MS NOW95%

DOGE was a dishonest sham — that still hurt our country0%

By Zeeshan Aleem94%

12/25/2025, 3:00:00 AM

BS Summary: This article contains 19 faulty reasoning types, including Framing Effect, Confirmation Bias, and Appeal to Authority, with Negativity Bias as the most egregious example at 59.6% saturation with 327 hits. Analysis detected 1,639 faulty-reasoning hits from 549 analyzed words, generating a BS Score of 0% and a BS Rank of 0% (0 of 21,887 articles). This article is better (less manipulative) than 100.00% of the article peer group.

Perhaps nothing so succinctly defines the brief and terrible existence of President Donald Trump’s Department of Government Efficiency, which appeared to shutter quietly in November, as much as deception. 
A comprehensive New York Times analysis found some startling data on DOGE that indicate its claims of having slashed government spending were packed with false advertising. 
According to the Times, DOGE’s 13 largest claims about cuts were all incorrect, and many were totally false: 
At the top were two Defense Department contracts, one for information technology, one for aircraft maintenance. 
Mr. Musk’s group listed them as “terminations,” and said their demise had saved taxpayers $7.9 billion. 
That was not true. 
The contracts are still alive and well, and those savings were an accounting mirage. 
Together, those two false entries were bigger than 25,000 of DOGE’s other claims combined. 
Of the 40 biggest claims on DOGE’s list, The Times found only 12 that appeared accurate  reflecting real reductions in what the government had committed to spend. 
When you put all the numbers together, they help explain why federal spending went up even as DOGE chief Elon Musk took a chainsaw to the government’s programs. 
As early as the spring there were signs that the claims of savings in Musk’s “wall of receipts” were riddled with misinformation. 
There was at least some hope that we’d eventually get an accurate accounting of what had been cut as Musk took down and tweaked information in response to call-outs from the media. 
But that didn’t happen. 
Like so much of the Trump era, the hype went unfulfilled. 
It’s hard to assess how much DOGE succeeded at what it set out to do. 
But we do know that it never even came close to achieving its absurd goal of saving $2 trillion. 
Just months into its existence it became clear that DOGE itself was inefficient in the way it executed its cuts  because obstacles and new costs, including lawsuits over its firings, lost productivity, re-hirings and huge amounts of paid leave, were expensive. 
At the same time, it was very clear that “efficiency” was largely a pretext for Trump to cut and discipline the federal civil service in an effort to create a government of unwavering loyalists. 
The chaos DOGE produced along the way undermined the putative goal of fiscal austerity, but it did succeed to some extent in degrading social services and cutting or intimidating government workers who might’ve been less compliant with some of Trump’s more extreme orders. 
As The New Republic summed it up, “DOGE’s legacy is both very stupid and very sad: It decimated the federal workforce, including Social Security personnel at local offices, and made it easier for hackers to access your data. 
The agency tore apart USAID, which resulted in hundreds of thousands of lives lost globally. 
And all this for projected savings—numbers that grew smaller and less ambitious every time Musk mentioned them.” 
That duality of stupid and sad  or as my colleague Hayes Brown has memorably put it, the achievement of “the dumbest dystopia”  is a useful analytic lens for the Trump era. 
We can be thankful that DOGE ran haphazardly into obstacles and that the project demoralized Musk. 
But it nevertheless degraded the government and left our country and the world worse off. 
Article reasoning-pattern comparisonThis article: 0.0%Zeeshan Aleem: 0.3%MS NOW: 0.3%Actor-Observer Bias0.0%This article: 7.3%Zeeshan Aleem: 1.0%MS NOW: 1.2%Anchoring Bias7.3%This article: 0.0%Zeeshan Aleem: 3.6%MS NOW: 3.7%Availability Heuristic0.0%This article: 0.0%Zeeshan Aleem: 0.1%MS NOW: 0.2%Blind-Spot Bias0.0%This article: 42.8%Zeeshan Aleem: 12.2%MS NOW: 7.5%Confirmation Bias42.8%This article: 0.0%Zeeshan Aleem: 0.0%MS NOW: 0.0%Dunning-Kruger Effect0.0%This article: 54.1%Zeeshan Aleem: 13.5%MS NOW: 15.4%Framing Effect54.1%This article: 6.2%Zeeshan Aleem: 5.8%MS NOW: 3.1%Fundamental Attribution Error6.2%This article: 0.0%Zeeshan Aleem: 1.9%MS NOW: 2.2%Halo Effect0.0%This article: 16.8%Zeeshan Aleem: 2.6%MS NOW: 1.3%Hindsight Bias16.8%This article: 0.0%Zeeshan Aleem: 2.0%MS NOW: 1.8%Horn Effect0.0%This article: 6.0%Zeeshan Aleem: 2.6%MS NOW: 3.5%In-Group Bias6.0%This article: 0.0%Zeeshan Aleem: 0.8%MS NOW: 0.7%Loss Aversion0.0%This article: 59.6%Zeeshan Aleem: 21.2%MS NOW: 19.2%Negativity Bias59.6%This article: 5.8%Zeeshan Aleem: 1.7%MS NOW: 2.4%Optimism Bias5.8%This article: 6.2%Zeeshan Aleem: 2.7%MS NOW: 2.5%Out-Group Homogeneity Bias6.2%This article: 0.0%Zeeshan Aleem: 2.5%MS NOW: 2.6%Overconfidence Bias0.0%This article: 0.7%Zeeshan Aleem: 6.0%MS NOW: 3.2%Pessimism Bias0.7%This article: 0.0%Zeeshan Aleem: 0.3%MS NOW: 0.6%Primacy Effect0.0%This article: 0.0%Zeeshan Aleem: 1.0%MS NOW: 1.8%Recency Bias0.0%This article: 0.0%Zeeshan Aleem: 1.2%MS NOW: 1.1%Representativeness Heuristic0.0%This article: 2.9%Zeeshan Aleem: 0.7%MS NOW: 2.4%Self-Serving Bias2.9%This article: 0.0%Zeeshan Aleem: 0.7%MS NOW: 0.9%Status Quo Bias0.0%This article: 0.0%Zeeshan Aleem: 0.3%MS NOW: 0.1%Sunk Cost Effect0.0%This article: 6.2%Zeeshan Aleem: 6.3%MS NOW: 4.7%Ad Hominem6.2%This article: 11.1%Zeeshan Aleem: 2.3%MS NOW: 1.7%Ambiguity (Equivocation)11.1%This article: 0.0%Zeeshan Aleem: 1.9%MS NOW: 2.6%Anecdotal0.0%This article: 26.0%Zeeshan Aleem: 6.5%MS NOW: 5.4%Appeal to Authority26.0%This article: 2.7%Zeeshan Aleem: 9.8%MS NOW: 9.8%Appeal to Emotion2.7%This article: 0.0%Zeeshan Aleem: 0.2%MS NOW: 0.1%Appeal to Nature0.0%This article: 0.0%Zeeshan Aleem: 0.4%MS NOW: 0.9%Bandwagon0.0%This article: 0.0%Zeeshan Aleem: 3.3%MS NOW: 2.4%Begging the Question0.0%This article: 0.0%Zeeshan Aleem: 0.8%MS NOW: 0.9%Burden of Proof0.0%This article: 0.0%Zeeshan Aleem: 0.2%MS NOW: 0.2%Circular Reasoning0.0%This article: 0.0%Zeeshan Aleem: 0.5%MS NOW: 0.3%Composition/Division0.0%This article: 0.0%Zeeshan Aleem: 2.4%MS NOW: 2.2%False Dilemma0.0%This article: 0.0%Zeeshan Aleem: 0.0%MS NOW: 0.0%Gambler’s Fallacy0.0%This article: 6.2%Zeeshan Aleem: 1.2%MS NOW: 1.3%Genetic Fallacy6.2%This article: 11.7%Zeeshan Aleem: 11.1%MS NOW: 8.1%Hasty Generalization11.7%This article: 0.0%Zeeshan Aleem: 0.1%MS NOW: 0.1%Middle Ground0.0%This article: 0.0%Zeeshan Aleem: 0.2%MS NOW: 0.2%No True Scotsman0.0%This article: 0.0%Zeeshan Aleem: 0.4%MS NOW: 0.3%Personal Incredulity0.0%This article: 18.4%Zeeshan Aleem: 3.5%MS NOW: 3.2%Post Hoc (False Cause)18.4%This article: 0.0%Zeeshan Aleem: 0.4%MS NOW: 0.7%Red Herring0.0%This article: 7.8%Zeeshan Aleem: 4.0%MS NOW: 2.2%Slippery Slope7.8%This article: 0.0%Zeeshan Aleem: 0.5%MS NOW: 0.3%Special Pleading0.0%This article: 0.0%Zeeshan Aleem: 1.4%MS NOW: 1.3%Straw Man0.0%This article: 0.0%Zeeshan Aleem: 0.6%MS NOW: 0.6%Tu Quoque0.0%

549 words analyzed.

Speakers

No attributed speakers were identified in this analysis.

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Analysis

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