Poetiq has emerged from stealth with 45.8 million dollars in Seed funding to scale its AI meta syste...
The AMW Read
The article introduces a model-agnostic reasoning layer that challenges the 'scaling laws only' paradigm by focusing on orchestration and recursive self-improvement to drive ROI.
NoveltySignificance
Foundation Models · Recurring PatternsScaling Laws
Poetiq has emerged from stealth with 45.8 million dollars in Seed funding to scale its AI meta system for complex reasoning. Using recursive self-improvement, it achieved a record 75 percent accuracy on the ARC-AGI 2 benchmark via GPT-5.2, beating leaders at half the cost. This model-agnostic layer optimizes foundation models using only a few hundred examples. Such modular architectures suggest that future enterprise ROI will depend on reasoning orchestration rather than model scale alone. 🚀