Post-Transformer Labs Test Alternatives To Standard AI Architectures

🧵 8/8 The strongest pattern in the map is that the top half is converging while the bottom half is spreading out.

The Frontier Model labs are becoming more similar because the transformer playbook is now widely shared: mixture-of-experts, long context, reasoning training, tool use, and multimodal input are becoming common.

The Post-Transformer side looks less tidy, but that is where the deeper architecture question is still open.

The gap that matters most on this map is @fchollet 's ARC-AGI, designed so that memorization doesn't help and only novel-task reasoning counts.

Top frontier models now reach the mid-80s on ARC-AGI-2. On the new interactive ARC-AGI-3, frontier models out of the box score below 1% while humans solve 100% of it. Even the best harness-built agents remain far from human efficiency.

Closing that gap is what every lab on this landscape is ultimately chasing.

🧵 6/8 Region 4 is the applied Post-Transformer region, where the new architecture bet is already tied to a specific product surface.

Cartesia uses state-space style models for real-time voice, Decart and Odyssey work on interactive world models, and Waabi and Wayve apply world-model ideas to autonomous driving.

These domains care a lot about low latency, continuous generation, and fast reaction.

A transformer can be powerful, but its attention step gets expensive as context grows, because the model keeps comparing many pieces of information against many other pieces.

That is why Post-Transformer systems are especially interesting in voice, driving, and interactive media.

What to watch. Whether the latency and real-time advantages that win in voice and driving generalize into the larger consumer surfaces, especially interactive media, where a real-time world model would be a genuinely new category rather than a faster version of an old one.

🧵 5/8 Region 3 is the Post-Transformer challenger region, which means general-purpose labs trying to move beyond the standard transformer recipe.

This includes Pathway, Liquid AI, NXAI, AI21 Labs, NVIDIA Nemotron, Inception, AMI Labs, World Labs, General Intuition, Sakana AI, and Safe Superintelligence.

Post-Transformer does not mean every lab has thrown away attention completely.

It means the lab is testing whether state-space models, recurrence, diffusion language models, world models, hybrid attention, or new undisclosed designs can handle memory, speed, long context, and continual learning better than today’s transformer-heavy systems.

What to watch. The first credible demonstration that a non-transformer or hybrid architecture matches a frontier transformer at frontier scale. That single result would convert this region from a thesis into a movement most labs would race towards.

🧵 3/8 Region 1 is the Frontier Model lab region, which means current-paradigm, general-purpose AI.

This includes OpenAI, Anthropic, Google DeepMind, xAI, Meta, Mistral, Cohere, Alibaba Qwen, DeepSeek, Moonshot, Thinking Machines, Reflection AI, Microsoft AI, MiniMax, Zhipu (now branded http://Z.ai), and ByteDance Seed.

A Frontier Model is a model near the top of the market on broad capability, so it can write, code, reason, use tools, follow long context, and handle many tasks instead of being narrow.

These labs are mostly still improving transformer-based systems, so the fight is now about data, reasoning quality, inference cost, latency, openness, safety work, product surface, and distribution.

What to watch. Whether the next gains come from reinforcement learning pushed beyond easily-verified domains and from continual learning that lets models keep learning during deployment, and whether the open-weight frontier, led by Chinese labs and entrants like Reflection, closes the gap to the closed one.