The Swedish AI company Berget AI, together with researchers from the Stockholm Environment Institute (SEI) and the organizations WattTime and Klimatkollen, has developed a new method for calculating how much carbon dioxide different AI requests generate.

The aim is to make the AI ​​industry’s climate impact more transparent and give companies the opportunity to compare different suppliers.

According to the method, for example, a typical request to the AI ​​model Kimi K3 results in around 0.4 grams of carbon dioxide emissions in Sweden, 1.6 grams in Ireland, 2.5 grams in Germany, and 3.2 grams in Texas. Despite identical hardware.

Berget AI has published the method as open source, meaning other companies can freely use it. A web-based calculator also makes it possible to compare emissions between different AI models and geographical regions.

The US Federal Bureau of Investigation has removed an Accenture contractor working on its PeopleSoft installation over their role in a data breach that exposed personal details of FBI employees, Reuters reported Monday.

This is the first time that anyone other than the hacker group Shinyhunters that claimed responsibility for the breach has linked it to PeopleSoft.

“To date, our review has determined that the incident occurred as the result of a security failure of a platform managed by a third-party organization — after a contractor failed to implement a security patch explicitly issued to secure the platform,” FBI Cyber Division Assistant Director Brett Leatherman told Reuters. “As such, the FBI has removed the contractor and taken all necessary steps to both mitigate any further risk and protect our workforce.”

Accenture was the third-party organization Leatherman would not identify, “two sources familiar with the matter” told Reuters.

As for the unnamed, unpatched platform, Reuters’ sources said that was Oracle PeopleSoft, the HR system that hacker group ShinyHunters claimed to have exploited in last month’s break-in.

This article first appeared on CSO.

A high school teacher had three girls sign up for her computer science class—three for the whole year. Then a group of ConocoPhillips technologists came in to talk about their careers, and more than 20 students registered. The school had to add a half-day CS teacher to handle them.

That is Girls Talk Tech working exactly as designed. A group of women in the ConocoPhillips IT organization formalized the program in 2018 with a clear mission: educate, encourage, and empower young women to embrace technology for the future. The gap it addresses is well documented. According to the National Center for Women & IT, women make up 57% of the US professional workforce and hold only 26% of IT roles, and the disparity widens in engineering and computing.

“It’s really an outreach education program,” says Billy-Joe Lafortune, general manager of Lower 48 Digital Technologies and Global Strategy at ConocoPhillips. “It’s about reaching out to young women in middle school and high school, and it’s about inspiring them and preparing them to consider or pursue a career in technology and STEM.”

Lafortune has been a co-sponsor since 2019, when the team asked her to take the role alongside the CIO at the time. Her day job spans two responsibilities: she is the business unit technology leader for ConocoPhillips’ Lower 48 segment, the largest piece of the portfolio, and she leads global digital strategy, deciding where the investments go and then delivering them. She reached that seat over two decades at the company, through IT leadership roles in the Canadian business unit and corporate functions. In April, the HoustonCIO chapter named her the 2026 Super Global ORBIE recipient, the category reserved for leaders at organizations above $7.5 billion in revenue with multinational operations.

How the program runs

Girls Talk Tech works in three tiers of depth.

School panels are the primary channel and the highest volume. Volunteers visit local schools, share their own career journeys, walk through the STEM pathways a student might take, field questions about college, and bring food and swag.

For a more immersive experience, students are invited to short on-campus events that typically span one or two days and provide hands-on time with real technology, breakout sessions and conversations with practitioners from cybersecurity, software development and other disciplines.

ConocoPhillips

Houston’s GTT Fest, a two-day event, draws close to 200 students. The deepest level of engagement comes through summer programs, where a small cohort of students spend about a week participating in mentorship, real-world projects and a capstone presentation back to the business. All programs are offered at no cost to participants.

Ninth year in, the numbers describe something that found demand rather than manufactured it. More than 7,000 girls reached. Three established affiliate programs in Houston, Bartlesville and Calgary. Pilots running in Midland and Equatorial Guinea. Twenty-plus global committee members and more than 150 volunteers.

“Schools want this, teachers want this, we’re seeing parents want this,” Lafortune says. “There’s this thing that’s drawing people in. And we’re not the only program like this out there. There are a lot of companies and nonprofits doing this, which I think is incredible, and the work has to continue. Clearly the gap still exists.”

She frames the case in pipeline terms rather than charitable ones. “It helps build the pipeline of talent needed for an AI-driven economy,” she says. “It’s making sure we have people equipped with the foundational skills, and that they participate rather than just consume.”

As AI plays an increasingly important role, it can help organizations improve productivity, decision quality and business performance. But the opportunity is not simply to use new tools. It is to help people develop the skills and judgment to apply them thoughtfully. “It’s more important than ever to bring young women on this journey so they can be part of building that future as we go along.”

The full loop

The story Lafortune returns to is Maddie’s. Maddie attended Girls Talk Tech events as a high school student, went on to earn a technology degree, and was hired into a ConocoPhillips rotation program. She is now a cybersecurity analyst at the company and sits on the Girls Talk Tech Houston committee. Her journey illustrates the human side of technology transformation: technical capabilities may continue to evolve, but expertise, mentorship and human judgment remain essential.

“She’s on the other side of the journey now,” Lafortune says. “She’s sharing her perspective, she’s mentoring students and she’s demonstrating what’s possible. For me that really hits home.”

Lafortune’s own role is sponsorship in the working sense of the word: air cover, prioritization and the executive weight that keeps a volunteer organization from drifting. That last piece is harder than it sounds. “It just keeps growing year over year, to the point where if we’re not careful this becomes a full-time job,” she says. “So, we really do have to prioritize how much we can do in a year and still have the impact we’re seeking. And everyone does it because they love it. They love to give back.”

Rationing volunteer capacity across three countries so a program stays effective while demand outruns supply is, functionally, portfolio management. It is the same work she does on the day job, in an arena where nobody has to do what she asks.

What leading something like this does for you

There is a secondary benefit here worth naming, because most technology executives underweight it.

Marketplace eminence is one of the dimensions we assess when we work with leaders on CIO readiness, and it is consistently the one they have thought about least. It is what puts you on a search firm’s short list before a role is posted, what makes a strong candidate return a recruiter’s call, and what earns you the benefit of the doubt in a board conversation.

Most of the ways leaders build it are good ways. Speaking on a panel puts your point of view in front of peers and gets it into circulation. Customer advisory boards, industry forums and analyst relationships all do real work, and the executives with the strongest reputations tend to do plenty of each.

Leading a community program complements those channels. It compounds, because you are there for years. It is genuine operating experience, since a program at this scale means governance, budget, volunteer labor and stakeholders across three countries who do not report to you. And it gives you something specific to say when the panel invitation comes, which is often the difference between an appearance people remember and one they do not.

Lafortune did not take this on as a career move. She took it on because she had seen what early access does. “It’s almost like you’re creating that spark to help people see what is actually possible,” she says. The standing came anyway, which is rather the point.

Call to action

If someone in your industry were asked tomorrow what you are known for beyond your own company, what would they say?

If the answer is thin, find the cause you would show up for even if nobody noticed and take a real leadership position in it. A STEM pipeline program. A workforce reentry initiative. A nonprofit that needs a technology strategy and cannot afford one. The cause matters less than the commitment, because the returns arrive slowly and they arrive as relationships, reputation and operating range you cannot buy or borrow.

Lafortune put it more plainly than I can. “The reality is that the future will be shaped by who shows up,” she says. “Programs like this help ensure who gets into the room, and whose voices are heard.”

That is true of the students. It is also true of the leaders who decide to show up for them.

I have spent enough time building cloud and AI infrastructure to know one thing: Efficiency problems never show up where teams expect them. They tend to sit just beneath the surface, quietly shaping outcomes long before they appear in the metrics anyone is tracking. Most enterprise conversations still center on token pricing. That focus makes sense. The per-unit cost of using models has fallen quickly, and organizations want to understand whether AI can scale without pushing budgets out of bounds.

That question has become more complicated than it looks. At the same time, the nature of each interaction has changed. What used to resemble a straightforward exchange now involves retrieval and ongoing reasoning that unfolds across multiple steps. Even with lower unit costs, total consumption continues to rise because each request requires more underlying work. This creates a contradiction. Tokens are cheaper, yet overall compute demand keeps increasing. That compute demand has to go somewhere, and where it goes is a growing problem. 

The answer, increasingly, is the power grid. The International Energy Agency has warned that AI and data centers are becoming a major source of electricity demand, and Goldman Sachs Research has projected that data center power demand could rise 165% by 2030 compared with 2023 levels. Today, electricity consumption from data centers already amounts to roughly 415 terawatt hours, or about 1.5% of global electricity use, and has been growing at roughly 12% annually over the past five years. Much of the response has focused on models or hardware. Both matter, but they do not explain the full picture. In enterprise environments, a meaningful share of inefficiency begins before a model processes anything at all. It begins in the data layer.

The paradox: Cheaper tokens, more total compute

I have seen this pattern play out before. When unit costs drop, usage expands and everyone is surprised, every time. AI is following that same trajectory.

Lower token pricing has made it easier to integrate models into more workflows. Those workflows have also become more involved. Systems retrieve broader context and revisit outputs before returning a result. The additional activity outweighs the savings from lower pricing. In many cases, a system prompt alone can consume 2,000–5,000 tokens before a user even contributes input, which means the baseline cost of each interaction has already expanded. This is no longer just a financial consideration. Increased compute demand brings increased energy consumption, which is becoming a central constraint for enterprise AI.

Why agentic AI changes the energy equation

Average load: It rarely tells the full story. A platform can appear stable while quietly absorbing constant internal work that never surfaces to users. Agentic AI introduces that kind of persistent demand.

An agent continues operating in the background even after a user interaction ends. It checks for new inputs, revisits its internal state, then prepares the next action. That ongoing loop changes how infrastructure is consumed. Instead of handling activity in bursts, systems begin to carry a steady baseline load. Compute usage becomes continuous rather than intermittent.

This shift exposes inefficiencies that might otherwise go unnoticed. A slow query or a fragmented data source affects far more than a single request. The same issue repeats continuously, which increases both cost and energy consumption over time. This matters even more as inference workloads scale, with projections suggesting they could account for more than 40% of data center demand by 2030.

The overlooked source of energy growth: The data layer

In almost every architecture review I’ve sat through, attention gravitates toward model performance or infrastructure spend. The data layer gets treated as an afterthought. That assumption falls apart the moment you move into retrieval-heavy AI. 

Agents depend on access to reliable context. They pull information from operational systems as well as analytical platforms. When those sources are disconnected, each interaction requires additional effort to assemble a usable view.

That effort accumulates quickly. Data must first be located before it can be used. It often needs to be moved into another environment, then reshaped into a format the model can process. Only after that does it become useful for decision-making. Each step introduces overhead that consumes compute and energy. In practice, engineers can spend up to 40% of their time preparing data before it is even usable for downstream systems. The model remains the most visible part of the system, but the surrounding data work often determines how much energy the system ultimately uses.

Fragmentation as the root cause

Enterprise architectures evolve over time, and fragmentation usually reflects a series of reasonable decisions rather than a single mistake. The challenge appears when AI systems need to operate across all environments at once.

Fragmentation forces repeated work. Systems retrieve overlapping datasets because no single source is trusted as authoritative. Pipelines reprocess information that already exists elsewhere. Teams build parallel structures instead of relying on shared ones.

This pattern increases demand in ways that are easy to overlook. When retrieval becomes inconsistent, applications compensate by sending more context than necessary. Models then process larger inputs, which increases token usage without improving the quality of the outcome. What appears to be a model efficiency issue often traces back to data architecture. It also helps explain why as many as 60% of AI projects are abandoned before reaching production, often due to gaps in data readiness rather than model capability.

Why sovereign, unified architecture changes the math

I have seen organizations improve efficiency without changing models simply by reducing friction in how data is accessed. The good news is that the fix is closer than most teams assume. 

A more unified architecture shortens the path between a question and the data needed to answer it. When systems can access authoritative data directly, they avoid repeated transformations and unnecessary duplication. Retrieval becomes more precise, which allows models to operate with less excess input.

This does not require consolidating everything into a single system. Enterprises will continue to operate across multiple environments. The objective is to reduce unnecessary movement and make data easier to use wherever it resides. Sovereignty and control play an important role as well. Organizations operating across different environments need a clear understanding of where data resides and how it can be used. When governance is built into the architecture, systems spend less time reconciling access and more time producing results. Reducing friction at this layer has a direct effect on both cost and energy use.

What 2026 will expose

The next phase of enterprise AI will shift attention from access cost to operating cost. Early efforts focused on whether organizations could use advanced models. The next stage focuses on whether those systems can run continuously across real workflows without creating unsustainable demand.

That shift will make energy consumption more visible. It will also make inefficiencies harder to ignore. Some increase in demand reflects real value. Systems that improve decision-making or streamline operations will naturally require more compute. The more difficult question is whether additional consumption reflects useful work or avoidable overhead.

When systems repeatedly move and reprocess the same data, the increase in energy use does not correspond to better outcomes. It reflects architectural inefficiency. CIOs will need to examine that distinction more closely.

The takeaway for tech leaders

Before adding infrastructure, ask an honest question: Is this capacity supporting real growth, or is it just hiding inefficiencies that should have been fixed first? Start with the data layer.

Review how systems retrieve context. Look for duplication across environments. Identify where data is repeatedly transformed before it becomes usable. These patterns reveal whether the architecture is enabling efficient AI or creating unnecessary demand.

Enterprise systems will always involve complexity. The objective is to ensure that complexity does not translate into avoidable work. In the agentic era, the efficiency of AI systems is closely tied to how much work happens before the model produces an answer. Data architecture plays a central role in determining whether that work remains controlled or expands beyond what is necessary.

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