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Future networks will need to do more than transport data reliably. AI can become an integral part of how networks understand intent, interact with their physical surroundings, support autonomous systems and continuously adapt to changing conditions. Combined with 6G, sensing and edge capabilities, this opens the door to networks that connect digital intelligence with the physical world.

We’re looking for innovations that explore this convergence and rethink what an AI-native network could enable — from intent-driven operation and intelligent infrastructure to autonomous field operations and new capabilities for Physical AI.

What We’re Looking For

Here are some priority areas, though we welcome bold, creative ideas beyond this list:

• Sense & Respond: How could networks use sensing of their physical environment to detect events, changes or risks and autonomously decide how to respond?
• Autonomous Field Operations: How could AI-powered robots, drones or other autonomous systems inspect, troubleshoot, maintain or repair network infrastructure with minimal human intervention?
• AI-Native Network Infrastructure: How could network infrastructure become more aware of its surroundings and autonomously adapt coverage, capacity, energy consumption or configuration based on what is happening in the physical world?
• Networks for Physical AI: How could networks autonomously provide the connectivity, compute and intelligence needed by robots, vehicles, drones and other Physical AI systems as they move and operate?
• Beyond Connectivity: What new autonomous-network use cases become possible when future networks can both communicate and sense people, objects, movement and their environment?
• AI & Intent: How could users or operators express what they want to achieve, while AI autonomously determines, executes and continuously adapts how the network delivers it?
• Convergence: What new use cases become possible when AI, 6G, sensing, intent and Physical AI come together to create networks that can understand goals, perceive their environment and autonomously act?

Why It Matters

AI-native networking has the potential to fundamentally expand the role of telecommunications infrastructure. Networks could evolve from systems that primarily provide connectivity into intelligent platforms that interpret intent, interact with their surroundings and enable autonomous machines and applications in the physical world.

Bringing together AI, 6G, sensing and Physical AI could unlock entirely new network capabilities, operational models and customer use cases. We’re interested in ideas that turn this convergence into practical, scalable solutions and demonstrate how intelligent networks can create value beyond connectivity.

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Can we build networks where energy consumption continuously follows actual demand?

Connectivity and computing demand continue to grow rapidly. AI, cloudification, edge computing and future 6G services will create unprecedented requirements for network and data-center capacity. Simply making individual components more efficient will not be enough.

For the next generation of networks, we need a fundamentally different capability: Energy Adaptability.

An energy-adaptable network dynamically adjusts the resources it activates—and therefore the energy it consumes—according to actual traffic, compute demand, service intent and operating conditions.

Our north star remains Zero Bit – Zero Watt: when there is no useful work, energy consumption should approach zero. But Energy Adaptability goes further. Across the entire load curve, from idle to peak demand, network and compute resources should activate only when, where and for as long as they are needed.

We are looking for startups, researchers and academic teams that can help make this vision a reality.

The Challenge

Today's telecom networks and data centers are designed primarily for availability, performance and peak capacity. As a consequence, energy consumption often remains significant even when traffic or compute utilization is low.

The challenge is to make energy consumption increasingly proportional to useful work, while maintaining—or improving—customer experience, reliability and network performance.

This may require innovation across software, algorithms, network architecture, signaling, silicon, AI, cloud infrastructure, cooling and control systems.

AI can be a powerful enabler, but it is not a prerequisite. We also welcome fundamental architectural, hardware and protocol innovations that unlock new energy-adaptation capabilities.

We are particularly interested in solutions capable of operating across large, heterogeneous and brownfield telecom environments in Europe and the United States.

What We Are Looking For

Strong proposals should demonstrate one or more of the following capabilities:

• Make energy consumption follow demand: Dynamically activate, scale, consolidate, relocate or deactivate resources according to traffic, compute workload or service requirements.
• Reduce idle and low-load power: Enable deeper sleep states, selective shutdown, resource consolidation or new architectures that substantially reduce the energy floor of network and computing infrastructure.
• Optimize across network layers: Coordinate energy decisions across RAN, transport, core, edge, cloud and data-center infrastructure instead of optimizing individual components in isolation.
• Use intelligence to anticipate demand: Apply AI, machine learning or advanced control techniques to predict traffic and workload patterns and proactively configure resources.
• Design intelligence into the infrastructure: Explore AI-native silicon, event-driven computing, photonics, hardware acceleration and other architectures that fundamentally improve the relationship between performance and energy consumption.
• Mitigate increasing data-center power demand: Enable power-aware computing, AI workload optimization, dynamic resource placement, accelerator utilization, cooling optimization and orchestration across servers, racks, clusters and data centers.
• Maintain service quality: Energy adaptation must preserve defined customer-experience, latency, throughput, resilience and availability requirements.
• Work in real networks: Solutions should provide a credible integration path into heterogeneous, multi-vendor and brownfield environments using clearly defined interfaces and APIs.
• Scale: Solutions should have the potential to operate across thousands or millions of network components and across multiple markets.
• Demonstrate measurable impact: Proposals should quantify both energy benefits and their impact on performance, utilization and total cost of ownership.

Areas of Particular Interest

• 1. Energy-Proportional Network & Compute Orchestration: Solutions that dynamically determine what resources need to run, where they should run and when they can be switched off, across network, edge and cloud infrastructure. Examples include traffic-aware CNF/VNF placement, workload consolidation, power-aware routing and resource orchestration.
• 2. Adaptive Core Networks: Intelligent 5G/6G Core solutions that minimize the resources required to deliver a service. Examples include self-optimizing service meshes, dynamic traffic steering, control-plane optimization, scaling of network functions and selection of the most energy-efficient processing path.
• 3. AI-Native and Energy-Native RAN: Innovations combining hardware and intelligence to fundamentally reduce radio-network energy consumption. Areas could include AI-native silicon, spiking or event-driven neural networks, energy-aware L1 processing, hardware acceleration and photonics-electronics convergence.
• 4. Adaptive 6G Signaling: New approaches that reduce the energy associated with continuously active signaling and network broadcasts. We welcome concepts such as adaptive synchronization signals, on-demand signaling, multi-layer carrier architectures and mechanisms that allow significantly deeper network sleep without sacrificing coverage or accessibility.
• 5. Collaborative Edge Intelligence: Architectures that intelligently distribute training and inference between devices, edge infrastructure and central cloud. Solutions could use federated learning, distributed inference or workload placement to minimize unnecessary data transport and computing while meeting application requirements.
• 6. Energy-Adaptable Data Centers & Telco Cloud: Solutions addressing the rapidly increasing power requirements of cloud and AI infrastructure. We are looking for innovations in areas such as power-aware workload placement, server and accelerator power management, dynamic capacity activation, liquid or intelligent cooling, cluster consolidation, AI workload scheduling and coordination between network demand and data-center resources. The objective is not simply a more efficient server—but a data center whose total power demand adapts dynamically to the useful computing work being performed.
• 7. Energy-Aware AI: Solutions that make AI itself more energy adaptable, including dynamic model selection, precision adaptation, inference placement, accelerator utilization, model compression and mechanisms that select the minimum computing resources necessary to satisfy a given intent.
• 8. Grid-Interactive Networks and Data Centers: Approaches that allow telecom infrastructure to adapt electricity demand in response to grid conditions, renewable-energy availability or energy prices—without compromising telecommunications services. This could include intelligent workload shifting, flexible cooling or compute demand, distributed energy resources and other mechanisms that turn networks and data centers into more flexible electricity consumers.

What Success Looks Like

We are looking beyond a one-time percentage reduction in energy consumption.

A compelling solution should demonstrate that the network or infrastructure becomes more adaptable:

• Less demand → fewer active resources → lower power consumption.
• More demand → resources activate where they create the greatest value.
• Demand changes → the infrastructure responds autonomously, quickly and safely.

The strongest solutions will combine significant energy impact, customer-safe operation, technical innovation, scalability and a credible path toward deployment within Deutsche Telekom and T-Mobile networks.

Help us shape a world of connected intelligence in which networks are not only intelligent—but intelligent about every watt they consume.

Autonomous Networks Level 5 — Trusted Decisions, Self-Evolving Networks

Imagine a network that runs itself end to end, and that you can trust to do so. Level-5 autonomy is not more automation. It is a network whose AI takes high-quality decisions without a human in the loop, knows the limits of its own judgement, can explain and prove why it acted, and keeps evolving while staying within a safe, verified envelope.

We’re looking for bold technical innovations that make this level of autonomy achievable: world models and network digital twins that let the network test an action before taking it, methods that make critical behaviour structurally impossible, explainability and provenance that turn trust into an engineering property, and ways to judge the quality of what a self-evolving network creates on its own.

What We’re Looking For

Here are some priority areas, though we welcome bold, creative ideas beyond this list:

• High-Quality Decisions: How could an autonomous network take consistently high-quality decisions at scale, using world models, network digital twins or learned simulators to evaluate an action before executing it in the live network?
• Safe by Design: How could critical or irreversible behaviour be prevented structurally rather than detected afterwards, for example through safety envelopes, verified action spaces, shielding of learning agents, blast-radius containment and guaranteed rollback?
• Knowing When Not to Decide: How could the network quantify its own uncertainty, recognise situations it has never seen, and escalate to a human as a calibrated decision rather than a fallback?
• Explainable and Provable Decisions: How could every autonomous decision be traced to its cause, explained in operational terms and audited afterwards, so that trust becomes measurable rather than assumed? What role could telco foundation models play in making decisions transparent?
• Robust Against Manipulation: How could autonomous decisions stay sound when the telemetry, models or agents they rely on are corrupted or attacked, for example through poisoned data, adversarial inputs or compromised agents holding write access to the network?
• Self-Evolving Networks: How could a network go beyond executing decisions and evolve itself, deriving new configurations, capabilities or service definitions on its own, while remaining reversible and inside a verified envelope?
• Judging Auto-Innovation: When a self-evolving network autonomously produces an artefact, such as a technical specification, a product description or a new service design, how could the quality of that output be assessed? What methodology tells us whether an auto-innovation loop is good enough to act on?

Why It Matters

Level-5 autonomy is the point where the network no longer needs a human to approve its decisions. That only works if those decisions are demonstrably good and demonstrably safe. Technical trust, not organisational sign-off, is what unlocks the next era of connected intelligence. We’re looking for scalable, verifiable ideas that can be tested in real networks and applied widely.

Sensing & Physical Intelligence

Imagine a mobile network that doesn’t just connect the world — it can sense, understand and respond to it. Integrated sensing could turn networks into a source of real-time intelligence, creating new possibilities for Physical AI, automation and smarter services.

We’re looking for bold solutions that make network sensing scalable, trustworthy, privacy-preserving and actionable.

What We’re Looking For

Here are some priority areas, though we welcome bold, creative ideas beyond this list:

• Sense, Understand & Act: How could mobile networks use integrated sensing to understand what is happening in the physical environment and autonomously respond to events, changes or risks?
• Sensing as a Network Service: How could operators turn network sensing into a scalable service, exposing useful insights about objects, movement and the environment to applications through standardized and easy-to-use APIs? How to enable intelligence on top of the sensing data to provide customers with answers to their questions, not just data about objects. How to know in “real-time” if we can meet the sensing request requirements from the customer. The Ray tracing approach is quite complex and compute intensive, on the other hand abstraction can be too far from the reality.
• ISAC for Physical AI: How could network-based sensing, connectivity and edge intelligence provide robots, drones, vehicles and other Physical AI systems with reliable situational awareness beyond what their onboard sensors can provide?
• Privacy-Preserving Sensing: How could network sensing deliver valuable information while minimizing collection and exposure of information about people and ensuring privacy, security, transparency and appropriate control over sensing data? The privacy is a high priority for us.
• Autonomous Communication–Sensing Orchestration: How could AI dynamically balance communication and sensing resources—including spectrum, beams, power and compute—to deliver sensing capabilities without compromising communication services or energy efficiency?
• Cooperative and Multi-Source Sensing: How could sensing information from multiple base stations, devices, vendors or sensing technologies be combined to create more reliable and accurate awareness of the physical environment? How to manage the presence or lack of trust in the data to provide trustworthy service?

Why It Matters

Network sensing could transform mobile networks from communication platforms into intelligent systems that understand the physical world. By combining sensing, connectivity and AI, networks can enable safer automation, smarter services and new capabilities for Physical AI. We’re looking for scalable, trustworthy and privacy-preserving ideas that can turn sensing into real-world value.

Quantum Technologies: From Quantum Resources to Network Services

Imagine a network where quantum resources can be accessed as easily as connectivity or cloud compute today. Entanglement, quantum-state transmission and other quantum capabilities could become dynamically available across users and locations — turning emerging quantum infrastructure into services that can be discovered, orchestrated and consumed on demand.

We’re looking for bold technical innovations that bring quantum technologies into real-world network environments: from architectures for distributing and orchestrating quantum resources to novel applications, integration with classical telecom and cloud infrastructure, and service models that can turn quantum capabilities into tangible customer value.

What We’re Looking For

Here are some priority areas, though we welcome bold, creative ideas beyond this list:

• How can quantum resources become a network service? How could the transmission of quantum states or the distribution of entanglement be offered on demand, between different users and locations, much like connectivity and cloud resources are provided today?
• What are the killer applications for networked quantum resources? Beyond established concepts such as QKD, which novel applications could create tangible value from remotely shared entanglement or the ability to transmit qubits?
• How should a quantum network be controlled and orchestrated? What new architectures, protocols and control mechanisms are needed to discover, allocate, route and assure quantum resources dynamically across heterogeneous network infrastructures?
• How can quantum and classical networks work together? How can quantum communication capabilities be integrated with existing telecom and cloud infrastructure, including classical optical networks, compute resources and applications, while remaining scalable and operationally viable?
• How can quantum-network services become commercially viable? What service models, performance metrics, SLAs and business models could turn scarce and technically demanding quantum resources into differentiated services that customers are willing to use and pay for?

Why It Matters

Quantum networking could extend telecommunications beyond the transmission of classical information and introduce entirely new network resources and capabilities. The opportunity is not only to build quantum links, but to make quantum resources accessible, orchestratable and useful as part of future network and cloud infrastructures.

We’re looking for scalable ideas that bridge the gap between quantum technology and real-world telecom networks — and help identify where networked quantum resources can create value that classical networks cannot.