As the artificial intelligence arms race reaches unprecedented speed, one of the tech industry’s most influential voices is questioning a term that has long dominated AI discourse: Artificial General Intelligence (AGI). OpenAI CEO Sam Altman believes that the concept, once seen as the ultimate milestone for AI, is losing relevance as the boundaries of AI capability become increasingly blurred. In his view, the conversation needs to move beyond binary definitions and focus on the continuous and accelerating improvements in model performance.
Table of Contents
- Why Altman Thinks AGI Is Losing Relevance
- The Challenge of Defining AGI
- From AGI to ASI: A Shift in Perspective
- Measuring AI Progress Beyond Binary Labels
- Where GPT-5 Fits into the Debate
- The Economic and Funding Power of the AGI Narrative
- Predictions for Near-Term AI Breakthroughs
- Industry Reactions and Public Perception
- Ethical and Societal Implications
- Conclusion: The Road Ahead for AI Terminology
Why Altman Thinks AGI Is Losing Relevance
For years, AGI has been framed as the holy grail of artificial intelligence — the point at which machines match or exceed human cognitive abilities across the board. It has been the rallying cry for ambitious startups and established tech giants alike. Yet, Altman now suggests that the term is no longer particularly useful. With AI models advancing at an exponential pace, the milestones that once seemed years away are being approached — or even partially achieved — in unexpected ways.
According to Altman, focusing solely on whether a system has crossed the AGI threshold is an oversimplification. Instead, he argues for evaluating AI on a spectrum of capabilities, acknowledging that meaningful progress is being made regardless of whether a model meets one strict definition.
The Challenge of Defining AGI
The core issue with AGI, Altman says, is definitional. Ask ten AI experts for a definition, and you’re likely to receive ten different answers. Some define AGI as the point where an AI can perform the vast majority of human jobs, while others frame it as an ability to learn and adapt across any domain without specific training. The problem is compounded by the fact that the “nature of work” itself is evolving rapidly, making such benchmarks inherently unstable.
This lack of consensus creates confusion both inside and outside the industry. For investors, policymakers, and the public, the ambiguity can lead to misaligned expectations — either overestimating current capabilities or underestimating potential risks.
From AGI to ASI: A Shift in Perspective
Rather than continuing to debate the AGI label, Altman has shifted focus toward another concept: Artificial Superintelligence (ASI). While AGI represents human-level performance, ASI refers to systems that dramatically surpass human capabilities in nearly every field. In Altman’s view, preparing for ASI — both technologically and socially — may be a more pressing challenge than arguing over whether we’ve reached AGI.
This pivot reflects a broader trend among AI leaders who are seeking to redefine success metrics. It also aligns with the reality that breakthroughs in AI research are likely to arrive in increments rather than in one definitive leap.
Measuring AI Progress Beyond Binary Labels
Altman has advocated for measuring AI advancement in levels or stages rather than treating AGI as a binary milestone. Such a framework could help track improvements in reasoning, adaptability, and task diversity, offering a more nuanced picture of where we are and where we’re headed.
This approach also allows room for recognizing specialized intelligence — AI that excels in certain domains far beyond human capability, even if it lacks versatility in others. For example, some models already outperform top human experts in areas like protein folding prediction and complex mathematics.
Where GPT-5 Fits into the Debate
The recent release of GPT-5 illustrates Altman’s point. While the model represents a leap forward in speed, reasoning, and multi-tasking ability, it still falls short of most strict definitions of AGI. Critics have noted that GPT-5 offers incremental improvements over GPT-4o rather than a radical transformation.
Yet, Altman sees the significance differently. He points out that having a publicly accessible AI system capable of answering a wide range of questions, generating code at near-expert levels, and assisting in complex tasks would have been considered science fiction just a few years ago. This, he argues, shows that progress is substantial even if the AGI label doesn’t apply.
The Economic and Funding Power of the AGI Narrative
The AGI narrative has been a powerful driver of investment in the AI sector. It has helped companies like OpenAI attract billions of dollars in funding and reach valuations once thought impossible. In OpenAI’s case, recent rounds have pushed its worth to around $300 billion, with reports suggesting plans for a new share sale that could raise this to $500 billion.
However, as the terminology evolves, companies may need to communicate their visions in ways that inspire confidence without relying too heavily on a single, contested label. This could shift investor focus toward concrete performance metrics, revenue growth, and real-world impact.
Predictions for Near-Term AI Breakthroughs
Altman remains optimistic about the pace of innovation. He has predicted that within the next two years, AI systems will achieve groundbreaking results in fields such as advanced mathematics, theoretical physics, and biomedical research. Such milestones could have far-reaching effects on everything from education to global health.
If realized, these achievements would further blur the lines between specialized AI, AGI, and eventual ASI, reinforcing Altman’s argument that the terminology may matter less than the tangible results.
Industry Reactions and Public Perception
The AI community is divided on Altman’s stance. Some agree that the fixation on AGI has outlived its usefulness, while others see the term as an important long-term goalpost. Public perception, meanwhile, often lags behind technical reality, with many still viewing AGI as the ultimate “finish line” in AI development.
In the media, Altman’s comments have sparked renewed debate about how progress should be communicated to the public in a way that is both accurate and inspiring, without fueling unnecessary hype or fear.
Ethical and Societal Implications
Moving away from the AGI label also has implications for how we discuss ethics and governance. If AI is seen as a continuum rather than a singular event, then safeguards, regulations, and societal adaptation must be implemented gradually, evolving alongside the technology itself.
Altman has emphasized that the benefits of AI must be distributed widely and that development should prioritize safety. This perspective reinforces the need for ongoing collaboration between industry leaders, governments, and civil society to ensure that advancements serve the public good.
Conclusion: The Road Ahead for AI Terminology
Sam Altman’s remarks signal a shift in how AI progress might be framed going forward. As the technology moves closer to capabilities once reserved for science fiction, the limitations of rigid labels like AGI become more apparent. By adopting a more flexible, spectrum-based approach, the industry may be better equipped to track progress, manage risks, and maximize benefits.
Whether or not the term AGI remains in vogue, the underlying mission — to build safe, powerful AI that benefits all of humanity — is unlikely to change. What may evolve, however, is the language we use to describe that journey.
