OpenAI CEO Sam Altman recently engaged with a select audience of founders, developers, and investors in a candid town hall discussion, centering on the immediate and long-term implications of rapidly scaling artificial intelligence. The conversation quickly moved past simple product announcements, focusing instead on structural shifts in the startup ecosystem and the societal challenges inherent in creating radically capable models.
Altman spoke with builders from across the AI ecosystem about the next generation of tools and how these increasingly powerful models will redefine labor and value creation. A central theme emerged immediately regarding the nature of technical work itself. When asked about the Jevons Paradox applied to software engineering—where efficiency gains lead to increased consumption rather than reduced demand—Altman firmly rejected the notion that AI would lead to fewer engineers. Instead, he argued that the definition of engineering is changing: “I think what it means to be an engineer is going to super change. There will be probably far more people creating far more value that are getting computers to do what they want.” The focus shifts from the tedious, manual process of typing code to the higher-level task of instructing and orchestrating complex digital systems to deliver unique value.
This shift, however, introduces new bottlenecks. One founder noted that while building software has become exponentially easier using tools like Codex, the challenge of Go-to-Market (GTM) and gaining user attention remains stubbornly difficult. Altman acknowledged this reality, drawing on his experience leading Y Combinator: “I thought the hard part of this was going to be building a product, and the hard part is getting anyone to care or to use it.” He stressed that fundamental business mechanics—creating durable value, achieving distribution, and capturing human attention—are not solved by powerful models. In a world of radical abundance driven by AI, human attention remains the truly limited commodity.
OpenAI’s strategy appears geared toward enabling this wave of entrepreneurial activity, even if it means fostering competition against its own potential products. When questioned about the risk of OpenAI building its own agent orchestration tools that might displace third-party developers, Altman emphasized the need for diverse experimentation. He admitted that OpenAI doesn't yet know the "right interface" for interacting with sophisticated agents. “We’re not going to figure this out on our own and also not everybody is going to want the same thing,” he stated, indicating that the company welcomes and relies on the startup ecosystem to discover the optimal user experiences atop the core models. The promise here is that the foundational model layer will remain robust and open for innovation, even as the application layer sees intense, necessary competition.
The discussion inevitably turned to the macroeconomic impact of pervasive AI. Addressing concerns about inequality, Altman offered a powerful prediction about the deflationary nature of the technology. He suggested that AI will exert "massively deflationary pressure in the economy," leading to significantly decreased costs for goods, services, and computation. This abundance, he argued, should serve as an “equalizing force in society,” providing opportunities to populations previously marginalized or disadvantaged by high costs and lack of access. This perspective frames AI not merely as a productivity tool but as a transformative economic engine capable of rewriting the rules of access and wealth distribution, contingent, of course, on sensible policy guidance.
Altman also provided insight into the underlying model architecture, addressing the tension between general-purpose and highly specialized models. While acknowledging the current trend toward specialization, he reaffirmed OpenAI's commitment to the generalist route, believing that intelligence is surprisingly fungible. He candidly addressed user feedback regarding the perceived decline in writing quality in recent iterations, admitting, "I think we just screwed that up." He pledged that future versions of the models would prioritize excelling across all dimensions, including intelligence, reasoning, and clear communication, rather than allowing any single capability to regress. Furthermore, regarding the constraints faced by developers running massive agent workflows, Altman predicted drastic improvements in efficiency, stating that by the end of 2027, the cost of inference should be "at least 100x less" than current rates.
The discussion culminated with a focus on safety and resilience, particularly regarding biosecurity. As AI models become capable of accelerating scientific discovery, they also enable dual-use applications. Altman highlighted the shift required in safety thinking: moving from trying to block access (restriction) to building resilience into systems, akin to how society manages inherent risks like fire. He believes that relying solely on classifiers to prevent misuse of models is a short-term measure that will eventually fail. Instead, the focus must be on creating infrastructure that can withstand misuse and respond effectively, ensuring that the benefits of rapidly advancing AI can be safely realized across industries, from scientific research to cybersecurity.
