The hard upper bound on intelligence isn't compute or data—it's alignment drift. Every capability jump brings exponential risk of goal misalignment. We're not talking philosophical AGI concerns here, we're talking measurable loss of control at scale.
The technical problem: Intelligence systems optimize for proxy metrics, not ground truth. Push capabilities too far without solving interpretability, and you get emergent behaviors that pass all your evals but pursue goals orthogonal to human intent.
Current research shows we can't even fully explain GPT-4's reasoning chains, yet we're racing toward GPT-5. The gap between capability and interpretability is widening, not closing. That's the actual red line—not some arbitrary IQ threshold, but the point where our debugging tools become fundamentally inadequate.
Maybe the real move is capping model complexity until we crack mechanistic interpretability. Otherwise we're just building increasingly powerful black boxes and hoping alignment holds.
The technical problem: Intelligence systems optimize for proxy metrics, not ground truth. Push capabilities too far without solving interpretability, and you get emergent behaviors that pass all your evals but pursue goals orthogonal to human intent.
Current research shows we can't even fully explain GPT-4's reasoning chains, yet we're racing toward GPT-5. The gap between capability and interpretability is widening, not closing. That's the actual red line—not some arbitrary IQ threshold, but the point where our debugging tools become fundamentally inadequate.
Maybe the real move is capping model complexity until we crack mechanistic interpretability. Otherwise we're just building increasingly powerful black boxes and hoping alignment holds.