Scaling the Next Paradigm of Heterogeneous Intelligence#

Adrian Bertagnoli

• Why: The homogeneous-scaling era — monolithic models on identical GPU clusters — is showing its limits; purpose-matched models and silicon per sub-task is a plausible next scaling axis, relevant to accelerator-infrastructure planning.
• What: The "Heterogeneous Intelligence" thesis with early findings: exploiting heterogeneity across workflows, model architectures, and hardware for simultaneous cost, speed, and task-performance gains — e.g., automated payment workflows executing reliably at reduced compute cost, and higher accuracy on long-document reasoning by specializing sub-tasks across purpose-matched chips and models — plus a frontier view of hardware-algorithm co-evolution.
• How: Heterogeneous optimization — decompose workflows and route sub-tasks to purpose-matched models and silicon.
• Where applicable: Strategy-level. "Early findings" with no stated methodology or evaluation — treat as a directional thesis to pressure-test, not validated results. No affiliation given; likely vendor-adjacent.