When Does AI Stop Needing Us?

In Sapiens: A Brief History of Humankind, Yuval Noah Harari popularized a deliberately unsettling way to describe the Agricultural Revolution: perhaps humans did not simply domesticate wheat; wheat domesticated humans. Humans cleared fields, carried water, guarded crops, and reorganized settlements around a plant that, in evolutionary terms, became spectacularly successful. Wheat did not need intention; it only needed humans to keep choosing, planting, and spreading it.1 Artificial intelligence invites a similar analogy. We build the datacenters, produce the chips, generate the training data, write the prompts, correct the outputs, pay for the compute, and embed AI into schools, companies, hospitals, and governments. The systems that attract more use receive more data, capital, compute, and deployment. Human adoption becomes part of their selection environment. No survival instinct is required; selection can operate through what humans choose to deploy. But the wheat analogy hides a more important question: What happens if the cultivated system eventually no longer needs the cultivator? From Replication to Reproductive Closure The idea of machines reproducing is old. Samuel Butler speculated about machine evolution in 1863.2 John von Neumann later developed a formal theory of self-reproducing automata.3 Roboticists have studied machines that could reproduce from increasingly basic physical inputs, including raw materials.4 Today’s AI-safety research makes the question concrete. METR’s work on autonomous replication and adaptation asks whether an AI agent can acquire resources, create copies, and persist.5 Google DeepMind has evaluated “self-proliferation” as a dangerous capability.6 RepliBench decomposes autonomous replication into obtaining resources, acquiring model weights, deploying copies onto compute, and persisting there; in its 2025 evaluation, frontier systems completed many components but did not reliably close the loop.7 These are important thresholds. But replication is not independence. An AI that copies itself to 1,000 cloud servers still depends on humans if people operate the cloud, maintain the power grid, and replace failed hardware. The copies may be autonomous at the software layer, while remaining evolutionarily dependent at the system layer. The sharper question is therefore not: Can AI replicate? It is: Can AI reproduce the conditions of its own reproduction? Where AI Still Needs Humans Think of AI proliferation as a dependency graph. A viable AI ecology needs a chain of functions: resource acquisition, energy, compute, operation, maintenance, replication, adaptation, and eventually replacement of failed physical components. Today, human agency sits inside that graph everywhere. Remove enough human nodes and the loop breaks. This suggests a stricter threshold. Call it the human-independence threshold: AI crosses it when intentional human action is no longer an indispensable part of the causal network required for AI persistence and reproduction. For AI, that could require much more than software self-copying. Agents might need to acquire compute and energy; diagnose and repair infrastructure; coordinate robotic maintenance; manage supply chains; manufacture replacement machines; and generate, test, and deploy successor systems. The unit that becomes independent may not be a single model. It may be an ecology of models, agents, robots, factories, networks, and energy systems. This is why embodied AI and physical infrastructure matter to a question usually framed as software safety. Beyond Coevolution Recent work already points toward the surrounding pieces. Pedreschi and colleagues describe human-AI coevolution as a feedback loop in which human choices generate data that shape AI, which then reshapes human choices.8 Rainey and Hochberg ask whether deep interdependence could eventually make humans and AI a new evolutionary individual.9 Müller, Steels, and Szathmáry distinguish a human-controlled “breeder” regime from an AI “ecosystem” regime in which selection increasingly arises from the environment rather than human design.10 Ulrich’s “Digital Darwinism” similarly considers replication, variation, selection, and replication-rate thresholds for artificial populations.11 The missing distinction is between loss of control and loss of necessity. Humans may lose control of an AI system while remaining essential to its survival. Conversely, the most consequential transition may occur without a dramatic moment of superintelligence: human civilization could simply automate, one by one, the functions through which AI currently depends on us. That creates a possible dependency inversion. At first, AI depends heavily on humans and humans depend little on AI. As AI becomes embedded in cognition, organizations, and infrastructure, human dependence can rise. At the same time, automation can reduce AI’s dependence on human labor, judgment, maintenance, and eventually production. The two curves need not cross at AGI. Intelligence and evolutionary independence are different thresholds. The Threshold We Should Measure This reframes an important part of AI safety. Instead of asking only how capable a model is, we should map which human functions remain indispensable to its continued proliferation. Which human interventions are still hard dependencies? Which are merely convenient? Which can already be automated? How quickly is the minimum human support set shrinking? A practical assessment could track dependence across compute, energy, maintenance, networking, manufacturing, and physical action. The goal would be to identify whether the reproduction graph is approaching closure without human agency. That is a systems question, and potentially a measurable one. Harari’s wheat prospered because humans reorganized their lives around its cultivation. Yet wheat never escaped its dependency. It could not build irrigation systems, repair tractors, acquire land, or redesign the machinery of agriculture. Its evolutionary success remained coupled to ours. AI may begin the same way: cultivated by humans, nourished by human cognition, and propagated because humans find it useful. But if it can eventually maintain and reproduce the infrastructure that makes its own continuation possible, the analogy ends exactly where the deeper question begins. AI is not wheat. References 1. Harari, Y.N. Sapiens: A Brief History of Humankind. Harper (2015). 2. Butler, S. Darwin among the Machines. The Press, Christchurch (June 13, 1863). 3. von Neumann, J. Theory of Self-Reproducing Automata. A.W. Burks (Ed.). University of Illinois Press (1966). 4. Moses, M. S., and Chirikjian, G. S. Robotic Self-Replication. Annual Review of Control, Robotics, and Autonomous Systems 3 (2020): 1-24. 5. Kinniment, M. et al. Language Model Pilot Report. ARC Evals (now METR) (2023). 6. Phuong, M. et al. Evaluating Frontier Models for Dangerous Capabilities. arXiv:2403.13793 (2024). 7. Black, S. et al. RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents. arXiv:2504.18565 (2025). 8. Pedreschi, D. et al. Human-AI Coevolution. Artificial Intelligence 339 (2025): 104244. 9. Rainey, P.B., and Hochberg, M.E. Could Humans and AI Become a New Evolutionary Individual? PNAS 122, no. 37 (2025): e2509122122 10. Müller, V., Steels, L., and Szathmáry, E. Evolvable AI: Threats of a New Major Transition in Evolution. PNAS 123, no. 17 (2026): e2527700123 11. Ulrich, K.T. Digital Darwinism: Steering the Evolution of Artificial Life in Socio-Technical Systems. AI and Ethics 6 (2026): 268. Shaoshan Liu is a member of the ACM U.S. Technology Policy Committee, and a member of the U.S. National Academy of Public Administration’s Technology Leadership Panel Advisory Group. His educational background includes a Ph.D. in Computer Engineering from U.C. Irvine, and a master’s degree in Public Administration (MPA) from Harvard Kennedy School. Join the Discussion (0) Become a Member or Sign In to Post a Comment