In 2025, global revenue from AI inference officially surpassed revenue from AI training for the first time. The industry has pivoted from “building AI” to “running AI at scale” – and this shift fundamentally changes which architectures win. Training rewards flexibility and ecosystem breadth. Inference rewards determinism, utilization, and memory locality. These are different physics regimes favoring different hardware.
Training economics: Large batch sizes amortize memory latency. GPUs achieve high utilization despite the memory wall. CUDA ecosystem lock-in with 3 million developers dominates decision-making. Switching costs are astronomical – rewriting years of code, retraining teams, accepting performance penalties.
Inference economics: Small, bursty workloads expose memory latency. GPU compute often sits idle waiting on HBM. Cost per token, latency, and energy efficiency dominate purchasing decisions. Workloads are more predictable, so workload-specific optimizations matter more than ecosystem breadth.
Framework Analysis
As Nvidia’s Groq acquisition reveals, this economic shift exposed a vulnerability in Nvidia’s position. Training remains essentially unbreakable for frontier model development – Nvidia maintains 80%+ share through 17 years of CUDA compounding. But inference? Purpose-built chips could outperform GPUs on the metrics that matter for production deployment.
This connects to the shift from software to substrate – as AI moves from experimentation to production, infrastructure economics become decisive. Training is capital-intensive but episodic. Inference is continuous, user-facing, and cost-sensitive.
Strategic Implications
The training/inference split creates a bifurcating market with different competitive dynamics. Companies optimized for training economics (Nvidia’s traditional strength) face pressure from purpose-built inference solutions. The CUDA advantage erodes when workloads are predictable and workload-specific optimizations matter more than ecosystem breadth.
Nvidia’s Groq deal neutralizes this bifurcation by controlling IP for both paradigms. The company can now offer general-purpose GPU training dominance AND purpose-built inference acceleration.
The Deeper Pattern
Technology markets often bifurcate along economic lines invisible in early stages. Training and inference seemed like the same market when both were nascent. As inference scales, its distinct economics create space for distinct architectures. Nvidia’s deal prevents this space from being captured by competitors.
Key Takeaway
The AI industry’s shift from training to inference changes which architectures win. Training favors GPUs with ecosystem lock-in. Inference favors purpose-built chips optimized for latency and efficiency. Nvidia’s Groq acquisition ensures it dominates both paradigms rather than being disrupted in one.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
