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Finding value with AI automation

In partnership withIntel In June 2023, technology leaders and IT services executives had a lightning bolt headed their way when McKinsey published the “The economic potential of generative AI: The next productivity frontier” report. It echoed a moment from the 2010s when Amazon Web Services launched an advertising campaign aimed at Main Street’s C-suite: Why would any fiscally responsible exec allow their IT teams to spend capex for servers and software when AWS only cost 10 cents per virtual machine?  Vendors understand that these kinds of reports and aggressive advertising around competitive risks projected onto an industry sector would drive many calls from boards to their C-suite, rolling from C-suite to their staff all asking, “What are we doing with AI?” When asked to “do something with AI,” technical leadership and their organizations promptly responded — sometimes begrudgingly and sometimes excitedly — for work-sanctioned opportunities to get their hands on a new technology. At that point, there was no time to sort between actual business returns from applying AI and “AI novelty” use cases that were more Rube Goldberg machines than tangible breakthroughs.  Today’s opportunity: Significant automation gains  When leaders respond to immediate panic, new business risks and mitigations often emerge.  Two recent examples highlight the consequences of rushing to implement and publish positive results from AI adoption. The Wall Street Journal reported in April 2025 on companies struggling to realize returns on AI. Just weeks later, it covered MIT’s retraction of a technical paper about AI where the results that led to its publication could not be substantiated.   While these reports demonstrate the pitfalls of over-reliance on AI without common-sense guardrails, not all is off track in the land of enterprise AI adoption. Incredible results being found from judicious use of AI and related technologies in automating processes across industries. Now that we are through the “fear of missing out” stage and can get down to business, where are the best places to look for value when applying AI to automation of your business?   While chatbots are almost as pervasive as new app downloads for mobile phones, the applications of AI realizing automation and productivity gains line up with the unique purpose and architecture of the underlying AI system they are built on. The dominant patterns where AI gains are realized currently boil down to two things: language (translation and patterns) and data (new format creation and data search).   Example one: Natural language processing   Manufacturing automation challenge: Failure Mode and Effects Analysis (FMEA) is both critical and often labor intensive. It is not always performed prior to a failure in manufacturing equipment, so very often FMEA occurs in a stressful manufacturing lines-down scenario. In Intel’s case, a global footprint of manufacturing facilities separated by large distances along with time zones and preferred language differences makes this even more difficult to find the root cause of a problem. Weeks of engineering effort are spent per FMEA analysis repeated across large fleets of tools spread between these facilities.   Solution: Leverage already deployed CPU compute servers for natural language processing (NLP) across the manufacturing tool logs, where observations about the tools’ operations are maintained by the local manufacturing technicians. The analysis also applied sentiment analysis to classify words as positive, negative, or neutral. The new system performed FMEA on six months of data in under one minute, saving weeks of engineering time and allowing the manufacturing line to proactively service equipment on a pre-emptive schedule rather than incurring unexpected downtime.   Financial institution challenge: Programming languages commonly used by software engineers have evolved. Mature bellwether institutions were often formed through a series of mergers and acquisitions over the years, and they continue to rely on critical systems that are based on 30-year-old programming languages that current-day software engineers are not familiar with.  Solution: Use NLP to translate between the old and new programming languages, giving software engineers a needed boost to improve the serviceability of critical operational systems. Use the power of AI rather than doing a risky rewrite or massive upgrade.  Example two: Company product specifications and generative AI models  Sales automation challenge: The time it takes to reformat a company’s product data into a specific customer RFP format has been an ongoing challenge across industries. Teams of sales and technical leads spend weeks of work across different accounts reformatting the same root data between the preferred PowerPoint or Word document formats. The customer response times were measured in weeks, especially if the RFPs required legal reviews.  Solution: By using generative AI combined with a data extraction and prompting technique called retrieval augmented generation (RAG), companies can rapidly reformat product information between different customer required RFP response formats. The time spent moving data between different documents and different document types only to find an unforced error in the move is reduced to hours instead of weeks.   HR policy automation challenge: Navigating internal processes can be time consuming and confusing for both HR and employees. The consequences of misinterpretation, access outages, and personal information or private data being exposed are massively important to the company and the individual.  Solution: Combine generative AI, RAG, and an interactive chatbot that uses employee-assigned assets to determine identity and access rights, provides employees interactive query-based chat formats to answer their questions in real time.  Finding your best use cases for AI  In a world where 80% to 90% of all AI proof of concepts fail to scale, now is the time to develop a framework that is based on caution. Consider starting with a data strategy and governance assessment. Then find opportunities to compare successful AI-based automation efforts at peer companies through peer discussions. Clear, rules-based policies and processes offer the best opportunities to begin a successful AI automation journey in your enterprise. Where you encounter disparate data sources (e.g., unstructured, video, structured databases) or unclear processes, maintain tighter human-in-the-loop decision controls to avoid unexpected data or token exposure and cost overruns.  As the AI hype cycle cools and business pressure mounts, now is the time to become practical. Apply AI to well-defined use cases and begin unlocking the automation benefits that will matter not just in 2025, but for years to come. This content was produced by Intel. It was not written by MIT Technology Review’s editorial staff.

In partnership withIntel

In June 2023, technology leaders and IT services executives had a lightning bolt headed their way when McKinsey published the “The economic potential of generative AI: The next productivity frontier” report. It echoed a moment from the 2010s when Amazon Web Services launched an advertising campaign aimed at Main Street’s C-suite: Why would any fiscally responsible exec allow their IT teams to spend capex for servers and software when AWS only cost 10 cents per virtual machine? 

Vendors understand that these kinds of reports and aggressive advertising around competitive risks projected onto an industry sector would drive many calls from boards to their C-suite, rolling from C-suite to their staff all asking, “What are we doing with AI?” When asked to “do something with AI,” technical leadership and their organizations promptly responded — sometimes begrudgingly and sometimes excitedly — for work-sanctioned opportunities to get their hands on a new technology. At that point, there was no time to sort between actual business returns from applying AI and “AI novelty” use cases that were more Rube Goldberg machines than tangible breakthroughs. 

Today’s opportunity: Significant automation gains 

When leaders respond to immediate panic, new business risks and mitigations often emerge.  Two recent examples highlight the consequences of rushing to implement and publish positive results from AI adoption. The Wall Street Journal reported in April 2025 on companies struggling to realize returns on AI. Just weeks later, it covered MIT’s retraction of a technical paper about AI where the results that led to its publication could not be substantiated.  

While these reports demonstrate the pitfalls of over-reliance on AI without common-sense guardrails, not all is off track in the land of enterprise AI adoption. Incredible results being found from judicious use of AI and related technologies in automating processes across industries. Now that we are through the “fear of missing out” stage and can get down to business, where are the best places to look for value when applying AI to automation of your business?  

While chatbots are almost as pervasive as new app downloads for mobile phones, the applications of AI realizing automation and productivity gains line up with the unique purpose and architecture of the underlying AI system they are built on. The dominant patterns where AI gains are realized currently boil down to two things: language (translation and patterns) and data (new format creation and data search).  

Example one: Natural language processing  

Manufacturing automation challenge: Failure Mode and Effects Analysis (FMEA) is both critical and often labor intensive. It is not always performed prior to a failure in manufacturing equipment, so very often FMEA occurs in a stressful manufacturing lines-down scenario. In Intel’s case, a global footprint of manufacturing facilities separated by large distances along with time zones and preferred language differences makes this even more difficult to find the root cause of a problem. Weeks of engineering effort are spent per FMEA analysis repeated across large fleets of tools spread between these facilities.  

Solution: Leverage already deployed CPU compute servers for natural language processing (NLP) across the manufacturing tool logs, where observations about the tools’ operations are maintained by the local manufacturing technicians. The analysis also applied sentiment analysis to classify words as positive, negative, or neutral. The new system performed FMEA on six months of data in under one minute, saving weeks of engineering time and allowing the manufacturing line to proactively service equipment on a pre-emptive schedule rather than incurring unexpected downtime.  

Financial institution challenge: Programming languages commonly used by software engineers have evolved. Mature bellwether institutions were often formed through a series of mergers and acquisitions over the years, and they continue to rely on critical systems that are based on 30-year-old programming languages that current-day software engineers are not familiar with. 

Solution: Use NLP to translate between the old and new programming languages, giving software engineers a needed boost to improve the serviceability of critical operational systems. Use the power of AI rather than doing a risky rewrite or massive upgrade. 

Example two: Company product specifications and generative AI models 

Sales automation challenge: The time it takes to reformat a company’s product data into a specific customer RFP format has been an ongoing challenge across industries. Teams of sales and technical leads spend weeks of work across different accounts reformatting the same root data between the preferred PowerPoint or Word document formats. The customer response times were measured in weeks, especially if the RFPs required legal reviews. 

Solution: By using generative AI combined with a data extraction and prompting technique called retrieval augmented generation (RAG), companies can rapidly reformat product information between different customer required RFP response formats. The time spent moving data between different documents and different document types only to find an unforced error in the move is reduced to hours instead of weeks.  

HR policy automation challenge: Navigating internal processes can be time consuming and confusing for both HR and employees. The consequences of misinterpretation, access outages, and personal information or private data being exposed are massively important to the company and the individual. 

Solution: Combine generative AI, RAG, and an interactive chatbot that uses employee-assigned assets to determine identity and access rights, provides employees interactive query-based chat formats to answer their questions in real time. 

Finding your best use cases for AI 

In a world where 80% to 90% of all AI proof of concepts fail to scale, now is the time to develop a framework that is based on caution. Consider starting with a data strategy and governance assessment. Then find opportunities to compare successful AI-based automation efforts at peer companies through peer discussions. Clear, rules-based policies and processes offer the best opportunities to begin a successful AI automation journey in your enterprise. Where you encounter disparate data sources (e.g., unstructured, video, structured databases) or unclear processes, maintain tighter human-in-the-loop decision controls to avoid unexpected data or token exposure and cost overruns. 

As the AI hype cycle cools and business pressure mounts, now is the time to become practical. Apply AI to well-defined use cases and begin unlocking the automation benefits that will matter not just in 2025, but for years to come.

This content was produced by Intel. It was not written by MIT Technology Review’s editorial staff.

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Broadcom scales up Ethernet with Tomahawk Ultra for low latency HPC and AI

Broadcom Support for minimum packet size allows streaming of those packets at full bandwidth. That capability is essential for efficient communication in scientific and computational workloads. It is particularly important for scale-up networks where GPU-to-switch-to-GPU communication happens in a single hop. Lossless Ethernet gets an ‘Ultra’ boost Another specific area

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Nvidia to restart H20 exports to China, unveils new export-compliant GPU

China re-entry impact Nvidia’s announcements mark a bid to re-enter the world’s second-largest AI market under tightened US export controls. But this return may not mean business as usual. “Despite Nvidia’s market re-entry, Chinese companies will likely continue diversifying suppliers to strengthen supply chain resilience,” said Prabhu Ram, VP of

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Macquarie Strategists See WoW USA Crude Inventory Build

In a report sent to Rigzone by the Macquarie team late Monday, Macquarie strategists revealed that they are forecasting that U.S. crude inventories will be up by 1.2 million barrels for the week ending July 11. “This follows a 7.1 million barrel build in the prior week, with the crude balance again realizing significantly looser than our expectations,” the strategists stated in the report. “For this week’s crude balance, from refineries, we model another small reduction in crude runs (-0.1 million barrels per day). Among net imports, we model a modest reduction, with exports (+0.7 million barrels per day) and imports (+0.4 million barrels per day) up on a nominal basis,” they added. The 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 reduction (-0.7 million barrels per day) on a nominal basis this week,” the strategists went on to note in the report. “Rounding out the picture, we anticipate a small draw in SPR [Strategic Petroleum Reserve] stocks (-0.3 million barrels) this week,” they said. The strategists also highlighted in the report that, “among products”, they “look for muted stats this week, with a gasoline draw (-0.8 million barrels), a jet build (+0.6 million barrels) and distillate stocks minimally lower”. “We model implied demand for these three products at ~14.5 million barrels per day for the week ending July 11,” they added. “We again note with the July 4th holiday falling on a Friday this year, the timing/magnitude of holiday impacts (particularly for distillate demand) could introduce additional volatility in this week’s stats,” the strategists continued. “Should the bulk of the July 4th distillate demand loss manifest in this week’s figures, a looser distillate balance could be realized,” they

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GAIL, OIL Add 15 Years to Their Existing Gas Sale and Purchase Deal

State-owned GAIL (India) Ltd. has extended its existing gas sale and purchase agreement with Oil India Ltd. (OIL) for another 15 years. GAIL said in a media release that the deal came into effect on July 1, 2025. Under the extended agreement, 900,000 standard cubic meters per day (31.7 million standard cubic feet per day) of natural gas will be supplied from OIL’s Bakhri Tibba Block of Rajasthan, which covers the Dandewala, Tanot and Bagi Tibba fields. “This agreement highlights the dedication of both Maharatna central public sector enterprises in production, transportation, and distribution of natural gas available from domestic gas fields demonstrating their collaborative approach to enhancing energy security and accessibility”, GAIL said.  “The sourced gas will be supplied to the state-run power plant of Rajasthan Rajya Vidyut Utpadan Nigam”, it said. GAIL owns and operates a 16,421-kilometer (10,200 miles) network of natural gas pipelines across India, transmitting over 127 million standard cubic meters per day (4.4 billion cubic feet per day) in fiscal year 2024-25, GAIL said. GAIL added it is simultaneously executing multiple pipeline projects to broaden its reach. GAIL also owns and manages a gas-based petrochemical complex at Pata, with capacities of 810 kilotons per annum (KTA) at Pata and 280 KTA at its subsidiary Brahmaputra Cracker and Polymer Ltd. GAIL also owns a liquefied natural gas (LNG) portfolio totaling 16.56 million tons per annum, representing 61 percent of India’s total LNG imports. Additionally GAIL, through its units and joint ventures, holds a significant market share in city gas distribution and is venturing into renewable energy sectors such as solar, wind and biofuels, according to the company. To contact the author, email [email protected] What do you think? We’d love to hear from you, join the conversation on the Rigzone Energy Network. The Rigzone Energy Network is

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Naftogaz Partners with Baker Hughes to Strengthen Ukraine’s Energy Sector

Naftogaz Group has signed a strategic memorandum of understanding (MoU) with Baker Hughes Co. to strengthen Ukraine’s energy sector. Naftogaz said in a media release that the two companies would explore new technical, operational, and commercial opportunities in key energy segments, including oil and gas extraction, transportation, storage, and processing, as well as geothermal projects, carbon capture technologies, and electricity generation. “Naftogaz Group is building collaboration frameworks that help Ukraine overcome wartime challenges, implement modern energy solutions, and strengthen energy independence. Baker Hughes has global expertise across the energy value chain, and we see great potential in this cooperation”, Sergii Koretskyi, Chief Executive Officer of Naftogaz, said. The collaboration will encompass a range of services and equipment vital for drilling and well construction. Additionally, Naftogaz said the cooperation targets critical advancements in environmental sustainability, focusing on solutions for emission reduction, carbon capture projects, and the development of hydrogen and geothermal energy initiatives. The partnership will also extend to enhancing digital services, automation, and analytics within operations, as well as providing specialized equipment for subsurface activities and production processes and developing software for asset management and drilling optimization. Naftogaz said that Baker Hughes signed a separate MoU with Naftogaz’s JSC Ukrtransgaz to assess the potential to leverage Baker Hughes’ gas technology equipment portfolio for underground gas storage and power generation projects in Ukraine. “Baker Hughes is committed to supporting Naftogaz Group and companies in Ukraine with its energy technology solutions portfolio and expertise. The ultimate goal of our companies is to contribute to the energy security and decarbonization of Ukraine’s energy sector and support the eventual reconstruction of the country’s economy through reliable and modern energy solutions”, Paolo Noccioni, President of Baker Hughes’ Nuovo Pignone, said. To contact the author, email [email protected] What do you think? We’d love to hear from

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CNOOC Marks Another Breakthrough in Buried Hills Exploration in SCS

CNOOC Ltd. said Wednesday it had achieved another “major breakthrough” in the exploration of buried hills in the South China Sea after drilling 3,362 meters (11,030.18 feet) deep in the Weizhou 10-5 South field. The field is in the Beibu Gulf, or the Gulf of Tonkin, in waters with an average depth of 37 meters, according to CNOOC Ltd., majority-owned by the state’s China National Offshore Oil Corp. Exploration well WZ10-5S-2d showed a 211-meter oil and gas pay zone. Tests yielded a production of 165,000 cubic feet of natural gas and 400 barrels of oil per day, CNOOC Ltd. said in a press release. The well “marks a major exploration breakthrough in the metamorphic sandstone and slate buried hills offshore China”, the announcement declared. “In recent years, CNOOC Ltd. has consistently intensified theoretical innovation and tackled key technology challenges in buried hills and deep plays exploration”, commented CNOOC Ltd. chief geologist Xu Changgui. “Breakthroughs have been achieved in the exploration of Paleozoic granite and Proterozoic metamorphic sandstone and slate buried hills within the Beibu Gulf Basin. “They demonstrate the vast exploration potential in buried hills formations, drive the secondary exploration process in mature areas, and mark the commencement of large-scale exploration of buried hills in the Beibu Gulf Basin”. CNOOC Ltd. chief executive Zhou Xinhuai added, “In the future, CNOOC Ltd. will continue to intensify research on key theories and technologies for deep play exploration, to enhance research and development capabilities, advance reserves and production growth, and to ensure stable supply of oil and gas”. In March CNOOC Ltd. claimed a breakthrough in the exploration of Paleozoic granite buried hills in the Beibu Gulf through an oil and gas discovery in the Weizhou 10-5 field. Drilled to a total depth of about 4,840 meters in an area with an average

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Crude Falls Amid Stronger Dollar, Supply Risk Doubts

Oil fell as the dollar strengthened and traders doubted US President Donald Trump’s plan to pressure Moscow would disrupt Russian exports. West Texas Intermediate slid 0.7% to settle above $66, extending Monday’s losses. Trump told reporters the US will impose a 19% tariff on goods from Indonesia after teasing the deal earlier in the day. The dollar strengthened, making commodities priced in the currency less attractive. Trump’s plan to pressure Russia into a ceasefire with Ukraine that was released Monday didn’t directly target energy infrastructure, a decision that brought some bears off of the sidelines. The administration intends to impose 100% tariffs on Russia only if the hostilities don’t end with a deal in 50 days, allaying fears of near-term supply tightness. “Since the start of the Ukraine war, it has become evident that halting Russian oil trade by targeting Russian sellers or the numerous shippers and payment intermediaries is nearly impossible,” JPMorgan Chase & Co. analysts led by Natasha Kaneva wrote in a note. Prices briefly popped on comments by US Energy Secretary Chris Wright that the US is considering creative ways to refill the Strategic Petroleum Reserve, before resuming their decline. Futures also came under pressure as investors liquidated their positions in WTI’s so-called prompt spread ahead of the contract expiry. US crude’s prompt spread — the difference between its two nearest contracts — held steady at around $1.16 a barrel in backwardation. While that’s still a bullish pattern, with nearer-term prices above those further out, it’s notably lower than Monday’s peak of $1.49. The gauge is set to be closely followed as the market’s focus shifts back to supply. The Organization of the Petroleum Exporting Countries partly pushed back against an International Energy Agency report that Saudi Arabia’ crude production surged in June. The cartel’s figures show

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Egypt Delays Some LNG Imports as New Terminals Yet to Start

Egypt is delaying some liquefied natural gas deliveries as its newest import facilities haven’t yet started operating. A small number of cargoes due to arrive in July are being rescheduled for next month, people familiar with the matter said, asking not to be identified discussing private information. The delay is not expected to be material or recurring, they said. The most populous Arab country has turned into a major LNG importer amid deteriorating domestic gas production and high consumption. Last month, Egyptian Natural Gas Holding Co. agreed on large LNG deals with suppliers including Saudi Aramco, Trafigura Group and Vitol Group, with contracts starting as soon as July and lasting as long as two and a half years. However, imports so far remain in line with recent months’ levels despite peak summer demand in the nation. Supplies have been coming in through the country’s only operational floating import terminal, the Hoegh Galleon vessel, ship-tracking data compiled by Bloomberg show.  Egypt has leased two more vessels for imports, Energos Power and Energos Eskimo. While the terminals have arrived in the country, and Egypt ordered expedited connection to the grid, they have not yet imported cargoes, according to the ship-tracking data. Both new terminals won’t be ready to receive LNG until the end of July, another person with knowledge of the matter said, as the onshore portion of the equipment is not ready yet. Egypt’s oil ministry didn’t respond to a request for comment. Egyptian Natural Gas Holding Co. declined to comment. 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.

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Equinix, AWS embrace liquid cooling to power AI implementations

With AWS, it deployed In-Row Heat Exchangers (IRHX), a custom-built liquid cooling system designed specifically for servers using Nvidia’s Blackwell GPUs, it’s most powerful but also its hottest running processors used for AI training and inference. The IRHX unit has three components: a water‑distribution cabinet, an integrated pumping unit, and in‑row fan‑coil modules. It uses direct to chip liquid cooling just like the equinox servers, where cold‑plates attached to the chip draw heat from the chips and is cooled by the liquid. The warmed coolant then flows through the coils of heat exchangers, where high‑speed fans Blow on the pipes to cool them, like a car radiator. This type of cooling is nothing new, and there are a few direct to chip liquid cooling solutions on the market from Vertiv, CoolIT, Motivair, and Delta Electronics all sell liquid cooling options. But AWS separates the pumping unit from the fan-coil modules, letting a single pumping system to support large number of fan units. These modular fans can be added or removed as cooling requirements evolve, giving AWS the flexibility to adjust the system per row and site. This led to some concern that Amazon would disrupt the market for liquid cooling, but as a Dell’Oro Group analyst put it, Amazon develops custom technologies for itself and does not go into competition or business with other data center infrastructure companies.

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Intel CEO: We are not in the top 10 semiconductor companies

The Q&A session came on the heels of layoffs across the company. Tan was hired in March, and almost immediately he began to promise to divest and reduce non-core assets. Gelsinger had also begun divesting the company of losers, but they were nibbles around the edge. Tan is promising to take an axe to the place. In addition to discontinuing products, the company has outsourced marketing and media relations — for the first time in more than 25 years of covering this company, I have no internal contacts at Intel. Many more workers are going to lose their jobs in coming weeks. So far about 500 have been cut in Oregon and California but many more is expected — as much as 20% of the overall company staff may go, and Intel has over 100,000 employees, according to published reports. Tan believes the company is bloated and too bogged down with layers of management to be reactive and responsive in the same way that AMD and Nvidia are. “The whole process of that (deciding) is so slow and eventually nobody makes a decision,” he is quoted as saying. Something he has decided on is AI, and he seems to have decided to give up. “On training, I think it is too late for us,” Tan said, adding that Nvidia’s position in that market is simply “too strong.” So there goes what sales Gaudi3 could muster. Instead, Tan said Intel will focus on “edge” artificial intelligence, where AI capabilities Are brought to PCs and other remote devices rather than big AI processors in data centers like Nvidia and AMD are doing. “That’s an area that I think is emerging, coming up very big and we want to make sure that we capture,” Tan said.

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AMD: Latest news and insights

Survey: AMD continues to take server share from Intel May 20, 2025: AMD continues to take market share from Intel, growing at a faster rate and closing the gap between the two companies to the narrowest it has ever been. AMD, Nvidia partner with Saudi startup to build multi-billion dollar AI service centers May 15, 2025: As part of the avalanche of business deals that came from President Trump’s Middle East tour, both AMD and Nvidia have struck multi-billion dollar deals with an emerging Saudi AI firm. AMD targets hosting providers with affordable EPYC 4005 processors May 14, 2025: AMD launched its latest set of data center processors, targeting hosted IT service providers. The EPYC 4005 series is purpose-built with enterprise-class features and support for modern infrastructure technologies at an affordable price, the company said. Jio teams with AMD, Cisco and Nokia to build AI-enabled telecom platform March 18, 2025: Jio has teamed up with AMD, Cisco and Nokia to build an AI-enabled platform for telecom networks. The goal is to make networks smarter, more secure and more efficient to help service providers cut costs and develop new services. AMD patches microcode security holes after accidental early disclosure February 3, 2025: AMD issued two patches for severe microcode security flaws, defects that AMD said “could lead to the loss of Secure Encrypted Virtualization (SEV) protection.” The bugs were inadvertently revealed by a partner.

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Nvidia hits $4T market cap as AI, high-performance semiconductors hit stride

“The company added $1 trillion in market value in less than a year, a pace that surpasses Apple and Microsoft’s previous trajectories. This rapid ascent reflects how indispensable AI chipmakers have become in today’s digital economy,” Kiran Raj, practice head, Strategic Intelligence (Disruptor) at GlobalData, said in a statement. According to GlobalData’s Innovation Radar report, “AI Chips – Trends, Market Dynamics and Innovations,” the global AI chip market is projected to reach $154 billion by 2030, growing at a compound annual growth rate (CAGR) of 20%. Nvidia has much of that market, but it also has a giant bullseye on its back with many competitors gunning for its crown. “With its AI chips powering everything from data centers and cloud computing to autonomous vehicles and robotics, Nvidia is uniquely positioned. However, competitive pressure is mounting. Players like AMD, Intel, Google, and Huawei are doubling down on custom silicon, while regulatory headwinds and export restrictions are reshaping the competitive dynamics,” he said.

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Enterprises will strengthen networks to take on AI, survey finds

Private data centers: 29.5% Traditional public cloud: 35.4% GPU as a service specialists: 18.5% Edge compute: 16.6% “There is little variation from training to inference, but the general pattern is workloads are concentrated a bit in traditional public cloud and then hyperscalers have significant presence in private data centers,” McGillicuddy explained. “There is emerging interest around deploying AI workloads at the corporate edge and edge compute environments as well, which allows them to have workloads residing closer to edge data in the enterprise, which helps them combat latency issues and things like that. The big key takeaway here is that the typical enterprise is going to need to make sure that its data center network is ready to support AI workloads.” AI networking challenges The popularity of AI doesn’t remove some of the business and technical concerns that the technology brings to enterprise leaders. According to the EMA survey, business concerns include security risk (39%), cost/budget (33%), rapid technology evolution (33%), and networking team skills gaps (29%). Respondents also indicated several concerns around both data center networking issues and WAN issues. Concerns related to data center networking included: Integration between AI network and legacy networks: 43% Bandwidth demand: 41% Coordinating traffic flows of synchronized AI workloads: 38% Latency: 36% WAN issues respondents shared included: Complexity of workload distribution across sites: 42% Latency between workloads and data at WAN edge: 39% Complexity of traffic prioritization: 36% Network congestion: 33% “It’s really not cheap to make your network AI ready,” McGillicuddy stated. “You might need to invest in a lot of new switches and you might need to upgrade your WAN or switch vendors. You might need to make some changes to your underlay around what kind of connectivity your AI traffic is going over.” Enterprise leaders intend to invest in infrastructure

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CoreWeave acquires Core Scientific for $9B to power AI infrastructure push

Such a shift, analysts say, could offer short-term benefits for enterprises, particularly in cost and access, but also introduces new operational risks. “This acquisition may potentially lower enterprise pricing through lease cost elimination and annual savings, while improving GPU access via expanded power capacity, enabling faster deployment of Nvidia chipsets and systems,” said Charlie Dai, VP and principal analyst at Forrester. “However, service reliability risks persist during this crypto-to-AI retrofitting.” This also indicates that struggling vendors such as Core Scientific and similar have a way to cash out, according to Yugal Joshi, partner at Everest Group. “However, it does not materially impact the availability of Nvidia GPUs and similar for enterprises,” Joshi added. “Consolidation does impact the pricing power of vendors.” Concerns for enterprises Rising demand for AI-ready infrastructure can raise concerns among enterprises, particularly over access to power-rich data centers and future capacity constraints. “The biggest concern that CIOs should have with this acquisition is that mature data center infrastructure with dedicated power is an acquisition target,” said Hyoun Park, CEO and chief analyst at Amalgam Insights. “This may turn out to create challenges for CIOs currently collocating data workloads or seeking to keep more of their data loads on private data centers rather than in the cloud.”

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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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