Centrus Energy (LEU) is Among the Energy Stocks that Gained the Most This Week
The share price of Centrus Energy Corp. (NYSEAMERICAN:LEU) surged by 14.44% between June 11 and June 18, 2025, putting it among the Energy Stocks that Gained the Most This Week.
A vast construction site with heavy machinery, materials, and workers, showcasing the company's global presence.
Centrus Energy Corp. (NYSEAMERICAN:LEU) is a trusted supplier of nuclear fuel and services for the nuclear energy industry.
Centrus Energy Corp. (NYSEAMERICAN:LEU) hit a 5-year high this week after a significant jump in the global price of uranium. Uranium futures in the U.S. are currently hovering around the $74.8 mark, up more than 7% over the last week, following a recent announcement by the Sprott Physical Uranium Trust that it would acquire around $200 million worth of physical uranium, twice the amount it initially signaled in its agreement with Canaccord Genuity.
Centrus Energy Corp. (NYSEAMERICAN:LEU) also received a boost after the analysts at Evercore ISI raised their price target from $145 to $205, while maintaining an 'Outperform' rating on the stock.
While we acknowledge the potential of LEU as an investment, we believe certain AI stocks offer greater upside potential and carry less downside risk. If you're looking for an extremely undervalued AI stock that also stands to benefit significantly from Trump-era tariffs and the onshoring trend, see our free report on the best short-term AI stock.
READ NEXT: 10 Best Nuclear Energy Stocks to Buy Right Now and
Disclosure: None.
擷取數據時發生錯誤
登入存取你的投資組合
擷取數據時發生錯誤
擷取數據時發生錯誤
擷取數據時發生錯誤
擷取數據時發生錯誤

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Bloomberg
34 minutes ago
- Bloomberg
Fed's Daly Says Muted Tariff Impact May Open Door to Cut in Fall
By Catarina Saraiva and Updated on Save Federal Reserve Bank of San Francisco President Mary Daly said she's seeing increasing evidence that tariffs may not lead to a large or sustained inflation surge, helping bolster the case for a rate cut in the fall. 'My modal outlook has been for some time that we would begin to be able to adjust the rates in the fall, and I haven't really changed that view,' Daly said Thursday in an interview on Bloomberg Television.


Forbes
34 minutes ago
- Forbes
AI Gave The World Infinite Content—Now What?
Tejas Manohar is the cofounder/co-CEO of Hightouch. Just a few years ago, generative AI (GenAI) felt more like a curiosity than a tool. We asked language models to write love letters in the style of tech bros or explain quantum physics to a 5-year-old. Visual platforms responded to prompts like "a dragon in a business suit, pixel art style" or "a Renaissance portrait of a barista." The results, while novel and amusing, were rarely practical for business. That has changed. By the end of 2024, GenAI outputs became sharper, more polished and increasingly indistinguishable from human-created work. In 2025, with tools like GPT-4, Midjourney, Runway and Canva AI becoming widely adopted, content creation is no longer the bottleneck it once was. Soon, marketing teams will be able to generate dozens of creative options in minutes. However, this shift introduces a new problem: With so much content, how do we decide what to use, for whom and when? Most marketers are now using GenAI to create assets. While Salesforce reports that 76% of marketers use AI to generate content, the processes for deploying that content haven't evolved. The typical workflow still involves pasting AI-generated copy into spreadsheets, testing a couple of variants, manually picking a winner and repeating it all. That might work in the short term, but it's not scalable. More importantly, it doesn't improve over time. More content is not the solution unless there's a system to decide which content to use and how. Imagine an orchestra where every musician trained at Juilliard, but there's no conductor. That's what marketing looks like in a GenAI world without decisioning. There's creativity, but no coordination. Marketers today face a flood of assets, but the bigger challenge is figuring out what to send, to which audience and when. These are not creation problems. These are decisioning problems. And we're still trying to solve them using tools and mental models—journey builders, marketing calendars and simple A/B tests—built for a world where content is scarce. Traditional workflows assume that you'll create a handful of subject lines, define a few segments and test some variations. But GenAI doesn't create one or two options—it creates hundreds. Suddenly, you're staring at thousands of possible combinations across messaging, timing, audience and channels. Marketers can't test every option. They can't manually orchestrate every journey, and they certainly can't rely on batch-and-blast methods anymore. A new approach is needed. For many organizations, AI decisioning has become a key part of their AI strategy. This new category of technology sits between content creation and content delivery. It enables marketers to deploy AI agents that make real-time decisions about which content to send to which user. These systems use reinforcement learning (the same type of machine learning behind self-driving cars and streaming recommendation engines) to optimize for business outcomes like conversions, retention or lifetime value. Think of how platforms like Google and Meta Ads operate. You set your goals, upload creative assets and the system optimizes combinations to deliver results. Now imagine that same model applied to email, push, in-app messaging and CRM. That's what AI decisioning aims to achieve, only this time with transparency and control built in. To adopt AI decisioning effectively, companies need to get the basics right first. That means clarifying goals, improving data access and identifying where manual decisions slow things down. Start small by pinpointing bottlenecks in your workflow, whether that's testing content, segmenting audiences or managing channels. Silos are a major hurdle. When teams like marketing, data and product work in isolation, decisioning falls flat. Aligning around shared goals, metrics and timelines helps break down these walls and ensures AI systems have the inputs they need to be effective. The best way to begin is with a focused use case, such as optimizing subject lines or send times. Prove value quickly and then scale. AI decisioning is not about replacing everything at once; it is about creating a system that learns and improves over time. Used together, these technologies form a closed-loop system. GenAI generates content while AI decisioning systems select the right assets for each user based on performance data. As results come in, those insights feed back into the content generation process, allowing both creation and decisioning to improve continuously. GenAI acts as the input layer, creating at scale. AI decisioning functions as the optimization layer, learning what works and when. Combined, they create a flywheel where content fuels decisions and decisions enhance future content. But none of this works without human oversight. Marketers still need to be involved. AI systems must be transparent, auditable and accountable. Teams need to know how decisions are made, what experiments are running and have the ability to approve content and manage risks. In the coming months, content bottlenecks will fade as GenAI becomes even more integrated into daily workflows. But that's only the first step. The true differentiator will be how effectively teams can deploy the content they generate to drive meaningful results. The winners in the next era of marketing won't be the ones who generate the most creative assets. They'll be the ones who build systems that know what to do with them and can adapt in real time. So keep prompting and creating. But remember: the next meaningful shift in marketing won't just come from creation—it will come from smarter decisioning. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?


Forbes
34 minutes ago
- Forbes
Edge AI Applications As The Catalyst For AI PC Market Growth
Ajith Sankaran, Executive Vice President, C5i. getty Despite all the buzz, the adoption of high-performance AI PCs with powerful neural processing units (NPUs) has been especially sluggish. Since their launch in mid-2024, these devices have captured just 5% of AI PC market sales. This can be attributed to several factors: • AI PCs typically command a significant price premium without clearly articulated benefits. Many users remain unconvinced that these costs translate to meaningful improvements in computing experiences. • Compatibility concerns persist, particularly with first-generation advanced RISC machine (ARM)-based systems that may not support legacy software. • There is a scarcity of software applications that fully harness AI PC capabilities. According to a 2024 ICD report, the global market for personal computing devices was "set to grow 3.8% in 2024, reaching 403.5 million units." However, this growth is primarily driven by a nearly double-digit growth in tablets. According to Jitesh Ubrani of IDC, 'There seems to be a big disconnect between supply and demand as PC and platform makers are gearing up for AI PCs and tablets to be the next big thing, but the lack of clear use cases and a bump in average selling prices has buyers questioning the utility.' I believe the answer to realizing the potential of AI PCs in enterprise scenarios lies in understanding and utilizing edge AI. To understand why, let's take a closer look at how these systems operate. Edge AI And Its Relationship With AI PCs Edge AI represents the convergence of AI and edge computing, enabling AI algorithms to run directly on local devices rather than in remote data centers. This approach processes data where it's generated, eliminating the need to send information to the cloud for analysis and returning results almost instantaneously. AI PCs are well-positioned to serve as powerful edge AI platforms due to their unique hardware architecture. They integrate three processing components: • A central processing unit (CPU) for general computing tasks. • A graphics processing unit (GPU) for parallel processing workloads. • A neural processing unit (NPU) optimized for AI computations. This triad of capabilities allows AI PCs to handle edge AI applications with efficiency. The performance benefits can be substantial; security company CrowdStrike reported that its software's CPU consumption dropped from 35% to 1% when running on machines equipped with Intel NPUs. Global shipments of AI PCs are projected to reach 114 million units in 2025, accounting for 43% of all PC shipments. I believe that edge AI that incorporates the latest advances in generative AI and agentic AI could provide tangible benefits that justify the premium pricing of AI PC for consumers and enterprises. As more developers create software that leverages NPUs and other specialized AI hardware, the value proposition should become clearer, driving increased adoption across both consumer and enterprise segments. Emerging Edge AI Applications Driving AI PC Demand • Manufacturing Intelligence Manufacturing environments are proving to be fertile ground for edge AI applications. AI systems running locally on AI PCs can monitor equipment health in real time, detecting anomalies and predicting potential failures before they occur. This can reduce costly downtime. Quality control represents another application. AI-powered cameras connected to edge computing systems can inspect products for defects with precision and consistency. • Healthcare Innovations The healthcare sector also stands to benefit from edge AI. Portable diagnostic devices equipped with edge5 AI can analyze medical images such as X-rays, MRIs, and CT scans locally, providing rapid insights without requiring cloud connectivity. This is particularly valuable in remote areas. And wearable health devices using edge AI can analyze biometric data locally, detect anomalies and alert healthcare providers without transmitting sensitive patient information to remote servers. • Retail Transformation In retail, edge AI applications are revolutionizing operations and customer experiences. AI-powered cameras and sensors can track inventory levels in real time, optimizing stock replenishment. The same infrastructure can analyze customer behavior patterns, enabling retailers to deliver personalized recommendations and promotions. These capabilities require significant local processing power that can be provided by AI PCs to analyze video feeds and sensor data in real time. • Security and Privacy Protection Edge AI can deliver faster performance while keeping sensitive data local instead of sending it to cloud services. For example, Bufferzone NoCloud "uses local NPU resources to analyze websites for phishing scams using computer vision and natural language processing." Edge AI applications can enhance banking security by detecting unusual transactions and immediately alerting users. Recommendations For Effective AI PC and Edge AI Adoption 1. Develop edge-native AI applications for real-time decision-making. Prioritize building edge-native AI applications that leverage the NPUs in your organization's AI PCs to execute machine learning models locally. For example, manufacturing firms can deploy vision systems on AI PCs to perform real-time quality inspections directly on production lines, reducing defect rates while eliminating cloud dependency. 2. Deploy agentic AI systems for autonomous workflow optimization. Agentic AI excel at autonomously managing complex, multi-step processes. In supply chain, running agentic AI systems on AI PCs can allow you to dynamically reroute shipments based on real-time traffic data processed at the edge, reducing delivery delays. Financial institutions can also combine agentic AI with edge computing to autonomously monitor transactions for fraud patterns, triggering immediate alerts while keeping sensitive financial data localized. 3. Implement privacy-centric AI architectures for regulated industries. Consider adopting hybrid edge-cloud AI architectures to balance computational demands with regulatory compliance. For example, banks can deploy on-premise AI PC clusters to run agentic AI fraud detection systems, ensuring customer transaction data never leaves internal networks. 4. Build scalable edge AI infrastructure with modular hardware. Invest in AI-optimized hardware ecosystems that support both current and emerging workloads. For instance, consider deploying AI PCs with dedicated NPUs for employee productivity tools and pairing them with edge servers containing GPU/TPU arrays for heavy computational tasks. 5. Integrate generative AI with edge computing for adaptive systems. By fusing generative AI with edge computing, you can enable dynamic system adaptation within your company. For example, manufacturers can deploy small language models on AI PCs to generate equipment repair instructions tailored to real-time sensor data, reducing machine downtime. Conclusion While initial adoption of AI PCs has been slow due to high costs, compatibility issues and a lack of applications, the emergence of edge AI use cases is beginning to demonstrate the value of local AI processing. As developers increasingly leverage NPUs to build edge-native and agentic AI solutions, I believe the value proposition of AI PCs will become more evident, driving broader adoption across consumer and enterprise markets. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?