DeepSeek’s breakthrough emboldens open-source AI models like Meta’s Llama
Omer Taha Cetin | Anadolu | Getty Images
DeepSeek’s powerful new artificial intelligence model isn’t just a win for China — it’s a victory for open-source versions of the tech from the likes of Meta, Databricks, Mistral and Hugging Face, according to industry experts who spoke with CNBC.
Last month, DeepSeek released R1, an open-source reasoning model that claims to rival the performance of OpenAI’s o1 model using a cheaper, less energy-intensive process.
The development caused the market values of Nvidia and other chipmakers to plummet on fears that it could lead to reduced spending on high-performance computing infrastructure.
DeepSeek is a Chinese AI lab that focuses on developing large language models with the ultimate aim of achieving artificial general intelligence, or AGI. It was founded in 2023 by Liang Wenfeng, co-founder of AI-focused quantitative hedge fund High-Flyer.
AGI loosely refers to the idea of an AI that equals or surpasses human intellect on a wide range of tasks.
What is open-source AI?
Since OpenAI’s ChatGPT burst onto the scene in November 2022, AI researchers have been working hard to understand and improve upon the advances of the foundational large language model technology that underpins it.
One area of focus for many labs has been open-source AI. Open source refers to software whose source code is made freely available on the open web for possible modification and redistribution.
Plenty of firms from tech giants like Meta to scrappier startups such as Mistral and Hugging Face have been betting on open-source as a way to improve on the technology while also sharing important developments with the wider research community.
How DeepSeek empowered open source
DeepSeek’s technological breakthrough has only made the case for open-source AI models stronger, according to some tech executives.
Seena Rejal, chief commercial officer of AI startup NetMind, told CNBC the Chinese firm’s success shows that open-source AI is “no longer just a non commercial research initiative but a viable, scalable alternative to closed models” like OpenAI’s GPT.
“DeepSeek R1 has demonstrated that open-source models can achieve state-of-the-art performance, rivaling proprietary models from OpenAI and others,” Rejal told CNBC. “This challenges the belief that only closed-source models can dominate innovation in this space.”
Rejal isn’t alone. Yann LeCun, Meta’s chief AI scientist, said DeepSeek’s success represents a victory for open-source AI models, not necessarily a win for China over the United States. Meta is behind a popular open-source AI model called Llama.
“To people who see the performance of DeepSeek and think: ‘China is surpassing the U.S. in AI.’ You are reading this wrong. The correct reading is: ‘Open source models are surpassing proprietary ones’,” he said in a post on LinkedIn.
“DeepSeek has profited from open research and open source (e.g. PyTorch and Llama from Meta). They came up with new ideas and built them on top of other people’s work. Because their work is published and open source, everyone can profit from it. That is the power of open research and open source.”
Open-source AI going global
Cut off by Washington from accessing advanced chips needed to train and run AI models, China has turned to open-source technology to boost the appeal of its AI models. Many Chinese firms — DeepSeek included — are pursuing open source models as a way to increase innovation and spread their use.
But the trend of companies turning to open-source technologies for success in AI isn’t limited to China. In Europe, an alliance of academics, companies and data centers have partnered on developing a family of high-performing, multilingual large language models, called OpenEuroLLM.
The alliance is led by Jan Hajič, a renowned computational linguist at Charles University, Czechia, and Peter Sarlin, the co-founder of Silo AI, an AI lab that was bought by U.S. chipmaker AMD last year.
The initiative forms part of a broader push for “AI sovereignty,” in which countries are encouraging investment in their own domestic AI labs and data centers to reduce a reliance on Silicon Valley.
What’s the catch?
There are downsides to open-source AI, however. Experts warn that, although open-source tech is a good thing for innovation, it is also more prone to cyber exploitation. That’s because it can be repackaged and modified by anyone.
Cybersecurity firms have already discovered vulnerabilities in DeepSeek’s AI models. Research that Cisco released last week revealed that R1 contained critical safety flaws.
Using “algorithmic jailbreaking techniques,” Cisco’s AI safety research team says it got R1 to provide affirmative responses to a series of harmful prompts from the popular HarmBench “with a 100% attack success rate.”
“DeepSeek R1 was purportedly trained with a fraction of the budgets that other frontier model providers spend on developing their models. However, it comes at a different cost: safety and security,” Cisco researchers Paul Kassianik and Amin Karbasi wrote.
Data leakage is also a concern. Data processed by DeepSeek’s R1 model via its website or app is sent straight to China. Chinese tech firms have long been dogged by allegations that Beijing uses their systems to spy on Western entities and individuals.
“DeepSeek, like other generative AI platforms, presents a double-edged sword for businesses and individuals alike,” said Matt Cooke, cybersecurity strategist EMEA at Proofpoint. “While the potential for innovation is undeniable, the risk of data leakage is a serious concern.”
“DeepSeek is relatively new, and it will take time to learn about the technology; however, what we do know is feeding sensitive company data or personal information into these systems is like handing attackers a loaded weapon,” Cooke added.
NetMind’s Rejal told CNBC that open-source AI models introduce cybersecurity risks which businesses need to consider, including software supply chain attacks, prompt jailbreaking and so-called “data poisoning” events that try to introduce biases or harmful outputs.
WATCH: Why China’s DeepSeek is putting America’s AI lead in jeopardy
Nvidia CEO Jensen Huang introduces new products as he delivers the keynote address at the GTC AI Conference in San Jose, California, on March 18, 2025.
Josh Edelson | AFP | Getty Images
At the end of Nvidia CEO Jensen Huang’s unscripted two-hour keynote on Tuesday, his message was clear: Get the fastest chips that the company makes.
Speaking at Nvidia’s GTC conference, Huang said that questions clients have about the cost and return on investment the company’s graphics processors, or GPUs, will go away with faster chips that can be digitally sliced and used to serve artificial intelligence to millions of people at the same time.
“Over the next 10 years, because we could see improving performance so dramatically, speed is the best cost-reduction system,” Huang said in a meeting with journalists shortly after his GTC keynote.
The company dedicated 10 minutes during Huang’s speech to explain the economics of faster chips for cloud providers, complete with Huang doing envelope math out loud on each chip’s cost-per-token, a measure of how much it costs to create one unit of AI output.
Huang told reporters that he presented the math because that’s what’s on the mind of hyperscale cloud and AI companies.
The company’s Blackwell Ultra systems, coming out this year, could provide data centers 50 times more revenue than its Hopper systems because it’s so much faster at serving AI to multiple users, Nvidia says.
Investors worry about whether the four major cloud providers — Microsoft, Google, Amazon and Oracle — could slow down their torrid pace of capital expenditures centered around pricey AI chips. Nvidia doesn’t reveal prices for its AI chips, but analysts say Blackwell can cost $40,000 per GPU.
Already, the four largest cloud providers have bought 3.6 million Blackwell GPUs, under Nvidia’s new convention that counts each Blackwell as 2 GPUs. That’s up from 1.3 million Hopper GPUs, Blackwell’s predecessor, Nvidia said Tuesday.
The company decided to announce its roadmap for 2027’s Rubin Next and 2028’s Feynman AI chips, Huang said, because cloud customers are already planning expensive data centers and want to know the broad strokes of Nvidia’s plans.
“We know right now, as we speak, in a couple of years, several hundred billion dollars of AI infrastructure” will be built, Huang said. “You’ve got the budget approved. You got the power approved. You got the land.”
Huang dismissed the notion that custom chips from cloud providers could challenge Nvidia’s GPUs, arguing they’re not flexible enough for fast-moving AI algorithms. He also expressed doubt that many of the recently announced custom AI chips, known within the industry as ASICs, would make it to market.
“A lot of ASICs get canceled,” Huang said. “The ASIC still has to be better than the best.”
Huang said his is focus on making sure those big projects use the latest and greatest Nvidia systems.
“So the question is, what do you want for several $100 billion?” Huang said.
Microsoft’s Amy Coleman (L) and Kathleen Hogan (R).
Source: Microsoft
Microsoft said Wednesday that company veteran Amy Coleman will become its new executive vice president and chief people officer, succeeding Kathleen Hogan, who has held the position for the past decade.
Hogan will remain an executive vice president but move to a newly established Office of Strategy and Transformation, which is an expansion of the office of the CEO. She will join Microsoft’s group of top executives, reporting directly to CEO Satya Nadella.
Coleman is stepping into a major role, given that Microsoft is among the largest employers in the U.S., with 228,000 total employees as of June 2024. She has worked at the company for more than 25 years over two stints, having first joined as a compensation manager in 1996.
Hogan will remain on the senior leadership team.
“Amy has led HR for our corporate functions across the company for the past six years, following various HR roles partnering across engineering, sales, marketing, and business development spanning 25 years,” Nadella wrote in a memo to employees.
“In that time, she has been a trusted advisor to both Kathleen and to me as she orchestrated many cross-company workstreams as we evolved our culture, improved our employee engagement model, established our employee relations team, and drove enterprise crisis response for our people,” he wrote.
Hogan arrived at Microsoft in 2003 after being a development manager at Oracle and a partner at McKinsey. Under Hogan, some of Microsoft’s human resources practices evolved. She has emphasized the importance of employees having a growth mindset instead of a fixed mindset, drawing on concepts from psychologist Carol Dweck.
“We came up with some big symbolic changes to show that we really were serious about driving culture change, from changing the performance-review system to changing our all-hands company meeting, to our monthly Q&A with the employees,” Hogan said in a 2019 interview with Business Insider.
Hogan pushed for managers to evaluate the inclusivity of employees and oversaw changes in the handling of internal sexual harassment cases.
Coleman had been Microsoft’s corporate vice president for human resources and corporate functions for the past four years. In that role, she was responsible for 200 HR workers and led the development of Microsoft’s hybrid work approach, as well as the HR aspect of the company’s Covid response, according to her LinkedIn profile.
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