What happens to work, power and value when AI takes the brain work?
NeoHumxn.io is a thinking surface for life after automation. Not which jobs will disappear, but which world we are quietly building while we argue about that question.
Code production is climbing. Headcount is falling. Governance is not keeping pace with either.
of all code on GitHub is now AI-generated, with Gartner projecting 60% by the end of 2026.
GitHub, Gartner 2026of employers globally plan AI-driven workforce reductions within five years.
WEF 2025of agentic AI projects are predicted to be cancelled by 2027 from governance gaps.
Gartner 2025Each number comes from a different institution measuring a different part of the same shift, and none of them were designed to answer the question underneath all three: who is accountable for what happens to the people in the gap?
Does AI concentrate power, or expand it?
Across the site these two futures are called Devil World and Saint World, shorthand for the two ends of a logic that most AI deployments quietly reinforce by default.
- Success is measured purely as cost saved, never as who absorbs the cost
- A name sits on the approval, but no one is actually answerable
- Systems ship before anyone asks who they affect
- Every decision asks: optimising for what, and for whom?
- A real person, not a role, carries the consequence
- Understanding the situation comes before acting on it
Each crack below is already visible somewhere in your organisation, and each one is forcing a question nobody has fully answered yet.
AI is absorbing the analytical, writing and coordination work that justified most professional roles.
What kinds of work stay non-negotiably human, beyond sentiment?
Agentic systems decide things inside organisations that were never mapped, never governed.
How do you deploy AI without hard-coding a faster Devil World by accident?
Burnout and meaning-crisis signals are visible everywhere, still filed under HR.
What does a Rehumanised model actually look like, in an org, a sector, a society?
The old operating model is not under pressure. It is already breaking.
Who carries the disorientation when systems outpace the humans inside them?
Most people who end up here are standing in one of three places.
You make the high-level decisions about where automation lands. This site gives you the diagnostic language to make sure those decisions hold systemic integrity.
You feel the friction where AI deployments meet real-world labour. These frameworks offer the bridge between boardroom intent and ground-level reality.
The challenge is the same everywhere: the system has lost your location. OI helps you find it again.
A human in the loop who does not understand what they are approving is not oversight. They are just the last click before the system moves on.
That is the gap most AI governance conversations skip past, because naming a reviewer satisfies the requirement on paper without asking whether that person was ever briefed, given context, or told when to actually intervene. Start here to see where your own deployments sit against that distinction.
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