At a recent developers conference, attendees rolled their eyes every time the term "AI" was mentioned. This backlash against artificial intelligence is not a rejection of the technology itself, but rather a cry for pragmatism and practicality. Developers want to know how they can harness AI for real-life use cases, not just hear about its transformative potential.

The hype surrounding AI has been overwhelming, with promises of robots taking over jobs, becoming sentient, and revolutionizing life as we know it. However, in reality, AI has quietly become an integral part of our daily lives, making tasks easier and more efficient. For instance, Google's AI-generated summaries of search results have become a convenient feature, but it's not exactly revolutionary.

The real challenge lies in scaling and integrating AI across organizations. This is where the AI backlash can be leveraged to create a more practical and seamless implementation of the technology. Developers, engineers, and operations personnel need AI to be as boring and unobtrusive as possible, allowing them to focus on their work without having to worry about the underlying technology.

Open-source projects like RamaLama are working towards making AI "boring" by providing tools that are easy to use, compatible with existing systems, and scalable. RamaLama uses OCI containers to facilitate the discovery, testing, and deployment of generative AI models, eliminating the need for complex configurations. This approach enables developers to focus on building intelligent applications without worrying about the underlying infrastructure.

Another key aspect of making AI more mainstream is the development of smaller, more specialized models that are fine-tuned for specific business needs. These models promote transparency, trust, and don't require state-of-the-art GPUs. As Matt Hicks, CEO of Red Hat, puts it, "Small models unlock adoption." By democratizing AI and making it more accessible, organizations can move beyond the rarefied air of data science and into the realm of practical application.

Linux containers are also playing a crucial role in making AI more usable. By providing a safe space for experimentation and development, containers enable developers to build, test, and deploy intelligent applications without having to worry about infrastructure. This approach empowers teams to effectively leverage AI capabilities while maintaining flexibility and control over their data across diverse environments.

The AI wave is not unlike other transformative technologies that have faced similar backlash and efforts to make them more usable. As the technology continues to evolve, it's likely that AI will become an integral part of our daily lives, just like the web and cloud-based technologies before it. The key to successful implementation lies in making AI "boring" and seamlessly integrated into existing infrastructure, allowing developers and organizations to focus on building innovative applications that drive real value.

In conclusion, the AI backlash is not a rejection of the technology itself, but rather a call to action for pragmatic implementation. By focusing on making AI more accessible, usable, and integrated into existing infrastructure, developers and organizations can unlock the true potential of artificial intelligence and drive real innovation.