Samsung Demonstrates Significant Advances in AI-RAN Technology and Ecosystem Development, Unleashing the Full Potential of Software-Based Networks with NVIDIA AI PlatformSamsung Electronics today announced a collaboration with NVIDIA to advance AI-RAN technology. The collaboration reflects Samsung’s commitment to fostering a strong ecosystem and diverse available computing platforms. The effort aims to support the smooth and easy application of AI in mobile networks by expanding the central processing unit (CPU) ecosystem and strengthening collaboration with graphics processing unit (GPU) companies.
To maximize the power of AI and incorporate it into the radio access network (RAN), Samsung has made significant technological advances since early 2024 by leveraging its internal expertise in AI and radio. One of the key milestones achieved was interoperability between Samsung’s O-RAN-enabled virtualized RAN (vRAN) and NVIDIA accelerated computing, which was achieved at Samsung Research Labs late last year. Samsung successfully demonstrated a proof of concept of how NVIDIA’s accelerated computing can be seamlessly integrated into software-based networks to help enhance AI capabilities.
This achievement further enhances Samsung’s advancement in unique innovations combining AI and RAN. Building on this, Samsung can integrate its vRAN (virtual distributed unit, vDU) with NVIDIA’s accelerated computing into commercial off-the-shelf (COTS) servers with Samsung vRAN software installed, enabling seamless delivery of AI-RAN.
In addition, the two companies will continue to explore the best combination of AI-RAN solutions, leveraging Samsung vRAN with NVIDIA Grace CPU and/or GPU-based AI platforms using Compute Unified Device Architecture (CUDA) technology. All of these options are optimal for every network deployment environment, from rural and suburban areas to densely populated cities.
At MWC 2025, Samsung demonstrated its leadership in AI-For-RAN innovation with two AI-RAN demonstrations. Both demonstrations were endorsed by the AI-RAN Alliance and developed in collaboration with multiple members, including NVIDIA. The demonstrations included AI-based physical uplink shared channel (PUSCH) estimation and non-uniform modulation, showcasing innovative ways to integrate AI into mobile networks.
“AI is changing the telecom landscape, and Samsung is helping operators build the network architecture and environment needed to enable AI with our proven AI-driven vRAN,” said June Moon, executive vice president of R&D for the Networks Business at Samsung Electronics. “This collaboration with NVIDIA reflects our ongoing efforts to expand the GPU and CPU ecosystem, and we look forward to exploring new opportunities in the future.” »
“AI-RAN is a critical technology that will significantly improve network utilization, efficiency, and performance while enabling new AI services,” said Ronnie Vasishta, senior vice president of telecommunications at NVIDIA. “Samsung is a leader in AI-RAN development. Its expertise and vRAN software will be invaluable to our customers.”
As a founding member of the AI-RAN Alliance, which was established in 2024, Samsung is actively collaborating with academic institutions and industry leaders such as NVIDIA to advance AI-RAN technology. As vice chair-elect of the board and Working Group 3 (AI-on-RAN), Samsung is leading the industry’s transformation to next-generation AI networks.
Samsung’s end-to-end software network architecture provides the best foundation for easy deployment and adoption of AI at every layer of the network. By doing so, Samsung will be able to support operators with flexible networks, enhance their competitive advantage, and maintain leadership in the AI era. This advancement paves the way for leveraging network infrastructure not only for mobile communications but also for general workloads, providing a data center-like network architecture that opens up new business opportunities
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China’s new AI model “Manus” attracts global attention, challenging OpenAI and Google
Manus is an advanced AI agent designed to think, plan, and perform real-world tasks independently. It can create websites, plan trips, analyze stocks, and more, all with a single user prompt. Just weeks after the launch of DeepSeek, China has unveiled another powerful artificial intelligence (AI) model, Manus, highlighting the country’s accelerating momentum to join the AI race. Developed by Chinese startup Monica, Manus is comparable to top AIsystems created by OpenAI, Google, and Anthropic. The company claims the model is a general AI that can perform tasks autonomously without human supervision.
What is Manus?
Manus is an advanced AI agent designed to think, plan, and perform real-world tasks independently. It can create websites, plan trips, analyze stocks, and more — all with a single user prompt. Unlike standard AI chatbots that provide answers, Manus takes comprehensive actions to complete tasks. For example, if asked to create a report on climate change, it will conduct research, write a report, create charts, and compile everything into a final document without further instructions. Chinese startup Monica has just launched Manus, the world’s first general AIagent. #Manus achieves state-of-the-art (SOTA) performance on all three difficulty levels, outperforming #OpenAI’s DeepResearch on the GAIA real-world problem-solving benchmarkWhat makes Manus so unique?
Manus was launched on March 6 and has quickly gained global attention. According to its creators, it outperforms OpenAI’s DeepResearch on the GAIA benchmark, a measure of AI performance. The demo video released by Monica shows Manus interacting with the internet, collecting data, and performing complex tasks in real time. It can browse websites, take screenshots, record online activity, and generate reports, spreadsheets, or presentations. This level of automation has many calling it a major leap forward in AI technologyMain features of Manus
Manus runs independently in the cloud, and it continues to perform assigned tasks even if the user disconnects the device. This feature ensures that long-term projects can proceed uninterrupted.
Unlike many AI models, Manus actively browses the web, interacts with websites, and displays its workflow in real time. This helps users understand how AI collects and processes information.
It learns from user interactions to provide customized results. Over time, it adapts to user preferences, improving the relevance and quality of responses.
The AI can access platforms like X (formerly Twitter), Telegram, and others to collect and process data. It can even manage multiple screens at once, as shown in its official video. Manus does more than just produce text-based results. It can create detailed reports, interactive presentations, and even code-based outputs like data visualizations and spreadsheets.How to use Manus AI
Manus functions similarly to AI chatbots like ChatGPT, but with greater autonomy. Users simply enter a task, such as “create a 7-day Bali itinerary within budget,” and Manus starts researching, collecting data, and formulating responses. The AI compiles all the relevant information and provides a complete itinerary with links, maps, and travel suggestions. If the user loses connection, the AIcontinues working in the cloud and notifies them when the task is complete.
Availability and future plans
Currently, Manus is available through an invite-only web preview. Monica has not announced a public release date, but hinted that it may be available soon. The company also plans to open source the model in the coming months, allowing developers to integrate it into their own projects. This move could lead to rapid improvement and widespread adoption of the technology.
Tencent releases new AI model, claims to be faster than DeepSeek-R1
Tencent on Thursday unveiled a new artificial intelligence model that it says can answer queries faster than global hit DeepSeek’s R1, the latest sign that the startup’s success at home and abroad is putting pressure on its larger Chinese rivals.Tencent said in a statement that Hunyuan Turbo S can answer queries in under a second, differentiating it from “DeepSeek R1, Hunyuan T1 and other slow-thinking models that need to ‘think for a while before answering.’” Tencent added that in tests of knowledge, math and reasoning, Turbo S’s capabilities were comparable to DeepSeek-V3, which powers DeepSeek’s AI chatbot that has surpassed OpenAI’s ChatGPT in app store downloads.DeepSeek did not immediately respond to a request for comment.The success of DeepSeek’s R1 and V3 models, the first time a Chinese company has received widespread acclaim and adoption in Silicon Valley, has also prompted Chinese tech giants such as Tencent to scramble to launch new versions of the AI models they began developing after OpenAI‘s ChatGPT came out in late 2022. Last month, just days after DeepSeek-R1 shook up the global tech order and triggered a sell-off in AI stocks outside of China, e-commerce giant Alibaba (9988.HK), opened in a new tab, released the Qwen 2.5-Max model, claiming that it outperformed DeepSeek-V3 in all aspects. Tencent also said that the new Turbo S is many times cheaper to use than previous generations, highlighting how DeepSeek’s open source and low-price strategy has forced other leading Chinese AI companies to charge users less.
DeepSeek’s AI breakthrough heralds big changes for data centers
While the debut of the DeepSeek AI model earlier this week sparked a sharp sell-off in U.S. tech stocks, its gains in AI processing efficiency could have big implications for data centers.
Market darling Nvidia shares fell more than 12% and the Nasdaq fell 2.7%, with analysts saying the reaction reflected concerns about whether huge investments in AI and its infrastructure are justified.
Meanwhile, U.S. power and utility stocks fell sharply on reports that DeepSeek’s model raised questions about expectations of an AI-driven surge in data center power demand.
Any shift toward cheaper, more powerful and more energy-efficient algorithms has the potential to significantly expand the scope of AI applications, which could ultimately drive demand for large-scale and distributed data center infrastructure.
“If the reports about DeepSeek are true, this will only drive AI innovation forward,” said Mitch Lenzi, vice president of sales and operations at Baxtel, an online platform dedicated to directories and reviews of managed data centers around the world.
The new model and reduced deployment costs will enable competitors to optimize their own AI strategies, driving demand and adoption, he said.
Lenzi said he believes AI advances like DeepSeek will ultimately accelerate, not slow, data center growth.
“Innovation in AI doesn’t reduce demand, it drives it,” he said. “As AI becomes more pervasive and cost-effective, the industry will continue to expand, maintaining demand for high-performance data center infrastructure.”
Sean Farney, vice president of data center strategy at JLL, agreed that the introduction of more efficient AI models like DeepSeek could reshape the data center market.
“That’s great news for the industry,” Farney said. “If someone finds a cheaper, more efficient way to do AI, it lowers the barrier to entry and makes AI accessible to a wider audience.”
Over time, that will drive increased usage and create new opportunities for data center growth.
Farney noted that AI GPU-focused data centers are already the fastest-growing segment of the market, with a compound annual growth rate (CAGR) of 39%, nearly double the overall data center growth rate of about 20%.
“AI-focused facilities are growing much faster than traditional data centers,” Farney said. “With innovations like DeepSeek, we may see an acceleration in this space.”
The financial implications of this growth are huge: According to Farney, annual spending on infrastructure by major hyperscale data center operators has soared from $200 billion to $300 billion.
“The industry is booming,” he said. “If technologies like DeepSeek make AI applications faster and easier to deploy, we will need more data centers to support this adoption.”
John Dinsdale, chief analyst and research director at Synergy Research Group, noted that it is generative AI (GenAI) that has led to some data center rethinking and re-architecting.
“If technology emerges that can significantly reduce the required power density, this may mean a return to pre-GenAI designs with more traditional cooling and power distribution,” he said.
Dinsdale explained that there is currently considerable investment in GenAI technology and products across the IT ecosystem, and this situation will not change in the short term.
“Will some technology emerge that can reduce the power consumption and cost of training and running AI models? Absolutely,” he said. “It’s the nature of technology development and lifecycles.”
When costs go down and capabilities go up, that tends to spur big increases in adoption and usage.
“Take the growth of cloud computing services over the past 15 years,” Dinsdale said.
The role of modular and edge data centers
Farney also highlighted the growing importance of small, modular, and edge data centers in this evolving environment.
While training large AI models will still require large centralized facilities, the growing focus on AI inference (using trained models to provide real-time insights) is likely to drive demand for distributed, latency-sensitive edge data centers.
“As we move into the inference phase of AI, there is a growing need for localized compute power,” Farney said.
Inference typically requires low latency and proximity to users, which makes smaller edge facilities more practical.
“We may end up covering the globe with small 1- or 2-megawatt data centers dedicated to AI tasks,” he said.
Farney envisions a hybrid future where giant hub data centers and distributed edge facilities coexist to meet the diverse needs of AI workloads.
“This is not a zero-sum game,” he explained. “We will see continued growth in large facilities for batch AI training, and a surge in small data centers for inference and real-time applications.”
The case for data decentralization
Phil Mataras, founder and CEO of AR.IO, a decentralized permanent cloud network provider, said that the current centralized data center approach to storing data
Clio: A privacy-preserving system that provides insights into real-world AI usage
What are people using AI models for? Despite the rapid growth in popularity of large language models, we still know very little about what they are used for.
This isn’t just out of curiosity, or even sociological research. Understanding how people actually use language models is important for security reasons: providers put a lot of effort into pre-deployment testing and use trust and safety systems to prevent misuse. But the scale and diversity of language models makes it hard to understand what they are used for (not to mention any kind of comprehensive security monitoring).
There’s another key factor that prevents us from having a clear understanding of how AI models are used: privacy. At Anthropic, our Claude model is not trained on user conversations by default, and we take protecting our users’ data very seriously. So how do we study and observe how our systems are used while strictly protecting our users’ privacy?
Cl aude Insights and Observations (“Clio” for short) is our attempt to answer this question. Clio is an automated analytics tool that performs privacy-preserving analysis of real-world language model usage. It gives us insight into everyday usage at claude.ai in a similar way to tools like Google Trends. It’s already helping us improve our security measures. In this post (with the full research paper attached), we describe Clio and some of its initial results.
How Clio Works: Privacy-Preserving Analytics at Scale
Traditional top-down security approaches (such as assessments and red teams) rely on knowing what to look for in advance. Clio takes a different approach, enabling bottom-up pattern discovery by distilling conversations into abstract, understandable clusters of topics. It does this while protecting user privacy: data is automatically anonymized and aggregated, and only higher-level clusters are visible to human analysts.
All of our privacy protections are extensively tested, as described in our research paper.
How People Use Claude: Insights from ClioUsing Clio, we were able to gain insight into how people use claude.ai in practice. While public datasets such as WildChat and LMSYS-Chat-1M provide useful information about how people use language models, they only capture specific contexts and use cases. Clio gives us a glimpse into the full real-world usage of claude.ai (which may differ from other AI systems due to differences in user base and model type).
Summary of Clio’s analysis steps, illustrated using fictional examples of conversations.
Here’s a brief overview of Clio’s multi-stage process:
Extracting Aspects: For each conversation, Clio extracts multiple “aspects” — specific properties or metadata, such as the topic of the conversation, the number of back-and-forths in the conversation, or the language used.
Semantic Clustering: Similar conversations are automatically grouped based on topics or general themes.
Cluster Descriptions: Each cluster receives a descriptive title and summary that captures common themes from the raw data while excluding private information.
Building Hierarchies: Clusters are organized into multi-level hierarchies for easier exploration. They can then be presented in an interactive interface that a human factors analyst can use to explore patterns along different dimensions (topics, language, etc.).
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Gli esperti di intelligenza artificiale rivelano il vero motivo per cui DeepSeek è così popolare
DeepSeek shocked the tech world last month. There’s a reason for that, according to AI experts, who say we’re likely just seeing the beginning of the Chinese tech startup’s influence in the field.
In late January, DeepSeek made headlines with its R1 AI model, which the company said roughly matched the performance of Open AI’s O1 model but cost a fraction of the price. DeepSeek briefly replaced ChatGPT as the top app in Apple’s App Store, sending tech stocks tumbling.
The achievement prompted U.S. tech giants to question America’s place in the AI race with China, and the billions of dollars behind those efforts. While Vice President JD Vance didn’t mention DeepSeek or China by name during his speech at the AI Action Summit in Paris on Tuesday, he did emphasize the importance of America’s lead in the field.
“The United States is a leader in AI , and our government plans to keep it that way,” he said, but added that “the United States wants to work with other countries.”
But there’s more to DeepSeek’s efficiency and capabilities than that. Experts say DeepSeek R1’s ability to reason and “think” answers to deliver high-quality results, combined with the company’s decision to make key parts of its technology public, will drive growth in the field.
While AI has long been used in tech products, it has reached a tipping point in the past two years thanks to the rise of ChatGPT and other generative AI services that have reshaped how people work, communicate and find information. It’s made companies like chipmaker Nvidia Wall Street darlings and upended the trajectory of Silicon Valley giants. So any development that helps build more powerful and efficient models is sure to be closely watched.
“This is definitely not hype,” said Oren Etzioni, former CEO of the Allen Institute for Artificial Intelligence. “But it’s also a world that’s changing very quickly.”
AI’s TikTok Moment
Tech leaders were quick to react to DeepSeek’s rise. Demis Hassabis, CEO of Google DeepMind, called the hype around DeepSeek “overblown,” but he also said the model was “probably the best work I’ve seen in China,” according to CNBC.
Microsoft CEO Satya Nadella said on the company’s quarterly earnings call in January that DeepSeek had some “real innovation,” while Apple CEO Tim Cook said on the iPhone maker’s earnings call that “innovation that drives efficiency is a good thing.”
But the attention isn’t all positive. Semiconductor research firm SemiAnalysis cast doubt on DeepSeek’s claim that it cost just $5.6 million to train. OpenAI told the Financial Times it found evidence that DeepSeek used the U.S. company’s models to train its own competitors.
“We are aware of and are reviewing indications that DeepSeek may have improperly improved our models, and we will share that information once we learn more,” an OpenAI spokesperson told CNN in a statement. DeepSeek was not immediately available for comment.
Two U.S. lawmakers called for a ban on the app on government devices after security researchers highlighted its possible links to the Chinese government, according to the Associated Press and ABC. Similar concerns have been raised about the popular social media app TikTok, which must be sold to a U.S. owner or risk being banned in the U.S.
“DeepSeek is the TikTok of (large language models),” Etzioni said.
How DeepSeek impressed the tech worldTech giants are already thinking about how DeepSeek’s technology will impact their products and services.
“DeepSeek basically gave us a solution in the form of a technical paper, but they didn’t provide the additional missing pieces,” said Lewis Tunstall, a senior research scientist at Hugging Face, an AI platform that provides tools for developers.
Tunstall is leading Hugging Face’s efforts to fully open source DeepSeek’s R1 model; while DeepSeek provided the research paper and model parameters, it did not reveal the code or training data.
Nadella said on Microsoft’s earnings call that Windows Copilot+ PCs (i.e. PCs built to specific specifications to support AI models) will be able to run AI models extracted from DeepSeek R1 locally. Mobile chip maker Qualcomm said on Tuesday that models extracted from DeepSeek R1 were running on smartphones and PCs equipped with its chips within a week.
AI researchers, academics and developers are still exploring what DeepSeek means for AI progress.
DeepSeek’s model isn’t the only open source model, nor is it the first that can reason about an answer before responding; OpenAI’s o1 model, which launched last year, can do that, too.
What makes DeepSeek so important is its ability to reason and learn from other models, and theAI community can see what’s going on behind the scenes. Those who use the R1 model in the DeepSeek app can also see how it “thinks” as it answers questions.
“You can see the wheels turning inside the machine,” Durga Malladi, senior vice president and general manager of technology planning and edge solutions at Qualcomm, told CNN.
iOS 18.3 is out, but AI is still waiting
An important milestone in the deployment of Apple intelligence in the United States, this iOS update still does not introduce AI on the European version of the iPhone.
We know what to expect, but impatience is the norm. While the American version of the iPhone can already benefit from numerous features related to artificial intelligence, in France, Apple intelligence will not show the end of its code until April at the latest. This does not prevent iOS 18.3, which has been launched on compatible smartphones since January 27, from offering some useful features. What does iOS 18.3 bring?
iOS 18.3 is not as harmful as it seems. At least in the United States, it brings the much-anticipated visual intelligence, Apple’s answer to Google Lens, which allows you to get information about objects, monuments, animals, just by putting them in the camera frame. Yes, it’s Pokémon. Too bad for us, we will have to wait a few months to find out.
The latest version of iOS is also the one that implements the deployment of Apple intelligence. Until now, Apple’s AI has been optional and enabled at the discretion of the user, now it will be enabled by default. To do this, iPhone users will have to go to Settings > Apple Intelligence and Siri.
In addition, the Genmoji image generation AIhas also been improved, and the news notification digest has been disabled. This is perhaps for the best, given the recent hype about the feature from Apple. What about us?Yes, everything described above is indeed reserved for iPhones on the other side of the Atlantic. For us Europeans, iOS 18.3 only fixes a few bugs. The calculator app has also restored its old functionality (and allows you to repeat actions by pressing the equals sign multiple times), and the keyboard no longer disappears when invoking Siri.
So, in our case, we can clearly talk about small updates. Anyway, who said that iOS 18.3 is finally public, also said that the iOS 18.4 beta is coming. And this time, it should be the right choice for European smartphone AI! We will be detailing the Apple Intelligence features that you can try in advance in this version in the coming weeks.
In the meantime, why not go and see what the iPhone SE 4 will look like?
ChatGPT just got OpenAI’s most powerful upgrade yet — and it’s ready for “deep research”
Artificial intelligence has already begun to change the way we conduct research, and now OpenAI has just released the latest update to ChatGPT, which it says will open up “deep research” to almost everyone.
The new deep research tool is rolling out to ChatGPT Pro subscribers today, just days after the launch of the 03-mini reasoning model.
“Today, we’re launching Deep Research in ChatGPT, a new agent capability that can conduct multi-step research on complex tasks on the internet,” a blog post reads, promising that the model can “do in just tens of minutes what would take a human hours to do.OpenAI ChatGPT’s powerful “deep research” upgrade gets open source copies in just 24 hours
I just tested ChatGPT’s new o3-mini model, rating its problem-solving and reasoning abilities with 7 prompts — and the results blew my mind
This new agent “can work independently for you,” “finding, analyzing, and synthesizing hundreds of online sources to create research analyst-level synthesis reports” based on a series of prompts.
It’s powered by the upcoming ChatGPT o3 model and is aimed at finance, science, policy, and engineering research, but OpenAI also says it can also be used to make purchasing decisions and help with detailed product research (which could be another warning for Google).
Using the new tool is simple, too. Users simply select “Drill down” in the ChatGPT message editor and enter a query, with the option to attach additional context via files and spreadsheets.
Doing so will see the cited articles in the sidebar, where you can also track your progress. OpenAI says it can take anywhere from five minutes to half an hour to complete a deep dive task.
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OpenAI launches new features for WhatsApp users — here’s what’s new
OpenAI has expanded the functionality of ChatGPT in WhatsApp to include uploading pictures, sending voice messages, and linking existing ChatGPT accounts directly through the messaging platform.
These updates have been rolled out to all users globally, ensuring that individuals around the world can benefit from the enhanced functionality. Users can now upload pictures within WhatsApp conversations using ChatGPT, similar to the functionality available in the standalone ChatGPT app. Users can now use AI to analyze and respond to visual content. ChatGPT is now on WhatsApp — here’s how to message the AI chatbot for free
You can now call or text 1-800-ChatGPT for free — here’s everything you need to knowIn addition, the integration of voice messages allows users to send voice notes to ChatGPT, which theAI will process and respond to in text form. While not exactly similar to ChatGPT Advanced Voice, this feature does provide a more natural interaction with ChatGPT within the platform, catering to a wider range of user preferences.
Account linking for expanded utilityIn addition, users now also have the option to link their existing ChatGPT account (whether Free, Plus, or Pro) to WhatsApp. This integration is designed to provide a more seamless experience, allowing users to manage their interactions and usage more effectively. By linking their accounts, users can enjoy extended usage and personalization, thereby enhancing the overall usefulness of ChatGPT in WhatsApp.
How to Use the New Features To use these new features, users should ensure that their WhatsApp application is updated to the latest version. Once updated, interacting with ChatGPT is very simple. Start by saving the number 1-800-CHATGPT (1-800-242-8478) to your phone contacts, then open WhatsApp and chat with your saved contacts to start a conversation. In the chat, use the attachment icon to select an image and send it directly to ChatGPT. To voice chat with ChatGPT, press and hold the microphone icon to record and send a voice message. ChatGPT will process these voice notes and reply in text.
If you want to get the best response, follow the prompts in the chat to link your ChatGPT account. You need to enable the extended usage and personalization features.
Benefits of the IntegrationThere are several benefits to integrating ChatGPT into WhatsApp. Accessing the AI assistant directly within the platform is much more convenient as it eliminates the need to switch between apps.
What is DeepSeek? Big tech companies continue to build AI in big ways.
About two weeks ago, Wall Street panicked when Chinese startup DeepSeek released an AI system that was far more effective than its American rivals.Investors, who have poured trillions of dollars into tech stocks over the past few years, wondered whether the tens of billions of dollars tech companies have spent on new data centers suddenly looked overextended.
But the biggest tech companies made clear in recent earnings reports that they don’t think they’re overextended in building new data centers.
Amazon hinted Thursday that its capital spending, which includes data center construction and other projects like warehouses, could exceed $100 billion this year. Microsoft said its spending could exceed $80 billion. Alphabet said it would spend $75 billion, while Meta reiterated plans for $65 billion in capital expenditures.
Combined, they could spend about $100 billion more on these projects than they did last year.
Executives urged patience. The problem, they said, is that customer demand for artificial intelligence outstrips the company’s capabilities. The only way to meet demand is to develop as many products as quickly as possible.
“Every time I see someone else doing something better, I say, ‘Ah, we should do that,’ ” Meta CEO Mark Zuckerberg told employees at a companywide meeting last week, according to a recording obtained by The New York Times. Competition is good, but we need to make sure we can win.”
Here are some keys to understanding this consumer-driven moment in the tech industry:
Tech companies need more data centers than they have now.
Many companies say they are constrained by the availability of chips, land and electricity to build data centers, so they are racing to open more. Microsoft, Alphabet and Amazon have all said their cloud sales could be higher if there was enough capacity. Cloud services are the quintessential way to deliver artificial intelligence to customers.
Alphabet Chief Financial Officer Anat Ashkenazi told investors that Alphabet is seeing “demand that exceeds our available capacity.” “So we’re going to work on that and make sure we can provide more capacity.”
Microsoft has been saying it has been constrained for some time, and previously told investors that pressure would ease early this year. But last week, when the company reported its latest earnings, executives told investors it might take until the summer to have enough capacity to meet all the demand. Shares of the company fell about 5% in after-hours trading after the report was released.
Greater efficiency, they say, will expand the use of and demand for AI
While many people think of data centers as expensive, power-hungry places to develop advanced AI systems, they are also where AI is deployed. Those are two different steps: training the model that underpins ChatGPT, and asking ChatGPT for recipe suggestions.
The industry calls deploying AI “inference”; a growing number of tech companies say their businesses are booming in this area.
Microsoft CEO Satya Nadella told investors last week that as costs fall, “AI I will become more pervasive.”
Amazon CEO Andy Jassy told investors Thursday that while a world where every application includes AI may be hard to imagine, “it’s a world we’ve been thinking about.” At the heart of that vision, he said, is inference.
He believes that lowering the cost of inference will follow the pattern of previous technology trends: As systems become cheaper to deploy, customers will “get excited about other things they can build that they previously thought were too expensive, and they usually end up spending more money on.”
These companies say they have to think long-term.
Cloud providers are used to giving customers the illusion of endless supply, which means they have to juggle having enough data centers online to play the video you want or answer your chatbot query. But they also can’t build too much in advance, which would tie up billions of dollars that could be deployed elsewhere. Balancing the two — especially when securing data center land, chips and electricity can take years — is a big challenge for these companies.
Executives say they can adjust how their investments are used, between building and deploying AI models and between serving their own core businesses and customers. Nadella said Microsoft’s infrastructure is “very flexible.” Ashkenazy said Google is flexible, too. For example, it can “repurpose capacity” to serve Google Search instead of cloud customers.
Zuckerberg said Meta was working on DeepSeek and how it could improve efficiency, but that investing heavily in data centers would be a strategic advantage against smaller, more nimble rivals.
“We serve over a billion people — that’s a lot of people, so more and more machines are going to be used to run inference,” he told employees.
Whatever the reason, cutting into profits — even the huge profits of tech giants — is unlikely to excite investors.