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Southern’s 17 GW Pipeline Puts AI Power Demand Into Utility Math

The headline number from Southern Company’s latest earnings report is hard to miss: electricity use by data centers across the utility’s system increased 55% in the second quarter compared with a year earlier. But the more consequential numbers may be the ones sitting behind it. Southern now has more than 1.2 GW of operating data […]

The headline number from Southern Company’s latest earnings report is hard to miss: electricity use by data centers across the utility’s system increased 55% in the second quarter compared with a year earlier.

But the more consequential numbers may be the ones sitting behind it.

Southern now has more than 1.2 GW of operating data center load, up by more than 500 MW from a year ago. At the same time, its electric utilities have signed contracts and large-load agreements totaling more than 17 GW by the mid-2030s, with another 8 GW in late-stage development and a prospective pipeline of large industrial and data center projects exceeding 75 GW.

That leaves an enormous gap between the data center megawatts consuming electricity today and the load Southern has contractually positioned itself to serve during the next decade.

For the data center industry, that gap may be the most important part of Southern’s second-quarter story.

It offers a look at how utilities are beginning to convert the AI infrastructure boom from forecasts and campus announcements into contracts, generation procurement, transmission investment and eventually energized capacity.

From Contracts to Megawatts

Southern added roughly 6 GW of contracted large load during the quarter alone.

Alabama Power signed three projects representing about 3 GW, while Georgia Power reached a 25-year agreement to serve OpenAI’s planned project in Effingham County near Savannah. That facility is expected to require approximately 3.2 GW and begin taking electric service in phases in 2028.

The numbers nevertheless require an important distinction.

Seventeen gigawatts contracted does not mean 17 GW will suddenly appear on Southern’s grid. Large data center campuses ramp gradually, often over several years, and Southern executives acknowledged that actual customer ramp schedules do not always match the assumptions made when projects are first approved.

CEO Chris Womack said the company has learned that it must work closely with customers because ramp rates “may not be what was projected” initially.

“The bottom line is the load is very real,” Womack said. “So we know it may not be there initially, but we know that it’s coming.”

That distinction is increasingly important for an industry trying to sort announced capacity from contracted power and contracted power from operating infrastructure.

Southern’s existing 1.2 GW of data center demand provides at least one useful benchmark: actual consumption is already accelerating sharply before most of the newly contracted AI load arrives.

The company said some projects now approaching final contracts would begin ramping in 2028 and continue into the next decade.

Generation Follows the Load

The other side of those contracts is the infrastructure required to serve them.

Southern has already secured approvals for approximately 10 GW of new company-owned generation resources, including thermal generation, batteries and solar, along with hundreds of miles of new transmission. But its latest large-load wins are beginning to push beyond that approved supply.

During the analyst Q&A, Southern CFO David Poroch said the company is now approximately 1 GW oversubscribed in Georgia relative to previously approved capacity, while the newly signed Alabama contracts represent another roughly 3 GW of load that will help determine future generation needs.

Asked whether investors could therefore think in terms of roughly 4 GW or more of additional generation requirements, Poroch said that approach was “directionally correct.”

He then supplied an unusually useful rule of thumb for the capital behind AI electricity demand: roughly $2 billion or slightly more per gigawatt of new generating capacity, depending on the generation resource.

That does not mean Southern has committed another $8 billion of generation spending. Projects must move through competitive RFPs, utility commissions must approve them, and Southern may not own every selected resource.

But the math illustrates the scale now beginning to accompany contracted AI demand.

Southern also emphasized that potential company-owned generation selected through current Georgia and Alabama RFP processes is not included in its existing capital forecast. If selected and approved, spending could begin appearing around 2028 for generation expected to enter service around 2031 and 2032.

In other words, the AI infrastructure capital cycle extends well beyond the construction of the data center itself.

The power contract can drive a second infrastructure cycle involving generating plants, battery systems, substations, transmission corridors, pipelines and financing that starts years before the associated load reaches full operation.

Making AI Load Bankable

Southern’s earnings call also offered a detailed look at how utilities are attempting to protect themselves against another risk: a customer reserving several gigawatts of capacity but ultimately consuming less electricity than expected.

The company’s large-load contracts include minimum bills designed to cover at least 100% of the incremental cost to serve the customer, along with termination payments and collateral requirements.

Poroch said Southern has approximately $21 billion of collateral supporting the full 17 GW contracted portfolio.

Those provisions matter because they change the economics of a delayed data center ramp.

Womack said minimum bills allow Southern to somewhat “decouple” revenue from the precise timing of customer consumption. A facility may take longer to reach its projected megawatt load, but the utility is not relying exclusively on electrons consumed during that ramp to recover the infrastructure costs incurred to serve it.

That makes the emerging large-load contract nearly as important to AI infrastructure deployment as the interconnection agreement itself.

Utilities increasingly need assurance that enormous load requests represent financeable projects rather than speculative queue positions. Data center developers, meanwhile, need enough contractual certainty around future power to finance campuses whose construction timelines may stretch across several years.

Southern is effectively building a financial bridge between those two requirements.

One Gigawatt of Flexibility

The OpenAI agreement adds another potentially important piece to that model.

Of the approximately 3.2 GW OpenAI expects to require in Effingham County, as much as 1 GW will be available as flexible demand response.

During periods of high system demand, Georgia Power will be able to reduce electricity delivered to the facility. Georgia Power says that flexibility can reduce the amount of additional generation the utility ultimately needs to build, producing longer-term savings for other customers.

Southern executives said it is the company’s first demand-response arrangement of this type with a data center, and Poroch indicated that load flexibility is now part of the discussion with other hyperscale customers as well.

The scale is notable.

One gigawatt represents nearly one-third of the OpenAI site’s projected maximum demand. That begins to recast a very large AI campus from a purely inflexible load into something that can participate, at least to a degree, in grid operations.

For utilities confronting unprecedented data center requests, flexibility at that scale could become another variable in the site-selection equation: not simply how much power a campus needs, but how much of that demand can be managed when the grid is stressed.

Georgia Power said OpenAI will pay the full infrastructure and electric-service costs associated with the facility under its 25-year agreement, while financial assurances are intended to protect other customers.

That framework also lands squarely in the growing national debate over whether households and small businesses should bear infrastructure costs created by AI data centers.

The Southeast Gains Gravity

Southern’s comments also reinforce the changing geography of hyperscale development.

Georgia has already emerged as one of the country’s largest data center growth markets, but Womack said large-load momentum is increasingly moving west across Southern’s territory.

Alabama accounted for approximately 3 GW of Southern’s newly contracted load during the quarter, while Womack pointed to 500 MW-scale data center activity in Mississippi and said the company’s broader pipeline reflects increasing interest there as well.

Southern argues that its vertically integrated, state-regulated utility structure is becoming a competitive advantage in that race.

Rather than requiring developers to separately navigate generation, transmission and power procurement across multiple market structures, Southern’s operating utilities can coordinate generation, transmission and distribution through integrated planning and state regulatory proceedings.

Whether that produces faster delivery in every case will depend on generation availability, transmission construction and regulatory execution. But hyperscale development is increasingly following jurisdictions where utilities can provide a credible path not merely to an interconnection agreement, but to the generation required to support it.

Southern’s 75-plus-GW prospective pipeline should therefore not be read as 75 GW of inevitable data center construction. The figure includes industrial projects as well as data centers, and projects remain at different stages of maturity.

What it does show is the intensity of the competition now underway for future power capacity across Georgia, Alabama and Mississippi.

AI Expands the Energy Stack

There is another layer to Southern’s earnings call that extends beyond its regulated utilities.

Southern Power is discussing new long-term energy and capacity agreements as existing power contracts expire, including potential arrangements with hyperscalers. Management is also evaluating brownfield opportunities and uprates at existing assets.

Southern subsidiary PowerSecure, meanwhile, is seeing expansion in distributed generation, backup generation and bridge-power markets—an increasingly relevant business as data center developers search for ways to begin operations before permanent grid capacity becomes available.

Natural gas infrastructure could also grow alongside the load. Southern executives said the generation RFPs in Georgia and Alabama could create additional opportunities for FERC-regulated pipeline investment.

The federal government is already helping finance a substantial portion of Southern’s broader power buildout. In February, the Department of Energy closed a $26.5 billion loan package for Georgia Power and Alabama Power supporting more than 16 GW of firm power and grid improvements, including 5 GW of new gas generation, nuclear uprates and license renewals, battery storage, hydropower modernization and more than 1,300 miles of transmission and grid enhancements.

Seen together, these pieces show why AI infrastructure can no longer be viewed as a data center construction story alone.

AI demand is reaching backward through the entire energy supply chain.

Nuclear Remains Further Out

Even nuclear surfaced in the discussion.

Womack said he believes the United States will need additional nuclear units operating by the mid-2030s, although he was explicit that Southern does not intend to be the next utility to build one.

More interesting for the data center industry was his acknowledgement that Southern is discussing with hyperscalers what role they could play in assuming some of the financial risk associated with future AP1000 projects, including potential construction cost overruns.

Those conversations remain preliminary.

But they illustrate how far the hyperscaler-power relationship is expanding. What began as the procurement of renewable energy and then moved into utility interconnection, nuclear PPAs and behind-the-meter generation is increasingly reaching into questions of who finances and bears risk for the next generation of power infrastructure.

When AI Demand Gets Real

Southern’s second-quarter numbers do not eliminate the uncertainty surrounding data center power forecasts.

Seventeen gigawatts of contracts will take years to materialize. Ramp schedules will change. Some prospective projects will never reach construction. Generation projects still face regulatory, permitting, supply-chain and construction constraints.

But Southern is providing something increasingly valuable in the AI infrastructure conversation: evidence of conversion.

Operating data center load has climbed above 1.2 GW. Another 500 MW-plus has appeared on the system in roughly a year. Six additional gigawatts were contracted in a single quarter. Generation procurement is following. Capital spending could follow that. Contract structures are being redesigned around gigawatt-scale customers. And one of the largest AI projects announced to date is giving the grid access to as much as 1 GW of flexible load.

That is a substantially different stage of development from the first wave of enormous AI power forecasts.

The question is no longer simply whether AI will require tens of gigawatts of additional electricity.

Across Southern Company’s territory, the harder work has begun: deciding which loads are real, what generation will serve them, who will finance it, who assumes the risk, and how quickly contracted megawatts can become operating infrastructure.

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The headline number from Southern Company’s latest earnings report is hard to miss: electricity use by data centers across the utility’s system increased 55% in the second quarter compared with a year earlier. But the more consequential numbers may be the ones sitting behind it. Southern now has more than 1.2 GW of operating data center load, up by more than 500 MW from a year ago. At the same time, its electric utilities have signed contracts and large-load agreements totaling more than 17 GW by the mid-2030s, with another 8 GW in late-stage development and a prospective pipeline of large industrial and data center projects exceeding 75 GW. That leaves an enormous gap between the data center megawatts consuming electricity today and the load Southern has contractually positioned itself to serve during the next decade. For the data center industry, that gap may be the most important part of Southern’s second-quarter story. It offers a look at how utilities are beginning to convert the AI infrastructure boom from forecasts and campus announcements into contracts, generation procurement, transmission investment and eventually energized capacity. From Contracts to Megawatts Southern added roughly 6 GW of contracted large load during the quarter alone. Alabama Power signed three projects representing about 3 GW, while Georgia Power reached a 25-year agreement to serve OpenAI’s planned project in Effingham County near Savannah. That facility is expected to require approximately 3.2 GW and begin taking electric service in phases in 2028. The numbers nevertheless require an important distinction. Seventeen gigawatts contracted does not mean 17 GW will suddenly appear on Southern’s grid. Large data center campuses ramp gradually, often over several years, and Southern executives acknowledged that actual customer ramp schedules do not always match the assumptions made when projects are first approved. CEO Chris Womack said

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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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Mother tongue

“Daddy?” Theo curled against my side in bed. “Where do words go when they die?” I’d orchestrated the bedtime routine flawlessly: bath (taken), teeth (brushed),

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