💡 Main Takeaway:
The cost of running AI models—specifically inference (using the model after training)—has dropped by as much as 100× since 2023, dramatically accelerating enterprise AI adoption.
📉 What’s Driving the Cost Drop?
- Custom AI chips (like NVIDIA’s Blackwell and AMD MI300X)
- Optimized software stacks (LLM distillation, quantization)
- Smarter workload placement across cloud and on-prem systems
As a result, companies can now deploy large language models (LLMs) and vision models in real time, affordably.
🏢 Which Industries Are Moving Fast?
- Banking: AI chatbots, fraud detection
- Retail: Personalized recommendations, supply chain AI
- Manufacturing: Predictive maintenance, quality control
- Healthcare: Diagnosis, patient data summarization
💰 The Money Is Flowing:
- Analysts say $30 billion+ is being invested in enterprise AI infrastructure in 2025 alone.
- Startups focused on agentic AI, data governance, and low-latency inference are especially hot.
🔐 A Word on Security & Data:
- Companies are shifting toward private AI deployments to protect customer data.
- “AI silos” and on-prem LLMs are becoming more common for privacy and compliance reasons.
