Inputs
Energy, water, food, transport, housing, healthcare, education and communication.
Ends Up Here · Connected research note
Research framework / Open question
What does
humanity become?
Ends Up Here is not only asking what AI can do. It is asking what happens to towns, families, status, education, culture and place after intelligence becomes abundant.
Research propositionThe mechanism is AI.
The subject is the future human.
The subject is the future human.
A practical test case
What would the Alice Springs–Darwin corridor look like if labour costs approached zero?
Remote communities, mining, freight, high energy costs and harsh maintenance conditions bring several future-world problems into one region.
The research question is practical: what would autonomous transport, continuous maintenance, local energy, AI-supported health and education, and improved communications change—and what would they not change?
Working boundary: describe infrastructure conditions and outcomes without explaining them through assumptions about culture.
Illustration and scene
The whole-world model
Constraints
Geography, climate, culture, governance, history and local authority.
Choices
What people want to preserve, change, build, share or leave alone.
The media project
Not “what can AI do?” but “what does humanity become?”
Video essays and future conversations can ask what happens to work, cities, families, education, status, loneliness, religion, land ownership and Indigenous communities when the old economic constraints loosen.
Each question can become a future memoir, a real-world case study or a research page. The corridor is one place to test the ideas against actual geography, services and community priorities.
03 / Who defines doing good?
When helping everyone becomes a calculation
“Martin’s not keen on altruism” is deliberately blunt. I am not against helping people. I am uneasy when altruism becomes a demand for people to keep sacrificing their own lives, or when a calculation made in humanity’s name gives a few people authority over everyone else.
A recent Economist leader on effective altruism and AI recognises the force of using evidence to do good, then warns that impartial moral arithmetic can lose sight of family, neighbours and individual rights. That is a serious criticism of some conclusions and priorities in the movement. Effective altruism’s own broad definition is the use of evidence and reason to find ways to benefit others; it should not be collapsed into the claim that every person must erase themselves.
The Luddite connection is about who benefits when technology changes work—not a blanket rejection of machines. Ayn Rand’s defence of individual interest and rights challenges moral systems that elevate self-sacrifice. Heidegger asks a different question: what happens when modern technology makes the world, including people, appear mainly as resources ready for measurement and optimisation?
These ideas do not agree with one another, and none settles how AI should be governed. Together they sharpen the question: can we reduce danger and share the gains of AI without turning people into inputs for someone else’s future?
My starting point: Helping matters. So do freedom, relationships, craft, curiosity and the right to choose what makes a life worth living. A future with less necessity should widen those choices, not measure every person by how much good they produce.
Read the Economist leader text · Effective altruism’s own introduction · Stanford Encyclopedia of Philosophy: Ayn Rand · Stanford Encyclopedia of Philosophy: Heidegger · The Luddites and modern AI
Research prompts
What remains difficult?
Who controls the infrastructure?
What does maintenance become?
How does culture shape the model?
What should not be optimised?
Who gets to choose?
Status note
This is a connected research framework, not a proposal for the Alice Springs–Darwin corridor. Any public work requires local voices, current evidence and appropriate authority.