OSS DeveloperAn indie developer working on AI, databases, and programming languages
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.