danyal akarca

AI workloads are shifting from homogeneous GPU clusters to orchestrated fleets of specialized chips (photonic, thermodynamic, wafer-scale, ASICs), creating a massive software opportunity to match tasks to optimal hardware for cost and speed.

As AI shifts from training to inference, workloads decompose into varied tasks requiring specialized chips; software that orchestrates model-chip matching will unlock massive cost and speed gains, making AI economically viable at scale.

Running AI economically requires matching diverse model types to specialized chip architectures (GPUs, LPUs, wafer-scale, photonic, thermodynamic) rather than relying on homogeneous GPU clusters; the software orchestration layer that maps tasks→models→chips becomes the critical bottleneck and value capture point.

As chip diversity increases, the orchestration layer that matches models to specialized hardware and enables co-design of algorithms and chips becomes the key value capture point. Callosum's thesis: heterogeneous scale > homogeneous scale because real-world problems are multi-step and multi-modal, requiring systems of models on mixed hardware. This 'connective tissue' breaks the hardware lottery by de-risking novel chip adoption.

Inference market size now justifies massive investment in novel architectures (Groq, Cerebras, Rebellions, Tendrils, photonic, thermodynamic), fragmenting the chip landscape and reducing Nvidia's share even as total market expands.

The inference market is now large enough to justify massive investment in novel chip architectures (Groq, Cerebras, Rebellions, Tendrils, hyperscaler ASICs), each with different memory hierarchies and compute models; the critical bottleneck shifts from chip design to software that can dynamically map workloads across this heterogeneous fleet.

Market growth justifies investment in diverse chip architectures (photonic, thermodynamic, wafer-scale, etc.), but the resulting fragmentation creates a software bottleneck that neutral orchestration layers must solve.

The paradigm shift from single-model reasoning to multi-agent systems (where models interact with tools, environment, and each other) creates inherently heterogeneous workloads. Different agents need different hardware (edge vs cloud, prefill vs decode, continual learning vs inference), making software orchestration essential for cost, speed, and performance.