HPE’s 2025 AI Strategy: Optimizing Wave Energy with Machine Learning
Hewlett Packard Enterprise Projects: Applying AI to Commercial Wave Energy
Hewlett Packard Enterprise is leveraging its advanced artificial intelligence and high-performance computing capabilities to optimize clean energy generation, shifting from broad infrastructure sales to targeted, high-value applications.
- Between 2021 and 2024, HPE established a foundational partnership with Carnegie Clean Energy to apply AI and machine learning to optimize the performance of Carnegie’s CETO wave energy technology, demonstrating an initial strategic entry into renewable energy optimization.
- This initiative was part of a larger pattern of engaging the energy sector, which also included building a next-generation supercomputer for Italian energy company Eni to support its energy transition research and partnering with Norwegian data center operator Green Mountain to sustainably host power-intensive AI workloads.
- In November 2024, HPE extended its agreement with Carnegie Clean Energy through November 2026, validating the initial project’s success and signaling a move from a pilot phase to a longer-term optimization and commercial application phase for its AI technology in the wave energy sector.
- Starting in 2025, this focused application in wave energy is supported by HPE‘s massive investment in a full-stack AI portfolio, including the $14 billion acquisition of Juniper Networks and the co-developed HPE Private Cloud AI with NVIDIA, which provides the necessary infrastructure to process complex energy data.
HPE’s AI Investment Analysis: Building the Foundation for Energy Sector Applications
Table: Hewlett Packard Enterprise’s Strategic AI Investments (2021-2024)
| Partner / Project | Time Frame | Details and Strategic Purpose | Source |
|---|---|---|---|
| Investment in Recogni (via Juniper Networks) | Nov 15, 2024 | Juniper’s $102 million Series C participation in AI inference startup Recogni adds capabilities in efficient AI processing, relevant for edge applications like analyzing data from distributed energy assets. | Juniper Networks To Back AI Startup Recogni In $102M … |
| Acquisition of Juniper Networks | Jan 9, 2024 | The ~$14 billion acquisition provides HPE with an AI-native networking portfolio, critical for managing the high-volume data streams generated by renewable energy projects and other distributed AI workloads. | HPE to Acquire Juniper Networks to Accelerate AI-Driven … |
| Investment in Aleph Alpha | Nov 6, 2023 | Through its venture arm, Hewlett Packard Pathfinder, HPE invested in Aleph Alpha’s >$500 million Series B, securing access to sovereign and trustworthy AI technology applicable to regulated sectors like national energy grids. | Aleph Alpha raises $500 million to build a European rival … |
| Acquisition of Pachyderm | Jan 12, 2023 | This acquisition added software for reproducible AI at scale, enabling complex, data-intensive modeling like that required for optimizing wave energy converter performance with verifiable and repeatable results. | Hewlett Packard Enterprise acquires Pachyderm to expand … |
| Investment in Ayar Labs | Apr 26, 2022 | HPE’s participation in a $130 million Series C round for Ayar Labs supports the development of optical I/O, a technology that reduces latency and improves performance for the large-scale AI models used in energy research. | Ayar Labs Raises $130 Million in Series C Funding … |
| Acquisition of Determined AI | Jun 21, 2021 | Acquiring this open-source platform accelerated HPE’s machine learning capabilities, providing the software tools needed to efficiently train the models used in projects like the Carnegie Clean Energy collaboration. | Hewlett Packard Enterprise Acquires Determined AI to … |
HPE Partnership Analysis: Building a Global Energy and AI Ecosystem
Table: Hewlett Packard Enterprise’s Key Energy and AI Partnerships (2021-2025)
| Partner / Project | Time Frame | Details and Strategic Purpose | Source |
|---|---|---|---|
| Carnegie Clean Energy | Nov 2024 (Extended) | The extended agreement to 2026 solidifies a multi-year effort to use HPE’s AI to optimize Carnegie’s CETO wave energy technology, serving as a key proof point for AI in a specialized clean tech application. | Carnegie Clean Energy, Hewlett Packard Enterprise push … |
| NVIDIA | Jun 2024 (Expanded) | This foundational alliance resulted in “NVIDIA AI Computing by HPE” and HPE Private Cloud AI, creating the core technology stack that underpins energy-focused AI solutions and other enterprise deployments. | Hewlett Packard Enterprise and NVIDIA Announce … |
| Green Mountain | Feb 2024 | HPE partnered with the Norwegian data center operator to host its AI and HPC infrastructure in an energy-efficient environment, addressing the high power consumption of AI workloads and aligning with sustainability goals. | Green Mountain data centers host HPE AI and HPC … |
| Eni | Feb 2024 | HPE was selected to build a next-generation supercomputer for the Italian energy company, enabling advanced AI modeling to support Eni’s energy transition research and goals. | Hewlett Packard Enterprise and Eni build one of the world’s … |
| Applied Digital | Jun 2023 | A collaboration and >$100 million purchase agreement for HPE’s Cray XD supercomputers to support Applied Digital’s AI cloud services, demonstrating demand for HPE’s high-performance hardware for large-scale AI. | Press Releases |
HPE’s Geographic Expansion: Targeting European and Australian Renewable Energy Hubs
Hewlett Packard Enterprise’s clean energy AI initiatives are strategically concentrated in Europe and Australia, aligning with regions that have strong renewable energy policies and innovation ecosystems.
- Between 2021 and 2024, HPE’s geographic focus for energy applications was established through key projects in Australia with Carnegie Clean Energy (wave energy), Italy with Eni (energy transition research), and Norway with Green Mountain (sustainable data centers).
- This initial footprint demonstrated HPE’s ability to support specialized energy projects in distinct international markets, moving beyond its traditional enterprise IT base.
- From 2025 onward, HPE is solidifying this focus by extending its Australian collaboration with Carnegie and establishing a new AI Factory Lab in Grenoble, France, in December 2025.
- While the French lab is not exclusively for energy, its location in Europe and focus on sovereign AI make it a strategic asset for serving European governments and regulated industries, including the energy sector, which increasingly requires data autonomy.
HPE Technology Maturity: Validating AI’s Role in Commercial Energy Optimization
Hewlett Packard Enterprise’s application of AI in wave energy has matured from an exploratory R&D project into a validated, long-term optimization initiative.
- In the 2021-2024 period, the initial agreement with Carnegie Clean Energy represented a pilot-stage application of HPE’s AI/ML capabilities, designed to test the feasibility of improving the performance and cost-effectiveness of CETO wave energy technology.
- The success of this phase was not guaranteed and served as a real-world test for applying complex AI models to the unpredictable physics of ocean waves.
- The extension of this partnership in late 2024 through 2026 marks a significant maturity milestone, confirming that the AI technology delivered tangible value and is now being integrated as a core optimization tool for the CETO system.
- This progression validates AI as a commercially viable tool for enhancing renewable energy performance, moving it beyond the lab and into practical, long-term industrial application.
Table: SWOT Analysis of Hewlett Packard Enterprise’s AI for Wave Energy Strategy
| SWOT Category | 2021 – 2023 | 2024 – 2025 | What Changed / Resolved / Validated |
|---|---|---|---|
| Strengths | Established leadership in high-performance computing (HPC) and initial AI software acquisitions like Determined AI. Proven ability to deliver supercomputers for research at institutions like the University of Wisconsin-Eau Claire. | Massive AI portfolio with HPE Private Cloud AI co-developed with NVIDIA. Record $1.6 billion in Q3 2025 AI systems revenue. An AI-native networking stack from the $14B Juniper acquisition. | HPE transformed from an HPC provider into a full-stack AI solutions company. Its financial strength and comprehensive product portfolio were validated by massive revenue growth and major customer deals like the >$1B contract with X. |
| Weaknesses | Portfolio was a collection of assets. Lacked a cohesive, turnkey AI solution and a strong, AI-native networking story. Dependent on partners for full-stack delivery. | High dependency on NVIDIA for GPUs, which impacts margins. Integration risk associated with the large-scale Juniper Networks acquisition. | The launch of HPE Private Cloud AI addressed the need for a turnkey solution. The Juniper acquisition is intended to resolve the networking weakness, but execution risk remains a key concern. |
| Opportunities | Growing enterprise interest in AI. Early-stage demand for AI in specialized fields like the energy transition, as seen with the initial Carnegie and Eni projects. | Surging demand for “sovereign AI” in Europe and other regions. A rapidly growing AI infrastructure market projected to reach $692B by 2028. Applying AI to optimize other clean tech sectors. | HPE validated its ability to capture the sovereign AI opportunity with projects in the UK, Canada, and France. The extension of the Carnegie partnership confirmed that specialized AI applications in clean energy are a viable market. |
| Threats | Competition from other hardware providers in the general server market. Uncertainty over the commercial viability of niche AI applications like wave energy. | Intensifying competition from rivals like Dell in the AI server market. The high cost of AI infrastructure could limit adoption for smaller-scale projects. Potential for economic slowdown to impact enterprise IT budgets. | Despite competition, HPE secured major deals and grew AI revenue significantly, proving its competitiveness. The extension of the Carnegie project suggests the commercial case for niche AI is strengthening, mitigating some risk. |
HPE’s Future Outlook: Expanding AI from Niche Projects to Broader Energy Market Solutions
Hewlett Packard Enterprise is positioned to scale its AI solutions from specialized energy projects to address broader opportunities across the energy value chain, including grid management and energy trading.
- The successful, multi-year collaboration with Carnegie Clean Energy provides a proven blueprint for applying advanced AI to optimize other renewable energy sources like wind and solar, creating a clear path for market expansion.
- The integration of Juniper Networks’ AI-native networking is a critical enabler for managing complex, distributed energy resources (DERs), positioning HPE to offer solutions for smart grid management and real-time energy optimization at scale.
- HPE’s growing focus on sovereign AI, demonstrated by its new lab in France and partnerships in the UK and Canada, directly addresses the data security and autonomy requirements of national governments and regulated utilities for managing critical energy infrastructure.
- The market’s strong adoption of HPE’s open architecture solutions, such as the AMD “Helios” platform, gives energy clients flexibility and avoids vendor lock-in, which is a key selling point for long-term infrastructure investments.
Frequently Asked Questions
What is HPE’s main project in the wave energy sector and who is their partner?
HPE’s main project involves a partnership with Carnegie Clean Energy to apply artificial intelligence and machine learning to optimize the performance of Carnegie’s CETO wave energy technology. This collaboration, which started between 2021-2024, has been extended through November 2026, moving it from a pilot phase to a longer-term commercial application.
How did HPE build the technological foundation for its AI strategy in energy?
HPE built its foundation through strategic acquisitions and partnerships. Key moves include the ~$14 billion acquisition of Juniper Networks for AI-native networking, the acquisition of Determined AI and Pachyderm for ML modeling capabilities, and a foundational alliance with NVIDIA to co-develop HPE Private Cloud AI, which provides the core infrastructure for processing complex energy data.
Has HPE’s AI application in wave energy been successful?
Yes, the project is considered a success. The initial pilot phase with Carnegie Clean Energy between 2021 and 2024 successfully validated the use of AI to improve wave energy performance. This success led to the partnership being extended in November 2024 through 2026, marking a shift from an R&D project to an integrated, long-term optimization tool for commercial application.
Besides wave energy, what other energy-related initiatives is HPE involved in?
HPE is engaged in several other energy sector initiatives. The company was selected to build a next-generation supercomputer for Italian energy company Eni to support its energy transition research. Additionally, HPE partnered with Norwegian data center operator Green Mountain to sustainably host power-intensive AI and HPC workloads.
Why is the acquisition of Juniper Networks significant for HPE’s energy strategy?
The acquisition of Juniper Networks is significant because it provides HPE with an AI-native networking portfolio. This is critical for managing the high-volume, real-time data streams generated by distributed energy resources, such as wave energy converters. It positions HPE to offer solutions for larger, more complex applications like smart grid management.
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