Replit CEO Amjad Masad argues that society should not fixate on creating an all-powerful, god-like form of artificial superintelligence. In his view, such an ambitious technological leap is unnecessary to transform global productivity, social systems, and economic structures. Instead, he contends that what humanity truly requires is a more pragmatic form of intelligent technology that he calls “functional AGI”—an artificial system advanced enough to replicate many forms of human-level performance without possessing actual consciousness, emotion, or full creative reasoning.
Speaking on the latest episode of the “a16z” podcast, Masad explained that Silicon Valley’s obsession with attaining a mythical version of true Artificial General Intelligence—the kind of omnipotent cognitive system often described in science fiction—may be missing the point entirely. He emphasized that a more attainable, operationally effective form of AGI is already within humanity’s grasp, and that such technology is adequate to automate vast portions of the modern economy. According to him, reaching this threshold will usher in an era of large-scale efficiency and profound social reconfiguration long before any theoretical form of conscious AI ever materializes.
Masad elaborated that “functional AGI” should be understood not as a sentient or self-aware machine but rather as a highly adaptable network of systems that can learn from real-world data, refine their abilities through feedback, and independently complete tasks that are measurable and verifiable. In his perspective, these systems need not replicate human thought processes or philosophical depth; they simply need to perform useful work with reliability and scalability. He envisions such functional intelligence targeting nearly every major sector—from manufacturing and logistics to education, finance, and creative industries—automating enormous portions of the labor force and driving economic evolution on an unprecedented scale.
Nonetheless, Masad expressed skepticism about whether true AGI, defined as a machine capable of seamlessly transferring knowledge and intuition across varying domains like a human mind, will ever be achieved. While acknowledging that a genuine AGI breakthrough could propel civilization into extraordinary frontiers of scientific and cultural progress, he admitted being doubtful that such a moment is imminent or even likely. His hesitancy stems from the observation that existing AI systems, though incomplete from a philosophical standpoint, already generate staggering commercial value and practical benefits. As he put it succinctly, the world may not need perfection to experience revolution—the functional forms of AI already deployed are transformative enough to reshape everything from workplace dynamics to macroeconomic systems.
Masad also introduced a cautionary note about what he refers to as a potential “local maximum trap.” This concept describes a tendency in the AI sector for companies to optimize incremental improvements in existing large models—pursuing higher short-term profits and performance gains rather than questioning foundational assumptions. By focusing on refinements rather than reinvention, the industry may be unintentionally restricting itself to a limited conceptual peak, missing opportunities for fundamental breakthroughs that lie beyond the boundaries of current paradigms. In other words, by continuously polishing models that work today, researchers risk overlooking entirely new pathways that might lead toward true general intelligence.
Reflecting on the immense complexity of the human mind and the limits of current computational approaches, Masad conceded that the ultimate problem of achieving real general intelligence might not be solvable within our lifetimes. The gap between narrow algorithmic intelligence and the integrated cognitive flexibility of humans, he suggested, could prove far more difficult to bridge than enthusiasts anticipate. “Who knows?” he mused, acknowledging the profound uncertainty that still governs this technological frontier.
Masad’s remarks coincide with a moment of growing debate across the AI landscape. A number of thought leaders and researchers are asking whether AGI remains a meaningful or even coherent target. Despite that philosophical questioning, many of the field’s largest players—OpenAI, Google, Meta, and Microsoft—continue to pursue AGI as their ultimate aspiration, dedicating some of their most sophisticated research divisions and computational resources to the quest. However, a rising chorus of experts has begun expressing doubts about whether large language models and related architectures can ever truly evolve into systems possessing broad, human-like versatility and understanding.
Among the skeptics is AI theorist and best-selling author Gary Marcus, who has been outspoken in his criticism of what he sees as misplaced confidence in scaling existing approaches. In a widely circulated essay published in August, Marcus warned that intellectually honest researchers should no longer assume that making models larger and feeding them more data will somehow result in true intelligence. He noted that even among high-profile technology advocates, there is a growing realization that earlier predictions—such as those claiming AGI would arrive by 2027—were driven more by marketing ambition than by scientific evidence.
The release of OpenAI’s GPT-5 further tempered this optimism. Although OpenAI’s own chief executive, Sam Altman, described the system as generally intelligent in the broad sense of the term, he candidly acknowledged that it still falls short of the deeper, integrative capacities most experts would associate with genuine AGI. Altman admitted that several essential elements of general cognition remain missing despite the model’s impressive performance across many domains.
Other luminaries in the field have echoed similar sentiments. Meta’s chief AI scientist, Yann LeCun, publicly observed that the path to true general intelligence could still stretch decades into the future. In an April lecture at the National University of Singapore, LeCun explained that many of the most fascinating challenges in AI scale poorly: simply increasing computational power or data volume does not automatically yield smarter or more capable systems. His warning underscores a recurring theme among researchers—namely, that progress in artificial intelligence is far from linear and may require conceptual leaps as much as computational ones.
Taken together, Masad’s position represents a pragmatic reorientation in how society might think about artificial intelligence. Rather than waiting for the theoretical arrival of a perfect, conscious mind within silicon, he urges embracing the extraordinary capabilities of the functional systems already available. These technologies, according to him, are not only economically beneficial but also socially transformative, marking a tangible step toward a future where human creativity and machine efficiency coexist in powerful balance.
Sourse: https://www.businessinsider.com/functional-agi-superintelligence-economy-replit-amjad-masad-2025-10
