Out-analyzing analysts: OpenAI’s Deep Research pairs reasoning LLMs with agentic RAG to automate work

Stay Ahead, Stay ONMINE

Out-analyzing analysts: OpenAI’s Deep Research pairs reasoning LLMs with agentic RAG to automate work — and replace jobs

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Enterprise companies need to take note of OpenAI’s Deep Research. It provides a powerful product based on new capabilities, and is so good that it could put a lot of people out of jobs. Deep Research […]

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More

Enterprise companies need to take note of OpenAI’s Deep Research. It provides a powerful product based on new capabilities, and is so good that it could put a lot of people out of jobs.

Deep Research is on the bleeding edge of a growing trend: integrating large language models (LLMs) with search engines and other tools to greatly expand their capabilities. (Just as this article was being reported, for example, Elon Musk’s xAI unveiled Grok 3, which claims similar capabilities, including a Deep Search product. However, it’s too early to assess Grok 3’s real-world performance, since most subscribers haven’t actually gotten their hands on it yet.)

OpenAI’s Deep Research, released on February 3, requires a Pro account with OpenAI, costing $200 per month, and is currently available only to U.S. users. So far, this restriction may have limited early feedback from the global developer community, which is typically quick to dissect new AI advancements.

With Deep Research mode, users can ask OpenAI’s leading o3 model any question. The result? A report often superior to what human analysts produce, delivered faster and at a fraction of the cost.

How Deep Research works

While Deep Research has been widely discussed, its broader implications have yet to fully register. Initial reactions praised its impressive research capabilities, despite its occasional hallucinations in its citations. There was the guy who said he used it to help his wife who had breast cancer. It provided deeper analysis than what her oncologists provided on how radiation therapy was the right course of action, he said. The consensus, summarized by Wharton AI professor Ethan Mollick, is that its advantages far outweigh occasional inaccuracies, as fact-checking takes less time than what the AI saves overall. This is something I agree with, based on my own usage.

Financial institutions are already exploring applications. BNY Mellon, for instance, sees potential in using Deep Research for credit risk assessments. Its impact will extend across industries, from healthcare to retail, manufacturing, and supply chain management — virtually any field that relies on knowledge work.

A smarter research agent

Unlike traditional AI models that attempt one-shot answers, Deep Research first asks clarifying questions. It might ask four or more questions to make sure it understands exactly what you want. It then develops a structured research plan, conducts multiple searches, revises its plan based on new insights, and iterates in a loop until it compiles a comprehensive, well-formatted report. This can take between a few minutes and half an hour. Reports range from 1,500 to 20,000 words, and typically include citations from 15 to 30 sources with exact URLs, at least according to my usage over the past week and a half.

The technology behind Deep Research: reasoning LLMs and agentic RAG

Deep Research does this by merging two technologies in a way we haven’t seen before in a mass-market product.

Reasoning LLMs: The first is OpenAI’s cutting-edge model, o3, which leads in logical reasoning and extended chain-of-thought processes. When it was announced in December 2024, o3 scored an unprecedented 87.5% on the super-difficult ARC-AGI benchmark designed to test novel problem-solving abilities. What’s interesting is that o3 hasn’t been released as a standalone model for developers to use. Indeed, OpenAI’s CEO Sam Altman announced last week that the model instead would be wrapped into a “unified intelligence” system, which would unite models with agentic tools like search, coding agents and more. Deep Research is an example of such a product. And while competitors like DeepSeek-R1 have approached o3’s capabilities (one of the reasons why there was so much excitement a few weeks ago), OpenAI is still widely considered to be slightly ahead.

Agentic RAG: The second, agentic RAG, is a technology that has been around for about a year now. It uses agents to autonomously seek out information and context from other sources, including searching the internet. This can include other tool-calling agents to find non-web information via APIs; coding agents that can complete complex sequences more efficiently; and database searches. Initially, OpenAI’s Deep Research is primarily searching the open web, but company leaders have suggested it would be able to search more sources over time.

OpenAI’s competitive edge (and its limits)

While these technologies are not entirely new, OpenAI’s refinements — enabled by things like its jump-start on working on these technologies, massive funding, and its closed-source development model — have taken Deep Research to a new level. It can work behind closed doors, and leverage feedback from the more than 300 million active users of OpenAI’s popular ChatGPT product. OpenAI has led in research in these areas, for example in how to do verification step by step to get better results. And it has clearly implemented search in an interesting way, perhaps borrowing from Microsoft’s Bing and other technologies.

While it is still hallucinating some results from its searches, it’s doing so less than competitors, perhaps in part because the underlying o3 model itself has set an industry low for these hallucinations at 8%. And there are ways to reduce mistakes still further, by using mechanisms like confidence thresholds, citation requirements and other sophisticated credibility checks.

At the same time, there are limits to OpenAI’s lead and capabilities. Within two days of Deep Research’s launch, HuggingFace introduced an open-source AI research agent called Open Deep Research that got results that weren’t too far off of OpenAI’s — similarly merging leading models and freely available agentic capabilities. There are few moats. Open-source competitors like DeepSeek appear set to stay close in the area of reasoning models, and Microsoft’s Magentic-One offers a framework for most of OpenAI’s agentic capabilities, to name just two more examples.

Furthermore, Deep Research has limitations. The product is really efficient at researching obscure information that can be found on the web. But in areas where there is not much online and where domain expertise is largely private — whether in peoples’ heads or in private databases — it doesn’t work at all. So this isn’t going to threaten the jobs of high-end hedge-fund researchers, for example, who are paid to go talk with real experts in an industry to find out otherwise very hard-to-obtain information, as Ben Thompson argued in a recent post (see graphic below). In most cases, OpenAI’s Deep Research is going to affect lower-skilled analyst jobs.

Deep Research’s value first increases as information online gets scarce, then drops off when it gets really scarce. Source: Stratechery.

The most intelligent product yet

When you merge top-tier reasoning with agentic retrieval, it’s not really surprising that you get such a powerful product. OpenAI’s Deep Research achieved 26.6% on Humanity’s Last Exam, arguably the best benchmark for intelligence. This is a relatively new AI benchmark designed to be the most difficult for any AI model to complete, covering 3,000 questions across 100 different subjects. On this benchmark, OpenAI’s Deep Research significantly outperforms Perplexity’s Deep Research (20.5%) and earlier models like o3-mini (13%) and DeepSeek-R1 (9.4%) that weren’t hooked up with agentic RAG. But early reviews suggest OpenAI leads in both quality and depth. Google’s Deep Research has yet to be tested against this benchmark, but early reviews suggest OpenAI leads in both quality and depth.

How it’s different: the first mass-market AI that could displace jobs

What’s different with this product is its potential to eliminate jobs. Sam Witteveen, cofounder of Red Dragon and a developer of AI agents, observed in a deep-dive video discussion with me that a lot of people are going to say: “Holy crap, I can get these reports for $200 that I could get from some top-4 consulting company that would cost me $20,000.” This, he said, is going to cause some real changes, including likely putting people out of jobs.

Which brings me back to my interview last week with Sarthak Pattanaik, head of engineering and AI at BNY Mellon, a major U.S. bank.

To be sure, Pattanaik didn’t say anything about the product’s ramifications for actual job counts at his bank. That’s going to be a particularly sensitive topic that any enterprise is probably going to shy away from addressing publicly. But he said he could see OpenAI’s Deep Research being used for credit underwriting reports and other “topline” activities, and having significant impact on a variety of jobs: “Now that doesn’t impact every job, but that does impact a set of jobs around strategy [and] research, like comparison vendor management, comparison of product A versus product B.” He added: “So I think everything which is more on system two thinking — more exploratory, where it may not have a right answer, because the right answer can be mounted once you have that scenario definition — I think that’s an opportunity.”

A historical perspective: job loss and job creation

Technological revolutions have historically displaced workers in the short term while creating new industries in the long run. From automobiles replacing horse-drawn carriages to computers automating clerical work, job markets evolve. New opportunities created by the disruptive technologies tend to spawn new hiring. Companies that fail to embrace these advances will fall behind their competitors.

OpenAI’s Altman acknowledged the link, even if indirect, between Deep Research and labor. At the AI Summit in Paris last week, he was asked about his vision for artificial general intelligence (AGI), or the stage at which AI can perform pretty much any task that a human can. As he answered, his first reference was to Deep Research: “It’s a model I think is capable of doing like a low-single-digit percentage of all the tasks in the economy in the world right now, which is a crazy statement, and a year ago I don’t think something that people thought is going to be coming.” (See minute three of this video). He continued: “For 50 cents of compute, you can do like $500 or $5,000 of work. Companies are implementing that to just be way more efficient.”

The takeaway: a new era for knowledge work

Deep Research represents a watershed moment for AI in knowledge-based industries. By integrating cutting-edge reasoning with autonomous research capabilities, OpenAI has created a tool that is smarter, faster and significantly more cost-effective than human analysts.

The implications are vast, from financial services to healthcare to enterprise decision-making. Organizations that leverage this technology effectively will gain a significant competitive edge. Those that ignore it do so at their peril.

For a deeper discussion on how OpenAI’s Deep Research works, and how it is reshaping knowledge work, check out my in-depth conversation with Sam Witteveen in our latest video:

Daily insights on business use cases with VB Daily

If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.

Read our Privacy Policy

Thanks for subscribing. Check out more VB newsletters here.

An error occured.

Stay Ahead

Explore More Insights

Stay ahead with more perspectives on cutting-edge power, infrastructure, energy, bitcoin and AI solutions. Explore these articles to uncover strategies and insights shaping the future of industries.

Microsoft’s largest quantum site to be built in Denmark

With this strategic move, Denmark will become Microsoft’s global quantum hub. According to the company, the expansion of the Lyngby laboratory will enable the complete core components of the Majorana chip to be manufactured directly on site. This research is based on years of cooperation with leading Danish research institutions,

Next-generation HPE supercomputer offers a mix of Nvidia and AMD silicon

HPE offers three different blades for the GX5000. First is the GX440n Accelerated Blade for mixed-precision computing, with four Nvidia Vera CPUs and eight Nvidia Rubin GPUs. Up to 24 of these blades can be configured per compute rack, which translates to 192 Rubin GPUs per rack. Vera Rubin is

StarlingX 11.0 addresses edge security, IPv4 exhaustion for massive deployments

“We are seeing increasing concern over security at the edge, where physical security is nowhere near as good as in a central office environment,” Waines said. “We are especially seeing this concern from StarlingX users in Europe.” Third-party security testing now assumes physical access to equipment. “It is standard for

Extreme plots enterprise marketplace for AI agents, tools, apps

Extreme Networks this week previewed an AI marketplace where it plans to offer a curated catalog of AI tools, agents and applications. Called Extreme Exchange, it’s designed to give enterprise customers a way to discover, deploy, and create AI agents, microapps, and workflows in minutes rather than developing such components

Adnoc Buy of Covestro Wins Conditional EU Approval

Abu Dhabi National Oil Co. won conditional European Union approval for its EUR 12 billion ($13.9 billion) takeover of Covestro AG after it dealt with EU concerns that its state subsidies could stifle competition. The European Commission said Friday that an offer from Adnoc to maintain Covestro’s intellectual property in Europe as well as concessions on the company’s unlimited state guarantee from the UAE settled its earlier fears. Those commitments are valid for 10 years. “Commitments offered by Adnoc effectively address the potential negative effects by allowing market participants to access key Covestro patents in the field of sustainability,” EU competition chief Teresa Ribera said in a statement. “Clear, pre-defined access to these patents will enable others to innovate and advance research in an area that is critical for Europe’s future.” The planned purchase of Covestro would give Adnoc – the biggest oil producer in the United Arab Emirates – control over a German company that supplies materials for some of the world’s most prominent phone and carmakers. Adnoc would own Covestro through its investment unit XRG, set up in last year as the company’s international platform for natural gas, chemicals and energy solutions. In July, the commission, the EU’s antitrust arm, opened a full-scale investigation into the deal under tough new foreign subsidies rules. These are aimed at preventing sovereign states from using their financial muscle to crush competition in the 27-nation bloc. EU officials warned at the time that Adnoc’s state funding may give it an unfair advantage over rivals with less-deep pockets. WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate or insulting comments will be removed.

The week in 5 numbers: Electricity prices extend rise, regulators rein in data centers

The upper end of Duke Energy’s expanded five-year capital spending plan, which it expects to roll out early next year. Executives attribute the rise in spending to rapid load growth, including many data centers, which they say is likely to continue into the early 2030s. Additional generation added to Duke’s system could exceed 13 GW in the next five years, including 7.5 GW of new gas facilities. Duke is one of many utilities that have bumped their spending in response to projected load growth from artificial intelligence, manufacturing and electrification.

Solar project delays decreased in Q3 2025: EIA

Listen to the article 2 min This audio is auto-generated. Please let us know if you have feedback. Fewer solar developers reported delays in the third quarter of 2025 compared to the same period last year, the Energy Information Administration said in a Monday report. In the third quarter this year, “solar projects representing about 20% of planned capacity reported a delay, a decrease from 25% in the same period in 2024,” EIA said. “Despite the relatively high number of projects reporting delays in 2024, that year was a record year for U.S. solar capacity additions,” EIA said. Developers added around 31 GW of utility-scale solar capacity last year, though their projections at the beginning of the year forecasted 36 GW in additions. Optional Caption Courtesy of Energy Information Administration “Because survey respondents may not anticipate the occurrence or duration of delays, ultimate capacity additions tend to be less than the expected amount that developers report to us at the beginning of the year,” EIA said. The agency said in February that it predicts 32.5 GW of utility-scale solar will be added this year, indicating that less solar may come online this year than last year, despite the decrease in delays. EIA also noted that delays are more common than cancellations, and “less than 1% of planned solar capacity is entirely cancelled in a typical month … Much of the reported delayed capacity occurs at projects that are in the late construction or testing phases just before they come online. These delays are typically only for a month or two.” Justin Baca, vice president of markets and research at the Solar Energy Industries Association, said in an email that it’s “important to note that most of the solar capacity that has come online this year began construction last year.” “The

Oil Rises as Geopolitics Heat Up

Oil rose after Ukraine attacked a key Russian oil port and Iran seized a tanker near the Strait of Hormuz, injecting a fresh geopolitical premium into prices. West Texas Intermediate rose 2.4% to settle above $60. Brent also advanced. A major drone attack damaged an oil depot and a vessel in the vital Black Sea port of Novorossiysk. About 700,000 barrels a day of Russian oil were shipped from there in September and October, according to vessel tracking data compiled by Bloomberg, while a nearby terminal handles more than 1.5 million barrels a day of Kazakh shipments. Ukraine’s General Staff also said that it struck Rosneft PJSC’s Saratov refinery in Russia’s Volga region. That’s the third attack this month on the facility. The attacks came on the same day that a US defense official said Iranian forces seized a tanker after it passed the vital Strait of Hormuz chokepoint, through which about a fifth of the world’s oil flows. The ship was smuggling 3,000 liters of fuel, state-run Islamic Republic News Agency reports. While authorities are still confirming the nature of the diversion toward the country’s territorial waters, Friday’s event would add to concerns that Iran is turning to hijacking merchant ships again. Though motive remains unclear, Iran’s moves appear less likely to be a concerted effort to inhibit crude flows than a potential response to a US action against the Middle Eastern nation’s exports, said Gregory Brew, a geopolitical analyst at the Eurasia Group. Iran’s exports have been in excess of two million barrels a day over September and October, he added. The twin concerns come against the backdrop of a tightening of US sanctions against Russia. Curbs on the country’s two largest oil companies, Rosneft and Lukoil PJSC, are due to kick in within days. Those restrictions won’t

Energy Department Announces $355 Million to Expand Domestic Production of Critical Minerals and Materials

WASHINGTON—The U.S. Department of Energy (DOE) today announced $355 million for two notices of funding opportunities issued by DOE’s Office of Fossil Energy (FE) to expand domestic production of critical materials essential for advancing U.S. energy production, manufacturing, transportation and national defense. The first funding opportunity provides up to $275 million for American industrial facilities capable of producing valuable minerals from existing industrial and coal byproducts. The second provides up to $80 million to establish Mine of the Future proving grounds for real-world testing of next-generation mining technologies. The Department announced in August its intent to invest $1 billion to advance and scale mining, processing, and manufacturing technologies, delivering on President Trump’s Executive Orders, Unleashing American Energy and Immediate Measures to Increase American Mineral Production. These actions will secure America’s critical material supply chain, increase domestic mineral production, reduce reliance on foreign sources, and strengthen U.S. energy independence. “For too long, the United States has relied on foreign nations for the minerals and materials that power our economy,” said U.S. Secretary of Energy Chris Wright. “We have these resources here at home, but years of complacency ceded America’s mining and industrial base to other nations. Thanks to President Trump’s leadership, we are reversing that trend, rebuilding America’s ability to mine, process, and manufacture the materials essential to our energy and economic security.” “The Mine of the Future – Proving Ground Initiative will be among the Department of Energy’s first major investments into mining technology research and development in almost four decades,” said U.S. Department of Energy Assistant Secretary of the Office of Fossil Energy Kyle Haustveit. “This effort will help establish the United States as the world’s leading producer and processor of non-fuel minerals—creating economic prosperity in fossil energy communities across the country while strengthening critical mineral supply chains for

Ukraine Drones Hit Russian Black Sea Oil Terminal

(Update) November 14, 2025, 9:45 AM GMT+1: Article updated with additional details. Ukrainian drones attacked Russia’s giant Black Sea port of Novorossiysk overnight, prompting a state of emergency, as Moscow launched a massive air strike on Kyiv that killed four and damaged several residential buildings. Falling drone debris caused a fire at the Russian depot located at Transneft PJSC’s Sheskharis oil terminal, the regional emergency service said on Telegram early Friday. The blaze was put out after more than 50 units of firefighting equipment were deployed at the site, authorities said, but provided no details on the damage. Novorossiysk Mayor Andrey Kravchenko announced the state of emergency on Telegram. Transneft didn’t immediately respond to a request for comment on the situation at the facility. Global benchmark Brent spiked as much as 3 percent in a rapid move toward $65 a barrel, before paring gains. A container terminal located in the port of Novorossiysk was damaged by falling debris, but continued to operate normally, Delo Group, which runs that facility, said in a statement on Telegram. Russia’s largest grain terminal, also operated by Delo Group, was impacted by drone debris, but continues to function, the Interfax news service reported, citing the terminal’s chief executive officer. Drones hit an unidentified civilian ship in the port of Novorossiysk as well, regional emergency services said, without specifying the type of the vessel. The city’s mayor reported damage to at least three residential buildings in separate statements on Telegram. In Ukraine, four people were killed after Russia launched about 430 drones and 18 missiles – including ballistic ones – in the strike, President Volodymyr Zelenskiy said on the X platform Friday. Dozens of apartment buildings were damaged in the capital Kyiv, he said. At least 26 people were injured, including two children, and several residential buildings were damaged,

Arista, Palo Alto bolster AI data center security

“Based on this inspection, the NGFW creates a comprehensive, application-aware security policy. It then instructs the Arista fabric to enforce that policy at wire speed for all subsequent, similar flows,” Kotamraju wrote. “This ‘inspect-once, enforce-many’ model delivers granular zero trust security without the performance bottlenecks of hairpinning all traffic through a firewall or forcing a costly, disruptive network redesign.” The second capability is a dynamic quarantine feature that enables the Palo Alto NGFWs to identify evasive threats using Cloud-Delivered Security Services (CDSS). “These services, such as Advanced WildFire for zero-day malware and Advanced Threat Prevention for unknown exploits, leverage global threat intelligence to detect and block attacks that traditional security misses,” Kotamraju wrote. The Arista fabric can intelligently offload trusted, high-bandwidth “elephant flows” from the firewall after inspection, freeing it to focus on high-risk traffic. When a threat is detected, the NGFW signals Arista CloudVision, which programs the network switches to automatically quarantine the compromised workload at hardware line-rate, according to Kotamraju: “This immediate response halts the lateral spread of a threat without creating a performance bottleneck or requiring manual intervention.” The third feature is unified policy orchestration, where Palo Alto Networks’ management plane centralizes zone-based and microperimeter policies, and CloudVision MSS responds with the offload and enforcement of Arista switches. “This treats the entire geo-distributed network as a single logical switch, allowing workloads to be migrated freely across cloud networks and security domains,” Srikanta and Barbieri wrote. Lastly, the Arista Validated Design (AVD) data models enable network-as-a-code, integrating with CI/CD pipelines. AVDs can also be generated by Arista’s AVA (Autonomous Virtual Assist) AI agents that incorporate best practices, testing, guardrails, and generated configurations. “Our integration directly resolves this conflict by creating a clean architectural separation that decouples the network fabric from security policy. This allows the NetOps team (managing the Arista

AMD outlines ambitious plan for AI-driven data centers

“There are very beefy workloads that you must have that performance for to run the enterprise,” he said. “The Fortune 500 mainstream enterprise customers are now … adopting Epyc faster than anyone. We’ve seen a 3x adoption this year. And what that does is drives back to the on-prem enterprise adoption, so that the hybrid multi-cloud is end-to-end on Epyc.” One of the key focus areas for AMD’s Epyc strategy has been our ecosystem build out. It has almost 180 platforms, from racks to blades to towers to edge devices, and 3,000 solutions in the market on top of those platforms. One of the areas where AMD pushes into the enterprise is what it calls industry or vertical workloads. “These are the workloads that drive the end business. So in semiconductors, that’s telco, it’s the network, and the goal there is to accelerate those workloads and either driving more throughput or drive faster time to market or faster time to results. And we almost double our competition in terms of faster time to results,” said McNamara. And it’s paying off. McNamara noted that over 60% of the Fortune 100 are using AMD, and that’s growing quarterly. “We track that very, very closely,” he said. The other question is are they getting new customer acquisitions, customers with Epyc for the first time? “We’ve doubled that year on year.” AMD didn’t just brag, it laid out a road map for the next two years, and 2026 is going to be a very busy year. That will be the year that new CPUs, both client and server, built on the Zen 6 architecture begin to appear. On the server side, that means the Venice generation of Epyc server processors. Zen 6 processors will be built on 2 nanometer design generated by (you guessed

Building the Regional Edge: DartPoints CEO Scott Willis on High-Density AI Workloads in Non-Tier-One Markets

When DartPoints CEO Scott Willis took the stage on “the Distributed Edge” panel at the 2025 Data Center Frontier Trends Summit, his message resonated across a room full of developers, operators, and hyperscale strategists: the future of AI infrastructure will be built far beyond the nation’s tier-one metros. On the latest episode of the Data Center Frontier Show, Willis expands on that thesis, mapping out how DartPoints has positioned itself for a moment when digital infrastructure inevitably becomes more distributed, and why that moment has now arrived. DartPoints’ strategy centers on what Willis calls the “regional edge”—markets in the Midwest, Southeast, and South Central regions that sit outside traditional cloud hubs but are increasingly essential to the evolving AI economy. These are not tower-edge micro-nodes, nor hyperscale mega-campuses. Instead, they are regional data centers designed to serve enterprises with colocation, cloud, hybrid cloud, multi-tenant cloud, DRaaS, and backup workloads, while increasingly accommodating the AI-driven use cases shaping the next phase of digital infrastructure. As inference expands and latency-sensitive applications proliferate, Willis sees the industry’s momentum bending toward the very markets DartPoints has spent years cultivating. Interconnection as Foundation for Regional AI Growth A key part of the company’s differentiation is its interconnection strategy. Every DartPoints facility is built to operate as a deeply interconnected environment, drawing in all available carriers within a market and stitching sites together through a regional fiber fabric. Willis describes fiber as the “nervous system” of the modern data center, and for DartPoints that means creating an interconnection model robust enough to support a mix of enterprise cloud, multi-site disaster recovery, and emerging AI inference workloads. The company is already hosting latency-sensitive deployments in select facilities—particularly inference AI and specialized healthcare applications—and Willis expects such deployments to expand significantly as regional AI architectures become more widely

Key takeaways from Cisco Partner Summit

Brian Ortbals, senior vice president from World Wide Technology, which is one of Cisco’s biggest and most important partners stated: “Cisco engaged partners early in the process and took our feedback along the way. We believe now is the right time for these changes as it will enable us to capitalize on the changes in the market.” The reality is, the more successful its more-than-half-a-million partners are, the more successful Cisco will be. Platform approach is coming together When Jeetu Patel took the reigns as chief product officer, one of his goals was to make the Cisco portfolio a “force multiple.” Patel has stated repeatedly that, historically, Cisco acted more as a technology holding company with good products in networking, security, collaboration, data center and other areas. In this case, product breadth was not an advantage, as everything must be sold as “best of breed,” which is a tough ask of the salesforce and partner community. Since then, there have been many examples of the coming together of the portfolio to create products that leverage the breadth of the platform. The latest is the Unified Edge appliance, an all-in-one solution that brings together compute, networking, storage and security. Cisco has been aggressive with AI products in the data center, and Cisco Unified Edge compliments that work with a device designed to bring AI to edge locations. This is ideally suited for retail, manufacturing, healthcare, factories and other industries where it’s more cost effecting and performative to run AI where the data lives.

AI networking demand fueled Cisco’s upbeat Q1 financials

Customers are very focused on modernizing their network infrastructure in the enterprise in preparation for inferencing and AI workloads, Robbins said. “These things are always multi-year efforts,” and this is only the beginning, Robbins said. The AI opportunity “As we look at the AI opportunity, we see customer use cases growing across training, inferencing, and connectivity, with secure networking increasingly critical as workloads move from the data center to end users, devices, and agents at the edge,” Robbins said. “Agents are transforming network traffic from predictable bursts to persistent high-intensity loads, with agentic AI queries generating up to 25 times more network traffic than chatbots.” “Instead of pulling data to and from the data center, AI workloads require models and infrastructure to be closer to where data is created and decisions are made, particularly in industries such as retail, healthcare, and manufacturing.” Robbins pointed to last week’s introduction of Cisco Unified Edge, a converged platform that integrates networking, compute and storage to help enterprise customers more efficiently handle data from AI and other workloads at the edge. “Unified Edge enables real-time inferencing for agentic and physical AI workloads, so enterprises can confidently deploy and manage AI at scale,” Robbins said. On the hyperscaler front, “we see a lot of solid pipeline throughout the rest of the year. The use cases, we see it expanding,” Robbins said. “Obviously, we’ve been selling networking infrastructure under the training models. We’ve been selling scale-out. We launched the P200-based router that will begin to address some of the scale-across opportunities.” Cisco has also seen great success with its pluggable optics, Robbins said. “All of the hyperscalers now are officially customers of our pluggable optics, so we feel like that’s a great opportunity. They not only plug into our products, but they can be used with other companies’

When the Cloud Leaves Earth: Google and NVIDIA Test Space Data Centers for the Orbital AI Era

On November 4, 2025, Google unveiled Project Suncatcher, a moonshot research initiative exploring the feasibility of AI data centers in space. The concept envisions constellations of solar-powered satellites in Low Earth Orbit (LEO), each equipped with Tensor Processing Units (TPUs) and interconnected via free-space optical laser links. Google’s stated objective is to launch prototype satellites by early 2027 to test the idea and evaluate scaling paths if the technology proves viable. Rather than a commitment to move production AI workloads off-planet, Suncatcher represents a time-bound research program designed to validate whether solar-powered, laser-linked LEO constellations can augment terrestrial AI factories, particularly for power-intensive, latency-tolerant tasks. The 2025–2027 window effectively serves as a go/no-go phase to assess key technical hurdles including thermal management, radiation resilience, launch economics, and optical-link reliability. If these milestones are met, Suncatcher could signal the emergence of a new cloud tier: one that scales AI with solar energy rather than substations. Inside Google’s Suncatcher Vision Google has released a detailed technical paper titled “Towards a Future Space-Based, Highly Scalable AI Infrastructure Design.” The accompanying Google Research blog describes Project Suncatcher as “a moonshot exploring a new frontier” – an early-stage effort to test whether AI compute clusters in orbit can become a viable complement to terrestrial data centers. The paper outlines several foundational design concepts: Orbit and Power Project Suncatcher targets Low Earth Orbit (LEO), where solar irradiance is significantly higher and can remain continuous in specific orbital paths. Google emphasizes that space-based solar generation will serve as the primary power source for the TPU-equipped satellites. Compute and Interconnect Each satellite would host Tensor Processing Unit (TPU) accelerators, forming a constellation connected through free-space optical inter-satellite links (ISLs). Together, these would function as a disaggregated orbital AI cluster, capable of executing large-scale batch and training workloads. Downlink

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

Stay Ahead, Stay ONMINE