TLDR: AI development is increasingly prioritizing inference, the process by which AI systems draw conclusions and make predictions from data. This shift is driven by the need for real-time applications, the rise of agentic AI, and advancements in efficient computing, with a growing emphasis on both large and smaller, specialized models for diverse deployment scenarios.
The landscape of Artificial Intelligence development is undergoing a significant transformation, with a pronounced shift towards inference—the ability of AI systems to process data, draw conclusions, and make predictions or take actions. This evolving focus underscores the industry’s move from primarily training large models to efficiently deploying them for real-world applications and immediate decision-making.
Experts at the RAISE Summit 2025 highlighted this evolution, emphasizing ‘Fast Inference’ as a critical component of the AI landscape. The discussion revolved around the accelerating innovation driven by open-source models, the strategic importance of AI chips, and energy-efficient processors in shaping future AI deployments. A key insight was the rise of agentic AI, which promises to automate workflows across enterprises and consumer applications, further necessitating robust inference capabilities.
Thomas Wolf, Co-founder & CSO of Hugging Face, noted that ‘all the reasoning is in the test time,’ underscoring that the practical utility of AI models is realized during the inference phase. The conversation also touched upon the ‘compute budget’ required, with reinforcement learning for advanced reasoning models demanding substantial inference compute.
The industry is observing a diversification in model strategies. While very large, often proprietary, models with 1 to 10 trillion parameters continue to push the frontier, there’s growing interest in pretty large open-source models (sub-trillion parameters) and, notably, smaller models in the 3-4 billion parameter range. These smaller, smarter AI models are crucial for enabling real-time inference at the edge, allowing for more localized and efficient AI operations.
The sheer scale of computational demand for AI inference is also a major factor. OpenAI, for instance, is projected to use approximately two gigawatts of compute in 2025, with ambitions to reach 10 gigawatts, illustrating the immense infrastructure required to power advanced AI systems.
Beyond just processing, the concept of ‘introspection of what the inference tells you’ is becoming vital, as AI systems are expected to not only produce results but also inform subsequent actions and learning. This is particularly evident in areas like coding and mathematics, where AI agents are rapidly advancing through reinforcement learning and self-verification, driven by both technological progress and capitalistic incentives.
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Overall, the emphasis on inference signifies a maturation of the AI field, moving beyond foundational model training to focus on the practical, efficient, and scalable deployment of AI for immediate impact across various sectors.
