Large language models (LLMs) have consumed massive parts of the internet, with the leading models using trillions of tokens during training. Meanwhile, AI startups are carving out niches by focusing on smaller, specialized models, leveraging more specific web data. As the race for the most comprehensive and accurate AI continues, competing giants are investing in three critical areas to maintain an edge: Compute, Talent, and Tokens.
Of these, tokens—the data that LLMs use during training and inference— can define success or lead to failure. Web data remains central to the evolution of these systems, offering an unprecedented opportunity to shape and enhance their accuracy and relevance. This targeted use of web data allows LLMs to excel in niche applications, delivering domain-specific accuracy that general training alone cannot achieve.
Yet, the role of web data extends far beyond the static datasets used during training and fine-tuning. As the demands placed on LLMs grow more dynamic, the use of web data has too, from its pivotal role in shaping LLMs to the emerging practice of using real-time web data for reasoning.
As we move forward, one thing is clear: inference-time data is not just an enhancement; it is the foundation of a smarter, more responsive AI future.
Tokens: The (potentially) unfair advantage.
In the context of web data and inference-time data, tokens represent the fundamental building blocks of an LLM’s capabilities, but the role of tokens doesn't stop at training.
Models using real-time data or inference data during the reasoning process can now fetch and process fresh tokens from live web data to enhance their outputs. The ability to dynamically integrate new information during inference supplements pre-trained and fine-tuned knowledge with the latest, most relevant data, ensuring outputs are accurate, timely, and context-aware.
This dual role of web data—providing tokens for training and live tokens for inference—creates opportunities for AI companies to gain a competitive advantage. While compute and talent are essential, they often level out across competitors. Web data, by contrast, is not only harder to replicate but can also vary dramatically in quality. High-quality inference-time data ensures that the tokens integrated during reasoning are precise, relevant, and timely, giving models a tangible edge in real-world applications. Conversely, poor-quality or irrelevant web data can degrade performance, underscoring the critical importance of data curation and infrastructure in both training and inference contexts.
Here are some examples of different contexts:
- Social media data: Tokens from social platforms help models understand informal language, slang, and real-time trends, which are essential for applications like sentiment analysis or chatbots.
- Structured datasets: Tokens from structured sources like product catalogs or financial reports enable precise, domain-specific understanding, critical for recommendation systems or financial forecasting.
- Niche contexts: Startups and specialized applications benefit from tokens sourced from hyper-relevant datasets tailored to their use cases, such as legal documents for legal tech or medical journals for healthcare AI.
The birth of ‘reasoning’
Reasoning in artificial intelligence, particularly within large language models (LLMs), represents a significant leap in their ability to solve complex problems and adapt to dynamic situations. It goes beyond simple pattern recognition, enabling AI systems to deliberate, synthesize information, and arrive at informed conclusions. Initially, reasoning in LLMs was rooted in increasing computational depth during inference. By deploying greater computational power, models could evaluate multiple possible outcomes, refining their responses through iterative processes. While this approach brought incremental improvements, it exposed inherent limitations in relying solely on static pre-trained knowledge and brute computational force.
Models trained on static datasets struggle to respond to dynamic, evolving contexts, leaving gaps in their ability to address real-world problems, which is how the true transformation in reasoning emerged. The integration of dynamic, real-time data into the decision-making process marked a pivotal shift from static reasoning, where models operated exclusively within the boundaries of their training datasets, to adaptive reasoning, which allows them to incorporate new, relevant information during inference.
Dynamic reasoning equips models to engage with ever-changing contexts, validating outputs against live data and aligning responses with current realities. This opens doors to unprecedented adaptability, enabling AI systems to address problems and scenarios that extend far beyond their original training.
Web data for reasoning
Dynamic reasoning reflects the trajectory of AI systems toward greater contextual intelligence, but it cannot be done without web data. For example, an LLM tasked with analyzing current geopolitical events can enhance its output by integrating up-to-the-minute news data, ensuring accuracy and relevance. Similarly, in fields like healthcare or finance, the ability to incorporate recent findings or market trends empowers models to deliver insights that are not only accurate but also timely. This approach reduces reliance on outdated or incomplete information, making AI tools more aligned with real-world needs.
Here’s the typical inference workflow:
- A user (either manually or via an API) enters a prompt into an LLM. This prompt could be anything from a simple query to a complex, multi-layered instruction.
- The LLM generates a response based on its pre-trained knowledge. Some advanced LLMs incorporate reasoning layers to improve the accuracy of their outputs. These layers may involve more computational effort or running agents to validate and refine the response.
- Add in real-time web data for additional information not included in training, process it at inference time, and incorporate it into the output.
An important consideration is whether incorporating inference-time data affects the weights and biases of the LLM itself. It does not directly alter the model’s underlying structure. Instead, this process functions as an external reasoning layer, giving the model the option to incorporate real-time insights into its responses. This creates an important distinction: AI developers can continuously improve model accuracy without retraining the underlying LLM.
The future of LLMs and real-time reasoning
The integration of real-time web data into reasoning processes transforms LLMs from static repositories of knowledge into adaptive, dynamic agents capable of navigating change. This is the next phase in LLM development and is set to dominate the industry over the next one to two years. AI companies are looking to develop model capabilities with real-time reasoning that improve accuracy by bridging the gap between static training data and the real world that creates a competitive edge.
An ecosystem is already emerging to meet the demands for real-time inference data, as companies look to outsource data collection to APIs. Companies and startups are already capitalizing on this trend, developing inference-time data as a service (ITDaaS) solutions. These solutions provide processed, context-specific data that models can query during inference, enabling them to enhance their reasoning capabilities without retraining their core architectures.
The future of reasoning in AI will likely revolve around increasingly sophisticated systems that blend static pre-trained knowledge with dynamic, real-time inputs. For AI developers, the challenge will be to build robust data pipelines that can seamlessly integrate these real-time inputs while maintaining high standards of accuracy and efficiency.
As the field matures, companies that invest in high-quality data sourcing, real-time integration infrastructure, and innovative inference-time tools will lead the way. In this, web data is the defining advantage that drives the evolution of AI.