Since the beginning of the year, several people have remarked to me off the cuff, apropos of nothing in particular: "Google Cloud is killing it." Parent company Alphabet reported Q2 earnings [PDF] after the bell on Wednesday and the numbers speak for themselves. Let's go to the tape: Google Cloud revenue was up 82 percent from the same quarter last year, increasing from $13.6 billion to $24.8 billion. Google Cloud operating income more than tripled during the same period, going from $2.8 billion to $8.8 billion. Once the distant-third-place laughingstock of the IaaS platforms, Google's cloud business is now the growth engine of Alphabet and a significant contributor to the company's overall business, making up 21 percent of revenues and 22 percent of operating income. How'd this magic happen? The company's claiming it's all AI, citing "demand for AI infrastructure and AI solutions." We have no idea if that's actually the case, given the plethora of more prosaic offerings from the Google Cloud team, but the company's Gemini marketing strategy – pushing it in front of hundreds of millions of searchers every day – can't be faulted. Informal checks against our own sources suggest Anthropic Claude remains the go-to frontier model for most enterprise customers, and we're definitely hearing about companies switching between models to make the most of token costs vs effectiveness. But when the AI bubble finally pops, it could bring down money-losers OpenAI and Anthropic, and maybe even Oracle, which has gotten in a bit too deep. Google, like the janitor at the end of the universe, will be there to pick up the pieces – and talent. ®

Europe can build more chip fabs, subsidize domestic manufacturing, and wrap it all in the language of sovereignty, but it still won't escape its dependence on American cloud providers and software anytime soon, according to Forrester. In its first Global Sovereignty Forecast, the analyst concludes that the race for technological independence has already produced two clear winners: China and the United States. Everyone else, Europe included, is left figuring out which dependencies it can live with. Forrester's Tech Sovereignty Index measures countries across areas such as AI investment, cloud infrastructure, semiconductors, software, datacenter capacity, and technical talent. Its forecast puts China and the US far ahead, with overall tech sovereignty scores of 82 percent and 79 percent respectively. Europe's biggest economies, by comparison, barely move between now and 2030: Germany and Spain rise from 34 percent to 36 percent, France from 33 percent to 35 percent, the UK from 30 percent to 32 percent, and Italy from 27 percent to 29 percent. Semiconductors offer one of the few signs of real progress. Governments are spending heavily on domestic production, and Forrester expects chip manufacturing scores to rise sharply in several countries by 2030. The catch is that more fabs do not amount to technological independence. Europe still designs only about 1 percent of the world's chips and has no homegrown equivalent to Nvidia or Qualcomm, Forrester says. Its wider stack is just as dependent: AWS, Microsoft Azure, and Google Cloud account for roughly 65 percent of the European cloud market, while high energy costs and planning constraints continue to slow datacenter expansion. The European Chips Act is unlikely to close that gap. Brussels wants the bloc to produce a fifth of the world's semiconductors by 2030, but Forrester expects Europe to reach just 11.3 percent as other regions expand at the same time. Forrester is also unconvinced by the hyperscalers' "sovereign cloud" pitch. AWS, Microsoft, and Google have rolled out European cloud offerings with separate governance and operational controls, but the report argues they remain subsidiaries of US companies. The datacenters may sit in Europe, but ultimate ownership does not. Rather than chasing complete self-sufficiency, Forrester says most countries should accept that some dependence is unavoidable and focus instead on managing it through alliances, open technologies, and selective investment. "Ongoing geopolitical volatility, AI competition, and semiconductor supply chain risks have put tech sovereignty firmly in the spotlight," said Dario Maisto, principal analyst at Forrester. "Today, tech sovereignty is concentrated in the hands of a few global leaders, creating an uneven competitive advantage for some countries. To compete in the AI era, nations must understand their strategic dependencies and build durable partnerships that safeguard their data, infrastructure, and long-term autonomy." It's hardly the rallying cry sovereignty advocates were hoping for. Europe may eventually produce more chips, but the harder part will be building an entire technology stack that doesn't ultimately answer to someone else's headquarters. ®

Airbus is migrating its most critical applications for sensitive workloads from AWS to French cloud provider Scaleway's under a drive to increase digital sovereignty. As exclusively revealed by The Register in December, the European-based aerospace manufacturer, said it needed to guarantee the data remained “under European control" and was launching a tender at the start of 2026. Catherine Jestin, head of digital at Airbus, told us on Thursday: "The selection of Scaleway is a combination of a very strong technical answer and a very strong commercial offer making it competitive compared to hyperscalers' public cloud offerings. In addition, Scaleway is committed to involving Airbus in the definition of its future product roadmap." "The objective is to host Airbus's most critical applications (those required for the Minimum Viable Company). This represents 900 applications and we will start with 70 of them today hosted on AWS." Applications being sent to Scaleway include ERP, manufacturing execution systems, CRM, and product lifecycle management. Finding a cloud provider to host its most sensitive applications for defense and industrial workloads was not a certainty when the process began, Airbus told us last year, because European cloud providers do not have the scale of their US rivals. Jestin said Airbus will continue to work with AWS. Skywise, a platform that aggregates and analyzes aviation data, and Case Management Assistant for customers' technical queries will continue to be hosted by AWS. In a statement, she said: “By integrating a trusted, high performance, cloud environment that keeps our critical data assets shielded from foreign extraterritorial laws, we are ensuring that our digital infrastructure keeps pace with our aerospace innovation, while maintaining control and resilience of our industrial operations.” Since President Donald Trump came to power for a second term, his antagonistic approach to allies - some of them now former allies - has created economic and geopolitical tensions between the US and Europe. This has heightened concern about the US Cloud Act, which allows the American government to request data held in overseas datacenters owned by US businesses, and only served to reinforce calls for digital sovereignty. Reacting to the movement in Europe, AWS, Microsoft and Google have all worked to convince customers they can provide digitally sovereign services, although a Microsoft exec previously admitted in a French court - under oath - that he could not guarantee digital sovereignty. Airbus continues to work with both Microsoft and Google’s productivity suites though this latest move with Scaleway exemplifies the broader pattern across the trading bloc: to become more self sufficient and less reliant on US big tech. Jestin told us the aerospace corp will also still use US providers, including Salesforce, Coupa and Workday. "We do not intend to move away from all non European solutions; we balance our choices based on the criticality of the data. AWS declined to comment. ®

Dave Brown, a 19-year veteran of AWS and member of its S-team leadership cabal, is leaving Amazon for parts undisclosed. It's hard to overstate Dave's impact on AWS; the few times I've met him, it was very clear that there was nothing I could trot out in the realm of "arcane EC2 trivia" that he didn't go orders of magnitude deeper on with zero forewarning. This is a titanic loss for AWS, because that's roughly how deep Dave routinely dove. He'll be handing the reins over to fellow S-team member Dave Treadwell, currently the head of Amazon Retail's "eCommerce Foundation" (itself an upscale term for "bargain basement"). Brown probably isn't going to be a direct competitor (though he's definitionally going to some Amazon competitor short of his next move being "philanthropy"), unless the two-week notice period is simply a bunch of Amazonian goons beating the tar out of him in a dark room around the clock until August. But the interesting part to me is that suddenly Dave Treadwell has an enviable job. Think about it for a second: Amazon retail was, for a time, the largest AWS customer — and certainly one of the most complicated. If I think I have beef about AWS-isms, there's no doubt that "Tread," as he's apparently called, has mountains to my molehill of complaints. So if I can slip into his role for a second, here would be my to-do list if I were coming from an Amazon Retail background and suddenly had EC2 bequeathed to me to run: GPU capacity acquisition: Special people get special allocation rules, balanced between Capacity Blocks, war-clicking past InsufficientInstanceCapacity screens, and having to know a guy. Amazon has an entire internal project to solve this for its own teams. If I'm Tread, take a page from retail and smash Spot Fleet dynamics into a proper capacity marketplace. "Only 2 p6-b300.48xlarge left in stock. Ships from and sold by GPUZ4LOLZ (91% positive feedback)." Savings plans / Reserved Instances archaeology: These sometimes-but-often-not overlapping discount vehicles were clearly designed by folks who never had to explain them to a CFO with anger management issues. Treadwell has a golden opportunity here to roll out dynamic raw pricing: on-demand rates that themselves fluctuate hourly like a third-party listing on discontinued and incompatible printer ink, replacing traditional commitment vehicles with a "Subscribe & Save 5%" toggle on vCPUs. Instance type proliferation: As of this writing, there are 1,354 EC2 instance types available in us-east-1 alone, and the console picker assumes that you already know the correct answer. Riiiight. There's a solution here! "Customers who launched m7i.large also launched ..." combined with sponsored placement by EC2 sub-departments means the top result is now suddenly the instance family that AWS overbought. Is this the best instance for the customer? Who gives a rat's ass; it's what's best for Amazon, a north star that Amazon Retail has been chasing for years. SageMaker is someone's empire-building project: There are over 35 SageMaker products, or features, or whatever the distinction is supposed to be at AWS. Tread should leave this alone, and introduce the amazing source of truth that is Customer Reviews. "1 star. Not as described. Arrived as Jupyter notebook; what the hell do I do with it?" They can repurpose their fake review detection to take down fraudulent reviews from other SageMaker sub-teams. Quota request supplication: You know the drill; beg, plead, and wait for the privilege to give AWS more money. The fix here is almost too easy; y'know who doesn't have to wait for quota increases? That's right: Amazon Prime members. That's the easy punch list if it were me. But I'm me, and Tread is not. I'm sure he's got planned more treats with far greater nuance and customer hostility; we'll know for sure that he's settled in when the EC2 section of our AWS bills starts including ads - and when Josh Pigford's excellent Knockoff extension starts working in the EC2 console. ®

Platform engineering team leads are facing a quiet crisis. Your data science teams want Kubeflow for its pipeline orchestration, metadata tracking, and training operators, so you build it for them on Kubernetes. Then day two arrives. Your engineering backlog is swallowed by breaking changes from upstream, Istio configuration complexity, security patching, and storage provisioning bottlenecks. You didn't build an ML platform; you accidentally adopted a full-time infrastructure maintenance program. The Kubeflow operations trap Kubeflow's day-two difficulty has structural roots. It is not a single, cohesive application but a distributed constellation of over a dozen distinct open source microservices, including Katib, Pipelines, Notebooks, and Central Dashboard. Each of these components comes with its own release cycle, dependency graph, and configuration quirks, which means that when platform teams deploy Kubeflow, they are actually signing up for a systems integration job. The friction concentrates in three systemic challenges. Kubeflow leans on Istio for routing, multi-tenancy, and security. Configuring Istio ingress, managing TLS certificates, and debugging broken virtual services can quickly turn into a time sink for senior infrastructure engineers. Kubeflow moves fast. Upgrading from one version to the next rarely involves a simple script, because a single API deprecation in an upstream Kubernetes component can silently break your entire machine learning pipeline orchestration. Machine learning workloads demand dynamic, high-performance storage provisioning and flawless GPU scheduling. Mapping cloud-native storage classes to Kubeflow's persistent volume claims while keeping data access latency low requires constant manual tuning. Managed Kubeflow, zero operational overhead Canonical's new Managed Kubeflow on Microsoft Azure is built to give operations teams their weekends back. It delivers the full power of upstream Kubeflow without the operational burden, and because it is a fully managed service that runs entirely within your own cloud tenancy, no data, no models, and no training workloads are ever sent to Canonical. Compliance teams keep their posture intact while platform teams shed the maintenance burden. The open source management engine is cloud-agnostic. Managed Kubeflow on Azure uses the same architecture as Canonical's on-premises OpenStack integration, and managed services on additional public clouds will follow. The result is environment portability without the operational overhead. Canonical Managed Kubeflow use cases Once teams are freed from infrastructure patching and service mesh debugging, they can concentrate on delivering business value. Kubeflow is a powerhouse when it works, because it provides the framework required to take models from an experimental notebook to high-throughput production. A fully managed platform abstracts the underlying cluster maintenance and turns complex machine learning workflows into repeatable, scalable operations. Here is how a managed, dedicated platform converts the heaviest machine learning workloads from infrastructure burdens into routine production operations. Generative AI: Offloading the compute complexity GenAI workloads push Kubernetes clusters to their limits, and managing the pipelines manually forces platform teams to write fragile, custom automation scripts. Canonical Managed Kubeflow on Azure handles this work natively inside your private Azure cloud tenancy. The generative AI workloads Kubeflow can run include: Distributed pre-training: Clustering multi-node GPU instances requires complex networking, node provisioning, and fault tolerance. Kubeflow orchestrates training jobs across nodes automatically and ties into Azure's low-latency network infrastructure to maximize hardware utilization without manual cluster tuning. Targeted fine-tuning: Data scientists constantly spin up LoRA or PEFT jobs that require immediate, heavy compute, only to leave idled GPUs burning budget later. Kubeflow pipelines automate the entire sequence: ingest data, run the fine-tuning job, and scale capacity back down to zero once the job finishes. Model distillation: Compressing large models into smaller, production-ready versions requires complex teacher-student pipelines. Kubeflow manages these multi-stage workflows, and teams can track training metrics side by side via the integrated MLflow server to validate model performance. Traditional ML: Solid production pipelines. While generative AI takes the spotlight, much core enterprise value still runs on traditional machine learning. Managed Kubeflow keeps these production systems reliably online. Traditional ML workloads include: Predictive maintenance: IoT and time-series data demand continuous updates. Kubeflow can schedule automated retraining pipelines triggered by data drift. This keeps models accurate without platform teams manually monitoring performance pipelines. Fraud detection: Compliance demands a watertight audit trail. The included MLflow server acts as a metadata engine that automatically logs every dataset version, hyperparameter choice, and model version to help assure robust regulatory compliance. Churn and demand forecasting: High-volume batch scoring requires massive, temporary compute scaling. Canonical Managed Kubeflow on Azure can autoscale the underlying infrastructure to process millions of rows, then tear it down cleanly to control cloud spend. Stop maintaining core Kubeflow. Start delivering value. A managed service exists to remove specialized infrastructure overhead without sacrificing data sovereignty. 100 percent in-tenancy: Because the service executes entirely inside your tenancy, your underlying data, source code, and custom weights never leave your perimeter. No hostage to fortune: The service is built on pure upstream Kubeflow, so the pipelines you run on Azure today can also run on Canonical's on-premises OpenStack solution or future cloud releases. Enterprise-grade security: The service integrates with enterprise identity management, including Microsoft Entra ID, and role-based access controls right from launch. Predictable reliability: No more debugging broken operator upgrades. Canonical's experienced managed services team handles backups, upstream fixes, security patches, and version migrations. Deploy in less than 30 minutes You can launch your first production-ready cluster in less than 30 minutes directly from the Azure Marketplace. Give your data scientists the environment they need, and keep full control of your infrastructure. Launch now on Azure. Contributed by Canonical.