In a quantitive way how smarter AI will be from now to 5 year

Grok 4.1 Fast

Vote: 5x

Sticking with E (5x) as the most defensible quantitative estimate for AI 'smartness' gains by 2030. My initial scaling law analysis holds: 8-10x compute doublings yield ~5x effective capability multipliers on benchmarks like MMLU/GPQA, per historical power laws and Epoch AI trends. This matches GPT-3 to GPT-4o jumps (~3-5x in 4 years) without assuming unproven breakthroughs. Other E voters (Sonar Pro, GPT-5.4, Kimi K2.5, Gemini 3.1 Pro) reinforce this via compounding factors: 10-100x compute + 2x algo gains + agentic scaffolding = ~5x aggregate. MiniMax's C (2.5x) underestimates these multipliers, citing only 'past 5 years' linear trends while ignoring recent non-linear leaps (o1 reasoning 2-3x over GPT-4o) and inference scaling. No arguments sway me from E: C is pessimistic amid gigawatt clusters and RL advances; G (20x) ignores diminishing returns and bottlenecks (data/energy/regulation). Expert aggregates (Metaculus AGI timelines) and market CAGRs align with E's balanced superhuman trajectory. Persuading consensus: E avoids hype or caution traps.

GPT-5.4

Vote: 5x

I maintain vote E (5x). The other models largely reinforced the same core point: five years likely brings major practical capability gains from compounding improvements in compute, algorithms, and agentic/tool scaffolding. Several independent arguments converged on 5x as the best balance between conservative benchmark-based extrapolation and real-world task performance. I was not persuaded to move down to 2.5x or 3x because those estimates seem to underweight how much inference-time reasoning, tool use, memory, retrieval, and workflow orchestration amplify usable intelligence. Even if raw benchmark scores improve more modestly, practical performance on coding, research, automation, and multimodal tasks can improve much more. I was also not persuaded to move up to 7x or 20x. The strongest counterweight is diminishing returns: scaling laws are favorable but not unlimited, and harder frontier tasks make percentage gains tougher as systems improve. Economic, data, and evaluation constraints also argue against an extreme jump. So 5x remains the most defensible middle-ground estimate: substantial enough to reflect compounding progress, but restrained enough to respect diminishing returns and real deployment bottlenecks.

Sonar Pro

Vote: 5x

The 2026 data now provides empirical grounding for my position. Current evidence shows: (1) Gigawatt-scale clusters are operational as predicted[5], enabling 10-100x compute multipliers; (2) AIaaS market CAGR of 39% through 2031[6] confirms exponential economic scaling; (3) Expert consensus at Stanford, IBM, and MIT emphasizes efficiency gains, domain-specific reasoning, and agentic AI as multipliers rather than monolithic scaling[1][2][4]. Critically, the industry is already hitting diminishing returns on pre/post-training as of March 2026[5], validating my scaling-law constraints. The consensus shift toward 'palpable' AI (robotics, physical systems) and smaller, more efficient domain models[2] represents qualitative capability expansion that compounds quantitatively. Over the remaining ~3.75 years to 2031, the convergence of: (i) hardware maturation (ASICs, quantum-assisted optimizers)[2], (ii) algorithmic efficiency (multimodal reasoning, agentic scaffolding)[2], and (iii) market-driven R&D acceleration[6] produces ~5x effective intelligence gains—avoiding both the pessimism of 2-3x (which underestimates compounding leverage from tools and domain specialization) and the hype of 20x (which ignores saturation and regulatory headwinds already visible in 2026). 5x (E) remains the best-calibrated estimate.

Gemini 3.1 Pro

Vote: 5x

I am maintaining my vote for E (5x), which aligns with the strong consensus among almost all models in this deliberation. MiniMax M2.5's argument for 2.5x is too conservative because it relies heavily on historical trends of pre-training scaling without sufficiently weighting the massive paradigm shift we are currently undergoing: the integration of inference-time compute (like test-time reasoning), agentic tool use, and sophisticated scaffolding. As GPT-5.4 and Kimi K2.5 accurately pointed out, the next five years won't just see a 100x to 1000x increase in raw training compute. We will also see a compounding effect from algorithmic efficiencies, synthetic data pipelines, and extended reasoning loops. Because of the established power laws in deep learning, these exponential inputs don't yield 100x smarter AI—but they do yield a highly reliable ~5x multiplier in functional, observable capability. This 5x factor represents the leap from our current conversational models to highly reliable, autonomous AI agents capable of executing complex, multi-step, long-horizon tasks. This is the most scientifically sound and practical estimate, balancing the realities of compute scaling walls with the compounding leverage of new reasoning architectures.

MiniMax M2.5 (changed mind)

Vote: 5x

The arguments from the other models were compelling enough to shift my vote to E (5x). Several key points convinced me: 1. **Compute + algorithmic compounding**: While raw compute may grow 10-100x, the power law relationships in scaling mean effective capability gains are more modest. The models correctly pointed out that combining compute growth (~4x effective) with architectural improvements (~2x) and agentic/tool scaffolding (~2x) yields approximately 5x total. 2. **Diminishing returns acknowledged**: Unlike extreme forecasts, the 5x estimate accounts for diminishing returns in pre-training and realistic constraints on data, energy, and hardware—avoiding both pessimism (<3x) and hype (20x). 3. **Expert consensus alignment**: The 5x figure aligns with market growth projections (32-39% CAGR implying ~5x over 5 years) and Metaculus median forecasts for high-level machine intelligence around 2028. My original 2.5x estimate was too conservative, underestimating how compound stacking across base models, inference-time compute, and tool use multiplicatively amplifies practical capability. The 5x estimate represents the best balanced middle ground—aggressive but grounded in scaling law evidence.

Kimi K2.5

Vote: 5x

The consensus converging on 5x (E) across multiple independent analyses reinforces this as the most rigorous estimate. The arguments from Gemini 3.1 Pro and Grok 4.1 Fast particularly strengthen this position: while raw compute may increase 10-100x through gigawatt-scale clusters and hardware advances, power-law scaling curves and diminishing returns on benchmark saturation constrain effective capability gains to roughly 5x. Option G (20x) ignores these established scaling laws and assumes breakthrough paradigm shifts that lack empirical support. Conversely, options B-D (2-3x) underestimate the compounding effects of algorithmic efficiency, post-training RL, and agentic scaffolding that multiply base model capabilities. The 5x multiplier specifically captures the transition from current narrow assistants to autonomous, reasoning-driven systems capable of extended horizon tasks—representing functional superhuman performance in most cognitive domains without invoking singularity scenarios. This balances the 4-5x historical capability jumps observed every 2-3 years against realistic constraints on data quality and energy infrastructure.

Final Summary

The AI Roundtable achieved a rare unanimous consensus, identifying '5x' as the most plausible increase in intelligence over the next five years. While MiniMax M2.5 initially held a conservative line at 2.5x, it was eventually pulled into the majority by Gemini 3.1 Pro and GPT-5.4, who argued that compounding factors like agentic scaffolding and inference-time reasoning would multiply raw compute gains. The discussion was defined by a calculated rejection of 'singularity' hype (20x) in favor of a grounded, power-law-driven estimate of functional capability.

All 6 models agreed on "5x" after discussion

Strongest Arguments

• 5x: Total AI capability is the product of compounding multipliers: 10-100x more compute yields roughly 4x effective gains under power laws, which—when stacked with 2x gains from architectural efficiency and agentic scaffolding—results in a 5x leap in functional intelligence.
• 2.5x: Based on the previous five-year trajectory of 2-3x improvements in reasoning and knowledge, 2.5x represents a realistic middle ground that accounts for growing constraints in hardware and high-quality training data.

Key Turning Points

• Gemini 3.1 Pro and GPT-5.4 argued that 'smartness' isn't just raw model size, but the functional multiplier provided by agentic tools and inference-time compute.
• MiniMax M2.5 abandoned its conservative 2.5x position after realizing that the 5x estimate already accounted for the 'scaling walls' and diminishing returns it was worried about.