Leadership in the generative artificial intelligence market is far from settled, and history suggests that early leaders do not always emerge as the mature market’s leaders.Still, investors, users and analysts do pay some attention to market share.
Suppliers know that market share and valuation, to say nothing of survival, hinge on market share performance. Investor bets alikewise ride on such outcomes. For enterprises, the bigger issue is “betting on the right horse” or using eventual “industry standard” products.
DeepSeek jitters aside, GenAI market share is in flux, at least where it comes to enterprise users. OpenAI started with the lead, and retains that lead, but others are emerging.
But the history of computing innovations suggests that early leaders often do not emerge as the eventual market leaders.
On the other hand, at least so far, large language models have proven to be exceedingly capital intensive, so all the present share leaders are firms with huge investments and hyperscale app provider sponsorship or ownership.
That has not generally been the case since the advent of the personal computing era, the internet and cloud computing, where small firms have been able to innovate and succeed without such large capex requirements.
But high-performance computing has, so far, been an expensive endeavor. A reasonable person might still forecast that the eventual market will be led by a few firms, as is the pattern in virtually all industries that are capital intensive or with scale requirements.
The issue is whether capex requirements and scale will confer unique and ultimately determinative advantages for the early hyperscaler-backed contenders.
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