Moltbot creator does not agree with Sam Altman’s ‘AGI obsession’: ‘What can one human being…’

While OpenAI CEO Sam Altman continues his quest to achieve for Artificial General Intelligence (AGI) – a system capable of exceeding human reasoning across all tasks – the creator of OpenClaw, commonly known as Moltbot, has called for a reality check. Peter Steinberger has distanced himself from the AGI, calling for specialised intelligence instead of generalised intelligence.Speaking on the Y Combinator podcast recently, Steinberger argued that the industry's focus on AGI ignores the fundamental way humans and technology actually succeed: through specialisation, according to Business Insider.“What can one human being actually achieve? Do you think one human being could make an iPhone or one human being could go to space? As a group we specialize, as a larger society we specialise even more” Steinberger said. In his view, the most effective AI systems are already specialised, citing startups building models for Erdos math problems or gene mutation identification.Steinberger is not alone in his skepticism. Anthropic president Daniela Amodei called the AI concept 'outdated' while Google DeepMind CEO said AGI cannot be achieved without “world models.”“AGI is such a funny term. Many years ago, it was kind of a useful concept to say, 'When will artificial intelligence be as capable as a human?'I think maybe the construct itself is now wrong-or maybe not wrong, but just outdated,” Amodei argued.“Today's large language models are phenomenal at pattern recognition. But they don't truly understand causality. They don't really know why A leads to B. They just predict the next token based on statistical correlations,” Hassabis explained on “The Tech Download.” Google DeepMind’s AlphaGenome system is designed specifically for predicting DNA mutations, a task far beyond the reach of a “general” chatbot.Timnit Gebru, founder of the Distributed AI Research Institute, recently called AGI a “fictional thing”. Aidan Gomez, CEO of Cohere, has similarly noted a shift toward "smaller, more efficient models" that prioritise intelligence and data quality over the expensive scaling required for AGI.