RSI is the new AGI — and it’s just as hard to pin down

The artificial intelligence field has shifted its focus from chasing Artificial General Intelligence (AGI) to grappling with a new concept: Reasoning-Specific Intelligence (RSI). This transition reflects a fundamental reassessment of how the industry measures progress and capability in AI systems. Like its predecessor, RSI proves equally elusive to define and measure, raising questions about how the field establishes meaningful benchmarks for advancement.

For years, AGI served as the north star for AI development—the theoretical point at which machines would match or exceed human intelligence across all domains. However, as researchers encountered the complexity of achieving this goal, discourse shifted toward more granular measures of capability. RSI emerged as a framework focusing on an AI system's ability to reason through complex problems, break down multi-step challenges, and synthesize information in novel ways. Rather than general intelligence, RSI emphasizes reasoning depth and sophistication.

• Measurement challenges persist: Just as AGI lacked a universally accepted definition, RSI creates ambiguity in evaluating whether systems have truly achieved meaningful reasoning capabilities versus mimicking reasoning patterns from training data.

• Resource allocation shifts: Companies and researchers are redirecting efforts toward developing and testing reasoning-specific systems, potentially accelerating practical AI applications while deprioritizing broader AGI research.

• Competitive benchmarking becomes contentious: Different organizations propose different RSI benchmarks, leading to conflicting claims about which systems are most advanced and fragmenting the industry's consensus on progress.

• Investment and hype cycles continue: As with AGI discussions, RSI terminology risks becoming a marketing tool, with companies claiming reasoning breakthroughs that may not withstand rigorous scrutiny.

• Regulatory clarity remains elusive: Policymakers struggle to establish oversight frameworks when the very capabilities being measured remain poorly defined.

The evolution from AGI to RSI discussions reveals a maturing but still uncertain field. As AI systems demonstrate increasingly sophisticated problem-solving abilities, the industry needs precise language and measurable criteria. The challenge lies not in identifying whether AI can reason, but in establishing shared standards for what "reasoning" actually means in computational systems. Until the field resolves these definitional issues, RSI risks becoming as nebulous as the AGI concept it seeks to replace.