Next-gen battery materials manufacturer Group14 Technologies laid off an undisclosed number of workers as tariff uncertainties and new federal policies create significant obstacles for the clean energy sector.

The company, which has facilities in Washington and South Korea, confirmed for GeekWire that it had taken steps on Tuesday to “rebalance our workforce to ensure the long-term resilience and competitiveness of our business.”

Group14 recently disclosed that it was delaying the start of production at its flagship battery materials plant in Eastern Washington’s Moses Lake from an initial target of late 2024 to a start date in early 2026.

“Shifts in demand, and uncertainties in global trade relationships have impacted our industry,” said spokesperson Katie Rolnick by email. “We are taking proactive steps to align our operations with current realities while positioning ourselves for future growth.”

Rolnick added that the company is continuing to support its joint venture in South Korea with electronics company SK materials. That plant will begin operating at full capacity this month and its output will be available to Chinese customers, avoiding the impact of U.S tariffs.

Group14’s headcount is 400 people. The layoffs impacted only its U.S. workforce.

The company has developed a silicon anode material for use in lithium-ion batteries that holds more power and requires less time for recharging. The product is suitable for use in electric vehicles, consumer electronics and utility-scale batteries that can help meet power demand for data centers.

The Senate on Tuesday narrowly passed a massive domestic policy bill that slashes clean energy supports. The Senate’s version of the legislation went slightly easier on battery, nuclear and geothermal projects than the previously approved House version of the bill. It retained a full tax credit for these projects if construction begins before the end of 2033, and phases out the credit completely by 2036.

But the Senate took a harder line on a $7,500 tax credit to cut the cost of EVs for consumers, eliminating the break by the end of September, instead of the House deadline of the end of the year. The two chambers are aiming to finalize the bill this week.

In a GeekWire interview last month, Group14 CEO Rick Luebbe shared areas of optimism for his company’s prospects despite recent headwinds. Luebbe said global battery demand will continue long term and he was hopeful that data centers represent a large, new market for batteries.

“We are behind a whole new opportunity in advanced materials production for all kinds of applications that are really critical,” Luebbe said.

AMC Theatres now warns customers that movies start 25-30 minutes after the listed showtime to account for ads and trailers, "making it easier for moviegoers to know the actual start time of their film screening," reports The Verge. From the report: Starting today, AMC will also show more ads than before, meaning its preshow lineup may have to be reconfigured to avoid exceeding the 30-minute mark. The company made an agreement with the National CineMedia ad network that includes as much as five minutes of commercials shown "after a movie's official start time," according to The Hollywood Reporter, and an additional 30-to-60-second "Platinum Spot" that plays before the last one or two trailers. AMC was the only major theater chain to reject the National CineMedia ad spot when it was pitched in 2019, telling Bloomberg at the time that it believed "US moviegoers would react quite negatively." Now struggling financially amid an overall decline in movie theater attendance and box-office grosses, AMC has reversed course, telling The Hollywood Reporter that its competitors "have fully participated for more than five years without any direct impact to their attendance."

Read more of this story at Slashdot.

The financials are impressive and founder CEO Dylan Field already cashed out $20 million worth of shares last year.

Senate Republicans today passed a sweeping spending bill that narrowly avoided punitive tax measures on renewable energy but still threatens to stall its growth in the US. 

After wrangling over hundreds of amendments for more than 24 hours in a so-called “vote-a-rama” on Monday and Tuesday, Senate Republicans advanced the bill with provisions that make it near impossible for many renewable energy projects to qualify for tax incentives Congress passed in 2022. But a last-minute amendment eliminated a proposal to impose a new excise tax on solar and wind projects, which industry groups and advocates warned could have done even more to cut jobs and raise electricity bills. 

The fate of renewable energy projects — many of them in Republican districts — was a major sticking point that nearly stopped GOP lawmakers from reaching an agreement on the “One Big Beautiful Bill Act” that’s a key pillar of Donald Trump’s agenda. As passed, the Senate bill could have wide-ranging effects on many Americans’ lives, including cuts to Medicaid and food assistance programs, a spectrum auction that could slow down Wi-Fi speeds, massive funding increases for Trump’s mass deportation campaign, and much more. (A 10-year moratorium on state AI laws was dropped at the last minute.) It’s now headed back to the House to vote on any changes that have been made to the text since it passed its own version of the bill in May. 

The fate of renewable energy projects — many of them in Republican districts — was a major sticking point

“There’s this push and pull between [GOP] members who … see the importance of doing things for their constituents, and a kind of ideological argument at a national level” that vilifies wind and solar energy, says Nat Keohane, president of the Center for Climate and Energy Solutions (C2ES).

Clean energy and climate advocates are still outraged by the bill’s slashing of incentives for renewables — on top of other language that gets rid of energy efficiency programs, guts protections for public lands, and winds down tax credits for electric vehicles.  

The bill nearly passed with a measure that could have devastated renewable energy companies. On Friday night, a surprise proposal was added for an excise tax on solar and wind projects. The tax would have essentially penalized developers who failed to meet requirements barring “material assistance from prohibited foreign entities.” In other words, they’d have needed to prove that their supply chains weren’t tainted by any materials or business ties to foreign governments the Trump administration deemed unacceptable — including China, which dominates supply chains for solar components and wind turbines. “It’s almost spiteful,” Keohane says of the plan.

The proposed tax was dropped this morning. But some advocates worry that introducing and removing the excise tax was a red herring, drawing attention away from other measures that could still derail renewable energy projects.  

Most prominently, the bill sets aggressive deadlines for any wind and solar developers hoping to take advantage of Biden-era tax credits for carbon-pollution-free energy. Biden committed the US to slashing greenhouse gas pollution roughly in half from peak levels by the end of the decade under the Paris Agreement. To reach that goal, Congress passed the Inflation Reduction Act (IRA), which expanded tax credits for pollution-cutting technologies including renewables, in 2022. 

Today, the Senate passed language that stipulates that solar and wind projects would need to either start construction within a year of the bill’s enactment or be placed in service by 2027 in order to qualify for IRA tax credits. Many projects would struggle to meet that truncated timeline given the long lead times needed to secure financing and permits and connect to the power grid.

Republican districts actually stand to benefit the most from IRA incentives for new solar and wind farms and factories. The tax bill’s future seemed to hinge largely on getting Sen. Lisa Murkowski (R-AK) on board with these limits, as well as cuts to Medicaid and food assistance programs. Murkowski co-sponsored the amendment to toss out the excise tax on renewables and eventually voted in favor of the bill — giving Republicans the 51-50 vote they needed to succeed (with Vice President JD Vance casting the tiebreaking vote). 

The fact that solar and wind — favorite punching bags of Trump and other right-wing culture warriors — were targeted rather than other carbon-pollution-free sources of electricity, like nuclear reactors, is telling. Trump, who campaigned with support from the fossil fuel industry, has been particularly vitriolic against wind and solar energy. Misleading claims about renewable energy projects harming wildlife and inaccurate claims about wind and solar leading to power outages have been flagged by researchers as leading talking points in disinformation campaigns about climate change.

Secretary of Energy Chris Wright repeated similar tropes in an op-ed he published in the New York Post last week in support of the bill. Before Trump tapped him for his current role, Wright was CEO of Liberty Energy, a major oil and gas service provider that claims that roughly 10 percent of total US primary energy production comes from wells it fracks. Wright also used to sit on the board of a nuclear energy startup with OpenAI CEO Sam Altman. Trump has signed executive orders aimed at speeding the development of new nuclear technologies. 

Tech giants, including Google, Microsoft, Meta, and Amazon, are also banking on nuclear energy to power data centers that are expanding to support the computing needs of AI. The growth of AI, domestic manufacturing, and the electric vehicle industry has led to electricity demand jumping for the first time in more than a decade in the US. 

That issue — on top of the health and climate risks posed by fossil fuel pollution — is a big reason why advocates say this is the worst time to kill new renewable energy projects. It could take years or decades to commercialize new nuclear technologies, but wind turbines and solar panels are already mature technologies.

“At a time when we need new energy more than ever, Republicans are punishing the plentiful wind and solar power that can be quickly added to the grid,” Manish Bapna, president of the nonprofit Natural Resources Defense Council, said in a press release.

Solar and wind together reached a milestone last year, when they produced more electricity than coal for the first time in the US. Gas still makes up the biggest chunk of the US electricity mix, however, at around 40 percent of power generation. 

“The intentional effort to undermine the fastest-growing sources of electric power will lead to increased energy bills, decreased grid reliability, and the loss of hundreds of thousands of jobs,” American Clean Power Association CEO Jason Grumet said in a press release. 

The Senate vote on this bill also reignited Trump’s feud with Tesla CEO Elon Musk. Elon Musk posted on X over the weekend that “a massive strategic error is being made right now to damage solar/battery that will leave America extremely vulnerable in the future” as he renewed calls to create a new political party. 

Not since the smartphone has technology been as truly disruptive as AI.

Which is why there are so many stories about AI changing the workforce. But many organizations are focused on the wrong thing: truncating their talent pipelines by getting rid of junior roles.

This shows a lack of foresight and a big void in leadership, because AI is going to impact every role at every level within an organization. Which means AI adoption has to be a top-down, strategic priority.

In comes the chief AI officer (CAIO).

Just like the creation of the CISO and CDO roles were in response to the 2010s’ need for holistic views of security and data, the CAIO responds to an ever-growing demand for cross-organizational, cross-functional AI leadership, which should include consideration of security and data.

If you answer yes to any of these questions, you likely need a CAIO or otherwise centralized AI leadership:

• Are you increasing your investment in AI?
• Are you unsure which teams are using what AI?
• Are you looking for reusable AI use cases or tooling?
• Are you trying to connect technology and business?
• Are you looking for cross-organizational efficiency?

According to a survey by Pltfrm, a tech recruiting company, 48% of the FTSE 100 now have a designated CAIO or equivalent AI leadership role. Of these, 67% have been appointed in the last two years, with 42% hired in the past 12 months alone.

So, just what is the role of the CAIO? What should a CAIO be in charge of — or not? Read on to learn from those already doing this multifaceted role and why your company would benefit from AI leadership soon.

Where Does the CAIO Sit?

AI is already disrupting the corporate hierarchy.

Everyone I interviewed for this piece has taken a different pathway to this AI leadership role. Top AI roles sit within different departments within organizations, at different levels of the org chart, and go by a variety of names.

According to the team at Pltfrm, who ran the aforementioned CAIO survey, the route to this role most often runs through software engineering, data science or consulting. Holding a Ph.D. is common, likely because the CAIO job title has been found in medical, university and other research institutions for about a decade now.

What’s the difference as the role goes mainstream?

“Here the CAIO isn’t building the next rocket; they’re making sure it doesn’t crash on launch. Their job? To manage AI-related risks (such as ethics), as well as adopt, scale and integrate systems responsibly,” the Pltfrm report read.

“The reality is that most CAIOs will require a mix of both offense and defense,” balancing speed of innovation with security and compliance. This role is a balance of staying ahead in the innovation race, along with protecting your organization from becoming the next bad headline.

At tech companies, AI leadership traditionally sat under the CTO. This is no longer advisable because:

• The CTO heads the engineering organization, while AI is being adopted across all departments.
• AI is about a lot more than technology. An AI strategy requires an understanding of people and processes, too.

This chief role includes more strategic vision and governance responsibilities, considering frameworks for ethics, corporate responsibility, data privacy and compliance. It’s risky to bias toward technical stakeholders.

The CAIO is also increasingly a public-facing role, communicating with both the press and customers about an organization’s AI policy.

“As a manager, a VP, a director, C-suite, you can focus on AI as much as you want until you’re given another task,” Ben Silverman, founder of Seatd IT consultancy, told The New Stack. If you haven’t set up your systems, then any AI transformation strategy will be left behind.

AI leadership, he argued, requires a dedicated C-suite role, with a deep understanding of:

• How to create automation systems.
• How to retain information.
• How to explain information in a way that doesn’t feel overwhelming.

This role that used to report to CIOs or CTOs is now speaking to CEOs, CFOs and full boards, defining return on investment and scaling costs nonlinearly.

How Do You Prepare for AI Leadership?

More than 10 years ago, SAP went through a restructuring to prioritize machine learning (ML). According to Jared Coyle, CAIO for the Americas at SAP, this restructuring was 70% focused on the human aspects.

Then, about 18 months ago, SAP moved its CAIO roles from the CIO office to directly within the regional presidents’ offices, as the role evolved from an IT focus to go-to-market and business enablement. IT enablement at SAP is run at the global level, but since AI factors in so many regional and local data privacy regulations, its CAIO roles are inherently regional.

Coyle’s role considers AI additions both within the SAP product suite and in the user experience of more than 100,000 employees.

For him, a great career path to chief AI officer looks similar to his own:

1. Product: Sitting where the problems are.
2. Consulting: Working close to the customer experience.
3. Sales: Understanding financial and operations perspectives.
4. Leadership: Bringing a full understanding of an organization to then apply AI to it.

“If you work in just one vertical, it creates this limited view of what the organization actually does,” Coyle said. “AI is a topic that transcends legal, government relations, public relations and our implementations.”

Naveena Allampalli has spent the last 15 years focused on the intersection of data, AI and product development across well-regulated industries. Two years ago, she joined CBRE, the world’s largest real estate and investment firm, as the global senior director for generative AI for enterprise architecture and AI solutions.

Her role centers on answering the question, “How do we gain efficiencies and productivities through generative AI [GenAI] in business platforms or in AI and the software delivery life cycle?” she told The New Stack. She also identifies “common solutions that we can implement enterprise-wide.”

As at many enterprises, this AI leadership role has shifted from a centralized AI strategy managed by a data science team, Allampalli said, to each of CBRE’s four business units, figuring out where AI could add value within its own product suite.

More and more AI leadership roles are cutting horizontally across departments like this, prioritizing business problems and then working with architecture, data and software teams to implement AI as part of the solution.

“There’s so much space for innovation with AI. We can use it in new and novel ways,” said Laura Tacho, CTO at DX, at LeadDev’s LDX3 in June. “But, at the end of the day, AI is a tool that needs to help us improve the system. We need to think about using AI at an organizational level if we really want to see the organizational benefits that are being promised.”

This impetus means this role is increasingly elevating to the C-suite.

A CAIO’s First Task: Governance

Every CAIO is different, but they usually share an early priority: setting up company-wide AI guidelines.

While double the previous year, according to the Traliant HR Report on AI, only 60% of companies have an AI acceptable use policy. A first priority of any AI leadership should be to create a GenAI policy, communicate it across their organization and, more frequently, to customers and other external stakeholders.

“My focus is ensuring we have the right policies, procedure and governance practices in place to develop, procure and operate AI responsibly,” said Amanda Muller of her role as chief of responsible AI at Northrop Grumman, the aerospace manufacturer.

In 2020, this led her to create the aerospace manufacturing company’s Responsible AI Working Group. After a few years of research, her team sitting under the chief information and digital office created a set of responsible AI principles, which aimed, she told The New Stack, to align customer stakeholders with industry best practices, compliance and legal requirements, and company values.

Muller’s first priority was to identify the potential risks and mitigations of AI use cases. Then, in 2022, with the deluge of generative AI, Northrop Grumman set up the AI governance board, which she chairs. This multidisciplinary team coordinates subject matter experts in law, compliance, global supply chain, human resources, privacy, cyber security and more, in charge of reviewing and approving or disapproving various AI use cases.

As AI can be included in everything, Muller emphasized that her work is very much focused on the AI that Northrop Grumman develops and procures to run the business, to enable employee productivity and job satisfaction. Currently, there isn’t one chief AI officer at the company of about 100,000 people worldwide. Instead, there is a network of AI experts across technology, information, compliance, cybersecurity, privacy, global supply chain and more.

Two months ago, David Low moved from director of client enablement to the first CAIO at Waracle, an IT consultancy. His role kicked off with a focus on upskilling his own organization of more than 200 developers, designers and strategists, in order to dogfood processes and develop best practices, so “we’re better equipped to be able to consult other people about it,” he told The New Stack.

While OpenAI CEO Sam Altman is arguing that AI is ready for the enterprise, most of Low’s well-regulated enterprise clients are still in what he calls the AI discovery phase, skeptical of its compliance and security.

“Most of our clients are now asking about AI that wouldn’t have done three months ago,” he said. “We’re building up their awareness to understand what they can do and what they can’t do.”

For instance, the European Union’s AI Act requires that, if a technology is classified as high risk — including critical infrastructure, education, employment and law enforcement — then organizations must make things audit-safe and explainable around what the AI is actually doing.

“Everyone goes into the role thinking, I’m going to bring magical, agentic-powered robots that are going to change my business entirely. What you’re probably going to do is automated approvals for a while so people get comfortable with that. Be ready for the human aspect, because you will become a salesperson for making people’s lives better.”

— Jared Coyle, SAP

Add to this Dario Amodei, CEO of Anthropic, creator of the large language model (LLM) Claude, who has publicly said his team doesn’t understand its own models because they’ve become too big for humans to comprehend.

If the LLM providers can’t provide provenance, what hope do enterprises have?

“If you take that to the banking space, they wouldn’t accept it because the risks are too high. They can’t explain what the thing is going to do,” Low said. “So what they’re asking us to do is to figure out how to manage that risk, explain the risk and explain the process that AI is actually going through.”

Thankfully, he said, the EU rules have clearly outlined a “template to judge the risk” and how to address it.

Once this awareness and governance is set up, this CAIO role often pivots to advising departments on running and measuring GenAI experiments, and then helping to facilitate the sharing of lessons learned — and hopefully shared tooling.

Cross-Functional AI: Start With Boring Use Cases

Are you looking for cross-organizational AI use cases? Start with the boring.

The highest measurable impact use case for GenAI adoption in software development is an increase in documentation quality — despite the much bigger focus and investment into AI code generation. At SAP, the most adopted AI use case is scanning and processing business expense receipts.

“The cool AI use cases that are all shown on keynote stages are not the ones that are going to be used. Be ready to work in the boring,” Coyle advised other CAIOs.

“Everyone goes into the role thinking, I’m going to bring magical, agentic-powered robots that are going to change my business entirely. What you’re probably going to do is automated approvals for a while so people get comfortable with that. Be ready for the human aspect, because you will become a salesperson for making people’s lives better.”

It’s human nature for people to be afraid of new systems. The chief role of the CAIO, he said, is to calm nerves and to help people understand how this new technology works.

Leadership should focus on AI use cases that aim to reduce boredom and toil, enabling teammates to focus on problem solving — not to reduce staff.

“I’m more of an ‘enhance with AI’ sort of person,” Silverman said, because AI plus mediocrity will lead to more mediocrity. AI enhancement, he said, is all about “what the person does with that information that matters.”

How To Scale AI Across an Organization

At organizations with tens of thousands of employees, challenges don’t occur in silos.

One common AI use case has enterprises looking at chat as a way to unlock data insights and to build more customizable dashboards. With such horizontal considerations, Allampalli said, AI leadership helps to understand the problem, determine if it even is an AI problem, and then evaluate third-party tooling — with serious security vetting — versus if a solution should be built internally.

“If we have to build internally, then we work with data teams, we work with AI teams, we work with product development teams, and all these teams come together to form a pod to address those solutions,” she said.

She added, “We try to identify and prioritize the problem and also calculate the return on investment of that specific problem we’re trying to solve. Then we also look into, do we have already that problem addressed, instead of jumping into and building that solution.”

When vetting a solution, its potential for reusability is also key. Then, her team considers if it is “a high-priority use case with high value of efficiency gains and productivity for the organization.”

Once you outline what you can or cannot do, Low said, the CAIO’s job becomes about scaling training and education about how to leverage AI.

“All different disciplines have different ways they’re going to use AI. And then they all have to interact with the software development life cycle that one person’s AI will hand off to another person’s,” he said. “We need to get that right, a mixture of baseline education and thinking about how to implement it across the business.”

Next, it’s time for AI leadership to assess the effort and scope of work, prioritizing data dependencies and how to either build or integrate an existing solution into the end-to-end data flow.

The majority of organizations have spent the last two years focused on their GenAI strategy and now they are pivoting to agentic AI considerations. As everyone across an organization comes up with agentic AI use cases, Allamalli recommends AI leadership ask:

• Can we build AI agents for this?
• Does this use case already have workflows that agentic AI can follow?
• Are we clear on the inputs and outputs?
• How much time will it take to build?
• Is it really an agentic AI use case or can GenAI solve this?

However, she advised, the focus of AI leadership should remain business first.

Scaling AI is “not just about applying the new technology,” she said. “First, it’s aligning your business problems directly to the AI solution, to the AI value it’s providing. You want to solve the problems for the business, leveraging the technology.”

Then, the role of the CAIO is to prioritize all these potential AI use cases, aligning them with C-suite stakeholder priorities across the enterprise.

Balancing the AI Budget

Another constant juggle for the chief AI officer is the cost of AI innovation.

“The brilliance in a well-run AI business is understanding the relative ratio of scaling your infrastructure costs to the commercial costs,” Coyle said. “The operational costs. The various infrastructure costs. The human cost, because you have to bring in different talent.”

Right now, the underlying infrastructure at SAP is evolving every six weeks, he said. This rate of change demands a cloud native software development life cycle with templated scalability.

In the face of AI everything, the CAIO helps dictate what’s off the table. For SAP, that’s deciding not to make its own LLMs. SAP has 52 years of highly structured data, Coyle noted — across accounting, customer experience, supply chain and human resources — which means that its financial and environmental costs are much less when training LLMs created by third-party providers.

AI leadership also has to consider how to communicate AI-driven cost changes to existing customers.

No longer is AI relegated to technical teams as it becomes approachable and potentially beneficial to all. This means this new AI role needs to have a unique cross-organizational perspective that only business typically has. The role isn’t easy to fill, but it can help your organization head in the right direction.

As Coyle said, “We have this opportunity, if we do this responsibly, to make the world better.”

The post Do You Need a CAIO? The Rise of the Chief AI Officer in 2025 appeared first on The New Stack.