According to In-Stat, hotspot usage is on the rise. One of the key concerns with the hotspot market over the past several years was whether there was a large enough audience of potential users for this service. While the market has increased over the past several years, there seems to be a more marked jump in usage this year. Many players report double digit usage growth month to month, and providers that have been very guarded on usage rates, such as T-Mobile, are now releasing information, which signals a positive shift in usage. This sudden trend in increased usage is largely a North American phenomena, as Asia Pacific usage has always been fairly robust, and Europe continues to have lower usage rates (often associated with the higher cost of access in that region). According to the Q2 2005 In-Stat Hotspot End-User Survey, nearly half of the 579 respondents use or have used hotspot services. Furthermore, 20 percent of respondents use these services frequently. While still slightly over half of the market has not used hotspots in the past, all respondents at least might consider using the service, with 17 percent indicating strong intent to do so. Nearly two-thirds of survey respondents that have used hotspots indicated plans to use the services more in the future. While 20 percent anticipated using the service at about the same level as they currently do, only 2 percent had plans to use the service less.
The prevailing narrative around artificial intelligence (AI) has been one of relentless scale. Bigger models, bigger clusters, bigger budgets. The assumption, largely unchallenged until recently, was that raw parameter count translated directly into competitive advantage. New research from Omdia suggests it's time to retire that assumption. According to the latest market study by Omdia, parameter growth in frontier AI models has slowed to around 5 percent annually since 2021, a stark contrast to the more than hundredfold expansion seen between 2019 and 2021. Enterprise AI Market Development For executives who have been making infrastructure and investment decisions based on the assumption that AI would keep demanding ever-larger, ever-more-expensive hardware, this finding deserves serious attention. The race to the top of the model size leaderboard has, at least for now, plateaued. Crucially, Omdia's analysts are not reading this as an AI winter. Alexander Harrowell, senior pri...