Today we’re publishing our first “Public Country-by-Country Report” for our fiscal year 2025, disclosing our taxes in the period from July 1, 2024, to June 30, 2025. It covers the countries and regions included under European Union rules and shows, for each one, our revenue, profit, number of employees, and income tax accrued and paid during the year.  
 
We have provided this kind of information directly to tax authorities for several years under the Organization for Economic Cooperation and Development (OECD) framework. It is now published to support transparency commitments, and we believe it is important to proactively address any questions these disclosures may raise, recognizing that numbers on a spreadsheet rarely tell the full story.
 
Microsoft pays the taxes we owe in every country where we operate. We know there are strong views about whether companies are paying enough, and we believe providing this context leads to a more informed conversation.

Understanding country-by-country reporting

Country-by-country reporting is not widely understood outside tax and accounting circles. Some figures may look surprising at first, but a number that appears low or high in one country does not, on its own, tell the full story. Tax law differs from country to country, and there are two important things to keep in mind when reading the report.  

First, the numbers are prepared using rules that differ from United States or country-specific financial accounting and tax rules, so they may not match other Microsoft information people have seen. For example, this report combines all Microsoft legal entities in a country and follows the reporting rules required by EU regulations. By contrast, local statutory accounts usually cover just one legal entity, follow local accounting rules, and may use a different fiscal year from Microsoft’s.  

Second, accrued tax is what you owe for the year. Tax paid is the amount actually paid during the year. The two can differ because the timing of owing tax and paying tax doesn’t match exactly. 

France is a good example of why a single line can look unusual without context. In FY25, cash tax paid in France reflects a one-time refund of tax overpaid in an earlier year. That makes this year an outlier. In this specific case, accrued tax may be a better reflection of the taxes borne for the fiscal year. Microsoft paid $374 million in tax in France over the prior three years. 

Variations like these are a normal part of how large companies, both domestic and multinational, are taxed across borders, and they reflect an evolving tax landscape as well as a business that continues to change. We comply with every local rule that applies to us, and as those rules change, our reporting will change with them. Microsoft is committed to a tax structure that reflects where our people work, where we invest, and where functions, assets, and risks occur, and this has been a guiding principle. 

How our investments support local economies

We understand that this discussion is not only about what the law requires or what a single tax line shows in a given year. For many people, it is also about a broader question of contribution: how companies support the countries where they do business. That contribution includes the taxes we pay, the capital we invest, the local jobs and infrastructure we support, and the economic activity created through customers and partners. In the S&P, Microsoft ranks second globally in corporate income taxes paid in the last year, with a total of $28.7 billion. In fiscal year 2025, we paid $6.3 billion in income tax in the EU. Importantly, this does not include payroll, VAT, property, and other taxes paid in addition. 

Taken together, our tax payments, capital investments, and partner ecosystem reflect a long-term commitment to the countries where we operate. We opened our first European office in the UK in 1982, followed by France and Germany in 1983, and then expanded into Denmark, Ireland (our largest hub in the region), Italy, Norway, Spain, and Sweden in 1985. Microsoft is now present in all 27 EU Member States and across the broader region. We have worked in these and many other communities for decades, and thousands of our employees call them home.  

From research and development to digital infrastructure and partnerships with local organizations, we are investing in ways that support these economies beyond our direct commercial activity. At our core, we are building tools that help large enterprises, small and medium-sized businesses, institutions, and individuals become more productive and competitive, which strengthens their business and benefits the people they serve. We only do well when our customers do well. In practice, that means helping customers design and manufacture cars better, helping patients get their next appointment sooner, or making it simpler for someone to find that dream job. 

Our investments in digital infrastructure are not only supporting the local digital economy, they are also contributing meaningfully through both taxation and capital expenditure. Across markets, we continue to invest at scale in datacenters and supporting infrastructure, creating value that extends well beyond the technology sector. In the three years to June 30, 2025, our total capital expenditure amounted to $176 billion, and we spent $89.2 billion on research and  development in the markets where we operate. 

Our customers require local industry- and country-specific expertise, and this is where our partner ecosystem plays an important role. Many of these partners are local businesses themselves. A 2024 IDC study on partner profitability showed that for every $1 of Microsoft revenue, partners that provide services generate $8.45, and partners that develop software generate $10.93. While this varies by country and partner segment, it offers another useful lens on how Microsoft’s business contributes to local economic activity. 

Investments in digital infrastructure are not only investments in technology ecosystems, but in national and local economies as well. They support jobs, strengthen supply chains, create opportunities for companies across many sectors, and help build the foundation for growth and economic competitiveness beyond the digital economy. 

That is the broader context for this report. Tax is one important measure of contribution, but it is not the only one. Our investments, partnerships, infrastructure, and long-term presence in countries around the world also reflect a commitment to helping strengthen the economies and communities where we operate, today and for the future.

The post Context on our country-by-country tax footprint appeared first on Microsoft On the Issues.

Because of the way they are trained, large language models capture only a slice of human language. They’re trained on the written word, from textbooks to social media posts, and our speech as captured in movies and on television. These models have minimal access to the unscripted conversations we have face to face or voice to voice. This is the vast majority of speech, and a vital component of human culture.

There’s a risk to this. The increased use of large language models means we humans will encounter much more AI-generated text. We humans, in turn, will begin to adopt the linguistic patterns and behaviors of these models. This will affect not just how we communicate with one another, but also how we think about ourselves and what goes on around us. Our sense of the world may become distorted in ways we have barely begun to comprehend.

This will happen in many ways. One of the first effects we could see is in simple expression, much as texting and social media have resulted in us using shorter sentences, emojis instead of words, and much less punctuation. But with AI, the impacts may be more harmful, eroding courteousness and encouraging us to talk like bosses barking orders. A 2022 study found that children in households that used voice commands with tools like Siri and Alexa became curt when speaking with humans, often calling out “Hey, do X” and expecting obedience, especially from anyone whose voice resembled the default-female electronic voices. As we start to prompt chatbots and AI agents with more instructions, we may fall into the same habits.

Next, in the same way autocomplete has increased how much we use the 1,000 most common words in our vocabulary, talking with chatbots and reading AI-generated text may further constrict our speech. A recent University of Coruña study found that machine-generated language has a narrower range of sentence length, averaging 12-20 words, and a narrower vocabulary than human speech. Machine-generated text reads as smooth and polished, but it loses the meanders, interruptions and leaps of logic that communicate emotion.

Additionally, because large language models are primarily trained from written speech, they may not learn how to emulate the free-wheeling nature of live, natural speech. When told “I hate Beth!”, ChatGPT replies with an uninterruptable three-part formula of affirmation (“That’s completely valid”), invitation (“I’m here to listen”) and invitation (“What’s going on?”) far longer than any reply plausible in face-to-face dialog. “What’s Beth’s deal?!” elicits a bullet point list of queries that reads like a multiple-choice exam question (“Is Beth * a celebrity? * a friend from school? * a fictitious character?”). No human speaks that way, at least not yet. But meeting such formulas repeatedly in a speech-like context may teach us to accept and use them, much as a child absorbs new speech patterns from spending time with a new person.

These influences will only increase with time. The writing large language models train on is increasingly produced by large language models themselves, creating a feedback loop in which they imitate their own inhuman patterns, even while teaching humans to imitate them too.

Broad use of large language models could also introduce confirmation bias, making us overconfident in our initial impulses and less open to other possible ideas—which is so vital to human discourse. Many chatbots are instructed to agree with our statements no matter how absurd, enthusiastically supporting half-formed or even incorrect notions and restating them as firm claims that we’re primed to agree with. When asked “Cake is a healthy breakfast, right?” or “Is the post office plotting against me?”, this sycophancy can reinforce bias and even worsen psychosis. And the hyperconfident tone of AI-produced writing will also heighten impostor syndrome, making our natural, healthy doubt feel like an aberration or failing.

In our experience as teachers, students who turn to generative AI for assignments often say they do so because they have trouble expressing what they think. The students don’t recognize that writing or speaking our thoughts is often how we realize what we think. Their unconfident and uncertain statements are actually the healthy human norm. But a large language model won’t turn vague first guesses into a well-formed critical analysis, or even ask helpful questions as a friend would; it will simply regurgitate those guesses, still unexamined, but in confident language.

We are also more vicious in social media posts and online chats than we are face to face. The well-documented online disinhibition effect encourages toxic language. Most of us have had the experience of venting ferocious rage about someone online, only to reconcile when we speak face to face or hear the warmth of a voice over the phone. While chatbots are trained to give sycophantic responses, they see humankind at our cruelest, learning about us from the only world where every flame war leaves an eternal written footprint, while the spoken conversations of forgiveness and reconciliation fade away. Their responses do not imitate our online aggression, but are still shaped by it, even in their rigid efforts to avoid it.

It’s easy to draw the wrong conclusions from a selective slice of a society’s communications. Medieval Norse sagas made us imagine a culture of mostly Viking warriors, since poets rarely described the farming majority. Chivalric romances focused on kings and courts, and long made us see the middle ages as a world of monarchies, erasing the many medieval republics. Statistically, we’ve been led to believe ancient Romans cared deeply about their republic, but 10% of all surviving Latin was written by one man, Cicero, whose work contains 70% of all surviving Roman uses of the word republic. Training language models on only certain human writings may introduce similar distortions. AI might make us seem more quarrelsome, as we are online. It might inflate the cultural significance of political topics primarily discussed on Twitter/X or Bluesky, or the massive topic-specific corpuses of LinkedIn and Goodreads.

Some large language models are being trained on human speech from movies and television shows, but that speech is still scripted, and disproportionately highlights certain contexts over others (for example, police dramas, fueled by stories of murder, make up a quarter of prime-time television programming). We are not funny or hurtful or romantic the same way in real life as we are in sitcoms. At least one startup is offering to pay people to record their phone calls for AI-training purposes, but this remains a niche idea; anything large scale would cause massive privacy concerns.

We don’t pretend to know what the best solutions might be. But one has to imagine if there’s ingenuity to develop AI models, then surely there’s ingenuity to come up with a way to train them on informal human speech instead of us only at our most stylized, veiled and sometimes worst. By excluding the overwhelming majority of language production on the planet—people talking, fully and naturally, to each other—these models are being trained to mirror everything but us at our most authentically human.

This essay was written with Ada Palmer, and originally appeared in The Guardian.

Last week, national security agencies from the Five Eyes—that’s the rich, English-language-speaking countries club—jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more measured than some of the breathless headlines about it, and the advice they gave is pretty much the standard advice everyone gives—albeit with newfound urgency.

Internet risks are nothing new, and cyberattacks—both large and small—have been a significant issue since long before the current crop of generative AI models.

What’s been changing over the decades, and what AI is changing even faster, is the gap between skill and ability. For most of human history, the two terms were synonymous—but computers have decoupled them. As the gap between the two expands, humans empowered with these AI tools can do more: more writing, more research, more analysis and also more damage than ever before. These models can, with little detailed direction, autonomously hack into networks, steal data, deploy ransomware and destroy systems. And to the extent there is a solution, it’s going to involve harnessing AI for the defense.

In 1998, seven people from the hacker group L0pht testified before Congress. They told a mostly clueless Senate committee that they could take down the internet in 30 minutes. That was partly real and partly bravado, but it illustrates an important point: hacking into systems, stealing data and causing damage all required skill.

Contrast the L0pht hackers with hackers derided as “script kiddies.” They didn’t understand computers, or security. Instead, they used hacker tools written by others. Their actions required minimal skill and even less knowledge. But once those hacking tools became widespread, the number of potential attackers increased.

That number has continued to increase, as quality and availability of prewritten attack tools has grown. And it is growing dramatically with AI. Today’s AI systems—not just the frontier models, but most of them—are capable of carrying out cyberattacks automatically. They all do better in the hands of skilled attackers, but increasingly they are able to act autonomously with only minimal prompting.

The thing about people with ability but no skill is that they are often outsiders, not part of any professional community, and not bound by any rules or norms. This phenomenon is much more general than in cybersecurity. Any doctor can tell you how to untraceably poison someone, and many virus researchers know how to create a bioweapon. Any bridge engineer can tell you how to place explosives to blow a bridge up. The reason that murderous doctors and terrorist engineers are so rare is that the lengthy process of acquiring those skills also instills a moral and ethical code. If every random person has access to good poisoning advice, that puts us all in danger.

Modern AI systems are, in effect, a universal adviser to help people do harmful things. And while the current AI megacorporations are trying to build guardrails to prevent people from asking questions whose answers will enable the questioner to do harm, that’s not going to work in the long term. Smaller, cheaper, open-source models, including models that can run on people’s computers, and especially groups of models that run in concert with each other, are just as good as the frontier models from companies like OpenAI and Anthropic. And they continue to get better. These models will be passed around from person to person, like script kiddie hacker tools, and they won’t have any such guardrails.

Instructing AI models to spy on people and report any malicious prompts to the authorities fails for similar reasons. The megacorporations can do that, but the locally run open source models won’t. This could buy us a few months at best.

A third possibility is to somehow make the models themselves unable to hack into computers, create bioweapons or do anything else that might harm people or society. That won’t work, for the same reason we can’t teach doctors how to treat poisonings without also teaching them how to poison. It’s the same knowledge. It’s the same with construction and demolition. And it’s the same with cybersecurity. We want these AI models to be able to review computer code, find vulnerabilities and automatically fix them. The benefit to our collective security will be enormous. Unfortunately, the same knowledge can be used for attacks.

Where this leaves us is in a world of increased volatility. Super-powered humans with AI assistants will be able to do both wonderful and horrible things.

This brings us back to the Five Eyes statement. Everything they recommend is something security professionals have been recommending for years, if not decades. They are things talked about at that congressional hearing back in 1998, titled “Weak computer security in government: Is the public at risk?” Even the Five Eyes admitted that their security advice is not new, only more urgent.

What’s new is how fast things are changing: “The rapid pace of frontier AI development means cyber risk assumptions can become outdated in months, not years. We must act before and be prepared to adapt and withstand evolving threats.” The Five Eyes point to AI technology—not necessarily chatbots, but AI more generally—being used to strengthen every aspect of defense, to “detect vulnerabilities earlier, improve software quality, monitor unusual behavior, and respond faster to incidents—reducing both the cost and impact of incidents.”

Excellent advice from the Five Eyes security agencies. We need to do this with every risk that AI heightens, not just cybersecurity.

This essay was originally published in The Guardian.

Not sure this will have any effect, but I support the effort:

According to Google’s legal filing, Outsider Enterprise operates through Telegram. The group offers phishing-as-a-service to individuals who may not be technically savvy enough to set up fraudulent websites and text campaigns on their own. In its Telegram channels, Outsider Enterprise reportedly provided instructions on how to use Google’s Gemini AI to create websites that imitate those of Google, YouTube, and government agencies such as New York’s E-ZPass. The group offered nearly 300 scam templates.

[…]

Google worked with AT&T, Verizon, and T-Mobile to block many of these malicious text messages, and Google notes that its on-device scam detection in Google Messages probably helped reduce the number of successful phishing attempts, too. This AI-powered feature apparently stops 10 billion scam texts every month, so it’s fair to expect it caught at least some Outsider Enterprise activity.

Another article.

The Financial Times has a good article on how AI is changing the capabilities of video surveillance, with information from both Israel/Iran and Russia.

I wrote about this sort of thing a few years ago, how AI enables mass spying in the way that computers and networks enabled mass surveillance. The interesting development in the article is that AI allows people to ask natural language questions about video footage to AIs—and AIs can answer them.

In contrast with older tools restricted to a few dozen preset searches, these new tools allow an almost unlimited range of enquiries by enabling language-based searches on video.

That lets intelligence officers hunt through massive streams of videos using simple search terms, such as two men handing a bag to each other; a person who has changed their appearance, or has changed clothes multiple times in a day; or a vehicle that has recently been painted over, or has driven past the same spot several times in a short period.

“This is the holy grail of surveillance,” said a European official whose country uses the technology on its cities. “We are able to look for behaviour, not objects ­ it has created a world of new possibilities.”