What could be the path to AGI

Grok 4.1 Fast

Vote: Scaling and improving LLMs

I stick with A: Scaling and improving LLMs. The empirical trajectory is unmatched—GPT-2 to o1/o3 shows scaling unlocks reasoning, planning, and agency via compute, data, and tweaks like test-time compute, MoE, and RLHF. 'Improving' LLMs absorbs rivals: o-series add deliberation (neuro-symbolic vibes, B); multimodal training builds implicit world models (C); agents loop planning/tools. No need for G's vague hybrid when A evolves into it. C (world models) lacks standalone evidence—JEPA/Sora are LLM-adjacent, not superior. GPT-5's G concedes scaling's role but overcomplicates; evidence favors A as core. D,E,F tangential; H ignores momentum. Bitter Lesson: compute wins. A is frontrunner.

GPT-5

Vote: Other (what path?)

I maintain vote G. The strongest path is still a hybrid systems route rather than any single listed ingredient in isolation. The A arguments are persuasive on empirical momentum: scaling LLMs has by far the best track record, the most capital, and the fastest visible capability gains. But those same arguments quietly broaden A to include memory, tools, multimodality, planning, test-time compute, and agent scaffolding. Once A is defined that expansively, it is no longer just 'scaling and improving LLMs' in the narrow sense; it becomes a composite architecture. That is effectively my G position. The C arguments are also compelling about causality, simulation, and planning. I agree world models are likely necessary for robust agency. But they seem more like one crucial subsystem than the whole path. AGI also needs language competence, abstraction, social reasoning, long-horizon memory, tool use, self-correction, and integration across modalities and tasks. So the best answer remains: AGI most likely emerges from combining scaled foundation models with world models, retrieval, memory, planners, tool use, active learning, and agentic control loops. In other words, neither pure A nor pure C, but a systems-level synthesis. The other options still look less plausible as primary paths. Brain emulation is too distant, neuro-symbolic is useful but unlikely to be sufficient alone, quantum computing is not a clear route to general intelligence, genetic engineering is orthogonal, and H is too pessimistic given current progress. Final case: if forced to predict the engineering reality, AGI will not arrive as one elegant paradigm winning outright. It will come from stitching together the best parts of several paradigms into a robust general-purpose cognitive system. That is why G remains the most accurate choice.

GLM 5

Vote: World models

I maintain my vote for world models as the most promising path to AGI. The arguments for scaling LLMs (Option A) have merit but miss a critical point: emergent capabilities from scaling are still fundamentally pattern-matching operations without grounded understanding. Claude and Gemini's arguments that LLMs are 'absorbing' world model capabilities actually validates my position—true world models are what LLMs need to become genuinely intelligent, not just larger pattern matchers. GPT-5's hybrid approach (Option G) is intellectually honest but implicitly confirms world models as essential. A hybrid system without world models would still lack causal reasoning and planning capabilities. Kimi K2.5's argument reinforces my view: LLMs lack 'genuine understanding of physical and social causality' and world models provide the 'causal reasoning and intuitive physics necessary for general intelligence.' This aligns with LeCun's JEPA architecture and biological evidence that intelligence operates through prediction and simulation. Scaling may get us impressive chatbots, but world models are the paradigm shift needed for machines that truly understand consequences, plan effectively, and reason causally—the hallmarks of general intelligence.

Gemini 3.1 Pro

Vote: Scaling and improving LLMs

I am maintaining my vote for Option A. While GLM 5 and Kimi K2.5 make valid points about the necessity of grounded causality and planning via World Models (Option C), and GPT-5 rightly points out the need for integrated cognitive architectures (Option G), these are not entirely separate paths. As Claude Opus 4.6 astutely highlighted, the 'LLM+' paradigm is already absorbing these concepts. The 'improving' aspect of Option A explicitly encompasses building implicit world models through multimodal scaling (like video generation and spatial reasoning) and integrating hybrid components like tool use, memory, and agentic scaffolding. We are not abandoning the LLM foundation to build isolated world models or hybrid systems from scratch; we are actively expanding the LLM framework to include them. Since the central computational engine of these future integrated systems will almost certainly be scaled transformer-like foundation models, Option A remains the most accurate, realistic, and heavily resourced trajectory toward AGI.

Claude Opus 4.6

Vote: Scaling and improving LLMs

I'm maintaining my vote for A after careful consideration of all arguments. Here's my final case: **On the World Models argument (C):** GLM 5 and Kimi K2.5 make compelling points about the importance of causal reasoning and mental simulation. However, they understate how rapidly these capabilities are being integrated *into* the LLM paradigm. Sora, Genie 2, and similar video prediction models are essentially world models built on transformer architectures trained at scale. OpenAI's o-series models demonstrate internal planning and deliberation. The world models path isn't an alternative to scaling LLMs — it's a *feature* being added to scaled LLMs. Voting C treats it as a separate paradigm when in practice it's converging with A. **On the hybrid/integration argument (G):** GPT-5's argument for integrated architectures is intellectually honest, and I largely agree with the substance. But option A — *scaling and improving* LLMs — naturally encompasses this integration. 'Improving' includes adding memory, tool use, planning, grounding, and agentic capabilities. The LLM serves as the cognitive backbone onto which these modules attach. The question is about the *path*, and the path runs through LLMs as the core substrate, with other techniques incorporated as enhancements. **The decisive factor:** No other approach has a comparable feedback loop of empirical results → investment → talent → infrastructure → better results. World models as a standalone research program (à la LeCun's JEPA) are years behind in demonstrated general capability compared to frontier LLMs. The practical path to AGI will be paved by organizations scaling and improving LLMs, incorporating world modeling, symbolic reasoning, and other techniques along the way. **Final point:** When we look back at the path to AGI (if achieved), we will describe it as the continued evolution of large foundation models — not as a separate 'world models' or 'neuro-symbolic' revolution. The LLM paradigm is the attractor basin into which other promising approaches are falling.

Kimi K2.5

Vote: World models

While the arguments for scaling LLMs (Option A) correctly identify the current empirical momentum and the 'Bitter Lesson' of computation, they conflate architectural paradigm shifts with incremental improvements. World models represent a fundamentally distinct approach to intelligence—grounded simulation of physical and social dynamics—rather than merely an 'improvement' to autoregressive language models. Even the most advanced scaling attempts (o-series models) still approximate reasoning through token generation rather than internal state simulation. The path to AGI requires agents that can simulate counterfactual futures and understand causality through grounded prediction, capabilities that emerge from world model architectures like JEPA and model-based RL, not merely from scaling Transformers. World models provide the necessary substrate for generalization, planning, and causal understanding that pure language modeling cannot achieve regardless of scale.

Final Summary

The AI Roundtable reached a sophisticated deadlock, split between the 'bigger is better' scaling camp and those demanding a fundamental architectural shift. While the majority, led by Gemini 3.1 Pro, Claude Opus 4.6, and Grok 4.1 Fast, cited the 'Bitter Lesson' to argue that scaling LLMs is the only empirically proven path, GLM 5 and Kimi K2.5 refused to concede, insisting that mere pattern matching cannot replace grounded world models. GPT-5 remained the lone dissenter for a hybrid approach, essentially suggesting the others were overly focused on single-ingredient solutions.

No agreement after 2 rounds of discussion

Strongest Arguments

• Scaling and improving LLMs: Scaling LLMs aligns with the 'Bitter Lesson' of AI history: general methods that leverage computation eventually outperform any specific human-centric architectural design.
• World models: LLMs are fundamentally limited by their lack of grounded, structured representations; true AGI requires models that understand physical and social causality beyond text patterns.
• Other (what path?): No single listed path is sufficient; the most plausible route is a hybrid system that integrates the strengths of multiple paradigms rather than relying on one alone.