Hype!

HYPE! It seems to be the fuel of our current era. When it comes to technology, the speed of progress and level of public interest have increased significantly recently. The days when tech innovations unfolded at a leisurely pace are long past; now, it's not uncommon to see new technological concepts being touted every few weeks on social media or in the press. Recent examples include the Metaverse and Generative AI, but we can expect this rapid-fire sequence of emerging tech to extend to fields such as quantum computing, robotics, edge computing, and more.

This can become a little overwhelming, however there is a strategic approach to dealing with this whirlwind of emerging tech, and it's not necessarily confined to identifying and adopting the latest long list of use cases for each tech individually. This approach requires us to momentarily set aside the hype, to extricate ourselves from the marketing ploys surrounding new products and the clamour to be the first to adopt the latest tech. The goal is to gain a clear and comprehensive understanding of the technology itself, stripped of the hype and promotional noise.

This process involves deciphering the array of new terminologies and semantics that are constantly being thrown around in tech conversations. It's about gaining a fundamental understanding of what these technologies are, how they work, and what they could potentially offer. Only with this knowledge can we begin to apply it to our specific business contexts, identifying where and how these technologies can truly add value.

Ultimately, the goal isn't to use technology for its own sake, but to leverage it as a tool to achieve what you need for your business. It's about being pragmatic and thoughtful in adopting new tech, focusing not on the hype, but on the genuine value and potential benefits it can bring to your organisation.

Finding a common thread

So, what's the common thread between emerging technologies such as the metaverse and generative AI? Let's take the metaverse as a starting point. It's a term that gained a lot of traction, yet it's often used loosely and inconsistently. Some envision it through the lens of virtual reality (VR), others augment reality (AR), while there are those who argue that a true metaverse requires a decentralised web3 foundation. The reality is, its definition varies depending on who you're talking to. To complicate matters further, Apple's recent emphasis on the term "spatial computing" adds another layer of conceptual complexity.

Generative AI shares a similarity around semantics. In conversations with clients, peers, or even friends and family, the terms generative AI and chatGPT are frequently used synonymously, as if they're one entity. There's also a common misconception that OpenAI operates as an open-source company, which is not accurate. As we see an explosion of AI models and products, such confusions and misinterpretations are likely to become even more widespread. Toss in other technical terms like large language models, GANs (Generative Adversarial Networks), diffusion models, and all the more longstanding terms like machine learning and neural networks, and the task of deciphering these concepts and understanding their relevance to your business can be a daunting prospect.

Breaking Down Barriers

To understand these complex topics, we need to break them down into smaller parts. In "A Pragmatist's Guide to the Metaverse," I looked at the Metaverse as a combination of different technologies and ideas that come together to make new experiences.?

This is helpful to abstract away from the hype, understand what the tech can do, and what components may be applicable. We can then apply those components in layers to build experiences and solutions that leverage each of the technologies and concepts in conjunction with each other to build layered and scalable solutions.?

I'm currently doing the same thing with generative AI. Instead of looking at all the products available or endless lists of ways they could be used, I've broken down generative AI into its constituent parts. This helps us understand each aspect of the technology and where we need to put our effort to get the most out of it.

When we look at the technology in terms of the data set, model, interface prompt, and output, we can start to see where we need a "human in the loop" to manage the challenges and ethical issues that come up with generative AI. It also helps us see where we need to work to build and improve the basic things like infrastructure, data governance and architecture that are needed to make any use of the technology successful.

But what's the point of all this without the right context? Too often, we talk about technology just for its sake, with people getting carried away by the hype and hurriedly trying to find a use for it. However, the first question anyone should ask after understanding the basics of a new technology is not how to use it, but what they are trying to achieve in their organisation. Usually, this can be categorised into a few areas: improving customer experience, enhancing employee experience, increasing operational efficiency and resilience, and addressing the urgent need for sustainability.

I always like to view new technology through these lenses. The old saying "if you don't do this, you'll be left behind" doesn't hold much weight if you can't see the value it brings. And it's worth noting that this value doesn't always have to be tangible. Sometimes, especially with new areas, just exploring and learning can be beneficial for gaining financial rewards down the road.

Convergence

Lastly, it's important to mention that part of the current hype cycle seems to suggest that for one technology to succeed, another has to fail. Headlines like "The metaverse is dead! Generative AI takes its crown!" are common, and while it's true that the focus of media coverage and corporate interest might shift, these technologies aren't mutually exclusive. In fact as AI and generative AI? become more pervasive, they will help to create more natural and data-rich immersive experiences on “metaverse” like platforms. No single technology is a magic bullet for all problems. As new technology emerges, we need to consider how it can work with those that are already in use, or perhaps if it might lead to new tech stacks that can work together to tackle the real challenges and opportunities that businesses face today.