Artificial Intelligence

For the last two years, Retrieval-Augmented Generation (RAG) has been the primary bridge between raw language models and private organizational data. We have largely relied on vector search (a method that converts text into numerical embeddings) to find information based on semantic similarity. This was a massive improvement over traditional keyword search; however, as we move through early 2026, the limitations of "pure" vector RAG have become clear. Now, federal agencies and government contractors are moving toward a more sophisticated architecture called Hybrid RAG.

For the last several years, the primary focus of AI development was raw capability; we wanted models that were faster, smarter, and more creative. However, as we move through early 2026, the conversation has shifted toward the physical and digital security of the models themselves. With the recent passage of the 2026 National Defense Authorization Act (NDAA), the federal government is officially treating AI as a critical supply chain asset. For any firm operating in the defense industrial base, this means that security is moving beyond simple access control; it is moving into the very data used to train the machine.

For the last few years, conversation around artificial intelligence has been dominated by what technology can do in a vacuum. We have spent countless hours marveling at the creative potential of chatbots and the speed of image generators. But as we move through early 2026, the novelty has officially worn off. In the halls of federal agencies and the boardrooms of government contractors, a new philosophy is emerging. It is called Business Led AI; it is the only way to turn experimental pilots into mission critical tools.

For the better part of the last three years, the tech world has been captivated by the novelty of the chat interface. We marveled at the ability of large language models to write poetry or summarize a meeting transcript with a single prompt. But as we move deeper into 2026, "talking to the machine" has led to realization that these systems have a lot more potential. The end goal was never to build a better chatbot; the goal was to build a teammate that can actually get the work done. This is the year we stop prompting and start delegating as the federal government and private sector alike shift toward truly agentic systems.

The rapid expansion of artificial intelligence has moved from the digital realm into physical reality. In 2026, the primary focus for AI is no longer just about who has the best model, but about who has the right to build the massive data centers required to run them. As these facilities consume significant amounts of energy and water they are facing a wave of environmental impact lawsuits that are reshaping the industry.

AI adoption is becoming more and more popular, and it is already changing what work looks like for the average employee. This shift is not limited to software engineers or data scientists. It is reaching office workers, analysts, marketers, administrators, and professionals across nearly every industry. Artificial intelligence is moving from being a specialized tool to becoming part of everyday workflows.

In late 2025, the U.S. Department of Defense launched GenAI.mil, a groundbreaking enterprise artificial intelligence platform designed to bring generative AI tools into everyday use across the military and defense workforce. Just two months after its launch, GenAI.mil has already surpassed 1 million unique users and is poised to transform how the Pentagon works, plans, and fights with AI-enabled capabilities. 

The concept of artificial intelligence can be traced back to ancient history, where myths and stories imagined intelligent machines brought to life by human hands. From mechanical automatons in Greek mythology to early clockwork inventions, the idea that intelligence could exist outside the human mind has fascinated people for centuries. However, it was not until the mid-20th century that artificial intelligence emerged as a formal field of study. In 1950, Alan Turing published his landmark paper “Computing Machinery and Intelligence,” introducing what would later be known as the Turing Test. This test proposed evaluating a machine’s intelligence based on its ability to exhibit behavior indistinguishable from that of a human during conversation.

For much of deep learning's history, neural networks were built around a simple constraint: information had to move through a model in order. If you wanted to process a sentence, a time series, or a sequence of events, the architecture itself was sequential. That shaped the first major wave of progress in natural language processing and sequence modeling. Recurrent neural networks, LSTMs, and GRUs dominated because they were designed to handle ordered data. They processed inputs step by step, carrying a hidden state forward through time. Transformers changed that pattern completely. They introduced a new way of handling sequence information, and that shift is why they now sit at the foundation of modern language models and many other AI systems. So what actually makes Transformers different?

Ethical concerns in AI often begin with abstract questions. Is the data biased? Are decisions explainable? Is the system being used appropriately? In early development, these questions are usually manageable. Teams work with limited data, narrow use cases, and a small group of stakeholders. Risks feel identifiable and contained. As AI systems scale, those same concerns change in scope, impact, and complexity. Ethical risk doesn’t disappear; it multiplies. Understanding how ethical considerations evolve as systems grow is critical for organizations that want to deploy AI responsibly over the long term.