The Future Lies at the Intersection
We’re standing at the threshold of an AI-driven industrial revolution, a moment of staggering momentum—the large AI labs and tech giants are racing to harness ever-growing computational power, all in pursuit of an audacious goal: Artificial General Intelligence (AGI). The foundation models they’re building are marvels of engineering, trained on oceans of unlabeled data to handle a vast array of tasks with minimal fine-tuning. The result is that we’re starting to see machines that show signs of thinking and reasoning, machines that increasingly feel human.
Despite their remarkable progress, these systems are not without boundaries: not all challenges bend to the will of human-like reasoning. Some problems require solutions far outside the limits of human cognition. That’s why we believe there is another horizon in AI, specialized intelligence, that will drive the next waves of innovation.
Specialized Intelligence
Foundation models are exceptional at generalizing across vast and varied datasets, but intrinsically complex systems often demand domain-specific precision that broad generalization struggles to achieve. Much like human cognition, generalized intelligence thrives on reasoning, learning, and creativity—but it also inherits the same cognitive limitations we face. Specialized intelligence, on the other hand, breaks free from these constraints, leveraging mathematical, computational, and representational methods beyond the reach of human thought. Unlike foundation models, which lack the architectural and algorithmic specificity to grasp these intricate patterns, specialized AI is deliberately non-human—designed for precision, optimized for focus, and unconcerned with anything outside its scope.
This distinction becomes critical when addressing intrinsically complex systems. Some systems, particularly many pockets of nature, are inherently unpredictable or chaotic, where even the most advanced AI can achieve only marginal improvements over current human or computational methods. For example, predicting protein folding or modeling the dynamic interactions within an ecosystem often involves such overwhelming intricacy that no generalized intelligence can fully untangle it. In these cases, specialized intelligence offers a different advantage—not by simplifying the complexity but by uncovering unique structures, constraints, and signals within these systems. We believe the future of AI lies in an ecosystem of specialized systems, each designed to tackle specific challenges—like modeling molecular interactions or understanding ecological dynamics. This mirrors how nature addresses complexity: through diverse, specialized systems working harmoniously.
Why Specialized Intelligence Matters
Achieving meaningful breakthroughs in fields that require nuanced, contextual understanding depends on leveraging deep expertise to reveal the intricate relationships hidden within data. Consider biology, nature’s ultimate complex system, marked by immense interconnectedness, context dependency, and dynamic feedback loops. To solve problems in domains like biology, we need models fluent in the unique languages of those fields, such as the molecular syntax of DNA or the structural grammar of proteins. We’re already starting to see biology evolving into an engineering discipline, transitioning from mere observation to active design and discovery. Just as large language models have learned to understand human language, they are now beginning to decode the biological “language” made up of the As, Ts, Gs, and Cs of DNA. This shift highlights how specialized AI can unlock transformative progress in understanding life’s instruction manual and designing solutions previously unimaginable.
Cybersecurity shares similar parallels. The adversarial nature of cybersecurity demands real-time adaptation and contextual intelligence that humans or generalized AI alone can’t manage. Moreover, cybersecurity operates in a highly specialized “language”—defined by patterns in network traffic, binary code, log files, and the behaviors of malicious actors. To be effective, AI systems need to understand this language in depth. Early AI systems often relied on surface-level pattern recognition. They couldn’t grasp deeper semantic meanings, such as whether a command was part of legitimate system maintenance or the start of a malicious process. Recent advancements, particularly in generative AI, are beginning to overcome these barriers by enabling models to simulate, interpret, and adapt to the complexities of cybersecurity’s unique linguistic space. And like the Cambrian explosion of data we’ve seen in biology from DNA sequencing and other similar efforts, the massive growth in security systems is creating rich tapestries of data that will provide the contextual awareness to differentiate between benign and anomalous and new, unseen tactics and tradecraft.
These domains require AI systems trained to understand and operate within their specific building blocks and rules, much like learning a new language tailored to their complexities.
A Framework
We believe that specialized intelligence will revolutionize various industries, and as we surveyed many different problems, we saw an underlying pattern emerge. These domains share a key property: intrinsic complexity and dynamic challenges.
We see three major factors that underlie the potential for transformative impact.
Complexity Beyond Human Cognition
The field deals with systems that exceed human cognition and predictive capacities.
Data-Rich but Inaccessible
Abundant data exists, but extracting meaningful insights requires specialized algorithms.
High-Stakes Impact
The domain benefits significantly from breakthroughs due to economic, safety, or scientific implications.
Deep learning and neural networks are on their way to becoming one of the most important technologies in history. We’re in the embryonic stage of this AI-driven revolution, and we think the next significant innovations will come from areas out of reach of general intelligence. We’re interested in solving things that may be too small for the big labs to care about—but our view is that it’s the collection of these specialized systems that will transform what is possible, driving the next big wave of technological and economic growth.