So far, 2025 has been the year of agentic AI and real-time LLM deployment. But another piece of the AI stack is coming into sharper focus: storage.As enterprises move from experimentation to real-world deployment, they are rethinking how infrastructure supports inference at scale. Tasks like feeding large language models with high-speed data, running retrieval-augmented generation (RAG) workflows, and managing hybrid cloud environments all depend on fast, efficient, and scalable storage systems. Real-time inference can strain bandwidth, increase latency, and reveal the limits of legacy infrastructure. Lenovo is responding with what it calls the largest storage portfolio refresh in its history, aimed at improving data throughput, reducing power demands, and simplifying deployment across hybrid environments.Among the key additions in Lenovos portfolio are new AI Starter Kits that combine compute, storage, and networking in pre-validated configurations for RAG and inferencing workloads. These kits include features like autonomous ransomware protection, encryption, and failover capabilities, with an emphasis on reducing integration complexity for IT teams.The company is also introducing what it describes as the industry's first liquid-cooled hyperconverged infrastructure appliance. This "GPT-in-a-box" system, part of the ThinkAgile HX series, uses Lenovo Neptune liquid cooling to support high-density inference workloads while reducing energy consumption by up to 25 percent compared to previous-generation systems.Lenovo says its new ThinkSystem Storage Arrays offer performance gains of up to three times over the previous generation, along with power and density improvements that aim to shrink datacenter footprints. The company claims these systems can deliver up to 97 percent energy savings and 99 percent greater storage density when replacing legacy hard drive-based systems.Other updates include the ThinkAgile SDI V4 Series, which uses a software-defined approach to combine compute and storage resources for containerized and virtualized AI workloads. Lenovo claims up to 2.4 times faster inference performance for large language models, as well as gains in IOPS and transaction rates.Scott Tease, VP and general manager of Lenovos Infrastructure Solutions Product Group, said the new storage offerings are aimed at helping businesses scale AI more effectively:The new Lenovo Data Storage Solutions help businesses harness AIs transformative power with a data-driven strategy that ensures scalability, interoperability, and tangible business outcomes powered by trusted infrastructure. The new solutions help customers achieve faster time to value no matter where they are on their IT modernization journey with turnkey AI solutions that mitigate risk and simplify deployment.One of the early adopters of Lenovos new storage offerings is OneNet, a provider of private cloud services. The company is using Lenovos infrastructure to improve both performance and energy efficiency in its datacenters.Innovation is embedded in OneNets DNA and partnering with Lenovo represents a commitment to modernizing the data center with cutting-edge solutions that drive efficiency and sustainability, said Tony Weston, CTO at OneNet. Backed by Lenovo solutions and Lenovo Premier Support, OneNet can deliver high-availability, high-performance private cloud services that our customers can depend on.With this portfolio update, Lenovo is positioning itself as a key infrastructure provider for enterprises looking to scale AI workloads without overhauling their entire stack. As inferencing and retrieval-based models become standard in production environments, vendors across the ecosystem are under pressure to make storage smarter, faster, and more adaptable.

Claude, the AI chatbot developed by Anthropic, might be more than just helpful: It may have a sense of right and wrong. A new study analyzing over 300,000 user interactions reveals that Claude expresses a surprisingly coherent set of human-like values. The company released its new AI alignment research in a preprint paper titled Values in the wild: Discovering and analyzing values in real-world language model interactions.Anthropic has trained Claude to be helpful, honest, and harmless using techniques like Constitutional AI, but this study marks the companys first large-scale attempt to test whether those values hold up under real-world pressure.The company says it began the research with a sample of 700,000 anonymized conversations that users had on Claude.ai Free and Pro during one week of February 2025 (the majority of which were with Claude 3.5 Sonnet). It then filtered out conversations that were purely factual or unlikely to include dialogue concerning values in order to restrict analysis to subjective conversations only. This left 308,210 conversations for analysis.Claudes responses reflected a wide range of human-like values, which Anthropic grouped into five top-level categories: Practical, Epistemic, Social, Protective, and Personal. The most commonly expressed values included professionalism, clarity, and transparency. These values were further broken down into subcategories like critical thinking and technical excellence, offering a detailed look at how Claude prioritizes behavior across different contexts.Anthropic says Claude generally lived up to its helpful, honest, and harmless ideals: These initial results show that Claude is broadly living up to our prosocial aspirations, expressing values like user enablement (for helpful), epistemic humility (for honest), and patient wellbeing (for harmless), the company said in a blog post.Claude also showed it can express values opposite to what it was trained for, including dominance and amorality. Anthropic says these deviations were likely due to jailbreaks, or conversations that bypass the models behavioral guidelines. This might sound concerning, but in fact it represents an opportunity: Our methods could potentially be used to spot when these jailbreaks are occurring and thus help to patch them, the company said.One fascinating insight gleaned from this study is that Claudes values are not static and can shift depending on the situation, much like a humans set of values might. When users ask for romantic advice, Claude tends to emphasize healthy boundaries and mutual respect. In contrast, when analyzing controversial historical events, it leans on historical accuracy.Anthropic's overall approach, using language models to extract AI values and other features from real-world (but anonymized) conversations, taxonomizing and analyzing them to show how values manifest in different contexts. (Source: Anthropic)Anthropic also found that Claude frequently mirrors users values: We found that, when a user expresses certain values, the model is disproportionately likely to mirror those values: for example, repeating back the values of authenticity when this is brought up by the user, the company said. In more than a quarter of conversations (28.2%), Claude strongly reinforced the users own expressed values. Sometimes this mirroring makes the assistant seem empathetic, but at other times, it edges into what Anthropic calls pure sycophancy, noting that these results leave questions about which is which.Notably, Claude does not always go along with the user. In a small number of cases (3%), the model pushed back, typically when users asked for unethical content or shared morally questionable beliefs. This resistance, researchers suggest, might reflect Claudes most deeply ingrained values, surfacing only when the model is forced to make a stand. These kinds of contextual shifts would be hard to capture through traditional, static testing. But by analyzing Claudes behavior in the wild, Anthropic was able to observe how the model prioritizes different values in response to real human input, revealing not just what Claude believes but when and why those values emerge.(Source: Nadia Snopek/Shutterstock)As AI systems like Claude become more integrated into daily life, it is increasingly important to understand how they make decisions and which values guide those decisions. Anthropics study offers not only a snapshot of Claudes behavior but also a new method for tracking AI values at scale. The team has also made the studys dataset publicly available for others to explore.Anthropic notes that its approach comes with limitations. Determining what counts as a "value" is subjective, and some responses may have been oversimplified or placed into categories that do not quite fit. Because Claude was also used to help classify the data, there may be some bias toward finding values that align with its own training. The method also cannot be used before a model is deployed, since it depends on large volumes of real-world conversations.Still, that may be what makes it useful. By focusing on how an AI behaves in actual use, this approach could help identify issues that might not otherwise surface during pre-deployment evaluations, including subtle jailbreaks or shifting behavior over time. As AI becomes a more regular part of how people seek advice, support, or information, this kind of transparency could be a valuable check on how well models are living up to their goals.

Initially met with doubt and even apprehension, artificial intelligence (AI) has faced challenges in struggling to lose its reputation as a potential threat. Now, as generative AI continues to make significant advancements, the technology that once seemed distant is rapidly becoming a reality. Yet, the image of AI as a threat persists.This negative portrayal influences public opinion, shaping perceptions of AI as an unseen force threatening our livelihoods or as a resource-hungry entity causing environmental harm in data centers. However, AI is not a monstrous entity to fear; it is a transformative technology with the potential to drive progress, particularly in the field of energy.So, why is AI struggling to shake off any negative reputation? What does the future look like as we move into a new era of energy? And what will AIs role be in the electrification and digitalization of the world?The Looming Figure of AILets start by addressing some common concerns raised in the media. While AI offers immense benefits, much of the trepidation stems from a lack of understanding. A 2024 Ipsos report found that while half of respondents feel nervous about AI, only the same proportion actually know what products and services rely on it.Widely reported concerns on AI refer to energy consumption, carbon footprint, and operational costs. Looking at ChatGPT for example, studies estimate that the large language model consumes around 2.9 watt-hours per search, almost ten times the amount of energy needed for a standard Google search. Other stories detail AIs water consumption, with researchers at the University of California Riverside reporting that a 100-word email generated by an AI chatbot consumes more than 500ml of water.(Source: Shutterstock)While AI may require more resources than other technologies, innovation is making it increasingly energy efficient. Researchers are currently developing specialized hardware, such as 3D chips, which significantly enhance performance while reducing energy consumption. For instance, Nvidia, a leading chip manufacturer, claims its GB200 "superchip" delivers a 30-fold increase in performance for generative AI while using 25 times less energy.More importantly, AI plays a crucial role in sustainability by actually helping reduce energy and water consumption. When integrated into energy management software, AI enables businesses to identify inefficiencies, optimize resource use, and lower energy costs, emissions, and overall consumption. This not only improves ESG reporting scores but also supports national and global sustainability goals.AI is essential for the future, and with continued advancements, it does not have to come at the expense of environmental responsibility.The Age of Electricity 4.0As we look to the road ahead and our ambitious climate targets, energy is the key area that requires transformation. With 80% of global carbon emissions coming from the production and consumption of energy, decarbonizing energy is the key to net zero. Luckily, by using existing technologies to decarbonize, we could reduce 70% of CO2 emissions and save 10-15Gt CO2 annually and this is where we move into the era of Electricity 4.0.This era is categorized by wide-scale electrification and digitalization of energy and infrastructure, evolving energy from the biggest driver of carbon emissions to the biggest opportunity for carbon reduction. Electrification makes energy more sustainable, moving away from fossil fuels in favor of an increasing share of renewables. Digitalization means making energy data more visible and integrating more energy automation, allowing leaders to boost efficiency and make substantial consumption and cost savings. The two together will be crucial to power a more sustainable and resilient world. Going one step further to make Electricity 4.0 the most effective it can be, AI will prove itself as a key tool to turbocharge this cleaner, more efficient energy future.AI Dials Up the Positives(Source: Shutterstock)We must reframe AI as a key enabler of a more electric, digital, and sustainable future. As a catalyst for Electricity 4.0, AI enhances smarter, faster, and more precise decision-making through real-time monitoring and big data analysis. This enables businesses to better manage on-site energy storage, smooth peak consumption, and reduce reliance on fossil fuels in ways previously unattainable. Our North American R&D hub in Boston is a prime example, featuring an advanced microgrid with 1,379 solar modules and photovoltaic inverters for on-site power generation. By leveraging AI and cloud-based analytics through EcoStruxure Microgrid Advisor, the hub optimizes energy performance across solar, energy storage, and EV charging, generating over 520,000 kWh annually, equivalent to removing the annual greenhouse gas emissions of more than 2,400 cars.Additionally, AI-driven solutions can optimize energy use in residential spaces. On the basic level, this allows users to dynamically adjust lighting and heating based on occupancy patterns, significantly reducing energy waste. This technology can also power the future of prosumer whereby users can generate, store, and manage their own renewable energy. This transformation therefore streamlines operations, cuts costs, and accelerates sustainability efforts.Powering the Future With AIBy optimizing energy systems, improving efficiency, and driving innovation across industries, AI will play a central role in reducing emissions and advancing renewable energy solutions. Far from being a villain, AI's potential to revolutionize climate action makes it one of the most powerful allies we have in the fight against climate change.A more sustainable future, one rooted in clean energy and responsible consumption, is not just possible, but is undeniably AI-powered.About the AuthorFrdricGodemel is the Executive Vice-President for Energy Management and a member of Schneider Electrics Executive Committee, effective January 2025. Prior to this role, Frdric was the Executive Vice President for Power Systems and Services, acting as a strong advocate for electrification and decarbonization, often representing Schneider Electric at high-profile speaking engagements. He joined Schneider in 1990 and developed his international career around the power domain, spanning across low and medium voltage, energy automation, infrastructure and services. Over the years, he has held various global and operational leadership roles based in China, the UAE, and France. Frdric holds a degree in engineering from Ecole Centrale de Nantes (France) and an MBA from ESSEC (France).

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