GPT reasoning models have "line of sight" to AGI, says OpenAI's Greg Brockman

OpenAI co-founder Greg Brockman says the debate about whether large language models can achieve general intelligence (AGI) is settled. The GPT architecture will lead to AGI.

"I think that we have definitively answered that question—it is going to go to AGI. Like we see line of sight," OpenAI President Greg Brockman says about the GPT reasoning models in the Big Technology Podcast.

It's a bold claim. Brockman is essentially declaring one of the central open questions in AI research settled: Can models primarily trained on text develop a real understanding of the world? Or does that require multimodal world models like Sora? Among AI researchers, the technical approach remains hotly debated.

OpenAI recently shut down the Sora app and model. World model research will continue for robotics, but on a smaller scale and without consumer-facing products.

Brockman calls Sora an "incredible model," but says it sits on "a different branch of the tech tree" than the GPT reasoning series. With limited computing power, pursuing both at the same time isn't feasible for OpenAI. For Brockman, it's less about the relative importance of the two approaches and more about "sequencing and timing." The applications "we've always dreamed of are starting to come into reach," and the way to get there is through the GPT architecture.

When host Alex Kantrowitz asks whether OpenAI could be missing something crucial by skipping Sora-style world models, pointing out that Deepmind's Demis Hassabis had said Google's "Nano Banana" image model felt particularly close to AGI, Brockman acknowledges the risk: "In this field you do have to make choices. Right? You have to make a bet."

Researchers remain divided on whether LLMs can reach general intelligence

Whether purely text-based models can achieve general intelligence is far from settled in the broader AI research community. Renowned AI researcher Yann LeCun has argued for years that LLMs won't lead to human-like intelligence. In his view, LLMs have a very limited understanding of logic, don't understand the physical world, have no permanent memory, cannot think rationally, and cannot plan hierarchically. Instead, he's betting on so-called world models to develop a comprehensive understanding of the environment. Deepmind founder Demis Hassabis holds a similar view: LLM scaling alone isn't enough, and further breakthroughs are needed.

AI researcher Francois Chollet defines intelligence as the ability to learn new skills efficiently. What matters is how well a system can independently form abstractions. While current language models can be placed on an intelligence scale, they rank very low. Outside their training domain, they have to relearn everything from scratch. Continuous learning could help address this gap.

This view aligns with a broader line of research. In a recent paper, Deepmind researcher Richard Sutton and former Deepmind researcher David Silver called for a paradigm shift. Instead of training on human knowledge, systems should learn from their own experience. Silver has since founded his own startup focused on simulation learning.

Ex-OpenAI researcher Jerry Tworek, one of the key minds behind OpenAI's reasoning model breakthroughs, also describes his research field of deep learning as "done." The next step, he says, is building simulations of human work where AI systems can learn skills. His new startup, Core Automation, is dedicated to this approach.

Not everyone shares this skepticism, though. Deepmind researcher Adam Brown recently defended the potential of the current LLM architecture. He compares the token prediction mechanism to biological evolution: a simple rule that, through massive scaling, creates emergent complexity that people perceive as understanding. Brown argues this complexity could even lead to consciousness.