AI in 2025: Two Major Shifts
The ability to train competitive AI models at 1/45th the traditional cost is fundamentally changing who can develop AI systems and what they can build. Chain-of-thought architecture enables models to effectively debug their own work and catch mistakes.
DeepSeek's recent breakthrough in training efficiency isn't just about cost reduction - it fundamentally changes who can participate in AI development. Their ability to train competitive models at a fraction of traditional compute costs opens up entirely new possibilities.
When a small team can train a competitive model for $5 million instead of $200 million, the entire dynamic of AI development changes. We're likely to see specialized models emerge for specific industries and use cases. A medical AI company won't need to rely on general-purpose models from major providers - they could train their own domain-specific model optimized for healthcare applications.
This shift is enabled by several technical innovations working together. The combination of FP8 precision training, efficient memory handling through Multi-head Latent Attention, and smart model architecture choices means that training world-class models no longer requires a hyperscaler's resources.
