Saad Ullah
⬤ The debate around artificial general intelligence keeps heating up as AI systems get more powerful, but serious limitations remain. Former OpenAI research lead Jerry Tworek recently laid out his take on why today's models haven't crossed into AGI territory yet. He zeroed in on a major weakness: when current systems hit a wall, they basically give up instead of finding ways around the problem.
⬤ Modern AI models tend to fall apart once their initial approach fails, needing human intervention to get back on track. Tworek highlighted the contrast with human intelligence, which naturally hunts for different angles until something works. Real resilience defines true intelligence—the ability to push through obstacles rather than freezing up after the first setback.
⬤ He emphasized that intelligence means constantly testing and adjusting, not just spitting out right answers when conditions are perfect. Today's AI systems can deliver impressive results, but they generally can't bounce back on their own when facing unexpected roadblocks. This lack of self-correction seriously limits their ability to work independently on complex, long-term challenges.
⬤ These insights carry weight because AGI expectations are driving research direction, funding decisions, and market narratives across the AI sector. Tworek's view exposes the real distance between powerful specialized models and genuinely general intelligence. Until AI can unstick itself, shift strategies mid-task, and keep working without constant human oversight, AGI progress will likely stay slow and steady rather than revolutionary.
Saad Ullah
