A Tom’s Guide analysis examines a provocative question in AI research: can artificial intelligence continue to get smarter indefinitely, or will it eventually “hit a wall” beyond which further advancements stall? The piece introduces the idea of the “Hapsburg AI” effect, a metaphor suggesting that as models are increasingly trained on data generated by other models rather than original human sources, their capacity for novel thought, creativity, and insight may degrade — similar to biological inbreeding limiting genetic diversity. This phenomenon, paired with the finite supply of high-quality training data, raises concerns that future progress could slow as these limitations accumulate.
Current AI systems excel at pattern recognition and interpolation within the scope of data they’ve seen, but they struggle with true creativity and extrapolation — generating genuinely new ideas or understanding concepts outside their training. The article argues that without grounding in the physical world or the ability to experiment and learn from real environments, AI may remain limited to bureaucratic efficiency rather than imaginative intelligence. This suggests that architecture and training data may impose deeper constraints on how “intelligent” AI can become.
Beyond data quality and training methodology, there are fundamental physical and computational limits that could bound intelligence — whether human or artificial. Concepts like Bremermann’s limit describe ultimate ceilings on computation speed and capacity based on physical laws (such as mass-energy equivalence and quantum uncertainty), indicating that any intelligent system operates within finite thermodynamic constraints. Such limits hint that there may be hard ceilings on how complex or powerful intelligence could be, even with exponential growth in hardware and algorithms.
Despite these potential barriers, many experts believe AI can continue to advance significantly, even if progress eventually slows. Some argue that breakthroughs will come not just from larger models but from novel architectures, embodied learning, and better integration with the physical world — areas where current systems remain rudimentary. Thus, the central debate isn’t only about whether intelligence has a limit, but how we define and measure it — and whether future AI systems can transcend current benchmarks of human-like reasoning and creativity.
