All new technologies with a futuristic promise have a common thread. If the dream holds true, the tech enjoys fast adoption, sustains its growth for long and becomes a trend. Just look at the smartphone. However, if the delivery falls short of the promise, the growth takes a dive and it becomes a fad.
Take the metaverse. A buzzword just a couple of years back, it has almost fizzled out, at least for now. Meta, the flagship of metaverses, has now become a symbol for the technology’s failure to live up to its vision.
In 2022, Meta lost $13.7 billion on Reality Labs. Several projects racing to be at the vanguard of the metaverse bandwagon have suffered ignominy, as well. Tinder reported a $10 million loss in 2022. Walt Disney eliminated its metaverse division, reducing headcount by around 7,000. Microsoft followed suit.
4 Reasons Generative AI Could Live Up to the Hype
- It has easy to understand use cases.
- It’s more efficient than other alternatives.
- It possesses significant economic potential for companies.
- It doesn’t require expensive equipment to utilize.
Why did the metaverse fail? Pampered as a novel technology not a long ago, the metaverse failed mainly because technology failed to deliver on what was promised. Lofty expectations based on an emerging set of technologies led to a difference between anticipation and reality.
Eventually, what turned out was starkly different.
Enter generative AI, the latest technology innovation that has engulfed the globe. Use cases of AI straddle across domains, from education to fashion to military, making a splash in tech as well as non-tech worlds. You’re unlikely to find a single industry or task that AI hasn’t been touted to improve.
But much like the metaverse, AI is a nascent technology that hasn’t reached its full potential. The question remains: Will this version of AI end up like the metaverse, or is it here to stay?
Why the Metaverse Failed to Live Up to the Hype
The first iteration of metaverse was based on a trio of technologies — augmented reality (AR), virtual reality (VR) and blockchain — all of which are emerging tech that have yet to reach maturity. The promise of the metaverse was based on what those technologies could become, not what they were able to currently do.
Metaverse aficionados also failed to take into account the cost factor, which is intrinsically associated with the success of any upcoming tech product. While AR and VR are promising technologies, equipment required for developing content is quite expensive. For VR development, a VR headset, such as the Oculus Rift, and a computer with strong processing power is needed for running the software.
As for developing AR content, digital cameras, optical sensors, GPS (global positioning system), gyroscopes, accelerometers,radio frequency identification (RFID), wireless sensors, and solid-state compasses are required. Even professionals who can use such equipment efficiently are few, and it has a bearing on the visuals in the metaverse. No one wanted to hop on a metaverse only to view ugly avatars attending a concert with poor visual quality and stupendous costs.
Before Meta founder Mark Zuckerberg’s dreams to make millions of people hop on the metaverse for their daily chores were realized, Meta was supposed to roll out several billions of dollars developing VR and AR technologies that form the core of the metaverse. For any company, it was going to be an uphill task. In the light of all these factors, it looks like that the first avatar of metaverse was more hype and less substance.
The Generative AI Boom
What makes AI a steady contender for long-term growth is its economic potential. According to a McKinsey Digital research, generative AI might add $2.6 trillion to $4.4 trillion annually across the 63 use cases analyzed by them, boosting productivity in a massive way.
Three sectors that generative AI is more likely to influence positively boosting their revenue are banking, high tech and life sciences. Application of AI across the banking industry is forecast to add an additional $200 billion to $340 billion a year. Retail and consumer packaged goods industry is projected to witness an addition of $400 billion to $660 billion annually.
Goldman Sachs economists Joseph Briggs and Devesh Kodnani stated in a report, “Despite significant uncertainty around the potential for generative AI, its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects.”
Adoption of AI will lead to a major shift in the technology-led economic environment. A large section of occupations will be disrupted but the scenario will also put in place several new kinds of jobs.
Rapid advances in AI are likely to have far-reaching implications for the business being conducted, further igniting interest in upcoming AI solutions.
Factors That Could Hinder Generative AI Growth
Two factors that might hinder the unrestricted growth of generative AI are evolving regulations and its negative environmental impact. Regulation always follows innovation, and lawmakers are still figuring out how to regulate the domain.
Moreover, contrary to what many think, training AI models and operations requires a voluminous amount of computational power with a large carbon and water footprint. A University of Massachusetts research disclosed that training a single AI model is likely to emit more than 626,000 pounds of carbon dioxide, which equals approximately five times the lifetime emissions of the average car in the US. Training large language models (LLMs) consumes even more energy.
2 Factors That Could Limit Generative AI Growth
- It consumes a lot of energy to train and has a significant environmental impact.
- AI regulations could place a limit on or disrupt its growth.
There can be no denial that AI can be a potent tool in the fight to preserve the environment. It can help in applications like climate modeling, optimizing power grids, facilitating precision agriculture, improving transportation systems, enabling smart buildings and more. However, steps need to be taken to reduce the carbon and water footprint of AI operations else there will be voices against application of AI.
However, AI developers are working to address this issue. Neural models like transformers process more data in less time, consuming less energy, which turns into lesser carbon emission. Settings of the cloud service can be changed to ensure less consumption of energy during training.
Companies in the domain are open to adopt a frugal AI approach, which focuses on designing more robust models with less data. Less data restricts the amount of computational power required, which reduces the resources used for training. Simone Larsson, an AI evangelist at Dataiku, an AI platform, states, “Companies are just now at the start of thinking of AI as part of the [sustainability] equation and it is not just heating and cooling.”
Why Generative AI Will Be Different From the Metaverse
When it comes down to it, AI’s value is much easier to understand than the metaverse. AI has the ability to process inputs logically, like humans, and arrive at a decision. On the contrary, some regarded the metaverse as an immersive virtual world, where one can shop, conduct business and play video games while using a headset. Others thought of it as a digital world closely connected with Web 3.0 elements such as cryptocurrencies and non-fungible tokens (NFTs).
Poor visual experience, cost factors and a lack of usefulness led to sharp fall in the number of the metaverse users.
In hindsight, the collapse wasn’t really surprising. At any time, hoping on a metaverse was just an option for the users. They could shop in a brick-and-mortar store or any other online venue, conduct business on Zoom and play a video game elsewhere.
AI is poised to be more sustainable because its solutions are often better than other alternatives. For instance, if someone wants to rephrase a piece of content or requires a description of an existing product in a certain number of words, they can use ChatGPT. In another use case, if a business owner needs a graphical analysis of a vast data set, AI offers the best solution in terms of both efficiency and speed.
What Will the Future of AI Hold?
A big question today is whether AI will be a fad like the metaverse or it will sustain the surge. Ultimately, its sustainability is assured by the number of use cases. If a technology resolves a set of genuine problems, it will continue to find takers.
AI solutions, whether they are LLMs or domain-specific software, have already created disruption across industries. As technology advances, the wrinkles in the efficiency will be ironed out and use cases will expand exponentially. With the current level of funding and tech development, it could reach unexpected and unprecedented heights.
Metaverse, though an interesting technology, wasn’t a pathbreaking one. While decentralization added a different dimension to centralized systems, its absence hasn’t had a major effect on daily lives. Metaverse didn’t even improve engagement within existing video games.
This doesn’t mean the metaverse can’t make a comeback. Metaverse projects could include AI to add zing to their quality of presentation and create use cases, and metaverse could arrive again in a new state. As technologies behind metaverse mature, costs are also likely to go down, eliminating the restraint factor. AI itself burst onto the scene in 2017 only to crash and rise again today.
We’ll have to wait and see if the metaverse conjures up a similar story,and if AI can sustain its success.
