Fundamentally, they are based on gathering an extraordinary amount of linguistic data (much of it codified on the internet), finding correlations between words (more accurately, sub-words called "tokens"), and then predicting what output should follow given a particular prompt as input. For all the alleged complexity of generative AI, at their core they really are models of language.
Despite the impressive achievements of current generative AI systems, the dream of Artificial General Intelligence remains far away, notwithstanding the hype offered by various tech CEOs.[1] The reasons are easy to state, if hard to quantify. Human intelligence requires three primary features, none of which have been fully cracked: logic, associative learning, and value sensitivity. I'll explain each in turn.
As a journalist who covers AI, I hear from countless people who seem utterly convinced that ChatGPT, Claude, or some other chatbot has achieved "sentience." Or "consciousness." Or-my personal favorite-"a mind of its own." The Turing test was aced a while back, yes, but unlike rote intelligence, these things are not so easily pinned down. Large language models will claim to think for themselves, even describe inner torments or profess undying loves, but such statements don't imply interiority.
As AI advances, so too does the desperation of those trying to stop it. Two men, worried about the threat AI poses to humanity's future, are now on hunger strike outside the offices of Anthropic and DeepMind. For Guido Reichstadter, a 45-year-old activist, Sunday marked a week of protest without food. Reichstadter told Business Insider he plans to remain until the company responds to his concerns about the direction of AI development.
AI hasn't progressed as quickly as many have predicted; specifically, the idea that AI will 'self-improve' and rapidly achieve 'godlike superintelligence' has been blown out of proportion.
OpenAI's new GPT-5 model represents a significant step toward artificial general intelligence, yet it lacks crucial elements like autonomous continuous learning, limiting its full potential.
Games provide a clear, unambiguous signal of success. Their structured nature and measurable outcomes make them the perfect testbed for evaluating models and agents. They force models to demonstrate many skills including strategic reasoning, long-term planning, and dynamic adaptation against an intelligent opponent, providing a robust signal of their general problem-solving intelligence.
