Bitcoin Mining Heat Reuse, From Waste to Opportunity

Stay Ahead, Stay ONMINE

Bitcoin Mining Heat Reuse, From Waste to Opportunity

Heat Generation by Mining Rigs Bitcoin mining machines consume large amounts of electricity, and roughly 90% of this energy is converted into heat. Managing this heat effectively is one of the biggest challenges miners face. Proper cooling is essential to optimize for ASIC efficiency, prevent hashboards from overheating or breaking down. Cooling methods include airflow […]

Heat Generation by Mining Rigs

Bitcoin mining machines consume large amounts of electricity, and roughly 90% of this energy is converted into heat. Managing this heat effectively is one of the biggest challenges miners face. Proper cooling is essential to optimize for ASIC efficiency, prevent hashboards from overheating or breaking down. Cooling methods include airflow management, immersion- and hydrocooling.

In many operations, the heat generated during mining is treated as waste and simply discarded. However, an increasing number of miners are recognizing the potential to repurpose this heat for productive uses. Some operations even prioritize heat production itself, using Bitcoin mining as a secondary function to support heating needs.

Why Repurpose Waste Heat?

Offset Operational Expenses Mining

Bitcoin mining is an energy-intensive industry, with energy costs typically accounting for around 80% of a miner’s operational expenses. To remain competitive, miners seek out the cheapest energy sources and innovative ways to offset these costs. Repurposing waste heat presents a significant opportunity to create an additional revenue stream and reduce operational expenses by effectively using the same energy twice.

Reduce Electricity Costs for Heat

On the other side, industries reliant on heat have been heavily impacted by rising gas and energy prices. For these businesses, integrating Bitcoin mining as a heat source can significantly reduce heating costs. Instead of consuming energy solely for direct heating, they can operate Bitcoin miners, generating both heat and revenue simultaneously.

Lower Emissions

Beyond financial benefits, repurposing heat also addresses environmental concerns. Heating systems are the single largest source of global CO₂ emissions, with many industrial and agricultural operations relying on coal, bunker fuel, propane, or natural gas. By co-locating Bitcoin mining with heat-dependent industries, the same energy is used twice, resulting in lower overall emissions compared to running two separate systems. The environmental benefits are amplified when Bitcoin miners use clean electricity to power their operations.

Address Food Insecurity

If waste heat is repurposed for food production can help address food insecurity, particularly in regions dependent on imported produce. Locally grown fruits and vegetables, supported by waste heat from mining, offer higher nutritional value, reduce food waste, and improve food reliability in colder climates.

Improve Public Perception of Mining

his not only marks a step toward more sustainable practices in Bitcoin and cryptocurrency mining but also has the potential to reshape public perceptions of the industry’s environmental impact.

How It Works

Repurposing heat from Bitcoin mining involves capturing the low-grade heat (40°C to 80°C) generated by mining hardware and redirecting it for productive uses, such as heating greenhouses, residential spaces, or industrial facilities.

This can be achieved through air-cooling, where heat is dispersed and redirected via fans; immersion-cooling, where mining equipment is submerged in a liquid that efficiently absorbs and transfers heat; or hydro-cooling, where water circulates through specialized cooling systems to extract and transport heat.

Both liquid-based systems—immersion and hydro-cooling—are particularly effective due to the superior heat density and conductivity of liquids compared to air. Bitcoin mining operations are modular and scalable, allowing miners to adjust heat output based on demand, and portable containers make it easy to locate operations near facilities needing heat.

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.

Cisco initiative targets device security

Cisco is announcing a security initiative that will push customers to update or replace aging infrastructure components, such as routers, switches and firewalls, as well as discourage them from using any insecure features. Called Resilient Infrastructure, the plan calls for Cisco to strengthen network security by increasing default protections, removing

What are TPUs? Your guide to tensor processing units and AI acceleration

How TPUs work AI platforms and their underlying ML models require intensive mathematical processing. Fundamentally, TPUs are optimized for a type of mathematical operation known as tensor computation. Tensors are multi-dimensional arrays, or matrixes, that store and process data. Think of them as fundamental data structures or “gears” in machine

NetOps teams struggle with AI readiness

Some 87% of respondents indicated that internet and cloud environments are creating network blind spots in many areas. Half of organizations reported a lack of adequate insight into public clouds, 44% of respondents indicated transit and peering networks created blind spots, and 43% said remote work environments lack visibility. Other

SAP touts Microsoft disaster recovery plan for Europe; Analysts doubt it will work

More risk management than compliance Setting aside the technical concerns, even the attempt to create such a partnership signals that Microsoft is either taking seriously the threat of a kill switch, or at least believes that enterprise executives are concerned enough to make this move to placate them. “This agreement

Russian Oil Giant Recommends Lowest Interim Dividends Since 2020

Russian oil giant Rosneft PJSC plans to pay the lowest interim dividends since the pandemic in 2020 as slumping crude prices, a stronger ruble and looming US sanctions bite. The board of directors of Russia’s largest state-controlled oil producer recommended to pay 11.56 rubles, $0.14, per share in interim dividends, according to a regulatory filing on Thursday. The recommendation comes just a day before unprecedented US sanctions are due to hit Rosneft and fellow Russian oil giant Lukoil PJSC. President Donald Trump’s administration last month stepped up restrictions on Russia’s oil industry, which together with gas accounts for about a quarter of the nation’s coffers. Rosneft’s earnings were already undermined by lower global oil prices amid fears of global surplus and much stronger ruble, with the appreciation of the nation’s currency meaning fewer rubles for each sold barrel. As a result, Rosneft’s net income shrank by 68% in the first half of the year from the same period in 2024. Rosneft, responsible for over a third of the nation’s oil output, has been paying dividends to the state since 1999, and to other shareholders since 2006 when it began trading publicly. The producer started to pay interim dividends in 2017, distributing half of its profit to shareholders. It scrapped the payouts for the first half of 2020 after posting a loss for the period. Lukoil’s board of directors will discuss recommendations on interim dividends on Friday. The oil producer initially planned to discuss nine-month payouts on Oct. 23, but postponed after US announced sanctions against the company on Oct. 22. Some Lukoil units on Friday received extensions to sanctions waivers that the Trump administration imposed. 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

Oil Slips as Peace Talks Advance

Oil fell after Ukrainian President Volodymyr Zelenskiy said he agreed to work on a peace plan drafted by the US and Russia aimed at ending the war in Ukraine. West Texas Intermediate fell 0.5% to settle above $59 a barrel on Thursday, paring some losses from intraday lows following Zelenskiy’s comments. A peace deal, if followed by the elimination of sanctions on Russian oil over its invasion of Ukraine, could unleash supply from the world’s third-largest producer. Oil markets are already staring down expectations for a surplus as OPEC+ and other producers ramp up output, with the commodity heading for a yearly loss amid concerns of a glut. The flurry of renewed activity to end the war comes just hours before US sanctions targeting Russia’s two largest oil companies, Rosneft PJSC and Lukoil PJSC, are due to come into effect. Russia has consistently found a way to sell its sanctioned oil through so-called “shadow” channels. But Moscow’s oil revenue is expected to stagnate amid falling global crude prices, posing a risk to its budget and broader economy. Still, any accord remains far from certain. The US has signaled to Zelenskiy that he should accept the deal drawn up in consultation with Moscow, according to a person familiar. But the plan outlines known Russian demands for concessions that Kyiv has repeatedly said are unacceptable and that have so far hindered any breakthrough in efforts to reach a ceasefire. “Notably, Ukraine is reiterating its openness to discuss ending the war, what’s uncertain is Russia’s real interest in ending the war,” said Rachel Ziemba, an adjunct senior fellow at the Center for a New American Security. “It remains to be seen if Russia is interested in ending the war or just in buying time to reduce more extensive sanctions.” Earlier in the day,

Exxon Lifts Force Majeure on Mozambique LNG Project

Exxon Mobil Corp. lifted a force majeure on its Rovuma liquefied natural gas project in Mozambique as security concerns subside, a key step toward sanctioning the development and committing construction funds. The force majeure was put in place after Islamic State-affiliated militants carried out an attack near its operations in northeastern Mozambique in 2021. Ending the force majeure will allow work to resume and is a crucial step toward Exxon making a final investment decision on the project, which is expected next year. TotalEnergies SE, which is building a separate $20 billion LNG plant nearby, ended its own suspension last month. “We have lifted force majeure for the Rovuma LNG project,” an Exxon spokesman said. “We are working with our partners and the government of Mozambique to ensure the safety of our people and facilities, as we look to develop a world-class LNG project that can help drive economic growth.” The Exxon and TotalEnergies projects are expected to be online by the early 2030s — assuming no further delays — and will enable Mozambique to ship gas around the world for decades. They also promise to transform the country’s economy, one of the world’s poorest, into an energy-export powerhouse. Mozambican President Daniel Chapo is keen to realize those promises and has worked in recent months with Rwandan troops to help secure the Cabo Delgado region. He called the area “relatively stable” in July and urged companies to resume work even if threats remain. “If we’re waiting for Cabo Delgado to be a heaven, we won’t lift force majeure,” he said at the time. Exxon used the delay to refine Rovuma’s design, envisioning it will produce as much as 18 million tons of gas annually, up from the original 15.2 million tons. Partners include China National Petroleum Corp. (CNPC), Abu Dhabi National Oil Co., Seoul’s

North America Drops Rigs Week on Week

North America dropped two rigs week on week, according to Baker Hughes’ latest North America rotary rig count, which was published on November 14. The total U.S. rig count increased by one week on week and the total Canada rig count dropped by three during the same period, taking the total North America rig count down to 737, comprising 549 rigs from the U.S. and 188 rigs from Canada, the count outlined. Of the total U.S. rig count of 549, 527 rigs are categorized as land rigs, 19 are categorized as offshore rigs, and three are categorized as inland water rigs. The total U.S. rig count is made up of 417 oil rigs, 125 gas rigs, and seven miscellaneous rigs, according to Baker Hughes’ count, which revealed that the U.S. total comprises 476 horizontal rigs, 62 directional rigs, and 11 vertical rigs. Week on week, the U.S. offshore and land rig counts remained unchanged, and the country’s inland water rig count increased by one, Baker Hughes highlighted. The U.S. oil rig count increased by three, its gas rig count dropped by three, and its miscellaneous rig count increased by one, week on week, the count showed. The U.S. directional rig count increased by three and its horizontal rig count dropped by two week on week, while the country’s vertical rig count remained unchanged during the period, the count revealed. A major state variances subcategory included in the rig count showed that, week on week, New Mexico added two rigs, Louisiana added one rig, and North Dakota and Oklahoma each dropped one rig. A major basin variances subcategory included in the rig count showed that, week on week, the Permian basin added two rigs, the Cana Woodford basin dropped three rigs, and the Eagle Ford, Granite Wash and Williston basins each

USA EIA Raises WTI Oil Price Forecasts

In its latest short term energy outlook (STEO), which was released on November 12, the U.S. Energy Information Administration (EIA) increased its West Texas Intermediate (WTI) spot average price forecast for 2025 and 2026. According to this STEO, the EIA now sees the WTI spot price averaging $65.15 per barrel in 2025 and $51.26 per barrel in 2026. In its previous STEO, which was released in October, the EIA projected that the WTI spot price would average $65.00 per barrel in 2025 and $48.50 per barrel in 2026. The EIA’s September STEO forecast that the WTI spot price average would come in at $64.16 per barrel this year and $47.77 per barrel next year. A quarterly breakdown included in the EIA’s latest STEO projected that the WTI spot price will average $58.65 per barrel in the fourth quarter of 2025, $50.30 per barrel in the first quarter of next year, $50.68 per barrel in the second quarter, and $52.00 per barrel across the third and fourth quarters of 2026. The EIA’s October STEO saw the WTI spot price averaging $58.05 per barrel in the fourth quarter of next year, $47.97 per barrel in the first quarter of next year, $48.33 per barrel in the second quarter, $48.68 per barrel in the third quarter, and $49.00 per barrel in the fourth quarter of 2026. In its September STEO, the EIA projected that the WTI spot price would come in at $65.14 per barrel in the third quarter of 2025, $55.41 per barrel in the fourth quarter, $45.97 per barrel in the first quarter of next year, $46.33 per barrel in the second quarter, $48.68 per barrel in the third quarter, and $50.00 per barrel in the fourth quarter of 2026. The EIA’s latest STEO showed that the WTI spot price averaged

Phillips 66 to Supply SAF to DHL for Three Years

Phillips 66 has won a three-year contract to deliver over 240,000 metric tons of sustainable aviation fuel (SAF) to DHL Group. “The SAF will be produced at Phillips 66’s Rodeo Renewable Energy Complex in California, one of the world’s largest renewable fuels facilities with a production capacity of 150 million gallons per year of neat SAF (i.e. SAF that is not blended with conventional jet fuel)”, DHL said in an online statement. The bulk of the supply is for the Los Angeles International Airport, “with future intended deliveries to other West Coast airports where DHL maintains operations, such as San Francisco International Airport”, the German logistics giant said. “The agreement with Phillips 66 represents one of the largest SAF deals by a U.S. producer and for the overall air cargo sector, paving the way for future collaborations in the SAF space”, DHL said. The volume represents an avoidance of about 737,000 metric tons of lifecycle greenhouse gas emissions, according to DHL. “DHL Express has been actively securing SAF partnerships worldwide including in the Europe, America and Asia-Pacific regions since 2021, and this new agreement exemplifies its dedication to leveraging sustainable aviation fuels to address its air freight carbon footprint effectively”, DHL said. “This agreement will contribute significantly to DHL’s GoGreen Plus service, which enables customers to reduce their Scope 3 greenhouse gas emissions using SAF”. Phillips 66 vice president for aviation Ronald Sanchez said in a separate statement, “Our integrated model is a competitive advantage that enables resilience and value creation in the SAF market. Our people, capabilities and assets allow for feedstock optionality; our supply chain agility accounts for an evolving environment”. Phillips 66 said that earlier this year it had signed agreements to supply SAF to Alaska Airlines, British Airways, Qantas Airlines and United Airlines. Last year Phillips

Nvidia is flying high: Is there anything left to say?

Supply chain risks, he said, “are numerous in nature; however, it is clear that Nvidia is customer Number One with all of their suppliers, which drives an inordinate allocation of resources to ensure that production flows. Any disruption would likely be materials-based as opposed to a process or labor issue from their vendor base.” He added, “geopolitical events would be the most likely origin of any type of medium to long term disruption, think China-Taiwan, expansion of the Russia-Ukraine conflict, or escalation in the US-China trade war.” For lower impact events, he said, “[Nvidia] does a nice job of setting conservative shipment goals and targets for Wall Street, which they almost invariably beat quarter after quarter. This provides some cushion for them to absorb a labor, process, or geopolitical hiccup and still meet their stated goals. Shipment volumes may not exceed targets, but shipments would continue to flow; the spice must flow after all.” In a worst-case scenario where shipments are materially impacted, there is little recourse for enterprises that are not large-scale cloud consumers with clout with the limited providers in the space, Bickley added. Enterprises joining a ‘very long queue’ According to Sanchit Vir Gogia, the chief analyst at Greyhound Research, the Nvidia earnings call “confirms that the bottleneck in enterprise AI is no longer imagination or budget. It is capacity. Nvidia reported $57 billion in quarterly revenue, with more than $51 billion from data center customers alone, yet still described itself as supply-constrained at record levels.” Blackwell and Blackwell Ultra, he said, have become the default currency of AI infrastructure, yet even at a build rate of roughly 1,000 GPU racks per week, the company cannot meet demand.

Server memory prices could double by 2026 as AI demand strains supply

Limited options for enterprise buyers As supply tightens, most enterprises face limited leverage in selecting suppliers. “Enterprise will have less control over what memory supplier they can choose unless you are a hyperscaler or tier-2 AI datacenter scale enterprise,” Neil Shah, VP for research and partner at Counterpoint Research, told NetworkWorld. “For most enterprises investing in AI infrastructure, they will rely on vendors such as Dell, Lenovo, HPE, Supermicro, and others on their judgment to select the best memory supplier.” Shah advised enterprises with control over their bill of materials to negotiate and lock in supply and costs in advance. “In most cases for long-tail enterprises, smaller buyers without volume leverage, they will have little control as demand outstrips supply, so the prudent thing would be to spread out the rollout over time to average out the cost spikes,” he said. Legacy shortage opens door for Chinese suppliers The current pricing pressure has its roots in production decisions made months ago. According to Counterpoint, the supply crunch originated at the low end of the market as Samsung, SK Hynix, and Micron redirected production toward high-bandwidth memory for AI accelerators, which commands higher margins but consumes three times the wafer capacity of standard DRAM. That shift created an unusual price inversion: DDR4 used in budget devices now trades at approximately $2.10 per gigabit, while server-grade DDR5 sells for around $1.50 per gigabit, according to the firm. This tightness is creating an opportunity for China’s CXMT, noted Shah. “DDR4 is being used in low- to mid-tier smart devices and considering bigger vendors such as Samsung and SK Hynix planned to ramp down DDR4 capacity, CXMT could gain advantage and balance the supply versus demand dynamics moving into the second half of next year,” Shah said.

Cobalt 200: Microsoft’s next-gen Arm CPU targets lower TCO for cloud workloads

These architectural improvements underpin Cobalt 200’s claimed increase in performance, which, according to Stephen Sopko, analyst at HyperFRAME Research, will lead to a reduction in total cost of ownership (TCO) compared to its predecessor. As a result, enterprise customers can benefit from consolidating workloads onto fewer machines. “For example, a 1k-instance cluster can see up to 30-40% TCO gains,” Sopko said, adding that this also helps enterprises free up resources to allocate to other workloads or projects. Moor Strategy and Insights principal analyst Matt Kimball noted that the claimed improvements in throughput-per-watt could be beneficial for compute-intensive workloads such as AI inferencing, microservices, and large-scale data processing. Some of Microsoft’s customers are already using Cobalt 100 virtual machines (VMs) for large-scale data processing workloads, and the chips are deployed across 32 Azure data centers, the company said. With Cobalt 200, the company will directly compete with AWS’s Graviton series and Google’s recently announced Axion processors, both of which leverage Arm architecture to deliver better price-performance for cloud workloads. Microsoft and other hyperscalers have been forced to design their own chips for data centers due to the skyrocketing costs for AI and cloud infrastructure, supply constraints around GPUs, and the need for energy-efficient yet customizable architectures to optimize workloads.

AWS boosts its long-distance cloud connections with custom DWDM transponder

By controlling the entire hardware stack, AWS can implement comprehensive security measures that would be challenging with third-party solutions, Rehder stated. “This initial long-haul deployment represents just the first implementation of the in-house technology across our extensive long-haul network. We have already extended deployment to Europe, with plans to use the AWS DWDM transponder for all new long-haul connections throughout our global infrastructure,” Rehder wrote. Cloud vendors are some of the largest optical users in the world, though not all develop their own DWDM or other optical systems, according to a variety of papers on the subject. Google develops its own DWDM, for example, but others like Microsoft Azure develop only parts and buy optical gear from third parties. Others such as IBM, Oracle and Alibaba have optical backbones but also utilize third-party equipment. “We are anticipating that the time has come to interconnect all those new AI data centers being built,” wrote Jimmy Yu, vice president at Dell’Oro Group, in a recent optical report. “We are forecasting data center interconnect to grow at twice the rate of the overall market, driven by increased spending from cloud providers. The direct purchases of equipment for DCI will encompass ZR/ZR+ optics for IPoDWDM, optical line systems for transport, and DWDM systems for high-performance, long-distance terrestrial and subsea transmission.”

Nvidia’s first exascale system is the 4th fastest supercomputer in the world

The world’s fourth exascale supercomputer has arrived, pitting Nvidia’s proprietary chip technologies against the x86 systems that have dominated supercomputing for decades. For the 66th edition of the TOP500, El Capitan holds steady at No. 1 while JUPITER Booster becomes the fourth exascale system on the list. The JUPITER Booster supercomputer, installed in Germany, uses Nvidia CPUs and GPUs and delivers a peak performance of exactly 1 exaflop, according to the November TOP500 list of supercomputers, released on Monday. The exaflop measurement is considered a major milestone in pushing computing performance to the limits. Today’s computers are typically measured in gigaflops and teraflops—and an exaflop translates to 1 billion gigaflops. Nvidia’s GPUs dominate AI servers installed in data centers as computing shifts to AI. As part of this shift, AI servers with Nvidia’s ARM-based Grace CPUs are emerging as a high-performance alternative to x86 chips. JUPITER is the fourth-fastest supercomputer in the world, behind three systems with x86 chips from AMD and Intel, according to TOP500. The top three supercomputers on the TOP500 list are in the U.S. and owned by the U.S. Department of Energy. The top two supercomputers—the 1.8-exaflop El Capitan at Lawrence Livermore National Laboratory and the 1.35-exaflop Frontier at Oak Ridge National Laboratory—use AMD CPUs and GPUs. The third-ranked 1.01-exaflop Aurora at Argonne National Laboratory uses Intel CPUs and GPUs. Intel scrapped its GPU roadmap after the release of Aurora and is now restructuring operations. The JUPITER Booster, which was assembled by France-based Eviden, has Nvidia’s GH200 superchip, which links two Nvidia Hopper GPUs with CPUs based on ARM designs. The CPU and GPU are connected via Nvidia’s proprietary NVLink interconnect, which is based on InfiniBand and provides bandwidth of up to 900 gigabytes per second. JUPITER first entered the Top500 list at 793 petaflops, but

Samsung’s 60% memory price hike signals higher data center costs for enterprises

Industry-wide price surge driven by AI Samsung is not alone in raising prices. In October, TrendForce reported that Samsung and SK Hynix raised DRAM and NAND flash prices by up to 30% for Q4. Similarly, SK Hynix said during its October earnings call that its HBM, DRAM, and NAND capacity is “essentially sold out” for 2026, with the company posting record quarterly operating profit exceeding $8 billion, driven by surging AI demand. Industry analysts attributed the price increases to manufacturers redirecting production capacity. HBM production for AI accelerators consumes three times the wafer capacity of standard DRAM, according to a TrendForce report, citing remarks from Micron’s Chief Business Officer. After two years of oversupply, memory inventories have dropped to approximately eight weeks from over 30 weeks in early 2023. “The memory industry is tightening faster than expected as AI server demand for HBM, DDR5, and enterprise SSDs far outpaces supply growth,” said Manish Rawat, semiconductor analyst at TechInsights. “Even with new fab capacity coming online, much of it is dedicated to HBM, leaving conventional DRAM and NAND undersupplied. Memory is shifting from a cyclical commodity to a strategic bottleneck where suppliers can confidently enforce price discipline.” This newfound pricing power was evident in Samsung’s approach to contract negotiations. “Samsung’s delayed pricing announcement signals tough behind-the-scenes negotiations, with Samsung ultimately securing the aggressive hike it wanted,” Rawat said. “The move reflects a clear power shift toward chipmakers: inventories are normalized, supply is tight, and AI demand is unavoidable, leaving buyers with little room to negotiate.” Charlie Dai, VP and principal analyst at Forrester, said the 60% increase “signals confidence in sustained AI infrastructure growth and underscores memory’s strategic role as the bottleneck in accelerated computing.” Servers to cost 10-25% more For enterprises building AI infrastructure, these supply dynamics translate directly into

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