AI Reasoning: The Next Leap Beyond Inferencing

In December 2024, OpenAI introduced O1, a groundbreaking AI model that moved beyond traditional inferencing, hinting at early forms of reasoning-based AI. The industry was already abuzz, but then, in early 2025, DeepSeek R1 changed the game entirely. This open-weight model showcased true multi-step reasoning capabilities, pushing the AI field closer than ever to Artificial General Intelligence (AGI).

We’re witnessing an AI revolution where models don’t just predict but actually reason which includes understanding complex concepts, solving multi-step problems, and making informed decisions. But what exactly is AI reasoning, and why is it crucial in 2025? Let’s break it down.

What is AI Reasoning, and How is it Different from Inferencing?

AI has traditionally relied on inferencing—a process of recognizing patterns and making predictions based on past data. However, as AI advances toward Artificial General Intelligence (AGI), it must go beyond inferencing and develop reasoning capabilities. Until recently, most AI models functioned through pattern recognition and inferencing—detecting trends in data and using them to make predictions. While powerful, these AI models don’t truly understand cause and effect. They operate on statistical correlations rather than logical reasoning.

?? AI Reasoning: A Leap Forward

With the emergence of DeepSeek R1 and OpenAI O1, (and I personally expect Llama 4 soon in Q1 of 2025 to provide the same); we’re now witnessing AI models that reason rather than just infer. These models can: Understand causality, rather than just recognizing statistical patterns. Adapt to new situations without requiring retraining. Explain decisions, improving AI transparency and trust. Solve multi-step problems autonomously, such as writing complex code or conducting scientific research.

Examples of reasoning AI in action: If you haven't tried it just go and use the DeepSeek R1 or OpenAI's o1 model (select from the model menu on top) and experience first hand how AI models reasons in real time; DeepSeek is giving us all the steps it has gone through vs OpenAI provides some summary about the reasoning process. So for conversational AI, advanced chat models engage in multi-turn, context-aware conversations, understanding the intent behind a query rather than just responding reflexively. More industry examples will follow like Autonomous Vehicles where AI doesn’t just detect objects but it predicts their behavior and makes real-time driving decisions based on logic. Also in Healthcare AI as an industry example instead of simply matching symptoms to diseases, AI now reasons through multiple possibilities before diagnosing conditions. I am planning to write an article on "Can I cure cancer" soon, related with reasoning so stay tuned for that also.

Why AI Reasoning Matters in 2025

As AI integrates deeper into decision-making processes, the demand for reasoning-based AI is rising across industries. First and foremost, I always cover this (including my Blockchain & AI article) trust & explainability; AI must justify its decisions, particularly in highly regulated fields like finance, healthcare, and law. We also need real-time decision-making where Autonomous systems (e.g., robots, self-driving cars, and cybersecurity AI) require real-time reasoning beyond simple inference. Finally in business & strategy; AI must generate insights, not just data summaries, to drive real decision-making.

Reasoning is path to AGI, for AI to evolve into AGI, it must develop the ability to think, reason, and act with intelligence.

The Compute Challenge: Why AI Reasoning Needs More Power

Unlike simple pattern-matching, reasoning AI requires significantly more computing power. First of all; we need more memory to store and process complex logical structures. You also need higher compute efficiency to execute multi-step logical reasoning. You need Scalable infrastructure to train and deploy reasoning-based AI models effectively.

How Intel is Powering AI Reasoning

Many businesses think AI all about GPUs, and this is a bias look because of training, many frontier AI models definitely will require more and more GPUs but the real-world usage of AI is happening at "inferencing" layer today and more and more it will happen for "reasoning" layers as I explained above. Intel’s AI solutions are designed to handle the increased computational demands of reasoning AI:

Staring with Intel Gaudi AI Accelerators which are optimized for deep learning, inference, and reasoning workloads, providing scalability for AI model training. Intel Xeon Processors which are equipped with built-in AI acceleration, enabling high-performance AI reasoning in enterprise and cloud environments. Intel AI PCs runs models like DeepSeek at the client with Intel Core Ultra processors; so reasoning for AI can be also done at your personal computer as well. Finally, Intel Edge AI Solutions brings real-time AI reasoning to robotics, autonomous vehicles, and cybersecurity applications. You can all test those on Intel Tiber AI Cloud today: Intel? Tiber? AI Cloud - Build and Deploy AI at Scale

What’s Next? AI Reasoning and the Road to AGI

The next two years will be pivotal in AI development.

The transition from inferencing to reasoning will drive AI that shifts from passive assistance to active decision-making.

More explainable, ethical, and trustworthy AI models. The foundation for AGI, where AI can truly think, reason, and act independently. Intel is actively shaping this future—developing AI solutions that bring reasoning AI to real-world applications at an unprecedented scale.

Are You Ready for Reasoning AI? Your thoughts?

2025 is the year AI goes beyond predictions and starts thinking. What’s your take? How do you see reasoning AI impacting your industry? Share your feedback below in the comments section.