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Are utility demand response program costs outstripping their benefits in California?

Collin Smith is a regulatory affairs manager at Leap, a company that helps distributed energy resources participate in wholesale markets. California’s electricity prices are skyrocketing. In October, Gov. Gavin Newsom issued an executive order aimed at tackling this issue, calling for the California Public Utilities Commission to evaluate the cost-effectiveness of ratepayer-funded programs and determine […]

Collin Smith is a regulatory affairs manager at Leap, a company that helps distributed energy resources participate in wholesale markets.

California’s electricity prices are skyrocketing. In October, Gov. Gavin Newsom issued an executive order aimed at tackling this issue, calling for the California Public Utilities Commission to evaluate the cost-effectiveness of ratepayer-funded programs and determine where program spending may be outstripping those programs’ benefits. Among the trends worth scrutinizing is the state’s increasing shift towards utility-run supply-side demand response programs.

In 2015, California sub-divided its DR programs, creating a class of programs known as “supply-side DR,” or SSDR. This class of programs allowed DR aggregators to bid demand-side resources directly into California’s wholesale market and earn capacity payments through its Resource Adequacy program. 

The logic was simple: both energy prices in the wholesale market and capacity prices in RA are determined by the laws of supply and demand, producing the most cost-effective pricing for these respective energy products. As a result, using these mechanisms to procure DR resources would naturally direct California’s load-serving entities to procure the least-cost options, supporting California’s longstanding goal of developing DR programs that can cost-effectively meet the needs of the grid

Who pays the price of a program?

Over the last decade, third-party DR providers, or DRPs, have begun participating directly in these markets at scale, but California also allows investor-owned utilities to participate via their own SSDR programs. However, unlike third-party DRPs, the costs for IOU-run programs are largely divorced from market dynamics. Administrative costs for third-party DRPs are covered by their balance sheets, so they cannot be higher than what those DRPs can recover from the market. By contrast, administrative costs for IOU-run programs are recovered directly from ratepayers, so their costs can be whatever the IOU says it needs to run them.

These administrative costs can be significant. For example, Pacific Gas & Electric’s costs for implementing its legacy Capacity Bidding Program in 2024-27 were 9% of its overall CBP budget for those years. The administrative costs for its new Automated Response Technology program (approved in 2023) were even higher, coming in at 12% of the program’s budget. The difference is even larger in objective terms, because ART has a larger budget overall due to the higher incentives it pays participants. 

In total, PG&E is approved to spend $4.76 million in administrative costs over the first four years of ART, more than twice the $2.35 million allocated for CBP over the same period — despite the fact that ART’s load reduction is expected to be roughly the same as CBP’s. Yet, just one year after ART was approved, PG&E requested an additional $1.97 million to expand its CBP program to serve the same customer base targeted by ART. It’s clear that PG&E’s recent proposals are increasing costs to customers; what’s less clear is whether the benefits of these programs are keeping pace.

Moving in the wrong direction

While it’s reasonable for IOUs to seek funding for programs that benefit their customers, rising electricity costs make it critical to ensure these budgets align with actual needs. Already, there’s evidence that this is not the case. For example, PG&E stated that it could fully fund the expansions to its CBP programs with unspent funds from its original budget. Although it’s great that this expansion wouldn’t further increase rates, why was so much of the budget unspent in the first place? And would it not be better to return these unspent funds to customers as a bill credit, as Gov. Newsom requested in his executive order?

The fact is that, because IOU costs aren’t subject to market forces, there’s a greater risk that their costs are higher than needed. Despite this, CPUC policy has steadily been pushing more customers to choose IOU-run SSDR programs over third-party options. Recent CPUC decisions have tied lucrative incentives like Automated DR and the Self-Generation Incentive Program to participation in CBP or certain IOU tariffs, despite the fact that third party options provide similar (if not greater) support to the grid. In fact, the reason PG&E proposed expanding its CBP program in the first place was to accommodate SGIP recipients that were unable to enroll in a DR program that fit the CPUC’s narrow list of “qualified” options. 

In the past, the CPUC has worked to maintain a level playing field between third-party and IOU run SSDR programs, recognizing that greater customer choice is an important component of a cost-effective DR sector. But in a December 2023 decision, the CPUC walked back this principle, stating that “any actions that are meant to prop up the third-party DRPs vis-à-vis the IOUs must also provide cost-effective benefits to ratepayers.” The focus on cost-effectiveness is correct, but the logic is backward. Third-party DRPs are funded by their shareholders and are already subject to competitive pressures that keep their costs low. It’s IOU programs that are required to justify their costs to ratepayers. 

Open competition naturally pushes costs down, a principle that is as applicable to DR as it is to other sections of the economy. California, however, is currently moving in the opposite direction with incentive policies that favor IOUs over third-party DRPs. This pushes customers into DR programs with less accountable cost structures and ultimately requires IOUs to request more ratepayer funds to expand these programs. If the intent of Gov. Newsom’s executive order was to tamp down costs from ratepayer-funded programs, current CPUC policy is doing the opposite.

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BP, Chevron Top US Gulf Lease Sale

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GasBuddy Flags ‘Fresh Multi-Year Low’ for USA Gasoline Price

In a blog posted on GasBuddy’s website this week, the company outlined that the average U.S. gasoline price reached a “fresh multi-year low”. “Gas prices continued to decline in most states last week, while some price-cycling states saw temporary spikes to restore margins,” Patrick De Haan, the head of petroleum analysis at the company, stated in the blog, which was published on Monday. “With the national average falling further, we’re now at multi-year lows heading into Christmas. Diesel prices are also easing, and in the cheapest cities, averages have dipped into the low-$2 range, with a few stations still offering gas under $2 per gallon,” De Haan added. “Barring any major disruptions, prices are likely to stay relatively low into the new year,” he continued. In Monday’s blog, GasBuddy noted that the nation’s average price of gasoline had fallen 5.0 cents over the last week and pointed out that it stood at $2.90 per gallon, “according to GasBuddy data compiled from more than 12 million individual price reports covering over 150,000 gas stations across the country”. “The national average is down 17.6 cents from a month ago and is 7.3 cents per gallon lower than a year ago,” GasBuddy highlighted in the blog. “The national average price of diesel has fallen 5.1 cents in the last week and stands at $3.671 per gallon,” it added. GasBuddy stated in the blog that the most common U.S. gas price encountered by motorists stood at $2.79 per gallon, which it said was down 20 cents from last week. This was followed by $2.89, $2.69, $2.99, and $2.59, GasBuddy highlighted. “The median U.S. gas price is $2.79 per gallon, down four cents from last week and about 11 cents lower than the national average,” GasBuddy said. “The top 10 percent of stations in the

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Oil Price Did Not Shift on Fed Cut

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Aramco, ExxonMobil Mull Petrochemical Complex at Samref

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Naftogaz Seals New PrivatBank Loan for Winter Gas

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CNOOC Ltd Announces 12th Startup in Chinese Waters in 2025

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New Nvidia software gives data centers deeper visibility into GPU thermals and reliability

Addressing the challenge Modern AI accelerators now draw more than 700W per GPU, and multi-GPU nodes can reach 6kW, creating concentrated heat zones, rapid power swings, and a higher risk of interconnect degradation in dense racks, according to Manish Rawat, semiconductor analyst at TechInsights. Traditional cooling methods and static power planning increasingly struggle to keep pace with these loads. “Rich vendor telemetry covering real-time power draw, bandwidth behavior, interconnect health, and airflow patterns shifts operators from reactive monitoring to proactive design,” Rawat said. “It enables thermally aware workload placement, faster adoption of liquid or hybrid cooling, and smarter network layouts that reduce heat-dense traffic clusters.” Rawat added that the software’s fleet-level configuration insights can also help operators catch silent errors caused by mismatched firmware or driver versions. This can improve training reproducibility and strengthen overall fleet stability. “Real-time error and interconnect health data also significantly accelerates root-cause analysis, reducing MTTR and minimizing cluster fragmentation,” Rawat said. These operational pressures can shape budget decisions and infrastructure strategy at the enterprise level.

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Arista goes big with campus wireless tech

In a white paper describing how VESPA works, Arista wrote: The first component of VESPA involves Arista access points creating VXLAN tunnels to Arista switches serving as WLAN Gateways…. Second, as device packets arrive via the AP, it dynamically creates an Ethernet Segment Identifier (Type 6 ESI) based on the AP’s VTEP IP address. These dynamically created tunnels can scale to 30K ESI’s spread across paired switches in the cluster which provide active/active load sharing (performance+HA) to the APs. Third, the gateway switches use Type 2 EVPN NLRI (Network Layer Reachability Information) to learn and exchange end point MAC addresses across the cluster. … With this architecture, adding more EVPN WLAN gateways scales both AP and user connections, to tens of thousands of end points. To manage the forwarding information for hundreds of thousands of clients (e.g: FIB next hop and rewrite) would prove very complex and expensive if using conventional networking solutions. Arista’s innovation is to distribute this function across the WiFi access points with a unique MAC Rewrite Offload feature (MRO). With MRO, the access point is responsible for servicing mobile client ARP requests (using its own mac address), building a localized MAC-IP binding table, and forwarding client IP addresses to the WLAN gateways with the APs MAC address. The WLAN Gateways therefore only learns one (MAC) address for all the clients associated with the AP. This improves the gateway’s scaling from 10X to 100X, allowing these cost effective gateways to support hundreds of thousands of clients attached to the APs. AVA system gets a boost In addition to the new wireless technology, Arista is also bolstering the capabilities of its natural-language, generative AI-based Autonomous Virtual Assist (AVA) system for delivering network insights and AIOps.  AVA is aimed at providing an intelligent assistant that’s not there to replace

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Most significant networking acquisitions of 2025

Cisco makes two AI deals: EzDubs and NeuralFabric Last month Cisco completed its acquisition of EzDubs, a privately held AI software company with speech-to-speech translation technology. EzDubs translates conversations across 31 languages and will accelerate Cisco’s delivery of next-generation features, such as live voice translation that preserves the characteristics of speech, the vendor stated. Cisco plans to incorporate EzDubs’ technology in its Cisco Collaboration portfolio. Also in November, Cisco bought AI platform company NeuralFabric, which offers a generative AI platform that lets organizations develop domain-specific small language models using their own proprietary data. Coreweave buys Core Scientific Nvidia-backed AI cloud provider CoreWeave acquired crypto miner Core Scientific for about $9 billion, giving it access to 1.3 gigawatts of contracted power to support growing demand for AI and high-performance computing workloads. CoreWeave said the deal augments its vertical integration by expanding its owned and operated data center footprint, allowing it to scale GPU-powered services for enterprise and research customers. F5 picks up three: CalypsoAI, Fletch and MantisNet F5 acquired Dublin, Ireland-based CalypsoAI for $180 million. CalypsoAI’s platform creates what the company calls an Inference Perimeter that protects across models, vendors, and environments. F5 says it will integrate CalypsoAI’s adaptive AI security capabilities into its F5 Application Delivery and Security Platform (ADSP). F5’s ADSP also stands to gain from F5’s acquisition of agentic AI and threat management startup Fletch. Fletch’s technology turns external threat intelligence and internal logs into real-time, prioritized insights; its agentic AI capabilities will be integrated into ADSP, according to F5. Lastly, F5 grabbed startup MantisNet to enhance cloud-native observability in F5’s ADSP. MantisNet leverages extended Berkeley Packet Filer (eBPF)-powered, kernel-level telemetry to provide real-time insights into encrypted protocol activity and allow organizations “to gain visibility into even the most elusive traffic, all without performance overhead,” according to an F5 blog

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Aviz Networks launches enterprise-grade community SONiC distribution

First, the company enabled FRR (Free Range Routing) features that exist in the community code but aren’t consistently implemented across different ASICs. VRRP (Virtual Router Redudancy Protocol) provides router redundancy for high availability. Spanning tree variants prevent network loops in layer 2 topologies. MLAG allows two switches to act as a single logical device for link aggregation. EVPN enhancements support layer 2 and layer 3 VPN services over VXLAN overlays. These protocols work differently depending on the underlying silicon, so Aviz normalized their implementation across Broadcom, Nvidia, Cisco and Marvell chips. Second, Aviz fixed bugs discovered in production deployments. One customer deployed community SONiC with OpenStack and started migrating virtual machines between hosts. The network fabric couldn’t handle the workload and broke. Aviz identified the failure modes and patched them.  Third, Aviz built a software component that normalizes monitoring data across vendors. Broadcom’s Tomahawk ASIC generates different telemetry formats than Nvidia’s Spectrum or Cisco’s Silicon One. Network operators need consistent data for troubleshooting and capacity planning. The software collects ASIC-specific logs and network operating system telemetry, then translates them into a standardized format that works the same way regardless of which silicon vendor’s chips are running in the switches. Validated for enterprise deployment scenarios The distribution supports common enterprise network architectures.  IP CLOS provides the leaf-spine topology used in modern data centers for predictable latency and scalability. EVPN/VXLAN creates layer 2 and layer 3 overlay networks that span physical network boundaries. MLAG configurations provide link redundancy without spanning tree limitations. Aviz provides validated runbooks for these deployments across data center, edge and AI fabric use cases. 

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US approves Nvidia H200 exports to China, raising questions about enterprise GPU supply

Shifting demand scenarios What remains unclear is how much demand Chinese firms will actually generate, given Beijing’s recent efforts to steer its tech companies away from US chips. Charlie Dai, VP and principal analyst at Forrester, said renewed H200 access is likely to have only a modest impact on global supply, as China is prioritizing domestic AI chips and the H200 remains below Nvidia’s latest Blackwell-class systems in performance and appeal. “While some allocation pressure may emerge, most enterprise customers outside China will see minimal disruption in pricing or lead times over the next few quarters,” Dai added. Neil Shah, VP for research and partner at Counterpoint Research, agreed that demand may not surge, citing structural shifts in China’s AI ecosystem. “The Chinese ecosystem is catching up fast, from semi to stack, with models optimized on the silicon and software,” Shah said. Chinese enterprises might think twice before adopting a US AI server stack, he said. Others caution that even selective demand from China could tighten global allocation at a time when supply of high-end accelerators remains stretched, and data center deployments continue to rise.

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What does Arm need to do to gain enterprise acceptance?

But in 2017, AMD released the Zen architecture, which was equal if not superior to the Intel architecture. Zen made AMD competitive, and it fueled an explosive rebirth for a company that was near death a few years prior. AMD now has about 30% market share, while Intel suffers from a loss of technology as well as corporate leadership. Now, customers have a choice of Intel or AMD, and they don’t have to worry about porting their applications to a new platform like they would have to do if they switched to Arm. Analysts weigh in on Arm Tim Crawford sees no demand for Arm in the data center. Crawford is president of AVOA, a CIO consultancy. In his role, he talks to IT professionals all the time, but he’s not hearing much interest in Arm. “I don’t see Arm really making a dent, ever, into the general-purpose processor space,” Crawford said. “I think the opportunity for Arm is special applications and special silicon. If you look at the major cloud providers, their custom silicon is specifically built to do training or optimized to do inference. Arm is kind of in the same situation in the sense that it has to be optimized.” “The problem [for Arm] is that there’s not necessarily a need to fulfill at this point in time,” said Rob Enderle, principal analyst with The Enderle Group. “Obviously, there’s always room for other solutions, but Arm is still going to face the challenge of software compatibility.” And therein lies what may be Arm’s greatest challenge: software compatibility. Software doesn’t care (usually) if it’s on Intel or AMD, because both use the x86 architecture, with some differences in extensions. But Arm is a whole new platform, and that requires porting and testing. Enterprises generally don’t like disruption —

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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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