In the middle of the 20th century, the world entered the age of information, the shift of industry to information technology. The era began with the miniaturization of computers and culminated with the invention of the World Wide Web, which put the ability to access information at nearly everyone\u2019s fingertips. Now, with the rise of AI, that age is over, according to some tech leaders, and a new age of technology has begun.<\/p>\n
\u201cWe have transitioned from [an] information era to intelligence era,\u201d Prakhar Mehrotra, PayPal senior vice president and global head of AI, said at the Fortune Brainstorm AI conference<\/a> earlier this month.<\/p>\n This \u201cintelligence era\u201d is marked by industries transitioning away from the model of storing and retrieving data, Mehrotra told Fortune reporter Sharon Goldman. Instead, because of the capabilities of AI, data can be more spontaneously generated, with the ultimate goal of achieving autonomy in some parts of the workplace.<\/p>\n Companies are racing to apply AI\u2014with its promises of increased productivity and output\u2014to their respective workplaces, but their successes have been mixed. An August MIT study<\/a> found 95% of enterprise AI workplace initiatives failed to reach rapid revenue acceleration.<\/p>\n \u201cIt\u2019s going to be a journey\u2026You have to go through this crawl, walk, and run,\u201d Mehrotra said. \u201cI think that adage has been true 10 years back, is also true in this era.\u201d<\/p>\n Marc Hamilton, Nvidia\u2019s vice president of solutions architecture and engineering who was interviewed alongside Mehrotra at the conference, said the future of building out AI in the workplace will be investing in AI factories, on a business\u2019s premises or in the cloud. That\u2019s because data needed to run companies will no longer be primarily retrieved by humans or computers, but rather generated by AI.<\/p>\n \u201cWhen you go and say, \u2018Generate a PowerPoint slide that says this,\u2019 or \u2018I\u2019m working on this coding function, can you go in and generate code?\u2019 It\u2019s not retrieving it from the database, it\u2019s taking a model and generating that data,\u201d Hamilton said.<\/p>\n Mehrotra noted in order for companies to effectively build out the computational power needed to create this data, there needs to be a new atomic unit prized by firms: tokens, or the fundamental component of text AI needs to understand and process a language. Tokens are both the snippet of information used to train data, as well as what is generated by AI after a model receives a prompt.<\/p>\n \u201cEvery company has to think about their data in terms of tokens, because then [they] can derive that intelligence from it,\u201d Mehrotra said.<\/p>\n A measure of input and output, token generation has become a key metric for tech companies in particular. In May, Nvidia<\/a> boasted that Microsoft<\/a>, which uses Nvidia\u2019s chips, generated more than 100 trillion tokens<\/a> in the first quarter of this year, a five-fold year-over-year increase. These indications of output can help these AI companies sell themselves to investors and boost valuations, though data<\/a> shows tokens\u2019 correlation with demand and profits are weaker than tech companies would suggest.<\/p>\n Mehrotra and Hamilton agreed that many companies today see the value of tokens in boosting AI capabilities, but are weighing how to best fit them into their needs, such as which tokens should be acquired or bought, what should be generated in-house, and for what purpose? Every company then has their own AI factory of sorts, both taking in tokens and outputting tokens that have value.<\/p>\n \u201cI see it as just building that muscle,\u201d Mehrotra said. \u201cLike if all the employees start thinking in terms of tokens, in terms of generating process, then, yeah, it\u2019s a different company.\u201d<\/p>\n