a body?"—the synthesis goes deep.
AI
GenAI
Part 4: Grow Leaders and Managers with an AI Team — Directing Is Developing
Break work down, share expectations and criteria, give feedback. The template of good management overlaps exactly with good instructions to an AI team. How the experience of leading AI grows leaders and managers, and how it turns the organization's learning loop.
AI
GenAI
Part 3: Make AI Your Student and Escape the Intermediate Trap — Graduating from "I Thought I Understood"
The key for intermediates who stall at "I basically get it" is to teach AI. Verbalizing your premises, constraints, criteria, and edge cases becomes training that turns tacit knowledge into explicit knowledge. A learning method that uses AI as a mirror of your understanding, plus a practical five-step routine.
AI
GenAI
Part 2: Beginners, Build Experience with AI — The Ones Who Grow Fastest Are Beginners
In an era where AI hands you answers, are beginners at a disadvantage? Quite the opposite. By trying sticky material—dev environments, containers, VMs—together with AI and failing together, beginners build experience the fastest through iteration. Includes how to let AI fail a lot and learn by watching. Part 2.
AI
GenAI
Part 1: How Not to Let AI Take Your Career — A Career Path for the AI Era
Many people worry that AI will take their jobs. But reframing the question opens a path forward. As the introduction to a five-part series, this piece maps out AI-era careers across the beginner, intermediate, and leader stages.
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The Skill of Not Building in the AI Agent Era (5) — Six Checks to Run Before You Start
Will that code still be needed once the next model ships? Six things worth checking before you start.
The Skill of Not Building in the AI Agent Era (4) — Implementations That Don't Repay the Effort, and the Case for Waiting
Homegrown long-term memory, a workflow DSL, a model router. Each one works, and each is genuinely interesting to build. But what if three months of waiting made them unnecessary?
The Skill of Not Building in the AI Agent Era (3) — Run the Loop Instead of Writing by Hand
Boris Cherny, who built Claude Code, says he hasn't hand-written a line of code in eight months. Is the craft of polishing prompts giving way to the craft of designing loops?
The Skill of Not Building in the AI Agent Era (2) — Implementations at the Platform's Mercy
The code runs and the tests pass. Only the value of using it has vanished. A look at this failure mode as a stranded asset rather than technical debt.
The Skill of Not Building in the AI Agent Era (1) — Third Parties Swallowed by the Platform
PDF chat, AI slides, ChatGPT plugins: all standard features of ChatGPT and Gemini now. Where did the implementations that once powered them go?
US vs. Japan IT Industries — "Product" and "Service," and the AI Reshuffle
In the US, an "IT company" means Microsoft or NVIDIA; in Japan, it means NTT Data or Fujitsu. The same words point to different things. One side mass-produces products for the world; the other supports each customer's bespoke operations. That structural gap has split revenue, talent, and competitiveness. How does generative AI reshape it? A look from Japan's weaknesses and strengths.
The Skill AI Can't Beat: Data Engineering
Coding agents are strong at logic and tests. But they can't tell what a piece of data means, who owns it, how fresh it flows, or which copy is authoritative. It follows from the fact that today's AI has no embodiment: the people who rise in value are those who can design the meaning, quality, lineage, and responsibility of data. A look at the trend with the latest data-engineering discussion.
Part 5: Those Who Grow AI Win — From "Users" to "Growers"
The finale. The core skill common to every part is turning experience into a form both people and AI can act on. Why it does not commoditize and instead compounds, the daily practices, and even "what if AI gains a body?"—the synthesis goes deep.
Part 4: Grow Leaders and Managers with an AI Team — Directing Is Developing
Break work down, share expectations and criteria, give feedback. The template of good management overlaps exactly with good instructions to an AI team. How the experience of leading AI grows leaders and managers, and how it turns the organization's learning loop.
Part 3: Make AI Your Student and Escape the Intermediate Trap — Graduating from "I Thought I Understood"
The key for intermediates who stall at "I basically get it" is to teach AI. Verbalizing your premises, constraints, criteria, and edge cases becomes training that turns tacit knowledge into explicit knowledge. A learning method that uses AI as a mirror of your understanding, plus a practical five-step routine.
Part 2: Beginners, Build Experience with AI — The Ones Who Grow Fastest Are Beginners
In an era where AI hands you answers, are beginners at a disadvantage? Quite the opposite. By trying sticky material—dev environments, containers, VMs—together with AI and failing together, beginners build experience the fastest through iteration. Includes how to let AI fail a lot and learn by watching. Part 2.
Part 1: How Not to Let AI Take Your Career — A Career Path for the AI Era
Many people worry that AI will take their jobs. But reframing the question opens a path forward. As the introduction to a five-part series, this piece maps out AI-era careers across the beginner, intermediate, and leader stages.