A worsening macroeconomic climate and the collapse of industry giants such as FTX and Terra have weighed on bitcoin’s price this year.
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Bitcoin fell through the $90,000 level overnight, weakened by sell pressure in equities as the crypto market awaits its next catalyst.
The price of bitcoin fell 6% to $88,519, according to Coin Metrics. Earlier, it fell as low as $87,736.
The decline puts the blue chip coin almost 20% off its all-time high reached on President Donald Trump’s inauguration day.
“Equities have faced a few difficult sessions over the last week, with top-performing stocks down many times the index, as markets grapple with increased uncertainty under the new administration,” said Steven Lubka, head of private clients and family offices at Swan Bitcoin. “This pressure has spilled over into bitcoin and crypto markets.”
The S&P 500 on Monday posted a three-day losing streak as it failed to recover from last week’s sell-off, driven by concern over a slowing economy and sticky inflation.
“Ultimately, the lack of visible short-term catalysts and pressure from equities creates an environment for profit-taking and pressure from shorts,” Lubka added.
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Bitcoin falls below the key $90,000 level Monday
Bitcoin kicked off the year in rally mode, fueled by optimism about the positive changes the new Trump administration was expected to make for the crypto industry. However, since the President issued his widely anticipated executive order on crypto at the end of January – the contents of which were well received by the industry despite its tamer than hoped for language on a strategic bitcoin reserve – the market has had little to look forward to.
While optimism about the long-term positive impact Trump’s policies could have for crypto remains high, its movements have been and may continue to be dictated by macroeconomic trends.
“From November through January, the market was very enthusiastic about pricing in a crypto-friendly U.S. administration,” said Joel Kruger, market strategist at LMAX Group. “Now it’s a question of waiting for that next catalyst. We know that all of this is in place, and the market is in a bit of a sell-the-fact consolidation sell as it kind of waits.”
The $90,000 level marks the bottom of the narrow range bitcoin has been trading in since the end of November. Analysts have warned that if bitcoin were to meaningfully break below the level, it could see a deeper pullback toward $80,000.
“There is room for bitcoin still to go back down towards the $70,000 to $75,000 area without doing anything to compromise the outlook,” Kruger said, “and we suspect that there will be plenty of demand as we head down towards those levels.”
Lubka said he believes bitcoin will finish digesting this move and resume its long-term move higher by mid-March.
Other cryptocurrencies fared worse on Monday. Ether and Solana’s sol token each tumbled 9%. The broader market of cryptocurrencies, as measured by the CoinDesk 20 index, lost more than 8%.
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Health-care software vendor Athenahealth on Tuesday said it will offer Abridge’s artificial intelligence scribing tool to its network of more than 160,000 clinicians.
Athenahealth has developed an electronic health record, revenue cycle management tools and patient engagement tools for ambulatory care providers, which include outpatient facilities like independent practices. The company introduced a solution called Ambient Notes in October that allows doctors to choose between various AI-powered documentation tools, and Abridge is the latest addition.
Abridge uses AI to draft clinical notes in real time as doctors consensually record their visits with patients. The startup is part of a red-hot market that has exploded as health-care executives search for solutions to help reduce staff burnout and daunting administrative workloads.
“The market is going to evolve rather rapidly, there are going to be winners and losers over time,” Athenahealth CEO Bob Segert told CNBC. “Different physicians will prefer different ways that notes are taken and that the information is delivered, and we want to be able to provide that flexibility.”
Athenahealth and Abridge declined to share the financial details of the partnership.
Clinicians spend nearly nine hours a week on documentation, according to an October study from Google Cloud. And more than 90% of physicians report feeling burned out on a “regular basis,” according to a survey commissioned by Athenahealth last February.
Companies including Abridge, Microsoft’s Nuance Communications, Suki and others say their AI scribing tools can help. Suki and iScribeHealth already offer their tools through Athenahealth’s Ambient Notes solution.
“It’ll be incumbent upon us to make sure that we’re able to demonstrate differentiation,” Abridge CEO Dr. Shiv Rao told CNBC. “So far, we’ve had good luck these last few years doing that.”
Abridge has deployed its technology across more than 100 health systems in the U.S., including organizations like the Mayo Clinic, Duke Health and Johns Hopkins Medicine.
The company announced a $250 million funding round earlier this month. It also unveiled a new Contextual Reasoning Engine that can pull information that’s relevant to a specific clinician and their clinic’s best practices. Abridge’s Rao said that technology will be available to Athenahealth clinicians.
Athenahealth’s Ambient Notes solution is currently available in a limited capacity, but the company said it plans to widen availability for clinicians through 2025.
“The more they try it, the more they like it, and I think we’re going to see a pretty steep adoption curve as this continues to move forward,” Segert said.
Nvidia is scheduled to report fourth-quarter financial results on Wednesday after the bell.
It’s expected to put the finishing touches on one of the most remarkable years from a large company ever. Analysts polled by FactSet expect $38 billion in sales for the quarter ended in January, which would be a 72% increase on an annual basis.
The January quarter will cap off the second fiscal year where Nvidia’s sales more than doubled. It’s a breathtaking streak driven by the fact that Nvidia’s data center graphics processing units, or GPUs, are essential hardware for building and deploying artificial intelligence services like OpenAI’s ChatGPT. In the past two years, Nvidia stock has risen 478%, making it the most valuable U.S. company at times with a market cap over $3 trillion.
But Nvidia’s stock has slowed in recent months as investors question where the chip company can go from here.
It’s trading at the same price as it did last October, and investors are wary of any signs that Nvidia’s most important customers might be tightening their belts after years of big capital expenditures. This is particularly concerning in the wake of recent breakthroughs in AI out of China.
Much of Nvidia’s sales go to a handful of companies building massive server farms, usually to rent out to other companies. These cloud companies are typically called “hyperscalers.” Last February, Nvidia said a single customer accounted for 19% of its total revenue in fiscal 2024.
Morgan Stanley analysts estimated this month that Microsoft will account for nearly 35% of spending in 2025 on Blackwell, Nvidia’s latest AI chip. Google is at 32.2%, Oracle at 7.4% and Amazon at 6.2%.
This is why any sign that Microsoft or its rivals might pull back spending plans can shake Nvidia stock.
Last week, TD Cowen analysts said that they’d learned that Microsoft had canceled leases with private data center operators, slowed its process of negotiating to enter into new leases and adjusted plans to spend on international data centers in favor of U.S. facilities.
The report raised fears about the sustainability of AI infrastructure growth. That could mean less demand for Nvidia’s chips. TD Cowen’s Michael Elias said his team’s finding points to “a potential oversupply position” for Microsoft. Shares of Nvidia fell 4% on Friday.
Microsoft pushed back Monday, saying it still planned to spend $80 billion on infrastructure in 2025.
“While we may strategically pace or adjust our infrastructure in some areas, we will continue to grow strongly in all regions. This allows us to invest and allocate resources to growth areas for our future,” a spokesperson told CNBC.
Over the last month, most of Nvidia’s key customers touted large investments. Alphabet is targeting $75 billion in capital expenditures this year, Meta will spend as much as $65 billion and Amazon is aiming to spend $100 billion.
Analysts say about half of AI infrastructure capital expenditures ends up with Nvidia. Many hyperscalers dabble in AMD’s GPUs and are developing their own AI chips to lessen their dependence on Nvidia, but the company holds the majority of the market for cutting-edge AI chips.
So far, these chips have been used primarily to train cutting-edge AI models, a process that can cost hundreds of millions dollars. After the AI is developed by companies like OpenAI, Google and Anthropic, warehouses full of Nvidia GPUs are required to serve those models to customers. That’s why Nvidia projects its revenue to continue growing.
Another challenge for Nvidia is last month’s emergence of Chinese startup DeepSeek, which released an efficient and “distilled” AI model. It had high enough performance that suggested billions of dollars of Nvidia GPUs aren’t needed to train and use cutting-edge AI. That temporarily sunk Nvidia’s stock, causing the company to lose almost $600 billion in market cap.
Nvidia CEO Jensen Huang will have an opportunity on Wednesday to explain why AI will continue to need even more GPU capacity even after last year’s massive build-out.
Recently, Huang has spoken about the “scaling law,” an observation from OpenAI in 2020 that AI models get better the more data and compute are used when creating them.
Huang said that DeepSeek’s R1 model points to a new wrinkle in the scaling law that Nvidia calls “Test Time Scaling.” Huang has contended that the next major path to AI improvement is by applying more GPUs to the process of deploying AI, or inference. That allows chatbots to “reason,” or generate a lot of data in the process of thinking through a problem.
AI models are trained only a few times to create and fine-tune them. But AI models can be called millions of times per month, so using more compute at inference will require more Nvidia chips deployed to customers.
“The market responded to R1 as in, ‘oh my gosh, AI is finished,’ that AI doesn’t need to do any more computing anymore,” Huang said in a pretaped interview last week. “It’s exactly the opposite.”
Chegg seen at the New York Stock Exchange on Feb. 13, 2025.
Danielle DeVries | CNBC
Chegg on Monday filed suit in federal district court against Google, claiming that artificial intelligence summaries of search results have hurt the online education company’s traffic and revenue.
The legal move come nearly two years after former CEO Dan Rosensweig said students engaging with OpenAI’s ChatGPT assistant were cutting into Chegg’s new customer growth.
Chegg is worth less than $200 million, and in after-hours trading Monday, the stock was trading just above $1 per share. Chegg has engaged Goldman Sachs and will look at strategic options, including getting acquired and going private, President and CEO Nathan Schultz told analysts on a Monday earnings call.
Chegg reported a $6.1 million net loss on $143.5 million in fourth-quarter revenue, a 24% decline year over year, according to a statement. Analysts polled by LSEG had expected $142.1 million in revenue. Management called for first-quarter revenue between $114 million and $116 million, but analysts had been targeting $138.1 million. The stock was down 23% in extended trading.
Google forces companies like Chegg to “supply our proprietary content in order to be included in Google’s search function,” said Schultz, adding that the search company uses its monopoly power, “reaping the financial benefits of Chegg’s content without having to spend a dime.”
Despite the suit, Chegg has its own AI strategy. It has drawn on Meta’s open-source Llama, as well as models from privately held Anthropic and Mistral, Schultz said. Chegg has also partnered with OpenAI, which the education company views as a competitor, alongside Google. The company reported that 3.6 million students had subscriptions in the fourth quarter, down 21%. Subscriptions include access to AI-powered learning assistance. Chegg also rents and sells textbooks.
AI Overviews, as Google’s artificial intelligence summaries are called, are available in the company’s search engine in over 100 countries, with more than 1 billion users, the company said in October. They show up above links to other pages in search results.
A Google spokesperson told CNBC that the company will defend itself against Chegg’s suit.
“Every day, Google sends billions of clicks to sites across the web, and AI Overviews send traffic to a greater diversity of sites,” the Google spokesperson said.
Chegg claimed that Google drew on Chegg’s collection of 135 million questions and answers on a variety of subjects in its model training data sets.
After training its models, Google can generate content that competes with information that publishers have on offer in search results, Chegg argued in its complaint. The online learning company included a screenshot of a Google AI Overview that borrows details from Chegg’s website but does not attribute the information. However, the relevant Chegg page does show up lower down in search results.
Chegg cited a federal judge’s ruling last August that Googleholds a monopoly in the search market. The decision came after the Department of Justice in 2020 filed its landmark case, alleging that Google controlled the general search market by creating strong barriers to entry and a feedback loop that sustained its dominance.