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Is it OK for AI to write science papers? Nature survey shows researchers are split
How much is the artificial intelligence (AI) revolution altering the process of communicating science? With generative AI tools such as ChatGPT improving so rapidly, attitudes about using them to write research papers are also evolving. The number of papers with signs of AI use is rising rapidly (D. Kobak et al. Preprint at arXiv https://doi.org/pkhp; 2024), raising questions around plagiarism and other ethical concerns.
To capture a sense of researchers’ thinking on this topic, Nature posed a variety of scenarios to some 5,000 academics around the world, to understand which uses of AI are considered ethically acceptable.
Take Nature’s AI research test: find out how your ethics compare
The survey results suggest that researchers are sharply divided on what they feel are appropriate practices. Whereas academics generally feel it’s acceptable to use AI chatbots to help to prepare manuscripts, relatively few report actually using AI for this purpose — and those who did often say they didn’t disclose it.
Past surveys reveal that researchers also use generative AI tools to help them with coding, to brainstorm research ideas and for a host of other tasks. In some cases, most in the academic community already agree that such applications are either appropriate or, as in the case of generating AI images, unacceptable. Nature’s latest poll focused on writing and reviewing manuscripts — areas in which the ethics aren’t as clear-cut.
A divided landscape
Nature’s survey laid out several scenarios in which a fictional academic, named Dr Bloggs, had used AI without disclosing it — such as to generate the first draft of a paper, to edit their own draft, to craft specific sections of the paper and to translate a paper. Other scenarios involved using AI to write a peer review or to provide suggestions about a manuscript Dr Bloggs was reviewing (see Supplementary information for full survey, data and methodology, and you can also test yourself against some of the survey questions).
Survey participants were asked what they thought was acceptable and whether they had used AI in these situations, or would be willing to. They were not informed about journal policies, because the intent was to reveal researchers’ underlying opinions. The survey was anonymous.
The 5,229 respondents were contacted in March, through e-mails sent to randomly chosen authors of research papers recently published worldwide and to some participants in Springer Nature’s market-research panel of authors and reviewers, or through an invitation from Nature’s daily briefing newsletter. They do not necessarily represent the views of researchers in general, because of inevitable response bias. However, they were drawn from all around the world — of those who stated a country, 21% were from the United States, 10% from India and 8% from Germany, for instance — and represent various career stages and fields. (Authors in China are under-represented, mainly because many didn’t respond to e-mail invitations).
The survey suggests that current opinions on AI use vary among academics — sometimes widely. Most respondents (more than 90%) think it is acceptable to use generative AI to edit one’s research paper or to translate it. But they differ on whether the AI use needs to be disclosed, and in what format: for instance, through a simple disclosure, or by giving details about the prompts given to an AI tool.
When it comes to generating text with AI —for instance, to write all or part of one’s paper — views are more divided. In general, a majority (65%) think it is ethically acceptable, but about one-third are against it.
Asked about using AI to draft specific sections of a paper, most researchers felt it was acceptable to do this for the paper’s abstract, but more were opposed to doing so for other sections.
Although publishers generally agree that substantive AI use in academic writing should be declared, the response from Nature’s survey suggests that not all researchers have the same opinion, says Alex Glynn, a research literacy and communications instructor at the University of Louisville in Kentucky. “Does the disconnect reflect a lack of familiarity with the issue or a principled disagreement with the publishing community?”
Using AI to generate an initial peer-review report was more frowned upon — with more than 60% of respondents saying it was not appropriate (about one-quarter of these cited privacy concerns). But the majority (57%) felt it was acceptable to use AI to assist in peer review by answering questions about a manuscript.
“I’m glad to see people seem to think using AI to draft a peer-review report is not acceptable, but I’m more surprised by the number of people who seem to think AI assistance for human reviewers is also out of bounds,” says Chris Leonard, a scholarly-communications consultant who writes about developments in AI and peer review in his newsletter, Scalene. (Leonard also works as a director of product solutions at Cactus Communications, a multinational firm in Mumbai, India.) “That hybrid approach is perfect to catch things reviewers may have missed.”
AI still used only by a minority
In general, few academics said they had actually used AI for the scenarios Nature posed. The most popular category was using AI to edit one’s research paper, but only around 28% said they had done this (another 43%, however, said they’d be willing to). Those numbers dropped to around 8% for writing a first draft, making summaries of other articles for use in one’s own paper, translating a paper and supporting peer review.
A mere 4% of respondents said they’d used AI to conduct an initial peer review.
Overall, about 65% reported that they had never used AI in any of the scenarios given, with people earlier in their careers being more likely to have used AI at least for one case. But when respondents did say they had used AI, they more often than not said they hadn’t disclosed it at the time.
“These results validate what we have also heard from researchers — that there’s great enthusiasm but low adoption of AI to support the research process,” says Josh Jarrett, a senior vice-president at Wiley, the multinational scholarly publisher, which has also surveyed researchers about use of AI.
Split opinions
When given the opportunity to comment on their views, researchers’ opinions varied drastically. On the one hand, some said that the broad adoption of generative AI tools made disclosure unnecessary. “AI will be, if not already is, a norm just like using a calculator,” says Aisawan Petchlorlian, a biomedical researcher at Chulalongkorn University in Bangkok. “‘Disclosure’ will not be an important issue.”
On the other hand, some said that AI use would always be unacceptable. “I will never condone using generative AI for writing or reviewing papers, it is pathetic cheating and fraud,” said an Earth-sciences researcher in Canada.
AI is transforming peer review — and many scientists are worried
Others were more ambivalent. Daniel Egan, who studies infectious diseases at the University of Cambridge, UK, says that although AI is a time-saver and excellent at synthesizing complex information from multiple sources, relying on it too heavily can feel like cheating oneself. “By using it, we rob ourselves of the opportunities to learn through engaging with these sometimes laborious processes.”
Respondents also raised a variety of concerns, from ethical questions around plagiarism and breaching trust and accountability in the publishing and peer-review process to worries about AI’s environmental impact.
Some said that although they generally accepted that the use of these tools could be ethical, their own experience revealed that AI often produced sub-par results — false citations, inaccurate statements and, as one person described it, “well-formulated crap”. Respondents also noted that the quality of an AI response could vary widely depending on the specific tool that was used.
There were also some positives: many respondents pointed out that AI could help to level the playing field for academics for whom English was not a first language.
Several also explained why they supported certain uses, but found others unacceptable. “I use AI to self-translate from Spanish to English and vice versa, complemented with intensive editing of the text, but I would never use AI to generate work from scratch because I enjoy the process of writing, editing and reviewing,” says a humanities researcher from Spain. “And I would never use AI to review because I would be horrified to be reviewed by AI.”
Career stage and location
Perhaps surprisingly, academics’ opinions didn’t generally seem to differ widely by their geographical location, research field or career stage. However, respondents’ self-reported experience with AI for writing or reviewing papers did correlate strongly with having favourable opinions of the scenarios, as might be expected.
Career stage did seem to matter when it came to the most popular use of AI — to edit papers. Here, younger researchers were both more likely to think the practice acceptable, and more likely to say they had done it.
And respondents from countries where English is not a first language were generally more likely than those in English-speaking nations to have used AI in the scenarios. Their underlying opinions on the ethics of AI use, however, did not seem to differ greatly.
Related surveys
Various researchers and publishers have conducted surveys of AI use in the academic community, looking broadly at how AI might be used in the scientific process. In January, Jeremy Ng, a health researcher at the Ottawa Hospital Research Institute in Canada, and his colleagues published a survey of more than 2,000 medical researchers, in which 45% of respondents said they had previously used AI chatbots (J. Y. Ng et al. Lancet Dig. Health 7, e94–e102; 2025). Of those, more than two-thirds said they had used it for writing or editing manuscripts — meaning that, overall, around 31% of the people surveyed had used AI for this purpose. That is slightly more than in Nature’s survey.
Science sleuths flag hundreds of papers that use AI without disclosing it
“Our findings revealed enthusiasm, but also hesitation,” Ng says. “They really reinforced the idea that there’s not a lot of consensus around how, where or for what these chatbots should be used for scientific research.”
In February, Wiley published a survey examining AI use in academia by nearly 5,000 researchers around the world (see go.nature.com/438yngu). Among other findings, this revealed that researchers felt most uses of AI (such as writing up documentation and increasing the speed and ease of peer review) would be commonly accepted in the next few years. But less than half of the respondents said they had actually used AI for work, with 40% saying they’d used it for translation and 38% for proofreading or editing of papers.
AI Research
Meet the Artificial Intelligence (AI) Stock With $368 Billion in Revenue Coming Down the Pipeline
Key Points
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A handful of big tech companies are set to spend over $300 billion building AI infrastructure this year.
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Demand for compute is growing just as fast as companies can stand up new servers.
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This giant has more commitments and is growing faster than almost everyone in the market.
- 10 stocks we like better than Microsoft ›
A handful of big tech companies are set to spend over $300 billion building AI infrastructure this year.
Demand for compute is growing just as fast as companies can stand up new servers.
This giant has more commitments and is growing faster than almost everyone in the market.
The artificial intelligence boom is only getting bigger, with just a handful of big tech companies on track to spend over $300 billion on AI infrastructure this year alone. Alphabet (NASDAQ: GOOG)(NASDAQ: GOOGL) recently bumped its capital expenditure outlook for the full year from $75 billion to $85 billion. Amazon (NASDAQ: AMZN) is on track to spend over $100 billion in capital expenditures, mostly going toward new data centers and servers to fill them. And Microsoft (NASDAQ: MSFT) is planning a whopping $10 billion of spending per month for the current quarter.
By far the biggest beneficiary from all that spending has been Nvidia (NASDAQ: NVDA). The chipmaker has seen its data center chip sales soar over the last few years, including a 56% jump in its most recent quarter. And demand doesn’t seem to be slowing down anytime soon, with analysts expecting revenue to grow nearly as much next year as this year (on an absolute basis).
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But another AI giant just disclosed it has $368 billion of contracted revenue, and it’s doing everything it can to keep up with the massive demand it’s seeing.
Image source: Getty Images.
Huge long-term commitments give this stock a massive runway
All three major cloud computing platforms disclose their backlog, or remaining performance obligations, and all three are seeing healthy growth.
- Alphabet said its Google Cloud backlog climbed to $108 billion last quarter, a 37% increase from a year ago.
- Amazon said its Amazon Web Services backlog totaled $195 billion, a 25% increase from a year ago.
- Microsoft revealed a $368 billion backlog last quarter, a 37% increase from last year.
Indeed, Microsoft is the AI giant with $368 billion coming down the pipeline.
There’s an important caveat about Microsoft’s remaining performance obligations. It includes contracts for commercial software and services like Microsoft 365 and its Azure cloud computing platform. As such, it’s not a perfect apples-to-apples comparison with Amazon or Alphabet. Still, the growth is impressive, and the metric Microsoft shares suggests Azure may be growing its commitments significantly faster than its two biggest rivals.
Importantly, Microsoft’s backlog growth is stemming from a growing number of long-term commitments. Microsoft said just 35% of those commitments will be recognized as revenue in the next 12 months, with the total increasing 21% year over year. The amount recognized beyond 12 months grew 49%. By comparison, Alphabet saw the percentage of commitments set to be recognized as revenue within 24 months drop from 55% to 50% last quarter. Amazon saw the average length of its long-term commitments get a slight bump from 3.9 years to 4 years.
That shift can skew just how much faster Azure is growing compared to its rivals. If Microsoft is extending the length of its contracts, it’ll naturally have a bigger backlog. Still, the long-term commitments put Microsoft in a position to generate strong growth for Azure for years to come. Management shared that Azure is now a $75 billion business, after exhibiting 39% year-over-year revenue growth last quarter. It expects 37% growth next quarter. That makes it roughly 50% larger than Google Cloud, but growing faster. And its massive backlog means it can continue outpacing the competition in the future.
Demand continues to outpace supply
Microsoft management has been telling investors for well over a year that demand for its cloud computing services, particularly its Azure AI services, is higher than its supply. That remained the case in the fourth quarter. To be sure, that’s not a situation unique to Microsoft. Both Amazon and Alphabet have made similar comments on their earnings calls.
But Microsoft is spending more than anyone building out its data centers. As mentioned, it committed to spending $30 billion on capex this quarter, and management refused to provide guidance on how much it might spend through the rest of fiscal 2026. But given the massive and rapidly growing backlog of demand for its AI services, investors should be happy to see Microsoft build as quickly as possible. It’s important to remember that Microsoft also holds a leading position as enterprises migrate more of their systems from on-premise to the cloud, specializing in hybrid cloud environments using Windows. As such, overbuilding shouldn’t be a huge concern.
Azure is the biggest growth driver for Microsoft right now, but it’s not the only one. As mentioned, that $368 billion backlog also includes commitments for Microsoft 365, Dynamics 365, and Microsoft’s other enterprise software and services. Those are getting a boost from AI as well, as Microsoft integrates its Copilot AI assistant into its software. That helps workers get more out of its products and increases productivity. As a result, Microsoft is able to charge more and gain bigger commitments from commercial customers.
Investors will have to pay a premium price to buy Microsoft stock. With a forward P/E ratio of 32, it trades for a much higher price than Alphabet, which sports a 23 multiple. It’s even approaching Amazon’s 34 times earnings multiple, despite the cloud computing leader historically trading for a much higher earnings multiple. But with a massive pipeline of long-term growth ahead for the company, it’s well worth paying up for.
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New Jersey, US State: “The global Artificial Intelligence For Video Surveillance market in the Information Technology and Telecom category is projected to reach USD 16.8 billion by 2031, growing at a CAGR of 11% from 2025 to 2031. With rising industrial adoption and continuous innovation in Information Technology and Telecom applications, the market is estimated to hit USD 6.5 billion in 2024, highlighting strong growth potential throughout the forecast period.”
Artificial Intelligence For Video Surveillance Market Size & Forecast 2033
The artificial intelligence for video surveillance market is expected to grow substantially by 2033, supported by rising demand for advanced security and monitoring solutions. AI-powered systems enhance surveillance by enabling real-time analytics, facial recognition, anomaly detection, and automated alerts. Increasing urbanization, smart city projects, and security concerns in public and private sectors are key drivers of adoption worldwide.
Technological innovations integrating AI with cloud storage, IoT devices, and edge computing are improving accuracy and scalability of surveillance systems. Vendors are focusing on developing customizable platforms for industries such as retail, transportation, and critical infrastructure. The emphasis on proactive threat detection and regulatory compliance is further boosting demand. By 2033, the AI for video surveillance market is forecast to reach robust global valuation, driven by continuous innovation and expanding applications across diverse sectors.
Key Players in the Artificial Intelligence For Video Surveillance Market
Hikvision
Dahua
Huawei
Cisco Meraki
Hanwha
ZTE
Honeywell Security
Simshine Intelligent Technology Co. Ltd.
For Further Detail, Download the Sample PDF with Complete TOC, Tables, Figures, Charts, And More @ https://www.marketresearchintellect.com/download-sample/?rid=1031128&utm_source=OpenprJune&utm_medium=023
Factors Supporting Growth of Artificial Intelligence For Video Surveillance Market in the Future:
1.Technological Advancements and Innovation:
The continuous evolution of technology is playing a vital role in driving the Artificial Intelligence For Video Surveillance market forward. Cutting-edge innovations are improving product functionality, enhancing performance, and reducing costs, making these solutions more accessible to a broader range of industries. Emerging technologies such as AI, IoT, advanced analytics, and automation are also enabling smarter and more efficient use cases, further expanding the scope of the market. These advancements are not only upgrading existing systems but are also creating entirely new application opportunities that will support long-term market expansion.
2. Expanding Applications Across End-Use Sectors:
The increasing integration of Artificial Intelligence For Video Surveillance solutions across diverse industries such as automotive, healthcare, consumer electronics, telecom, and industrial manufacturing is significantly boosting market demand. Each sector brings unique requirements, pushing companies to diversify their offerings and customize solutions. This cross-industry relevance ensures consistent demand growth, while rising digitalization and adoption of smart technologies amplify the market potential across both developed and developing regions.
3. Favorable Government Policies and Infrastructure Push:
Supportive initiatives by governments around the world, including funding programs, tax incentives, and policy frameworks, are providing a strong foundation for market development. Efforts to strengthen digital infrastructure, promote energy efficiency, and drive sustainable development are fueling demand for advanced Artificial Intelligence For Video Surveillance technologies. Moreover, public-private partnerships and national transformation agendas such as smart cities and Industry 4.0 are creating favorable conditions for rapid market expansion, especially in emerging economies
4. Increased Investment and Focus on Research & Development:
The Artificial Intelligence For Video Surveillance market is experiencing a surge in investment from both private and public entities, driven by the urgency to innovate and stay competitive. Companies are dedicating substantial resources to research and development to create next-generation products with higher efficiency, scalability, and environmental sustainability. Venture capital funding, mergers, acquisitions, and collaborations are also contributing to a dynamic ecosystem that fosters experimentation and accelerates commercialization of novel solutions, ensuring sustained market growth in the future.
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Key Segments Covered in Our Report: Artificial Intelligence For Video Surveillance Industry
Artificial Intelligence For Video Surveillance Market by Type
Software
Hardware
Artificial Intelligence For Video Surveillance Market by Application
Public & Government Infrastructure
Commercial
Residential
The Application segment showcases the industries and sectors that use Artificial Intelligence For Video Surveillance products for example Artificial Intelligence For Video Surveillance targeting healthcare and automotive industries etc. It also provides a perspective of the market rate of acceptance, usage of the products, and new applications that are paving the way for the future of the market.
Global Artificial Intelligence For Video Surveillance Market Regional Analysis
The Global Artificial Intelligence For Video Surveillance Market is examined in dimensions of regions, wherein each region has its own market growth, trends as well as dynamics. This section highlights on the detailed market performance, major shifts, and trends and underlying factors explaining growth in different places around the world.
North America: North America accounts for a large share of the Artificial Intelligence For Video Surveillance market which is a result of the developed technology, intense consumer market, and huge investments in the Artificial Intelligence For Video Surveillance industry. To add, the U.S. market also plays a crucial role as this economy is more concerned with innovation and was also one of the first to implement Artificial Intelligence For Video Surveillance products in its Artificial Intelligence For Video Surveillance sectors. The region is expected to see a gradual rise till 2031 and this is because of its reinforced infrastructure and existing regulation mechanisms.
Europe: Global has the fastest growing Artificial Intelligence For Video Surveillance market and is oriented around environmental protection, renewed efforts and environmental awareness. The market is dominated by countries like Germany, the UK, and France that have improved their technologies and have a strong industrial structure. Increased request for green solutions along with regulatory efforts are increasing demand in the market’s key areas such as Artificial Intelligence For Video Surveillance sectors.
Asia-Pacific: The growth potential in the Artificial Intelligence For Video Surveillance market is expected to be maximum for Asia-Pacific region. Increased maturation, urban migration as well as expanding middle class in China, India, and Japan and other developing economies are great constituents of market growth. Further, there is an increasing contribution to investments in the Artificial Intelligence For Video Surveillance sector which is increasing the demand for Artificial Intelligence For Video Surveillance regions-supplying throughout the area.
Rest of the World: Countries and areas like Latin America, Middle East & Africa have also been showing moderate Artificial Intelligence For Video Surveillance market growth. Although still developing, these markets are fueled by a fast increasing infrastructure, expending industrial activities and growing consumer demand for Artificial Intelligence For Video Surveillance goods. These regions pose great opportunities for the market players to tap into other sources of growth.
Frequently Asked Questions (FAQ) – Artificial Intelligence For Video Surveillance Market
Q1: What is the anticipated growth rate of the Global Artificial Intelligence For Video Surveillance Market?
A1: With a growth rate of CAGR of 11%, the Global Artificial Intelligence For Video Surveillance Market is anticipated to reach USD 16.8 billion by 2031. Industrial demand and innovation will lead it to reach USD 6.5 billion by 2024.
Q2: Which regions provide the highest growth opportunities for the Artificial Intelligence For Video Surveillance Market?
A2: Asia-Pacific is likely to provide the highest growth prospects based on speedy industrialization and infrastructure growth, followed by robust markets in Europe and North America.
Q3: Which are the primary drivers of market growth?
A3: The primary drivers are technology innovation, growing industrial applications, heightened government initiatives, and expanding use of Artificial Intelligence For Video Surveillance solutions in different industries.
Q4: What are the challenges faced by the Artificial Intelligence For Video Surveillance Market?
A4: The challenges are tight regulatory systems, high upfront capital expenditures, fragmentation of the market in the emerging markets, and geopolitical risks in some regions.
Q5: Which are the major players in the Global Artificial Intelligence For Video Surveillance Market?
A5: The market has a number of leading players with a focus on innovation, strategic alliances, and global expansion.
Q6: How does innovation influence the Artificial Intelligence For Video Surveillance Market?
A6: Market growth is driven by innovation, which enhances product efficiency, lowers costs, and facilitates new applications, making the overall market potential broader.
Q7: Which industries utilize Artificial Intelligence For Video Surveillance products mostly?
A7: Major industries include manufacturing, automotive, energy, electronics, and infrastructure, among others, where Artificial Intelligence For Video Surveillance solutions deliver operational efficiency and sustainability.
Q8: How is the market anticipated to change after 2031?
A8: Although projections beyond 2031 are uncertain, continued technological advancement and increasing industrial demand are expected to continue supporting long-run growth patterns.
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Both stocks have been flying high in the past year, but one of them looks like a much better buy right now.
The spending on artificial intelligence (AI) software and tools has been picking up momentum at a solid pace of late, and that’s not surprising as this technology is expected to deliver terrific productivity gains. According to McKinsey, AI has the potential to deliver $4.4 trillion worth of productivity gains in the long run.
Palantir Technologies (PLTR -2.04%) and BigBear.ai (BBAI 0.71%) are two companies that can help investors benefit from the massive generative AI software market that’s expected to clock a compound annual growth rate (CAGR) of 36% through 2030. But if you have to choose from one of these two AI stocks for your portfolio right now, which one should it be?
Let’s find out.
Image source: Getty Images.
The case for Palantir Technologies
Palantir is considered to be the leading player in the AI software platforms market by third-party research firms such as Forrester and IDC. That explains why the company has been landing new customers for its AI software solutions at a terrific clip.
Its overall customer count was up by 43% year over year in the second quarter of 2025. But more importantly, the productivity gains delivered by Palantir’s AI solutions are helping it expand its business with existing customers. As a result, the company’s deal size is improving, allowing it to close 157 deals worth at least $1 million last quarter. That was a jump of 64% from the year-ago period, exceeding the growth in its customer base.
It is easy to see why customers spend more money on Palantir’s AI software if we take a look at management’s comments on the recent earnings conference call. As pointed out by Chief Revenue Officer Ryan Taylor:
The impact our software is delivering for our customers as they cross the chasm is ever widening their advantage over the AI have-nots. Citibank shared that the customer onboarding process and relevant KYC and security checks that once took them nine days now take seconds. Fannie Mae recently announced they’re working with Palantir, decreasing the time to uncover mortgage fraud from two months down to seconds.
These are just two of the many examples highlighted by management about how its Artificial Intelligence Platform (AIP) is helping it win more customers and strengthen its relationship with existing ones. As such, it won’t be surprising to see Palantir sustain its healthy growth rates following the 45% spike in revenue forecast for 2025.
Another important thing worth noting is that Palantir’s ability to gain more business from existing customers drives stronger bottom-line growth. Its earnings are expected to jump 57% this year to $0.64 per share, followed by impressive growth over the next couple of years as well.
Data by YCharts.
So, Palantir is likely to remain a top AI stock for a long time to come, thanks to the secular growth opportunity in the AI software market.
The case for BigBear.ai
Just like Palantir, BigBear.ai also provides AI software solutions that help its customers make faster and better decisions. The stock has more than tripled in value in the past year, as investors buy it in anticipation that it could become a big winner of the lucrative opportunity in the AI software market. However, investors can buy this stock at a much cheaper valuation despite its red-hot rally.
BigBear.ai stock trades at 9 times sales as compared to Palantir’s way more expensive price-to-sales ratio of 115. Another thing working in BigBear’s favor is its fast-growing revenue backlog that could lead to an acceleration in the company’s growth. It ended the second quarter with a backlog of $380 million, up by 43% from the year-ago period.
However, a closer look at BigBear.ai will tell us that the company’s growth is nowhere near that of Palantir’s. Its revenue slid 18% year over year in Q2 to $32.5 million, as it was unable to convert some of its Army contracts into revenue. This brings us to the reason why BigBear.ai has been in hot water of late.
The company relies on government contracts for a majority of its revenue. So, its business is dependent on government budgets and policies, which is why it was forced to lower its 2025 guidance when it released its Q2 results. Investors pressed the panic button, and BigBear.ai stock went into free-fall mode since the earnings’ release on Aug. 11.
The company’s updated revenue guidance of $132.5 million for 2025 would be lower than the $158 million in revenue it generated last year. Moreover, BigBear.ai’s backlog doesn’t necessarily guarantee that its growth will pick up due to certain caveats associated with that metric. As such, just because BigBear.ai is cheaper than Palantir doesn’t make it a better buy than the latter.
The verdict
Palantir, though extremely expensive right now, has the ability to justify its rich valuation thanks to its solid position in the fast-growing AI software space. The company is quickly building up a solid customer base and is also winning a bigger share of their wallets. That’s the reason why its forward sales multiples are significantly lower than the trailing multiple.
Data by YCharts.
So, investors looking to choose from one of these two AI stocks for their portfolio right now would be better off buying Palantir, given the company’s fast pace of growth and sunny prospects considering its leading position in the AI software market.
Citigroup is an advertising partner of Motley Fool Money. Harsh Chauhan has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Palantir Technologies. The Motley Fool has a disclosure policy.
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