The Hugging Face website on a smartphone arranged in New York, Aug. 17, 2023.
Gabby Jones | Bloomberg | Getty Images
Hugging Face, an AI firm based in New York, has raised $235 million at a $4.5 billion valuation from some of technology’s biggest companies.
Google, Amazon, Nvidia, Salesforce, AMD, Intel, IBM and Qualcomm all contributed to the round, the company said. Hugging Face CEO Clement Delangue said the funds are to be focused on hiring talent to be competitive in the artificial intelligence space.
Startups working on AI models have reached high valuations as big companies and venture capitalists seek to plow money in the recent AI boom, which kicked off last year when Microsoft-backed OpenAI released its ChatGPT chatbot.
Hugging Face’s big valuation and crop of prominent backers reflect how a more collaborative approach to building AI has been gaining steam in recent months, especially after Facebook parent Meta released its Llama large language model, which is free to use for the vast majority of companies.
Other highly valued AI startups, like OpenAI or Cohere, work on the technology directly and guard the results as a trade secret, then charge customers to access them through application programming interfaces, or AIs.
But Hugging Face produces a platform where AI developers can share code, models, data sets, and use the company’s developer tools to get open-source artificial intelligence models running more easily. In particular, Hugging Face often hosts weights, or large files with lists of numbers, which are the heart of most modern AI models.
While Hugging Face has developed some models, like BLOOM, its primary product is its website platform, where users can upload models and their weights. It also develops a series of software tools called libraries that allow users to get models working quickly, to clean up large datasets, or to evaluate their performance. It also hosts some AI models in a web interface so end users can experiment with them.
It’s similar in theme and practice to code-repository GitHub (which Microsoft acquired in 2018), where coders from around the world post their projects while they’re working on them.
Hugging Face endorses the belief that most companies working with AI will want to develop their own models or technology, and will need tools to do so, co-founder and CEO Delangue told CNBC. He hopes that AI developers will rely on Hugging Face on a daily basis to get their work done.
One reason the big companies are investing: Their employees are actively using the platform, he said.
“AI builders are using Hugging Face all day, every day,” Delangue said. He predicted that the number of software developers working with AI models would grow in the coming years.
“Maybe in five years, you’re going to have like 100 million AI builders. And if all of them use Hugging Face all day, every day, we’ll obviously be in a good position,” he said.
Although most attention in recent weeks has been on so-called large language models like ChatGPT or Llama that focus on generating text, Hugging Face hosts any AI model, including ones that generate music or images, translate languages, or identify objects inside images. Hugging Face hosts 500,000 different AI models, 250,000 data sets, and has 10,000 paying customers, the company said.
Hugging Face is named after an emoji, the hugging face, a smiley face framed by two open hands.
The name and logo date back to the company’s founding. Hugging Face was originally a iPhone chatbot app, but when the company open-sourced some its machine-learning code, it realized that it was catching on with AI developers, and pivoted toward that.
“When we started the company, with my co founders Julien Chaumond and Thomas Wolf, we joked that we wanted to be the first company to go public with an emoji instead of the three letter ticker,” Delangue said.
“Maybe during this round we should start our lobbying exercise with the Nadsaq for them to allow us to use emojis on their board,” he quipped.
An employee works at Shopify’s headquarters in Ottawa, Ontario in Canada.
Chris Wattie | Reuters
Shopify on Tuesday reported better-than-expected sales for the fourth quarter but missed on earnings. Shares whipsawed in premarket trading.
Here’s how the company did:
Earnings: 39 cents per share vs. 43 cents per share expected by LSEG
Revenue: $2.81 billion vs. $2.73 billion expected by LSEG
Shopify forecasted revenue in the first quarter to grow at a mid-20% percentage rate, which is roughly in line with analysts’ expectations of 24.4% revenue growth, according to LSEG.
“We expect the strong merchant momentum from Q4 to carry over into Q1, recognizing that Q1 is consistently our lowest [gross merchandise volume] quarter seasonally,” the company said in its earnings release.
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The first quarter includes the results of the holiday shopping season. Online spending jumped nearly 9% to $241.1 billion in November and December, according to data from Adobe Analytics, which tracks sales on retailers’ websites. That was slightly higher than analysts’ forecast for sales of $240.8 billion.
The company said it expects operating expense as a percentage of revenue to be 41% to 42% in the current quarter. That’s a step up from 31.5% in the fourth quarter.
Net income nearly doubled to $1.3 billion, or 99 cents per share, from $657 million, or 51 cents per share, a year ago.
Revenue in the fourth quarter jumped 31% from $2.14 billion in the same quarter a year earlier.
Gross merchandise volume, or the total volume of merchandise sold on the platform, came in at $94.5 billion. Analysts surveyed by FactSet were looking for GMV of $93 billion.
Shopify sells software for merchants who run online businesses as well as services such as advertising and payment processing tools. The company has made its name as a platform for small businesses and direct-to-consumer brands to launch online storefronts. More recently, it has looked to attract bigger customers, such as Reebok, Mattel and Barnes & Noble, as a way to boost its growth.
While the details on just how DeepSeek did it remain incomplete, and its success doesn’t mean export controls don’t have a place in markets and national security policy, it does show that a focus on stopping the competition can’t keep pace with innovation. Now, the debate is underway over just how far the U.S. government should go in the future in blocking access to U.S. chip technology.
President Biden’s Department of Commerce issued its rules to “regulate the global diffusion” of AI chips and models in the administration’s waning days. The rules already have been heavily criticized by tech companies, including Nvidia, as well as policy experts. A Brookings analysis argues the the AI diffusion rules seek to create “a centrally planned global computing economy.”
“A decade from now, we will look back and recognize how quixotic it was for the U.S. government of the mid-2020s to attempt to limit the ability of people in 150 countries to perform fast multiplications,” wrote John Villasenor, a nonresident senior fellow at Brookings and professor of electrical engineering, law, public policy, and management at UCLA.
In any technology war, questions about what countermove the U.S. should make next inevitably run up against the awareness that any notion of controlling innovation through measures like restricting exports is not guaranteed to work – and may even backfire. Among the risks cited by Brookings: spurring the development of a global AI ecosystem anchored outside the U.S.; pushing more nations into building stronger technology ties with China; and allowing non-U.S. makers of advanced chips to grow global market share at the expense of the U.S. companies behind the original innovations.
“I worry that we will have a knee-jerk response to ratchet up controls heavily, before we fully think through the trade-offs,” said Martin Chorzempa, senior fellow at the Peterson Institute for International Economics.
There is a 120-day comment period that ends on May 15 on the AI diffusion rules, unless Trump reverses or revises the rule before then. While the president has spoken in general about the need to protect the U.S. technological lead, he has not specifically addressed this rule. It’s unknown what stance the current administration will take – expanding, curtailing, or overturning chip export rules already in place.
“Some authoritarian regimes have stolen and used AI to strengthen their military intelligence and surveillance capabilities, capture foreign data and create propaganda to undermine other nations’ national security,” Vance said in an address at France’s AI Action Summit in Paris. “I want to be clear, this administration will block such efforts, full stop,” Vance said. “We will safeguard American AI and chip technologies from theft and misuse, work with our allies and partners to strengthen and extend these protections and close pathways to adversaries attaining AI capabilities that threaten all of our people,” he added.
Trump’s first-day signing of an executive order to “identify and eliminate loopholes in existing export controls,” suggest he could take a hard line. It said the government will “assess and make recommendations regarding how to maintain, obtain, and enhance our Nation’s technological edge and how to identify and eliminate loopholes in existing export controls – especially those that enable the transfer of strategic goods, software, services, and technology to countries to strategic rivals and their proxies.”
The tech sector was quick to do its outreach to the new administration, with several major CEOs at the inauguration, and Nvidia CEO Jensen Huang meeting with President Trump at the White House in recent weeks for a discussion that included chip restrictions to China.
Trump also called Deepseek a “wake-up call for our industries that we need to be laser-focused on competing to win.”
Particularly relevant to Deepseek in the AI diffusion rules are controls surrounding closed AI model weights, essential to the training process that develops how AI systems think and respond to queries.
“In part, DeepSeek was able to get around the speed limit imposed on chips allowed for sale to China in 2022, but banned in 2023, when the U.S. realized that the limit imposed was the wrong one,” said Chorzempa.
When the U.S. put controls on China in 2022, Chorzempa explained, they set a specific parameter concerning the speed of communication between chips. It was thought that if you control the power of an individual chip that might not be enough, because if you bring enough less powerful chips together, it’s possible to have supercomputer-like capabilities at a level the U.S. government didn’t want China to obtain. It appears from what DeepSeek described in its R1 paper that the company was able to overcome that speed limit.
“Experts in the technical community in at least early 2023 were pointing out that other restrictions were required to have an effective control as the technology evolved,” Chorzempa said.
In 2023, the U.S. government added additional layers of restriction that made the Nvidia chips DeepSeek says it trained the model on no longer legal for export. Tightened controls could be further strengthened by subsequent initiatives from the Trump administration.
But through a combination of having a limited number of advanced chips available and innovation spurred on by that limit, DeepSeek was able to build a better, and potentially cheaper, mousetrap.
“DeepSeeks seems to have optimized heavily with clever software and hardware engineering to sort of neuter the speed limit meant to hold those chips back,” Chorzempa said.
AI rivals will continue to do more with less
There are other aspects to the evolving AI race which show gaps that are narrowing for other reasons.
“The story is really about the gap being closed between open source and closed source models,” said Alexandra Mousavizadeh, CEO of Evident, an AI consulting firm. “Now the open source models are getting much closer to the capabilities of the closed ones, and we see the price driving down to zero,” Mousavizadeh said.
DeepSeek has already shown that you don’t need maximum computing power, and you can you use open-source as alternative when building a viable LLM, and in fact, according to Mousavizadeh, these factors can be a driver of innovation.
“We’re seeing that limits forced them to use scientific methods and systems that compress data onto a much smaller pool that uses much less power using mixed expert models,” she said. AI rivals can “do more with less,” Mousavizadeh added.
“You can’t really gatekeep,” Mousavizadeh said, noting that there is lots of sharing that occurs in the open source environment, “regardless of governmental policy.”
If DeepSeek’s success leads to export controls on advanced chips intended to slow Chinese AI efforts that become even stricter, it should also be clear they are no silver bullet. “They’re not a way to duck the competition between the US and China,” wrote Dario Amodei, CEO of gen AI startup Anthropic, in a blog post last week. “In the end, AI companies in the US and other democracies must have better models than those in China if we want to prevail. But we shouldn’t hand the Chinese Communist Party technological advantages when we don’t have to.”
His issue isn’t with the AI researchers in China, but the government to which they are ultimately beholden. “In interviews they’ve done, they seem like smart, curious researchers who just want to make useful technology,” Amodei wrote about DeepSeek. “But they’re beholden to an authoritarian government that has committed human rights violations, has behaved aggressively on the world stage, and will be far more unfettered in these actions if they’re able to match the US in AI.”
To be sure, there are many reasons to be wary of doing anything to contribute to China’s AI advances and successes like DeepSeek, from national security concerns about data sharing with the Chinese government, to ongoing hacking risks, to Chinese AI apps becoming popular enough to be used by Chinese intelligence to learn about Americans and American industries, and to sow division among the public.
Palantir Technologies CEO Alex Karp told CNBC’s Sara Eisen in a recent interview that “we have to run harder, run faster, have an all-country effort.”
“The second-mover can move very quickly, especially if we’ve already done the innovation,” Karp said, describing DeepSeek as derivative of U.S. models with “improvements at the margins.”
He expects a “huge policy discussion” to make sure innovations are not exported, but Karp added that in the end, “the real advantage goes to the first mover as long as the first mover is running hard. … We have the lead, we have to focus on making sure we keep it. Our adversaries are gonna copy anything they can.”
Palantir’s rise — its shares soared 340% last year to lead the S&P 500 — didn’t come by trying to stop others, and in that there may be a lesson. “We don’t focus on the competition,” Karp said. “We focus on how do we execute.”
The Google Calendar logo is displayed on a tablet.
Igor Golovniov | Sopa Images | Lightrocket | Getty Images
Google‘s popular online and mobile calendars no longer include reference to the first day of Black History Month or Women’s History month, among other holidays and events.
The company’s calendar previously had those days marked at the start of February and March, respectively, but they don’t appear for 2025.
The Verge first reported on the removals from Google Calendar late last week, which followed comments from users.
A Google spokesperson said the changes took place in the middle of last year.
“Some years ago, the Calendar team started manually adding a broader set of cultural moments in a wide number of countries around the world,” the spokesperson said in an email. “We got feedback that some other events and countries were missing — and maintaining hundreds of moments manually and consistently globally wasn’t scalable or sustainable,” the spokesperson added.
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Google has made numerous changes lately that align with an altered political environment in the U.S. The company recently began scrapping its diversity hiring goals, becoming the latest tech giant to change its approach to hiring and promotions following the election of President Donald Trump. One of Trump’s first acts as president after taking office in January was to sign an executive order ending the government’s DEI programs and putting federal officials overseeing those initiatives on leave.
In late January, the company said it would change the name of the Gulf of Mexico to the “Gulf of America” in Google Maps after the Trump administration updates its “official government sources.” Google also said it would follow Trump and start using the name “Mount McKinley” for the mountain in Alaska currently called Denali.
On Google Calendar, the company has removed other events as well. It previously had Nov. 1 as the first day of Indigenous Peoples Month and June 1 as the start of LGBTQ+ Pride month.
The company spokesperson said that in mid-2024, the company “returned to showing only public holidays and national observances from timeanddate.com globally, while allowing users to manually add other important moments.” The timeanddate.com website says its company has 40 employees and is based in Norway.
Google Calendar users noticed the changes and left comments in the user support web pages and on social media. The user support site previously received comments from people upset about the company adding such observances.