Testing the Limits of AI (1985)
inference
| Source: Lobsters | Original article
Recent reflections revisit the 1985 work “The Limits of AI,” emphasizing that intelligence—including artificial—appears asymptotic and fundamentally bounded.
A recent essay has brought the 1985 treatise “The Limits of AI” back into the spotlight, prompting renewed debate over how far artificial intelligence can ultimately go. In a March 19, 2026 post, writer Hugh Howey revisited the original argument that intelligence does not follow an unbounded exponential curve but instead approaches an asymptote. He reiterated his long‑standing contention that there is a ceiling on what can be known and inferred, a view echoed in a growing body of scholarship that distinguishes quantitative computing capacity from deeper ontological constraints on machine cognition.
The resurfacing of the 1985 work matters because it challenges the prevailing narrative that ever‑larger models and more powerful hardware will inevitably lead to artificial general intelligence (AGI). Recent analyses, such as a February 2, 2025 discussion of whole‑brain emulation, suggest that even a hypothetical substrate capable of 1 exaflop and 1 petabyte may still fall short of the unknown thresholds required for true AGI. Meanwhile, a November 6, 2025 Medium article highlighted how current large language models excel at pattern detection yet remain bounded by the data they ingest, underscoring the practical limits highlighted in the 1985 text.
Looking ahead, observers will watch for concrete research that either narrows or expands the perceived ceiling. Key signals include advances in neuromorphic hardware, empirical tests of whole‑brain emulation, and policy initiatives aimed at aligning AI systems with human values before any potential breakthrough. As scholars continue to parse the “ontological boundaries” that separate machine processing from human experience, the conversation sparked by the 1985 essay may shape both technical roadmaps and regulatory frameworks for the next decade of AI development.
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