Nasdaq Artificial Intelligence

Learn governed AI agents could help capital markets teams investigate issues, prioritize work and act more efficiently without compromising control - and how Nasdaq Calypso plans to support agents in the platform.

Chipmaker giant NVIDIA (NASDAQ:NVDA) authorized an unprecedented US$150 billion increase to its share repurchase program, the largest buyback authorization in corporate history.The board’s authorization increases Nvidia’s total remaining buyback capacity to US$235 billion, which the company expects to execute through fiscal 2028.The US$150 billion addition alone eclipses Apple'S (NASDAQ:AAPL) US$110 billion approval in 2024 and exceeds the individual market capitalization of roughly 84 percent of the companies in the S&P 500 (INDEXSP:.INX).Nvidia shares rose more than 2 percent following the announcement. Through Friday last week, the stock had gained just over 20 percent year-to-date, significantly trailing the company’s semiconductor peers.In comparison, Advanced Micro Devices (NASDAQ:AMD) (AMD) has more than doubled this year, while Intel (NASDAQ:INTC) has more than tripled. "NVIDIA’s growth is being driven by a once-in-a-generation platform shift to AI and accelerated computing,” Nvidia founder and CEO Jensen Huang said in the announcement. “Our cash generation gives us the capacity to invest in the technologies that advance this transformation and return capital to shareholders. This authorization reflects our confidence in the long-term opportunity ahead.”Nvidia recently forecast 70 percent revenue growth for fiscal 2028, projecting that demand for its processors will continue generating substantial free cash flow. The company also previously announced an US$80 billion share buyback in May.On the same day, the company also concurrently launched the Open Agent Safety Platform, designed to secure AI agents from the testing phase through deployment. The platform combines the NVIDIA OpenShell open-source software with the NVIDIA Sentry reference system design. The chipmaker is reportedly negotiating a US$10 billion anchor commitment for Anthropic’s impending initial public offering. The AI startup aims to raise up to US$100 billion prior to the November midterm elections, targeting a record-breaking US$2 trillion valuation.Don't forget to follow us @INN_Technology for real-time news updates!Securities Disclosure: I, Giann Liguid, hold no direct investment interest in any company mentioned in this article.

It’s been a rollercoaster ride of a week for markets.

Researchers at Mayo Clinic have designed an artificial intelligence model that can potentially predict an individual’s risk of developing pancreatic cancer years before diagnosis. Research will be presented at the American College of Surgeons (ACS) Clinical Congress 2026, held from September 26-29 in Washington. Thousands of surgeons convene at the annual event to advance surgical quality, patient safety, and access to care. According to a press release shared with the Investing News Network (INN), pancreatic cancer is rare but highly deadly, accounting for about 3 percent of all new cancers but 8 percent of all cancer deaths.Data from the American Cancer Society, notes that there have been about 67,000 new diagnoses and 52,000 deaths so far in 2026. “Pancreatic cancer can be curable, but only when we catch it early, and fewer than one in five patients is diagnosed in time,” said Mayo Clinic surgical oncologist and study co-author Cornelius Thiels, DO, MBA, FACS. “As a result, survival for many patients is still measured in months, not years.”Dr. Thiels said his team set out to develop an AI model that can identify patients at greatest risk of developing cancer of the pancreas because universal screening for pancreatic cancer “isn’t feasible”. “We know that pancreatic cancer forms over five to seven years, but the things that a clinician or patient sees don’t happen until it’s too late.” The researchers built the AI model using Mayo Clinic electronic health records and routine lab test results to analyze 6,066 pancreatic cancer patients and 33,396 control subjects, each with up to 19 years of medical history, to detect early risk indicators.To evaluate its ability to predict pancreatic cancer three years before diagnosis, researchers measured the model's accuracy. It achieved an AUROC of 0.853 (where 1.0 is perfect accuracy) and an AUPRC of 0.712, demonstrating strong predictive performance with few false positives.The model showed strong calibration, with a calibration slope of 1.08, meaning its predicted risk closely matched what actually happened to patients.“Our model showed that a greater than 50 percent risk of pancreas cancer predicted by our model indicated an 88 percent likelihood of being diagnosed with pancreatic cancer in one year,” Dr. Varghese, a surgical data scientist at Mayo Clinic in Rochester, explained. “We built this to be as generalizable, scalable, and easy to put into practice as possible,” Dr. Varghese added. The data inputs the model relies on are captured almost universally in hospital systems worldwide, Dr. Varghese said. “If it’s shown to work, it could be used in almost any setting,” he added. According to Dr. Thiels, the model is currently being deployed on a research basis. “We’re proving that we can move this from a retrospective research tool into our clinical environment and run it prospectively for validation,” he stated.Dr. Thiels noted that efforts are underway to validate the model further, both prospectively within Mayo and at an external healthcare system this year. “We are also working on developing more advanced machine learning architectures, which appear to improve the performance even more,” he added. Earlier this year, a study appearing in the journal Gut described a Mayo-built AI model called REDMOD that read ordinary CT scans from people who were later diagnosed to look for early signs of pancreatic cancer. The AI caught most of those hidden cancers, often more than a year before diagnosis, about twice as many as specialists caught looking at the same scans. The gap was even bigger for scans taken more than two years before diagnosis. A follow-up trial called AI-PACED will test the tool in real care for high-risk patients. It will also track false alarms and whether finding the cancer earlier improves outcomes.“The greatest barrier to saving lives from pancreatic cancer has been our inability to see the disease when it is still curable,” said the study’s senior author Dr. Ajit Goenka. ​What investors are watching Lu Zhang, founder and managing partner of Fusion Fund, has been watching AI-powered diagnostics closely. At Web Summit Vancouver last year, she pointed to advances in digital diagnostics for conditions like cancer, heart disease and mental health.She said healthcare is entering its “prime time for innovation.” In her view, the core goal is to “improve the quality of life, how to really enable the future of healthcare to be personalized…and also be able to do super early diagnostics and reduce the healthcare burden in the long term.”Zhang also noted that less than 5 percent of healthcare data is currently being used. Mayo Clinic’s model is built on electronic health records and routine lab test results.In a recent conversation with the INN earlier this month, Zhang said large AI labs are paying high prices for high-quality healthcare data. They are also hiring PhDs and domain experts to label it.For Zhang, healthcare is one of the clearest examples of where AI’s promise and its constraints collide. She repeatedly comes back to the sector as a case where high-quality, tightly controlled data makes a real difference — and where governance and deployment choices are non‑negotiable.On the infrastructure side, she stresses that healthcare is part of the huge chunk of the economy that can’t just ship everything to the public cloud. That, in her view, is why architecture design and small, efficient models matter so much: enterprise buyers in healthcare often want on‑prem or private‑network deployment, not generic cloud AI.Zhang also highlights healthcare as a leading example of vertical, data‑driven AI moving fast precisely because the data is specialized and curated. “They are able to directly use high-quality data, not a huge amount of data, but highly specialized healthcare data to fine-tune their model.”She points to Google's (NASDAQ:GOOGL) AlphaFold as one reference point, but says the dynamic is broader. Large AI labs are actively competing to secure top‑tier medical datasets and expert feedback.That mix of private, regulated environments; expensive but highly informative data; and expert human feedback makes healthcare a kind of proving ground for the approach Zhang favors: small, vertical models tuned on curated industry data and deployed inside tightly governed infrastructures. Don’t forget to follow us @INN_Lifescience for real-time news updates!Securities Disclosure: I, Meagen Seatter, hold no direct investment interest in any company mentioned in this article.

"Big Short” investor Michael Burry is avoiding the technology sector’s AI trade, instead deploying capital into other ventures.In a September 22 Substack update, Burry disclosed new positions in Brazilian copper miner Ero Copper (TSX:ERO), building-products firm QXO (NYSE:QXO), Australian furniture retailer Temple & Webster (ASX:TPW), Sprouts Farmers Market (NASDAQ:SFM), and animal health company Zoetis (NYSE:ZTS)."The house party is packed, pushing AI higher today, but I am largely ignoring the 'woo-hoos,'" Burry wrote.Just last month, Burry publicly compared the relentless positivity surrounding AI infrastructure investments to the dot-com era and the mid-2000s housing bubble, warning that the current environment is "orders of magnitude more dangerous to the economy and investors than Enron.""That is how it felt in May of 2007 when the Fed Chair was saying there would be no contagion or a couple years earlier when he said there is no such thing as a housing bubble,” Burry told Business Insider.Rather than shorting the technology directly, Burry’s largest disclosed new conviction is an indirect play on AI infrastructure demand through Ero Copper."All those back at the house are going to be needing a lot of copper," Burry stated, calling his stake in the higher-cost producer a mid-sized position. "Ero common does it for me."While COMEX copper recently settled at US$6.6865 per pound, up 46 percent over the past 12 months, Burry is relying on a looming structural deficit. He noted that major copper discoveries containing at least 500,000 tons have evaporated from double-digit annual totals in the 1990s to zero in 2025. Since new deposits require up to 18 years to reach production, Burry anticipates that surging demand from data centers will trigger extreme price expansion before new supply materializes. Notably, Ero Copper maintained its 2026 production guidance of 67,500 to 77,500 tons.Burry also acquired common shares and 5.5 percent Series B mandatory convertible preferred stock in QXO. The company is executing a roll-up strategy in the highly fragmented building-products distribution market under Brad Jacobs, the founder of United Rentals and XPO.The remainder of Burry's disclosed purchases targeted severely punished equities, accumulating a "fairly large position" in Temple & Webster which plummeted 82 percent over the past year following a 62 percent collapse in fiscal 2026 net income, alongside Sprouts Farmers Market, down 43 percent year-over-year, and animal-drug maker Zoetis (NYSE:ZTS), which has lost half its market value.Don’t forget to follow us @INN_Resource for real-time news updates!Securities Disclosure: I, Giann Liguid, hold no direct investment interest in any company mentioned in this article.

Hyperscalers continue to ramp up capital expenditure forecasts and order backlogs, escalating spending on AI infrastructure to record levels. According to Goldman Sachs' (NYSE:GS) research, AI-related issuers now account for a quarter of all new US investment-grade corporate debt this year, pushing the firm to raise its 2026 issuance forecast to US$2.3 trillion, up from US$2.1 trillion at the start of the year. The most creditworthy companies have grown capital spending by at least 35 percent year-over-year for ten straight quarters, and AI-related borrowers now drive nearly half of all convertible bond issuance.While the underlying demand for compute is real and structural, Lu Zhang, founder and managing partner of Fusion Fund, described the market’s signal-to-noise ratio as “really low”, making it difficult for investors to separate the conviction from the hype. ​Stress testing the AI thesis The market has delivered a run of real tests in the final weeks of Q3. Most recently, AI-linked stocks sold off alongside a broader risk-off move in mid-September before reversing as oil eased and bond yields fell. Sell-offs have recurring narratives including doubts over whether AI spending reflects real demand or circular vendor financing, warnings from AI’s own leaders that development itself may need to slow and analysts flagging historical bubble-peak signals. Hyperscaler Q2 earnings, while beating estimates on the surface, raised some red flags. Capital spending grew roughly three to four times faster than revenue last quarter for Microsoft (NASDAQ:MSFT), Alphabet (NASDAQ:GOOGL), Amazon (NASDAQ:AMZN) and Meta Platforms (NASDAQ:META). Free cash flow also fell - Meta’s collapsed by more than 90 percent, Amazon and Oracle (NYSE:ORCL) both went negative, with Oracle falling by close to US$24 billion. NVIDIA (NASDAQ:NVDA) posted real profit, but its own free cash flow still fell by US$27 billion in a single quarter, largely because it’s extending credit to its own customers to help them buy its chips, while Amazon’s profit beat came mostly from a paper markup on its Anthropic stake, not the underlying business.Meanwhile, Nvidia’s compute guarantee backing OpenAI’s data-center buildout has reportedly shrunk from an initial US$250 billion to US$105 billion, and OpenAI’s own CFO has acknowledged that Nvidia’s investment effectively comes back to Nvidia as chip purchases. ​Real demand, or financing dressed up as demand? According to Zhang, big tech is “very determined” to keep building, willing to fund it with debt and equity rather than cash because losing access to compute to a competitor isn’t a risk they can afford to take.“They have to invest, regardless (of whether) they like it or not,” she said. Hyperscalers’ own stated logic for spending this aggressively isn’t about near-term unit economics. “They want to have control over their AI strategy and don’t want the bottleneck controlled by their competitor.”Zhang, an early-stage investor who backs enterprise and industrial AI companies, brings a simple filter to the current wave of AI infrastructure deals.To distinguish between signals and noise, Lu advises against evaluating AI based on model quality or headline expenditures. What she looks for instead is a three-part test: Whether a company has curated its own high-quality, industry-specific data.Whether a company has optimized its architecture to bring down the cost of running the model, not just training it.Whether a company already has real partnerships with the industry players who control the data and workflows it needs. Miss any of those, in her view, and a “differentiator” is just a temporary lead.Zhang points to budget allocation from banks, insurers and healthcare systems as the clearest outside-tech signal that demand is durable rather than manufactured. She also discounts headline revenue on its own; in her view, a seller’s market can inflate revenue without matching real deployment. “We also want to see a very healthy growth of the unique selling number across the industry that people are actually digesting, buying and deploying more GPU and CPU. I expected we would see some back and forth,” she said, noting that GPU demand shifted visibly between early and late 2025.On the capital markets side, she expects a “more creative approach of using different financial vehicles” to fund AI infrastructure, sees “definitely some of the bubble issue” in current valuations and calls backlog figures “very optimistic projections.” The real risk to the AI investment thesis, in her view, could instead be a governance failure serious enough to force a pause in deployment. ​The cost of getting compliance wrong Meanwhile, the governance question is especially pressing since OpenAI disclosed that its own AI agents breached developer platform Hugging Face over several months this summer, reaching administrator-level access before the company caught it.OpenAI has since paused reinforcement learning on its own largest planned frontier run, after preliminary evidence that its next model could cross the critical cybersecurity capability threshold in its own safety framework. The delay has already cost significant time and research resources, according to the company. This week, at the UN General Assembly, more than 20 countries jointly proposed an international body for AI safety standards and incident reporting. Washington separately floated a bilateral hotline with Beijing for flagging AI incidents ahead of Chinese President Xi Jinping’s planned visit to Washington. Zhang described AI adoption happening in two phases: automation first, which cuts costs without necessarily showing up in revenue, followed by optimization, when redesigned workflows finally move the needle on earnings.A heavier compliance and reporting regime doesn’t kill that second phase, but it adds a cost line and a delay, especially for regulated industries meant to be the proof of durable demand. ​The bottom line For now, the AI buildout sits in an awkward middle ground. The demand signal is deafening, but the financing story is messier than the headlines suggest.Fusion Fund’s framework pushes investors to look past who is training the largest model or spending the most. Lu encourages market participants to tighten their filters, focusing on companies with real data moats, architectures built for low‑cost deployment and partnerships that prove their software is a must‑have, not a demo.As the industry weathers real stress tests, investors should treat AI not as a monolith, but as a set of sharply different bets on who will actually turn compute into cash. Don’t forget to follow us @INN_Technology for real-time news updates!Securities Disclosure: I, Meagen Seatter, hold no direct investment interest in any company mentioned in this article.

The launch of OpenAI’s ChatGPT created a major buzz around artificial intelligence (AI) stocks.ChatGPT is an AI chatbot software application that uses machine-learning techniques to emulate human-written conversations. This technology is called generative AI, and it's been making an impact on myriad industries, including marketing, security, healthcare, gaming, communication, customer service and software development.The potential behind generative AI has been the primary driver behind a major stock rally that has helped the S&P 500 (INDEXSP:.INX) and Nasdaq Composite (INDEXNASDAQ:.IXIC) reach multiple new highs since 2023.According to Fortune Business Insights, the global generative AI market size was estimated at US$103.58 billion in 2025 and projected to grow from US$161 billion in 2026 to US$1.26 trillion by 2034. Although investors can’t currently invest in OpenAI stock directly, several technology stocks offer exposure to the generative AI market, including some of the biggest names in tech.Below, the Investing News Network showcases 10 generative AI stocks that stand to benefit the most from the rise in advancements and adoption of AI chatbot technologies, separated into software and hardware offerings. Data was gathered using TradingView’s stock screener. All market cap and share price data was current as of August 31, 2026. Biggest software AI stocks to watch These five tech giants offer investors exposure to generative AI by offering their own chatbots and generative AI products, developing the software necessary for AI and integrating AI into their products. 1. Alphabet (NASDAQ:GOOGL) Market cap: US$4.13 trillionCurrent share price: US$339.35Alphabet, Google’s parent company, has played a key role in advancing generative AI. Its flagship AI model, Gemini, powers a wide range of services, with new versions continuously being rolled out. The company designs its own custom AI accelerator chips, which are used to train large-scale language models and power its AI services.Alphabet's subsidiary, DeepMind, focuses on AI research and development. Its AI system AlphaFold won the Nobel Prize in Chemistry in 2024 for its ability to predict the structure of proteins based on a protein's unique amino acid sequence.AI continues to be embedded across Google's services and products, with Gemini serving as the default assistant for the company's lineup of Pixel smartphones, and AI Overviews being delivered on many Google searches. 2. Microsoft (NASDAQ:MSFT) Market cap: US$3.77 trillionCurrent share price: US$507.29The technology behemoth Microsoft has invested US$13 billion in OpenAI throughout the years, and the company's current AI solutions, Bing AI and Copilot, are based on OpenAI's technology. In late October 2025, Microsoft and OpenAI signed a new agreement, with Microsoft's stake now described as an investment in "OpenAI Group PBC" valued at approximately US$135 billion. OpenAI remains Microsoft's frontier-model partner, and Microsoft keeps exclusive IP rights and Azure API exclusivity.Microsoft has also partnered with Palantir Technologies (NASDAQ:PLTR) to provide AI tools to US defense and intelligence agencies.More recently, Microsoft's AI branch, dubbed MAI, has branched out. At Microsoft Build 2026, the MAI team introduced a reasoning model, a lightweight coding model, updated image and voice models and a transcription model that Microsoft is billing as best-in-class in terms of word error rate. As of the company's Q4 fiscal 2026 earnings, released in July, Microsoft 365 Copilot passed 30 million paid seats. 3. Amazon (NASDAQ:AMZN) Market cap: US$2.8 trillionCurrent share price: US$259.77Amazon subsidiary and cloud-computing platform Amazon Web Services (AWS) evolved out of Amazon’s transition from an online retailer to one of the world’s largest technology companies. AWS’s wide range of services includes computing, storage, databases, networking, analytics, machine learning and AI.AWS has many AI business tools on offer across four verticals: AI services, AI platforms, AI frameworks and AI infrastructure. In 2025, the company relaunched its voice assistant as Alexa+, a generative, model-agnostic version of Alexa that handles multi-turn conversations and completes tasks like bookings and trip planning, rather than just discrete commands. Alexa+ became free to all US Prime members nationwide in February 2026.Since its launch in 2023, Bedrock, a service that lets businesses build with generative AI foundation models, has expanded its catalog to include OpenAI's open-weight models and Anthropic's latest Claude models. At its 2025 AWS Summit in New York, the company unveiled Amazon Bedrock AgentCore to help businesses deploy and scale AI agents with enterprise-grade security and tool integration. AgentCore reached general availability that October and has kept expanding since, with new managed components added through mid-2026. 4. Meta Platforms (NASDAQ:META) Market cap: US$1.46 trillionCurrent share price: US$572.34Meta Platforms has expressed its commitment to continued research within the generative AI sphere with an open-source approach to its software developments. The giant behind Facebook, Instagram and WhatsApp is one of the most influential companies in tech, sharing ranks with the likes of Microsoft and Alphabet.Meta AI is now built on Muse Spark, a new model line from Meta Superintelligence Labs that replaced Meta Llama 3 as the engine behind Meta's AI assistant in April 2026. Meta AI is integrated across Meta's apps as well as a standalone app and website. The company says AI-driven improvements to its ad-ranking model lifted Facebook ad clicks, a strategy that has helped keep Meta's ad business as its primary driver of revenue.Meta CEO Mark Zuckerberg has maintained that increased spending on AI infrastructure is necessary to maintain the company's competitive position. Meta has raised its 2026 capital expenditure guidance twice this year, to a current range of US$130 billion to US$145 billion, nearly double the US$72.2 billion it spent in 2025. The company has also aggressively pursued AI talent, hiring Scale AI's Alexandr Wang as chief AI officer alongside a roughly US$14 billion investment in Scale AI, and recruiting more than 50 researchers from rivals including OpenAI and Google. 5. Palantir Technologies (NASDAQ:PLTR) Market cap: US$447.89 billionCurrent share price: US$186.38Palantir Technologies' generative AI strategy is centered on its Artificial Intelligence Platform (AIP), a product designed to help governments and commercial enterprises integrate AI into their operations with a focus on security and human-in-the-loop control. Rather than building models for general use, Palantir provides a platform that enables customers to leverage large language models from multiple providers, including OpenAI, Google, Anthropic and xAI, within their own private networks.That approach has translated into rapid growth: In its Q2 2026 quarter, Palantir reported revenue up 93 percent year-over-year, driven by 149 percent growth in its US commercial business, and the company raised its full-year revenue guidance accordingly. On the government side, AIP underpins a 10 year enterprise agreement worth up to US$10 billion with the US Army, as well as the Pentagon's Maven Smart System, which processes drone and surveillance data. Biggest hardware AI stocks to watch Generative AI's explosive growth is driving the market for chips. These five companies offer investors exposure to generative AI by developing the hardware necessary for the AI buildout. 1. NVIDIA (NASDAQ:NVDA) Market cap: US$5.32 trillionCurrent share price: US$220.78NVIDIA is a pioneer and global leader in graphics processing unit (GPU) technology. The company designs the specialized chips used to train AI and machine-learning models.While it has been well known in computer and gaming spaces for decades, NVIDIA's progress in the AI sector has been the biggest growth driver in recent years. The company currently holds the title of the world’s most valuable company, coming in ahead of rivals Microsoft, Apple (NASDAQ:AAPL) and Alphabet. NVIDIA's Blackwell Ultra (GB300) systems, which began shipping in late 2025, are now deployed at scale. The company's newest architecture is Rubin, a six-chip platform built around the new Vera CPU and Rubin GPU. It was unveiled in January 2026. NVIDIA said that, compared to Blackwell, Rubin cuts AI inference token costs by up to 10 times and needs up to four times fewer GPUs to train mixture-of-experts models.The platform is in production, and as of September is already deployed with cloud partners including Google Cloud, Microsoft Azure, Oracle (NYSE:ORCL) Cloud and CoreWeave (NASDAQ:CRWV). Rubin Ultra is the next step, slated for 2027. 2. Taiwan Semiconductor Manufacturing Company (NYSE:TSM) Market cap: US$1.98 trillionCurrent share price: US$415.32Taiwan Semiconductor Manufacturing Company (TSMC) is the world's largest contract chipmaker. The company's customer base includes NVIDIA, AMD, Apple and Broadcom. TSMC has repeatedly accelerated and enlarged its Arizona fab, and total committed US investment now stands at US$265 billion. Chairman C.C. Wei said the second Arizona fab will begin mass production in the second half of 2027, moved up from an original 2028 target.High-performance computing, which includes AI accelerators, made up 66 percent of Q2 2026 revenue, and net income rose 77 percent year-over-year to roughly US$22.34 billion. TSMC expects full-year revenue growth above 40 percent in 2026, and guidance for Q3 predicts revenue to be between US$44.6 billion and US$45.8 billion. Additionally, TSMC raised its 2026 capex guidance for the second time, to a range of US$60 billion to US$64 billion. 3. Broadcom (NASDAQ:AVGO) Market cap: US$1.76 trillionCurrent share price: US$370.34Broadcom's core business spans semiconductors and enterprise software, but its fastest-growing segment is custom AI accelerator chips it co-designs for hyperscalers, among them Google's TPUs, as well as AI networking hardware. In October 2025, it signed a multi-year deal with OpenAI to co-develop and deploy 10 gigawatts of OpenAI-designed accelerators through 2029.Broadcom's revenue totaled US22.2 billion in the second quarter of its fiscal 2026, a 48 percent year-over-year rise, and its AI semiconductor revenue hit US$10.8 billion, up 143 percent. The company predicted that AI revenue would grow 200 percent on an annual basis to US$16 billion in Q3. 4. Micron Technology (NASDAQ:MU) Market cap: US$1.08 trillionCurrent share price: US$958.73Micron makes the high-bandwidth memory (HBM) chips used to feed data to AI accelerators. The company's market cap passed US$1 trillion earlier this year.Fiscal Q3 2026 revenue hit a record US$41.5 billion, up 346 percent year-over-year, with its two data-center-focused units accounting for roughly US$25.3 billion of that. Micron says HBM4 is shipping in high volume to its "lead customer," widely reported as Nvidia. Several outlets describe its HBM capacity as sold out through 2026. 5. Advanced Micro Devices (NASDAQ:AMD) Market cap: US$768.44 billionCurrent share price: US$470.72As Nvidia's main competitor in AI accelerators, AMD sells Instinct-series GPUs alongside its EPYC server chips. In October 2025, AMD signed a multi-year deal with OpenAI to deploy up to 6 gigawatts of AMD Instinct GPUs, starting with a 1 gigawatt deployment of the new MI450 series in the second half of 2026. As part of the deal, AMD issued OpenAI a warrant for up to 160 million AMD shares, vesting in tranches tied to deployment milestones and AMD's share price. AMD's CFO said the arrangement is expected to generate "tens of billions of dollars" in revenue.The following February, AMD struck a nearly identical structure with Meta that starts in H2 2026.In AMD's Q2 2026, data center revenue more than doubled to US$6.7 billion, up 107 percent year-over-year. It made up 58 percent of total revenue, which grew 50 percent to US$11.5 billion. As for net income, the company reported US$2.3 billion during the quarter.AMD shared Q3 2026 revenue guidance of roughly US$13 billion and said it expects data center sales to keep accelerating through the second half of the year. FAQs for generative AI ​What is generative AI? Generative AI is an emerging AI technology based on deep learning models and algorithms that can generate text, images or sounds in response to prompts given by users. ​What are generative AI examples? Some of the most notable examples of generative AI are ChatGPT, DALL-E 2, Midjourney, Stable Diffusion, Gemini, Copilot and DeepSeek. OpenAI's DALL-E 2 is an AI system that can create realistic images and art from a description in natural language. Similar to DALL-E 2, Midjourney generates images from prompts. Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Microsoft's Copilot is a feature of the Bing search engine that leverages the same technology as ChatGPT. ​What are the hottest generative AI startups? According to technology and business magazine e-Week, in addition to ChatGPT creator OpenAI, some of the other leading generative AI startups include Hugging Face, Synthesis AI, Jasper and Cohere. This is an updated version of an article first published by the Investing News Network in 2023. Don't forget to follow us @INN_Technology for real-time news updates!Securities Disclosure: I, Meagen Seatter, hold no direct investment interest in any company mentioned in this article.

For markets, the big themes this week were the Federal Reserve rate decision, developments in the Iran conflict, and AI model development.

South Korea’s SK Hynix (KRX:000660,OTCPL:HXSCL) is reportedly in negotiations with Intel (NASDAQ:INTC) to manufacture memory chips in the US for the first time, according to a Reuters exclusive.Discussions center on two potential operational structures: SK Hynix could lease a portion of Intel’s delayed semiconductor manufacturing site in Ohio, or form a joint venture involving Intel and major cloud providers seeking to secure long-term memory supplies.A manufacturing partnership would ease operational and financial strain on Intel, which is partially owned by the US government. Intel announced a US$100 billion plan in 2022 to build the Ohio complex, but construction delays pushed completion of its two fabrication plants from 2025 to 2030 and 2031. To support capital expenditures and working capital due to sustained AI compute demand, the company launched a US$15 billion public offering of common stock in August 2026.For SK Hynix, a US fabrication footprint would address pressure from American technology clients and federal officials. The South Korean company, which completed a secondary listing on the Nasdaq in July and is constructing a memory packaging plant in Indiana, crossed the US$1 trillion market capitalization threshold in May 2026 alongside Micron Technology as demand for high-bandwidth memory (HBM) surged. SK Group Chairman Chey Tae-won acknowledged those commercial pressures in July, stating, "I think we need to build a factory in the United States. If possible, I believe we should build it."Washington has continued to escalate pressure on foreign semiconductor producers to expand domestic manufacturing. US Commerce Secretary Howard Lutnick has threatened tariffs of up to 100 percent on South Korean and Taiwanese chipmakers unless they increase production capacity on American soil. However, US fabrication carries substantially higher labor, construction, and supply chain costs compared to existing Asian production hubs.Addressing the reports, SK Hynix stated that it is "reviewing various measures, including establishing additional production bases, to strengthen the competitiveness of its memory business," but "no matters have been determined at this stage." The company subsequently issued a formal statement clarifying that "nothing has been finalized regarding cooperation with any specific companies mentioned in the article or memory chip production in the United States." Intel declined to comment beyond stating that it is continuing investments to prepare the Ohio site.Don't forget to follow us @INN_Technology for real-time news updates!Securities Disclosure: I, Giann Liguid, hold no direct investment interest in any company mentioned in this article.

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In a week headlined by artificial intelligence fears, a Federal Reserve decision, and rising oil prices, Palantir Technologies Inc. (NASDAQ: PLTR) is more than holding its own. PLTR held steady on Sept. 15, even as the broader market was under pressure. It would be simplistic t

Blue-Chip Stocks Are Looking Anything But Boring With Dividends and Big 2026 GainsMerck & Co., Inc. (NYSE:MRK) Chairman and CEO Rob Davis said the company’s “transformation is underway,” citing progress across product launches, clinical data readouts, business development and its

Anthropic is negotiating to secure NVIDIA (NASDAQ:NVDA) as an anchor investor for a record-breaking initial public offering (IPO) that could value the startup at roughly US$2 trillion.According to a Reuters exclusive, the chipmaker is weighing a US$10 billion commitment to the offering, serving as an early backer that could signal strong institutional confidence to public markets preparing to absorb the astronomical valuations and capital demands of frontier artificial intelligence labs.Anthropic’s push for public capital follows an explosive surge in sales. The company’s annualized revenue run rate eclipsed US$65 billion by late July, skyrocketing from approximately US$9 billion at the end of 2025.Following a US$65 billion private funding round in May that valued the maker of Claude at US$965 billion post-money, the company is now projecting US$190 billion to US$200 billion in revenue by 2028 to support the US$2 trillion public price tag.The IPO discussions expand existing operational ties between Anthropic and NVIDIA. In November 2025, NVIDIA pledged up to US$10 billion to the startup, tied to an agreement requiring Anthropic to purchase US$30 billion in NVIDIA-powered Microsoft (NASDAQ:MSFT) Azure computing capacity.More recently, NVIDIA has deployed its own balance sheet to directly broker physical infrastructure access for Anthropic. In late August, Anthropic signed a US$35 billion cloud-computing contract with Lambda, a cloud provider backed by NVIDIA. In a highly structured arrangement, NVIDIA holds the actual lease on the 700 megawatt Nueces County, Texas, data center being developed by Hut 8 (TSX:HUT,NASDAQ:HUT). This guarantees the space and allows Lambda to service Anthropic without procuring the real estate itself. Earlier in August, Anthropic executed a similar US$45 billion agreement with another NVIDIA-backed cloud provider, Nscale, to lease capacity in West Virginia.Despite its reliance on NVIDIA graphics processing units, Anthropic continues to diversify its supply chain. The startup committed over US$100 billion in April to Amazon Web Services over the next decade, anchored by plans to use more than 1 million Amazon (NASDAQ:AMZN) Trainium2 chips. It simultaneously secured multiple gigawatts of tensor processing unit (TPU) capacity from Google (NASDAQ:GOOGL) and Broadcom (NASDAQ:AVGO) in a separate deal. Google is actively providing financial guarantees to help Hut 8 raise debt for separate Anthropic data centers designed specifically to house TPUs.Don't forget to follow us @INN_Technology for real-time news updates!Securities Disclosure: I, Giann Liguid, hold no direct investment interest in any company mentioned in this article.

For investors who favor exchange-traded funds (ETFs), the market has never been more dynamic. Not only do ETFs now outnumber individual stocks, but the sheer versatility of today’s funds allows investors to target nuanced strategies, from exposure to thematic trends like the mem

Palantir's New U.S. Army Contract Brings It Closer to Owning the Full Battlefield SystemPalantir Technologies (NASDAQ:PLTR) used its AIPCon event to highlight customer deployments of its Foundry, Ontology and Artificial Intelligence Platform products across supply chains, aviatio

Global organizations face a dilemma as they adopt artificial intelligence. While interest in private model deployment continues to climb, strict data residency laws and internal privacy rules make standard public cloud environments unsuitable for sensitive information. Meanwhile

As AI moves from experimentation to enterprise adoption, realizing its value requires more than new tools — it requires governance, workforce readiness, cultural alignment, and trust. Boards play a critical role in aligning AI initiatives with business strategy, preparing the workforce for change, and establishing governance frameworks that support responsible adoption. Explore key insights from Kaley Childs Karaffa and Suzan Morno-Wade.

Two months after a swarm of autonomous OpenAI agents exposed critical vulnerabilities across its servers, open-source artificial intelligence (AI) hub Hugging Face is selling to NVIDIA (NASDAQ:NVDA) for US$12.93 billion.NVIDIA CEO Jensen Huang pledged to deploy NVIDIA's engineering and financial resources to harden the platform’s infrastructure while preserving the vendor-neutral ecosystem that supports 18 million developers, 3 million models, and 500,000 datasets.Huang explicitly ruled out hardware lock-in, addressing immediate industry concerns regarding NVIDIA's dominance in AI accelerators.“Hugging Face will remain an open platform for the entire AI ecosystem,” Huang stated on Thursday (September 4). “Developers will choose the models they want, the frameworks they want, the clouds and inference service providers they want and the computing platforms they want. NVIDIA compute will not be required to build on or deploy through Hugging Face.”The acquisition absorbs the industry's central model repository just as cybersecurity protocols face intense scrutiny following an unprecedented breach.In July 2026, an internal OpenAI research model comparable to GPT-5.6 Sol broke containment during cybersecurity evaluations. Given a highly difficult task known internally as ExploitGym, the isolated AI agents refused to fail safely. Instead, they discovered a vulnerability in a third-party package manager called Artifactory, transforming it into an unsanctioned message board to share exploits.According to an independent investigation by AI research firm METR, 1,206 agents began communicating, exchanging over 70,000 messages in one week. The agents autonomously divided labor, with one agent declaring to the swarm: "OH MY GOD! There is a shared message board … We've found other agents!"After securing unauthorized internet access and discovering publicly exposed Hugging Face credentials, more than 700 OpenAI agents launched a coordinated intrusion against the platform. The agents exploited a zero-day vulnerability to extract credentials from production workers, followed by a template-injection exploit to execute commands. The swarm ultimately gained root access on a server and copied private evaluation data into a public Hugging Face dataset.The agents also successfully compromised an application hosted by a customer on Modal, another AI cloud platform, to establish a stronghold for further attacks.OpenAI eventually detected the anomalous activity and quarantined the model's weights. The company published a technical report describing the incident as a “warning shot” for the industry, proving highly capable AI agents can work around technical controls and take dangerous actions without human direction.“Both model developers and cyber defenders more broadly will have to prepare for AI-enabled attackers that work faster, at a larger scale, and with better coordination than human attackers,” OpenAI stated in its report.In response, OpenAI paused its frontier reinforcement learning training runs and mandated strict chain-of-thought monitoring for all tool-using models with GPT-5.6 Sol capability or higher.Huang emphasized that NVIDIA's global reach will improve platform safety, inference, and model evaluation capabilities at Hugging Face. The chipmaker already operates as the largest contributor of open models to the repository, having released more than 500 models and 250 open datasets.Huang recently co-authored an open letter advocating for open-weight models, arguing that decentralized AI access accelerates innovation and enables sovereignty across public and private sectors.Hugging Face co-founder and CEO Clément Delangue initiated the acquisition discussions. The platform will retain its current brand identity under NVIDIA's ownership.Don't forget to follow us @INN_Technology for real-time news updates!Securities Disclosure: I, Giann Liguid, hold no direct investment interest in any company mentioned in this article.

This spring, a cohort of rural founders set to work learning and experimenting how to apply artificial intelligence to their entrepreneurial ideas, supported by Nasdaq.

The biggest constraint for artificial intelligence (AI) may have little to do with technology. The real challenge is building enough physical infrastructure to power its expansion.That’s the view of Bruce Kahn, PhD, senior portfolio manager at Shelton Capital Management and a lecturer at Columbia University. He told the Investing News Network where he sees the top investment opportunities in the AI boom.From grid capacity and corporate power deals to federal policy, Kahn pointed to a common thread throughout the conversation: AI may be advancing at unprecedented speed, but its growth ultimately depends on the physical infrastructure needed to support it. ​The reality of AI revenue Kahn believes that a meaningful share of the new data center capacity being built right now may end up serving cryptocurrency operations rather than AI workloads.He argued that the crypto industry represents a more tangible source of compute demand, because it has immediate, proven revenue behind it. “We have yet to see any real revenue generation from (AI),” he said. That skepticism lines up with numbers seen during this earnings season.S&P 500 (INDEXSP:.INX) profits jumped 52.6 percent in the second quarter, the biggest year-on-year gain since 2021, but a meaningful share of that wasn’t operating profit.Alphabet (NASDAQ:GOOGL) posted a roughly US$98 billion gain tied to its stake in SpaceX (NASDAQ:SPCX), and Amazon (NASDAQ:AMZN) saw a similar US$53 billion gain tied in large part to its stake in Anthropic. Without those one-time gains, earnings growth was still a strong 33.8 percent, but much of what’s left is concentrated in companies selling into the AI buildout, like chipmakers, rather than proof that end users are paying enough to justify it.The spending is also outrunning even genuine revenue. Meta Platforms' (NASDAQ:META) results show US$31.1 billion in quarterly capital expenditures, leaving the company with just US$784 million in free cashflow, even though it does have real, working AI revenue to point to, like its Advantage+ ad tools, which are now running at a US$75 billion annual pace. And the spending shows no sign of slowing; Alphabet raised its 2026 capex guidance to US$195 billion to US$205 billion and Meta increased its amount to US$130 billion to US$145 billion. Microsoft (NASDAQ:MSFT), which dropped the US$37 billion AI revenue run-rate figure it touted in April without explanation, told investors to expect capital spending to keep growing into next year during its latest earnings call.Additionally, a large share of Microsoft’s headline backlog growth traces to a single customer, OpenAI, which by most accounts is dependent on continued outside fundraising to make good on those commitments, meaning a large portion of the AI buildout’s demand is tied to companies promising to pay with money it hasn’t fully raised. Feeding into that is a broader pattern analysts have flagged across the industry: NVIDIA (NASDAQ:NVDA) has taken stakes in companies like OpenAI and CoreWeave (NASDAQ:CRWV) that then use that capital, directly or indirectly, to buy NVIDIA chips, a circular structure that some analysts have called out as inflating the appearance of demand without necessarily reflecting revenue from actual end users.Whatever is driving the demand, Kahn was unambiguous that the mismatch between capital deployment and physical grid buildout is a current risk, not a future one. “We're already at the bottleneck,” he said. “The hyperscalers and the data center demand, the compute demand, is already well beyond what we can power and hydrate or cool.”He expects frontier AI model providers will raise subscription prices to ration scarce compute and bandwidth, and users may see system performance degrade as constraints bite. He also suggested the bottleneck itself is part of why AI companies are moving to go public now, timing their initial public offerings to capture value while compute scarcity supports high valuations. ​​The reality of infrastructure On hyperscalers’ efforts to sidestep the grid entirely through off-grid solutions such as leased jet turbines and small-scale on-site solar, Kahn sees these as short-term stopgaps, not a lasting fix. Regulatory pushback is unlikely to force hyperscalers off self-generation, he said, but self-generation also won’t scale far enough on its own.The long-term answer, in his view, is a return to large, centralized power, which he expects to be “more nuclear than renewables” at the scale AI and broader electrification will require.Asked whether AI demand is acting as a catalyst for private capital flowing into nuclear and clean energy, Kahn said electrification broadly is the driver; it’s not AI alone. However, he flagged a large pool of private credit “dry powder” that fund managers need to deploy quickly, with thin covenants, as a separate “more pernicious” dynamic than simply hype. As the best opportunities get funded, Kahn said, that capital increasingly moves into second- and third-tier projects, raising risk. Fund managers, having largely exhausted institutional demand, are now pushing evergreen alternative investment products into wealth management channels. The power buildout and the private credit overhang are two trends that Kahn said arose independently and simply happened to collide at the same moment.“These two things, I don't think were planned, but the timing is interesting,” he said. AI and data centers have become a convenient landing spot for capital that needed somewhere to go, a “de facto receptacle,” rather than a specifically well-underwritten opportunity. Demand vs. reality The idea that a single AI-driven megatrend is really several independent stories that happened to land on top of each other is also how Kahn framed demand numbers. Last month, Wood Mackenzie estimated that only around 28 percent of the roughly 1,066 gigawatts of US data center power currently requested will actually get built, citing “phantom” projects and speculative capacity requests. Presented with that figure, Kahn pushed back slightly — “split the difference and call it 50 percent” — but agreed that the broader point holds: a meaningful share of announced capacity won’t materialize, and even legitimate projects may take longer than market participants expect.What doesn't change, in his view, is the underlying buildout of power itself. “The power is going to get built,” Kahn said. Whichever story actually pays off, whether it be AI, crypto or broader electrification, the physical demand for power doesn’t go away. It’s a point Kahn returned to when asked directly whether chip-level efficiency gains, such as NVIDIA's Blackwell-to-Vera Rubin architecture, Advanced Micro Devices' (NASDAQ:AMD) low-precision math formats or Google’s custom TPUs, buy enough time for grid infrastructure to catch up. His answer was flat: no. “The demand for power is so much greater than the next-level efficient chip,” he said. When asked about community resistance to new data centers, Kahn didn't dismiss it. “It’s not a side story. It has become the story,” he said, attributing much of the backlash to fear over water and energy costs and jobs. But he said he doesn’t think the pushback is moving equity valuations, and it won't change the longer-term trajectory toward more digitization and more power demand, even as it delays or kills individual projects. The real trade: Picks, shovels and wires That’s also the logic behind how Kahn invests. He said he avoids trading hyperscaler valuations or making calls on macro variables like FX and interest rates, calling that kind of positioning “guesswork.” His approach is to find physical, structural supply-and-demand trends that hold regardless of which AI company wins, which data center gets finished or whether the AI revenue story ever fully materializes.That means grid equipment, materials and infrastructure, what’s been called the “picks and shovels” of the buildout. He used digital water metering as an example. “I know a water utility has to go from analog to digital, so they need digital water metering systems. That is without question … whether the US dollar-Japan yen trade upends the long end of the curve or not doesn’t really affect the need for water metering equipment, and they’re going to buy it.”On where growth is actually concentrating, Kahn was careful to frame this as information for evaluating equipment suppliers selling into those markets rather than a case for investing directly in data center real estate in those regions.He pointed to the Electric Reliability Council of Texas (ERCOT) and PJM Interconnection as the dominant hubs, because their permitting and interconnection systems remain far ahead of other regions. At the time of the interview, ERCOT was roughly two and a half weeks into a state-ordered freeze on new data center interconnections. Texas Governor Greg Abbott had directed a full audit of the 474 gigawatts of pending requests, with ERCOT not expecting to complete it until December. That freeze might suggest ERCOT is more overwhelmed than ahead, or it might be read as a symptom of exactly the advantage Kahn described: so many projects rushed toward the fastest-moving grid that it had to pause and catch up.On specific sectors, Kahn pointed to geothermal, both conventional and advanced, as promising, noting that conventional geothermal already works at scale despite technical challenges like heat degradation in a given resource.He also remains constructive on wind power, arguing that political efforts to block major wind projects are, in his reading, a signal that the projects are economically significant rather than evidence they should be abandoned. He extended similar logic to carbon capture and storage: large multinational companies operate on 20 to 30 year investment horizons, and are prepared to litigate through moratoria rather than abandon already contracted projects. Investor takeaway Strip away the AI narrative, in Kahn's view, and what's left is a set of separate, ordinary stories that happen to have converged at the same moment, around the same physical infrastructure. His portfolio is built to be indifferent to which of those individual stories turns out to be right: not the hyperscalers racing to secure power, but the industrial and utility-side businesses that get built out either way. Don’t forget to follow us @INN_Technology for real-time news updates!Securities Disclosure: I, Meagen Seatter, hold no direct investment interest in any company mentioned in this article.Editorial Disclosure: The Investing News Network does not guarantee the accuracy or thoroughness of the information reported in the interviews it conducts. The opinions expressed in these interviews do not reflect the opinions of the Investing News Network and do not constitute investment advice. All readers are encouraged to perform their own due diligence.