OpenAI for Germany: Sovereign AI for Public Sector with SAP Delos Cloud and Azure

OpenAI and SAP have announced a high-profile partnership — branded "OpenAI for Germany" — to deliver a sovereign AI service for Germany’s public sector by combining OpenAI’s models with SAP’s Delos Cloud and Microsoft Azure infrastructure, with a planned rollout in 2026 and an initial infrastructure buildout of 4,000 GPUs in Germany.

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Background​

Germany has pushed digital sovereignty to the front of its public-policy agenda for several years, driving investment in national and regional cloud capabilities that promise strict data residency, auditability, and legal accountability. The "OpenAI for Germany" initiative directly ties into those ambitions by promising AI services that are operated in Germany under German governance.
SAP positions Delos Cloud as its German sovereign-cloud offering designed for public-sector needs, and the company has publicly committed large, ongoing investments in sovereign infrastructure across Europe. OpenAI’s announcement frames the collaboration as a way to bring applied generative AI into administrative workflows while meeting Germany’s legal and security standards. Microsoft Azure will provide the hyperscale platform layer that Delos will run on.

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What the announcement says — the essentials​

• The launch name: OpenAI for Germany.
• Planned public-sector focus: governments, administrations, research institutions and other public bodies.
• Platform: SAP’s Delos Cloud, operated with Azure technology supporting the stack.
• Timeline: a public-facing rollout planned for 2026.
• Capacity baseline: SAP intends to expand Delos Cloud capacity in Germany to 4,000 GPUs for AI workloads, with the potential to scale further by demand.

SAP, OpenAI and Microsoft statement highlights are explicit in the announcement: SAP casts the work as an extension of its sovereign-cloud strategy; OpenAI emphasizes enabling safe, locally operated uses of its models in Germany’s public sector; Microsoft underscores Azure’s role in delivering compliance and resilience for government-grade services.

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Why this matters: practical and strategic context​

Germany’s public administration manages highly regulated datasets and mission-critical processes that make data sovereignty non-negotiable in many procurements. The OpenAI–SAP–Microsoft triad attempts a pragmatic compromise: deliver state-of-the-art models while keeping operations and data within German jurisdiction and under enhanced local governance. That approach seeks to balance capability, speed, and the legal safeguards demanded in regulated environments.
From a market perspective, the pact is notable for pairing a U.S.-based model developer (OpenAI) with a major German enterprise operator (SAP/Delos Cloud) and a global hyperscaler (Microsoft Azure). This configuration aims to preserve the technical advantages of modern AI while answering political and legal demands for national control and auditability.

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Technical architecture and sovereignty claims​

How Delos Cloud, Azure, and OpenAI are described to interoperate​

Public statements present a layered architecture:

• Delos Cloud acts as the sovereign operator: responsible for German governance, operational controls and legal contracts that keep administrative data within German jurisdiction.
• Microsoft Azure supplies the hyperscale compute, storage, platform services (identity, logging, SIEM), and networking fabric beneath Delos. Microsoft positions Azure as the regional substrate that can meet government-grade compliance.
• OpenAI supplies the foundation models and application-level services that will be operated in Delos Cloud for public-sector use. The models and inference workloads are stated to run in Germany on Delos-owned/operated infrastructure.

Unspecified technical details that matter​

Public materials state the intent and high-level guarantees, but several pivotal implementation details are not yet public:

• GPU families and exact hardware stack (GPU model types, NUMA topology, NVLink usage, networking fabrics) are not specified. These details determine latency, power consumption, performance-per-euro, and model-compatibility trade-offs.
• Telemetry, model-update mechanics, and training/provenance controls: statements promise local operation and governance, but the technical channels for updates, telemetry collection, and incident telemetry sharing across vendor boundaries are as-yet unspecified. These are critical to attesting to sovereignty in practice rather than only in contract.
• Containerization, orchestration and API layers: precise service boundary definitions (what runs in Delos vs. what requires cross-border operations) will matter for procurement teams validating compliance. Public materials do not fully disclose these operational constructs.

These gaps are not unusual for an initial announcement, but they are material for auditors, procurement teams and security architects. Any claim that the platform is fully sovereign should be treated as contingent on contractual controls, independent audits, and verifiable operational practices. Where such technical specifics remain unpublished, the claims about sovereignty should be flagged for additional verification during procurement.

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Use cases and immediate public-sector benefits​

Early use cases described by the partners and analysts emphasize productivity, risk reduction and better citizen services:

• Records and case management: automatic document summarization, metadata extraction, and assisted search over archival repositories.
• Administrative data analysis: faster synthesis of budget, policy and research datasets to accelerate decision cycles.
• Workflow automation: AI agents integrated into SAP-based administrative processes that propose or execute routine, auditable transactions.
• Research assistance: secure, sovereign model access for federal labs and universities that can speed literature reviews, code prototyping, and data analysis.

The core benefits claimed are measurable: time savings for civil servants, reduced backlog in citizen-facing services, and better information synthesis for policy teams. These are plausible outcomes when applied AI is integrated into well-scoped administrative tasks under strong governance.

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Key strengths of the approach​

• Pragmatic hybrid model: combines advanced model capabilities with local operational control rather than seeking full stack nationalism (which would delay access to leading models). This reduces time-to-value while attempting to respect sovereignty constraints.
• Scale intent: the stated target of 4,000 GPUs indicates planning for realistic production workloads beyond toy pilots; it signals commitment to meaningful deployment capacity rather than a small trial.
• Enterprise integration: SAP’s history in public-sector ERP and its Business Technology Platform give it domain credibility to embed AI into administrative workflows rather than offering disjointed consumer experiences.
• Political signalling: the partnership aligns with Germany’s High‑Tech Agenda and the “Made for Germany” initiative, offering a visible industry-government pathway to scale AI in the public sector.

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Risks, open questions, and governance failures to guard against​

• Vendor dependency and indirect access risk
  Even if infrastructure is operated in Germany, critical components — hypervisor patches, firmware updates, and model operator tooling — may originate beyond Germany’s borders. Unless contracts tightly limit cross-border flows and provide verifiable audit rights, sovereignty guarantees can be weakened in practice. Procurement teams should demand explicit contractual limitations and technical evidence of enforcement.
• Transparency of model updates and training data
  Governments must know model update cadence, training data sources for any fine-tuning, and the procedures used to mitigate bias and hallucinations. Public announcements lack a concrete audit roadmap for model provenance and update governance; this must be codified in procurement documents and independently verifiable.
• Telemetry and data leakage
  Metadata, usage signals, or aggregate telemetry can unintentionally leak information about workloads or citizens. The partners must define the exact telemetry scopes, retention policies, and access controls. Without those specifics, claims of sovereignty remain incomplete.
• Cost and procurement complexity
  Sovereign cloud offerings often carry price premiums. Public budgets and procurement procedures in Germany (and across Länder) could slow large-scale adoption without coordinated funding or subsidies. Smaller municipal bodies may struggle with cost and technical integration.
• Legal and administrative accountability
  Any automation that affects citizens (benefits decisions, legal statuses, etc.) must preserve contestability and explainability under German administrative law. Pilot projects should be carefully scoped to internal administrative tasks before moving to decisions that affect rights.
• Geopolitical optics and political scrutiny
  Using U.S.-based AI models and a U.S. hyperscaler beneath a German operational layer will be scrutinized by privacy advocates and policymakers who prefer fully local stacks or open-source alternatives. The partnership is a political compromise — sensible operationally, but not universally accepted.

Where public statements are high-level and technical specifics are absent, procurement teams should treat vendor claims as hypotheses to be validated by pilots, third-party audits, and contractual protections. Any single claim that cannot be independently verified should be flagged in procurement documentation.

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A short checklist for government IT and procurement teams​

• Start with narrow pilots limited to internal administrative tasks and high-auditability processes.
• Require contractual commitments on telemetry scope, retention, and cross-border data flows, with penalties for violations.
• Insist on independent model audits and continuous monitoring for drift, bias, and hallucination rates.
• Demand exportability of logs and the right to archive inputs/outputs for forensic review.
• Budget for governance, monitoring, and specialized teams — sovereign AI is operationally intensive.

These steps align with the broader governance framework recommended by independent analysts: pilot, instrument, audit, and then scale where evidence shows consistent gains.

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Competitive landscape and European policy context​

The announcement is consistent with a wider European movement toward sovereign or regionally operated AI offerings. Several vendors and national initiatives are racing to provide local alternatives that emphasize inspectability and legal compliance. OpenAI for Germany may become a de facto model for other European public-sector projects — or it may compete with open-source and locally owned alternatives, depending on cost, transparency, and audit results.
The partnership explicitly ties into Germany’s High‑Tech Agenda, which includes ambitious targets for AI-driven value creation, and into the “Made for Germany” initiative, which reports large-scale industrial commitments. Those policy anchors give the program political traction but also raise expectations for measurable outcomes and legal robustness.

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Strategic implications for the partners​

• For SAP: The deal reinforces its sovereign-cloud positioning and gives Delos Cloud a high-profile public-sector anchor that could expand SAP’s reach into government transformation projects across Europe. SAP’s multi-billion euro investment messaging signals long-term commitment.
• For OpenAI: The partnership opens regulated public-sector markets in Europe under a local-operational model, a route to scale in spaces where direct cloud-hosting or consumer offerings would not meet legal constraints. It also tests OpenAI’s ability to adapt governance promises to sovereign frameworks.
• For Microsoft: Azure’s role validates Microsoft’s business strategy of being the hyperscaler partner for sovereign platforms. Azure gains another enterprise/government anchor and reinforces its compliance messaging for public-sector customers.

Market reactions (shares and analyst commentary) showed immediate investor interest, reflecting the commercial significance of large public-sector engagements. However, execution risk — not strategy risk — will determine whether this partnership delivers the promised benefits.

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Short and medium-term milestones to watch​

• Detailed technical disclosures: Where the 4,000 GPUs will be physically located, which GPU families will be used, and the orchestration/telemetry architecture. These details will shape cost, resilience and compliance profiles.
• Procurement contracts from federal ministries and Länder: The first procurement templates and legal terms will set precedents for future deals.
• Independent audits and oversight frameworks: Publication of third-party audit results or government evaluation frameworks will determine whether the platform meets public accountability standards.
• Pilot outcomes and KPIs: Evidence of time saved, error rates, citizen service improvements and cost impacts from early pilots will determine adoption decisions.

Where public statements currently lack detail, the first round of procurement documents and audit reports will be decisive in validating sovereignty claims in practice.

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Final assessment​

OpenAI for Germany is a pragmatic, well‑resourced effort to reconcile two pressing realities: governments want the productivity gains offered by modern AI, and national regulators demand legal and operational sovereignty. By combining OpenAI’s models, SAP’s Delos Cloud and Microsoft Azure, the partnership offers a realistic route to deliver advanced AI into regulated public-sector environments — but its success depends on execution, contractual clarity, and independent verification.
The announcement’s strengths are its pragmatic hybrid architecture, scale intent, and enterprise integration potential. The risks are equally tangible: technical opacity on key implementation details, telemetry and update governance, vendor dependency, and procurement/cost barriers for smaller public bodies. These unresolved areas must be addressed in procurement contracts, independent audits and clear operational playbooks before governments shift mission‑critical workloads onto the platform.
Until the partners publish detailed technical, contractual and audit artifacts — or until independent auditors validate the promised controls — claims about full sovereignty should be treated as conditional and subject to verification. Procurement teams should insist on pilots, measurable KPIs, exportable logs, and enforceable contractual safeguards before expanding deployments beyond narrowly scoped administrative tasks.
OpenAI for Germany marks an important chapter in Europe’s sovereign-AI story: it demonstrates how commercial AI advances can be adapted for regulated environments, but it also underscores a central truth — sovereignty in name is not sovereignty in practice unless it is backed by auditable engineering, enforceable contracts, and continuous independent oversight.

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Source: The Economic Times OpenAI for Germany: SAP, OpenAI partner to boost public sector AI - The Economic Times

 

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OpenAI and SAP have announced a high‑profile collaboration to deliver a sovereign AI service targeted at Germany’s public sector, pairing OpenAI’s models with SAP’s Delos Cloud and Microsoft Azure infrastructure in an effort to meet strict German data‑sovereignty, security, and compliance requirements. The initiative — marketed as OpenAI for Germany — promises to run model inference and related workloads inside Germany, with SAP planning a substantial GPU capacity expansion and Microsoft supplying the hyperscale cloud foundation; public statements and reporting place an initial capacity target at roughly 4,000 GPUs and indicate a staged rollout aimed at government and research customers.

Background​

Germany has pursued digital sovereignty as a strategic policy priority for years. That push has produced a crowded market of sovereign‑cloud projects and supplier arrangements designed to keep public‑sector data under German legal control while still leveraging the technical power of global cloud and AI vendors. SAP’s Delos Cloud is explicitly positioned as a German‑operated sovereign platform that layers national governance, cryptographic controls and operational oversight on top of hyperscaler technologies to satisfy the Federal Office for Information Security (BSI) and procurement rules. The OpenAI–SAP–Microsoft triad attempts to reconcile the tension between capability (modern foundation models) and control (local operation and legal jurisdiction).
This collaboration is notable for its pragmatic compromise: rather than attempt to re‑build world‑class LLM capabilities inside Europe from scratch, the project wraps U.S. model provider technology with a German operational shell and hyperscaler capacity hosted in Germany. That hybrid model is the same architecture increasingly proposed by governments that want advanced AI without ceding legal control over sensitive data.

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What the announcement says — the essential facts​

• The program is a three‑party initiative among OpenAI, SAP/Delos Cloud, and Microsoft Azure designed for German federal and regional public administrations, research institutions, and regulated entities.
• Public materials state models and inference workloads will execute on Delos Cloud infrastructure physically located in Germany.
• SAP has signaled a GPU capacity target of about 4,000 GPUs as part of the initial buildout to host model workloads; this is presented as a baseline for future scaling.
• The rollout is framed as phased and practical: initial pilots and narrow public‑sector use cases are expected prior to broader adoption.

These headline claims are the parts of the announcement most relevant to procurement teams, IT architects and policy makers. Where details are sparse — notably on exact hardware families, telemetry flows, update cadence, and contractual limits on cross‑border dependencies — commentators and analysts have flagged those as critical follow‑ups before any large‑scale procurement.

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Technical architecture: how sovereignty is being defined​

The hybrid stack: Delos Cloud + Azure + OpenAI models​

The declared architecture is layered:

• Microsoft Azure supplies hyperscale compute, storage, networking, identity and platform services — the raw cloud primitives that support large model hosting at scale.
• SAP Delos Cloud operates the sovereign layer: local operational governance, cryptographic services, certificate management, and contractual assurances that administrative data remains under German jurisdiction.
• OpenAI provides the foundation models and model lifecycle (updates, fine‑tuning, model management) while those models are hosted and served inside the Delos Cloud environment.

This model attempts to separate jurisdictional control (who operates services and where data sits) from technical dependency (who supplies the AI models and core cloud platform). In practice, sovereignty is being operationalized through location controls, governance overlays, and contractual oversight rather than by complete technology stack indigenization.

What remains unspecified and why it matters​

Public statements leave several low‑level but consequential implementation details unspecified:

• The exact GPU types (NVIDIA Ampere vs Hopper variants, A100 vs H100, etc.), the networking fabric and hypervisor/container orchestration choices are not disclosed publicly. These choices materially affect latency, cost, power, and cooling requirements.
• Model update mechanics — how OpenAI will deliver model patches, telemetry, and training‑data provenance information to Delos Cloud customers — are not fully described. That matters for auditability and regulatory proof of compliance.
• Telemetry, logging and metadata flows: even small telemetry signals can expose operational control points or trigger legal cross‑border transfer concerns; contracts and technical measures must explicitly constrain and disclose these flows.

Because these are implementation issues rather than marketing claims, procurement teams should treat the public announcement as a functional roadmap and insist on technical addenda or attachments that detail these elements before signing large contracts.

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Use cases and near‑term value for public administration​

The announced use‑cases focus on administrative productivity and process automation rather than policy‑deciding systems. Typical early applications include:

• Records and case management: automatic summarization, metadata extraction and assisted search across document repositories.
• Administrative data analysis: faster synthesis of budgets, reports and structured datasets to accelerate policy evaluation and planning.
• Workflow automation: embedding agents in SAP‑based workflows to propose or execute routine administrative transactions with auditable trails.
• Research acceleration: allowing public labs and universities to use sovereignly hosted models for literature review, code assistance and prototype generation.

These are practical starting points because they can be scoped narrowly, instrumented for audit, and limited to internal processes that don’t immediately affect citizens’ legal rights. Early pilots in these areas can produce measurable KPIs (time saved, error reduction, throughput improvements) and create the governance templates needed for more sensitive use.

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Strengths and strategic positives​

This initiative brings several real advantages to the table:

• Pragmatic compromise between sovereignty and capability. By combining a German operational layer with global cloud and model expertise the project aims to unlock immediate production‑grade AI capabilities without waiting years for indigenous model development.
• Operational scale planning. A publicly stated GPU capacity target (about 4,000 GPUs) signals an intent to support meaningful, production workloads rather than small experiments. That capacity enables larger inference loads, vector stores and retrieval‑heavy applications common in enterprise AI.
• Enterprise integration potential. SAP’s historic footprint in German public administration — through ERP, BTP and sector verticals — gives the project an integration advantage: the AI can be embedded into existing administrative systems rather than remaining a point product.
• Political and investment signaling. The pact is likely to catalyze further public and private investment into sovereign AI infrastructure, showing a practical route to modern AI under European jurisdictional controls.

These strengths explain why both officials and industry watchers see the initiative as a credible path for near‑term sovereign AI adoption.

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Risks, gaps and governance red flags​

The announcement also exposes several real risks that must be mitigated through procurement, technical audits, and independent oversight:

• Vendor dependency and hidden cross‑border flows. Even when infrastructure is operated locally, the stack depends on non‑German suppliers. Unless contracts and technical controls strictly forbid and monitor cross‑border telemetry and code update mechanisms, legal sovereignty can become an illusion.
• Opaque model provenance and update cycles. Public bodies will need to know what training data affects model behavior and how model updates are governed. Without explicit auditability and patch transparency, models deployed in administrative settings risk introducing systemic bias or hallucinations into decision processes.
• Telemetry and metadata leakage. Aggregated or seemingly benign metadata could leak patterns that regulators consider personal data or sensitive. Procurement documentation should mandatorily limit telemetry, and grant audit rights to independent third parties.
• Cost and procurement complexity. Large‑scale GPU capacity and managed sovereign operations are expensive. Smaller municipal administrations may struggle with cost unless national subsidy or shared procurement frameworks are established.
• Regulatory and legal scrutiny. EU and German procurement law, data‑protection statutes, and administrative law principles require transparency, contestability and auditability for systems that process public data or assist in public decisions. Any solution automating administrative outcomes must be designed with legal rollback and human‑in‑the‑loop guarantees.

In short, the partnership is promising but not yet a turnkey solution: the technical and contractual fine print will determine whether government sovereignty is preserved in practice.

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Procurement checklist: what governments should demand​

• Require a technical annex that specifies hardware families, GPU SKUs, hypervisor/OS baselines and physical data‑center locations.
• Insist on explicit telemetry and update policies that restrict cross‑border data flows and mandate machine‑readable logs of model updates and provenance.
• Include independent audit rights and periodic third‑party model audits (bias, safety, provenance), plus forensic access to inputs/outputs on request.
• Define clear human‑in‑the‑loop requirements for any decision that affects legal entitlements, with rollback and remediation procedures.
• Negotiate exit and portability clauses (data export, model weights where possible, reproducible pipelines) to avoid irreversible vendor lock‑in.
• Budget for ongoing governance costs: specialized staff, legal oversight, logging storage and incident response.

These are practical guardrails to ensure that promising AI technology yields public value while limiting legal and operational exposure.

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Competitive landscape and alternatives​

This partnership is not the only path to sovereign AI in Europe. Alternatives include:

• European model providers and open‑source models that can be hosted in local data centers to provide maximal code‑level transparency and fewer third‑country dependencies.
• Private‑cloud managed offerings from local providers that combine European software stacks with certified facilities, targeting SMEs and municipalities that cannot afford custom sovereign builds.
• Hybrid architectures that mix local models for sensitive tasks with cloud‑based large models for non‑sensitive workloads, controlled via strict data‑handling policies.

Procurement choices will likely reflect a spectrum: fully local stacks for the most sensitive workloads, hybrid sovereign‑hybrid options for large administrative systems, and commercial sovereign offerings (like Delos) where scale and integration with enterprise software (SAP) are decisive.

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What to watch next — milestones and verification points​

• Technical disclosure of GPU types and locations. Confirm whether the quoted 4,000 GPUs are a mixture of CPU/GPU classes and where those servers are physically hosted. This will affect energy, latency and TCO calculations.
• Procurement decisions from federal ministries and Länder. Early contracts will reveal templates and audit clauses that other agencies will follow.
• Publication of audit frameworks by German authorities. Expect the BSI and independent auditors to publish guidelines that will shape permissible deployments.
• Third‑party model audits and transparency reports. Watch for independent verification of model behavior, update cadence, and telemetry controls that will determine trustworthiness at scale.

These milestones will decide whether the project is a practical vehicle for sovereign AI or only a partial fix that still channels trust outside national boundaries.

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Final assessment — pragmatic promise with necessary caveats​

The OpenAI + SAP + Microsoft arrangement represents a pragmatic and well‑resourced attempt to reconcile two hard realities: governments want the productivity gains from modern AI, and regulators demand legal, operational, and data sovereignty. The approach — wrapping global AI capability in a German‑operated sovereign layer — is logically compelling and could deliver measurable benefits for administrative efficiency and research productivity.
However, the public statements released so far are high level and leave several critical operational questions unanswered: telemetry and model‑update governance, detailed hardware and networking architecture, contractual limits on cross‑border dependencies, and enforceable independent audit mechanisms. If procurement and technical teams fail to insist on those details, the sovereignty guarantees risk becoming symbolic rather than substantive.
The most realistic near‑term outcome is that this hybrid model will become one viable route for German public‑sector AI, particularly where integration with SAP applications matters. Whether it becomes the dominant approach will depend on how rigorously contracts, audits and technical disclosures are enforced — and whether credible, truly local alternatives gain traction. Until the implementation details are publicly documented and independently verified, agencies should proceed with cautious, measured pilots and strict governance guardrails.

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OpenAI for Germany has the potential to accelerate public‑sector modernization under a sovereignty banner — but real sovereignty requires more than a local datacenter pin on a map. It demands contractual clarity, technical transparency and enforceable oversight that together turn the promise of local control into operational reality.

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Source: Mobile World Live OpenAI, SAP up Germany sovereignty efforts