The emergence of frontier artificial intelligence is the new front in Silicon Valley\u2019s open- vs. closed-model Forever Wars, and the battle lines are now clear.<\/p>\n
On one side: OpenAI and Anthropic, championing a closed-model ecosystem in the name of safety. On the other side: just about everyone else in the tech world, including Mark Zuckerberg\u2019s Meta, and perhaps most importantly, Nvidia. In March, Nvidia CEO Jensen Huang spent much of Nvidia\u2019s developers conference<\/a> positioning Nvidia\u2019s chips as the perfect infrastructure for open-source tools, and last month, Huang created an X-n\u00e9e-Twitter account<\/a> seemingly for the sole purpose of publicly praising the democratizing power of open-source AI. On Tuesday, the semiconductor king made another stand, announcing its new open-source Nemotron 3.5 Lightning model.<\/p>\n With Open Arms<\/p>\n Still, Nvidia didn\u2019t become the world\u2019s biggest company by practicing pure digital altruism, and the company has plenty to gain from a more open AI ecosystem. \u201cSoftware traditionally is where the value is, and that is also where a lot of the costs are,\u201d Bill Wong, AI research fellow at Info-Tech Research Group, told The Daily Upside. \u201cWhat Nvidia is doing is removing that cost\u201d by offering and supporting open-source models, \u201cwhich means [enterprise clients] have more money for hardware.\u201d\u00a0<\/p>\n In other words, greater demand for and access to open models will push value back down the tech stack to where the scarcity exists: hardware and infrastructure, Nvidia\u2019s bread and butter. The flow of money away from powerful models also drains potential capital that closed-model makers like OpenAI, Google and Anthropic could use (and are already using<\/a>) to craft in-house chips and reduce their reliance on Nvidia. In turn, model right-sizing only increases compute demand, which is also good for Nvidia:<\/p>\n Global spending on AI inference (or the actual usage of AI models) is expected to reach $23.3 billion this year, according to a Gartner report published Monday<\/a>. That would surpass spending on AI training (expected to reach $19.9 billion this year) for the first time ever.<\/p>\n