The big three areas that will now shape the future of generative AI.
The first wave of generative artificial intelligence (ai) was like nothing else the digitalised world had seen before. Within just 60 days of its launch, Chatgpt had racked up an amazing 100m thirsty users desperate to add a cute cat to Monet’s lillies painting or write a blog about tax returns for Uber drivers. Internet searches for “artificial intelligence” dominated and an incredible $40bn in venture capital money flowed into ai related businesses in the first six months of 2023.
After a period of serious (and often not so) consumer experimentation, it has of course throttled back a little and Chatgpt now has fewer users and there are less people Googling “ai”. A few investors well known in the technology market are said to be interested in now entering, mainly aligning with the bigger players, but are we now entering another phase of the maturity of ai? It is thought three main elements will determine the mature market and of course whether the current big innovators stay dominant or are usurped by the baying pack of chasers.
The first is cost, in the form of computing power which is driving the model-builders to have to be more efficient. Faced with painful costs of training and running ever more powerful models, it is just not going to work currently. Even Openai is not training its next big model, gpt-5, but still working on gpt-4.5 a more efficient version of its existing product. That opening might just give cash rich competitors such as Google a chance to power ahead and Gemini, the tech giant’s launching model, is expected to be more powerful than Openai’s current version. But where there are challenges there are always solutions. High computing costs have created many smaller more niche ai models, trained on very defined data just to do very specific things. And open-source models are making it easier in general for the dive into the world of generative ai. Because of this it is estimated there are already over 1,500 versions of largely data optimised models.
The second is data. Every single one of them are now seeking more data which is further shaping the generative-ai industry. The biggest players have models scouring the planet, trained on more than 1trn words (imagine that) and as they grow, they will of course get hungrier. But also, mind blowingly, the internet is now close to being tapped out. Many model-makers are now looking elsewhere and are forced into signing deals with news, media and even photography agencies as they hunt for more data. A few are even currently creating training data using algorithms whilst others are trying to work with newer forms of data, such as video. The big prize is a model that beats the rivals.
Generative ai’s hunger for power and data makes the third ingredient more important still: Monetisation. Many model-makers are already turning away from Chatgpt-style bots for the general public, now looking instead to licencing or subscription-based services. Openai, which started life in 2015 as a non-profit venture, has been especially energetic in this regard. It has not just licensed its models to Microsoft but is setting up bespoke tools for companies including Morgan Stanley, Salesforce and even Buzzfeed. Abu Dhabi plan to establish a company to help commercialise applications of Falcon, its own open source ai model.
So who will emerge victorious in what could become a bloody and ultra valuable battle? Firms like ChatGpt with its first to market position and vast number of users? Google, with its absurdly deep pockets and reach? Or will it be an unknown who waits for the perfect wave to surf past everyone else? For as long as computing power and data remain big constraints, the rewards for clever ways around them will be big. Any model-builder finding the most efficient approach, the most intelligent method towards data or the most appealing marketing to customers could win out as the champion.
The global hype may have cooled. But grab some popcorn and settle down, the big fight is just beginning.
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