Part V of VIII
The previous four sections are not separate problems. They’re parts of the same structure: a fast self-amplifying loop, a slow broken self-correcting loop, and a paradox that turns alarm into acceleration.
Where are the self-amplifying loops? Which loops are supposed to correct them, and which is winning? In the AI system, the self-amplifying loops are large, fast, and well-capitalized. The stabilizing loops are under-resourced, and as Parts 3 and 4 showed, actively weakened by the same forces they’re meant to check.
Watch what happens here. Safety research gets published. Safety policies get announced. The pressure for structural reform (liability law, mandatory audits, hard deployment gates) quietly drops. Not because the research is fake. Because its existence signals that the problem is being handled. The visible fix absorbs the urgency that would otherwise push toward the structural fix. Meadows calls this shifting the burden: the symptomatic solution works just well enough to prevent the fundamental one. The symbolic safety apparatus and unconstrained deployment coexist, and the capacity for deeper correction gradually weakens.
A third loop is forming. AI systems are now used in AI research itself: suggesting architectures, running experiments, writing training code. DeepMind’s AlphaProof17 and Sakana AI’s AI Scientist18 are early documented instances, with widespread use of AI coding tools in ML workflows accelerating behind them. As this matures, it adds a second self-amplifying loop on top of the existing capital loop: better AI accelerates the research that produces even better AI. The feedback delay in this loop is shrinking. How fast it fires is genuinely uncertain. The original AI 2027 scenario (Kokotajlo et al., April 2025) was conditional, not a central forecast; the authors’ own updated timelines model puts full R&D automation at roughly 2030-2032.19 The mechanism is real and already in motion.
The one constraint that's already permanent: open weights
Compute is a flow: you can regulate chips, tax energy, slow permits. But model weights are a stock. DeepSeek-R1 (MIT license, January 20, 2025), Llama 3.x and its distillation chains have permanently exported reasoning-class capabilities outside any government’s reach. Export controls on chips might slow who builds the next generation. They cannot un-release what already exists. AI capability, once in open weights, requires nothing further from the original lab. This irreversibility is structurally distinct from nuclear or biological weapons, both of which require ongoing access to controlled materials. AI capability doesn’t.
Part 6 looks at what these loops are actually doing to people, starting with where labor displacement is already concentrated and what the industry’s framing leaves out.