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China is focusing on large language models (LLMs) in the artificial intelligence space. 

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China is embracing open-source AI models in a trend market watchers and insiders say is boosting AI adoption and innovation in the country, with some suggesting it is an ‘Android moment’ for the sector.

The open-source shift has been spearheaded by AI startup DeepSeek, whose R1 model released earlier this year challenged American tech dominance and raised questions over Big Tech’s massive spending on large language models and data centers. 

While R1 created a splash in the sector due to its performance and claims of lower costs, some analysts say the most significant impact of DeepSeek has been in catalyzing the adoption of open-source AI models. 

“DeepSeek’s success proves that open-source strategies can lead to faster innovation and broad adoption,” said Wei Sun, principal analyst of artificial intelligence at Counterpoint Research, noting a large number of firms have implemented the model. 

“Now, we see that R1 is actively reshaping China’s AI landscape, with large companies like Baidu moving to open source their own LLMs in a strategic response,” she added. 

On March 16, Baidu released the latest version of its AI model, Ernie 4.5, as well as a new reasoning model, Ernie X1, making them free for individual users. Baidu also plans to make the Ernie 4.5 model series open-source from end-June. 

Experts say that Baidu’s open-source plans represent a broader shift in China, away from a business strategy that focuses on proprietary licensing. 

“Baidu has always been very supportive of its proprietary business model and was vocal against open-source, but disruptors like DeepSeek have proven that open-source models can be as competitive and reliable as proprietary ones,” Lian Jye Su, chief analyst with technology research and advisory group Omdia previously told CNBC.

Open-source vs proprietary models

Open-source generally refers to software in which the source code is made freely available on the web for possible modification and redistribution.

AI models that call themselves open-source had existed before the emergence of DeepSeek, with Meta‘s Llama and Google‘s Gemma being prime examples in the U.S. However, some experts argue that these models aren’t really open source as their licenses restrict certain uses and modifications, and their training data sets aren’t public.

DeepSeek’s R1 is distributed under an ‘MIT License,’ which Counterpoint’s Sun describes as one of the most permissive and widely adopted open-source licenses, facilitating unrestricted use, modification and distribution, including for commercial purposes.

The DeepSeek team even held an “Open-Source Week” last month, which saw it release more technical details about the development of its R1 model. 

While DeepSeek’s model itself is free, the start-up charges for Application Programming Interface, which enables the integration of AI models and their capabilities into other companies’ applications. However, its API charges are advertised to be far cheaper compared with OpenAI and Anthropic’s latest offerings.

OpenAI and Anthropic also generate revenue by charging individual users and enterprises to access some of their models. These models are considered to be ‘closed-source,’ as their datasets, and algorithms are not open for public access.

China opens up

In addition to Baidu, other Chinese tech giants such as Alibaba Group and Tencent have increasingly been providing their AI offerings for free and are making more models open source.

For example, Alibaba Cloud said last month it was open-sourcing its AI models for video generation, while Tencent reportedly released five new open-source models earlier this month with the ability to convert text and images into 3D visuals.

Smaller players are also furthering the trend. ManusAI, a Chinese AI firm that recently unveiled an AI agent that claims to outperform OpenAI’s Deep Research, has said it would shift towards open source.

“This wouldn’t be possible without the amazing open-source community, which is why we’re committed to giving back” co-founder Ji Yichao said in a product demo video. “ManusAI operates as a multi-agent system powered by several distinct models, so later this year, we’re going to open source some of these models,” he added.

Zhipu AI, one of the country’s leading AI startups, this month announced on WeChat that 2025 would be “the year of open source.”

Ray Wang, principal analyst and founder of Constellation Research, told CNBC that companies have been compelled to make these moves following the emergence of DeepSeek.

“With DeepSeek free, it’s impossible for any other Chinese competitors to charge for the same thing. They have to move to open-source business models in order to compete,” said Wang. 

AI scholar and entrepreneur Kai-Fu Lee also believes this dynamic will impact OpenAI, noting in a recent social media post that it would be difficult for the company to justify its pricing when the competition is “free and formidable.”

“The biggest revelation from DeepSeek is that open-source has won,” said Lee, whose Chinese startup 01.AI has built an LLM platform for enterprises seeking to use DeepSeek.

U.S.-China competition

OpenAI — which started the AI frenzy when it released its ChatGPT bot in November 2022— has not signaled that it plans to shift from its proprietary business model. The company which started as a nonprofit in 2015 is moving towards towards a for-profit structure.

Sun says that OpenAI and DeepSeek both represent very different ends of the AI space. She adds that the sector could continue to see division between open-source players that innovate off one another and closed-source companies that have come under pressure to maintain high-cost cutting-edge models. 

The open-source trend has put in to question the massive funds raised by companies such as OpenAI. Microsoft has invested $13 billion into the company. It is in talks to raise up to $40 billion in a funding round that would lift its valuation to as high as $340 billion, CNBC confirmed at the end of January.

In September, CNBC confirmed the company expects about $5 billion in losses, with revenue pegged at $3.7 billion revenue. OpenAI CFO Sarah Friar, has also said that $11 billion in revenue is “definitely in the realm of possibility” for the company this year.

China's open-source AI push is an Android moment and a huge sentiment boost: Hedge fund manager

On the other hand, Chinese companies have chosen the open-source route as they compete with the more proprietary approach of U.S. firms, said Constellation Research’s Wang. “They are hoping for faster adoption than the closed models of the U.S.,” he added. 

Speaking to CNBC’s “Street Signs Asia” on Wednesday, Tim Wang, managing partner of tech-focused hedge fund Monolith Management, said that models from companies such as DeepSeek have been “great enablers and multipliers in China,” demonstrating how things can be done with more limited resources.

According to Wang, open-source models have pushed down costs, opening doors for product innovation — something he says Chinese companies historically have been very good at.

He calls the development the “Android moment,” referring to when Google’s Android made its operating system source code freely available, fostering innovation and development in the non-Apple app ecosystem.

“We used to think China was 12 to 24 months behind [the U.S.] in AI and now we think that’s probably three to six months,” said Wang.

However, other experts have downplayed the idea that open-source AI should be seen through the lens of China and U.S. competition. In fact, several U.S. companies have integrated and benefited from DeepSeek’s R1. 

“I think the so-called DeepSeek moment is not about whether China has better AI than the U.S. or vice versa. It’s really about the power of open-source,” Alibaba Group Chairperson Joe Tsai told CNBC’s CONVERGE conference in Singapore earlier this month. 

Tsai added that open-source models give the power of AI to everyone from small entrepreneurs to large corporations, which will lead to more development, innovation and a proliferation of AI applications.

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Americans are heating their homes with bitcoin this winter

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Americans are heating their homes with bitcoin this winter

As winter’s chill settles in across the U.S., and electricity bills become a bigger budgeting issue, most Americans will rely on their usual sources of warmth, such as home heating oil, natural gas, and electric furnaces. But in a few cases, crypto is generating the heat, and if some of the nascent crypto heat industry’s proponents are correct, someday its use as a source within homes and buildings will be much more widespread.

Let’s start with the basics: the computing power of crypto mining generates a lot of heat, most which just ends up vented into the air. According to digital assets brokerage, K33, the bitcoin mining industry generates about 100 TWh of heat annually — enough to heat all of Finland. This energy waste within a very energy-intense industry is leading entrepreneurs to look for ways to repurpose the heat for homes, offices, or other locations, especially in colder weather months.

During a frigid snap earlier this year, The New York Times reviewed HeatTrio, a $900 space heater that also doubles as a bitcoin mining rig. Others use the heat from their own in-home cryptocurrency mining to spread warmth throughout their house.

“I’ve seen bitcoin rigs running quietly in attics, with the heat they generate rerouted through the home’s ventilation system to offset heating costs. It’s a clever use of what would otherwise be wasted energy,” said Jill Ford, CEO of Bitford Digital, a sustainable bitcoin mining company based in Dallas. “Using the heat is another example of how crypto miners can be energy allies if you apply some creativity to their potential,” Ford said.

It’s not necessarily going to save someone money on their electric bill — the economics will vary greatly from place to place and person to person, based on factors including local electricity rates and how fast a mining machine is — but the approach might make money to offset heating costs.

“Same price as heating the house, but the perk is that you are mining bitcoin,” Ford said.

A single mining machine — even an older model — is sufficient. Solo miners can join mining pools to share computing power and receive proportional payouts, making returns more predictable and changing the economic equation.  

“The concept of using crypto mining or GPU compute to heat homes is clever in theory because almost all the energy consumed by computation is released as heat,” said Andrew Sobko, founder of Argentum AI, which is creating a marketplace for the sharing of computing power. But he added that the concept makes the most sense in larger settings, particularly in colder climates or high-density buildings, such as data centers, where compute heat shows real promise as a form of industrial-scale heat recapture.

To make it work — it’s not like you can transport the heat somewhere by truck or train — you have to identity where the computing heat is needed and route it to that place, such as co-locating GPUs in environments from industrial parks to residential buildings.

“We’re working with partners who are already redirecting compute heat into building heating systems and even agricultural greenhouse warming. That’s where the economics and environmental benefits make real sense,” Sobko said. “Instead of trying to move the heat physically, you move the compute closer to where that heat provides value,” he added.

Why skeptics say crypto home heating won’t work

There are plenty of skeptics.

Derek Mohr, clinical associate professor at the University of Rochester Simon School of Business, does not think the future of home heating lies in crypto and says even industrial crypto is problematic.

Bitcoin mining is so specialized now that a home computer, or even network of home computers, would have almost zero chance of being helpful in mining a block of bitcoin, according to Mohr, with mining farms use of specialized chips that are created to mine bitcoin much faster than a home computer.

“While bitcoin mining at home — and in networks of home computers — was a thing that had small success 10 years ago, it no longer is,” Mohr said.

“The bitcoin heat devices I have seen appear to be simple space heaters that use your own electricity to heat the room … which is not an efficient way to heat a house,” he said. “Yes, bitcoin mining generates a lot of heat, but the only way to get that to your house is to use your own electricity,” Mohr said.

He added that while running your computer non-stop would generate heat, it has a very low probability of successfully mining a bitcoin block.

“In my opinion, this is not a real opportunity that will work. Instead it is taking advantage of things people have heard of — excess heat from bitcoin mining and profits from mining — and is giving false hope that there is a way for an individual to benefit from this,” Mohr said.

But some experts say more widespread use of plug-and-play, free-standing mining rigs, might make the concept viable in more locations over time. In the least, they say it is worth studying the dual use economic and environmental benefits based on the underlying fact that crypto mining generates significant heat as a byproduct of the computer processing.

“How can we capture the excess heat from the operation to power something else? That could range from heating a home to warming water, even in a swimming pool. As a result, your operating efficiency is higher on your power consumption,” said Nikki Morris, the executive director of the Texas Christian University Ralph Lowe Energy Institute.

She says the concept of crypto heating is still in its earliest stages, and most people don’t yet understand how it works or what the broader implications could be. “That’s part of what makes it so interesting. At Texas Christian University, we see opportunities to help people build both the vocabulary and the business use feasibility with industry partners,” Morris said.

Because crypto mining produces a digital asset that can be traded, it introduces a new source of revenue from power consumption, and the power source could be anything from the grid to natural gas to solar to wind or battery generation, according to Morris. She cited charging an electric vehicle at mixed-use buildings or apartment complexes as an example.

“Picture a similar scenario where an apartment complex’s crypto mining setup produces both digital currency and usable heat energy. That opens the door to distributed energy innovation to a broader stakeholder base, an approach that could complement existing heating systems and renewable generation strategies,” Morris said.

There are many questions to explore, including efficiency at different scales, integration with other energy sources, regulatory considerations, and overall environmental impact, “but as these technologies evolve, it’s worth viewing crypto heating not just as a curiosity, but as a small window into how digital and physical energy systems might increasingly converge in the future,” Morris said.

Testing bitcoin heat in the real world

The crypto-heated future may be unfolding in the town of Challis, Idaho, where Cade Peterson’s company, Softwarm, is repurposing bitcoin heat to ward off the winter.

Several shops and businesses in town are experimenting with Softwarm’s rigs to mine and heat. At TC Car, Truck and RV Wash, Peterson says, the owner was spending $25 a day to heat his wash bays to melt snow and warm up the water.

“Traditional heaters would consume energy with no returns. They installed bitcoin miners and it produces more money in bitcoin than it costs to run,” Peterson said. Meanwhile, an industrial concrete company is offsetting its $1,000 a month bill to heat its 2,500-gallon water tank by heating it with bitcoin.

Peterson has heated his own home for two-and-a-half years using bitcoin mining equipment and believes that heat will power almost everything in the future. “You will go to Home Depot in a few years and buy a water heater with a data port on it and your water will be heated with bitcoin,” Peterson said. 

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These underperforming groups may deliver AI-electric appeal. Here’s why.

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These underperforming groups may deliver AI-electric appeal. Here's why.

Reshoring and infrastructure products could be the next ETF play after AI, say ETF experts

Industrial and infrastructure stocks may soon share the spotlight with the artificial intelligence trade.

According to ETF Action’s Mike Atkins, there’s a bullish setup taking shape due to both policy and consumer trends. His prediction comes during a volatile month for Big Tech and AI stocks.

“You’re seeing kind of the old-school infrastructure, industrial products that have not done as well over the years,” the firm’s founding partner told CNBC’s “ETF Edge” this week. “But there’s a big drive… kind of away from globalization into this reshoring concept, and I think that has legs.”

Global X CEO Ryan O’Connor is also optimistic because the groups support the AI boom. His firm runs the Global X U.S. Infrastructure Development ETF (PAVE), which tracks companies involved in construction and industrial projects.

“Infrastructure is something that’s near and dear to our heart based off of PAVE, which is our largest ETF in the market,” said O’Connor in the same interview. “We think some of these reshoring efforts that you can get through some of these infrastructure places are an interesting one.”

The Global X’s infrastructure exchange-traded fund is up 16% so far this year, while the VanEck Semiconductor ETF (SMH), which includes AI bellwethers Nvidia, Taiwan Semiconductor and Broadcom, is up 42%, as of Friday’s close.

Both ETFs are lower so far this month — but Global X’s infrastructure ETF is performing better. Its top holdings, according to the firm’s website, are Howmet Aerospace, Quanta Services and Parker Hannifin.

Supporting the AI boom

He also sees electrification as a positive driver.

“All of the things that are going to be required for us to continue to support this AI boom, the electrification of the U.S. economy, is certainly one of them,” he said, noting the firm’s U.S. Electrification ETF (ZAP) gives investors exposure to them. The ETF is up almost 24% so far this year.

The Global X U.S. Electrification ETF is also performing a few percentage points better than the VanEck Semiconductor ETF for the month.

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How tariffs and AI are giving secondhand platforms like ThredUp a boost

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How tariffs and AI are giving secondhand platforms like ThredUp a boost

At ThredUp‘s 600,000-square-foot warehouse in Suwanee, Georgia, roughly 40,000 pieces of used clothing are processed each day. The company’s logistics network — four facilities across the U.S. — now rivals that of some fast-fashion giants.

“This is the largest garment-on-hanger system in the world,” said Justin Pina, ThredUp’s senior director of operations. “We can hold more than 3.5 million items here.”

Secondhand shopping is booming. The global secondhand apparel market is expected to reach $367 billion by 2029, growing almost three times faster than the overall apparel market, according to GlobalData.

President Donald Trump’s tariffs were billed as a way to bring manufacturing back home. But the measures hit one of America’s most import-dependent industries: fashion.

About 97 percent of clothing sold in the U.S. is imported, mostly from China, Vietnam, Bangladesh and India, according to the American Apparel and Footwear Association.

For years, Gen Z shoppers have been driving the rise of secondhand fashion, but now more Americans are catching on.

“When tariffs raise those costs, resale platforms suddenly look like the smart buy. This isn’t just a fad,” said Jasmine Enberg, co-CEO of Scalable. “Tariffs are accelerating trends that were already reshaping the way Americans shop.”

For James Reinhart, ThredUp’s CEO, the company is already seeing it play out.

“The business is free-cash-flow positive and growing double digits,” said Reinhart. “We feel really good about the economics, gross margins near 80% and operations built entirely within the U.S.”

ThredUp reported that revenue grew 34% year over year in the third quarter. The company also said it acquired more new customers in the quarter than at any other time in its history, with new buyer growth up 54% from the same period last year.

“If tariffs add 20% to 30% to retail prices, that’s a huge advantage for resale,” said Dylan Carden, research analyst at William Blair & Company. “Pre-owned items aren’t subject to those duties, so demand naturally shifts.”

Inside the ThredUp warehouse, where CNBC got a behind-the-scenes look. automation hums alongside human workers. AI systems photograph, categorize, and price thousands of garments per hour. For Reinhart, the technology is key to scaling resale like retail.

“AI has really accelerated adoption,” said Reinhart. “It’s helping us improve discovery, styling, and personalization for buyers.”

That tech wave extends beyond ThredUp. Fashion-tech startups Phia, co-founded by Phoebe Gates and Sophia Kianni, is using AI to scan thousands of listings across retail and resale in seconds.

“The fact that we’ve driven millions in transaction volume shows how big this need is,” Gates said. “People want smarter, cheaper ways to shop.”

ThredUp is betting that domestic infrastructure, automation, and AI will keep it ahead of the curve, and that tariffs meant to revive U.S. manufacturing could end up powering a new kind of American fashion economy.

“The future of fashion will be more sustainable than it is today,” said Reinhart. “And secondhand will be at the center of it.”

Watch the video to learn more.

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