The Intelligence Revolution

We are witnessing a new industrial revolution, one powered by accelerated computing, where intelligence is the primary product. This isn't just a minor shift; it's a transformative wave sweeping across every market and institution globally. Intelligence is rapidly becoming a ubiquitous utility, much like electricity fueled past industrial economies. The very infrastructure for this intelligence is being built, turning computing into a generative force for every industry.

At the heart of this era are AI factories. Think of them as the engine rooms of this new age. They are a new class of infrastructure purpose-built to transform raw data into "tokens of intelligence". In this new computing model, intelligence is no longer just stored and retrieved; it's continuously generated by these AI factories.

Computing machines are the "thinking machines" that generate these tokens, which are considered the "currency of the AI age".

The fundamental units of this digital intelligence are tokens, described as fragments of meaning – a word, a piece of code, a strand of DNA. When sequenced, they form thoughts, instructions, stories, and reasoning. Computing machines are the "thinking machines" that generate these tokens, which are considered the "currency of the AI age". This entire process relies fundamentally on data as the raw material being converted into intelligent output.

The Intelligence Revolution Distinct Phases

The intelligence revolution is evolving through distinct phases, each demanding significant data and computation:

• Perception AI allowed computers to understand the world, enabling breakthroughs in areas like speech recognition and medical imaging.

• Generative AI allows AI to create new content like text, images, or code, shifting from processing or retrieving data to generating original material.

• Currently, we are entering the era of Agentic AI, where intelligent systems can analyze, reason, plan, and use tools to solve problems, functioning as digital teammates.

• The next wave is Physical AI – robots and autonomous machines that perceive, reason, and act in the real world, requiring an understanding of physics.

Meeting the demands of this revolution, particularly the need for increasingly sophisticated AI, adheres to a fundamental principle, a scaling law: more data equals more capable AI. This is true for the initial training phase (Pre-Training Scaling), where more data, larger models, and more compute produce more capable systems. But it's equally true for the crucial refinement that makes AI truly powerful (Post-Training Scaling). Models continue to improve significantly after initial training through methods like fine-tuning, reinforcement learning, human feedback, and synthetic data generation. At scale, this post-training phase can be even more compute-intensive than pre-training. Furthermore, Inference (Test-Time) Scaling is no longer a simple one-shot process; it's real-time reasoning. Agentic AI requires models to dynamically allocate compute to "think through" complex tasks and plan actions, a reasoning process that can demand substantially more compute, potentially 100x more than simple inference. As users interact with AI and agentic systems operate continuously, inference is becoming the most compute-intensive phase, driving massive deployments of AI factories.

Data, the Fuel for the Intelligence Supply Change

Effectively fueling these AI factories and meeting the demands of these scaling laws requires not only vast amounts of general data but also specialized, contextual insights. This is particularly true for AI being built to understand, predict, and engage with human behavior in the ever-evolving AI landscape. This is where the opportunity sits. This is where Concrete delivers.

The Concrete AI model is being designed to incorporate human psychology, cognitive biases, and emotional triggers to forecast behavior more accurately.

Concrete AI is pioneering behavioral intelligence, transforming nearly two decades of aggregated learning and generalized knowledge from research into a predictive AI-driven Large Behavior Model. Combined with years of proprietary and original research on social behavior, this model aims to bridge the gap between raw data and deep human insight, offering real-time, actionable intelligence. Unlike conventional AI that primarily relies on transactional data, the Concrete AI model is being designed to incorporate human psychology, cognitive biases, and emotional triggers to forecast behavior more accurately. This approach is intended to provide contextual understanding, interpreting why people make decisions, not just what they do.

Concrete’s AI-driven behavioral intelligence coupled with their mixed methods research team are intended to help fuel AI factories across different phases needing data:

• Feeding Models (Initial Training - Pre-Training Scaling): The foundation of the Concrete AI model is built upon years of proprietary, high-value social behavioral data and aggregated learning and generalized knowledge amassed from user research, observations, and interviews. This isn't just historical information; it is the essential raw material needed to fuel the AI factories aimed at understanding, predicting, or simulating human behavior. This extensive dataset on human behavior provides a unique historical perspective and competitive advantage, serving as a vital, specialized data source for the Pre-Training Scaling phase, offering foundational knowledge for AI models built to understand and interact with humans.

• Feeding with New Insights (Ongoing Refinement and Real-Time Inference - Post-Training and Inference Scaling): As AI systems evolve into agentic and physical AI, interacting directly with humans in the real world, continuous feedback and nuanced understanding of behavior are critical for Post-Training and Inference Scaling. Concrete AI is being built to provide actionable, evidence-based recommendations based on predictive analysis of real-world scenarios. Its model will adapt to evolving trends, offering deeper audience understanding, predictive behavioral analytics, and insights into optimizing user interfaces and communication strategies. Crucially, Concrete's mixed methods research team will be able to conduct research as needed to produce new forms of insights and data needed to fuel AI models built to understand, predict, and engage with human behavior in the ever-evolving AI landscape. This continuous stream of contextual insights into human motivations and behavior serves as essential ongoing data for refining models, improving real-time reasoning, and ensuring AI agents, as they are deployed, can function effectively and safely alongside humans.

Accumulated Data is Currency

In the new intelligence economy, unique, accumulated data is currency. Concrete's behavioral insights are a high-value asset. It's the critical supply your AI Factory needs. The intelligence revolution fundamentally requires continuous feeding of data and insights to the AI factories that produce intelligence. As AI evolves through its phases and scaling laws drive ever-increasing demands for computation and refinement, the need for deep, contextual understanding of human behavior becomes paramount. Concrete, with their specialized behavioral data and future ability to generate new insights through research, are poised to play a key role in providing the necessary inputs to build more capable, understanding, and effective AI systems across every phase of this revolution.

For a more detailed look at the new industrial revolution, see the 2025 Nvidia Corporation Annual Review.

Watch Nvidia's CEO keynote at COMPUTEX.