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13/06/2026

Dassault Systèmes reports first quarter 2026 results in line with objectives and confirms full-year outlook

VELIZY-VILLACOUBLAY, France — April 23, 2026 — Dassault Systèmes reports its IFRS unaudited estimated financial results for the first quarter 2026 ended March 31, 2026. The Group’s Board of Directors approved these estimated results on April 22, 2026. This press release also includes financial information on a non-IFRS basis and reconciliations with IFRS figures in the Appendix.

First Quarter 2026 Summary Highlights (1)

(unaudited, IFRS & non-IFRS unless otherwise noted, all growth rates in constant currencies)

Total revenue and software revenue up 3%, both in line with objectives, with strong performance of Mainstream Innovation
Annual Run Rate growth of 6% versus last year (2), reaching €4.4 billion, reflecting good recurring activity
IFRS Operating cash flow totaled €0.95 billion up 22%
3DEXPERIENCE software revenue up 7%, and cloud software revenue growth of 8%
Non-IFRS operating margin of 30.3%, underscoring healthy operational efficiency
Non-IFRS diluted EPS up 4% at €0.30
Confirming FY26 non-IFRS objectives, capitalizing on first quarter achievements




1) IFRS figures for 1Q26: Total revenue of €1.51 billion, operating margin of 23.0% compared to 19.4% in 1Q25, and diluted EPS of €0.22 compared to €0.20 in 1Q25
2) At FY26 plan currency rates

13/06/2026

Dassault Systèmes Partners with PariSanté Campus to Advance a Sovereign Healthcare Ecosystem in Europe

Dassault Systèmes has announced its partnership with PariSanté Campus, the French hub for AI and digital innovation applied to health, to support and accelerate healthcare startups in France and across Europe that are driving the future of medicine.

The collaboration uniquely combines expertise in virtual twins and cloud technologies with Europe’s premier digital health innovation ecosystem, offering startups access to artificial intelligence capabilities, sovereign cloud infrastructure and mentoring to transform ideas into scalable, sustainable solutions that comply with European data security regulations.

Under the agreement, eligible startups supported at PariSanté Campus - established under France’s “Innovation Santé 2030” strategy to drive sovereign digital health initiatives - will have the opportunity to participate in Dassault Systèmes’ 3DEXPERIENCE Lab and OUTSCALE for Entrepreneurs accelerator programs.

This priority access places Dassault Systèmes’ 40-plus years of industry expertise at the core of groundbreaking healthcare projects at a time where industry and government are prioritizing the development of trusted solutions that protect sensitive data without stifling innovation, agility and competitiveness.

PariSanté Campus startups can use AI-powered virtual twins on the 3DEXPERIENCE platform to model, test and validate real-world solutions virtually before clinical or industrial deployment, reducing development cycles, the need for costly physical prototypes, and time to market.

Startups can also support AI-driven healthcare applications with Dassault Systèmes’ certified OUTSCALE sovereign cloud infrastructure and high-performance computing resources, in addition to receiving mentoring and training from a rich community of experts and partners.

10/06/2026

NVIDIA.

Its partner NAVER (in South Korea) Expands AI Infrastructure With NVIDIA to Serve Surging Global AI Demand

NAVER, an AI Cloud, will build AI factories on the NVIDIA DSX platform at gigawatt scale, starting with AI infrastructure expansion at GAK Sejong.
NVIDIA DSX gives NAVER a proven AI factory blueprint that will serve Korea’s industries and global AI cloud customers as demand for useful AI surges at lowest token cost.
NAVER will use NVIDIA full-stack AI platforms, models and software to advance regional AI models including next-generation HyperCLOVA X models, its Seoul World Model development and agentic AI services.

NAVER will expand sovereign AI infrastructure, starting at 55 megawatts with plans to move to gigawatt scale using the NVIDIA DSX™ platform to rapidly design, build and scale full-stack, end-to-end AI platforms that can serve enterprises, industries and government.

“Useful AI has arrived, and demand for AI factories is extraordinary,” said Jensen Huang, founder and CEO of NVIDIA. “NAVER is building AI factory infrastructure that will serve its companies, developers and industries. With NVIDIA DSX, we can help Korea scale sovereign intelligence infrastructure for the agentic era — from AI agents to AI factories and physical AI.”

“NAVER is building sovereign AI infrastructure that can serve Korea’s industries and global customers with trusted, high-performance AI,” said Haejin Lee, founder and chairman of NAVER. “By building on the NVIDIA DSX platform, we can help customers move from AI experimentation to production-scale AI factories that power models, agents and real-world services.”

As useful AI increasingly moves to production, AI factories are becoming critical infrastructure for training, post-training and inference. Built with the NVIDIA DSX platform with NVIDIA accelerated computing, NAVER’s AI factories will give Korea a sovereign foundation to create intelligence for enterprises, manufacturers, government organizations and AI cloud customers.

10/06/2026

At Computex 2026 Intel unveiled new innovations that address customers’ chip- to-systems-level AI needs with solutions tailored to address their specific industry challenges, including:

New rackscale AI infrastructure:
Intel announced rackscale AI infrastructure for customers interested in scaling their inference and agentic workloads based on Intel Xeon processors and SambaNova SN-50 Reconfigurable Dataflow Units (RDUs).

Agentic Cloud Offering for Disaggregated Inference:
Vector Core Compute, a new purpose-built enterprise inference cloud formed by Vista Equity Partners and Cambium Capital, unveiled fully disaggregated inference running on Intel Xeon processors, SambaNova RDUs, and NVIDIA Blackwell GPUs.

Deep industry solutions:
Strategic collaborations with industry leaders, including Foxconn, Siemens, Hitachi, Echo Neurotechnologies, and Greenstone Biosciences focused on delivering integrated vertical customer solutions based on Intel processors and purpose-built silicon.
Intel Xeon 6+ processors: Next-generation data center CPU built on Intel 18A and designed for high-density, scale-out workloads.

PC, gaming handheld, and physical AI momentum:

Broad partner support and customer uptake for the Series 3 family of processors.

“For more than five decades, Intel and its ecosystem partners have brought the world the foundational technologies for the PC, Internet, and now AI eras,” said Lip-Bu Tan, CEO of Intel. “Today, with the rise of inference, agentic, and physical AI, Intel is poised to bring the world new innovations from the chip to systems level that promise to transform industry and society for the better. We are proud to join all our partners in building great products that will delight customers and bring the power of AI to more people as we create a brighter future together.”

Rackscale AI Infrastructure for Inference and Agentic Workloads

As the training of AI models has matured, and more AI applications have moved into production, the industry has witnessed an exponential rise in the demand for cost-effective and power-efficient AI inference. With the emergence of agentic AI, the growing demand for AI inference is changing the balance of power in the data center, returning the CPU to a position of prominence.

According to Creative Strategies CEO and principal analyst Ben Bajarin, while “the training-era world looked closer to a one-CPU-per-four-GPU relation in AI deployments, agentic inference changes that relationship to roughly a one-CPU-to-one-GPU (or less) ratio.”

Intel, SambaNova, and Foxconn announced their intent to build rackscale AI infrastructure for data center, hyperscale, and intelligence center deployments—built on Intel Xeon processors.

The companies are demonstrating production-ready racks that combine Intel Xeon processors with SambaNova SN-50 RDUs, which together are designed to deliver high performance AI inference with improved cost and power efficiency. As part of the collaboration, Foxconn will provide system integration capabilities for the new rackscale AI infrastructure. Foxconn also plans to manufacture a CPU-dense variant of the rackscale infrastructure for workloads that do not require additional acceleration, including cost-optimized inference, data processing, and hybrid AI.

Agentic Cloud Offering for Fully Disaggregated Inference

Vector Core Compute, a new purpose-built enterprise inference cloud formed by Vista Equity Partners and Cambium Capital, unveiled fully disaggregated inference. Running onstage at Computex, Intel, SambaNova, Vista Equity Partners and Cambium Capital showcased the first real-world demonstration of a disaggregated inference system, using Intel Xeon 6 processors for orchestration and ex*****on, SambaNova SN40 RDUs for decode, and NVIDIA Blackwell GPUs for prefill—operating from a Vector Core Compute data center in Los Angeles, California.

Together.ai is the first commercial customer running workloads on Vector Core Compute’s agentic cloud, which delivered the fastest enterprise inference on the MiniMax 2.5 model of any architecture to date. Vista Equity Partners has secured early access to the company’s high-quality, low-cost inference solutions for its 90+ portfolio companies which serve more than 2.5 million enterprise customers and 750 million users worldwide.

Industry Specific Solutions Based on Intel Processors and Purpose-built Silicon

It is often stated that AI is transforming every industry. It is also true that the computing needs of specific industries vary widely due to differences in their business environments, processes, workflows, and customers.

Intel went in to announce several strategic partnerships designed to co-develop industry-specific vertical solutions based on Intel processors and purpose-built silicon, including:

Foxconn: The world’s largest electronics manufacturer is working with Intel to provide systems integration capabilities for rackscale AI infrastructure and explore collaboration in design services and custom silicon development.

Siemens: The leading technology company focused on industry, infrastructure, transport, and healthcare and Intel have expanded their existing collaboration. In 2023, Siemens and Intel first joined forces; now the two companies are strengthening their collaboration across the entire value chain from design to manufacturing to chips embedded in Siemens products. Siemens brings its capabilities for the design, manufacturing, and lifecycle management of chips, as well as fab digitalization, automation, and electrification. This collaboration will enable the exploration of use cases for purpose-built Intel silicon for Siemens’ varied compute requirements, which may include edge devices, high-performance computing (HPC), and robotics.

Hitachi: A global leader in digital innovation and sustainable solutions and Intel intend to work together on a range of solutions including foundry tools and quantum computing.

Intel also announced the availability of Intel Xeon 6+ processors, which provide greater performance density, power efficiency, and operational scale for cloud-native, agentic AI, and network-intensive workloads.

Built on Intel 18A—its first use in a data center CPU—Xeon 6+ is engineered for sustained performance under real-world power constraints—addressing the orchestration, concurrency, and data movement demands of emerging agentic AI.

Xeon 6+ can be configured for AI rackscale infrastructure purpose-built for hosting agents at maximum density. For example, a single liquid-cooled rack can deliver 36,864 cores using 32U of compute space, which provides the highest agent density available (at approximately 100-kilowatt rack power compute).

Optimized for environments where watts per rack, throughput per core, and latency predictability are critical, Xeon 6+ emphasizes scale-out performance—making room for new AI workloads without requiring disruptive data center redesign.

Core Ultra Series 3, built on Intel 18A, continues to experience strong customer uptake for a platform that now powers more than 325 consumer and commercial PC designs. Leveraging the same advanced IP as Ultra, the recently launched Core processors are enabling a new class of thin, sleek, powerful, and efficient PCs at affordable price points. Series 3 also pushes into the growing market of handheld gaming with the new Intel Arc G-series processors, which will be available starting this month. The expansion of the Series 3 processor family is being accelerated by increased 18A yields and strong customer and partner engagement.

Beyond the PC, Intel has powered edge devices in manufacturing, robotics, retail, and smart cities for decades. For the first time, the latest Series 3 IP scaling in the PC ecosystem will deploy in parallel to thousands of edge customers globally. Over 130 customers have already chosen Series 3 to power edge AI and robotics designs.

Build what's next on the AI Native Cloud. Full-stack AI platform for inference, fine-tuning, and GPU clusters — powered by cutting-edge research.

10/06/2026

Intel held a conference in Taiwan a few days ago called Computex 2026:

It introduced New Intel Core Series 3 laptops for Everyday Creation and All-day Productivity

One thing: Up to 20 hours of battery life versus the previous generation

One trend stood out across the Computex 2026 show floor: users want laptops that can keep up with their day without constantly searching for a charger.

Intel showcased six new thin-and-light laptop designs powered by the new Intel® Core™ Series 3 processor family (formerly codenamed Wildcat Lake), bringing together long battery life, built-in AI capabilities and the latest connectivity technologies for everyday users.

More than 70 designs from leading PC makers, including Acer, ASUS, Dell, HP, Lenovo, MSI and others, are expected to come to market in the months ahead.

Why it matters

The Intel Core Series 3 processor family is designed to deliver the experiences mainstream users care about most:

Built-in AI acceleration through an integrated NPU
Fast, modern connectivity with Thunderbolt™ 4, Wi-Fi 7 and Bluetooth® 6
Thin-and-light designs built for work, learning and everyday creation

"With the all-new Series 3 processor family, you get the latest connectivity features including Thunderbolt 4, Wi-Fi 7 and Bluetooth 6 technologies, along with an extremely capable NPU and GPU for everyday users," said Joseph Broderick, Intel technical marketing engineer.

As new Intel Core Series 3 systems begin arriving on shelves, consumers will see a new generation of mainstream laptops that combine portability, performance and efficiency in increasingly sleek designs.

09/06/2026

IBM:

Banks, governments and researchers are using synthetic data to protect privacy and save lives. But combining anonymity with accuracy remains a challenge.

In August 2024, the United States National Science Foundation gave Dr. Wei Zhai USD 75,000 to meticulously photograph, document and track the interiors of people’s houses.

He and his team of researchers, mostly his colleagues at the University of Texas San Antonio, planned to gather personal data about the interiors and exteriors of private residences in San Antonio’s Westside neighborhood and then build digital twins, or hyper-realistic simulations, of these homes.

Zhai is researching extreme heat in Westside, the hottest neighborhood in one of the hottest cities in America. With those digital twins, he and his colleagues hope to gather enough information about how houses trap heat to develop newer and more cost-effective methods of cooling. This, Zhai hopes, will help save lives in Texas, where at least 334 people and possibly hundreds more died from heat in 2023.

When Zhai pitched the project to Westside residents in 2025, they were understandably wary of the significant intrusion on their privacy this would entail. Because there simply isn’t much data on how homes are constructed and used in under-resourced communities like Westside, Zhai’s team needed to collect data on the homes’ interior layouts and properties. That required constant camera monitoring; data on temperature requiring their own finely-tuned sensors; and information about the construction of the homes themselves, which in turn required highly sophisticated LIDAR sensors to produce an accurate 3D model of the buildings from the foundation up.

Gathering the volume of real-world data necessary to build a realistic heat model of an entire neighborhood would be a challenge in any setting, but it would be especially daunting given the extensive detail they needed to collect about the most private areas of people’s homes, armed only with a USD 75,000 grant and a homespun PR campaign. Their problem, then, was twofold: convincing an understandably reluctant population to fork over private information, and then figuring out how to build a usable, highly detailed model based on what data they could glean.

To solve both problems, Zhai and his colleagues turned to an AI-powered solution: synthetic data, or artificial sets of training data that statistically replicate real-world sets that are too sensitive, or too meager, to use in live AI tools.

“We already have at least 20 homes where we’ve installed sensors, but the community is still kind of a data desert,” Zhai told IBM Think. “That’s why we’re using synthetic data to simulate data for other homes where they might not have the resources to install the sensors.”

Zhai has become an evangelist of sorts for the use of synthetic data for research in low-income communities, writing in a November 2025 essay about how it can ensure not just accuracy, but privacy for neighborhood residents. He also co-wrote an October 2025 article for the Journal of Planning Education and Research about how synthetic data can address “key challenges of privacy, reproducibility and technical feasibility.”

09/06/2026

IBM about AI:

Organizations that bolt AI controls on after deployment spend four times more of their AI budgets than those that build governance in from the start.

That’s one of the clearest and most surprising findings from a new study from the IBM Institute for Business Value (IBV) on AI governance. IBM researchers also found that organizations that design governance into their AI systems deploy up to 16 times more AI agents and achieve 18% higher operating margins.

As AI agents move from pilot programs into full agentic systems, organizations are entering a very different operating environment. According to the report, 77% of the organizations surveyed said that AI adoption was outpacing their ability to govern it. That gap matters. Traditional governance models—built around checkpoints, approvals and human review—simply can’t keep up with systems operating in real time.

Instead, leading organizations are rethinking governance as something embedded directly into how AI systems function. As Dena Almansoori, Group Chief Technology and Innovation Officer at ADNOC, puts it in the report: “Control has shifted from approving inputs to continuously supervising outputs and outcomes—from gates to guardrails.”

What does that look like in practice? According to the report authors, it starts with shared ownership across teams. Platform teams build the technical guardrails—things like telemetry, model registries, identity management, and logging and rollback capabilities. Risk and compliance teams define the policies: thresholds, audit requirements and escalation triggers. Architecture teams establish patterns and standards, ensuring that systems can scale and work together. Meanwhile, business teams take responsibility for outcomes, managing how AI is applied within defined limits. And when something goes wrong, incident response teams are ready with predefined containment and recovery plans.

This “orchestrated control” approach will only get increasingly important as the number of agents skyrockets. The IBV report found that by 2027, enterprises expect to deploy an average of 1,661 AI agents, a 38% increase from today.

For that reason, it’s critical that organizations shift governance from something reactive to something continuous and built in, the report authors argue. “Traditional governance functions design their processes around making it easy for the function to do its job, forcing the business to adapt,” Charles Newhouse, CTO at Leidos UK and Europe, told IBV. “AI is flipping that model—pushing governance, IT and security to think like service providers whose job is to enable the business, not slow it down.”

07/06/2026

Veeam:

Traditional manual privacy practices are insufficient for the agentic era. Veeam’s new AI agents operationalize privacy, consent, compliance, and AI governance at machine speed across modern enterprise data ecosystems

Veeam Advances Operational Privacy and AI Governance for the Agentic Era on the DataAI Command Platform

Veeam Software, the Data and AI Trust Company, today announced new agentic AI capabilities for the Veeam DataAI Command Platform designed to help organizations operationalize governance at the speed and scale of modern AI systems. Built on more than a decade of leadership in privacy operations, data intelligence, governance automation, and operational trust, Veeam's three new AI agents achieve what traditional privacy programs have struggled to achieve at enterprise scale: to continuously prove, with evidence, that policies are actually working across complex data and AI ecosystems.

Organizations are navigating the most complex regulatory environment in a generation. GDPR, the EU AI Act, ePrivacy, DORA, and emerging national and state AI regulations create obligations that span not just data but also AI models, consent signals, and cross-border transfers. Fines under these frameworks now reach up to 7 percent of global annual revenue. Yet many privacy programs still rely on manual assessments, spreadsheets, and disconnected workflows not designed to cope with AI agents that act on enterprise data at machine speed, generating compliance events faster than any human-operated program can track.

When a person makes privacy choices, such as opting out of tracking technologies or restricting how their data feeds an AI model, those preferences need to be respected by every system the data touches, not just the one where they clicked the button. The Consent Agent is a compliance detection and auto-remediation agent that gives privacy practitioners, legal professionals, and marketing operations like real-time visibility and reduced regulatory risk under GDPR, CCPA, and global laws. Alongside the Consent Agent, Veeam is introducing two additional new AI agents that take the most time-consuming operational work off privacy teams' plates, including automating web forms for privacy requests and common compliance assessments.

PrivacyOps agents built on the DataAI Command Platform agent framework

The three new PrivacyOps AI agents are designed to automate high-effort tasks that slow down privacy and AI governance programs. Together, they reduce operational overhead, eliminate implementation friction, and free privacy teams to focus on the judgment calls that actually require human expertise.

Consent Agent: A full-stack consent compliance and remediation agent that manages the end-to-end consent lifecycle, from banner creation and automated testing to continuous monitoring and auto-remediation. From customers' domains, it captures user consent signals, including cookie preferences, marketing opt-outs, revoked permissions for AI personalization, and downstream processing restrictions, then helps propagate and enforce them across every system that must honor them, including analytics platforms, AI pipelines, advertising technologies, SaaS applications, and third-party ecosystems. Powered by Veeam's regulatory database, it closes that gap with jurisdiction-aware risk scoring, centralized dashboards, and audit-ready evidence.

Data Subject Request Agent: Generates and maintains data subject request intake forms configured to your operational and regulatory footprint. Teams can stand up compliant forms in minutes and keep them current as regulations evolve, all without queuing legal review and developer time for every legislative change. This will reduce time to launch a Data Subject Rights (DSR) form by roughly 50 percent.

Assessment Agent: Analyzes supporting evidence to generate high-quality, tailored assessment responses with a single click, covering Data Protection Impact Assessments (DPIA), EU AI Act conformity assessments, and vendor risk questionnaires.
Built for operational trust across privacy, compliance, and AI governance

These capabilities are delivered through the Veeam DataAI Command Platform, the industry's first unified data and AI trust infrastructure for the agentic era. The platform unifies key domains, including DataAI Security, DataAI Governance, DataAI Compliance, DataAI Privacy, and DataAI Resilience, powered by the DataAI Command Graph, Veeam's intelligence layer with broadest coverage with hundreds of connectors across every cloud, SaaS application, and on-premises environment.

DataAI Privacy is powered by the People Data Graph, the industry's most advanced identity intelligence graph, to unify structured and unstructured personal data across hybrid multi-cloud environments. This enables real-time, jurisdiction-aware policy enforcement and produces audit-ready evidence of how intent and policy are applied. As a result, Veeam's new AI agents operate on live, continuously updated context, not assumptions or point-in-time snapshots, so governance can keep pace with the agentic era.

07/06/2026

Veeam Software, the Data and AI Trust Company, has unveiled new global research at VeeamON London. The new Data and AI Trust Gap report from Veeam exposes a stark and widening gap at the heart of enterprise AI. While 88 percent of organizations are already using or piloting AI agents, only 7 percent qualify as truly AI-ready and 95 percent say data challenges have already slowed their AI progress. As agentic AI moves from pilots into production, organizations face an urgent challenge: ensuring that the data powering those systems is visible, governed, secure and resilient.

The research, based on a global survey of 600 senior executives across financial services, healthcare, manufacturing, retail, and technology, reveals that AI adoption is scaling dramatically faster than the governance structures designed to manage it. Despite strong executive investment and intent, the ability to control, monitor, and recover from AI failures is critically underdeveloped.

Key findings show AI is scaling faster than control:

Only 7 percent of organizations are truly AI-ready.
88 percent are already using or piloting AI agents.
Only 28 percent are confident they can detect AI systems operating outside approved parameters.
95 percent say data challenges have already slowed AI progress.
The figures show a clear trust gap between AI adoption and the governance, visibility, and control required to support it.

07/06/2026

Akamai:

Cybercriminals are now targeting financial services more than any other industry for web and API (Layers 3 and 4) distributed denial-of-service (DDoS) attacks, Akamai reveals in its AI-Empowered Botnets and API Visibility Gaps: Attack Trends in Financial Services State of the Internet (SOTI) Security report. The findings highlight a dangerous shift as pro-Iran hacktivists and AI-driven bots weaponize DDoS tactics to disrupt online banking, payment systems, and critical applications.

Driven by AI-powered infrastructure, the median duration of global Layers 3 and 4 DDoS attacks targeting the financial services sector is up 738 percent since 2024. This shows that while digital transformation has enabled advances such as online banking and real-time payments, it has also widened the doors for attackers.

Key findings of the report include:

Among the financial service leaders surveyed for the 2026 API Security Impact Study, 96 percent reported at least one API security incident over the past 12 months, the highest among all industries.

In 2025, 60 percent of total web attacks and 83 percent of incursions against API endpoints targeted banking.

Nearly 80 percent of financial institutions have faced ransomware attacks in the past two years, yet less than half have adopted advanced security technologies.

Advanced bot activity surged by 147 percent in late 2025 — and, in one case study, a staggering 96 percent of all site traffic was identified as malicious scraping bots.

Cyberattack methods against financial services vary significantly by region: EMEA is the primary target for Layers 3 and 4 DDoS (62 percent), APAC is the most targeted by Layer 7 DDoS (52 percent), and in North America, web attacks are the most prevalent (44 percent).

“Cybercriminals and hacktivists continue to escalate DDoS from nuisance attacks to a sustained siege encompassing both hacktivism and cybercrime, and financial services are in the crosshairs," said Steve Winterfeld, Advisory CISO of Akamai. “In addition, the data shows that APIs are increasingly targeted as AI doesn't reduce traditional security risks, it puts them on steroids. Fortunately, financial services organizations can leverage the security strategies and best practices detailed in this report."

AI-Empowered Botnets and API Visibility Gaps: Attack Trends in Financial Services also includes: data-supported trends on criminal activity, a guest column by the CISO of FS-ISAC, a security spotlight on MITRE capabilities, a cloud spotlight on the differences among AI architectures, and practical DNS and DDoS attack mitigation strategies.