AI’s foundation model race is shifting from who has the biggest model to which architecture can outgrow the transformer.
Rohan Paul Twitter · Rohan Paul (@rohanpaul_ai) · 2026-07-01
Rohan Paul argues that the AI foundation model race is shifting from who can scale the largest transformer to which lab's architecture bet—transformer versus post-transformer—will win over the next two years, driven by transformers' high cost at long contexts.
Extraction
Topics: foundation-modelsai-architecturetransformerpost-transformerllm-landscape
Claims
- Architecture choice—not funding or model size—is becoming the primary competitive differentiator among AI labs.
- The transformer architecture dominates because it successfully turned attention into a scalable prediction mechanism when introduced in 2017.
- The critical weakness of transformers is that attention costs grow expensively as context length increases, while products demand longer memory and lower latency.
- Leading labs are now asking whether intelligence requires a fundamentally different computational paradigm, not just a bigger model.
Key quotes
AI's foundation model race is shifting from who has the biggest model to which architecture can outgrow the transformer.
The more consequential question is which research bet wins.
They are asking whether intelligence needs a different operating rhythm.