OpenAI’s GPT-5, while impressive, is emphatically not AGI (Artificial General Intelligence). OpenAI CEO Sam Altman himself has cautioned against overhyping its capabilities. During a recent discussion, he admitted the model’s power made him feel “useless,” saying, “I felt useless compared to the AI in this thing that I felt I should have been able to do, and I could not, and it was really hard.” Yet he also reaffirmed that GPT-5 lacks fundamental AGI traits like real-time autonomous learning.
Despite marketing unleashing expectations that GPT-5 signals the dawn of AGI, analysts and critics are urging restraint. Grace Huckins of MIT Technology Review calls GPT-5 “a refined product” that “falls far short of the transformative AI future that Altman has spent much of the past year hyping.”
Expert Skepticism and Highlighted Flaws
Gary Marcus—a persistent and respected critic—was equally measured. On X, he stated that after “nearly three years and billions of dollars, GPT-5 made ‘good progress on many fronts’ but is ‘still part of the pack, not a giant leap forward.’ He concluded it is ‘obviously not AGI.'”
Additionally, emergent research continues to show that LLMs falter even on simple tasks. A recent Apple study demonstrated that even leading LLMs stumble on classic logic puzzles such as the Tower of Hanoi—the kind solvable by children—revealing that more scaling does not equal more reasoning.
Hype vs. Historical Precedents
The current excitement around GPT-5 mirrors past technology hype—like the early-2020s promise that self-driving cars were just around the corner. Despite massive investment and innovation, full autonomy remains elusive a decade later. People today still glimpse the potential in pilot programs and advanced features, but widespread adoption of fully autonomous vehicles remains distant.
Similarly, while LLMs like GPT-5 offer impressive pattern recognition, their underlying architecture—transformers—is fundamentally flawed for achieving AGI. These models rely on statistical pattern-matching, not understanding. As researchers like John Mark Bishop and Judea Pearl—and echoed by Marcus—have argued, deep learning remains “just curve fitting,” lacking causal reasoning and generalizable logic.
The Technology Wall Ahead
Transformers, despite their scalability, hit diminishing returns. Larger models do not necessarily understand context or reasoning better—they just get better at predicting the next word. The Tower of Hanoi results, combined with Marcus’s analysis, suggests we’ve hit a “deep learning wall.”
Thus, even after another decade of incremental improvements, substantial progress toward AGI is unlikely unless new paradigms—like neurosymbolic, causal, or hybrid systems—emerge.
Final Word
GPT-5 is undeniably a leap forward in usability and fluency. But it remains a refined LLM—a tool, not a self-aware intellect. Altman’s “useless” remark underscores this uneasy dissonance between progress and promise. Vocal critics like Gary Marcus remind us: we are still far from true AGI.
Much like the still-distant era of autonomous vehicles, the vision of AGI remains one for the future—perhaps generations away. For now, we should value LLMs for their immediate utility but temper expectations and invest in robust, causal, and verifiable AI foundations.
