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.

F5 to acquire CalypsoAI for advanced AI security capabilities

CalypsoAI’s platform creates what the company calls an Inference Perimeter that protects across models, vendors, and environments. The offers several products including Inference Red Team, Inference Defend, and Inference Observe, which deliver adversarial testing, threat detection and prevention, and enterprise oversight, respectively, among other capabilities. CalypsoAI says its platform proactively

HomeLM: A foundation model for ambient AI

Capabilities of a HomeLM What makes a foundation model like HomeLM powerful is its ability to learn generalizable representations of sensor streams, allowing them to be reused, recombined and adapted across diverse tasks. This fundamentally differs from traditional signal processing and machine learning pipelines in RF sensing, which are typically

Google enlists new ‘free’ data transfer service as the EU Data Act deadline approaches

Google beyond the basic requirement When asked about the rationale behind the introduction of the new service, a Google spokesperson said that the hyperscaler was going “above and beyond what the data act requires by eliminating the cost of in-parallel processing fees for customers in the EU and UK.” In

Cisco’s Splunk embeds agentic AI into security and observability products

AI-powered observability enhancements Cisco also announced it has updated Splunk Observability to use Cisco AgenticOps, which deploys AI agents to automate telemetry collection, detect issues, identify root causes, and apply fixes. The agentic AI updates help enterprise customers automate incident detection, root-cause analysis, and routine fixes. “We are making sure

India LNG Demand Set to Fall in 2025

India’s annual liquefied natural gas demand is set to contract in 2025 for the first time in years, as buyers hold out for a surge in production that is expected to push down prices. The world’s fourth-biggest LNG importer bought about 16 million tons of the super-chilled gas in the eight months through August, down 10 percent from a year earlier, according to ship-tracking data compiled by Bloomberg. Purchases slowed as elevated spot prices made LNG less competitive against alternative fuels, while monsoon rains brought cooler weather and reduced power demand. The pullback offers some relief to a global gas market that’s remained tight since Russia’s 2022 invasion of Ukraine forced Europe to pivot to LNG, boosting competition with Asia. India’s imports are expected to rebound as soon as next year, helped by a looming supply glut that should drag prices lower. Projects coming online from the US to Qatar starting in 2026 are set to add volumes that will outstrip demand growth through the rest of the decade. “We expect the dip in 2025 is a temporary price-driven phenomenon,” said Kaushal Ramesh, vice president for gas & LNG research at Rystad Energy. “The years ahead will see more contracts ramp up and also lower spot prices.” Demand for gas from industries, refineries and the fertilizer sector in the South Asian nation has plunged this year, according to oil ministry data, mainly due to high prices. Asian spot LNG has traded at more than $11 per million British thermal units this year – above the level at which price-sensitive Indian companies typically step in to buy. Still, Rystad sees India’s annual LNG demand exceeding 40 million tons by 2030, compared with about 26 million tons last year. The government has promoted gas for industries and households as a way to reduce the country’s dependence

Strategists Expect ‘Meaningful’ USA Crude Draw This Week

In an oil and gas report sent to Rigzone late Monday by the Macquarie team, Macquarie strategists, including Walt Chancellor, revealed that they are expecting a “meaningful [U.S.] crude draw alongside product builds” this week. “We are forecasting U.S. crude inventories down 6.4 million barrels for the week ending September 12,” the strategists noted in the report. “This follows a 3.9 million barrel build in the prior week, with the crude balance again realizing looser than our expectations,” they added. “For this week’s balance, from refineries, we model another slight reduction in crude runs (-0.1 million barrels per day). Among net imports, we model a very large reduction, with exports sharply higher (+1.7 million barrels per day) and imports lower (-0.5 million barrels per day) on a nominal basis,” they continued. The Macquarie strategists warned in the report that timing of cargoes remains a source of potential volatility in this week’s crude balance. “From implied domestic supply (prod. +adj.+transfers), we look for a bounce (+0.6 million barrels per day) on a nominal basis this week,” the strategists said in the report. “Rounding out the picture, we anticipate a similar increase (+0.5 MM BBL) in SPR [Strategic Petroleum Reserve] stocks this week,” they added. The strategists went on to state in the report that, “among products”, they “look for a modest gasoline draw (-0.9 million barrels) with builds in distillate (+1.8 million barrels) and jet (+1.3 million barrels)”. “We model implied demand for these three products at ~14.5 million barrels per day for the week ending September 12,” the strategists noted. In its latest weekly petroleum status report at the time of writing, which was released on September 10 and included data for the week ending September 5, the U.S. Energy Information Administration (EIA) highlighted that U.S. commercial crude oil inventories,

Allseas Orders New Heavy Transport Vessel

Offshore engineering and construction company Allseas Group SA has signed a construction contract for a purpose-built semi-submersible heavy transport vessel with Guangzhou Shipyard International in China. Allseas said in a media release that the vessel is scheduled for delivery in the first quarter of 2028. The vessel, to be named Grand Tour, will have a 40,000-tonne load capacity, specifically built to transport the world’s largest offshore structures across oceans and seamlessly transfer them to Pioneering Spirit for installation. Allseas said Grand Tour is designed to fit precisely into the bow slot of Pioneering Spirit. This integration aims to simplify offshore installation, providing clients with a single solution for transporting and installing large structures fabricated away from the installation site, it said. Allseas said Grand Tour boasts a semi-submersible hull with a 57-meter (187.01 feet) beam to enhance stability and enable shallow-draft access at global yards. The vessel has an advanced ballast system capable of pumping 24,000 cubic meters (847,552 cubic feet) per hour for precise load transfers. According to Allseas, its methanol-ready 24 MW propulsion system is capable of transitioning to e-methanol, while an air lubrication system and podded propulsion will reduce drag and improve fuel efficiency. The 180 x 57-meter cargo deck is designed for direct skidding, roll-on/roll-off, and float-on/float-off operations. “Grand Tour will play a key role in Allseas’ execution of TenneT’s landmark 2 GW offshore wind program, which will deliver 28 GW of clean offshore wind power to European homes and businesses by 2032”, Allseas said. The vessel will carry converter stations from fabrication facilities in Asia and Europe to installation locations in the North Sea off the coasts of the Netherlands and Germany, where Pioneering Spirit will take over for the installation using a single lift, Allseas said. “This addition to our fleet is more than an expansion; it’s a

Afreximbank, MDGIF Eye $500MM Investment in Nigerian Gas Infrastructure

Nigeria’s Midstream and Downstream Gas Infrastructure Fund (MDGIF) and the African Export-Import Bank (Afreximbank) have signed a memorandum of understanding (MoU) for a four-year debt and equity plan of up to $500 million to expand and modernize Nigeria’s natural gas infrastructure. “Afreximbank will consider providing direct financing and credit risk guarantees to support project finance transactions, working alongside local financial institutions”, Afreximbank and MDGIF said in a joint statement. “MDGIF will consider equity contributions to complement Afreximbank’s senior debt, enabling full capital structuring for eligible projects”, the statement said. The partners also eye “a structured program to enhance MDGIF’s institutional capabilities in project structuring, risk management and innovative financing”. MDGIF, established under the West African country’s Petroleum Industry Act, says its primary purpose is to make equity investments “in infrastructure related to midstream and downstream gas operations aimed at increasing the domestic consumption of natural gas in Nigeria in projects which are financed in part by private investment”. Nigerian Petroleum Resources (Gas) Minister Ekperikpe Ekpo was quoted as saying in the statement, “Through this partnership, we are unlocking the potential to mobilize up to $500 million over the next four years for Nigeria’s gas infrastructure. More importantly, we are creating a pipeline of bankable projects, supported by feasibility studies, project preparation and risk-sharing mechanisms, that will accelerate the pace of investment in pipelines, processing”. Kanayo Awani, executive vice president for intra-African trade and export development at Afreximbank, said, “By combining Afreximbank’s deep expertise in trade and project finance with MDGIF’s national investment reach, we are poised to unlock new opportunities for inclusive growth and sustainable development across Nigeria and, potentially, across the West Africa sub-region”. MDGIF executive director Oluwole Adama commented, “[T]his partnership with Afreximbank enables MDGIF to mobilize capital, expand critical midstream and downstream infrastructure, reduce flaring and deliver

GreenIT Secures $434MM for Renewable Energy Projects

GreenIT SpA has signed a new project finance agreement for EUR 370 million ($434.2 million) to support its renewable energy projects. GreenIT, a joint venture between Eni SpA’s Plenitude and CDP Equity, plans to invest the funds in the development of a portfolio of greenfield projects onshore Italy. In a media release, Eni said that the construction of the projects is expected to be completed by 2028, in line with GreenIT’s industrial plan, which targets 1 gigawatt (GW) of installed renewable capacity by 2030. “The completion of this strategic transaction strengthens GreenIT’s financial structure, providing new resources to support the investments planned for the next few years by our ambitious industrial plan. The strong confidence shown by the lending institutions reinforces GreenIT’s strategic vision to play a key role in Italy’s energy transition”, Paolo Bellucci, CEO of GreenIT, said. The European Investment Bank has committed $258 million, including $211 million in direct loans and $46.9 million through financial intermediaries. The remainder was sourced from prominent European financial institutions, such as BNP Paribas, Credit Agricole Corporate & Investment Bank, ING Bank NV, and Societe Generale. GreenIT enhances the offerings of Plenitude. Plenitude operates in more than 15 countries worldwide, utilizing a business model that combines electricity generation from renewable sources, with over 4 GW of installed capacity, alongside providing energy and energy solutions to more than 10 million customers across Europe, according to Eni. Plenitude also has an extensive network of 21,500 electric vehicle charging stations, according to Eni. To contact the author, email [email protected] 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. MORE FROM THIS AUTHOR

Hafnia to Acquire 14 Percent in TORM

Oaktree Capital Management LP and Hafnia Ltd have progressed a recently announced preliminary agreement into a binding one for Hafnia’s purchase of around 14.1 million TORM PLC shares held by Oaktree Capital for $311.43 million, or $22 per share. “Upon completion Hafnia will hold approximately 14.45 percent of issued share capital in TORM”, Hafnia, part of Singapore-based BW Group, said in a statement on its website. Hafnia and TORM own fleets that ship crude, oil products and chemicals. Hafnia says it owns about 200 vessels. TORM says it owns over 80 vessels. Hafnia trades on the Oslo Stock Exchange and the New York Stock Exchange while TORM is listed on the Copenhagen Stock Exchange and Nasdaq in New York. Oaktree Capital meanwhile is a Los Angeles-based investor. The statement clarified, “Following market reports referring to the possibility of a business combination on a net asset value basis, Hafnia agrees that it is to the benefit of shareholders in both companies to explore such strategic opportunities. However, discussions have yet to take place and there can be no assurance that this will lead to any transaction”. The transaction is subject to customary conditions including regulatory approvals and the appointment of a new independent board chair for TORM, Hafnia said. TORM said September 3, when Hafnia announced the preliminary deal, “TORM has not been involved in the transaction”. Strong Demand For the second quarter Hafnia earlier reported $346.56 million in operating revenue, down from $563.1 million for 2Q 2024. Operating profit was $83.09 million, down from $262.14 million for 2Q 2024. Profit before income tax landed at $78 million, down from $260.77 million. Net profit was $75.34 million, or $0.15 per share – down from $259.2 million. “Strong product demand, low global inventories, improving refining margins and high export volumes have gradually supported

Arista touts liquid cooling, optical tech to reduce power consumption for AI networking

Both technologies will likely find a role in future AI and optical networks, experts say, as both promise to reduce power consumption and support improved bandwidth density. Both have advantages and disadvantages as well – CPOs are more complex to deploy given the amount of technology included in a CPO package, whereas LPOs promise more simplicity. Bechtolsheim said that LPO can provide an additional 20% power savings over other optical forms. Early tests show good receiver performance even under degraded conditions, though transmit paths remain sensitive to reflections and crosstalk at the connector level, Bechtolsheim added. At the recent Hot Interconnects conference, he said: “The path to energy-efficient optics is constrained by high-volume manufacturing,” stressing that advanced optics packaging remains difficult and risky without proven production scale. “We are nonreligious about CPO, LPO, whatever it is. But we are religious about one thing, which is the ability to ship very high volumes in a very predictable fashion,” Bechtolsheim said at the investor event. “So, to put this in quantity numbers here, the industry expects to ship something like 50 million OSFP modules next calendar year. The current shipment rate of CPO is zero, okay? So going from zero to 50 million is just not possible. The supply chain doesn’t exist. So, even if the technology works and can be demonstrated in a lab, to get to the volume required to meet the needs of the industry is just an incredible effort.” “We’re all in on liquid cooling to reduce power, eliminating fan power, supporting the linear pluggable optics to reduce power and cost, increasing rack density, which reduces data center footprint and related costs, and most importantly, optimizing these fabrics for the AI data center use case,” Bechtolsheim added. “So what we call the ‘purpose-built AI data center fabric’ around Ethernet

Network and cloud implications of agentic AI

The chain analogy is critical here. Realistic uses of AI agents will require core database access; what can possibly make an AI business case that isn’t tied to a company’s critical data? The four critical elements of these applications—the agent, the MCP server, the tools, and the data— are all dragged along with each other, and traffic on the network is the linkage in the chain. How much traffic is generated? Here, enterprises had another surprise. Enterprises told me that their initial view of their AI hosting was an “AI cluster” with a casual data link to their main data center network. With AI agents, they now see smaller AI servers actually installed within their primary data centers, and all the traffic AI creates, within the model and to and from it, now flows on the data center network. Vendors who told enterprises that AI networking would have a profound impact are proving correct. You can run a query or perform a task with an agent and have that task parse an entire database of thousands or millions of records. Someone not aware of what an agent application implies in terms of data usage can easily create as much traffic as a whole week’s normal access-and-update would create. Enough, they say, to impact network capacity and the QoE of other applications. And, enterprises remind us, if that traffic crosses in/out of the cloud, the cloud costs could skyrocket. About a third of the enterprises said that issues with AI agents generated enough traffic to create local congestion on the network or a blip in cloud costs large enough to trigger a financial review. MCP tool use by agents is also a major security and governance headache. Enterprises point out that MCP standards haven’t always required strong authentication, and they also

There are 121 AI processor companies. How many will succeed?

The US currently leads in AI hardware and software, but China’s DeepSeek and Huawei continue to push advanced chips, India has announced an indigenous GPU program targeting production by 2029, and policy shifts in Washington are reshaping the playing field. In Q2, the rollback of export restrictions allowed US companies like Nvidia and AMD to strike multibillion-dollar deals in Saudi Arabia. JPR categorizes vendors into five segments: IoT (ultra-low-power inference in microcontrollers or small SoCs); Edge (on-device or near-device inference in 1–100W range, used outside data centers); Automotive (distinct enough to break out from Edge); data center training; and data center inference. There is some overlap between segments as many vendors play in multiple segments. Of the five categories, inference has the most startups with 90. Peddie says the inference application list is “humongous,” with everything from wearable health monitors to smart vehicle sensor arrays, to personal items in the home, and every imaginable machine in every imaginable manufacturing and production line, plus robotic box movers and surgeons. Inference also offers the most versatility. “Smart devices” in the past, like washing machines or coffee makers, could do basically one thing and couldn’t adapt to any changes. “Inference-based systems will be able to duck and weave, adjust in real time, and find alternative solutions, quickly,” said Peddie. Peddie said despite his apparent cynicism, this is an exciting time. “There are really novel ideas being tried like analog neuron processors, and in-memory processors,” he said.

Data Center Jobs: Engineering, Construction, Commissioning, Sales, Field Service and Facility Tech Jobs Available in Major Data Center Hotspots

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting. Looking for Data Center Candidates? Check out Pkaza’s Active Candidate / Featured Candidate Hotlist (and coming soon free Data Center Intern listing). Data Center Critical Facility Manager Impact, TX There position is also available in: Cheyenne, WY; Ashburn, VA or Manassas, VA. This opportunity is working directly with a leading mission-critical data center developer / wholesaler / colo provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations (enterprise and hyperscale customers). This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OH This traveling position is also available in: Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Cedar Rapids, IA; Phoenix, AZ; Dallas, TX or Chicago, IL. *** ALSO looking for a LEAD EE and ME CxA Agents and CxA PMs. *** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Data Center Engineering Design ManagerAshburn, VA This opportunity is working directly with a leading mission-critical data center developer /

Modernizing Legacy Data Centers for the AI Revolution with Schneider Electric’s Steven Carlini

As artificial intelligence workloads drive unprecedented compute density, the U.S. data center industry faces a formidable challenge: modernizing aging facilities that were never designed to support today’s high-density AI servers. In a recent Data Center Frontier podcast, Steven Carlini, Vice President of Innovation and Data Centers at Schneider Electric, shared his insights on how operators are confronting these transformative pressures. “Many of these data centers were built with the expectation they would go through three, four, five IT refresh cycles,” Carlini explains. “Back then, growth in rack density was moderate. Facilities were designed for 10, 12 kilowatts per rack. Now with systems like Nvidia’s Blackwell, we’re seeing 132 kilowatts per rack, and each rack can weigh 5,000 pounds.” The implications are seismic. Legacy racks, floor layouts, power distribution systems, and cooling infrastructure were simply not engineered for such extreme densities. “With densification, a lot of the power distribution, cooling systems, even the rack systems — the new servers don’t fit in those racks. You need more room behind the racks for power and cooling. Almost everything needs to be changed,” Carlini notes. For operators, the first questions are inevitably about power availability. At 132 kilowatts per rack, even a single cluster can challenge the limits of older infrastructure. Many facilities are conducting rigorous evaluations to decide whether retrofitting is feasible or whether building new sites is the more practical solution. Carlini adds, “You may have transformers spaced every hundred yards, twenty of them. Now, one larger transformer can replace that footprint, and power distribution units feed busways that supply each accelerated compute rack. The scale and complexity are unlike anything we’ve seen before.” Safety considerations also intensify with these densifications. “At 132 kilowatts, maintenance is still feasible,” Carlini says, “but as voltages rise, data centers are moving toward environments where

Google Backs Advanced Nuclear at TVA’s Clinch River as ORNL Pushes Quantum Frontiers

Inside the Hermes Reactor Design Kairos Power’s Hermes reactor is based on its KP-FHR architecture — short for fluoride salt–cooled, high-temperature reactor. Unlike conventional water-cooled reactors, Hermes uses a molten salt mixture called FLiBe (lithium fluoride and beryllium fluoride) as a coolant. Because FLiBe operates at atmospheric pressure, the design eliminates the risk of high-pressure ruptures and allows for inherently safer operation. Fuel for Hermes comes in the form of TRISO particles rather than traditional enriched uranium fuel rods. Each TRISO particle is encapsulated within ceramic layers that function like miniature containment vessels. These particles can withstand temperatures above 1,600 °C — far beyond the reactor’s normal operating range of about 700 °C. In combination with the salt coolant, Hermes achieves outlet temperatures between 650–750 °C, enabling efficient power generation and potential industrial applications such as hydrogen production. Because the salt coolant is chemically stable and requires no pressurization, the reactor can shut down and dissipate heat passively, without external power or operator intervention. This passive safety profile differentiates Hermes from traditional light-water reactors and reflects the Generation IV industry focus on safer, modular designs. From Hermes-1 to Hermes-2: Iterative Nuclear Development The first step in Kairos’ roadmap is Hermes-1, a 35 MW thermal demonstration reactor now under construction at TVA’s Clinch River site under a 2023 NRC license. Hermes-1 is not designed to generate electricity but will validate reactor physics, fuel handling, licensing strategies, and construction techniques. Building on that experience, Hermes-2 will be a 50 MW electric reactor connected to TVA’s grid, with operations targeted for 2030. Under the agreement, TVA will purchase electricity from Hermes-2 and supply it to Google’s data centers in Tennessee and Alabama. Kairos describes its development philosophy as “iterative,” scaling incrementally rather than attempting to deploy large fleets of units at once. By

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