Update, September 3, 2026: This article records an October 2020 corporate presentation. The $40 billion NVIDIA-Arm transaction discussed below was proposed, not completed: NVIDIA and SoftBank terminated the agreement on February 8, 2022, citing significant regulatory challenges. Arm’s American depositary shares subsequently began trading on Nasdaq on September 14, 2023. The forecasts preserved here remain the speakers’ dated claims rather than present-day findings. ([nvidianews.nvidia.com](https://nvidianews.nvidia.com/news/nvidia-to-acquire-arm-for-40-billion-creating-worlds-premier-computing-company-for-the-age-of-ai))
In October 2020, the message was unabashedly optimistic: AI would soon be everywhere—and, better, would be put to work by everyone.
NVIDIA CEO Jensen Huang joined SoftBank Group Chairman and CEO Masayoshi Son during the online SoftBank World 2020 keynote to discuss AI, edge computing and the companies’ proposed combination with Arm. ([group.softbank](https://group.softbank/en/news/webcast/20201029))
“For the first time, we’re going to democratize software programming,” Huang said. “You don’t have to program the computer; you just have to teach the computer.”
Watch the full keynote. Highlighted segments cover the Arm ecosystem, edge AI and the democratization of programming.
A Technological Jewel
About six weeks before the conversation, NVIDIA and SoftBank had announced a definitive agreement under which NVIDIA would acquire Arm in a transaction valued at $40 billion. The proposed acquisition remained subject to regulatory approvals and other closing conditions. ([nvidianews.nvidia.com](https://nvidianews.nvidia.com/news/nvidia-to-acquire-arm-for-40-billion-creating-worlds-premier-computing-company-for-the-age-of-ai))
Huang described Arm as “one of the technology world’s great jewels.” The strategic argument was that NVIDIA could distribute its AI technology through Arm’s extensive licensing ecosystem and across the many systems-on-a-chip, or SoCs, built with Arm intellectual property.
“The reason why combining Arm and NVIDIA makes so much sense is because we can then bring NVIDIA’s AI to the most popular edge CPU in the world,” Huang said.
Son explained that Arm licensed intellectual property and development tools to chipset vendors serving many kinds of devices and applications. Huang said a combined company would “absolutely” continue that model—a commitment also included in NVIDIA’s acquisition announcement. Because the acquisition never closed, the statement should be read as a conditional promise made in 2020 rather than a policy NVIDIA ultimately implemented as Arm’s owner.
An Ecosystem Like No Other
For Huang, Arm’s value lay not only in its energy-efficient CPU designs but in the network of licensees and partners building on them.
“Our dream is to bring NVIDIA’s AI to Arm’s ecosystem, and the only way to bring it to the Arm ecosystem is through all of the existing customers, licensees and partners,” Huang said. “We would like to offer the licensees more, even more.”
At the time of the acquisition announcement, NVIDIA said Arm licensees had shipped 180 billion chips. Son predicted that cumulative shipments would eventually reach one trillion and argued that making NVIDIA AI available across such a base would be “an amazing combination.” The trillion-chip figure was a forecast, not an achieved total. ([nvidianews.nvidia.com](https://nvidianews.nvidia.com/news/nvidia-to-acquire-arm-for-40-billion-creating-worlds-premier-computing-company-for-the-age-of-ai))
Son described an ecosystem spanning game machines, home appliances and robots that fly, run or swim. In the architecture he and Huang outlined, these edge devices would communicate with cloud AI systems and contribute data to recurring learning processes.
“Intelligence at Scale”
The executives presented AI as a major change in how software is created and computers are used.
“AI is a new kind of computer science; the software is different, the chips are different, the methodology is different,” Huang said.
Son offered a simplified progression: computers first advanced calculation, then enabled the storage of massive quantities of data, and were now becoming “the ears and the eyes” that could recognize voices, speech and other inputs.
“It’s intelligence at scale,” Huang responded. “That’s the reason why this age of AI is such an important time.”
Extending Human Capabilities
The presentation named AstraZeneca and GlaxoSmithKline in drug discovery, American Express in banking, Walmart in retail, Microsoft in software and Kubota in agriculture as examples of organizations adopting NVIDIA AI tools. In this restored article, those examples remain attributed to the corporate presentation rather than treated as independent assessments of each deployment.
Huang also cited recommender systems, which help people navigate large collections of products, music and other online choices. He and Son described such systems—and AI more broadly—as tools for extending rather than replacing human judgment.
“Humans will always be in the loop,” Huang said.
“We have a heart, a desire to be nice to other humans,” Son said. “We will utilize AI as a tool, for our happiness, for our joy—humans will choose which recommendations to take.”
“Perpetually Learning Machines”
Son and Huang envisioned smart, connected edge systems working with more powerful cloud systems. Devices in the physical world would provide inputs; cloud infrastructure would aggregate information; and the resulting improvements could flow back into deployed systems.
Huang called the process a “learning loop” that could produce “perpetually learning machines.”
“The cloud side will aggregate information from edge AI; it will become smarter and smarter,” Son said.
This was the proposed technical mechanism at the center of their argument: Arm’s device distribution, NVIDIA’s AI software and cloud-edge feedback loops would reinforce one another.
Democratizing AI
Huang predicted that AI would make computing more accessible because people could teach systems through examples or requests instead of expressing every task as conventional program code.
“You will just ask the computer, ‘This is what I want to do; can you give me a solution?’” Son responded. “Then the computer will give us the solution and the tools to make it happen.”
Huang argued that such tools could amplify Japan’s strengths in precision engineering and manufacturing.
“This is the time of AI for Japan,” Huang said.
He described how tools such as NVIDIA Omniverse could be used to model a digital factory, simulate its robots and processes, and refine the design before or alongside construction of a corresponding physical facility.
“This robotic factory will be filled with robots that will build robots in virtual reality,” Huang said. “The whole thing will be simulated … and when you come in in the morning the whole thing will be optimized more than it was when you went to bed.”
Son connected the idea to the “metaverse,” invoking the shared virtual world in Neal Stephenson’s 1992 novel Snow Crash.
“… and it’s right in front of us now,” Huang added.
Connecting Humans With One Another
Huang forecast that video conferencing would eventually account for most internet traffic. He cited AI-based reconstruction of a speaker’s facial expressions as a possible way to reduce the amount of data that needed to be transmitted.
He described a factor-of-10 bandwidth reduction, along with possible features such as eye-contact correction and real-time translation. Related NVIDIA research reported a 10× result against H.264 on a benchmark talking-head dataset. That controlled result was narrower than a universal claim that all video conferencing could use one-tenth the bandwidth. ([research.nvidia.com](https://research.nvidia.com/labs/dir/face-vid2vid/main.pdf?utm_source=openai))
“So you can speak to me in the future in Japanese and I can speak to you in English, and you will hear Japanese and I will hear English,” Huang said.
Enabling Big Dreams
The executives concluded by describing a future in which human judgment, AI systems, autonomous machines and closely connected teams could help people pursue more ambitious projects.
Son pointed to hoped-for applications in earlier detection of cardiac risks, faster discovery of cancer treatments and safer transportation. These were presented as aspirations for AI-enabled technologies, not evidence that heart attacks or car accidents could already be reliably predicted or eliminated.
“It is a big help,” Son said. “So we should be having a big smile, and big excitement, welcoming this revolution in AI.”
Source note: This is a technically restored version of an October 28, 2020 post from The Official NVIDIA Blog, imported through a syndication feed. It records a corporate presentation and the speakers’ views at the time; it is not independent reporting. Tracking images, feed boilerplate and embedded players were removed, while the video was retained through links.
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