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AI & Tech

Nvidia Says Finance-Backed AI Labs Could Drive a Quarter of Next Year’s Sales

Nic Reeve3 min read
Nvidia Says Finance-Backed AI Labs Could Drive a Quarter of Next Year’s Sales

Nvidia says demand from AI labs it helps finance could account for about a quarter of its business next year, highlighting how deeply the chipmaker is now tied to the build-out of artificial intelligence infrastructure. The company’s comments came as it outlined a year-ahead forecast that pointed to continued rapid growth, while also underscoring a financing model that is drawing fresh scrutiny across the AI industry.

Chief Financial Officer Colette Kress told analysts that demand from AI labs backed by Nvidia’s balance sheet will contribute roughly 25% of the company’s business next year. Reuters reported that Nvidia paired that guidance with an expectation of 70% sales growth next year, signaling that the company still sees broad demand beyond the biggest cloud providers. Yahoo Finance similarly quoted Nvidia as saying that demand from AI labs will account for about a quarter of business next year.

The disclosure matters because Nvidia is not only selling chips to these companies; it is also helping finance parts of the ecosystem that buy its hardware. Reporting this week said Nvidia has invested nearly $50 billion in frontier AI labs and has helped line up more than $500 billion in third-party capital for AI infrastructure through partnerships with major firms including Apollo, BlackRock, Blackstone, Goldman Sachs and KKR. That creates a tightly linked loop: Nvidia supports the financing, the financed companies build data centers, and those facilities are then filled with Nvidia’s GPUs.

Nvidia says the arrangement is not purely dependent on any one customer or project. Kress said the company’s platform is “fungible and durable,” meaning chips and systems can be redeployed if a partner changes plans or if demand shifts. Reuters added that Nvidia described demand from AI labs as part of a more diversified customer base, alongside hyperscale cloud providers and so-called neo-clouds.

Still, the scale of the financing has become a key story in its own right. Artificial Intelligence News described the setup as “circular financing,” noting that Nvidia’s capital support can help labs build data centers that in turn purchase Nvidia hardware. The report also said Kress referred to credit support covering nearly two gigawatts for one unnamed lab, though she did not identify which company would receive that backing.

The broader backdrop is Nvidia’s continued financial dominance in the AI chip market. In its most recent fiscal fourth quarter, the company reported record revenue of $68.1 billion, up 73% from a year earlier, with data center sales accounting for $62.3 billion of that total. That performance has helped make Nvidia one of the most closely watched companies in global markets, especially as investors try to assess how much of AI demand is driven by genuine end-user adoption versus financing-heavy expansion.

Supporters of Nvidia’s approach argue that it is simply helping accelerate infrastructure build-out at a moment when AI companies need vast amounts of compute power and capital. Critics, however, see the risk of overdependence on a self-reinforcing cycle in which funding, purchasing, and revenue are increasingly intertwined. For now, Nvidia’s message is that the demand is real, broadening, and large enough to keep the company growing at extraordinary speed.

What remains to be watched is whether this financing-backed demand proves durable if the AI market cools, or whether it becomes a warning sign that some of the industry’s biggest growth projections were built on unusually aggressive capital support. Nvidia’s latest guidance suggests the company is confident the answer is the former, at least for now.

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Moomoo Boosts AI News Tools to Turn Market Headlines into Trading Insight
AI & Tech

Moomoo Boosts AI News Tools to Turn Market Headlines into Trading Insight

Moomoo is deepening its push into AI-powered investing with upgraded news‑analysis features that automatically sift, summarize and contextualize market headlines for retail traders. The tools, bundled under the broader Moomoo AI and “Skills” ecosystem, aim to help users track fast‑moving news without manually scanning dozens of sources. AI at the Core of Moomoo’s News Experience At the center of the experience is Moomoo AI, the app’s always‑on investing assistant. Integrated directly into the trading platform, it monitors global markets in real time, pulls in corporate filings and news, and distills them into concise summaries that are easier for individual traders to act on. According to Moomoo’s product materials, Moomoo AI can automatically collect and filter key information from a vast stream of market data and news, then condense it into a single‑page brief that is updated both pre‑market and post‑market. These briefs highlight what has changed, which stocks or sectors are driving the move, and what risks or opportunities may be emerging. From Headlines to Briefs: How the “News Analyzer” Works Rather than functioning as a traditional news feed, Moomoo’s AI features are designed to behave like a “news analyzer” layer on top of existing data. For individual tickers, users can go to the stock’s quote or chart page and scroll down to a dedicated Moomoo AI section. There, the system surfaces a structured brief that combines: Key news events related to the company, sector or macro environment Summaries of earnings reports and announcements , translating dense filings into bullet‑point takeaways Directional context , such as whether recent coverage has skewed positive or negative Supporting links to original news, filings and research on the Moomoo platform For earnings and corporate events, users can tap into the News > Announcements section and have the AI instantly summarize key figures and management commentary, reducing the need to read full‑length reports word‑for‑word. News Search and Skills: AI Plug‑Ins for Market Monitoring Beyond the in‑app briefs, Moomoo is also promoting a set of modular AI “Skills” that extend its news‑analysis capabilities. The flagship example is News Search a skill that lets users or AI agents pull the latest headlines, filings and research reports associated with a ticker or topic in a single query, without leaving the platform. These skills connect AI agents directly to Moomoo’s live data feeds and news infrastructure. When a user asks what is happening with a specific stock like Apple, the system can respond with current quotes, fresh corporate filings, premium news and community sentiment in one integrated answer, rather than simply returning a list of links. Additional skills, such as Stock Digest and Sentiment Gauge, are designed to condense the latest news into a narrative summary and gauge how bullish or bearish community discussions have become. Together, these tools effectively turn Moomoo’s data and community activity into a continuous stream of machine‑readable signals. Coverage from Premium News Sources Underpinning the AI layer is a broad content pipeline. Moomoo says its AI pulls news from more than 200 top sources, including major outlets like The Wall Street Journal, Bloomberg and Benzinga, alongside corporate filings and in‑house research. The platform emphasizes that it is drawing on real‑time feeds rather than static or outdated web content, helping its AI keep pace with intraday developments. This is particularly relevant for traders who need to respond quickly to surprise earnings, regulatory announcements or macroeconomic data. By summarizing many items at once, the AI aims to help users see which stories are most material rather than simply the most recent. Community Response: Faster Research, Less Noise Within the Moomoo Community, early adopters have highlighted news‑analysis and market‑summary features as some of their favorite tools. Users report that the AI can quickly organize complex market headlines, analyze individual stock data and summarize daily ETF movements, shaving hours off their research process. For many retail traders, the appeal lies less in fully automating decisions and more in filtering noise. Instead of scrolling through long lists of articles and posts, traders can scan a concise AI‑generated digest to understand what is driving sentiment in a stock or sector that day, then drill down into original sources as needed. How to Access the AI News Tools in the App Investors can access Moomoo’s AI news‑analysis capabilities in several ways: On a stock’s quote or chart page, by scrolling down to the Moomoo AI section to see daily or weekly briefs. Through the News > Announcements tab, where earnings and event summaries are generated on demand. Via the AI assistant interface, by asking natural‑language questions such as “What’s going on with Tesla today?” or “Summarize the latest news on semiconductor stocks.” Inside the Skills Hub, where News Search and other AI skills can be enabled for more specialized workflows or connected AI agents. Positioning in the AI Trading Landscape Moomoo’s push into AI‑driven news analysis comes as brokers and fintech platforms race to build intelligent tools that can keep up with the speed and volume of modern markets. By combining large language models with real‑time market feeds and premium news sources, Moomoo is positioning its AI not just as a chatbot, but as a research companion that stays on call around the clock. The company presents these tools as a way to improve efficiency and comprehension, particularly for newer investors who may be overwhelmed by jargon‑heavy filings and fast‑moving headlines. At the same time, it emphasizes that AI output should complement, not replace, users’ own judgment and risk management. As Moomoo continues to roll out new AI skills and refine its summarization models, its “news analyzer” capabilities are likely to play an increasingly central role in how retail traders on the platform discover, interpret and respond to information.

Nic Reeve·
Mistral’s Agentic Search Aims to Redefine Enterprise AI Retrieval
AI & Tech

Mistral’s Agentic Search Aims to Redefine Enterprise AI Retrieval

A New Retrieval Layer for Enterprise-Grade AI Mistral AI has introduced Agentic Search , a new retrieval layer designed to make AI systems markedly more accurate and efficient when working over complex enterprise data. The feature sits between large language models (LLMs) and an organization’s existing indexes, allowing models to actively search, open, navigate and verify information inside lengthy and heterogeneous documents rather than relying on a single retrieval pass. Unlike conventional retrieval‑augmented generation (RAG), which typically performs one vector or keyword lookup and feeds the top chunks into a model, Agentic Search orchestrates a multi‑step search loop . The model can iteratively refine queries, inspect multiple sources, and gather corroborating evidence before producing an answer, a workflow aimed squarely at the hard cases of enterprise question answering across filings, contracts, scanned PDFs and other long‑form content. How Agentic Search Works At the core of Mistral’s approach is a set of file‑style tools that the model can call autonomously during a session. Public documentation describes five primary capabilities exposed to the model: search – find relevant documents across an existing index or corpus. open – select and open a specific document returned by search. navigate – move to a particular page, section or region within the open document. read – retrieve the content at the current location for inspection. grep – search within the open document for exact strings or patterns. Together, these tools enable a model to behave more like an analyst than a passive text generator. When the first retrieved chunk does not contain the answer, the model can issue a new search, open a more promising file, jump to likely sections based on headings or page numbers, and then scan for precise terminology using in‑document search, all before finalizing its response. Already‑inspected chunks can be excluded, reducing redundancy and cost. This approach is especially important for questions that require cross‑document reasoning or precise citation of clauses buried deep within long documents. Instead of hoping that chunking and ranking surface the right passages on the first attempt, Agentic Search allows the model to keep looking until it has collected enough evidence. Benchmark Results: From One‑Shot RAG to Iterative Evidence Gathering Mistral is positioning Agentic Search as a measurable improvement over traditional RAG pipelines, backing the launch with benchmark data on datasets such as FinanceBench and OfficeQA Pro . According to the company’s published figures, running the full agentic loop – including both iterative search and document navigation – drove a dramatic jump in accuracy on FinanceBench compared with single‑shot RAG. Public reports summarizing Mistral’s results highlight accuracy gains from roughly the high‑20% range to the mid‑80% range for certain models when moving from one‑shot retrieval to Agentic Search. Iterative search alone contributed the majority of the improvement, with navigation and in‑document tools accounting for several additional percentage points. In other words, allowing the model to keep searching and then move intelligently within documents can more than triple its ability to answer complex financial questions correctly. The company also reports significant efficiency gains . By avoiding repeated retrieval of the same or irrelevant chunks and focusing only on promising portions of documents, Agentic Search reduces token usage and latency compared with a naive search‑only loop. Mistral’s internal tests indicate reductions in token consumption of around a quarter to a third for evaluated models, alongside double‑digit percentage cuts in tail latency on FinanceBench queries. On the OfficeQA Pro benchmark, which targets questions about office productivity and business documents, the multi‑step navigation stack similarly lowered token usage and improved responsiveness. These metrics are critical for enterprises deploying AI at scale, where every marginal improvement in latency and token cost can translate into substantial infrastructure savings. Built Into Mistral’s Search Toolkit, Studio and Vibe Agentic Search is being shipped as part of the broader Mistral Search Toolkit , a composable framework for building production‑grade search pipelines for AI applications. The toolkit is designed to run in diverse environments, from public cloud to on‑premises infrastructure, giving organizations flexibility over where their data and compute reside. For developers, Agentic Search is exposed through an SDK that can be integrated into custom agents, workflows and end‑user applications. The company emphasizes that it wraps around existing indexes rather than requiring new fine‑tuning of models or wholesale changes to data infrastructure. Teams can plug the agentic layer into their current search stack and let it handle the multi‑turn retrieval logic. For users of Mistral’s own products, the feature comes pre‑integrated into the Libraries capability within both the Studio interface and the Vibe agent. That means customers who store their knowledge bases in Libraries can benefit from the multi‑step retrieval loop without writing any additional code; the system automatically allows the model to search, open, navigate and read within the indexed content as it answers questions or executes tasks. Agentic Search also aligns with Mistral’s broader strategy around agentic AI. The company has previously rolled out an Agents API with web search connectors and continues to expand tools like Vibe for long‑horizon coding and research tasks. In this context, Agentic Search marks a focused push into enterprise knowledge search , complementing those agent capabilities with deeper document‑level understanding. Target Use Cases: From Filings to Contracts Mistral is explicitly targeting organizations with large, complex and often messy corpora. Typical examples include regulatory filings, annual reports, internal policy manuals, technical documentation, legal contracts and scanned PDFs produced by legacy workflows. These documents tend to be long, inconsistently formatted, and heavy on domain‑specific language – a challenging environment for standard chunk‑based RAG. In these scenarios, a user might ask, for example, about how a particular risk is disclosed across several years of a company’s filings, or which clause in a contract governs a specific edge case. Agentic Search allows the model to conduct a multi‑hop investigation: search across the corpus, open the most relevant documents, jump to sections that mention the relevant risk or clause, and cross‑check multiple sources before responding with a grounded, cited answer. Industry observers note that the approach is distinct from consumer‑oriented web search. Rather than crawling the open internet, Agentic Search is focused on private, high‑value enterprise data where precision and verifiability are more important than breadth. The trade‑off is that the agentic loop can take longer – sometimes running for minutes on especially hard questions – but the resulting accuracy and auditability are intended to justify the added compute. Competitive and Strategic Implications The launch of Agentic Search underscores a broader shift in the AI industry toward agentic retrieval , where models are given toolkits and autonomy to orchestrate their own information‑gathering processes. By releasing a production‑ready framework and integrating it into its flagship products, Mistral is positioning itself as a serious contender in enterprise AI, not only on the strength of its base models but also through specialized infrastructure for knowledge‑intensive workloads. For enterprises, the technology offers a path to move beyond proof‑of‑concept chatbots and into applications where AI systems must routinely answer difficult, regulated or high‑stakes questions. If the reported benchmark gains generalize in real deployments, Agentic Search could enable more reliable AI copilots for finance, legal, compliance and operations teams, while also easing concerns about hallucinations and incomplete retrieval. As organizations continue to evaluate vendors on both model quality and retrieval performance, Mistral’s agentic layer may become a key differentiator – especially for customers who want the option to run search pipelines on‑premises while still tapping into state‑of‑the‑art agent capabilities.

Nic Reeve·
AI Stocks In 2026: Cooling Cloud Spend, New Leaders And The Robotaxi Push
AI & Tech

AI Stocks In 2026: Cooling Cloud Spend, New Leaders And The Robotaxi Push

AI investing moves beyond the initial boom Artificial intelligence has shifted from hype cycle to business reality, and the stock market is adjusting accordingly. After two years in which a handful of semiconductor and cloud leaders dominated returns, 2026 is bringing a more complex picture: cooling capital spending, sector rotation, and new pockets of strength in data center infrastructure and networking. Investor's Business Daily (IBD) has framed this period as an inflection point for AI stocks, urging investors to look past headline names like Nvidia and track the broader ecosystem of companies supplying chips, cloud capacity, software, and physical data center build‑out. Cloud and AI spending: still growing, but at a slower pace A key driver of AI equity performance has been massive investment by the largest cloud providers in infrastructure to support generative AI workloads. Industry estimates cited by market research and Wall Street analysts indicate that combined cloud capital expenditures by the five leading providers are on track to approach $400 billion by 2025. Growth, however, is expected to decelerate meaningfully from 2026 onward, with forecast increases in capex falling from more than 50% in the current year to under 20% in 2026 and potentially single‑digit growth by 2027 and 2028. This slowdown does not imply an end to AI investment, but it does suggest a transition from rapid build‑out to more disciplined deployment and optimization. For equity investors, that shift tends to favor companies with proven profitability and pricing power over high‑growth, cash‑burning names that depended on ever‑rising infrastructure budgets. Leadership rotates: from megacap chips to networking and data centers Early in the AI boom, the market narrative centered on a small group of companies supplying the graphics processing units (GPUs) that power large language models. Nvidia, in particular, became the emblem of the generative AI rally, with its data center revenue and share price soaring on demand for training chips. By 2026, however, several of those early winners have cooled, and some have even exhibited "death cross" technical patternsa bearish signal in chart analysis that occurs when a shorter‑term moving average falls below a longer‑term one. IBD's coverage in 2026 highlights how leadership has shifted toward less‑celebrated but strategically important players: Optical networking specialists such as Lumentum Holdings and Ciena have emerged as top performers, benefiting from surging demand for high‑bandwidth connectivity between AI servers inside and across data centers. Data center infrastructure providers like Vertiv Holdings have posted strong gains as hyperscale and enterprise customers invest in power, cooling, and racks capable of handling dense AI compute clusters. Cloud and enterprise software names tied directly to AI deploymentincluding security platforms, data analytics, and edge networkinghave seen significant appreciation, even as some core chip stocks consolidate. This rotation illustrates a broader theme: as AI implementation spreads, value is migrating along the supply chain, rewarding companies that solve bottlenecks in throughput, energy efficiency, and systems integration. Is there an AI bubble? Sentiment points to normalization Talk of an "AI bubble" was common in 2023 and 2024, as valuations of some popular names detached from near‑term fundamentals. Recent indicators suggest that bubble concerns have eased. IBD noted that searches for the term "AI bubble" on Google have fallen to their lowest levels since late 2023, signaling a shift from speculative enthusiasm to more measured interest. The price action supports that view: many of last year's top AI performers have given back a portion of their gains, while other areas of the stock marketincluding energy, materials, consumer staples, and health carehave attracted capital as investors rebalance away from concentrated tech bets. Volatility in AI names remains elevated, but the pattern looks more like a maturing theme than a classic boom‑and‑bust. Notable AI‑related stocks drawing attention in 2026 Investor's Business Daily and other market observers are tracking a wide range of companies as potential AI leaders or turnaround stories this year. Among those frequently cited: Nvidia (NVDA)  Still considered a cornerstone of AI infrastructure thanks to its GPUs and software stack. After sharp gains in earlier years and a major sell‑off tied to competitive concerns, the stock's 2025 performance has been more moderate, with investors watching closely for the next wave of product cycles and demand catalysts. Microsoft (MSFT) and Alphabet (GOOGL)  Both have integrated AI across their cloud and consumer platforms, from productivity tools to search and developer services. Their shares have climbed steadily as investors focus on how AI can deepen moats in cloud computing and software rather than simply drive short‑term revenue spikes. Oracle (ORCL)  The enterprise software and cloud provider has benefited from its role in large AI infrastructure projects, including capacity linked to OpenAI's "Stargate" initiative. Oracle's stock recorded a double‑digit percentage gain in 2025, reflecting renewed confidence in its cloud strategy. Arista Networks (ANET)  A key supplier of high‑speed networking equipment to cloud titans, Arista has seen its shares rise on the back of strong earnings and guidance that emphasize AI‑driven demand for data center switching and routing. Cloudflare (NET) and Palantir (PLTR)  These companies, focused respectively on edge networking/security and data‑driven decision platforms, have enjoyed substantial stock price increases, underscoring investor belief that AI value lies in secure, scalable delivery and real‑world analytics as much as in raw compute. Outside the best‑known names, IBD has flagged more specialized AI plays. An example is Everus Construction, a North Dakota‑based company that designs and builds advanced data centers tailored for AI workloads. Its shares have surged in 2026, and technical analysis suggests the stock is approaching a fresh buy point after rebounding from key support levels. Coverage of such names reflects investor interest in companies that profit directly from the physical expansion of AI capacity. Under‑the‑radar beneficiaries: brokers and industrials AI's reach into financial services and manufacturing is creating opportunities beyond pure technology. IBD recently spotlighted Robinhood Markets as a potential "next AI play" as the brokerage invests in automation, personalization, and new product offerings built on machine learning. At the same time, names such as Dell Technologies, Howmet Aerospace, and Cognex have been cited as stocks near technical buy points that are tied indirectly to AI, either through supplying hardware for data centers, providing components used in advanced manufacturing, or delivering machine‑vision systems that rely on AI algorithms. Robotaxis and real‑world AI deployment Beyond the data center, AI is beginning to reshape transportation. A recent development covered by IBD is the decision by Nevada regulators to grant robotaxi permits to Tesla, Waymo, and Uber, allowing them to operate autonomous ride‑hailing services in Las Vegas. The move follows years of testing and limited pilots, and it positions Las Vegas as one of the most advanced U.S. markets for commercialized self‑driving operations. For investors, robotaxis highlight how AI can evolve from software running in the cloud to a revenue‑generating service with visible urban impact. The companies involved range from pure technology players to diversified automakers and platform businesses, further blurring the line between "AI stock" and traditional sectors. What investors are watching next The central question for AI investors heading into the remainder of 2026 is whether the sector can sustain earnings growth in a more restrained spending environment. Key factors on watch include: The pace of new AI chip launches and whether they drive replacement cycles in existing data centers. Adoption of generative AI in enterprise workflows and its impact on software licensing and cloud consumption. Regulatory developments, particularly around data privacy, AI safety, and autonomous vehicles. The ability of second‑tier and infrastructure‑focused companies to maintain margins as competition increases. In its ongoing "AI News: Artificial Intelligence Trends And Top AI Stocks To Watch" coverage, Investor's Business Daily continues to emphasize disciplined stock selection, technical buy and sell rules, and diversification across the AI value chainfrom chips and cloud providers to networking, infrastructure, and real‑world applications such as robotaxis.

Nic Reeve·