Windows seemingly lost 400 million users in the past three years — official Microsoft statements show hints of a shrinking user base
Microsoft EVP Yusuf Mehdi said in a blog post last week that Windows powers over a billion active devices globally. This might sound like a healthy number, but according to ZDNET, the Microsoft annual report for 2022 said that more than 1.4 billion devices were running Windows 10 or 11. Given that these documents contain material information and have allegedly been pored over by the tech giant's lawyers, we can safely assume that Windows' user base has been quietly shrinking in the past three years, shedding around 400 million users.
This is probably why Microsoft has been aggressively pushing users to upgrade to Windows 11 after the previous version of the OS loses support — so that its users would install the latest version of Windows on their current system (or get a new PC if their system is incapable of running the latest version). Although macOS is a threat to Windows, especially with the launch of Apple Silicon, we cannot say that those 400 million users all went and bought a MacBook. That's because, as far back as 2023, Mac sales have also been dropping, with Statista reporting the computer line, once holding more than 85% of the company revenue, now making up just 7.7%.
Instead, people are slowly ditching their computers for smartphones and tablets, especially as they've become more powerful than ever. The only remaining major consumer markets for Windows PCs are gamers and specialized professionals who rely on software that only runs on Windows, ZDNET observed back in 2019. While the pandemic caused a three-year blip in PC sales, it seems that this trend has continued.
The looming Windows 10 end-of-support date will likely cause a jump in corporate sales, especially as companies rely on current software to help secure their systems. However, this is not a sure thing with consumers. After all, their Windows 10 PC will still work perfectly well, even if they're not getting updates — they might be a bit more vulnerable to threats, but it seems that the average person does not care about that.
Those who are getting a replacement also have attractive alternatives: if you have a budget of around $800 to $1,000, the Apple Silicon MacBook Air is a tempting offer, especially with its amazing battery life. On the other hand, if money is a bit tight, but you still want something decent and new, there are a ton of Chromebook options out there. And with many schools using this system, students — who will eventually become adults and buy their own computers — are so used to them that they might just choose Google over Microsoft when they need a computer.
Alternative operating systems weren't a major threat in the past, as the ubiquity of the Microsoft Office suite, various software that is only available for Windows, and the cheaper price of PCs over Macs meant that they were the best options for those who simply needed a computer for basic tasks. But with the prevalence of Google Docs (which is free, by the way) and web-based apps, it seems that many no longer find a reason to choose Microsoft.
Follow Tom's Hardware on Google News to get our up-to-date news, analysis, and reviews in your feeds. Make sure to click the Follow button.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Forbes
39 minutes ago
- Forbes
2025 Halftime: AI's Four Forces - What Happened, What's Coming
Demis Hassabis, Co-Founder and CEO, Google DeepMind, speaks at a Google I/O event in Mountain View, ... More Calif., Tuesday, May 20, 2025. (AP Photo/Jeff Chiu) Monday, June 30th delivered a triple whammy of news that perfectly captures AI's current state. Meta unveiled its Superintelligence Labs after spending $14.3 billion to invest in Scale AI and poach talent from OpenAI, Anthropic, Google and others. Microsoft claimed its AI diagnoses patients 4x better than doctors. And the White House launched an AI youth education pledge, acknowledging AI literacy is as essential as reading. These weren't isolated events. Over the past six months we've seen some of the most dramatic AI disruptions since ChatGPT's debut in November 2022. If you zoom out, they're symptoms of four converging AI forces reshaping at unprecedented speed every aspect of society from people to businesses to governments: compute, data, algorithms, and robotics (often referred to as 'physical AI'). Compute costs have plummeted 25x, synthetic data is reducing AI training expenses, breakthroughs like DeepSeek along with model updates from OpenAI, Anthropic, xAI, and Google continue to push the limits of scaling laws, and humanoid robot pioneers such as Tesla (Optimus), Figure AI and Agility Robotics are preparing to commercialize physical AI-powered robots starting in late 2025. Progress in any one of these forces would be impressive on their own. When combined, these forces are amplifying each other to create unprecedented opportunities for prepared businesses and existential threats for the unprepared. As we cross 2025's midpoint, it's time to assess what just happened and brace for what's coming. AI Force #1 • Compute: The Paradox of Plenty Compute is the raw processing power driving AI - the brain power. The first half of 2025 revealed a paradox: while compute options multiplied, actual GPU availability remained critically constrained. NVIDIA's Blackwell Ultra announcement promised 50% better performance at 25x less power—if you could get them. With 36-52 week lead times and allocation politics determining who gets chips, the "democratization" remains theoretical. We're seeing the rise of accelerated computing as GPU performance is growing 2x per year While NVIDIA's Project DIGITS offers $3,000 desktop AI, the real action stayed in data centers where Microsoft Azure's 35% growth meant fierce competition for H100s and A100s. Google's TPU v7 claims 24x better economics, but its hard to access them outside Google Cloud. Intel's Gaudi 3 at $125,000 looks attractive until you realize the software ecosystem barely exists. The brutal truth: despite AMD's quantization efforts and edge computing promises, if you need serious training compute in H1 2025, you're either paying NVIDIA's prices, waiting in line, or making do with inferior alternatives. DeepSeek's $6 million miracle wasn't about abundant compute - it was about doing more with less because they had no choice. What's next: Blackwell production ramps from 200,000 to 2 million units by December, finally breaking the GPU stranglehold. Expect large training cost reductions and mid-market companies achieving GPT-4 capabilities for under $10,000/month. AI Force #2 • Data : The Insatiable Hunger Data is the oxygen of AI. Without quality data, even the most sophisticated algorithms suffocate, making the difference between AI that demos well and AI that delivers measurable business value. The first half of 2025 marked data's shift from volume obsession to quality management, especially as all publicly-available internet data has essentially already been ingested into LLMs, creating a scramble for new data sources. Synthetic data - AI-generated information that mimics real-world patterns without containing actual user data - is a key part of AI's growth story. The synthetic data market is expected to reach $3.7 billion by 2030, but most implementations remain basic data augmentation. Every player, both small and large, continue to search for more data. Just look at Meta's $14.3 billion Scale AI investment - part 'acqui-hire' for sure, but it was also for direct access to Scale's data labeling expertise and access to enterprise data partnerships. As IP rights are being debated, a major ruling came when Anthropic's copyright victory legitimized training on copyrighted material. The hunger for data is ravenous. Scale AI is a key player in the AI data industry, specializing in data annotation and model ... More evaluation services that are essential for developing and deploying advanced artificial intelligence applications. The last two data frontiers remain stubbornly out of reach. First-party enterprise data is locked behind corporate firewalls (containing decades of proprietary business intelligence), so every AI firm is now focused on how best to partner (and penetrate) the enterprise. The other source is real-world sensor data that's critical for physical AI. While Tesla's builds its humanoid robot fleet Optimus, it will benefit from the billions of miles of driving data that synthetic generation can't replicate. What's next: The majority of major companies will adopt synthetic data strategies by December. The first AI model trained entirely on synthetic data will outperform human-trained models, ending the "data is the new oil" era. But the real battle shifts to enterprise data - expect aggressive partnerships and "data-for-compute" deals. AI Force #3 • Algorithms : Surpassing Scaling Laws If compute is brain power and data is oxygen, then algorithms are the neural pathways - the connections and patterns that determine how efficiently the brain uses oxygen to produce intelligence. And we are on the road to superintelligence - just read the essays from OpenAI's Sam Altman or Anthropic's Dario Amodei. The first half of 2025 shattered every assumption about scaling laws and compute requirements. DeepSeek's R1 bombshell - reportedly achieving GPT-4 parity for $6 million versus $100+ million - wiped out $1 trillion dollars in market capitalization and sparked global panic back in January. However, by June the markets were back at new highs as the industry realized that this wasn't just cost reduction, it was algorithmic innovation as they used mixture-of-experts, aggressive sparsity, and clever routing. There have been 50 major model releases in just the first six months of 2025, with lots of different sizes, features, and use cases (see the table below). 50 major large language models have been released in the first six months of 2025 Open-weight models closed the gap with their closed-weight counterparts. In January 2024, the leading closed-weight model outperformed the top open-weight model by 8.04% on the Chatbot Arena Leaderboard, and by February 2025 this gap had narrowed to 1.70%. Claude 4 Opus hitting 72.5% on SWE-bench while coding autonomously for 7+ hours showed reasoning, not size, wins. Google's Gemini 2.5 Flash at 742 tokens/second redefined inference economics. By June, enterprise costs plummeted from $10,000 to sub-$1,000 monthly for equivalent performance. The truth that every LLM researcher knows is that most top models are now within the range of +/- 5% of each other, so we're waiting for the next step-function in innovation. Some of the focus is shifting from model training to system design - companies seeing 60% higher ROI focus on prompt engineering, RAG implementation, and workflow integration. While the "bigger is better" model was questioned in H125, what is becoming clear is that there will be many flavors for lots of different use cases. What's next: The "bigger is better" era ends. Agentic AI takes over - expect many companies to start having customer service handled by autonomous agents and perhaps whole departments being run entirely by AI. Salesforce already reports 30-50% of work done by AI. AI Force #4: Robotics : From Labs to Loading Docks Robotics and physical AI represent the final frontier in business transformation, with over 4 million industrial robots now operating globally and installations growing at 7% annually. Tesla aims to produce "several thousand" Optimus humanoid robots in 2025 for internal use, targeting sub-$30,000 pricing that could revolutionize labor economics. Figure AI's $39.5 billion valuation after raising $1.5 billion demonstrates investor confidence in embodied intelligence, while Agility Robotics' Digit achieved the first commercial humanoid deployment at GXO Logistics. Figure Unveils Next-Gen Conversational Humanoid Robot With 3x AI Computing for Fully Autonomous ... More Tasks The business case has shifted from future promise to present reality. Industrial automation delivers 12-24 month payback periods for large-scale deployments, with robots operating at $0.75/hour versus human labor costs. Manufacturing labor costs drop 20-30% with robotic automation while productivity increases 150% in equipment manufacturing. Agricultural drones, numbering 620,000 worldwide and growing 40% annually, exemplify how physical AI transforms traditional industries through precision and scale. Breakthrough capabilities in 2025 include 24+ hour autonomous operation with 99%+ reliability, multimodal perception combining vision and touch, and natural language control eliminating specialized programming. Yet adoption barriers remain: battery limitations, integration complexity with legacy systems, and a critical skills gap in robotics operation. The winners embrace Robotics-as-a-Service models to reduce capital requirements, invest in workforce training for human-robot collaboration, and pilot solutions in controlled environments before scaling. What's next: The first 10,000+ humanoid deployment hits warehouses. China deploys over a million service robots. We start to see the first "dark factories" operating 24/7 without humans. Robot-as-a-Service becomes a new growth market as companies offload capital expenditures. Business leaders must act on converging opportunities The four forces create immediate risks and opportunities for today's business leaders. First, reassess infrastructure investments given that algorithmic advances can deliver 95% cost savings—your planned GPU purchases may already be obsolete. Second, implement edge computing to reduce cloud dependencies by 60-90% while improving response times and data sovereignty. Third, embrace synthetic data to accelerate AI development while maintaining privacy compliance, joining the 60% of projects already benefiting from this approach. Medium-term strategies should focus on building AI implementation expertise rather than model development capabilities, as the 1.7% performance gap between open and proprietary models makes execution more important than selection. Develop hybrid human-robot workflows in operations, targeting the proven 2-year ROI rather than full automation fantasies. Create comprehensive data governance frameworks that treat information as a strategic asset, enabling the multimodal integration that drives next-generation business models. For long-term positioning, prepare for the algorithmic efficiency paradigm where smaller, optimized models outperform larger ones, making capital-intensive infrastructure strategies obsolete. Build partnerships that provide access to specialized capabilities rather than attempting to develop everything internally. Most critically, invest in workforce transformation. The organizations lacking sufficient AI talent will become exposed and lose out to those who develop these capabilities. The convergence of plummeting compute costs, synthetic data accessibility, algorithmic breakthroughs, and practical robotics has created a unique window for business transformation. Organizations that recognize these forces aren't developing independently but amplifying each other will capture disproportionate value. The question isn't whether to embrace AI-driven transformation, but whether you'll lead it or be disrupted by competitors who do. The tools are accessible, the economics are proven, and the early movers are already capturing market share. What remains is execution and the window for strategic advantage is narrowing rapidly.
The Onion
an hour ago
- The Onion
Windows To Phase Out ‘Blue Screen Of Death'
Windows will no longer display the operating system's infamous 'blue screen of death' when something goes wrong, removing the signature frowning face that accompanied the crash notice in favor of a shorter message and plain black screen. What do you think? 'How am I going to know when I'm supposed to punch my computer monitor?' Derrick Wozniak, Package Claimer 'A great reminder to hug your error messages while you still can.' Carla Maron, Raspberry Lobbyist 'You mean the blue screen of second chances?' David Rosenbaum, Box Sealer
Yahoo
an hour ago
- Yahoo
How AI is impacting lawyers, auditors, and accountants holds lessons for us all
Hello and welcome to Eye on AI. In today's edition…the U.S. Senate rejects moratorium on state-level AI laws…Meta unveils its new AI organization…Microsoft says AI can out diagnose doctors…and Anthropic shows why you shouldn't let an AI agent run your business just is rapidly changing work for many of those in professional services—lawyers, accountants, auditors, compliance officers, consultants, and tax advisors. In many ways, the experience of these professionals, and the businesses they work for, are a harbinger of what's likely to happen for other kinds of knowledge workers in the near future. Because of this, it was interesting to hear the discussion yesterday at a conference on the 'Future of Professionals' at Oxford University's Said School of Business. The conference was sponsored by Thomson Reuters, in part to coincide with the publication of a report it commissioned on trends in professionals' use of report, based on a global survey of 2,275 professionals in February and March, found that professional services firms seem to be finding a return on their AI investment at a higher rate than in other sectors. Slightly more than half—53%—of the respondents said their firm had found at least one AI use case that was earning a return, which is about twice what other, broader surveys have tended to surprisingly, Thomson Reuters found it was the professional firms where AI usage was part of a well-defined strategy and that had implemented AI governance structures were the most likely to see gains from the technology. Interestingly, among firms where AI adoption was less structured, 64% of those surveyed still reported ROI from at least one use case, which may reflect how powerful and time-saving these tools can be even when used by individuals to improve their own biggest factors holding back AI use cases, the respondents said, included concerns about inaccuracy (with 50% of those surveyed noting this was a problem) and data security (42%). For more on how law firms are using AI, check out this feature from my Fortune colleague Jeff John Roberts. Here are a few tidbits from the conference worth highlighting:Mari Sako, the Oxford professor of management studies who helped organize the conference, talked about the three gaps that professionals needed to watch out for in trying to manage AI implementation: One was the responsibility gap between model developers, application builders, and end users of AI models. Who bears responsibility for the model's accuracy and possible harms?A second was the principles to practice gap. Businesses enact high-minded 'Responsible AI' principles but then the teams building or deploying AI products struggle to operationalize them. One reason this happens is that first gap—it means that teams building AI applications may not have visibility into the data used to train a model they are deploying or detailed information about how it may perform. This can make it hard to apply AI principles about transparency and mitigating bias, among other she said, there is a goals gap. Is everyone in the business aligned about why AI is being used in the first place? Is it for human augmentation or automation? Is it operational efficiency or revenue growth? Is the goal to be more accurate than a human, or simply to come close to human performance at a lower cost? What role should environmental sustainability play in these decisions? All good questions. Ian Freeman, a partner at KPMG UK, talked about his firm's increasing use of AI tools to help auditors. In the past, auditors were forced to rely on sampling transactions, trying to apply more scrutiny to those that presented a bigger business risk. But now, with AI, it is possible to run a screen on every single transaction. Still, it is the riskiest transactions that should get the most scrutiny and AI can help identify those. Freeman said AI could also help more junior auditors understand the rationale for probing certain transactions. And he said AI models could help with a lot of routine financial he said KPMG had a policy of not deploying AI in situations that called for human judgment. Auditing is full of such cases, such as deciding on materiality thresholds, making a call about whether a client has submitted enough evidence to justify a particular accounting treatment, or deciding on appropriate warranty reserves for a new product. That sounds good, but I also wonder about the ability of AI models to act as tutors or digital mentors to junior auditors, helping them to develop their professional judgment? Surely, that seems like it might be a good use case for AI too.A senior partner from a large law firm (parts of the conference were conducted under Chatham House Rules, so I can't name them) noted that many corporate legal departments are embracing AI faster than legal firms—something the Thomson Reuters survey also showed—and that this disparity was putting pressure on the firms. Corporate counsel are demanding that external lawyers be more transparent about their AI usage—and critically, putting pressure on legal bills on the theory that many legal tasks can now be done in far fewer billable hours. AI is also possibly going to change how professional service firms think about career paths within their business and even who leads these firms, several lawyers at the conference said. AI expertise is increasingly important to how these firms operate, and yet it is difficult to attract the talent these businesses need if these 'non-qualified' technical experts (the term 'non-qualified' is simply used to denote an employee who has not been admitted to the bar, but its pejorative connotations are hard to escape) know they will always be treated as second-class compared to the client-facing lawyers and also are ineligible for promotion to the highest ranks of the firm's management. Michael Buenger, executive vice president and chief operating officer at the National Center for State Courts in the U.S., said that if large law firms had trouble attracting and retaining AI expertise, the situation was far worse for governments. And he pointed out that judges and juries were increasingly being asked to rule on evidence, particularly video evidence, but also other kinds of documentary evidence, that might be AI manipulated, but without access to independent expertise to help them make calls about what has been altered by AI and how. If not addressed, he said, this could seriously undermine faith in the courts to deliver justice. There were lots more insights from the conference, but that's all we have space for today. Here's more AI The essay above was written and edited by Fortune staff. The news items below were selected by the newsletter author, created using AI, and then edited and fact-checked. Jeremy to know more about how to use AI to transform your business? Interested in what AI will mean for the fate of companies, and countries? Then join me at the Ritz-Carlton, Millenia in Singapore on July 22 and 23 for Fortune Brainstorm AI Singapore. This year's theme is The Age of Intelligence. We will be joined by leading executives from DBS Bank, Walmart, OpenAI, Arm, Qualcomm, Standard Chartered, Temasek, and our founding partner Accenture, plus many others, along with key government ministers from Singapore and the region, top academics, investors and analysts. We will dive deep into the latest on AI agents, examine the data center build out in Asia, examine how to create AI systems that produce business value, and talk about how to ensure AI is deployed responsibly and safely. You can apply to attend here and, as loyal Eye on AI readers, I'm able to offer complimentary tickets to the event. Just use the discount code BAI100JeremyK when you checkout. This story was originally featured on Sign in to access your portfolio
