Townhall⁠84%

John Fetterman Has Just Two Words for New York Following Gov. Hochul's Data Center Moratorium ⁠69%

By Amy Curtis⁠89%

7/15/2026, 1:30:44 AM

BS Summary: This article contains 23 faulty reasoning types, including Appeal to Emotion, Pessimism Bias, and Confirmation Bias, with Negativity Bias as the most egregious example at 37.7% saturation with 116 hits. Analysis detected 895 faulty-reasoning hits from 308 analyzed words, generating a BS Score of 62.4% and a BS Rank of ⁠69% (6,776 of 21,887 articles). This article is worse (more manipulative) than 69.00% of the article peer group.

New York Governor Kathy Hochul was the first governor in the nation to issue a moratorium on data centers. 
New York is the first state in the nation to enact a moratorium on data centers, pausing construction on new facilities for one year. 
An executive order by Gov. 
Kathy Hochul bans state lawmakers from approving environmental permits for hyperscale data centers. 
Hochul said Tuesday the pause will give lawmakers time to create a framework to protect residents and the environment. 
Hyperscale data centers contain thousands of computer servers and typically use 50 or more megawatts of power, the equivalent of 50,000 homes, to operate. 
They also require a steady supply of water to keep cool. 
The moratorium only applies to new projects, so ones that were previously approved will still go forward. 
As Townhall LIVE showed back in April, data centers—part of President Trump's AI framework—are critical for beating China in the AI race. 
We do not want China to control AI and the future of technology, and there are ways to address the concerns people have about data centers. 
But Democrats have come out against data centers, and likely because doing so helps our enemies. 
John Fetterman had two words for Kathy Hochul and his fellow Dems applauding this: China wins. 
Yes, it does. 
Never forget that Hochul had a Chinese spy on her staff. 
He's right, again, and not afraid to break with his party. 
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Article reasoning-pattern comparisonThis article: 18.8%Amy Curtis: 8.6%Townhall: 5.3%Confirmation Bias18.8%This article: 7.8%Amy Curtis: 0.9%Townhall: 0.7%Anchoring Bias7.8%This article: 14.9%Amy Curtis: 3.8%Townhall: 2.8%Availability Heuristic14.9%This article: 6.2%Amy Curtis: 0.7%Townhall: 0.8%Representativeness Heuristic6.2%This article: 0.0%Amy Curtis: 0.5%Townhall: 0.7%Hindsight Bias0.0%This article: 0.0%Amy Curtis: 2.1%Townhall: 1.7%Overconfidence Bias0.0%This article: 18.8%Amy Curtis: 10.8%Townhall: 11.2%Framing Effect18.8%This article: 0.0%Amy Curtis: 0.4%Townhall: 0.4%Loss Aversion0.0%This article: 5.5%Amy Curtis: 0.2%Townhall: 0.3%Status Quo Bias5.5%This article: 0.0%Amy Curtis: 0.1%Townhall: 0.1%Sunk Cost Effect0.0%This article: 0.0%Amy Curtis: 1.0%Townhall: 1.4%Optimism Bias0.0%This article: 20.8%Amy Curtis: 2.8%Townhall: 1.9%Pessimism Bias20.8%This article: 37.7%Amy Curtis: 15.5%Townhall: 13.5%Negativity Bias37.7%This article: 9.7%Amy Curtis: 0.6%Townhall: 1.8%Self-Serving Bias9.7%This article: 0.0%Amy Curtis: 3.8%Townhall: 1.8%Fundamental Attribution Error0.0%This article: 0.0%Amy Curtis: 0.1%Townhall: 0.1%Actor-Observer Bias0.0%This article: 15.9%Amy Curtis: 4.7%Townhall: 4.6%In-Group Bias15.9%This article: 0.0%Amy Curtis: 6.0%Townhall: 3.2%Out-Group Homogeneity Bias0.0%This article: 3.6%Amy Curtis: 0.8%Townhall: 1.8%Halo Effect3.6%This article: 0.0%Amy Curtis: 0.7%Townhall: 0.9%Horn Effect0.0%This article: 0.0%Amy Curtis: 0.1%Townhall: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Amy Curtis: 1.2%Townhall: 1.3%Recency Bias0.0%This article: 11.4%Amy Curtis: 0.8%Townhall: 0.6%Primacy Effect11.4%This article: 0.0%Amy Curtis: 0.0%Townhall: 0.0%Blind-Spot Bias0.0%This article: 12.3%Amy Curtis: 8.3%Townhall: 5.0%Ad Hominem12.3%This article: 0.0%Amy Curtis: 4.3%Townhall: 2.0%Straw Man0.0%This article: 11.7%Amy Curtis: 4.2%Townhall: 4.4%Appeal to Authority11.7%This article: 8.4%Amy Curtis: 2.6%Townhall: 1.9%False Dilemma8.4%This article: 0.0%Amy Curtis: 2.5%Townhall: 1.6%Slippery Slope0.0%This article: 0.0%Amy Curtis: 0.2%Townhall: 0.2%Circular Reasoning0.0%This article: 7.1%Amy Curtis: 16.6%Townhall: 9.1%Hasty Generalization7.1%This article: 0.0%Amy Curtis: 0.9%Townhall: 0.6%Red Herring0.0%This article: 0.0%Amy Curtis: 1.6%Townhall: 1.3%Bandwagon0.0%This article: 29.9%Amy Curtis: 9.9%Townhall: 11.1%Appeal to Emotion29.9%This article: 6.2%Amy Curtis: 2.9%Townhall: 2.2%Begging the Question6.2%This article: 0.0%Amy Curtis: 2.9%Townhall: 2.4%Post Hoc (False Cause)0.0%This article: 0.0%Amy Curtis: 1.5%Townhall: 0.6%Tu Quoque0.0%This article: 0.0%Amy Curtis: 2.5%Townhall: 1.5%Burden of Proof0.0%This article: 0.0%Amy Curtis: 0.2%Townhall: 0.1%Appeal to Nature0.0%This article: 0.0%Amy Curtis: 0.2%Townhall: 0.1%Composition/Division0.0%This article: 0.0%Amy Curtis: 2.7%Townhall: 1.4%Anecdotal0.0%This article: 0.0%Amy Curtis: 0.1%Townhall: 0.1%No True Scotsman0.0%This article: 0.0%Amy Curtis: 2.3%Townhall: 2.2%Ambiguity (Equivocation)0.0%This article: 0.0%Amy Curtis: 0.0%Townhall: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Amy Curtis: 0.0%Townhall: 0.1%Middle Ground0.0%This article: 0.0%Amy Curtis: 0.1%Townhall: 0.2%Personal Incredulity0.0%This article: 0.0%Amy Curtis: 0.4%Townhall: 0.2%Special Pleading0.0%This article: 0.0%Amy Curtis: 0.6%Townhall: 0.4%Genetic Fallacy0.0%This article: 3.6%Amy Curtis: 4.4%Townhall: 3.4%Unattributed Quote3.6%This article: 8.8%Amy Curtis: 2.5%Townhall: 3.0%Quote-first Misdirection8.8%This article: 8.8%Amy Curtis: 24.5%Townhall: 16.1%Biased Writer Voice8.8%This article: 13.6%Amy Curtis: 8.1%Townhall: 5.6%Indoctrination13.6%This article: 0.0%Amy Curtis: 0.2%Townhall: 0.2%Politically Left Leaning Bias0.0%This article: 0.0%Amy Curtis: 22.0%Townhall: 13.8%Politically Right Leaning Bias0.0%This article: 9.1%Amy Curtis: 6.1%Townhall: 5.0%Attempt to Sell a Product or S…9.1%

308 words analyzed.

Speakers

4speakers36%attributed speech196writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 100.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageKathy Hochul • 13 words • 0.0% coverageKathy Hochul • 19 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageTownhall LIVE • 22 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageJohn Fetterman • 16 words • 100.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 11 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageTownhall • 28 words • 100.0% coverageWriter's voice • 14 words • 100.0% coverageTownhall • 14 words • 100.0% coverage
Selected voice

Townhall LIVE

100%flagged-word coverage
22 attributed words20% of attributed speech97% writer coverage
0%5.0%10.0%Indoctrination-7.1 ptsWriter: 7.1%Townhall LIVE: 0.0%0.0%Attempt to Sell a Product -7.1 ptsWriter: 7.1%Townhall LIVE: 0.0%0.0%Unattributed Quote-5.6 ptsWriter: 5.6%Townhall LIVE: 0.0%0.0%Quote-first Misdirection-5.6 ptsWriter: 5.6%Townhall LIVE: 0.0%0.0%Biased Writer Voice-5.6 ptsWriter: 5.6%Townhall LIVE: 0.0%0.0%

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.