What Happened to Baidu (BIDU) Stock This Year?
Baidu's stock has dropped nearly 75% from its all-time high.
Its artificial intelligence (AI) and cloud businesses are expanding, but its advertising business is shriveling.
The company looks cheap, but it could deserve its discount valuation.
10 stocks we like better than Baidu ›
Baidu (NASDAQ: BIDU), the largest online search engine provider in China, was once considered a great growth stock. It went public in 2005, and its annual revenue grew at a CAGR of 45% from 319 million yuan in 2005 to 124.5 billion yuan ($19.5 billion) in 2021.
The Chinese tech giant experienced a major growth spurt after cybersecurity and censorship issues drove Alphabet's Google to shutter its search engine in mainland China in 2010. Its stock closed at a record high of $339.91 on Feb. 19, 2021, which represented a 12,489% gain from its split-adjusted IPO price of $2.70 per share.
But from 2021 to 2024, Baidu's revenue only grew at a CAGR of 2%. Its ad sales cooled off as it dealt with fierce macro headwinds in China and intense competition from ByteDance's Douyin (known as TikTok in overseas markets), Tencent's Weixin (also known as WeChat), and other nimbler apps that changed how people conducted online searches.
That's why its stock now trades at about $88. It's up less than 4% year to date, even as lower interest rates and milder macro headwinds drove many investors back toward tech stocks. Let's see why Baidu isn't impressing the bulls -- and what it would take for its stock to soar again.
The biggest challenges for Baidu
Back in 2021, Baidu generated 78% of its revenue from its online marketing services segment, which sells traditional search-based and display ads. That business is struggling to stay relevant as more internet users shift their searches to the newer mobile apps. To offset that pressure, Baidu expanded its AI Cloud platform to boost its non-online marketing services revenue. That business grew much faster than its online marketing business over the following years.
In 2024, Baidu only generated 55% of its revenue from its online marketing services, while 24% of its revenue came from its non-online marketing services. The rest mainly came from its streaming video platform iQiyi (NASDAQ: IQ), which struggled over the past year as it launched fewer hit shows and attracted fewer advertisers.
Metric
2021
2022
2023
2024
Q1 2025
Online marketing services revenue growth (YOY)
12%
(6%)
8%
(3%)
(6%)
Non-online marketing services revenue growth (YOY)
71%
22%
9%
12%
40%
Total revenue growth
16%
(1%)
9%
(1%)
3%
Data source: Baidu. RMB terms. YOY = Year over year.
Baidu's non-online marketing services segment is growing rapidly as the AI boom drives more businesses to its AI Cloud platform -- which bundles together its ERNIE large language model (LLM) for generative AI applications, self-developed AI chips (including its Kunlun 800) for servers, data storage and analytics tools, and cloud infrastructure services. Its AI Cloud is also tethered to Apollo, its open-source software platform for driverless vehicles.
Baidu's goal is to keep expanding its non-online marketing services segment to curb its dependence on its fading online marketing services segment. It's also been reportedly mulling a full spinoff or divestment of iQiyi over the past few years. That sale would free up a lot of cash for the expansion of its AI Cloud business.
Why didn't Baidu's stock impress the bulls this year?
For 2025, analysts expect Baidu's revenue to stay nearly flat as its EPS drops 17%. Its online marketing services and iQiyi segments should remain weak, but its AI Cloud business could grow rapidly enough to offset those declines. However, its earnings will be reduced by its higher investments in its AI Cloud platform, driverless vehicles, and fresh media content for iQiyi.
That mix of flat revenue growth and rising expenses, along with the macro pressure from the unresolved tariffs and trade issues between China and the U.S., made Baidu a tough stock to love.
Baidu's stock seems cheap at 12 times this year's earnings, but it might deserve that discount valuation. Meanwhile, China's e-commerce and cloud leader Alibaba -- which is growing faster and more broadly diversified -- might be a better value at 17 times this year's earnings.
For 2026, analysts expect Baidu's revenue and EPS to grow 5% and 3%, respectively. That stabilization would be a step in the right direction, but Baidu would still be a slow-growth stock with limited upside potential. Unless Baidu finds fresh ways to ignite its growth -- possibly by spinning off its weaker segments -- it will likely stay in the penalty box.
Should you buy stock in Baidu right now?
Before you buy stock in Baidu, consider this:
The Motley Fool Stock Advisor analyst team just identified what they believe are the for investors to buy now… and Baidu 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 $625,254!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $1,090,257!*
Now, it's worth noting Stock Advisor's total average return is 1,036% — a market-crushing outperformance compared to 181% for the S&P 500. Don't miss out on the latest top 10 list, available when you join Stock Advisor.
See the 10 stocks »
*Stock Advisor returns as of July 29, 2025
Leo Sun has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Alphabet, Baidu, and Tencent. The Motley Fool recommends Alibaba Group and iQIYI. The Motley Fool has a disclosure policy.
What Happened to Baidu (BIDU) Stock This Year? was originally published by The Motley Fool
登入存取你的投資組合
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Forbes
12 minutes ago
- Forbes
Office Hellscapes And AI Process Mapping
Why are human workplaces so disorganized? In some ways, it's a question people have been asking themselves ever since the first cubicle dwellers rose up from the primordial swamp - whenever that was. We know that larger systems tend to be disordered, especially if they're administrated by humans. Just go read Joseph Conrad's Heart of Darkness, and it might remind you of the modern office – people and products and materials strewn about a gigantic footprint, with very little centralized control. You get the same kind of idea reading the most recent piece by Ethan Mollick on the site where he posts his essays, One Useful Thing. I always follow his posts, interested in his emerging take on the technologies that are so new to all of us. Mollick has MIT ties, and an excellent track record looking at the AI revolution from a fresh perspective. The Office Dilemma Human wothis most recent piece, he talks about process mapping and how AI can help people to sort through the disorganization of a business. Think of a company with 100 or more employees, and probably a dozen locations. The first thing you tend to find is that sense of disorder. Mollick talks about a 'Garbage Can' principle, which posits that most businesses are a collection of disparate processes thrown into a large, disorganized bin. To me, you could use the analogy of what programmers used to call 'DLL hell' in the earlier days of the Internet. DLLs are digital libraries. Their application was often chaotic and disordered. There were dependencies that would flummox even the most seasoned engineers, because things were complicated and chaotic. That's what a large company is often like. Everyone for Themselves Mollick also pointed to some numbers that I've seen in various studies, and presented at conferences where we've talked about AI over the past year. His number was 43% – the number of employees who are using AI in the workplace. But as Mollick points out, and as I've heard before, most of them are using AI in personal ways. The use of the tools is not ordered across an organization – it's piecemeal. It's people using an AI tool like you would use a hammer, or a saw, or a drill, or a lathe --- largely in an unsupervised way. However, in general, it seems AI is largely catching on, especially when it comes to product development. You have resources like this one from the Texas Workforce Commission, referencing thousands of AI jobs. So even if there's not much centralized AI in the boardroom, there is abundant AI in business processes. It's just that those processes may or may not be unified. The Bitter Lesson Then Mollick references something called the 'Bitter lesson' that's attributed to Robert Sutton in 2019. It's the idea that AI will prove to be cognitively superior to humans without a lot of poking and prodding – but given enough time and compute, the system will find its own way to solve problems. That phrase, problem solving, is what people have been saying is the unique province of humans. It's the idea that AI can do the data-crunching, but people are still doing the creative problem-solving. Well, that bastion of human ingenuity doesn't seem that safe anymore. Mollick references the early days of chess machine evolution, where eventually Deep Blue beat Kasparov. He notes that there are two ways to go about this – you can program in innumerable chess rules, and have the system sort through them and apply them, or you can just show the system thousands of chess games, and it will make those connections on its own. Back to Machine Learning Principles Reading through this, I was reminded of the early days of machine learning, where people talked a good bit about supervised versus unsupervised learning. We often used the analogy of fruit in a digital software program enhanced with machine learning properties. Supervised learning would be labeling each fruit with its own tag – banana – apple - or grapes. The program would then learn to correlate between its training data and new real-world data. That comparison would be its main method. And that comparison isn't hugely cognitive. It follows the tradition of deterministic programming. The unsupervised version would be simply to tell the program that bananas are yellow and long, that grapes are purple or green and have clusters, and the apples are red or green and round. Then the system goes out, looks at the pictures and applies that logic. The interesting thing here is taking that analogy to the bitter lesson. Is AI more powerful if it simply analyzes reams of training data without applied logic? Or is it more powerful if it can actually distinguish between various kinds of outcomes based on requested logical processes? Which came first: the chicken or the egg? The theory of the bitter lesson seems to be that the system can actually do better through supervised learning. But that supervision doesn't necessarily have to be human oversight. The machine gets a practically infinite set of training data, and makes all of its own conclusions. That's contrasted to an approach where people tell the machine what to do, and it learns based on those suggestions. Back in the era of supervised versus unsupervised learning, the unsupervised learning seemed more powerful. It seemed more resource-intensive. But AI might finally show us up just by doing things in a more efficient way – if I can use one more analogy, it's the traditional idea of the Laplace demon, an invention of the physicist Pierre-Simon Laplace who suggested that if you know enough data points, you can predict the future. In other words, brute force programming is king. We learned a lot of this in the big data age, before we learned to use LLMs, and now we're seeing the big data age on steroids. In Conclusion I also found a very interesting take at the end of Mollick's essay where he talks about businesses going down one or the other avenue of progress. Sure enough, he suggested that these companies are playing chess with each other – that one of these chess teams consists of companies using AI to be logical, and that another chess team consists of businesses using it for brute force programming and classification. If all of this is a little hard to follow, it's because we're pretty securely in the realm of AI philosophy here. It makes you think about not just whether AI is going to win out over human workers, but how it's going to do it. I forgot to mention the exponential graph that Mollick includes showing that we're closer to AGI then most people would imagine. Let's look back at the end of this year and see how this plays out.
Yahoo
26 minutes ago
- Yahoo
Analyst updates Palantir stock forecast before key earnings
Analyst updates Palantir stock forecast before key earnings originally appeared on TheStreet. Palantir () , the hot AI stock that's doubled this year, is making waves again. The company has recently secured another contract with the U.S. Army worth up to $10 billion over the next ten years. According to a release, Palantir will improve the Army's operational efficiency by creating a 'comprehensive framework for the Army's future software and data needs.' 💵💰Don't miss the move: Subscribe to TheStreet's free daily newsletter 💰💵 The deal consolidates multiple contracts into a single enterprise agreement, giving the Army more flexibility in purchasing software and services. It also reduces procurement delays and contract-related fees. Palantir is known for providing AI-driven data analytics software to the U.S. government, military, and commercial clients. The stock soared 340% in 2024 as demand for AI infrastructure surged across sectors. Investors are now watching closely ahead of the company's second-quarter earnings report, which is set for August 4 after the bell. What to watch for Palantir's second quarter earnings Three months ago, Palantir delivered robust earnings for the first quarter and issued a strong forecast for the second, yet the stock tumbled 12% on the following trading day. Palantir's first-quarter revenue beat was primarily fueled by robust U.S. sales, which rose 55% year-over-year. U.S. commercial revenue jumped 71%, while government revenue grew 45%. U.S. revenue accounted for about 71% of the total."We are in the middle of a tectonic shift in the adoption of our software," CEO Alex Karp said. 'We are delivering the operating system for the modern enterprise in the era of AI. Consequently, we are raising our full-year guidance for total revenue growth to 36% and our guidance for U.S. commercial revenue growth to 68%.' Palantir projects second-quarter revenue of $934 million to $938 million, surpassing the consensus estimate of $899.1 million. Just days ahead of its second-quarter earnings report, Palantir was trading near record levels. On July 31, the stock reached an all-time intraday high of $160.89. It closed at $154.27 on August 1, weighed down by a broader market slump. With that sharp surge, investors question whether Palantir's valuation is running too far ahead of its fundamentals. The stock currently trades at 277x forward earnings, well above other software providers like Microsoft () , which is 34x, and Salesforce () , which is 22x. Still, D.A. Davidson's head of technology research, Gil Luria, argued that Palantir is being driven up by 'very loyal' individual investors who prioritize the company's mission over its valuation. Analyst bullish on Palantir stock before earnings Wedbush reiterated an outperform rating and a price target of $160 for Palantir stock ahead of its earnings, according to a recent research note. Analysts led by Daniel Ives said the recent deal with the U.S. Army represents an additional tailwind, which places Palantir in the sweet spot to benefit from "a tidal wave of federal spending on AI.""We continue to believe that Palantir's unique AI software approach will be a positive growth catalyst as governments look to further increase efficiency with more software/lower headcount," Ives wrote. The analysts expect to hear more about this deal and strong AI-driven results when Palantir reports earnings. More Palantir Veteran trader surprises with Palantir price target and comments Musk moves xAI, Grok onto Palantir turf Veteran analyst sends bold message on Palantir stock target Palantir makes surprise move into weather This note follows Wedbush's earlier update on July 10, when the firm raised its price target on the stock from $140 to $160. "We believe Palantir has a 'golden path to become the next Oracle' over the coming years and while the valuation is expensive today we see the Messi of AI as a core winner in the trillions of AI spend over the next few years," Ives updates Palantir stock forecast before key earnings first appeared on TheStreet on Aug 3, 2025 This story was originally reported by TheStreet on Aug 3, 2025, where it first appeared. 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
Yahoo
30 minutes ago
- Yahoo
Apple reportedly has a 'stripped-down' AI chatbot to compete with ChatGPT in the works
Apple has fallen far behind in the competitive market of AI-powered chatbots, but it may have a plan for an in-house option that aligns with the company's "not first, but best" philosophy. According to Bloomberg's Mark Gurman, Apple created a team called "Answers, Knowledge and Information" earlier this year that's tasked with developing a "stripped-down rival to ChatGPT." Forming this team represents a major change in direction from Apple's earlier stance on artificial intelligence when the company partnered with OpenAI in 2024 to infuse Siri with ChatGPT instead of developing its own AI chatbot. Now, Gurman reported that this new Apple team will instead develop in-house AI services for a "new ChatGPT-like search experience" that can crawl the web to respond to questions. These new capabilities could be a standalone app, as well as support AI capabilities for Apple's Siri, Spotlight and Safari, according to Gurman's sources. The team is reportedly led by Robby Walker, who previously oversaw Siri, and has been advertising job openings that mention wanting experience with search algorithms and engine development. Beyond the Answers, Knowledge and Information team, Apple has experienced some growing pains when it comes to artificial intelligence. Earlier this year, the company said it would delay the release of the new-and-improved Siri, adding only that it would roll out "in the coming year." However, Apple is aware of the need to accelerate its Apple Intelligence program to stay competitive. In the latest third-quarter earnings call, the company's CEO Tim Cook said that Apple is open to acquisitions to fast-track its AI roadmap.
