New AI regulations in the US and EU, along with data privacy laws like the EU’s GDPR and rulings like Schrems II, are indirectly affecting where AI models can be trained and where inference can occur. These laws dictate how (and whether) AI data can move between jurisdictions, with data residency requirements in countries like China (under the 2017 Cybersecurity Law and 2021 Data Security Law) further restricting data transfers. Organizations must carefully consider where they train and run their AI models, particularly when personal data is involved, to ensure compliance with data processing regulations.
These factors require location-aware dedicated clouds or edge computing to ensure compliance without sacrificing performance. Location-aware clouds facilitate AI model training and inference in jurisdictions where data residency, sovereignty, and privacy laws are observed. Edge computing allows for distributing AI workloads across multiple geographic locations, ensuring compliance with local laws and regulations while minimizing latency.
This approach is essential for businesses operating in multiple countries, as it ensures data sovereignty and residency requirements are met without having to sacrifice speed, agility, or data protection. Additionally, it provides flexibility by decentralizing infrastructure, which can be more cost-effective than building large, dedicated data centers.
Let’s dig into these terms in more detail to understand why your AI training and inference locations matter and how you can easily control them.
What Does AI Training in a Sovereign Cloud Mean?
A sovereign cloud is a specific kind of private cloud that focuses on geographical location and compliance with local laws and regulations. The different principles of sovereign clouds relate to AI training in the following ways:
- Data sovereignty refers to the principle that data is subject to the laws of the country where it is generated. For AI, this means that the training data must comply with the regulations of the originating country. For example, under the EU’s General Data Protection Regulation (GDPR), the location where data is generated or collected dictates how it can be used for AI training. With the introduction of the EU AI Act, AI models must adhere to these laws, meaning the source of training data determines the legal constraints applied to the entire AI lifecycle.
- Data residency defines that data is governed by the laws of the country where it is stored, regardless of where it was generated. This can have a significant impact on AI models, especially when law enforcement or regulatory authorities require access to stored data. For example, if AI training data is stored in a specific country, local authorities may request access under legal frameworks. Therefore, data residency ensures that AI model training and data storage remain within the legal boundaries of the region, giving local regulators the power to enforce their laws.
- Operational sovereignty focuses on how cloud infrastructure and operational processes are managed, subject to local regulations. It’s not just about data location but also about how systems handle data. In AI training, this means organizations must ensure their operational processes, like data backups, disaster recovery, and system resilience, comply with the laws of the region where the cloud resources are located. Ensuring operational sovereignty allows local authorities access to models and systems during critical situations, further embedding AI operations within the legal structure of the country.
- Digital sovereignty addresses permission management and access control—who has access to the data and systems and how that access is audited. In terms of AI training, this means strict controls over who can interact with the models and their training data. Even if data complies with residency and sovereignty laws, poor access control could lead to unauthorized use or breaches. With digital sovereignty, organizations must enforce stringent access policies and maintain robust audit trails to ensure that data is protected and compliant with national security concerns. For AI, this means ensuring that sensitive data used for training is accessed only by authorized personnel and that access is thoroughly logged.
AI training in a sovereign cloud ensures compliance with local data laws, safeguards sensitive information, and provides organizations with full control over their operations. AI models can be developed securely and legally, reducing regulatory risks and helping to ensure that sensitive data is handled appropriately.
What Are the Options for Sovereign Cloud AI Training?
To enforce location-bound AI training, organizations have two main options: a dedicated private cloud data center within the country of operation or the use of edge computing to ensure training happens at the location closest to the data’s origin.
- Dedicated private cloud data centers: Running a private data center within the country provides organizations with complete control over their AI training environment. This option adheres to sovereign cloud principles by ensuring all training data remains within the country’s borders, which can be crucial for compliance with local regulations. While the costs of building and maintaining a dedicated data center can be significant, this approach offers centralized management, minimizing the need for software refactoring or adaptations. For large-scale AI projects or those requiring heightened security and compliance, the centralized control of a dedicated cloud can be a worthwhile investment, ensuring data never leaves the country while keeping the AI training process localized and secure.
- Edge computing: Edge computing is a more cost-effective alternative, offering a distributed approach to AI training and data storage. It enables organizations to train AI models closer to the source of the data without building an entire data center. Although edge computing requires software to be refactored to handle its decentralized nature, it offers significant flexibility. Training and storing data at edge locations within the country of origin ensures compliance with data sovereignty and residency laws while reducing latency and improving performance. For organizations looking to balance regulatory compliance with scalability and cost, edge computing provides a flexible and efficient solution for location-bound AI training.
Read on for part two on the benefits of deploying AI models at the edge.
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How to achieve compliance and security in AI inference
AI inference applications today handle an immense volume of confidential information, so prioritizing data privacy is paramount. Industries such as finance, healthcare, and government rely on AI to process sensitive data—detecting fraudulent transactions, analyzing patient records, and identifying cybersecurity threats in real time. While AI inference enhances efficiency, decision-making, and automation, neglecting security and compliance can lead to severe financial penalties, regulatory violations, and data breaches. Industries handling sensitive information—such as finance, healthcare, and government—must carefully manage AI deployments to avoid costly fines, legal action, and reputational damage.Without robust security measures, AI inference environments present a unique security challenge as they process real-time data and interact directly with users. This article explores the security challenges enterprises face and best practices for guaranteeing compliance and protecting AI inference workloads.Key inference security and compliance challengesAs businesses scale AI-powered applications, they will likely encounter challenges in meeting regulatory requirements, preventing unauthorized access, and making sure that AI models (whether proprietary or open source) produce reliable and unaltered outputs.Data privacy and sovereigntyRegulations such as GDPR (Europe), CCPA (California), HIPAA (United States, healthcare), and PCI DSS (finance) impose strict rules on data handling, dictating where and how AI models can be deployed. Businesses using public cloud-based AI models must verify that data is processed and stored in appropriate locations to avoid compliance violations.Additionally, compliance constraints restrict certain AI models in specific regions. Companies must carefully evaluate whether their chosen models align with regulatory requirements in their operational areas.Best practices:To maintain compliance and avoid legal risks:Deploy AI models in regionally restricted environments to keep sensitive data within legally approved jurisdictions.Use Smart Routing with edge inference to process data closer to its source, reducing cross-border security risks.Model security risksBad actors can manipulate AI models to produce incorrect outputs, compromising their reliability and integrity. This is known as adversarial manipulation, where small, intentional alterations to input data can deceive AI models. For example, researchers have demonstrated that minor changes to medical images can trick AI diagnostic models into misclassifying benign tumors as malignant. In a security context, attackers could exploit these vulnerabilities to bypass fraud detection in finance or manipulate AI-driven cybersecurity systems, leading to unauthorized transactions or undetected threats.To prevent such threats, businesses must implement strong authentication, encryption strategies, and access control policies for AI models.Best practices:To prevent adversarial attacks and maintain model integrity:Enforce strong authentication and authorization controls to limit access to AI models.Encrypt model inputs and outputs to prevent data interception and tampering.Endpoint protection for AI deploymentsThe security of AI inference does not stop at the model level. It also depends on where and how models are deployed.For private deployments, securing AI endpoints is crucial to prevent unauthorized access.For public cloud inference, leveraging CDN-based security can help protect workloads against cyber threats.Processing data within the country of origin can further reduce compliance risks while improving latency and security.AI models rely on low-latency, high-performance processing, but securing these workloads against cyber threats is as critical as optimizing performance. CDN-based security strengthens AI inference protection in the following ways:Encrypts model interactions with SSL/TLS to safeguard data transmissions.Implements rate limiting to prevent excessive API requests and automated attacks.Enhances authentication controls to restrict access to authorized users and applications.Blocks bot-driven threats that attempt to exploit AI vulnerabilities.Additionally, CDN-based security supports compliance by:Using Smart Routing to direct AI workloads to designated inference nodes, helping align processing with data sovereignty laws.Optimizing delivery and security while maintaining adherence to regional compliance requirements.While CDNs enhance security and performance by managing traffic flow, compliance ultimately depends on where the AI model is hosted and processed. Smart Routing allows organizations to define policies that help keep inference within legally approved regions, reducing compliance risks.Best practices:To protect AI inference environments from endpoint-related threats, you should:Deploy monitoring tools to detect unauthorized access, anomalies, and potential security breaches in real-time.Implement logging and auditing mechanisms for compliance reporting and proactive security tracking.Secure AI inference with Gcore Everywhere InferenceAI inference security and compliance are critical as businesses handle sensitive data across multiple regions. Organizations need a robust, security-first AI infrastructure to mitigate risks, reduce latency, and maintain compliance with data sovereignty laws.Gcore’s edge network and CDN-based security provide multi-layered protection for AI workloads, combining DDoS protection and WAAP (web application and API protection. By keeping inference closer to users and securing every stage of the AI pipeline, Gcore helps businesses protect data, optimize performance, and meet industry regulations.Explore Gcore AI Inference
Mobile World Congress 2025: the year of AI
As Mobile World Congress wrapped up for another year, it was apparent that only one topic was on everyone’s minds: artificial intelligence.Major players—such as Google, Ericsson, and Deutsche Telekom—showcased the various ways in which they’re piloting AI applications—from operations to infrastructure management and customer interactions. It’s clear there is a great desire to see AI move from the research lab into the real world, where it can make a real difference to people’s everyday lives. The days of more theoretical projects and gimmicky robots seem to be behind us: this year, it was all about real-world applications.MWC has long been an event for telecommunications companies to launch their latest innovations, and this year was no different. Telco companies demonstrated how AI is now essential in managing network performance, reducing operational downtime, and driving significant cost savings. The industry consensus is that AI is no longer experimental but a critical component of modern telecommunications. While many of the applications showcased were early-stage pilots and stakeholders are still figuring out what wide-scale, real-time AI means in practice, the ambition to innovate and move forward on adoption is clear.Here are three of the most exciting AI developments that caught our eye in Barcelona:Conversational AIChatbots were probably the key telco application showcased across MWC, with applications ranging from contact centers, in-field repairs, personal assistants transcribing calls, booking taxis and making restaurant reservations, to emergency responders using intelligent assistants to manage critical incidents. The easy-to-use, conversational nature of chatbots makes them an attractive means to deploy AI across functions, as it doesn’t require users to have any prior hands-on machine learning expertise.AI for first respondersEmergency responders often rely on telco partners to access novel, technology-enabled solutions to address their challenges. One such example is the collaboration between telcos and large language model (LLM) companies to deliver emergency-response chatbots. These tailored chatbots integrate various decision-making models, enabling them to quickly parse vast data streams and suggest actionable steps for human operators in real time.This collaboration not only speeds up response times during critical situations but also enhances the overall effectiveness of emergency services, ensuring that support reaches those in need faster.Another interesting example in this field was the Deutsche Telekom drone with an integrated LTE base station, which can be deployed in emergencies to deliver temporary coverage to an affected area or extend the service footprint during sports events and festivals, for example.Enhancing Radio Access Networks (RAN)Telecommunication companies are increasingly turning to advanced applications to manage the growing complexity of their networks and provide high-quality, uninterrupted service for their customers.By leveraging artificial intelligence, these applications can proactively monitor network performance, detect anomalies in real time, and automatically implement corrective measures. This not only enhances network reliability but reduces operational costs and minimizes downtime, paving the way for more efficient, agile, and customer-focused network management.One notable example was the Deutsche Telekom and Google Cloud collaboration: RAN Guardian. Built using Gemini 2.0, this agent analyzes network behavior, identifies performance issues, and takes corrective measures to boost reliability, lower operational costs, and improve customer experience.As telecom networks become more complex, conventional rule-based automation struggles to handle real-time challenges. In contrast, agentic AI employs large language models (LLMs) and sophisticated reasoning frameworks to create intelligent systems capable of independent thought, action, and learning.What’s next in the world of AI?The innovation on show at MWC 2025 confirms that AI is rapidly transitioning from a research topic to a fundamental component of telecom and enterprise operations. Wide-scale AI adoption is, however, a balancing act between cost, benefit, and risk management.Telcos are global by design, operating in multiple regions with varying business needs and local regulations. Ensuring service continuity and a good return on investment from AI-driven applications while carefully navigating regional laws around data privacy and security is no mean feat.If you want to learn more about incorporating AI into your business operations, we can help.Gcore Everywhere Inference significantly simplifies large-scale AI deployments by providing a simple-to-use serverless inference tool that abstracts the complexity of AI hardware and allows users to deploy and manage AI inference globally with just a few clicks. It enables fully automated, auto-scalable deployment of inference workloads across multiple geographic locations, making it easier to handle fluctuating requirements, thus simplifying deployment and maintenance.Learn more about Gcore Everywhere Inference
Everywhere Inference updates: new AI models and enhanced product documentation
This month, we’re rolling out new features and updates to enhance AI model accessibility, performance, and cost-efficiency for Everywhere Inference. From new model options to updated product documentation, here’s what’s new in February.Expanding the model libraryWe’ve added several powerful models to Gcore Everywhere Inference, providing more options for AI inference and fine-tuning. This includes three DeepSeek R1 options, state-of-the-art open-weight models optimized for various NLP tasks.DeepSeek’s recent rise represents a major shift in AI accessibility and enterprise adoption. Learn more about DeepSeek’s rise and what it means for businesses in our dedicated blog. Or, explore what DeepSeek’s popularity means for Europe.The following new models are available now in our model library:QVQ-72B-Preview: A large-scale language model designed for advanced reasoning and language understanding.DeepSeek-R1-Distill-Qwen-14B: A distilled version of DeepSeek R1, providing a balance between efficiency and performance for language processing tasks.DeepSeek-R1-Distill-Qwen-32B: A more robust distilled model designed for enterprise-scale AI applications requiring high accuracy and inference speed.DeepSeek-R1-Distill-Llama-70B: A distilled version of Llama 70B, offering significant improvements in efficiency while maintaining strong performance in complex NLP tasks.Phi-3.5-MoE-instruct: A high-quality, reasoning-focused model supporting multilingual capabilities with a 128K context length.Phi-4: A 14-billion-parameter language model excelling in mathematics and advanced language processing.Mistral-Small-24B-Instruct-2501: A 24-billion-parameter model optimized for low-latency AI tasks, performing competitively with larger models.These additions give developers more flexibility in selecting the right models for their use cases, whether they require large-scale reasoning, multimodal capabilities, or optimized inference efficiency. The Gcore model library offers numerous popular models available at the click of a button, but you can also bring your own custom model just as easily.Everywhere Inference product documentationTo help you get the most out of Gcore Everywhere Inference, we’ve expanded our product documentation. Whether you’re deploying AI models, fine-tuning performance, or scaling inference workloads, our docs provide in-depth guidance, API references, and best practices for seamless AI deployment.Choose Gcore for intuitive, powerful AI deploymentWith these updates, Gcore Everywhere Inference continues to provide the latest and best in AI inference. If you need speed, efficiency, and flexibility, get in touch. We’d love to explore how we can support and enhance your AI workloads.Get a complimentary AI consultation
How to optimize ROI with intelligent AI deployment
As generative AI evolves, the cost of running AI workloads has become a pressing concern. A significant portion of these costs will come from inference—the process of applying trained AI models to real-world data to generate responses, predictions, or decisions. Unlike training, which occurs periodically, inference happens continuously, handling vast amounts of user queries and data in real-time. This persistent demand makes managing inference costs a critical challenge, as inefficiencies can gradually drive up expenses.Cost considerations for AI inferenceOptimizing AI inference isn’t just about improving performance—it’s also about controlling costs. Several factors influence the total expense of running AI models at scale, from the choice of hardware to deployment strategies. As businesses expand their AI capabilities, they must navigate the financial trade-offs between speed, accuracy, and infrastructure efficiency.Several factors contribute to inference costs:Compute costs: AI inference relies heavily on GPUs and specialized hardware. These resources are expensive, and as demand grows, so do the associated costs of maintaining and scaling them.Latency vs. cost trade-off: Real-time applications like recommendation systems or conversational AI require ultra-fast processing. Achieving low latency often demands premium resources, creating a challenging trade-off between performance and cost.Operational overheads: Managing inference at scale can lead to rising expenses, particularly as query volumes increase. While cloud-based inference platforms offer flexibility and scalability, it’s important to implement cost-control measures to avoid unnecessary overhead. Optimizing workload distribution and leveraging adaptive scaling can help mitigate these costs.Balancing performance, cost, and efficiency in AI deploymentThe AI marketplace is teeming with different options and configurations. This can make critical decisions about inference optimization, like model selection, infrastructure, and operational management, feel overwhelming and easy to get wrong. We recommend these key considerations when navigating the choices available:Selecting the right model sizeAI models range from massive foundational models to smaller, task-specific in-house solutions. While large models excel in complex reasoning and general-purpose tasks, smaller models can deliver cost-efficient, accurate results for specific applications. Finding the right balance often involves:Experimenting during the proof-of-concept (POC) phase to test different model sizes and accuracy levels.Prioritizing smaller models where possible without compromising task performance.Matching compute with task requirementsNot every workload requires the same level of computational power. By matching hardware resources to model and task requirements, businesses can significantly reduce costs while maintaining performance.Optimizing infrastructure for cost-effective inferenceInfrastructure plays a pivotal role in determining inference efficiency. Here are three emerging trends:Leveraging edge inference: Moving inference closer to the data source can minimize latency and reduce reliance on more expensive centralized cloud solutions. This approach can optimize costs and improve regulatory compliance for data-sensitive industries.Repatriating compute: Many businesses are moving away from hyperscalers—large cloud providers like AWS, Google Cloud, and Microsoft Azure—to local, in-country cloud providers for simplified compliance and often lower costs. This shift enables tighter cost control and can mitigate the unpredictable expenses often associated with cloud platforms.Dynamic inference management tools: Advanced monitoring tools help track real-time performance and spending, enabling proactive adjustments to optimize ROI.Building a sustainable AI futureGcore’s solutions are designed to help you achieve the ideal balance between cost, performance, and scalability. Here’s how:Smart workload routing: Gcore’s intelligent routing technology ensures workloads are processed at the most suitable edge location. While proximity to the user is prioritized for lower latency and compliance, this approach can also save cost by keeping inference closer to data sources.Per-minute billing and cost tracking: Gcore’s platform offers unparalleled budget control with granular per-minute billing. This transparency allows businesses to monitor and optimize their spending closely.Adaptive scaling: Gcore’s adaptive scaling capabilities allocate just the right amount of compute power needed for each workload, reducing resource waste without compromising performance.How Gcore enhances AI inference efficiencyAs AI adoption grows, optimizing inference efficiency becomes critical for sustainable deployment. Carefully balancing model size, infrastructure, and operational strategies can significantly enhance your ROI.Gcore’s Everywhere Inference solution provides a reliable framework to achieve this balance, delivering cost-effective, high-performance AI deployment at scale.Explore Everywhere Inference
The future of AI workloads: scalable inference at the edge
Although artificial intelligence (AI) is rapidly transforming various industries, its value ultimately hinges on inference—the process of running trained models to generate predictions and insights on data it has never seen before. Historically, AI training has been centralized, meaning that models have been developed and trained in large, remote data centers with vast computational resources. However, we’re witnessing a significant shift toward edge-based decentralized inference, where models can operate closer to end users or data. Low-latency processing, cost-effectiveness, and data privacy compliance are the driving forces for this evolution. For most AI-driven projects, efficient inference scaling is essential, though some low-traffic or batch-processing tasks may require less of it.The evolution of AI workloadsThe way businesses handle AI workloads is changing as AI adoption increases. In contrast to early AI efforts, which focused primarily on training complex models, today’s focus lies in optimizing inference or applying these trained models in real-time. The increasing demand for scalability, cost-effectiveness, and real-time processing drives this shift, guaranteeing that AI can generate valuable insights quickly and at a large scale.Training vs. inferenceTraining and inference are the two key processes involved in developing and operating AI workloads. Building AI models through training is a resource-intensive process that requires massive data sets and computational capacity. Inference is how these trained models are used in real-time to process incoming data and generate predictions. For example, an AI model trained on historical banking transactions to detect fraud can then infer fraudulent activity in real-time by analyzing new transactions and flagging suspicious patterns. While training defines an AI model’s potential, inference determines its real-world usability.The growing focus on InferenceBusinesses are increasingly prioritizing inference as part of the natural evolution of AI projects. Once a model has been trained or a suitable pretrained model has been identified and procured, it transitions into the inference phase, where the model interacts with real-world data. A few factors drive the increase in inference activity we’re seeing in the marketplace:Businesses have now gained experience experimenting with AI and are ready to deploy models in the real world.Many projects that invested significant time in training have reached a stage where the models meet desired performance levels and are ready for production.The availability of high-performing, pretrained models like ChatGPT has simplified the inference process, reducing the need to train models from scratch.This evolution underscores the growing role of inference in AI workloads as organizations leverage advancements and experience to move models from experimentation to real-world application.The rise of dynamic inference cloudsDue to increasing requirements for AI models to scale, the need for flexible and cost-effective inference environments has also grown. Traditional, static infrastructure struggles to keep up with fluctuating AI workloads, often leading to inefficiencies in performance and cost. This challenge has given rise to dynamic inference clouds—platforms that enable businesses to adjust their compute resources based on workload complexity, latency requirements, and budget constraints.Centralized vs. edge-based inferenceAs AI applications scale, the drawbacks of centralized cloud-based inference become more apparent. Businesses need faster, more efficient ways to process AI workloads while reducing costs and guaranteeing data privacy. Edge-based inference overcomes these issues by bringing AI processing closer to users or data, reducing latency, lowering operating costs, and improving compliance.The challenges of AI inferenceCentralized cloud-based inference is still used in many AI applications; however, this approach presents multiple drawbacks:High latency: Data must travel back and forth between distant locations to centralized servers, resulting in higher latency. This issue is especially relevant for real-time applications like fraud detection and driverless cars.Operational costs: Running inference in centralized environments often involves higher expenses due to cross-region traffic and compute resource requirements. By keeping traffic localized within the country or region of the workload, businesses can significantly reduce these costs and improve efficiency.Data privacy and compliance risks: Multiple industries, including healthcare and financial services, are subject to strict data privacy laws. It is more challenging to guarantee compliance with regional requirements with centralized inference than keeping workloads in their originating region.On the other hand, edge-based inference comes with its own set of challenges. Deploying and managing distributed infrastructure can be complex, requiring careful allocation of resources across multiple locations. Additionally, edge devices often have limited computational power, making it crucial to optimize models for efficiency. Guaranteeing consistent performance and reliability across diverse environments also adds an extra layer or operational complexity.The benefits of edge-based inferenceAs the demand for real-time AI applications grows, centralized inference often falls short of meeting performance, cost, and compliance requirements. Let’s have a look at how edge-based inference addresses these challenges:Low latency: By running inference closer to end users or data, delays are minimized, enabling real-time applications.Cost optimization: Traffic is optimized within the country, optimizing operational costs.Compliance-friendly processing: By keeping sensitive data local, edge-based inference simplifies compliance with regional regulations.While centralized inference offers high computational power and simplicity, it can introduce latency and rising costs at scale. Edge-based inference reduces these issues by processing data closer to the source, enhancing both speed and compliance. The right approach depends on workload demands, budget constraints, and infrastructure capabilities. In practice, combining centralized and edge-based inference often strikes the optimal balance, enabling businesses to achieve both performance and cost-efficiency while maintaining flexibility.Scale AI inference seamlessly with GcoreScalable, dynamic inference is essential for deploying AI efficiently. As your AI applications grow, you need a solution that optimizes performance, reduces latency, and keeps data compliant with privacy regulations. Gcore Everywhere Inference lets you deploy AI workloads dynamically, bringing inference closer to users and data sources. With a global edge infrastructure, smart routing, and autoscaling capabilities, Gcore guarantees your AI runs efficiently, cost-effectively, and adapts to real-world demands.Ready to scale your AI workloads with edge inference?Explore Everywhere Inference
What do the Stargate and DeepSeek AI announcements mean for Europe?
Within the last week, we’ve seen the announcement of two major AI developments: Last week, President Trump unveiled The Stargate Project, a $500bn venture to build up AI infrastructure in the US, while Chinese start-up DeepSeek blindsided the technology and finance worlds with the surprise launch of its new high-quality and cost-efficient AI models. Seemingly in a rushed response to this news, fellow Chinese tech company Alibaba yesterday announced a new version of its own AI model—which it claims outperforms the latest DeepSeek iteration.President Trump immediately declared DeepSeek a wake-up call for the US, while Meta was said to be “scrambling war rooms of engineers” seeking ways to compete with DeepSeek in terms of low costs and computing power. But if the normally bullish American government and tech giants are rattled by DeepSeek, where does that leave the more highly regulated and divided Europe in terms of keeping up with these AI titans?Multiple sources have already expressed concerns about Europe’s role in the AI age, including the CEO of German software developer SAP, who blamed the silos that come with individual countries having different domestic priorities. European venture capitalists had a more mixed view, with some lamenting the slower speed of European innovation but some also citing DeepSeek’s seeming cost-effectiveness as an inspiration for more low-cost AI development across the continent.With an apparent AI arms race developing between the US and China, is Europe really being left behind, or is that a misperception? Does it matter? And how should the continent respond to these global leaps in AI advancement?Why does it seem like the US and China are outpacing Europe?China and the US are racing ahead in AI due to massive investments in research, talent, and infrastructure. China’s government plays a significant role by backing AI as a national priority, with strategic plans, large data sets (due to its population size), and a more flexible regulatory environment than Europe.Similarly, the US benefits from its robust tech industry with major players like Google, OpenAI, Meta, and Microsoft, as well as a long-standing culture of innovation and risk-taking in the private sector. The US is also the home of some of the world’s leading academic institutions, which are driving AI breakthroughs. Europe, by contrast, lacks some of these major drivers, and the hurdles that AI innovators face in Europe include the following:Fragmented markets and regulationUnlike China and the US, Europe is made up of individual countries, each with their own regulatory frameworks. This can create delays and complexities for scaling AI initiatives. While Europe is leading the way on data privacy with laws like GDPR, these regulations can also slow innovation. Forward-thinking EU initiatives such as the AI Act and Horizon Europe are also in progress, albeit in the early stages.Compare this to China and the US, where regulations are minimalist with the goal of driving innovation. For instance, collecting large datasets, essential for training AI models, is much easier in the US and China due to looser privacy concerns. This creates an innovation lag, especially in consumer-facing AI.The US used to have national-level regulation, but that was revoked in January 2025 with Trump’s Executive Order, and some states have little to no regulation, leaving businesses free to innovate without barriers. China has relatively strict AI laws, but they’re all applied consistently across the vast country, making their application simple compared to Europe’s piecemeal approach. All of this has the potential to incentivize AI innovators to set up shop outside of Europe for the sake of speed and simplicity—although plenty remain in Europe!Talent drainThe US and China can attract the best AI talent due to financial incentives, fewer regulatory barriers, and more concentrated hubs (Silicon Valley, Beijing). While many AI experts trained in Europe, they often move abroad or work with multinational corporations that are based elsewhere. Europe has excellent academic institutions, but the private sector can struggle to keep talent within the region.Funding gapsStartups in Europe face more challenges in terms of funding and scaling compared to those in the US or China. Venture capital is more abundant and aggressive in the US, and the Chinese government heavily invests in AI companies with a clear, state-backed direction. In contrast, European investors are often more risk-averse, and many AI startups struggle to get the same level of backing.How should Europe respond to global AI innovations?While Europe may not be able to compete with the wealth, unification, and autonomy of either China or the US, there are plenty of important areas in which it excels, even leading these other players. Besides that, caution and stricter adherence to ethical regulations may be beneficial in the long run. Last year, the previous US administration commissioned a report warning of the dangers of AI evolving too quickly. Europe’s more “slow and steady” approach is more likely to mitigate these risks.At the same time, Europe should aim to foster innovation as well as take advantage of AI developments in other markets. Here are some more ways in which European companies can take advantage of their regional positioning to get ahead in the global AI market:Innovation in niche areas: Europe may not be able to lead in general-purpose AI like the US or China, but it can carve out spaces in areas like ethical AI, AI governance, and privacy-focused AI. European companies could also specialize in areas like AI for healthcare, environmental sustainability, or manufacturing, where the continent has existing strengths.Collaboration over competition: European nations might need to focus on collaborative efforts. By pooling resources, sharing expertise, and aligning on common goals, Europe can build a unified approach to AI that is both innovative and cohesive. This collaborative model could help Europe create AI frameworks that are sustainable, inclusive, and ethically responsible, all while fostering a spirit of teamwork rather than rivalry.AI sovereignty: AI sovereignty means aiming to ensure that Europe isn’t overly dependent on American or Chinese tech giants and keeps European data in Europe. This involves building localized infrastructure, developing homegrown AI solutions, and protecting European data—while ensuring European AI remains competitive globally. European sovereignty and the region’s tight regulations are likely to catch the eye of the international AI market in light of the already-emerging concerns regarding DeepSeek and censorship, which may be offputting for markets outside of China.So, while the US and China are making the headlines right now, Europe is more quietly paving its own areas of AI specialization, characterized by concern for data privacy and ethics. We’re curious to see whether the global AI market will turn its attention to the benefits Europe offers during 2025. Whether or not European AI companies become top news stories, there’s no doubt that we’re already seeing incredible quality AI models coming out of the continent, and exciting projects in the works that build on key industries and expertise in the region.Talk to us about your AI needsNo matter where in the world your business operates, it’s essential to keep up with changes in the fast-paced AI world. These constant shifts in the market and rapid innovation cycles can create both opportunities and challenges for businesses. While it may be tempting to jump on the latest bandwagon, businesses should carefully examine the pros and cons for their specific use case, and keep in mind their regulatory responsibilities.Whether you’re operating in Europe or globally, our innovative solutions can help you navigate the fast-moving world of AI. Get in touch to learn more about how Gcore Everywhere Inference can support your AI innovation journey.Get a personalized AI consultation
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