- Nvidia CEO Jensen Huang predicts companies will become AI factories that generate tokens.
- Tokens are numerical representations used by AI models to process and understand data.
- Here’s what the Nvidia CEO means by an “AI factory.”
In his keynote at Nvidia’s AI conference this week, CEO Jensen Huang predicted every company will become an “AI factory.”
It’s a big idea that could make businesses of all types successful in the future. So it’s worth explaining.
I first heard about this last year when chatting with Guillermo Rauch, CEO of AI startup Vercel. He explained the role of tokens in artificial intelligence, and noted that Huang likes to say “every company will become a token factory.”
‘Everything’s token’
If data is the raw material of generative AI, tokens are the language. AI models break down words and other inputs into numerical tokens to make them easier to process and understand. One token is about ¾ of a word.
An example from Nvidia: The word “darkness” might be tokenized into the numbers 271 for “dark” and 655 for “ness.” The opposite word, “brightness,” would be represented by 491 and 655. This way, the AI model can spot the same 655 number twice and understand that these words are related.
Trillions of these numbers, or tokens, are used to train AI models like this, then fine-tune and run them. Instead of “everything’s computer,” you might say that “everything’s token” in AI.
‘One job and one job only’
Huang, Rauch, and other technologists think modern companies will succeed by generating the most tokens. They will be AI factories churning out tokens that will be used to improve and run AI systems that help businesses make better products and services.
“I call them AI factories,” Huang said on Tuesday at the GTC conference. “They’re AI factories because they have one job and one job only — generating these incredible tokens that we then reconstitute into music, into words, into videos, into research, into chemicals or proteins.”
He said these token-generating facilities will sometimes sit alongside companies’ more traditional operations.
“Every industry, every company that has factories will have two factories in the future,” Huang predicted. “The factory for what they build and the factory for the mathematics, the factory for the AI.”
He cited auto manufacturing as an example, describing “a factory for cars” and “a factory for AI for the cars.”
He then put some meat on those theoretical bones by announcing a partnership with General Motors in which Nvidia will help that company use AI to manufacture cars while also making GM vehicles more autonomous with AI.
Does Tesla make cars or token-generating machines?
Jason Liu, a machine-learning engineer and AI consultant, made a similar point with Tesla’s electric vehicles and Elon Musk’s goal to make them fully autonomous.
When a Tesla is driven around a city, it has sensors that collect mountains of information about its surroundings. That data is collected and turned into tokens that are used to improve Tesla’s AI models. That, in theory, produces better self-driving software to guide the vehicles more accurately and safely.
In an AI world, most companies’ roles will be to generate more data,” Liu said.
He argued that Tesla’s approach of putting as many cars as possible on the road to scoop up as much data as possible has been better than Waymo’s strategy, “where engineers sat in a cave for years working on this in relative isolation, not collecting as much data, or tokens.”
Making better business decisions
Liu shared another example, this time theoretical: How can companies make better business decisions by looking back at their process and tokenizing it?
“For any major decision, there’s probably six months of back and forth debate between employees on Slack chats, Zoom video meetings, board meetings, and data dashboards,” he said.
Companies can now turn all that into tokens and use that to train an AI system to make better decisions in the future or to help human executives and employees make better decisions next time.
“The job of the company and the software is to pull all that out of the humans involved and turn that into tokens for AI training,” he added.
Token factory examples
Rauch says Vercel is doing this with its v0 tool, which helps developers and non-technical people build websites and applications.
“v0 takes in user requirements in English and outputs an application,” Rauch explained. “Those are our tokens.”
How also cited a Vercel customer called OpenEvidence, which uses AI to synthesize mountains of medical research into digestible information for busy medicine professionals.
“Their tokens are the research data that doctors need in order to make better decisions,” Rauch said. “It’s medical intelligence tokens.”
Liu cited the example of Mercor, a startup that is hiring technical PhDs to harvest their specific knowledge and turn that into tokens that are used by AI labs to improve their models.
“The job of every company will become to produce intelligence, like a token factory,” Rauch said. “Companies build up institutional knowledge over time, they accrue best practices, operational principles and procedures, training manuals, brand guidelines, and even taste. All of that will become part of the pre- and post-training of AI models and the data that gets inserted on top.”
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