generative AI, GenAI, AI regulation, AI growth

Today’s hype surrounding artificial intelligence (AI) is reminiscent of the great fiber optic build-out of the 1990s. Back then, significant investments in fiber infrastructure resulted in an abundance of optical fiber, but a dearth of “killer apps” to utilize it effectively for at least a decade. It wasn’t until the advent of smart mobile phones, along with apps, video streaming, social media and gaming, that the massive fiber build-out found its true purpose, balancing supply and demand.

Similarly, we are in the early stages of AI development, where tech companies are creating foundational models, and the demand for AI far exceeds the supply of GPUs. Major suppliers like Microsoft report surging demand, indicating that the industry is on the brink of significant advancements. However, the real value capture will happen closer to the application layer, where AI can enhance user experience through full automation, offering massive and measurable ROI. This drive for automation will prompt organizations to rapidly implement new AI solutions.

The Build

In the 1990s, the deregulation of telecommunications and the advent of the Internet accelerated the growth of fiber technology. Although fiber had been invented decades earlier, it wasn’t transformative until applications emerged that maximized its potential. Similarly, AI has a long history, unbeknownst to the general public. Deep Learning, a cornerstone of modern AI, has been around since the 1980s, and AI as a concept has been theorized for centuries. A significant milestone was in 1997 when IBM’s Deep Blue chess AI defeated a human champion, marking a pivotal moment in AI development—and doing so nearly 30 years ago.

AI began gaining momentum in the 2000s with user algorithms optimizing social media and virtual assistants like Siri. These technologies laid the groundwork for the widespread use of AI today. However, as with fiber optics, AI is truly beginning to flourish with the advent of Generative AI (GenAI). This increased accessibility and rapid evolution are propelling AI into the mainstream.

The Next Steps

Virtual assistants like Siri exemplify the early stages of AI development. Siri, short for Speech Interpretation and Recognition Interface, was developed in 2007 and popularized by Apple in 2011. It marked a breakthrough in natural language processing (NLP). The difference between past AI iterations like Siri and Alexa and the emerging AI agents lies in the shift from NLP to human-like natural language understanding and generation.

AI agents, built on that same foundation of Deep Learning and NLP, utilize GenAI to enhance their capabilities. Incorporating technologies like sentiment analysis, these AI agents are now capable of what could be termed “emotional intelligence,” allowing them to adapt and respond in real-time. This leap from basic virtual assistants to sophisticated AI agents marks a monumental shift in AI applications.

Virtual assistants like Siri also laid the groundwork for the new, high-tech AI agents we’re starting to see today. The big difference between what we have seen in the past with Siri and Alexa and what we’re going to see with the new and improved virtual agents, though, is natural language processing versus actual human-like natural language understanding and generation. AI agents use GenAI to take their performance to the next level.

The Value

AI has long provided value through optimization analytics, data-driven insights, and automating tedious, manual processes. With the integration of GenAI, this value will grow exponentially. Data-driven insights can now transform into actionable recommendations and even direct application implementation by the AI itself. The “I” part of ROI will be less than organizations think—because the foundation for AI has already been laid, much like the preexisting fiber networks before their widespread utilization.

There’s nothing left but to get started on implementing the AI equivalent of the apps that drove fiber that will then support the “R” in ROI. That’s the good news for the aforementioned AI “scramble” – the foundation for automation is already laid, much like fiber networks had already been installed prior to being widely leveraged.

Up Next

Although the foundation is in place, AI—bolstered by GenAI—is only just beginning its transformative journey. GenAI is evolving rapidly, with its value and capabilities continuously increasing. Future AI developments will likely surpass today’s GenAI approaches and models. However, similar to the fiber optic revolution, organizations do not need to wait for the most advanced AI to become available, and significant value can already be extracted from today’s AI technologies.

Organizations that embrace automation now will witness a real-time increase in value. The opportunity to leverage AI for substantial returns is here, and the time to act is now. Other forms of AI will continue to develop and soon exceed today’s GenAI approaches and models. But just like with fiber, businesses don’t need to wait until the fastest broadband is available.

There is a lot of value and ROI that can be extracted from today’s AI, with an eye on future, more powerful AI. If organizations automate now, they’ll be able to see that value go up in real time.