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Don’t let hype about AI agents get ahead of reality

Google’s recent unveiling of what it calls a “new class of agentic experiences” feels like a turning point. At its I/O 2025 event in May, for example, the company showed off a digital assistant that didn’t just answer questions; it helped work on a bicycle repair by finding a matching user manual, locating a YouTube tutorial, and even calling a local store to ask about a part, all with minimal human nudging. Such capabilities could soon extend far outside the Google ecosystem. The company has introduced an open standard called Agent-to-Agent, or A2A, which aims to let agents from different companies talk to each other and work together. The vision is exciting: Intelligent software agents that act like digital coworkers, booking your flights, rescheduling meetings, filing expenses, and talking to each other behind the scenes to get things done. But if we’re not careful, we’re going to derail the whole idea before it has a chance to deliver real benefits. As with many tech trends, there’s a risk of hype racing ahead of reality. And when expectations get out of hand, a backlash isn’t far behind. Let’s start with the term “agent” itself. Right now, it’s being slapped on everything from simple scripts to sophisticated AI workflows. There’s no shared definition, which leaves plenty of room for companies to market basic automation as something much more advanced. That kind of “agentwashing” doesn’t just confuse customers; it invites disappointment. We don’t necessarily need a rigid standard, but we do need clearer expectations about what these systems are supposed to do, how autonomously they operate, and how reliably they perform. And reliability is the next big challenge. Most of today’s agents are powered by large language models (LLMs), which generate probabilistic responses. These systems are powerful, but they’re also unpredictable. They can make things up, go off track, or fail in subtle ways—especially when they’re asked to complete multistep tasks, pulling in external tools and chaining LLM responses together. A recent example: Users of Cursor, a popular AI programming assistant, were told by an automated support agent that they couldn’t use the software on more than one device. There were widespread complaints and reports of users cancelling their subscriptions. But it turned out the policy didn’t exist. The AI had invented it. In enterprise settings, this kind of mistake could create immense damage. We need to stop treating LLMs as standalone products and start building complete systems around them—systems that account for uncertainty, monitor outputs, manage costs, and layer in guardrails for safety and accuracy. These measures can help ensure that the output adheres to the requirements expressed by the user, obeys the company’s policies regarding access to information, respects privacy issues, and so on. Some companies, including AI21 (which I cofounded and which has received funding from Google), are already moving in that direction, wrapping language models in more deliberate, structured architectures. Our latest launch, Maestro, is designed for enterprise reliability, combining LLMs with company data, public information, and other tools to ensure dependable outputs. Still, even the smartest agent won’t be useful in a vacuum. For the agent model to work, different agents need to cooperate (booking your travel, checking the weather, submitting your expense report) without constant human supervision. That’s where Google’s A2A protocol comes in. It’s meant to be a universal language that lets agents share what they can do and divide up tasks. In principle, it’s a great idea.In practice, A2A still falls short. It defines how agents talk to each other, but not what they actually mean. If one agent says it can provide “wind conditions,” another has to guess whether that’s useful for evaluating weather on a flight route. Without a shared vocabulary or context, coordination becomes brittle. We’ve seen this problem before in distributed computing. Solving it at scale is far from trivial. There’s also the assumption that agents are naturally cooperative. That may hold inside Google or another single company’s ecosystem, but in the real world, agents will represent different vendors, customers, or even competitors. For example, if my travel planning agent is requesting price quotes from your airline booking agent, and your agent is incentivized to favor certain airlines, my agent might not be able to get me the best or least expensive itinerary. Without some way to align incentives through contracts, payments, or game-theoretic mechanisms, expecting seamless collaboration may be wishful thinking. None of these issues are insurmountable. Shared semantics can be developed. Protocols can evolve. Agents can be taught to negotiate and collaborate in more sophisticated ways. But these problems won’t solve themselves, and if we ignore them, the term “agent” will go the way of other overhyped tech buzzwords. Already, some CIOs are rolling their eyes when they hear it. That’s a warning sign. We don’t want the excitement to paper over the pitfalls, only to let developers and users discover them the hard way and develop a negative perspective on the whole endeavor. That would be a shame. The potential here is real. But we need to match the ambition with thoughtful design, clear definitions, and realistic expectations. If we can do that, agents won’t just be another passing trend; they could become the backbone of how we get things done in the digital world. Yoav Shoham is a professor emeritus at Stanford University and cofounder of AI21 Labs. His 1993 paper on agent-oriented programming received the AI Journal Classic Paper Award. He is coauthor of Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, a standard textbook in the field.

Google’s recent unveiling of what it calls a “new class of agentic experiences” feels like a turning point. At its I/O 2025 event in May, for example, the company showed off a digital assistant that didn’t just answer questions; it helped work on a bicycle repair by finding a matching user manual, locating a YouTube tutorial, and even calling a local store to ask about a part, all with minimal human nudging. Such capabilities could soon extend far outside the Google ecosystem. The company has introduced an open standard called Agent-to-Agent, or A2A, which aims to let agents from different companies talk to each other and work together.

The vision is exciting: Intelligent software agents that act like digital coworkers, booking your flights, rescheduling meetings, filing expenses, and talking to each other behind the scenes to get things done. But if we’re not careful, we’re going to derail the whole idea before it has a chance to deliver real benefits. As with many tech trends, there’s a risk of hype racing ahead of reality. And when expectations get out of hand, a backlash isn’t far behind.

Let’s start with the term “agent” itself. Right now, it’s being slapped on everything from simple scripts to sophisticated AI workflows. There’s no shared definition, which leaves plenty of room for companies to market basic automation as something much more advanced. That kind of “agentwashing” doesn’t just confuse customers; it invites disappointment. We don’t necessarily need a rigid standard, but we do need clearer expectations about what these systems are supposed to do, how autonomously they operate, and how reliably they perform.

And reliability is the next big challenge. Most of today’s agents are powered by large language models (LLMs), which generate probabilistic responses. These systems are powerful, but they’re also unpredictable. They can make things up, go off track, or fail in subtle ways—especially when they’re asked to complete multistep tasks, pulling in external tools and chaining LLM responses together. A recent example: Users of Cursor, a popular AI programming assistant, were told by an automated support agent that they couldn’t use the software on more than one device. There were widespread complaints and reports of users cancelling their subscriptions. But it turned out the policy didn’t exist. The AI had invented it.

In enterprise settings, this kind of mistake could create immense damage. We need to stop treating LLMs as standalone products and start building complete systems around them—systems that account for uncertainty, monitor outputs, manage costs, and layer in guardrails for safety and accuracy. These measures can help ensure that the output adheres to the requirements expressed by the user, obeys the company’s policies regarding access to information, respects privacy issues, and so on. Some companies, including AI21 (which I cofounded and which has received funding from Google), are already moving in that direction, wrapping language models in more deliberate, structured architectures. Our latest launch, Maestro, is designed for enterprise reliability, combining LLMs with company data, public information, and other tools to ensure dependable outputs.

Still, even the smartest agent won’t be useful in a vacuum. For the agent model to work, different agents need to cooperate (booking your travel, checking the weather, submitting your expense report) without constant human supervision. That’s where Google’s A2A protocol comes in. It’s meant to be a universal language that lets agents share what they can do and divide up tasks. In principle, it’s a great idea.

In practice, A2A still falls short. It defines how agents talk to each other, but not what they actually mean. If one agent says it can provide “wind conditions,” another has to guess whether that’s useful for evaluating weather on a flight route. Without a shared vocabulary or context, coordination becomes brittle. We’ve seen this problem before in distributed computing. Solving it at scale is far from trivial.

There’s also the assumption that agents are naturally cooperative. That may hold inside Google or another single company’s ecosystem, but in the real world, agents will represent different vendors, customers, or even competitors. For example, if my travel planning agent is requesting price quotes from your airline booking agent, and your agent is incentivized to favor certain airlines, my agent might not be able to get me the best or least expensive itinerary. Without some way to align incentives through contracts, payments, or game-theoretic mechanisms, expecting seamless collaboration may be wishful thinking.

None of these issues are insurmountable. Shared semantics can be developed. Protocols can evolve. Agents can be taught to negotiate and collaborate in more sophisticated ways. But these problems won’t solve themselves, and if we ignore them, the term “agent” will go the way of other overhyped tech buzzwords. Already, some CIOs are rolling their eyes when they hear it.

That’s a warning sign. We don’t want the excitement to paper over the pitfalls, only to let developers and users discover them the hard way and develop a negative perspective on the whole endeavor. That would be a shame. The potential here is real. But we need to match the ambition with thoughtful design, clear definitions, and realistic expectations. If we can do that, agents won’t just be another passing trend; they could become the backbone of how we get things done in the digital world.

Yoav Shoham is a professor emeritus at Stanford University and cofounder of AI21 Labs. His 1993 paper on agent-oriented programming received the AI Journal Classic Paper Award. He is coauthor of Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, a standard textbook in the field.

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Hardcoded root credentials in Cisco Unified CM trigger max-severity alert

The affected products-Cisco Unified CM and Unified CM SME–are core components of enterprise telephony infrastructure, widely deployed across government agencies, financial institutions, and large corporations to manage voice, video, and messaging at scale. A flaw in these systems could allow attackers to compromise an organization’s communications, letting them log in

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USA Crude Oil Inventories Rise by Almost 4MM Barrels WoW

U.S. commercial crude oil inventories, excluding those in the Strategic Petroleum Reserve (SPR), increased by 3.8 million barrels from the week ending June 20 to the week ending June 27, the U.S. Energy Information Administration (EIA) highlighted in its latest weekly petroleum status report. That report was released on July 2 and included data for the week ending June 27. It showed that crude oil stocks, not including the SPR, stood at 419.0 million barrels on June 27, 415.1 million barrels on June 20, and 448.5 million barrels on June 28, 2024. The EIA report highlighted that data may not add up to totals due to independent rounding. Crude oil in the SPR stood at 402.8 million barrels on June 27, 402.5 million barrels on June 20, and 372.6 million barrels on June 28, 2024, the report revealed. Total petroleum stocks – including crude oil, total motor gasoline, fuel ethanol, kerosene type jet fuel, distillate fuel oil, residual fuel oil, propane/propylene, and other oils – stood at 1.642 billion barrels on June 27, the EIA report pointed out. Total petroleum stocks were up 9.6 million barrels week on week and down 12.8 million barrels year on year, the report showed. “At 419 million barrels, U.S. crude oil inventories are about nine percent below the five year average for this time of year,” the EIA said in its latest weekly petroleum status report. “Total motor gasoline inventories increased by 4.2 million barrels from last week and are about one percent below the five year average for this time of year. Both Finished gasoline inventories and blending components inventories increased last week,” it added. “Distillate fuel inventories decreased by 1.7 million barrels last week and are about 21 percent below the five year average for this time of year. Propane/propylene inventories increased

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TotalEnergies Raises Stake in AES Renewables Portfolio in Caribbean

TotalEnergies SE said Wednesday it had completed the purchase of a 50 percent stake in AES Corp.’s renewable energy and battery energy storage system (BESS) portfolio of over one gigawatt in the Dominican Republic. Last year the French energy giant acquired a 30 percent stake in Arlington, Virginia-based AES’ renewables portfolio in Puerto Rico. “The combined portfolio now exceeds 1.5 GW of renewable energy and BESS capacity across the Caribbean”, TotalEnergies said in an online statement. The Dominican portfolio includes wind, solar and BESS projects, 410 megawatts of which are operational or under construction. Under-development wind and solar total over 500 MW. The BESS projects will be integrated into solar plants to mitigate intermittency, according to TotalEnergies. The company already owns a 103-MW solar plant under construction and a partially solarized network of 184 service stations, as well as operates natural gas distribution, in the Dominican Republic. The Puerto Rican portfolio includes 200 MW of solar and 285 MW/1,140 MW hours of BESS projects under construction. “TotalEnergies is pursuing deployment of its multi-energy strategy on the island, where it is already active in the fuel, lubricants, and aviation sectors, and operates a network of 200 service stations between Puerto Rico and the island of St Thomas”, the company said. Stephane Michel, TotalEnergies president for gas, renewables and power, said, “We are pleased to expand our multi-energy strategy through this partnership with AES, focusing on renewables and battery storage in a region where TotalEnergies is already a leading supplier of LNG, notably for power generation. Since 2018, we have been supplying LNG to AES’s subsidiaries in Panama and the Dominican Republic”. On April 15, 2025, TotalEnergies announced a heads of agreement with Energia Natural Dominicana (EnaDom), the joint venture between AES and Energas in the Dominican Republic, for the supply of 400,000 metric tons a year of LNG for 15 years from 2027. The

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EU Commission Launches Matchmaking Platform for Energy, Materials Sourcing

The European Commission rolled out Wednesday a mechanism for coordinated purchases of hydrogen, the first mechanism under the EU Energy and Raw Materials Platform. The online platform allows EU buyers to offer demand for biomethane, natural gas, hydrogen and raw materials. It seeks to give EU companies a cost-effective and efficient access to such commodities by enabling negotiations with competing suppliers. The Commission’s Directorate-General for Energy said more products may be covered in the future. The Hydrogen Mechanism is “designed to support the market development of renewable and low-carbon hydrogen and its derivatives (ammonia, methanol, electro-sustainable aviation fuel)”, the Directorate said in an online statement. “The first round of matching demand and supply is planned for September 2025”, it added. The Hydrogen Mechanism will operate until 2029 under the European Hydrogen Bank, as specified under the European Union’s “Regulation on the Internal Markets for Renewable Gas, Natural Gas and Hydrogen”. The Hydrogen Bank is an EU Innovation Fund financing platform to scale up the renewable hydrogen value chain in the 27-nation bloc and partner countries. Energy and Housing Commissioner Dan Jorgensen said, “With the Hydrogen Mechanism launched today, we empower the European industry to seize competitive opportunities while advancing towards greater security of supply and decarbonization”. A Commission information page about the platform says, “The EU Energy and Raw Materials Platform enables the collection and exchange of market data, information about demand and supply, demand aggregation, and joint purchasing of energy-related products and raw materials. The Platform fosters collaboration, efficiency, and transparency in finding counterparts”. “It does not directly provide financing or support negotiations which may take place, outside of the Platform, between Participants following their connections through the Platform”, the page says. However, financial institutions may participate in the platform to publish information about their financing offers, the page says.

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Who Is The World’s Top Oil Producer?

According to the Energy Institute’s (EI) latest statistical review of world energy report, which was published recently, the world’s top oil producer is the United States. The report showed that the U.S. produced 20.135 million barrels of oil per day in 2024. That represented a 3.6 percent increase year on year and 20.8 percent of the total oil production in 2024, according to the report, which highlighted that U.S. oil production has seen an average yearly growth rate of 5.5 percent from 2014 to 2024. Saudi Arabia was shown in the report to be second biggest oil producer in the world last year, with 10.856 million barrels per day. That figure marked a 3.6 percent drop year on year and 11.2 percent of the world’s total, the report outlined. From 2014 to 2024, Saudi Arabia has seen an average yearly oil production decline rate of 0.6 percent, the report pointed out. Russia was ranked third for oil production in the EI report, with 10.752 million barrels per day. That figure represented a 2.9 percent drop year on year and 11.1 percent of the global total for 2024, the report highlighted. From 2014 to 2024, Russia has seen an average yearly oil production decline rate of 0.2 percent, the report showed. Total world oil production came in at 96.890 million barrels per day in 2024, according to the EI’s latest report, which showed that this was a year on year increase of 0.6 percent. Total world oil output has increased by a yearly average of 0.9 percent from 2014 to 2024, the report highlighted. The report pointed out that its oil production figures include “crude oil, shale oil, oil sands, condensates (lease condensate or gas condensates that require further refining), and NGLs (natural gas liquids – ethane, LPG and naphtha separated

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Analysts Reveal What They Expect at Next OPEC+ 8 Meeting

In a report sent to Rigzone by the Standard Chartered Bank team late Tuesday, analysts at the bank, including the company’s commodities research head Paul Horsnell, revealed their projection for the next meeting of the eight OPEC+ countries that made additional voluntary cuts in April and November 2023, which is set for Sunday. “We expect ministers will continue the unwinding of the November 2023 tranche of cuts, increasing targets by 411,000 barrels per day for the fourth successive month,” the analysts said in the report. “We expect a further 411,000 barrel per day increase at the August meeting, which will result in the full unwinding of the November 2023 voluntary cuts that totaled about 2.2 million barrels per day,” they added. In the report, the analysts noted that they expect the market to absorb extra OPEC+ production easily in the short term and revealed that they forecast a global stock draw of 0.9 million barrels per day in the third quarter of this year and a 0.2 million barrel per day build in the second quarter. “The tightening in Q3 is primarily the result of a 1.4 million barrel per day quarter on quarter increase in demand while non-OPEC+ output is fairly flat and OPEC+ output increases by significantly less than the nominal unwinding of the target cuts,” the analysts said in the report. “While the targets (not including compensation for past overproduction) will average about one million barrels per day higher week on week in Q3, we expect total OPEC+ output to rise quarter on quarter by about 0.4 million barrels per day,” they added. The Standard Chartered Bank analysts stated in the report that rapid unwinding of the November 2023 cuts has proved a highly successful strategy. “It has simplified a situation that many traders found too complicated,

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Eni Next, Azimut Partner to Accelerate Clean Tech Deployment

Eni S.p.A.’s corporate venture capital company, Eni Next, has signed a collaboration agreement with Azimut Group. Under the agreement, Azimut will launch a new European Long Term Investment Fund (ELTIF) of venture capital, leveraging Eni Next’s consulting and expertise on technological developments in the energy sector. Eni said in a media release the ELTIF’s launch is expected in September 2025, and the fund will support investments in the energy tech sector.  With a EUR 100 million ($118 million) fundraising target, the Luxembourg-based fund, which is currently awaiting authorization from the relevant authorities, will be open to a broad range of investors, both institutional and private, in line with the new ELTIF 2.0 Regulation’s criteria. The portfolio will comprise U.S.-based startups and scale-ups in the clean tech sector, focusing on decarbonization, energy efficiency, sustainable mobility, and the circular economy. The fund may also invest in European and international companies, Eni said. “This strategic collaboration initiated with Azimut provides Eni Next with an additional lever to support innovative companies in the energy sector. By combining our specialized expertise with Azimut’s fundraising capabilities, the partnership will further accelerate and enhance the growth of the Eni Next portfolio”, Clara Andreoletti, CEO of Eni Next, said. “The energy sector, like many other industrial sectors, is undergoing a profound transformation driven by technological innovation. To support this transition and ensure its economic sustainability, private capital plays a crucial role in enabling new technological solutions to emerge and scale rapidly”. “As new technologies reshape the energy sector, driving a generational shift toward increasingly efficient solutions, this fund aims to give investors access to the most promising and high-potential opportunities”, Giorgio Medda, CEO of Azimut Holding, commented. “This will help bring the Group’s total investments since 2022, dedicated to global energy transition and environmental sustainability, to at least

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Oracle to power OpenAI’s AGI ambitions with 4.5GW expansion

“For CIOs, this shift means more competition for AI infrastructure. Over the next 12–24 months, securing capacity for AI workloads will likely get harder, not easier. Though cost is coming down but demand is increasing as well, due to which CIOs must plan earlier and build stronger partnerships to ensure availability,” said Pareekh Jain, CEO at EIIRTrend & Pareekh Consulting. He added that CIOs should expect longer wait times for AI infrastructure. To mitigate this, they should lock in capacity through reserved instances, diversify across regions and cloud providers, and work with vendors to align on long-term demand forecasts.  “Enterprises stand to benefit from more efficient and cost-effective AI infrastructure tailored to specialized AI workloads, significantly lower their overall future AI-related investments and expenses. Consequently, CIOs face a critical task: to analyze and predict the diverse AI workloads that will prevail across their organizations, business units, functions, and employee personas in the future. This foresight will be crucial in prioritizing and optimizing AI workloads for either in-house deployment or outsourced infrastructure, ensuring strategic and efficient resource allocation,” said Neil Shah, vice president at Counterpoint Research. Strategic pivot toward AI data centers The OpenAI-Oracle deal comes in stark contrast to developments earlier this year. In April, AWS was reported to be scaling back its plans for leasing new colocation capacity — a move that AWS Vice President for global data centers Kevin Miller described as routine capacity management, not a shift in long-term expansion plans. Still, these announcements raised questions around whether the hyperscale data center boom was beginning to plateau. “This isn’t a slowdown, it’s a strategic pivot. The era of building generic data center capacity is over. The new global imperative is a race for specialized, high-density, AI-ready compute. Hyperscalers are not slowing down; they are reallocating their capital to

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Arista Buys VeloCloud to reboot SD-WANs amid AI infrastructure shift

What this doesn’t answer is how Arista Networks plans to add newer, security-oriented Secure Access Service Edge (SASE) capabilities to VeloCloud’s older SD-WAN technology. Post-acquisition, it still has only some of the building blocks necessary to achieve this. Mapping AI However, in 2025 there is always more going on with networking acquisitions than simply adding another brick to the wall, and in this case it’s the way AI is changing data flows across networks. “In the new AI era, the concepts of what comprises a user and a site in a WAN have changed fundamentally. The introduction of agentic AI even changes what might be considered a user,” wrote Arista Networks CEO, Jayshree Ullal, in a blog highlighting AI’s effect on WAN architectures. “In addition to people accessing data on demand, new AI agents will be deployed to access data independently, adapting over time to solve problems and enhance user productivity,” she said. Specifically, WANs needed modernization to cope with the effect AI traffic flows are having on data center traffic. Sanjay Uppal, now VP and general manager of the new VeloCloud Division at Arista Networks, elaborated. “The next step in SD-WAN is to identify, secure and optimize agentic AI traffic across that distributed enterprise, this time from all end points across to branches, campus sites, and the different data center locations, both public and private,” he wrote. “The best way to grab this opportunity was in partnership with a networking systems leader, as customers were increasingly looking for a comprehensive solution from LAN/Campus across the WAN to the data center.”

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Data center capacity continues to shift to hyperscalers

However, even though colocation and on-premises data centers will continue to lose share, they will still continue to grow. They just won’t be growing as fast as hyperscalers. So, it creates the illusion of shrinkage when it’s actually just slower growth. In fact, after a sustained period of essentially no growth, on-premises data center capacity is receiving a boost thanks to genAI applications and GPU infrastructure. “While most enterprise workloads are gravitating towards cloud providers or to off-premise colo facilities, a substantial subset are staying on-premise, driving a substantial increase in enterprise GPU servers,” said John Dinsdale, a chief analyst at Synergy Research Group.

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Oracle inks $30 billion cloud deal, continuing its strong push into AI infrastructure.

He pointed out that, in addition to its continued growth, OCI has a remaining performance obligation (RPO) — total future revenue expected from contracts not yet reported as revenue — of $138 billion, a 41% increase, year over year. The company is benefiting from the immense demand for cloud computing largely driven by AI models. While traditionally an enterprise resource planning (ERP) company, Oracle launched OCI in 2016 and has been strategically investing in AI and data center infrastructure that can support gigawatts of capacity. Notably, it is a partner in the $500 billion SoftBank-backed Stargate project, along with OpenAI, Arm, Microsoft, and Nvidia, that will build out data center infrastructure in the US. Along with that, the company is reportedly spending about $40 billion on Nvidia chips for a massive new data center in Abilene, Texas, that will serve as Stargate’s first location in the country. Further, the company has signaled its plans to significantly increase its investment in Abu Dhabi to grow out its cloud and AI offerings in the UAE; has partnered with IBM to advance agentic AI; has launched more than 50 genAI use cases with Cohere; and is a key provider for ByteDance, which has said it plans to invest $20 billion in global cloud infrastructure this year, notably in Johor, Malaysia. Ellison’s plan: dominate the cloud world CTO and co-founder Larry Ellison announced in a recent earnings call Oracle’s intent to become No. 1 in cloud databases, cloud applications, and the construction and operation of cloud data centers. He said Oracle is uniquely positioned because it has so much enterprise data stored in its databases. He also highlighted the company’s flexible multi-cloud strategy and said that the latest version of its database, Oracle 23ai, is specifically tailored to the needs of AI workloads. Oracle

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Datacenter industry calls for investment after EU issues water consumption warning

CISPE’s response to the European Commission’s report warns that the resulting regulatory uncertainty could hurt the region’s economy. “Imposing new, standalone water regulations could increase costs, create regulatory fragmentation, and deter investment. This risks shifting infrastructure outside the EU, undermining both sustainability and sovereignty goals,” CISPE said in its latest policy recommendation, Advancing water resilience through digital innovation and responsible stewardship. “Such regulatory uncertainty could also reduce Europe’s attractiveness for climate-neutral infrastructure investment at a time when other regions offer clear and stable frameworks for green data growth,” it added. CISPE’s recommendations are a mix of regulatory harmonization, increased investment, and technological improvement. Currently, water reuse regulation is directed towards agriculture. Updated regulation across the bloc would encourage more efficient use of water in industrial settings such as datacenters, the asosciation said. At the same time, countries struggling with limited public sector budgets are not investing enough in water infrastructure. This could only be addressed by tapping new investment by encouraging formal public-private partnerships (PPPs), it suggested: “Such a framework would enable the development of sustainable financing models that harness private sector innovation and capital, while ensuring robust public oversight and accountability.” Nevertheless, better water management would also require real-time data gathered through networks of IoT sensors coupled to AI analytics and prediction systems. To that end, cloud datacenters were less a drain on water resources than part of the answer: “A cloud-based approach would allow water utilities and industrial users to centralize data collection, automate operational processes, and leverage machine learning algorithms for improved decision-making,” argued CISPE.

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HPE-Juniper deal clears DOJ hurdle, but settlement requires divestitures

In HPE’s press release following the court’s decision, the vendor wrote that “After close, HPE will facilitate limited access to Juniper’s advanced Mist AIOps technology.” In addition, the DOJ stated that the settlement requires HPE to divest its Instant On business and mandates that the merged firm license critical Juniper software to independent competitors. Specifically, HPE must divest its global Instant On campus and branch WLAN business, including all assets, intellectual property, R&D personnel, and customer relationships, to a DOJ-approved buyer within 180 days. Instant On is aimed primarily at the SMB arena and offers a cloud-based package of wired and wireless networking gear that’s designed for so-called out-of-the-box installation and minimal IT involvement, according to HPE. HPE and Juniper focused on the positive in reacting to the settlement. “Our agreement with the DOJ paves the way to close HPE’s acquisition of Juniper Networks and preserves the intended benefits of this deal for our customers and shareholders, while creating greater competition in the global networking market,” HPE CEO Antonio Neri said in a statement. “For the first time, customers will now have a modern network architecture alternative that can best support the demands of AI workloads. The combination of HPE Aruba Networking and Juniper Networks will provide customers with a comprehensive portfolio of secure, AI-native networking solutions, and accelerate HPE’s ability to grow in the AI data center, service provider and cloud segments.” “This marks an exciting step forward in delivering on a critical customer need – a complete portfolio of modern, secure networking solutions to connect their organizations and provide essential foundations for hybrid cloud and AI,” said Juniper Networks CEO Rami Rahim. “We look forward to closing this transaction and turning our shared vision into reality for enterprise, service provider and cloud customers.”

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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.

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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

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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

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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

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