Beyond the Hype: Rethinking AI's Role in Industry and Infrastructure

Introduction: The Current AI Obsession

In recent years, the world has been captivated by the advancements in Artificial Intelligence, particularly with the rise of Large Language Models (LLMs). These models have showcased impressive capabilities—from generating human-like text to powering conversational agents and even creating art. But as the industry continues to pour resources into LLMs and generative AI, we risk losing sight of the broader potential of AI in transforming industries, cities, law enforcement, and more.

While LLMs have certainly revolutionized how we interact with technology, it’s time to shift the conversation. We need to start thinking beyond the novelty of generative AI and recognize that LLMs are just one piece of the AI puzzle. The real transformative power of AI lies in its application across diverse sectors, where real-time interaction, low latency, and robust infrastructure are critical. This is what we should call Applied AI—and it’s time we give it the attention it deserves.

The LLM-Centric Mindset: A Narrow Focus

The industry’s current focus on LLMs has created a narrow view of AI's capabilities. Yes, LLMs are powerful tools for text generation, customer service, and creative applications. But when we consider the vast array of AI models being developed—ranging from computer vision and predictive maintenance to anomaly detection and edge computing—it becomes clear that we need a broader perspective.

LLMs Are Important, But Not the Whole Picture

LLMs have been lauded for their ability to process and generate language with uncanny accuracy, making them a cornerstone of new user interfaces where natural language interaction is key. However, their role is largely confined to applications that involve text and speech. They are not the solution for every AI challenge, especially in environments where real-time decision-making and interaction are critical.

The Expanding AI Ecosystem

Beyond LLMs, there exists a diverse ecosystem of AI models that are designed for specific tasks in industrial, enterprise, and municipal settings. These models are not just novelty items—they are the engines driving automation, enhancing public safety, improving healthcare, and optimizing operations across the globe. This diverse AI landscape also holds the potential to empower smaller businesses, enabling them to leverage cutting-edge technology that was previously reserved for larger enterprises.

Applied AI: Moving Beyond LLMs

Real-Time Interaction and Low Latency: The Edge Imperative

In industrial and enterprise applications, the need for real-time interaction is paramount. Consider a city using computer vision to monitor traffic in real-time. When an accident occurs, a traffic issue arises, or a pedestrian is hit, the AI system must immediately detect the event, change traffic lights to reroute vehicles, and alert authorities, enabling a rapid response. Delays of even a few microseconds in these automated processes could mean the difference between a successful intervention and a tragedy.

These applications require edge AI—where processing happens closer to the data source, reducing latency and ensuring rapid response times. Unlike LLMs, which are often run in cloud environments and can tolerate some latency, edge AI must operate with near-instantaneous feedback. This is where we need to focus our efforts, particularly in building the necessary infrastructure.

Data Sovereignty and Infrastructure Needs

Another critical aspect of Applied AI is data sovereignty. As AI models process increasingly sensitive information—whether it’s patient data in healthcare or surveillance footage in law enforcement—organizations must ensure that data is handled in compliance with local regulations. This often means processing data locally, at the edge, rather than relying on centralized cloud servers.

To support this, we need a robust digital infrastructure that includes edge computing, high-performance networking, and the ability to deploy AI models in distributed environments. This infrastructure must be scalable, secure, and capable of handling the unique demands of real-time AI applications. Importantly, this infrastructure should be accessible not just to tech giants, but to smaller businesses as well, allowing them to participate in the AI revolution.

The Economics of AI: The True Cost of Implementation

Training AI models is undeniably expensive, but it’s a cost that can often be managed in a centralized data center, wherever that may be. However, once these models are trained, the real challenge begins—using these models in ways that require significant computational resources, like GPUs, which are costly and difficult to manage.

When these AI models need to operate in real-time, at the edge, the expense of deploying and maintaining such hardware can be prohibitive for many organizations. This is particularly true for smaller businesses that may not have the capital to invest in sophisticated AI infrastructure. One solution that the industry needs to explore is the concept of fractionalized GPUs at the edge. By renting access to these resources through a distributed edge infrastructure, organizations can reduce costs and scale their AI operations as needed. This approach is far more affordable than purchasing and maintaining the hardware themselves, making advanced AI accessible to a wider range of industries, including SMEs.

Imagine a future where even a small business can harness the power of Applied AI through an AI app model and service marketplace. This marketplace would allow businesses to easily customize applications to their specific needs, run them at the edge, rent the necessary GPUs, and ensure low latency and redundancy. Such a development would democratize AI, enabling smaller players to compete on a more level playing field and drive innovation across sectors.

Applied AI: A New Frontier

Revolutionizing Industries

Healthcare, manufacturing, law enforcement, and smart cities are just a few examples of sectors that stand to benefit from Applied AI. By moving beyond the hype of LLMs and focusing on real-world applications, we can drive innovation in areas that truly matter.

In healthcare, for example, AI models are already being used to analyze medical images, predict patient outcomes, and optimize treatment plans. In manufacturing, AI is enhancing predictive maintenance, reducing downtime, and improving efficiency. In law enforcement, AI-driven surveillance and analysis tools are helping to keep communities safe while respecting privacy and data sovereignty.

Crucially, the potential of Applied AI extends to businesses of all sizes. Smaller companies, which might previously have been excluded from utilizing advanced AI due to cost and complexity, should be able to access and deploy these technologies. This shift will have a profound impact on economic growth, fostering innovation at all levels of industry.

The Future of AI: A Call to Action

The future of AI is not just about generating text or images—it’s about solving real-world problems, enhancing human capabilities, and driving progress across every sector. As we stand on the brink of this new era, it’s crucial that we broaden our focus from the narrow confines of LLMs to the expansive potential of Applied AI.

The infrastructure and platforms that support this evolution will be critical in bringing Applied AI to life. Whether in smart cities, healthcare, or industrial automation, the possibilities are limitless when we think beyond the hype. By also making these tools accessible to smaller businesses through innovative service models and marketplaces, we can ensure that the benefits of AI are distributed more evenly across society.

Conclusion: A Balanced Approach to AI

In conclusion, while the excitement around LLMs and generative AI is understandable, we must not lose sight of the broader potential of AI. Applied AI represents the next frontier, where the real impact of AI will be felt in industries, municipalities, and beyond.

By focusing on the practical applications of AI, investing in the necessary infrastructure, and considering the unique challenges of edge deployment, we can ensure that AI delivers on its promise—not just as a novelty, but as a transformative force for good. And by democratizing access to these technologies, we can create a future where AI serves as a powerful tool for businesses of all sizes, driving innovation and growth across the board.

Let’s start thinking beyond the hype. Let’s start talking about Applied AI.