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Americans Feel the Pinch of High Electricity Costs Data Centres are Sucking People’s Ability to Pay to Heat or Cool their Home By Christina Catenacci, human writer Oct 17, 2025 Key Points: American residents are experiencing energy poverty, an inability to afford to keep their homes warm or cool Though the cost of electricity is based on several factors, a main driver of the spike in energy prices involves the energy required to power data centres due to the demand from AI Many states are pushing back (passing laws or reaching settlements with large tech companies) in order to keep the prices fair for residents According to CBS News , the cost of electricity has increased from $0.14 per kilowatt hour in 2019 to $0.18 per kilowatt hour in 2024—this represents a change of more than 28.5 percent. The result: the average American is now paying nearly $300 a month just in utilities. This phenomenon is referred to as energy poverty. Why is this happening? To be sure, the cost of electricity is based on several factors, including the volatile prices for natural gas, wildfire risk, electricity transmission and distribution, regulations and inflation. That being said, there is also the heat—rising temperatures fuel extreme weather events, such as heat waves in the summer and snowstorms in the winter, which then increases energy consumption as people try to keep their homes warm or cool. Climate change has only exacerbated the frequency and intensity of these extreme weather events. But there are also data centres . In fact, data centers are projected to consume up to 12 percent of American electricity within the next three years. How is this happening? Simply put, the expansion of power-hungry data centers that is required to support the surge in AI usage is a main factor in the rising costs that Americans are experiencing. As a consequence, American states are feeling the pressure to act . However, it us unclear that any state has a solution to the issue of data centers wreaking havoc on people’s electricity bills. Many have noted that answering this question may require taking a hard line against large tech companies that are rapidly investing in and using a large number of data centres. To be clear, we are talking about data centres that may require more electricity than entire cities—large factories would pale in comparison. Ari Peskoe, who directs the Electricity Law Initiative at Harvard University, states: “A lot of this infrastructure, billions of dollars of it, is being built just for a few customers and a few facilities and these happen to be the wealthiest companies in the world. I think some of the fundamental assumptions behind all this just kind of breaks down” In fact, Peskoe suggests that there could be a can of worms that pits ratepayer classes against each other. Moreover, Tricia Pridemore, who sits on Georgia’s Public Service Commission and is president of the National Association of Regulatory Utility Commissioners, noted that there is already a tightened electricity supply and increasing costs for power lines, utility poles, transformers and generators as utilities replace aging equipment or harden it against extreme weather. Pridemore mentioned that the data centers that are required to deal with the AI boom are still in the regulatory planning stages. But it is important to keep in mind that unless utilities negotiate higher specialized rates, other ratepayer classes (residential, commercial and industrial) are likely paying for data center power needs. For now, there is recent research conducted by Monitoring Analytics, the independent market watchdog for the mid-Atlantic grid, showing that 70 percent, or $9.3 billion, of last year’s increased electricity cost was the result of data center demand. How are states responding? In short, five governors led by Pennsylvania’s Josh Shapiro began pushing back against power prices set by the mid-Atlantic grid operator, PJM Interconnection, after the amount spiked nearly sevenfold. In response, PJM Interconnection has not yet proposed ways that would guarantee that data centres pay their fair share. On the other hand, Monitoring Analytics is floating the idea that data centers should be required to procure their own power. The company likened the residents’ payment for electricity as a “massive wealth transfer” from average people to tech companies. In addition, at least 12 states are considering ways to make data centers pay higher local transmission costs. For example, in Oregon, a law was passed in June that orders state utility regulators to develop new power rates for data centers. The Oregon Citizens’ Utility Board has said that there is clear evidence that costs going to serve data centers are being spread across all customers at a time when some electric bills are up 50 percent over the past four years. By way of another example, New Jersey’s governor signed legislation last month commissioning state utility regulators to study whether ratepayers are being hit with “unreasonable rate increases” in order to connect data centers and to develop a specialized rate to charge data centers. Some states are trying to reach settlements. For example, in Indiana, state utility regulators approved a settlement between Indiana Michigan Power Co., Amazon, Google, Microsoft and consumer advocates that set parameters for data center payments for service. In Pennsylvania, the state utility commission is drafting a model rate structure for utilities to consider adopting. While it is important for utilities and states to attract big customers like data centres, it is necessary to appreciate what is fair; transmission upgrades and other similar initiatives could costs millions of dollars, and it is not fair to put it all on residents. Large AI companies will need to take the above discussion into consideration when making plans to expand and adding power-hungry data centres. They may anticipate being approached by various states in order to create fair settlements so that the cost of energy is not transferred entirely to residents. Previous Next

California Bill on AI Companion Chatbots A New Law Emerges due to Concerns About the Impacts on Mental Health and Real-World Relationships By Christina Catenacci, human writer Oct 31, 2025 Key Points On October 13, 2025, California SB 243, Companion chatbots, was signed into law by Governor Newsom SB 243 addresses concerns about teen suicide and other impacts on mental health and real-world relationships since people have used companion chatbots as romantic partners California is the first state to enact this law—this law is a welcome development On October 13, 2025, California SB 243 , Companion chatbots, was signed into law by Governor Newsom. As can be seen in the recent Bill Analyses on the Senate Floor , AI companion chatbots that are created through genAI have become increasingly prevalent since they seek to offer consumers the benefits of convenience and personalized interaction. These chatbots learn intimate details and preferences of users based on their interactions and user customization. Millions of consumers use these chatbots as friends, mentors, and even romantic partners. However, there are serious concerns about their effects on users, including impacts on mental health and real-world relationships. In fact, many studies and reports point to the addictive nature of these chatbots and call for more research into their effects and for meaningful guardrails. Unfortunately, incidents resulting in users harming themselves and even committing suicide have been reported in the last year. To that end, SB 243 addresses these concerns by requiring operators of companion chatbot platforms to maintain certain protocols aimed at preventing some of the worst outcomes. What Does the New Law Say? The law defines a “companion chatbot” as an AI system with a natural language interface that provides adaptive, human-like responses to user inputs and is capable of meeting a user’s social needs, including by exhibiting anthropomorphic features and being able to sustain a relationship across multiple interactions. However, it does not include any of the following: A bot that is used only for customer service, a business’ operational purposes, productivity and analysis related to source information, internal research, or technical assistance A bot that is a feature of a video game and is limited to replies related to the video game that cannot discuss topics related to mental health, self-harm, sexually explicit conduct, or maintain a dialogue on other topics unrelated to the video game A stand-alone consumer electronic device that functions as a speaker and voice command interface, acts as a voice-activated virtual assistant, and does not sustain a relationship across multiple interactions or generate outputs that are likely to elicit emotional responses in the user The law also defines an “operator” as a person who makes a companion chatbot platform available to a user in the state. A “companion chatbot platform” is a platform that allows a user to engage with companion chatbots. Beginning on July 1, 2027, requires the following: If a reasonable person interacting with a companion chatbot would be misled to believe that the person is interacting with a human, the operator must issue a clear and conspicuous notification indicating that the companion chatbot is artificially generated and not human Operators must prevent a companion chatbot on its companion chatbot platform from engaging with users unless they maintain a protocol for preventing the production of suicidal ideation, suicide, or self-harm content to the user, including, but not limited to, by providing a notification to the user that refers the user to crisis service providers, including a suicide hotline or crisis text line, if the user expresses suicidal ideation, suicide, or self-harm. Operators must publish the details of this protocol on the operator’s internet website For a user that the operator knows is a minor, operators must do all of the following: (1) Disclose to the user that the user is interacting with AI; (2) Provide by default a clear and conspicuous notification to the user at least every three hours for continuing companion chatbot interactions that reminds the user to take a break and that the companion chatbot is artificially generated and not human; and (3) Institute reasonable measures to prevent its companion chatbot from producing visual material of sexually explicit conduct or directly stating that the minor should engage in sexually explicit conduct Operators must annually report to the Office of Suicide Prevention all of the following: (1) The number of times the operator has issued a crisis service provider referral notification in the preceding calendar year; (2) Protocols put in place to detect, remove, and respond to instances of suicidal ideation by users; and (3) Protocols put in place to prohibit a companion chatbot response about suicidal ideation or actions with the user. This report must not include any identifiers or personal information about users Operators must disclose to a user of its companion chatbot platform, on the application, the browser, or any other format that a user can use to access the companion chatbot platform, that companion chatbots may not be suitable for some minors A person who suffers injury as a result of a violation of this law may bring a civil action to recover all of the following relief: injunctive relief damages in an amount equal to the greater of actual damages or $1,000 per violation reasonable attorney’s fees and costs What Can We Take from This Development? This landmark bill is the first law in the United States to regulate AI companions. Given that teenagers have committed suicide following questionable conversations with these AI companion chatbots, the new transparency requirements are a welcome development. Previous Next

Whose Ethics Matter Most and Why Ethics is a declaration of whose voices, opinions, and values matter By Tommy Cooke Sep 19, 2024 Key Points: Engage with voices and ideas outside your organization as a litmus test for your ethical priorities Treat AI ethics frameworks as living documents that are organic and change over time Strive to think globally - not locally When we talk about ethics in AI, it’s easy to overlook their underlying complexity. Many organizations treat ethics as a straightforward process: identify some standards, create some policies, and follow compliance. But this approach overlooks a key reality. This reality is that ethics are subjective . Ethics reflect values, and values differ in context, culture, and stakeholder expectations. This subjectivity can be particularly challenging for organizations operating in diverse industries and global markets. In AI development and AI use, writing ethics is a declaration . It’s a statement of your organization's beliefs about right and wrong, good and bad. But it’s also a reflection of the values you’re embedding into your AI systems, and the choices you make along the way are critical. To make things a bit more complicated, one key question often goes unanswered: Whose ethics are we prioritizing? In an increasingly interconnected world, it’s vital to consider whose perspectives are included - and whose might be missed. Ethics is a Living Framework At its core, ethics are codified morals. They are an attempt to translate abstract ideas and values into solid standards for behaviour. However, in AI, where the implications of decisions are far-reaching, the ethics landscape is complex and shifting. For example, in Europe, the General Data Protection Regulation (GDPR) provides a comprehensive ethical framework for data use. It is largely individual-centric , focused on protecting the privacy and rights of individuals, requiring transparency and consent for how data is collected and used. In contrast, the U.S. takes a more business-centric approach to AI ethics. There is less comprehensive regulation, and ethical standards tend to focus on enabling innovation while mitigating harm through self-regulation and sector-specific guidelines . For companies operating in both the US and the EU, this difference creates a challenge: ethical beliefs around privacy, autonomy, and transparency can be misaligned depending on where you are, resulting in inconsistencies in AI governance. The Subjectivity of Ethics The subjective nature of ethics means that what is considered ethical in one context might not be viewed the same way elsewhere. For instance, the concept of fairness is interpreted differently across cultural boundaries . In Western countries , fairness in AI often focuses on preventing discrimination based on race, gender, or disability. In contrast, in China or other parts of East Asia, fairness might emphasize collective welfare and societal harmony , even if it means sacrificing some degree of individual privacy. This raises critical questions for organizations: when you create an ethical framework, whose values are you representing? Which ones do you prioritize, and why? And, just as importantly, whose values are being left out? As businesses develop AI systems that impact people across borders, the need for a more inclusive and adaptable ethical framework becomes apparent. Without it, companies risk ethical blind spots that can lead to reputational damage, loss of trust, and, in extreme cases, legal action. Questions for Organizations So, how can organizations navigate this complex ethical landscape? Here are three essential questions to ask: Whose voices are included in your ethics frameworks? Consider the diversity of stakeholders impacted by your AI systems, including employees, customers, communities, and global regulators. Do your ethical standards reflect this diversity, or are they shaped by the dominant voices within your organization? It is becoming increasingly clear around the globe that inclusive frameworks tend to be more robust and resilient because they account for a wider range of perspectives. How often do you revisit your ethical guidelines? Ethics cannot be static. As technology evolves and societal expectations shift, so too must your ethical frameworks. For example, the rise of generative AI and large language models has created new ethical dilemmas around intellectual property, misinformation, and AI autonomy. Organizations should regularly assess whether their ethical guidelines remain relevant and effective. Are you balancing internal and external values? Often, companies prioritize their internal values—whether it’s innovation, efficiency, or profitability—over external stakeholder concerns. But ethics are about building trust , and to do so, organizations need to align their values with those of the communities they serve. Practical Advice for Ethical AI Governance Building an adaptable, inclusive ethical framework doesn’t have to be overwhelming. Here are three practical tips for organizations looking to strengthen their AI ethics: Engage with external voices. Ethics should be informed by a diversity of perspectives, both internal and external. Regularly engage with stakeholders—including regulators, customers, and community leaders—to ensure that your ethical frameworks are inclusive and reflective of broader societal values. Make ethics a living document. Ethical standards should be dynamic, not static. Establish a regular process for reviewing and updating your ethics policies to reflect the latest technological developments, regulatory changes, and societal shifts. Think globally, act locally. Your ethics should be adaptable to different cultural and legal contexts. Strive for a balance between global standards and local values to ensure your AI systems are both responsible and contextually appropriate. Ethics as a Strategy In the end, ethics in AI is not just about doing what’s right. It’s about being strategic. In a world where AI systems can influence lives across continents, ethical governance is about building trust, ensuring accountability, and safeguarding the future of your organization. By asking the right questions and adopting a flexible, evolving approach, companies can develop ethical frameworks that are not only reflective of their values but adaptable to a rapidly changing world. Previous Next

Budget 2025 Canada’s Plans for AI By Christina Catenacci, human writer Nov 7, 2025 Key Points On November 4, 2025, the Government of Canada released Budget 2025: Canada Strong The federal government made several proposals to invest in AI and quantum computing One of the first things that Canadians will likely see is fresh feedback on how the consultations went, along with an update on the status of the development of the AI Strategy On November 4, 2025, the Government of Canada announced the release of Budget 2025: Canada Strong. Generally speaking, the federal government plans to transform Canada’s economy from one that is reliant on a single trade partner, to one that is stronger, more self-sufficient, and more resilient to global shocks. Essentially, Canada has just delivered an investment budget: the goal is to spend less on government operations and invest more in the workers, businesses, and nation-building infrastructure that will grow the economy. More specifically, the budget includes a total of $60 billion in savings and revenues over five years, and makes generational investments in housing, infrastructure, defence, productivity, and competitiveness. These strategic investments will enable $1 trillion in total investments over the next five years through smarter public spending and stronger capital investment. Budget 2025 rests on two fiscal anchors: Balancing day-to-day operating spending with revenues by 2028–29, shifting spending toward investments that grow the economy Maintaining a declining deficit-to-GDP ratio to ensure disciplined fiscal management for future generations Indeed, Budget 2025 was passionately delivered by The Honourable François-Philippe Champagne, Minister of Finance and National Revenue. He noted that these are difficult times, but we need to rest assured that the government will not back down, will be there for Canadians now and for as long as it takes, and will do what Canadians do best in times of need—we look after each other and help each other. He stated, “That’s the Canadian way, our way!” That said, he acknowledged that meeting this challenge requires both ambition and discipline. To mark the day, Champagne even made some shoes for the occasion: they were made by Canadians for Canadians to hammer home the point that we need to be our own best customers. We cannot forget the ending of the speech: “Long live Canada!” The entire budget is a lengthy document; this article deals with what the budget has articulated with respect to Canada’s plans for AI. What Does Budget 2025 Say about AI? Canada wants to seize the full potential of AI. The purpose is to create opportunities for millions of Canadians, businesses, and the economy. Budget 2025 will facilitate the creation of AI compute infrastructure, including the development of a Sovereign Canadian Cloud. Ultimately, AI will help to create new jobs and economic growth. It is not only about AI: the government plans to allocate funds to foster innovation in both AI and quantum technologies. More precisely, Budget 2025 aims to: Provide $925.6 million over five years, starting in 2025-26 : this is, to support a large-scale sovereign public AI infrastructure that will boost AI compute availability and support access to sovereign AI compute capacity for public and private research. The investment will ensure that Canada has the capacity needed to be globally competitive in a secure and sovereign environment. Of this amount, $800 million will be sourced from funds that were previously provisioned in the fiscal framework. This means that $800 million of the $925.6-million investment will come from funds that were set aside by the last federal budget, which announced a total of $2 billion to boost domestic AI compute capacity and build public supercomputing infrastructure Enable the Minister of Artificial Intelligence and Digital Innovation, Evan Solomon, to engage with industry to identify new promising AI infrastructure projects and enter into Memoranda of Understanding with those projects. Along the same lines, the government intends to enable the Canada Infrastructure Bank to invest in AI infrastructure projects Allocate $25 million over six years, starting in 2025-26, and $4.5 million ongoing for Statistics Canada to implement the Artificial Intelligence and Technology Measurement Program (TechStat). TechStat will use data and insights to measure how AI is used by organizations, and understand its impact on Canadian society, the labour force, and the economy Explore options for the National Research Council of Canada’s Canadian Photonics Fabrication Centre to best position it to attract private capital, scale its operations, and serve as a platform for Canadian innovation and new photonic applications, including in the face of the rise of AI and related compute infrastructure Provide, through the Defence Industrial Strategy, $334.3 million over five years to strengthen Canada’s quantum ecosystem. It is important to note that computing problems that are currently considered to be intractable even with the most powerful classical computers could be solved using quantum computers Enable Canada to unlock significant economic benefits through commercialising the associated intellectual property (IP) and being among the first to put it to use. For example, when it comes to IP, the budge plans on providing $84.4 million over four years, starting in 2026-27, to Innovation, Science and Economic Development Canada to extend the Elevate IP program, as well as $22.5 million over three years, starting in 2026-27, to renew support for the Innovation Asset Collective’s Patent Collective Establish a new Office of Digital Transformation that will lead the adoption of AI and other new technologies across government. On top of that, there will be near-term procurement of made-in-Canada sovereign AI tools for the public service, which will lead to a more efficient government Enable the Shared Services Canada (SSC) and the Department of National Defence and the Communications Security Establishment to will develop a made-in-Canada AI tool which will be deployed across the federal government. The goal is to facilitate the partnership between the SSC and leading Canadian AI companies to develop the internal tool As I wrote about here , the government announced in September, 2025 the launch of an AI Strategy Task Force and a “30-day national sprint” (consultations) that will help shape Canada’s approach to AI. The government is set to develop a new AI strategy by the end of 2025. It will also consider whether new AI incentives and supports should be provided. Already, the government has decided to work with Cohere to use AI to improve the public service . In fact, the parties signed an agreement to set up an early-stage collaboration so that Cohere can identify areas where AI can enhance public service operations. What Can We Take from Budget 2025? As Champagne has highlighted, Canada is strong and has a lot going for it. AI and quantum computing are part of this. In the context of this investment budget, we see that the government has allocated significant resources to improve Canada’s AI and quantum computing posture. One of the first things that Canadians will likely see is fresh feedback on how the consultations went, along with an update on the status of the development of the AI Strategy. We can only wait and see if the above proposals will come to fruition. Previous Next

Why the AI Chip Controversy Matters How Semiconductor Tensions Shape AI Strategy By Tommy Cooke, fueled by light roast coffee May 23, 2025 Key Points: AI strategy now depends as much on chip supply and trade stability as on internal capability Semiconductor restrictions are fragmenting the global AI landscape, creating risks and perhaps some opportunities for business leaders as well Business leaders must proactively monitor supply chains, policy shifts, and emerging markets to future-proof their AI investments The semiconductor tensions between the U.S. and China aren’t just about geopolitics. They reveal a deeper truth about the future of artificial intelligence. A semiconductor is a material (usually silicon) that conducts electricity under some conditions—and not others. This characteristic makes them ideal for controlling electrical signals. It is also why they are used as the foundation for microchips, which of course power everything from smartphones to cars and AI systems. In the case of AI, microchips are used in the processors used to handle the massive calculations that AI requires. You’ve probably heard of them: graphics processing units (GPUs) and tensor processing units (TPUs). Back to the controversy at issue: at its core, the controversy isn’t about semiconductors and microchips. It’s about who controls the speed, shape, and scale of AI innovation globally. For business leaders exploring AI adoption, understanding these supply-side dynamics is crucial. AI systems are only as powerful as the chips that run them, and those chips are subject to competition, trade restrictions, and access limitations. That means that today’s decisions around AI aren’t just about what tools to use. They’re also about where those tools come from, how stable the supply pipeline is, and whether your organization is prepared for the long-term implications of this shifting terrain. Simply put, if you are investing in AI now, the controversy may impact your ROI calculations. Understanding the Core of the Controversy At the heart of the issue lies the U.S. government's implementation of strict export controls on advanced AI chips. The intention is to limit China's access to cutting-edge semiconductor technology. These measures, including the recently rescinded AI Diffusion Rule, sought to categorize countries and restrict chip exports accordingly. Industry leaders, like Nvidia's CEO Jensen Huang, have criticized these policies as counterproductive. He argues that they not only diminish U.S. companies' market share, but they also inadvertently accelerate domestic innovation within China. Implications for the AI Landscape While the chip export restrictions may seem like merely a trade issue, they are already reshaping how and where AI systems are being built and deployed. These changes have ripple effects across industries, from vendor availability and cost structures to innovation cycles and long-term planning. Here are some of the most prevalent implications on the horizon: Acceleration of Domestic Alternatives. The restrictions have spurred Chinese companies to invest heavily in developing local semiconductor technologies. This means that China is investing in a capacity for self-reliance, which could lead to the emergence of competitive alternatives to U.S. and European products. Market Share and Revenue Impact. U.S. companies like Nvidia have experienced significant reductions in their Chinese market share, dropping from 95 percent to 50 percent over four years . These declines not only affect revenues, but they also influence global competitiveness and innovation leadership. On this point alone, we ought to pay close attention to Nvidia’s future ability to supply GPUs required for supporting U.S.-driven AI innovation. Global AI Development Dynamics. Building from the previous point, the export controls may inadvertently fragment the global AI development landscape. This may, in turn, lead to parallel ecosystems with differing standards and technologies. This is what is referred to as a bifurcation: the division of something into two or more branches or parts, like a river that splits into two because of elevated terrain. A marketplace bifurcation may eventually encourage further self-reliance and innovation, but it will almost certainly complicate international collaboration and AI system interoperability at the same time. Partnerships and trust are at threat, to say the least. Strategic Considerations for Business Leaders in the Wake of the AI Chip Controversy This controversy is a warning sign. It reveals how AI adoption is no longer just about internal capability or budget. It’s also about navigating a volatile global landscape. Business leaders must now consider not only what AI tools can do, but also where those tools originate, whether future access will be reliable, and how international policy may affect ongoing AI strategies. As the supply side of AI becomes more political, leaders must become more strategic. Here are some tips that you should consider when internally canvasing the right fit, especially as a reflection of your ROI priorities: Assess Supply Chains and Diversify. Assess and diversify your supply chains. It’s important to mitigate risks associated with geopolitical tensions and export restrictions. Who is selling? Where are they sourcing their solutions from? Where are your vendors’ data farms? Ask these questions now to avoid issues later. Invest in R&D. To maintain a competitive edge, invest in research and development. Start now because it will become important later, particularly in areas less susceptible to export controls. The idea is to, at the very least, begin exposing yourself to an R&D process so that you can learn more about strategic AI-related investments downstream (no pun intended). Monitor, Monitor, Monitor. The everchanging regulatory landscape matters a lot here. Stay informed about evolving export regulations and international trade policies. It is essential for strategic planning, let alone compliance. Explore New Markets. With certain markets becoming less accessible due to restrictions, identifying and cultivating alternative markets can help offset potential losses. Who are the emerging suppliers around the globe? Where are AI innovations specific to your industry and use cases growing? Expand your horizon. The AI chip export controversy is as a reminder of the intricate balance between national priorities and global technological development. For business leaders, navigating this landscape requires awareness, agility, and informed decision-making. This is what a proactive approach looks like. Remember, AI adoption doesn’t happen in a vacuum. The semiconductor debate makes it clear that the tools we choose, and the ecosystems we rely on, matter more than ever. Previous Next

As AI continues to reshape industries, understanding its organizational, legal, social, and ethical impacts is essential for successfully running an organization. Our collection of articles offers both depth and breadth on critical AI topics, from legal compliance to ethical deployment, providing you with the knowledge necessary to integrate AI successfully and responsibly into your operations. With 85% of CEOs affirming that AI will significantly change the way they do business in the next five years, the urgency to adopt AI ethically and fairly cannot be overstated. Dive into our resources to ensure your growth with AI is both innovative and just, positioning your organization as a leader in the conscientious application of advanced technology. Insights Articles to increase awareness and understanding of AI adoption and integration Canada’s Innovation Crossroads Jan 16, 2026 New York Governor Hochul Signs AI Safety and Transparency Bill into Law Jan 23, 2026 Privacy Commissioner Investigation into Social Media Platform, X Jan 23, 2026 Trump Signs Executive Order on AI Dec 15, 2025 Legal Tech Woes Dec 5, 2025 Meta Wins the Antitrust Case Against It Nov 27, 2025 Cohere Loses Motion to Dismiss Nov 21, 2025 What is “AI Augmentation”, and How Do You Achieve It? Nov 14, 2025 Budget 2025 Nov 7, 2025 When Technology Stops Amplifying Artists and Starts Replacing Them California Bill on AI Companion Chatbots Oct 31, 2025 Reddit Sues Data Scrapers and AI Companies Oct 24, 2025 Data Governance & Why Business Leaders Can’t Ignore It Oct 13, 2025 Canada’s AI Brain Drain Oct 17, 2025 Americans Feel the Pinch of High Electricity Costs Oct 17, 2025 Newsom Signs Bill S53 Into Law Oct 10, 2025 The Government of Canada launches an AI Strategy Task Force and Public Engagement Oct 3, 2025 Privacy Commissioner of Canada (OPC) Releases Findings of Joint Investigation into TikTok Sep 26, 2025 How an Infrastructure Race is Defining AI’s Future Sep 26, 2025 Google Decision on Remedies for Unlawful Monopolization Sep 19, 2025 1 2 3 4 5 1 ... 1 2 3 4 5 ... 5

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Cohere Loses Motion to Dismiss Copyright and Trademark Infringement Lawsuit Must Proceed By Christina Catenacci, human writer Nov 21, 2025 Key Points A large number of news publishers (Publishers) previously sued AI company Cohere for copyright and trademark infringement Cohere just brought a partial motion to dismiss the lawsuit, and it lost—on November 13, 2025, McMahon, J of the United States District Court Southern District of New York denied Cohere’s partial motion to dismiss the lawsuit This dispute is one of more than 50 lawsuits that are currently before the courts challenging the use of copyrighted works by AI companies to train their large language models—each case depends on the circumstances, and we will have to wait and see Back in March, 2025, I wrote about how several news publishers (Publishers) sued AI company Cohere for copyright and trademark infringement. As a refresher, the Publishers alleged that Cohere, without permission or compensation, used scraped copies of their articles, through training, real-time use, and in outputs, to power its AI service, which in turn competed with Publisher offerings and the emerging market for AI licensing. The Publishers accused Cohere of stealing their works to the point where actual verbatim copies were produced in outputs, and of blatantly manufacturing fake pieces and attributing them to the Publishers, which misled the public and tarnished their brands. What’s more, when RAG was turned off, the AI model, Command, hallucinated answers. Ultimately, the Publishers claimed that Cohere’s actions amounted to “massive, systematic copyright infringement and trademark infringement, and have caused significant injury to Publishers”. Well now, a new development has emerged—Cohere brought a partial motion to dismiss the lawsuit, and it lost. That is, on November 13, 2025, McMahon, J of the United States District Court Southern District of New York denied Cohere’s partial motion to dismiss Counts II, III, and IV of the Publishers' complaint. Why Did the Court Deny Cohere’s Motion? In this motion, the judge found the following: Cohere's Motion to Dismiss the Publishers' Direct Copyright Infringement Claim was Denied . Cohere wanted to dismiss the Publishers' claim for direct copyright infringement only to the extent it alleged that Cohere was directly liable for generating "substitutive summaries" of the Publishers' work. In a nutshell, Cohere argued that though the Publishers could show they owned valid copyrights, Command’s summaries were not substantially similar to the Publishers’ works. The court disagreed with Cohere and concluded that the Publishers adequately alleged that Command's outputs were quantitatively and qualitatively similar—they argued that Command's output heavily paraphrased and copied phrases verbatim from the source article, and that these summaries went well beyond a limited recitation of facts. Also, the Publishers provided 75 examples of Cohere's alleged copyright infringement (50 allegedly included verbatim copies and a further 25 examples had a mix of verbatim copying and close paraphrasing) Cohere's Motion to Dismiss the Publishers' Secondary Copyright Infringement Claim was Denied . The Publishers claimed that Cohere was secondarily liable for unlawfully reproducing, displaying, distributing, and preparing derivatives of the Publishers' copyrighted works under each of three theories: contributory infringement by material contribution, contributory infringement by inducement, and vicarious infringement. Cohere agued that all three theories failed, but the court held that each of Cohere’s arguments were without merit. In particular, the Publishers adequately alleged underlying direct infringement; the Publishers adequately alleged Cohere’s knowledge of direct infringement; and the Publishers adequately alleged inducement Cohere's Motion to Dismiss the Publishers' Lanham Act (trademark) Claims was Denied . Cohere argued that the Publishers failed to allege use of their marks in commerce and a plausible likelihood of consumer confusion and that Cohere's use of the marks was lawful as nominative fair use. However, the court disagreed with Cohere because the Publishers adequately alleged Cohere’s use in commerce; the Publishers adequately alleged a likelihood of confusion; and the nominative fair use doctrine did not apply on the facts of this case—"All I can and will do is conclude that the complaint adequately alleges facts that could, if proved, cause a trier of fact to reject application of that doctrine” Needless to say, the court was simply unconvinced by Cohere’s arguments and shot them all down. Since Cohere was unsuccessful, it will have to prepare for a trial. What Does This Mean for the Case? This is not good for Cohere. At this point in the case, it is striking that the Publishers put Cohere on notice that it was not allowed to do what it was doing—the Publishers had copyright notices and terms of service on their websites, and they also sent do-not-crawl instructions to Cohere’s bots using robots.txt protocols. In fact, the Publishers also claimed that Cohere kept unlawfully copying their works even after they sent a cease-and-desist letter. From the perspective of the Publishers, this motion went well; if this is an indication of what is to come later down the line, they will be content. What Does This Mean for the Other AI Infringement Cases? The court noted that this dispute is one of more than 50 lawsuits that are currently before the courts challenging the use of copyrighted works by AI companies to train their large language models. Some may view the decision as a foreshadowing of what could transpire in some of the other cases, but it is important to note that this was just one motion in one case; the decisions of those other cases will depend on the circumstances of those cases. We can only wait and see. Previous Next

What Apple’s $500 Billion AI Investment Means to You The Continued Normalization of AI as Core Infrastructure By Tommy Cooke, powered by caffeine and curiousity Mar 3, 2025 Key Points: Apple's $500 billion AI investment signals that AI is shifting from an innovation tool to core business infrastructure Increased AI integration in Apple’s ecosystem will shape consumer, employee, and investor expectations, pushing businesses to adapt Businesses should focus on preparing for AI-driven shifts in consumer expectations, workforce dynamics, and regulatory landscapes Apple recently announced a $500 billion investment in AI. The moment is not merely a landmark in the technology work. It is also monumental for U.S. manufacturing, U.S. talent development, and the U.S.’s foothold in the global AI economy. This news is not merely a corporate push for technology. It’s a sign that AI is becoming intimately embedded in business infrastructure; quickly fading are the days of thinking of AI as merely an emerging, experimental tool. With AI-capable smartphones forecasting to grow significantly over the next three years at a compounded annual growth rate of nearly 63 percent , coupled with the fact that Apple accounts for more than half of the smartphone device market share in the U.S., what business leaders need to recognize that their employees, investors, partners, and customers alike – the Apple device lovers in your professional and personal networks – will be interfacing with AI at unprecedented rates in the few short years to come. Whether your organization is adopting AI or not, here’s why Apple’s announcement matters to you. AI as Infrastructure, Not Merely Innovation For years, AI has been treated as an innovation driver or a business enabler, something that enhances products, streamlines workflows, or creates new capabilities. But with Apple’s recent announcement, a deeper reality is setting in: AI is increasingly recognized as an operational necessity. Apple’s announcement, which includes an AI server manufacturing facility in Texas and 20,000 new research and development jobs along with a new AI academy in Michigan, signals a broader shift—AI is no longer niche, it is foundation. This reclassification matters. Apple’s investment will push AI further into the mainstream. It is altering expectations for AI-readiness across multiple industries. Additionally, and as Apple continues to integrate AI more deeply into its own ecosystem, more consumers, employees, partners, and investors will be regularly exposed to AI-driven interactions and functionalities. This broad exposure means that businesses need to be prepared for shifting human expectations of AI. AI Normalization and Business Implications As AI becomes more infrastructural, normalization will follow. What is important to recognize here is that this level of financial investment will create jobs, accelerate workforce transformation, and even generate a new AI training and research facility—this is about much, much more than declaring AI is crucial to the company’s internal operations. It will also significantly affect their external ecosystem in sending a very clear message about the value of AI. Here are three reasons why Apple’s investment matters to you: AI is Becoming More Accessible. As AI infrastructure expands, smaller enterprises will have increased access to AI capabilities. This means even organizations without extensive tech teams must begin discussing AI integration and management. Consumers and Employees Expect AI. With AI becoming more embedded in Apple’s ecosystem (through Siri advancements, AI-enhanced applications, and automated workflows) customer and employee expectations around AI-driven interactions will evolve as well. Businesses must anticipate and meet these new expectations. Remember, whether your leadership believes in AI or not, the people working with you and for you have ideas, dreams, and visions of AI making their jobs easier. AI will be an integral, core component of Apple devices moving forward. Accordingly, expectations will change. Policy and Regulation Will Evolve. Large-scale AI investment has a high likelihood to accelerate regulation. As AI becomes a fundamental part of economic infrastructure, governments will refine legal frameworks around AI use, data privacy, and corporate accountability. While regulatory change is rather cumbersome in North America, it will be important to keep an eye on global regulators and civil society discourse as there will be adjustments in the tone, frame, and focus of AI law and AI ethics concepts. The Takeaway: A Wake-Up Call for Businesses Regardless of whether your organization is navigating AI, it is important to start thinking about the relationship between you, your people, and their increasingly AI-driven Apple devices. Businesses are recommended to invest in AI literacy, establish decision-making plans, and if they are on the cusp of integrating AI, lead the charge on the conversation. In this way, businesses will be more equipped to respond to the fact that people outside and inside their organizations are comparing their agility, creativity, and flexibility to new standards driven by AI models. Previous Next

Closing the Gap: from Policies and Procedures to Practice Overcoming the policy/procedure-practice paradox requires focus and commitment By Tommy Cooke Sep 24, 2024 Key Points: Having AI policies doesn't automatically ensure ethical AI practices Regular audits and cross-functional teams are crucial for aligning AI with ethical standards Explainability and stakeholder engagement are key to responsible AI implementation Closing the Gap: from Policies and Procedures to Practice Organizations pride themselves on having comprehensive AI policies and procedures. They show care, diligence, and signal to your staff and stakeholders that you are take AI use and employee behaviour seriously as part of your business plan. However, AI policies and procedures don’t guarantee ethical AI. Even when multiple policies reference AI, there's often a gap between policy and procedures on the one hand, and practice on the other. This gap is a problem because it can catalyze unintended consequences and ethical breaches that undermine the very principles they otherwise uphold. The Policy/Procedure-Practice Paradox This problem is a paradox that is common in virtually every industry using AI. By paradox we mean a contradictory statement that, when investigated and explained, proves to be true. For example, say aloud to yourself, “the beginning is the end”. It sounds absurd, but when you think it through, it makes sense. This same phenomenon presents itself when thinking about “policies and procedures in practice”. Policies and procedures are documents, so how exactly are they practiced? The initial thought that a document practices anything is absurd. But when we read them, they guide how people ought to use and not use AI. The policy/procedure-practice paradox is a problem because failing to understand it means failing to address it. And in failing to address it, policies and procedures about AI often lead to broken and misinformed practices. Let’s consider a real-world example: Despite a company having an anti-bias policy in place, a facial recognition system used in stores across the United States for over eight years exhibited significant bias . The system struggled to accurately identify people with darker skin tones, leading to higher error rates for certain demographics. This occurred because the AI was trained on datasets disproportionately representing lighter skin tones. And so, even well-intentioned policies can fail in practice. The example above is not isolated. It’s a symptom of a larger issue in AI implementation. While the example I provided was caused by biased data, there are several other reasons why the policy/procedure paradox exists: Lack of Explainability: many AI systems operate as " black boxes ," making it difficult to understand their decision-making processes, even with transparency policies in place Rigid Rule Adherence: AI systems may strictly follow their programmed rules without understanding the nuanced ethical priorities of an organization Complexity of Ethical Standards: Translating abstract ethical concepts into concrete, programmable instructions is a complex task that often leaves room for interpretation and error Closing the Gap To mitigate the paradox, we need to close the gap that often exists between AI policies and procedures with AI practices. Here are some strategies to achieve this: Translate Policies into AI-Specific Guidelines: high-level policy language needs to be converted into actionable steps that can be implemented in AI systems. This translation ensures that AI operates on the same definitions of privacy, fairness, and transparency as the organization. Engage with your AI vendor to discuss how your policies can be integrated into the system's operations. Remember, AI systems often require fine-tuning to align with specific organizational needs . Conduct Regular Audits: periodic reviews of AI systems are essential to ensure they're behaving in line with ethical standards. These audits should be thorough and look for potential blind spots. They’re also excellent at discovering and mitigating issues that an organization may have previously missed . Compare your system's training data with the data your organization provides. Analyze the differences and involve your ethics and analytics teams in prioritizing findings for policy amendments. Build a Cross-Functional Ethics Team: bringing together technology champions, legal experts, and individuals with strong ethical compasses can provide a well-rounded perspective on AI implementation. Ensure this team regularly communicates with your AI vendor, especially during the implementation of new systems. When building this team, diversify it. As academics say, make it multidisciplinary, meaning the combination of professional specializations when approaching a problem . Promote Explainability: as the Electronic Frontier Foundation has advocated for years, explainability is crucial when using AI . Why? If an AI system's decisions can't be explained, it becomes difficult for an organization to claim accountability for its actions. Work with your vendor to ensure AI models are interpretable. Position the right people to explain system outputs to anyone in your organization and verify that these align with your founding principles. Engage External Stakeholders: as AI ethics expert Kristofer Bouchard recently argued , external perspectives, especially from customers, communities, and marginalized groups, are crucial when using AI. This is especially the case when it comes to identifying ethical blind spots. Regularly seek feedback from these groups when evaluating your AI systems. Their insights can be invaluable in uncovering unforeseen ethical implications. The Path Forward: Ongoing Oversight and Proactive Management The close the gap between AI policy and ethical practice requires keep the gap shut. Unfortunately, it’s not as simple as closing a door once-and-for-all. It needs to be closed because it can easily reopen many times during your AI journey. Closing the gap requires ongoing oversight, regular policy updates, and a commitment to aligning AI behavior with organizational values. Actively integrate the five strategies above as doing so can significantly minimize risks associated with AI use. Being proactive not only ensures compliance with ethical standards but also builds trust with stakeholders and positions the organization as a responsible leader in AI adoption. Remember, in the world of AI, accountability and responsibility are critical. The power of these systems demands continuous vigilance and active management. By committing to this process, organizations can harness the full potential of AI while upholding their ethical principles and societal responsibilities. Previous Next

Meta Refuses to Sign the EU’s AI Code of Practice A closer look at the reasons why By Christina Catenacci, human writer Jul 30, 2025 Key Points On July 18, 2025, the European Commission released its General-Purpose AI Code of Practice and its Guidelines on the scope of the obligations for providers of general-purpose AI models under the AI Act Many companies have complained about the Code of Practice, and some have gone so far as to refuse to sign it—like Meta Businesses who are in the European Union and who are outside but do business with the EU (see Article 2 regarding application) are recommended to review the AI Act, Code of Practice, and Guidelines and comply Meta has just refused to sign the European Union’s General-Purpose AI Code of Practice for the AI Act . That’s right—Joel Kaplan, the Chief Global Affairs Officer of Meta, said in a LinkedIn post on July 18, 2025 that “Meta won’t be signing it”. By general-purpose AI, I mean an AI model, including when trained with a large amount of data using self-supervision at scale, that displays significant generality and is capable of competently performing a wide range of distinct tasks regardless of the way the model is placed on the market and that can be integrated into a variety of downstream systems or applications. This does not cover AI models that are used before release on the market for research, development and prototyping activities. What is the purpose of the AI Act? As you may recall, section (1) of the Preamble of the AI Act states that the purpose is to: “The purpose of this Regulation is to improve the functioning of the internal market by laying down a uniform legal framework in particular for the development, the placing on the market, the putting into service and the use of artificial intelligence systems (AI systems) in the Union, in accordance with Union values, to promote the uptake of human centric and trustworthy artificial intelligence (AI) while ensuring a high level of protection of health, safety, fundamental rights as enshrined in the Charter of Fundamental Rights of the European Union (the ‘Charter’), including democracy, the rule of law and environmental protection, to protect against the harmful effects of AI systems in the Union, and to support innovation. This Regulation ensures the free movement, cross-border, of AI-based goods and services, thus preventing Member States from imposing restrictions on the development, marketing and use of AI systems, unless explicitly authorized by this Regulation” The AI Act classifies AI according to risk and prohibits unacceptable risk like social scoring systems and manipulative AI. High-risk AI is regulated, limited risk has lighter obligations, and minimal risk is unregulated. The AI Act entered into force on August 1, 2024, but its prohibitions will be phased in over time. The first set of regulations, which take effect on February 2, 2025, ban certain unacceptable risk AI systems. After this, a wave of obligations over the next two to three years, with full compliance for high-risk AI systems expected by 2027 (August 2, 2025, February 2, 2026, and August 2, 2027 have certain requirements). Those involved in general-purpose AI may have to take additional steps (e.g., develop of Codes of Practice by 2025), and may be subject to specific provisions for general-purpose AI models and systems. See the timeline for particulars. What is the Code of Practice for the AI Act ? The Code of Practice is a voluntary tool (not a binding law), prepared by independent experts in a multi-stakeholder process, designed to help industry comply with the AI Act’s obligations for providers of general-purpose AI models. More specifically, the specific objectives of the Code of Practice are to: serve as a guiding document for demonstrating compliance with the obligations provided for in the AI Act , while recognising that adherence to the Code of Practice does not constitute conclusive evidence of compliance with these obligations under the AI Act ensure providers of general-purpose AI models comply with their obligations under the AI Act and enable the AI Office to assess compliance of providers of general-purpose AI models who choose to rely on the Code of Practice to demonstrate compliance with their obligations under the AI Act Released on July 10, 2025, it has three parts: Transparency : Commitments of Signatories include Documentation (there is a Model Documentation Form containing general information, model properties, methods of distribution and licenses, use, training process, information on the data used for training, testing, and validation, computational resources, and energy consumption during training and inference) Copyright : Commitments of Signatories include putting in place a Copyright policy Safety and Security : Commitments of Signatories include adopting a Safety and security framework; Systemic risk identification; Systemic risk analysis; Systemic risk acceptance determination; Safety mitigations; Security mitigations; Safety and security model reports; Systemic risk responsibility allocation; Serious incident reporting; Additional documentation and transparency For each Commitment that Signatories sign onto, there is a corresponding Article of the AI Act to which it relates. In this way, Signatories can understand what parts of the AI Act are being triggered and complied with. For example, the Transparency chapter deals with obligations under Article 53(1)(a) and (b), 53(2), 53(7), and Annexes XI and XII of the AI Act . Similarly, the Copyright chapter deals with obligations under Article 53(1)(c) of the AI Act . And the Safety and Security chapter deals with obligations under Articles 53, 55, and 56 and Recitals 110, 114, and 115 AI Act. In a nutshell, adhering to the Code of Practice that is assessed as adequate by the AI Office and the Board will offer a simple and transparent way to demonstrate compliance with the AI Ac t. The plan is that the Code of Practice will be complemented by Commission guidelines on key concepts related to general-purpose AI models, also published in July. An explanation of these guidelines is set out below. Why are tech companies not happy with the Code of Practice? To start, we should examine the infamous LinkedIn post: “Europe is heading down the wrong path on AI. We have carefully reviewed the European Commission’s Code of Practice for general-purpose AI (GPAI) models and Meta won’t be signing it. This Code introduces a number of legal uncertainties for model developers, as well as measures which go far beyond the scope of the AI Act. Businesses and policymakers across Europe have spoken out against this regulation. Earlier this month, over 40 of Europe’s largest businesses signed a letter calling for the Commission to ‘Stop the Clock’ in its implementation. We share concerns raised by these businesses that this over-reach will throttle the development and deployment of frontier AI models in Europe, and stunt European companies looking to build businesses on top of them. The post criticizes the European Union for going down the wrong path. It also talks about legal uncertainties, measures which go far beyond the scope of the AI Act , as well as stunting development of AI models and companies. There was also mention of other companies wanting to delay the need to comply. To be sure, CEOs from more than 40 European companies including ASML, Philips, Siemens and Mistral, asked for a “two-year clock-stop” on the AI Act before key obligations enter into force this August. In fact, the bottom part of the open letter to European Commission President Ursula von der Leyen called “Stop the Clock” asked for more simplified and practical AI regulation and spoke of a need to postpone the enforcement of the AI Act . Essentially, the companies want a pause on obligations on high-risk AI systems that are due to take effect as of August 2026, and to obligations for general-purpose AI models that are due to enter into force as of August 2025. Contrastingly, the top of the document is entitled “EU Champions AI Initiative”, with logos of over 110 organizations that have over $3 billion in market cap and over 3.7 million jobs across Europe. In response to the feedback, the European Commission is mulling giving companies who sign a Code of Practice on general-purpose AI a grace period before they need to comply with the European Union's AI Ac t. This is a switch from the July 10, 2025 announcement that the EU would be moving forward notwithstanding the complaints. The final word appears to be that there is no stop the clock or pauses or grace periods, period. New guidelines also released July 18, 2025 In addition, the European Commission published detailed Guidelines on the scope of the obligations for providers of general-purpose AI models under the AI Act (Regulation EU 2024/1689)—right before the AI Act’s key compliance date, August 2, 2025. The goal is to help AI developers and downstream providers by providing clarification. For example, it explains which providers of general-purpose AI models are in and out of scope of the AI Act’s obligations. In fact, the European Commission stated that “The aim is to provide legal certainty to actors across the AI value chain by clarifying when and how they are required to comply with these obligations”. The Guidelines focus on four main areas: General-purpose AI model Providers of general-purpose AI models Exemptions from certain obligations Enforcement of obligations The intention is to use clear definitions, a pragmatic approach, and exemptions for open-source. That said, the Guidelines consist of 36 pages of dense material that need to be reviewed and understood. For instance, the Guidelines answer the question, “When is a model a general-purpose AI model? Examples are provided for models in scope and out of scope. What happens next? As we can see from the above discussion, there are serious obligations that need to be complied with—soon. To that end, businesses in the European Union or who do business in the European Union (see Article 2 regarding application) are recommended to review the AI Act, the Code of Practice, and the Guidelines to ensure that they are ready for August 2, 2025. After August 2, 2025, providers placing general-purpose AI models on the market must comply with their respective AI Act obligations. Providers of general-purpose AI models that will be classified as general-purpose AI models with systemic risk must notify the AI Office without delay. In the first year after entry into application of these obligations, the AI Office will work closely with providers, in particular those who adhere to the General-Purpose AI Code of Practice, to help them comply with the rules. From 2 August 2026, the Commission’s enforcement powers enter into application. And by August 2, 2027, providers of general-purpose AI models placed on the market before August 2, 2025 must comply. Previous Next

AI policies and frameworks to help your organization meet legal and ethical standards. Compliance At voyAIge strategy, compliance is a foundation for our analysis on the legal, policy, and ethical dimensions of AI. We understand the intricacies of the laws of many jurisdictions and can guide you through every step of your compliance journey. Today's rapidly evolving digital landscape is fueled by the exponential rate that AI transforms not just business practices and ways of seeing but entire industries. However, with innovation comes new challenges - particularly in compliance. Governments and regulatory bodies around the world are racing to keep up. They are creating complex legal requirements with which business must comply. For businesses, navigating this complexity is not just about avoiding fines and penalties. It's about safeguarding reputation, building trust with stakeholders, and ensuring sustainability. What's Your Compliance Challenge? Understanding jurisdiction, sector, applicable legislation, data types, and data flows are some of the many considerations we take into account when identifying regulatory bodies relevant to your organization. The following are some examples that may apply to a business now or in the future: GDPR Enforces the General Data Protection Regulation (GDPR). Applies to all businesses with clients and customers in Europe NYC LL 144 New York City regulates how business can and cannot use AI to assist in hiring employees AI ACT Regulates and governs the use of all Artificial Intelligence inside the European Union FDA The US Food and Drug Administration contains specific AI regulations governing medical devices, evidence curation, and market monitoring CCPA The California Consumer Privacy Act has a significant impact on how AI systems used in businesses handle data AIDA Enforces the General Data Protection Regulation (GDPR). Applies to all businesses with clients and customers in Europe OESA Governs employee monitoring as well as using AI to recruit employees in Ontario, Canada SB 1047 California's Safe and Secure Innovation for Frontier AI Models Act imposes safety restrictions on advanced AI PIPEDA Canada's Personal Information Protection and Electronic Documents Act governs how companies collect, use, and share personal information Did You know? Up to €30 million or 6% of Global Annual Turnover for prohibited AI practices under the GDPR. Amazon was fined €746 million by Luxembourg’s data protection authority for how it processed personal data for targeted advertising using AI-driven systems. Canada's proposed Artificial Intelligence and Data Act plans to impose administrative monetary penalties $10 million or 3% of Global Annual Revenue - including criminal penalties such as jail time for AI decisions causing significant harm. Expert Insights, Experience & Resources Book a free consultation to chat with us about correctly and accurately identifying compliance and regulation applicable to your organization. Stay informed by subscribing to our VS-AI Observer Substack , where we offer articles, whitepapers, case studies, and video content that will keep your organization ahead of emerging compliance challenges, requirements, and issues. Book a Free Consultation