Texas has spent the past decade building one of the most data center-friendly policy environments in the United States. But the state’s political posture is tightening. The emerging message from Austin is that continued data center growth will face greater scrutiny over grid costs, water use, tax incentives and community impacts. What is interesting about this policy conversation is that the Texas Legislature is not in regular session. The 89th regular session ended June 2, 2025, and the 90th Legislature does not convene until January 12, 2027. What has occurred instead is a concentrated period of interim committee work, gubernatorial recommendations, implementation of Senate Bill 6, calls for a special session, and regulatory action by the Public Utility Commission of Texas and the Electric Reliability Council of Texas. Together, those efforts are creating the framework for a broader legislative debate in 2027 while already affecting projects seeking ERCOT interconnection, infrastructure costs and site-selection decisions. Abbott Sets Out a New Policy Framework The policy shift accelerated June 10, when Gov. Greg Abbott directed the PUCT to require data centers to fully fund the electric infrastructure needed to serve their operations and directed PUCT and ERCOT to identify additional actions available under existing authority. Separately, Abbott pledged to work with lawmakers in 2027 on legislation requiring data centers to add electric capacity, use water-efficient cooling systems, report electricity and water use, phase out outdated tax incentives and adopt additional protections for neighboring communities. The most consequential shift began June 10, when Gov. Greg Abbott sent state electricity regulators a sweeping list of data center policy priorities. Abbott called for future legislation requiring new facilities to add generation to the Texas grid, pay the full cost of their interconnection and related infrastructure, use closed-loop or similarly water-efficient cooling systems, and file annual reports

Making Compute Underwritable Huang expanded the argument a day later in an NVIDIA blog describing AI factory compute as an emerging investable asset class. NVIDIA’s case begins with a definition. The company does not describe its compute platform simply as a GPU. It includes accelerated computing, networking, systems software, AI frameworks and the CUDA software ecosystem surrounding the hardware. That wider platform matters to the financing thesis because NVIDIA argues it increases the number of potential users for an installed AI system. An NVIDIA DSX AI factory could support language models, vision, speech, biological computing, robotics, physical AI and other workloads. The same infrastructure could potentially move among customers, clouds or operators as demand changes. In financial terms, NVIDIA is arguing for fungibility. That could become particularly important to lenders and infrastructure investors trying to determine what happens if an original customer disappears, a contract expires or the economics of a particular workload change. A GPU cluster tied economically to one speculative tenant is one thing. Compute that can be redeployed across a large global market of clouds, enterprises, AI developers and model providers is a different risk proposition. NVIDIA contends that this breadth of potential offtakers helps protect residual value. Whether institutional markets ultimately price that risk the way NVIDIA hopes remains to be seen. But the company is now explicitly trying to establish a financial framework around that premise. Challenging the Traditional Depreciation Curve NVIDIA’s second argument is that software can extend the economic life of installed hardware. CUDA is central to that case. The company maintains that successive software improvements can increase the performance and efficiency of systems that have already been deployed, allowing the same hardware to produce more useful work at lower cost over time. That does not eliminate hardware obsolescence. New GPU generations continue

When Facts Aren’t Enough Few places offer a more revealing test case than Loudoun County, Virginia. Data Center Alley has spent decades living with data center development at a scale most emerging markets will never approach. Rizer said Loudoun’s experience gives the county an unusually deep record with which to answer questions about environmental impacts, infrastructure and economic benefits. But those facts increasingly struggle to penetrate the broader public debate. Rizer said Loudoun today has more than 250 data centers, while the entire sector uses less than 10% of the county water system. He also pointed to improved air quality over the past decade and approximately $1.2 billion in tax revenue from the industry. Yet he acknowledged that simply producing another data point does little good when residents no longer trust the people presenting it. “I call it community concern whack-a-mole, because every time you address one thing, there are three others that pop up,” Rizer said. The problem, in his view, has become partly emotional rather than informational. “You can’t change how people think until you change how they feel,” he said. “And right now they feel angry, they feel confused, they are fearful, they are mistrustful, both of government and the big tech industry.” That distinction matters. The industry’s instinct has often been to counter criticism with facts: tax receipts, job numbers, water-use calculations, emissions data or explanations of how a particular cooling system works. Those facts remain important. But Rizer’s argument is that the industry must first rebuild enough credibility for communities to hear them. The Industry’s Unforced Errors Not all of the distrust has arrived from outside the industry. Rizer and Waitkunas were equally pointed about mistakes by developers and operators that have given opponents powerful examples to use against data center projects elsewhere. “The industry

AI Density Is Arriving Unevenly Inside the data center, the AI transition remains equally uneven. Uptime’s 2026 survey found the average modal, or most common, rack density across respondents exceeding 11 kW for the first time, up from 9 kW in 2025. But that number requires context. A relatively small group of very high-density facilities pulls the average upward. Without those facilities, Uptime puts average modal rack density at 7.8 kW, only modestly higher than 7.5 kW in 2025. The industry therefore continues to operate two realities at once: a vast installed base running conventional rack densities and a rapidly emerging class of AI facilities pushing far beyond them. The latter is becoming more visible. Some 24% of Uptime respondents now report racks at 30 kW or higher, up from 19% last year. Much of the increase occurred above 50 kW, and some operators reported deployments exceeding 100 kW. Still, most surveyed facilities have no racks at 30 kW or above. AI inference is also moving up the density curve. For the first time in Uptime’s survey, generative AI inference matched AI training as a driver of respondents’ highest-density deployments, with 21% citing each workload. That matters because inference potentially pushes AI infrastructure requirements beyond a relatively concentrated population of model-training campuses and into a broader set of facilities and markets. Power Is Both Constraint and Risk No issue connects the three reports more consistently than power. It limits new site availability. It redirects development toward emerging markets. It shapes community debates. It affects density and cooling architecture. And once a facility is operating, it remains the largest source of outage risk. Uptime says 56% of operators who experienced an impactful outage identified power as the primary cause of their most recent incident. The institute cautions against treating the increase

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

As data centers evolve into AI factories, compute has shifted from a cost center to a revenue driver. “Compute is revenue,” said Jensen Huang, co-founder and CEO of NVIDIA. “Without compute, there is no way to generate tokens. Without tokens, there’s no way to generate revenue. So, in this new world of AI, compute equals revenue.” This reframe changes an organizations’ calculus. If compute is revenue, what do you optimize for? Here are 5 questions to consider: Are you measuring what actually drives AI factory revenue? Most AI factories are power-constrained, so tokens per watt dictate how much revenue you can generate and the cost per token impacts the AI factory profit margin. But neither of these metrics should be evaluated at a single operating point. Batch jobs, real-time chat, and agentic workloads demand different points on the throughput-latency curve. AI chips that perform well at only a few points will underserve the full range of workloads. Additional key operational metrics like time to first token (TTFT), mean time between interruptions (MTBI), and platform useful life are the bedrock of AI factory efficiency. They dictate how quickly an AI factory comes online to generate tokens, the reliability of its revenue streams, and its long-term ability to remain productive as AI workloads evolve. How does agentic AI change what your CPU needs to deliver? Data center CPUs have historically been optimized for parallel throughput, where more cores improve aggregate capacity.  Agentic workloads run in loops and make different demands. The model reasons on the GPU, the CPU executes tool calls such as code compilation and data retrieval, and the result returns to the GPU so the model can reason again. Every step runs in sequence, gated by the one before it. Per-core performance and memory latency determine how fast each step