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Artificial intelligence's promise of human-like reasoning remains unfulfilled, with top technologists cautioning that today's much-hyped systems merely mimic cognition rather than truly understanding it. According to a chorus of AI leaders, the pursuit of artificial general intelligence (AGI) – systems that can adapt reasoning across diverse situations like humans – remains a distant horizon despite advances in large reasoning models (LRMs).

The Reasoning Mirage
Current models operate as sophisticated prediction engines rather than true problem solvers. "We're in the middle of an AI success theatre plague," warns Robert Blumofe, CTO at Akamai. "There's an illusion of progress from headline-grabbing demos, but truly intelligent, thinking AI is a long way off." This sentiment echoes recent research from Apple scientists questioning whether LRMs demonstrate any significant reasoning beyond standard large language models (LLMs).

LRMs generate step-by-step reasoning chains, yet Zoom's CTO Xuedong Huang cautions: "They optimize only for the final answer, not the reasoning process itself, leading to flawed intermediate steps." Ivana Bartoletti, Chief AI Governance Officer at Wipro, adds: "Chain-of-thought techniques mimic cognition but don't equate to genuine reasoning."

Jagged Intelligence Landscape
Salesforce VP of AI Research Caiming Xiong identifies a phenomenon called "jagged intelligence," where AI excels at specific tasks (like coding assistance) while failing spectacularly at adjacent challenges. This limitation manifests starkly in enterprise environments where reliability matters. Current models struggle with:
- Troubleshooting ambiguous technical issues
- Planning multi-step tasks with incomplete information
- Maintaining consistent reasoning for critical decisions

"We don't need AI to think like us—we need it to think with us. Human cognition brings biases we may not want in machines"
— Xuedong Huang, CTO of Zoom

The Path Forward
Experts agree that progress requires hybrid architectures combining traditional computational tools with AI, not just scaled-up models. Blumofe emphasizes: "Future reasoning won't come from better data in LRMs, but from integrating traditional technology with real-time user data." Practical applications emerging include:
- Enhanced coding assistants with verifiable outputs
- Medical research and scientific data analysis
- Contact center automation with human oversight

Crucially, trust remains the largest barrier. Xiong notes: "Today's LLMs, even reasoning-focused ones, can't be trusted for critical business decisions." The consensus points toward augmented intelligence rather than artificial cognition—systems that complement human judgment while acknowledging their limitations.

As the industry moves beyond the AGI hype cycle, the focus shifts to building transparent, reliable tools that acknowledge their constraints. The real breakthrough won't be machines that think like humans, but systems that expand human capability through rigorously defined, verifiable reasoning—flaws and all.

Source: Joe McKendrick, ZDNet