EV nonprofit holds out hope for embattled IRA tax credits

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EV nonprofit holds out hope for embattled IRA tax credits

Dive Brief: National nonprofits dedicated to promoting EVs have urged people in their networks to call their legislators and tell them they have benefited from federal tax credits for purchasing EVs, according to Stuart Gardner, executive director of Generation180. Despite President Donald Trump’s vocal opposition to EVs, his executive order rolling back nationwide EV targets left […]

Dive Brief:

National nonprofits dedicated to promoting EVs have urged people in their networks to call their legislators and tell them they have benefited from federal tax credits for purchasing EVs, according to Stuart Gardner, executive director of Generation180.
Despite President Donald Trump’s vocal opposition to EVs, his executive order rolling back nationwide EV targets left the EV tax credits created by the Inflation Reduction Act intact.
Tax credits for the purchase of EVs may be more difficult to roll back than incentives for EV charging infrastructure—and the loss of infrastructure incentives could have a greater impact on the EV industry than losing the tax credits, according to John Higham of the Electric Vehicle Association.

Dive Insight:

Generation180, the Electric Vehicle Association and other national nonprofits say EV enthusiasts should contact their legislators and talk to them about how they have personally benefited from tax credits for electric vehicles in an effort to head off cuts to incentives that so far remain untouched by the Trump administration.

Despite the executive order ending EV sales targets, the Trump administration has left the 30D tax credit for the purchase of electric vehicles untouched — and revising the tax credit may require an act of Congress that could be difficult to push through the House, Higham said. With new battery and EV plants set to create jobs in states like North Caolina and Georgia, representatives from those districts are likely to feel protective of those particular tax credits, Higham said.

On top of this, the Trump administration has not altered a Biden-era policy on incentives for leasing electric vehicles. Higham described this as a likely oversight on the administration’s part, given that President Donald Trump could easily overturn the policy, which was based on the Biden administration’s interpretation of existing law.

The Trump administration’s spending freeze, which has impacted loans and incentives for EV charging infrastructure, is more concerning to the EV industry, said Higham, citing a Harvard survey of Electric Vehicle Association members that concluded that incentives for EV charging infrastructure have a greater impact on EV adoption than incentives for the vehicles themselves.

“It’s easier to [overturn incentives for charging infrastructure] because the money spigot doesn’t go to thousands of places, only a few dozen,” Higham said. “And it’s not $7,500 checks, it’s $1 million checks. It’s a lot easier to shut off, and it will have more of an impact.”

Even so, Higham and Gardner said, ending incentives for electric vehicles would likely only slow EV adoption, not stop it altogether. Sales of EVs hit a record last year, representing 8% of all new cars, and the vast majority of consumers who make the switch ultimately find they prefer an electric drive train and do not go back to gasoline vehicles with subsequent purchases, Gardner said. Consumers prefer EVs for the convenience and savings of decreased fueling and maintenance, Higham said.

“There are reasons why I say the electrification of transportation is inevitable,” he said. “Being green or better for the environment is what I call a happy coincidence. And I am being a little facetious, but that is not the reason why people are going to start driving electric.”

U.S. automakers are aware of this trend and have invested heavily in new battery and assembly plants — deals they are not likely to abandon, Gardner said. Members of the Electric Drive Transportation Association, including General Motors and Toyota, also called on the U.S. Department of Transporation earlier this month to resume funding for EV charging infrastructure. But Higham said he has heard from automakers who say that if the Trump administration does not reinstate the funds, they will find a way to raise the funds for the chargers themselves.

But the loss of federal incentives for the vehicles could still impact lower-income Americans who will be priced out of the market without the tax credits, Gardner said. Fewer incentives for EVs and manufacturing could cause U.S. companies to fall behind their overseas competitors, he said.

“I think there will always be an interest there to attract the U.S. consumer, but those incentives inspired us to build those factories and develop the workforce and really plan for long-term competitiveness,” Gardner said. “It doesn’t have to be a social change conversation. It can be a topic of innovation and competitiveness, and that’s how I think we should be thinking about EVs.”

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OpenAI–Broadcom alliance signals a shift to open infrastructure for AI

The decision also reflects a future of AI workloads running on heterogeneous computing and networking infrastructure, said Lian Jye Su, chief analyst at Omdia. “While it makes sense for enterprises to first rely on Nvidia’s full stack solution to roll out AI, they will generally integrate alternative solutions such as AMD and self-developed chips for cost efficiency, supply chain diversity, and chip availability,” Su said. “This means data center networking vendors will need to consider interoperability and open standards as ways to address the diversification of AI chip architecture.” Hyperscalers and enterprise CIOs are increasingly focused on how to efficiently scale up or scale out AI servers as workloads expand. Nvidia’s GPUs still underpin most large-scale AI training, but companies are looking for ways to integrate them with other accelerators. Neil Shah, VP for research at Counterpoint Research, said that Nvidia’s recent decision to open its NVLink interconnect to ecosystem players earlier this year gives hyperscalers more flexibility to pair Nvidia GPUs with custom accelerators from vendors such as Broadcom or Marvell. “While this reduces the dependence on Nvidia for a complete solution, it actually increases the total addressable market for Nvidia to be the most preferred solution to be tightly paired with the hyperscaler’s custom compute,” Shah said. Most hyperscalers have moved toward custom compute architectures to diversify beyond x86-based Intel or AMD processors, Shah added. Many are exploring Arm or RISC-V designs that can be tailored to specific workloads for greater power efficiency and lower infrastructure costs. Shifting AI infrastructure strategies The collaboration also highlights how networking choices are becoming as strategic as chip design itself, suggesting a change in how AI workloads are powered and connected.

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Cisco seriously amps-up Silicon One chip, router for AI data center connectivity

Some say deep buffers shouldn’t be used to handle this type of traffic; the contention is that these buffers fill and drain, creating jitter in the workloads, and that slows things down, Chopra told Network World. “But the real source of that challenge is not the buffers. It’s a poor congestion management scheme and poor load balancing with AI workloads, which are completely deterministic and predictable. You can actually proactively figure out how to place flows across the network and avoid the congestion,” he said. The 8223’s deep-buffer design provides ample memory to temporarily store packets during congestion or traffic bursts, an essential feature for AI networks where inter-GPU communication can create unpredictable, high-volume data flows, according to Gurudatt Shenoy, vice president of Cisco Provider Connectivity. “Combined with its high-radix architecture, the 8223 allows more devices to connect directly, reducing latency, saving rack space, and further lowering power consumption. The result is a flatter, more efficient network topology supporting high-bandwidth, low-latency communication that is critical for AI workloads,” Shenoy wrote in a blog post. NOS options Notably, the first operating systems that the 8223 supports are the Linux Foundation’s Software for Open Networking in the Cloud (SONiC) and Facebook open switching system (FBOSS) – not Cisco’s own IOS XR. IXR will be supported, too, but at a later date, according to Cisco. SONiC decouples network software from the underlying hardware and lets it run on hundreds of switches and ASICs from multiple vendors while supporting a full suite of network features such as Border Gateway Protocol (BGP), remote direct memory access (RDMA), QoS, and Ethernet/IP. One of the keys to SONiC is its switch-abstraction interface, which defines an API to provide a vendor-independent way of controlling forwarding elements such as a switching ASIC, an NPU, or a software switch in a uniform

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[ Related: More Intel news and insights ] AMD has lagged behind Nvidia in the AI business but has done well in the federal supercomputing business, holding numerous top spots with supercomputers like El Capitan and Frontier. Manufacturing its chips in the United States would be a good way to get the Trump administration off its back given its push for domestic manufacturing of semiconductors. The Trump administration is pushing for 50% of chips sold in America to be manufactured domestically, and tariffs on chips that are not. It also faces outbound restrictions. Earlier this year, AMD faced export restrictions GPUs meant for China as part of U.S. export controls against China’s AI business. “I believe this is a smart move by AMD to secure capacity in the local market without fighting against Nvidia and Apple and their deeper pockets for the limited capacity at TSMC,” said Alvi Nguyen, senior analyst with Forrester Research.” With the US investment in Intel, followed by Nvidia, this is can be seen as diversifying their supply chain and providing cheaper, locally sourced parts.” For Intel, this will continue a streak of good news it has enjoyed recently. “Having customers take up capacity at their foundries will go a long way in legitimizing their semiconductor processes and hopefully create the snowball effect of getting even more US-based customers,” said Nguyen. In recent weeks, Intel has partnered with Nvidia to jointly make PC and data center chips. Nvidia also took a $5B stake in Intel. Earlier the Trump administration made a $11.1B, or 10%, stake in Intel.

Microsoft will invest $80B in AI data centers in fiscal 2025

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John Deere unveils more autonomous farm machines to address skill labor shortage

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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

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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