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Utah’s 4 GW AI Campus Tests the Limits of Speed-to-Power

Back in September 2025, we examined an ambitious proposal from infrastructure developer Joule Capital Partners – often branding the effort as “Joule Power” – in partnership with Caterpillar. The concept is straightforward but consequential: acquire a vast rural tract in Millard County, Utah, and pair an AI-focused data center campus with large-scale, on-site “behind-the-meter” generation […]

Back in September 2025, we examined an ambitious proposal from infrastructure developer Joule Capital Partners – often branding the effort as “Joule Power” – in partnership with Caterpillar. The concept is straightforward but consequential: acquire a vast rural tract in Millard County, Utah, and pair an AI-focused data center campus with large-scale, on-site “behind-the-meter” generation to bypass the interconnection queues, transmission constraints, and substation bottlenecks slowing projects nationwide.

The appeal is clear: speed-to-power and greater control over delivery timelines. But that speed shifts the project’s risk profile. Instead of navigating traditional utility procurement, the development begins to resemble a distributed power plant subject to industrial permitting, fuel supply logistics, air emissions scrutiny, noise controls, and groundwater governance. These are issues communities typically associate with generation facilities, not hyperscale data centers.

Our earlier coverage focused on the technical and strategic logic of pairing compute with on-site generation. Now the story has evolved. Community opposition is emerging as a material variable that could influence schedule and scope. Although groundbreaking was held in November 2025, final site plans and key conditional use permits remain pending at the time of publication.

What Is Actually Being Proposed?

Public records from Millard County show Joule pursuing a zone change for approximately 4,000 acres (about 6.25 square miles), converting agricultural land near 11000 N McCornick Road to Heavy Industrial use. At a July 2025 public meeting, residents raised familiar concerns that surface when a rural landscape is targeted for hyperscale development: labor influx and housing strain, water use, traffic, dust and wildfire risk, wildlife disruption, and the broader loss of farmland and local character.

What has proven less clear is the precise scale and sequencing of the buildout.

Local reporting describes an initial phase of six data center buildings, each supported by a substantial fleet of Caterpillar natural-gas generators, with construction beginning “this spring.” Other accounts reference a significantly larger first phase, with entitlement discussions including as many as 32 buildings of roughly one million square feet each, even if only a portion would be constructed in the near term.

These descriptions are not necessarily contradictory. Developers often seek entitlements for a maximum buildout while actual construction proceeds in phased increments based on financing and customer commitments. But the distinction matters. Community impact (particularly around noise, emissions, traffic, and water) will be evaluated based on what is permitted and installed in the near term, not on long-range conceptual buildout plans.

What is clear is that the project’s critical path is not simply data center construction. It is prime power generation.

The Salt Lake Tribune reported that early plans pair each of six buildings with 69 Caterpillar natural-gas generators. At that scale, the site would require hundreds of engines, with community members describing projected sound levels as comparable to “more than 400 semi-trucks idling.” Joule and Caterpillar’s own materials position the campus as a 4-gigawatt development featuring combined cooling, heat and power (CCHP), liquid cooling by design, and a fleet of Caterpillar G3520K generator sets.

This is not a conventional hyperscale construction program. It is the development of a distributed generation campus: foundations for large engine arrays, exhaust and aftertreatment systems, high-voltage switchyards, synchronization controls, fire and life-safety systems, fuel interconnections, and a commissioning process that resembles a utility-scale plant more than a colocation facility.

That shift introduces additional long-lead and integration risks, including:

  • Generator manufacturing capacity and delivery sequencing.

  • Emissions-control configuration and regulatory compliance.

  • Synchronization and islanding controls.

  • Black-start capability and ride-through design.

  • Commissioning under load, particularly before full tenant occupancy.

In short, the engineering challenge extends well beyond compute density. It centers on whether a modular, engine-based generation strategy can be permitted, delivered, and synchronized at multi-gigawatt scale without triggering schedule friction from the very industrial systems that make the project possible.

Construction Logistics: When Rural Scale Meets Industrial Volume

County proceedings reflect immediate concern about the practical realities of building at this scale. Residents raised questions about labor influx, temporary housing, traffic, and the strain placed on a rural road network not designed for sustained heavy industrial movement.

On a 4,000-acre site, construction logistics become a program of their own. Hundreds of large generator units, transformers, switchgear lineups, cooling systems, and potentially battery containers must be delivered, staged, installed, and commissioned. That translates into prolonged heavy trucking, haul-route coordination, road upgrades, laydown yards, pre-assembly zones, and ongoing dust management across miles of internal access roads.

Rural land availability is often cited as an advantage in hyperscale siting. But the same locations frequently lack depth in supporting infrastructure, from workforce housing and emergency response capacity to medical services and road maintenance budgets. That imbalance surfaced directly in public meetings, where residents asked whether the community is equipped to absorb the scale and duration of construction activity being proposed.

Dust and fire risk were raised explicitly in the record. In arid regions, dust affects more than local quality of life; it can degrade construction productivity and equipment reliability, increasing filtration and maintenance requirements for cooling and electrical systems. Fire risk, meanwhile, introduces questions about defensible space, fire-water supply, response times, and whether local emergency services would require expansion to support an industrial campus of this magnitude.

For a traditional data center, these concerns are manageable extensions of site work. For a multi-gigawatt campus anchored by engine-based generation, they become material schedule and community-relations variables.

Emissions Strategy Shapes the Site Plan

County meeting minutes note that the buildings are intentionally spread across the property “due to the emissions and not dispersing them all in one area.” That single comment reveals a great deal about the permitting strategy behind the layout.

At multi-gigawatt scale, particularly with engine-based generation, site geometry becomes an emissions-management tool. Distributing buildings and associated generator blocks across thousands of acres may help manage:

  • Localized pollutant concentration modeling results.

  • Noise contours and setback compliance.

  • Stack and exhaust dispersion dynamics.

  • Regulatory thresholds that can shift when large numbers of emission sources are co-located.

In other words, the physical layout is not driven solely by operational efficiency or campus aesthetics. It may also be structured to navigate air-quality modeling and permitting categories.

There is a trade-off. Spreading structures across a large footprint increases civil and electrical complexity: longer internal roads, extended medium-voltage distribution runs, more trenching, additional switchgear segmentation, and greater redundancy requirements. That raises both capital cost and coordination demands.

But concentration carries its own risk. Clustering large generator arrays can intensify modeled emission “hot spots,” tighten setback constraints, and elevate the regulatory classification of the project.

In this case, the master plan appears to reflect a calculated balance between construction efficiency and emissions dispersion; a reminder that, at this scale, environmental modeling is influencing not just equipment selection, but the geography of the campus itself.

Water Rights vs. Water Reality

Joule’s public case for the project emphasizes water independence. The company has reportedly secured rights to more than 10,000 acre-feet of groundwater annually (over 3 billion gallons) and has stressed that the campus will not rely on a municipal system.

Project materials also point to a closed-loop, direct-to-chip cooling architecture designed to minimize evaporative losses. According to Trellis, engineers estimate the data center would use significantly less water than the alfalfa farming currently supported on the land — potentially as much as 75% less on an annual basis.

In arid regions, however, possessing legal rights does not automatically resolve public concern.

Opposition tends to focus less on annual totals and more on long-term basin health: aquifer drawdown over time, impacts on neighboring wells, drought-cycle variability, and the transparency of monitoring and reporting regimes. There is also a precedent question. If one multi-gigawatt industrial campus can rely on privately controlled groundwater at this scale, others may attempt to follow.

At the state level, scrutiny is increasing. Utah lawmakers have signaled interest in expanding water-use reporting requirements for data centers, reflecting broader concern about transparency and sustainability in water-constrained regions.

The core issue is not simply consumption. It is governance, verification, and public trust in how withdrawals will be measured and managed over decades of operation.

Air Permitting: The Project’s Central Flashpoint

The defining feature of the Utah campus — large-scale, on-site gas-fired generation — is also its most direct environmental vulnerability.

According to Trellis, air permit applications filed with Utah regulators indicate that the initial six-building phase could emit approximately 4,380 tons per year of regulated pollutants (excluding CO₂), including roughly 1,380 tons annually of nitrogen oxides (NOx). Trellis further reports that the projected NOx rate is materially higher than that of Utah’s gas-fueled Lake Side Power Plant, based on EPA data comparisons.

Those figures shift the conversation. This is no longer simply a data center debate; it is an air-quality and industrial-generation discussion.

The Salt Lake Tribune has highlighted a broader concern emerging statewide: when utility interconnection timelines stretch too long, some data center developers are choosing to build generation on-site, effectively relocating power plant emissions closer to new industrial campuses. For environmental advocates, that raises questions about cumulative air-quality impacts and regulatory precedent.

From a construction and delivery standpoint, the risk becomes schedule-driven. Air permitting — including dispersion modeling and determinations around Best Available Control Technology (BACT) or, if triggered, Lowest Achievable Emissions Rate (LAER) requirements — can materially influence equipment configuration. If regulators tighten emissions controls or modeling assumptions late in the process, engine specifications, aftertreatment systems, or operating limits may need to be revised after procurement decisions have been made.

At multi-gigawatt scale, those revisions are not minor adjustments. They can mean redesign, additional capital expenditure, extended lead times, and commissioning delays.

In effect, the project’s speed-to-power advantage hinges on successfully navigating a regulatory pathway more commonly associated with utility-scale generation than with hyperscale data halls.

Noise: Operational Reality, Not Rhetoric

Noise concerns surfaced directly in county proceedings, including discussion of projected decibel levels. The Salt Lake Tribune characterized the anticipated generator output as comparable to “hundreds of idling semi-trucks,” a description vivid enough to resonate well beyond technical modeling.

At the scale proposed, acoustic mitigation becomes a core design requirement, not a secondary engineering detail. Engine enclosures, exhaust mufflers, sound walls, berming, building orientation, and setback distances all influence both compliance and community acceptance.

Noise also carries enforcement implications. Even where permitted limits are met on paper, persistent complaints can lead to additional monitoring, operational restrictions, or pressure to retrofit mitigation measures. For a campus built around continuous engine-based generation, acoustic performance becomes an operational variable with both regulatory and reputational consequences.

From Farmland to Industrial Power Campus

Beyond engineering metrics lies a broader land-use shift. Residents have voiced concern about the conversion of agricultural land, described by Trellis as a family alfalfa operation, into a multi-gigawatt industrial complex.

Even if the data center ultimately consumes less water than the existing agricultural activity, the transformation is not simply volumetric. It represents a permanent change in landscape function: new road networks, fencing, lighting, substations, and generation yards replacing open farmland.

That transition introduces habitat fragmentation, construction disturbance, and long-term industrialization of a rural corridor. For some in the community, the question is not only environmental impact but identity, i.e. whether the region is prepared to redefine itself from agricultural base to energy-intensive digital infrastructure hub.

Power at Utility Scale

Caterpillar’s announcement frames the project as a 4-gigawatt campus incorporating 1.1 GWh of grid-forming battery energy storage, combined cooling, heat and power (CCHP), and what it describes as “diverse fuel sources.” Our earlier coverage reflected those figures. Subsequent reporting from Trellis suggests the broader site could ultimately scale toward 12 gigawatts, depending on entitlement and demand.

Even at the lower bound, 4 GW is not simply a large substation. It is utility-scale generation.

If a meaningful portion of that capacity is installed in early phases, fuel logistics, emissions permitting, and operational oversight become regional planning issues rather than purely site-level considerations.

The design direction outlined publicly includes:

  • Large fleets of Caterpillar natural-gas generator sets.

  • A planned pipeline interconnection (with county minutes referencing the Kern River system and Trellis citing nearby gas infrastructure).

  • Battery storage positioned for load smoothing, firming, and grid-forming capability, with the system described as “permitted to accept cleaner electricity” in the future, including potential fuel cells, geothermal, or small modular reactors.

This approach reflects a broader pattern emerging in AI-oriented campus development: deploy modular gas generation and storage to secure immediate power availability, then pursue incremental decarbonization as alternative supply chains mature.

Trellis cites Utah Clean Energy describing the engine-based approach as less efficient than combined-cycle turbine plants, and therefore potentially more emissions-intensive, while acknowledging that reciprocating engines are deployable today.

That is the central trade-off.

Modular engines and battery systems offer speed, sequencing flexibility, and independence from grid interconnection timelines.

But they also anchor the project in an industrial permitting regime defined by air quality, fuel supply, noise, and long-term emissions intensity; considerations that diverge from the renewables-backed narrative often associated with hyperscale data center expansion.

What Must Be Resolved

For community support to solidify, several questions need clear, enforceable answers.

First, what precisely constitutes Phase I? Whether the near-term build is limited to six buildings or represents the leading edge of a much larger sequence materially changes projected impacts on traffic, emissions, water, and noise. Entitlement scale and construction scale must align transparently.

Second, what are the binding air-permit limits and monitoring protocols? The reported tonnage and NOx comparisons suggest the air program will sit at the center of regulatory scrutiny. Modeling assumptions, control technologies, and compliance verification will define both schedule and public confidence.

Third, what noise mitigation commitments are contractually embedded in the design, and how will compliance be validated over time? Acoustic performance is not theoretical at this scale; it is measurable and enforceable.

Fourth, how will groundwater withdrawals be metered, reported, and made publicly auditable? Legal rights alone do not guarantee community trust in a drought-sensitive basin.

Finally, who absorbs the cost of ancillary impacts, i.e. road upgrades, emergency-response expansion, workforce housing strain? County proceedings suggest these concerns are already embedded in local discourse.

A Test Case for the AI Power Model

In many respects, Joule’s 4,000-acre Utah campus represents more than a single development proposal. It is a case study in the next phase of AI-era infrastructure strategy.

When grid interconnection timelines stretch beyond acceptable delivery windows, developers are increasingly bringing the power plant to the servers.

That shift changes the development equation. The core question is no longer simply whether a data center can be constructed on time and on budget. It becomes whether a utility-scale distributed generation system can be entitled, financed, built, and operated without sustained opposition over air emissions, noise, water use, and land conversion.

Scale does not eliminate local scrutiny. Even in rural settings, multi-gigawatt projects introduce industrial impacts that communities recognize and evaluate accordingly. In that sense, the Utah proposal may foreshadow a broader industry reality: as AI campuses grow to industrial dimensions, they inherit industrial politics.

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From Lab to Gigawatt: CoreWeave’s ARENA and the AI Validation Imperative

The Production Readiness Gap AI teams continue to confront a familiar challenge: moving from experimentation to predictable production performance. Models that train successfully on small clusters or sandbox environments often behave very differently when deployed at scale. Performance characteristics shift. Data pipelines strain under sustained load. Cost assumptions unravel. Synthetic benchmarks and reduced test sets rarely capture the complex interactions between compute, storage, networking, and orchestration that define real-world AI systems. The result can be an expensive “Day One” surprise:  unexpected infrastructure costs, bottlenecks across distributed components, and delays that ripple across product timelines. CoreWeave’s view is that benchmarking and production launch can no longer be treated as separate phases. Instead, validation must occur in environments that replicate the architectural, operational, and economic realities of live deployment. ARENA is designed around that premise. The platform allows customers to run full workloads on CoreWeave’s production-grade GPU infrastructure, using standardized compute stacks, network configurations, data paths, and service integrations that mirror actual deployment environments. Rather than approximating production behavior, the goal is to observe it directly. Key capabilities include: Running real workloads on GPU clusters that match production configurations. Benchmarking both performance and cost under realistic operational conditions. Diagnosing bottlenecks and scaling behavior across compute, storage, and networking layers. Leveraging standardized observability tools and guided engineering support. CoreWeave positions ARENA as an alternative to traditional demo or sandbox environments; one informed by its own experience operating large-scale AI infrastructure. By validating workloads under production conditions early in the lifecycle, teams gain empirical insight into performance dynamics and cost curves before committing capital and operational resources. Why Production-Scale Validation Has Become Strategic The demand for environments like ARENA reflects how fundamentally AI workloads have changed. Several structural shifts are driving the need for production-scale validation: Continuous, Multi-Layered Workloads AI systems are no longer

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GenAI Pushes Cloud to $119B Quarter as AI Networking Race Intensifies

Cisco Targets the AI Fabric Bottleneck Cisco introduced its Silicon One G300, a new switching ASIC delivering 102.4 Tbps of throughput and designed specifically for large-scale AI cluster deployments. The chip will power next-generation Cisco Nexus 9000 and 8000 systems aimed at hyperscalers, neocloud providers, sovereign cloud operators, and enterprises building AI infrastructure. The company is positioning the platform around a simple premise: at AI-factory scale, the network becomes part of the compute plane. According to Cisco, the G300 architecture enables: 33% higher network utilization 28% reduction in AI job completion time Support for emerging 1.6T Ethernet environments Integrated telemetry and path-based load balancing Martin Lund, EVP of Cisco’s Common Hardware Group, emphasized the growing centrality of data movement. “As AI training and inference continues to scale, data movement is the key to efficient AI compute; the network becomes part of the compute itself,” Lund said. The new systems also reflect another emerging trend in AI infrastructure: the spread of liquid cooling beyond servers and into the networking layer. Cisco says its fully liquid-cooled switch designs can deliver nearly 70% energy efficiency improvement compared with prior approaches, while new 800G linear pluggable optics aim to reduce optical power consumption by up to 50%. Ethernet’s Next Big Test Industry analysts increasingly view AI networking as one of the most consequential battlegrounds in the current infrastructure cycle. Alan Weckel, founder of 650 Group, noted that backend AI networks are rapidly moving toward 1.6T architectures, a shift that could push the Ethernet data center switch market above $100 billion annually. SemiAnalysis founder Dylan Patel was even more direct in framing the stakes. “Networking has been the fundamental constraint to scaling AI,” Patel said. “At this scale, networking directly determines how much AI compute can actually be utilized.” That reality is driving intense innovation

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Meta scoops up more of Nvidia’s AI chip output

“No one deploys AI at Meta’s scale,” Nvidia CEO Jensen Huang said in a news release. Meta plans capital expenditure, mostly on data centers and the computing infrastructure they contain, of $115 billion-$135 billion this year — more than some hyperscalers, which rent their computing capacity to others. Meta is keeping it all for itself. This could be bad news for other enterprises, as the demands of the hyperscalers and big customers like Meta is leading to a decrease in the availability of chips to train and run AI models. IDC is predicting that the broader AI-driven chip shortage will have a significant effect on the IT market over the next two years as companies struggle to replace everything from laptops to servers. In particular, businesses looking for Nvidia processors may be forced to look elsewhere.

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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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The bird is a beautiful silver-gray, and as she dies twitching in the lasernet I’m grateful for two things: First, that she didn’t make a

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