What's Happening With CoreWeave Stock?
A laptop keyboard and CoreWeave logo displayed on a phone screen are seen in this illustration photo ... More taken in Krakow, Poland on March 28, 2025. (Photo by Jakub Porzycki/NurPhoto via Getty Images)
CoreWeave (NASDAQ: CRWV), an AI infrastructure provider, recently announced its Q1 results, reporting a loss of $1.49 per share on revenue of $982 million. This exceeded market expectations of $853 million in revenue. However, its capital expenditures plan of $20-23 billion was much higher than the $18 billion anticipated by the analysts and it didn't sit well with investors - triggering a decline in the stock in after-hours trading. Separately, see – Is UNH Stock Now A Falling Knife?
Since its IPO at $40 per share in March, CRWV stock has surged past $65 earlier this week, significantly outperforming the NASDAQ index's 11% gain over the same period. CoreWeave's strong backlog of over $25 billion has contributed to its stock's impressive performance. However, if you prefer a less volatile investment alternative, consider the High Quality Portfolio, which has consistently outpaced the S&P 500 and has delivered over 91% returns since inception.
CoreWeave's Q1 revenue of $982 million reflects an impressive 420% year-over-year increase. CoreWeave has secured significant contracts with leading AI labs, hyperscalers, and enterprises, including OpenAI, Microsoft, IBM, Meta, and Mistral AI, aiding its revenue growth. The first quarter also saw OpenAI solidify its relationship with CoreWeave by committing to a five-year deal worth up to $11.9 billion. This significant agreement underscores the growing importance of CoreWeave's infrastructure for OpenAI.
Additionally, CoreWeave's adjusted EBITDA margin increased by 700 bps to 62%, up from 55% in the previous year's quarter. However, the adjusted net loss widened to $150 million from $24 million in the prior-year quarter. Higher revenue offset by a contraction in net margin resulted in adjusted net loss of $0.61 per share.
Looking ahead, CoreWeave expects Q2'25 revenue of $1.08 billion and full-year 2025 revenue of $5.0 billion, at the mid-point of the provided range. This fares better than the consensus estimates of $987 million and $4.6 billion, respectively.
CRWV stock has shown some volatility since its debut. In contrast, the Trefis High Quality Portfolio, which consists of 30 stocks, is significantly more stable. It has also comfortably outperformed the S&P 500 over the past four years. Why? This portfolio comprises stocks that have historically provided stronger returns with lower risk compared to the broader index, offering a more predictable performance, as evident in the HQ Portfolio performance metrics.
Given the current economic uncertainty surrounding tariffs and ongoing trade disputes, could CoreWeave sustain its upward trajectory? Since the stock was only listed last year, there is no historical data to assess its historical valuation multiple. At its current price of approximately $63 (after market hours), CRWV trades at 13 times trailing revenue. With sales increasing by over 700% in 2024, over 400% in Q1, and it's expected to grow 2.3x this year, the company's high valuation multiple appears justified. However, the company's high capital expenditures and widening losses remain key near-term concerns.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Yahoo
an hour ago
- Yahoo
Why is AI halllucinating more frequently, and how can we stop it?
When you buy through links on our articles, Future and its syndication partners may earn a commission. The more advanced artificial intelligence (AI) gets, the more it "hallucinates" and provides incorrect and inaccurate information. Research conducted by OpenAI found that its latest and most powerful reasoning models, o3 and o4-mini, hallucinated 33% and 48% of the time, respectively, when tested by OpenAI's PersonQA benchmark. That's more than double the rate of the older o1 model. While o3 delivers more accurate information than its predecessor, it appears to come at the cost of more inaccurate hallucinations. This raises a concern over the accuracy and reliability of large language models (LLMs) such as AI chatbots, said Eleanor Watson, an Institute of Electrical and Electronics Engineers (IEEE) member and AI ethics engineer at Singularity University. "When a system outputs fabricated information — such as invented facts, citations or events — with the same fluency and coherence it uses for accurate content, it risks misleading users in subtle and consequential ways," Watson told Live Science. Related: Cutting-edge AI models from OpenAI and DeepSeek undergo 'complete collapse' when problems get too difficult, study reveals The issue of hallucination highlights the need to carefully assess and supervise the information AI systems produce when using LLMs and reasoning models, experts say. The crux of a reasoning model is that it can handle complex tasks by essentially breaking them down into individual components and coming up with solutions to tackle them. Rather than seeking to kick out answers based on statistical probability, reasoning models come up with strategies to solve a problem, much like how humans think. In order to develop creative, and potentially novel, solutions to problems, AI needs to hallucinate —otherwise it's limited by rigid data its LLM ingests. "It's important to note that hallucination is a feature, not a bug, of AI," Sohrob Kazerounian, an AI researcher at Vectra AI, told Live Science. "To paraphrase a colleague of mine, 'Everything an LLM outputs is a hallucination. It's just that some of those hallucinations are true.' If an AI only generated verbatim outputs that it had seen during training, all of AI would reduce to a massive search problem." "You would only be able to generate computer code that had been written before, find proteins and molecules whose properties had already been studied and described, and answer homework questions that had already previously been asked before. You would not, however, be able to ask the LLM to write the lyrics for a concept album focused on the AI singularity, blending the lyrical stylings of Snoop Dogg and Bob Dylan." In effect, LLMs and the AI systems they power need to hallucinate in order to create, rather than simply serve up existing information. It is similar, conceptually, to the way that humans dream or imagine scenarios when conjuring new ideas. However, AI hallucinations present a problem when it comes to delivering accurate and correct information, especially if users take the information at face value without any checks or oversight. "This is especially problematic in domains where decisions depend on factual precision, like medicine, law or finance," Watson said. "While more advanced models may reduce the frequency of obvious factual mistakes, the issue persists in more subtle forms. Over time, confabulation erodes the perception of AI systems as trustworthy instruments and can produce material harms when unverified content is acted upon." And this problem looks to be exacerbated as AI advances. "As model capabilities improve, errors often become less overt but more difficult to detect," Watson noted. "Fabricated content is increasingly embedded within plausible narratives and coherent reasoning chains. This introduces a particular risk: users may be unaware that errors are present and may treat outputs as definitive when they are not. The problem shifts from filtering out crude errors to identifying subtle distortions that may only reveal themselves under close scrutiny." Kazerounian backed this viewpoint up. "Despite the general belief that the problem of AI hallucination can and will get better over time, it appears that the most recent generation of advanced reasoning models may have actually begun to hallucinate more than their simpler counterparts — and there are no agreed-upon explanations for why this is," he said. The situation is further complicated because it can be very difficult to ascertain how LLMs come up with their answers; a parallel could be drawn here with how we still don't really know, comprehensively, how a human brain works. In a recent essay, Dario Amodei, the CEO of AI company Anthropic, highlighted a lack of understanding in how AIs come up with answers and information. "When a generative AI system does something, like summarize a financial document, we have no idea, at a specific or precise level, why it makes the choices it does — why it chooses certain words over others, or why it occasionally makes a mistake despite usually being accurate," he wrote. The problems caused by AI hallucinating inaccurate information are already very real, Kazerounian noted. "There is no universal, verifiable, way to get an LLM to correctly answer questions being asked about some corpus of data it has access to," he said. "The examples of non-existent hallucinated references, customer-facing chatbots making up company policy, and so on, are now all too common." Both Kazerounian and Watson told Live Science that, ultimately, AI hallucinations may be difficult to eliminate. But there could be ways to mitigate the issue. Watson suggested that "retrieval-augmented generation," which grounds a model's outputs in curated external knowledge sources, could help ensure that AI-produced information is anchored by verifiable data. "Another approach involves introducing structure into the model's reasoning. By prompting it to check its own outputs, compare different perspectives, or follow logical steps, scaffolded reasoning frameworks reduce the risk of unconstrained speculation and improve consistency," Watson, noting this could be aided by training to shape a model to prioritize accuracy, and reinforcement training from human or AI evaluators to encourage an LLM to deliver more disciplined, grounded responses. RELATED STORIES —AI benchmarking platform is helping top companies rig their model performances, study claims —AI can handle tasks twice as complex every few months. What does this exponential growth mean for how we use it? —What is the Turing test? How the rise of generative AI may have broken the famous imitation game "Finally, systems can be designed to recognise their own uncertainty. Rather than defaulting to confident answers, models can be taught to flag when they're unsure or to defer to human judgement when appropriate," Watson added. "While these strategies don't eliminate the risk of confabulation entirely, they offer a practical path forward to make AI outputs more reliable." Given that AI hallucination may be nearly impossible to eliminate, especially in advanced models, Kazerounian concluded that ultimately the information that LLMs produce will need to be treated with the "same skepticism we reserve for human counterparts."
Yahoo
an hour ago
- Yahoo
Why a top market strategist says his base case is still a 25% stock drop and a recession in 2025
Peter Berezin of BCA Research maintains a bearish outlook despite a tariff pause. Berezin predicts a 60% chance of recession, with the S&P 500 dropping to 4,500. Economic concerns include trade uncertainty, rising delinquencies, and a weakening labor market. At a time when strategists across Wall Street are dialing back their recession probabilities, Peter Berezin of BCA Research is doubling down. President Donald Trump's 90-day tariff pause was enough to ease the worries of some investors, but the chief global strategist at BCA has maintained his bearish outlook. While Berezin has lowered his recession outlook from Liberation Day levels, he still expects an economic slowdown to unfold this year. "I've brought down my recession probability from 75% to 60%, so it's not an overwhelming likelihood of recession, but it is still my base case. And in that base case, I would expect the S&P to trade down to around 4,500," Berezin told Business Insider. That would mark a 25% decline for the benchmark index from levels on Friday. While 4,500 sounds like a steep drop from the near-record highs the stock market is trading out now, Berezin doesn't think it'll take much to trigger the fall. A plunge to that level would require the S&P 500 to trade at 18 times earnings with EPS of $250. The index is currently trading at around 23 times earnings with EPS of around $260 — not too far off, in Berezin's opinion. "At this point, it's hard to make a case to be very optimistic on either the stock market or economy," Berezin said. The economy was already showing signs of weakness prior to the trade war fallout, Berezin said. Back in December of 2024, Berezin was calling for a recession in 2025 coupled with a stock market plunge of over 20%. His S&P 500 target of 4,452 was one of the lowest on Wall Street. Today, Berezin is concerned about continued trade uncertainty, a growing deficit, and a weakening consumer. Job openings have been on a downward trend since early 2022, "removing a lot of insulation that had protected the labor market," Berezin said. Indeed, other economists agree that the labor market might be weaker than it seems — Sam Tombs of Pantheon Macroeconomics is concerned with slowing hiring and declining small business sentiment. Berezin also points out that consumer delinquency rates on credit cards and auto loans have been rising. In the first quarter of 2025, credit card delinquencies hit 3.05%. That's the highest level since 2011, "a year in which the unemployment rate was 8%," Berezin said. Furthermore, as student loan collections restart after a five-year hiatus amid the pandemic, consumers' credit scores are taking an even bigger hit. The housing market has also been a pressure point in the economy since COVID, with home affordability and inventory challenges mounting for buyers. Berezin pointed to falling construction in May—housing starts dropped 9.8% in the month— as another sign of a slowdown. The effective tariff rate is hovering around 15%, which is still a level that Berezin considers dangerous. "There's probably no ideal for a tariff rate, but there are numbers that are more punitive for the economy than others," he said. If Trump doesn't solidify trade deals soon, the economy could be in store for some major pain as businesses start to pass along price increases to consumers. A tariff rate lower than 10% would be less disruptive to the economy, but Berezin isn't hopeful that Trump will lower his policies to that level. "Since tariffs on China probably will be higher than tariffs in other countries, that means Trump would have to roll back his 10% base tariff that he's applied to almost all countries," Berezin said. "I don't see him doing that unless the market forces him to do it." In fact, Berezin thinks Trump might even increase tariffs on some industries such as pharmaceuticals, semiconductors, and lumber. Berezin doesn't see an easy way around an impending recession. Some strategists might be hoping for Trump's Big Beautiful Bill to boost the economy through tax cuts, but unfunded tax cuts could push bond yields higher and cancel out any any stimulus. According to the Congressional Budget Office, while the tax bill would increase GDP growth by 0.5% on average over the next 10 years, it would also push up 10-year Treasury yields by 14 basis points and increase the deficit by $2.8 trillion. A stock market crash and economic downturn could actually be the turning point for Trump to reverse course on his policies, Berezin said. The S&P 500 dipping below 5,000 and the 10-year Treasury yield spiking above 4.5% probably influenced Trump to paus tariffs for 90 days, Berezin added. "We could get more tariff relief, but the market has to force that. I don't think it's going to come from any other source," he said. Read the original article on Business Insider
Yahoo
an hour ago
- Yahoo
1 AI Super Stock Is Starting to Rebound, but Shares Still Look Cheap
Datadog stock remains more than 35% below the all-time high it reached four years ago. Many organizations are rapidly adopting AI-powered tools, which presents an opportunity for Datadog. The shares are trading near their cheapest price-to-sales ratio ever. 10 stocks we like better than Datadog › The rise of artificial intelligence (AI) is generating plenty of wealth on Wall Street -- and the winners won't be limited to just semiconductor stocks like Nvidia. Tech stocks across several subsectors will benefit, too. Let's take a look at one such stock, Datadog (NASDAQ: DDOG). Between 2019 and 2021, Datadog was one of the hottest names in the stock market. Shares advanced by more than 400% in only three years. However, as the stock market soured on tech stocks and speculative companies in 2022, Datadog shares plummeted. All told, the shares cratered by 68%, erasing the majority of their earlier gains. As of this writing, Datadog stock remains more than 35% off of the all-time high it touched in late 2021. Yet sentiment regarding the stock appears to have shifted. Datadog, which provides cloud monitoring services for enterprises, now boasts strong ratings from the analyst community. According to data compiled by Yahoo! Finance, there are 46 analysts covering Datadog. Of those, 10 rate it as a strong buy, 28 rate it a buy, and eight call it a hold. None of them rate it a sell or strong sell. Moreover, the average 12-month price target for Datadog shares is nearly $139. That's about 9% higher than the stock trades as of this writing. Datadog's business model is to sell monitoring services to organizations with significant cloud assets. This type of monitoring is critical to enterprises today, as operational downtime can result in serious consequences, including lost revenue, customer dissatisfaction, and even legal action. It already serves tens of thousands of clients across a range of industries, including e-commerce, gaming, and finance. While the type of monitoring that Datadog offers isn't new, what it is monitoring is changing. New large language models (LLMs) powered by AI algorithms have become much more important to organizations. Use of these models is rapidly spreading into the day-to-day operations of countless organizations. As this happens, their performance must be monitored, too. That has created a new source of revenue for Datadog, which is helping boost its growth. Consider the company's first-quarter results. Datadog noted that about 8.5% of its total revenue came from AI-native customers. That was up from 3.5% one year earlier -- showing meaningful growth for this new source of revenue. Management also raised its revenue guidance for the year by about $40 million, or 1%, on the back of this fast-growing new source of sales. Ultimately, these figures aren't game changers for Datadog, but they demonstrate that the AI revolution is benefiting the company. If it can continue to deliver on its higher guidance -- or even surpass it -- the stock should respond positively. In part, that's because Datadog's valuation remains near multiyear lows. Though Datadog's stock price has recovered significantly from its 2022 low point, its valuation -- as measured by its price-to-sales (P/S) ratio -- remains near the bottom of its range. As of this writing, Datadog shares trade at a P/S ratio of around 16. That's well below the peak levels above 60 that it reached in 2020 and 2021. It's also far below the stock's average of 28. Granted, a P/S ratio of 16 is still high compared to many stocks -- even within the tech sector. However, for long-term investors who want to establish a position in Datadog shares, it should be comforting that it doesn't appear to be overvalued. The stock appears to be rebounding after a steep decline. The AI revolution is playing a role in that comeback, and the analyst community is moderately bullish on the company's prospects. Finally, its current valuation is well below its long-term average. Overall, that suggests that this could be a good time for long-term investors to buy. Before you buy stock in Datadog, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the for investors to buy now… and Datadog wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $664,089!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $881,731!* Now, it's worth noting Stock Advisor's total average return is 994% — a market-crushing outperformance compared to 172% for the S&P 500. Don't miss out on the latest top 10 list, available when you join . See the 10 stocks » *Stock Advisor returns as of June 9, 2025 Jake Lerch has positions in Nvidia. The Motley Fool has positions in and recommends Datadog and Nvidia. The Motley Fool has a disclosure policy. 1 AI Super Stock Is Starting to Rebound, but Shares Still Look Cheap was originally published by The Motley Fool Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
