Jehovah’s Witnesses buys former IBM campus near Warwick headquarters 3%

By Maria M. Silva0%

8/7/2026, 3:00:00 AM

BS Summary: This article contains 3 faulty reasoning types, including Hasty Generalization and Optimism Bias, with Negativity Bias as the most egregious example at 17% saturation with 88 hits. Analysis detected 147 faulty-reasoning hits from 519 analyzed words, generating a BS Score of 10.1% and a BS Rank of 3% (27,974 of 28,846 articles). This article is better (less manipulative) than 97.00% of the article peer group.

WARWICK  The Jehovah’s Witnesses have purchased a former IBM office and data center near the organization’s world headquarters in Orange County. 
The religious group  through its companies Watchtower Bible and Tract Society of New York  bought the property at 299-300 Long Meadow Road in the town of Warwick from Kyndryl, IBM’s former infrastructure services unit that separated into an independent, publicly traded company in 2021. 
The property spans a 68-acre campus that dates back to 1972 and includes a 433,000-square-foot data center. 
The Jehovah’s Witnesses world headquarters, which opened in 2016 after relocating from Brooklyn, is about a mile away from the site. 
An unnamed spokesperson for the organization based in Putnam County said in an email that planning for the property is still in the early stages and that the religious group is evaluating how the facility will be used “to support our religious activities.” 
The spokesperson declined to provide more details about the site’s intended use  including whether the group will continue operating the existing data center and whether the organization plans to pursue tax exemptions or permits  and referred a Times Union reporter to the organization’s website and a Religion News Service article. 
U.S. 
Jehovah’s Witnesses spokesman Jason Hohl told Religion News Service in July that the organization decided to purchase the neighboring property after it became available about six months ago, citing its proximity to the group’s headquarters and potential to provide for future office, dining and other organizational needs. 
“This property will provide additional flexibility in view of our growing operations,” Hohl said in a release. 
The purchase expands the Jehovah’s Witnesses’ footprint in the Hudson Valley, which includes the Patterson Bethel Visitor Center at the Watchtower Educational Center in Putnam County, the Wallkill Watchtower Farms in Ulster County and the newly opened Fishkill Support Center in Dutchess County, which features medical offices, event spaces, and recreation facilities. 
Jehovah’s Witnesses are also currently building a new media center on a 249-acre property in Rockland County. 
It’s unclear whether Jehovah’s Witnesses intend to continue operating the data center on the campus it bought in Orange County. 
Such facilities  large warehouses that host computer infrastructure requiring vast amounts of energy and water  have emerged as a political focal point across the nation as private companies have poured billions of dollars into their construction to feed the ongoing development of artificial intelligence. 
New York recently became the first U.S. state to pass a moratorium on building new data centers amid strong opposition from environmental advocacy groups and some localities over their potential impacts, fears about AI technology’s impact on jobs, energy and water consumption. 
In a 2023 permit application filed with the state Department of Environmental Conservation, Kyndryl said it would need permission to withdraw up to 130,000 gallons of water a day as an emergency backup to keep the data center’s cooling system operating during interruptions to its normal water supply. 
Hohl told Religion News Service environmental stewardship is “an important consideration in all of our construction and development projects.” 
Article reasoning-pattern comparisonThis article: 0.0%Maria M. Silva: 0.0%Times Union: 2.3%Confirmation Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.7%Anchoring Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 1.8%Availability Heuristic0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.5%Representativeness Heuristic0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.6%Hindsight Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.9%Overconfidence Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 2.4%Framing Effect0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.3%Loss Aversion0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.6%Status Quo Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Sunk Cost Effect0.0%This article: 3.3%Maria M. Silva: 1.6%Times Union: 1.4%Optimism Bias3.3%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.9%Pessimism Bias0.0%This article: 17.0%Maria M. Silva: 8.5%Times Union: 5.8%Negativity Bias17.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 1.4%Self-Serving Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.5%Fundamental Attribution Error0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Actor-Observer Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%In-Group Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.0%Out-Group Homogeneity Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.7%Halo Effect0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Horn Effect0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.9%Recency Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Primacy Effect0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Blind-Spot Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.5%Ad Hominem0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Straw Man0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 1.7%Appeal to Authority0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.4%False Dilemma0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.3%Slippery Slope0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.0%Circular Reasoning0.0%This article: 8.1%Maria M. Silva: 4.0%Times Union: 1.4%Hasty Generalization8.1%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Red Herring0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.2%Bandwagon0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 2.2%Appeal to Emotion0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.5%Begging the Question0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 1.0%Post Hoc (False Cause)0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Tu Quoque0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.6%Burden of Proof0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Appeal to Nature0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Composition/Division0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 1.0%Anecdotal0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%No True Scotsman0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.8%Ambiguity (Equivocation)0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Middle Ground0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.0%Personal Incredulity0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.1%Special Pleading0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.0%Genetic Fallacy0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 1.9%Unattributed Quote0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.8%Quote-first Misdirection0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 1.1%Biased Writer Voice0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.4%Indoctrination0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.0%Politically Right Leaning Bias0.0%This article: 0.0%Maria M. Silva: 0.0%Times Union: 0.2%Attempt to Sell a Product or S…0.0%

519 words analyzed.

Speakers

1speaker16%attributed speech436writer words
Selected voice

Jason Hohl

20%flagged-word coverage
83 attributed words100% of attributed speech20% writer coverage

No manipulation-pattern hits were found in this speaker's attributed words or the writer's voice.

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.