Crypto Critic Maxine Waters’s New Primary Foe Got Over Two-Thirds of Money From Crypto ⁠85%

By Matt Sledge⁠63%

4/18/2026, 9:00:00 AM

BS Summary: This article contains 24 faulty reasoning types, including Negativity Bias, Unattributed Quote, and Appeal to Emotion, with Biased Writer Voice as the most egregious example at 40.4% saturation with 243 hits. Analysis detected 1,469 faulty-reasoning hits from 601 analyzed words, generating a BS Score of 76.6% and a BS Rank of ⁠85% (3,389 of 21,886 articles). This article is worse (more manipulative) than 84.50% of the article peer group.

Rep. 
Maxine Waters, D-Calif., is the scourge of cryptocurrencies on Capitol Hill, burnishing her bona fides by supporting tighter oversight from her perch as ranking member of the House Financial Services Committee. 
If Democrats win the midterm elections, Waters is poised to become the chair of the influential committee. 
Crypto donors are trying to make sure that never happens. 
The woman mounting a long-shot challenge to Waters in California’s 43rd Congressional District has drawn more than two-thirds of her donations from the cryptocurrency industry. 
Nonprofit executive Myla Rahman, who is running as a younger alternative to the 87-year-old Waters, has taken 69 percent of her campaign contributions from crypto figures. 
Rahman’s biggest single donor is Ripple Labs CEO Brad Garlinghouse, a leading voice pushing for looser regulations on crypto who has been active in the debate over pending crypto legislation in Congress. 
Garlinghouse’s $6,600 donation last month helped bring Rahman’s total haul to $14,540 since announcing her long-shot campaign in February. 
The total haul is a pittance compared to what it would take to mount a viable campaign against Waters, a legendary figure who is serving her 18th term in the House. 
California’s primary election takes place on June 2. 
(Ripple Labs declined to comment.) 
The total haul is a pittance compared to what it would take to mount a viable campaign against Waters, a legendary figure. 
Still, any opposition funding could serve as a nuisance to Waters, a relative lightweight when it comes to fundraising compared to other top names in Congress. 
(Neither Waters’s nor Rahman’s campaigns responded to requests for comment.) 
Rahman’s second biggest benefactor was Colin McLaren, the head of government relations at the crypto advocacy nonprofit Solana Policy Institute. 
He chipped in $3,500. 
The crypto industry has ample reason to target Waters. 
While other Democrats have proven more accommodating, Waters has supported tighter oversight from her powerful position in the House Financial Services Committee, which has jurisdiction over the crypto industry. 
With Waters potentially assuming the helm of the committee next year, crypto is racing to win passage of a favorable regulatory framework in the form of a bill called the Clarity Act. 
Despite widespread support among the Republicans, the industry has faced intense pushback from banks and credit unions who worry that passage of the law could lead to a stampede of deposits out of their institutions and into crypto exchanges. 
Ripple, which has an estimated valuation of $50 billion, fought a yearslong legal battle with the Securities and Exchange Commission that centered on the issues under debate in Congress right now. 
Waters’s most recent campaign filing on April 15 showed that she had a little over $300,000 on hand. 
Many recent contributions came from the banks and credit unions squaring off against crypto on Capitol Hill. 
Despite her stance on crypto regulation, Waters also received a campaign donation from Ripple Labs co-founder and Democratic megadonor Chris Larsen. 
He gave $3,300 to Waters on March 6, only a few days after Garlinghouse made his donation to Rahman. 
Larsen gave one of the crypto industry’s highest-profile contributions to Kamala Harris’s 2024 presidential campaign. 
Rahman’s campaign does not mark crypto’s first quixotic campaign against a prominent congressional industry critic. 
The crypto industry also funded a Republican challenger in 2024 in an attempt to unseat Democratic Sen. 
Elizabeth Warren in deep-blue Massachusetts and a since-suspended primary challenge to Democratic California Rep. 
Brad Sherman. 
In Sherman’s race, the crypto industry made clear its intention to leverage a message of generational change against critics of blockchain currencies. 
Article reasoning-pattern comparisonThis article: 9.7%Matt Sledge: 2.8%The Intercept: 5.3%Confirmation Bias9.7%This article: 0.0%Matt Sledge: 0.6%The Intercept: 0.5%Anchoring Bias0.0%This article: 5.2%Matt Sledge: 2.4%The Intercept: 3.0%Availability Heuristic5.2%This article: 8.5%Matt Sledge: 1.1%The Intercept: 1.0%Representativeness Heuristic8.5%This article: 0.0%Matt Sledge: 0.3%The Intercept: 0.7%Hindsight Bias0.0%This article: 0.0%Matt Sledge: 0.8%The Intercept: 1.5%Overconfidence Bias0.0%This article: 11.1%Matt Sledge: 11.2%The Intercept: 9.2%Framing Effect11.1%This article: 0.0%Matt Sledge: 0.1%The Intercept: 0.4%Loss Aversion0.0%This article: 4.8%Matt Sledge: 0.4%The Intercept: 0.4%Status Quo Bias4.8%This article: 0.0%Matt Sledge: 0.1%The Intercept: 0.1%Sunk Cost Effect0.0%This article: 8.2%Matt Sledge: 1.2%The Intercept: 1.0%Optimism Bias8.2%This article: 1.7%Matt Sledge: 1.5%The Intercept: 2.2%Pessimism Bias1.7%This article: 23.3%Matt Sledge: 11.8%The Intercept: 12.3%Negativity Bias23.3%This article: 0.0%Matt Sledge: 0.6%The Intercept: 0.9%Self-Serving Bias0.0%This article: 3.5%Matt Sledge: 1.4%The Intercept: 1.4%Fundamental Attribution Error3.5%This article: 0.0%Matt Sledge: 0.2%The Intercept: 0.2%Actor-Observer Bias0.0%This article: 2.8%Matt Sledge: 1.8%The Intercept: 1.7%In-Group Bias2.8%This article: 3.3%Matt Sledge: 1.0%The Intercept: 1.0%Out-Group Homogeneity Bias3.3%This article: 16.6%Matt Sledge: 1.1%The Intercept: 1.0%Halo Effect16.6%This article: 0.0%Matt Sledge: 0.4%The Intercept: 0.2%Horn Effect0.0%This article: 0.0%Matt Sledge: 0.0%The Intercept: 0.0%Dunning-Kruger Effect0.0%This article: 3.2%Matt Sledge: 1.2%The Intercept: 1.3%Recency Bias3.2%This article: 5.2%Matt Sledge: 0.3%The Intercept: 0.3%Primacy Effect5.2%This article: 0.0%Matt Sledge: 0.0%The Intercept: 0.0%Blind-Spot Bias0.0%This article: 0.0%Matt Sledge: 2.7%The Intercept: 2.4%Ad Hominem0.0%This article: 0.0%Matt Sledge: 0.9%The Intercept: 0.7%Straw Man0.0%This article: 5.3%Matt Sledge: 2.9%The Intercept: 3.4%Appeal to Authority5.3%This article: 0.0%Matt Sledge: 2.0%The Intercept: 2.0%False Dilemma0.0%This article: 5.3%Matt Sledge: 1.5%The Intercept: 1.7%Slippery Slope5.3%This article: 0.0%Matt Sledge: 0.2%The Intercept: 0.1%Circular Reasoning0.0%This article: 4.8%Matt Sledge: 5.8%The Intercept: 7.2%Hasty Generalization4.8%This article: 0.0%Matt Sledge: 0.2%The Intercept: 0.3%Red Herring0.0%This article: 0.0%Matt Sledge: 0.3%The Intercept: 0.4%Bandwagon0.0%This article: 17.8%Matt Sledge: 6.3%The Intercept: 6.4%Appeal to Emotion17.8%This article: 5.2%Matt Sledge: 1.6%The Intercept: 1.4%Begging the Question5.2%This article: 16.0%Matt Sledge: 2.6%The Intercept: 3.1%Post Hoc (False Cause)16.0%This article: 0.0%Matt Sledge: 0.2%The Intercept: 0.2%Tu Quoque0.0%This article: 0.0%Matt Sledge: 0.6%The Intercept: 0.7%Burden of Proof0.0%This article: 0.0%Matt Sledge: 0.0%The Intercept: 0.1%Appeal to Nature0.0%This article: 0.0%Matt Sledge: 0.3%The Intercept: 0.4%Composition/Division0.0%This article: 11.8%Matt Sledge: 1.1%The Intercept: 2.3%Anecdotal11.8%This article: 0.0%Matt Sledge: 0.1%The Intercept: 0.1%No True Scotsman0.0%This article: 10.6%Matt Sledge: 1.1%The Intercept: 1.5%Ambiguity (Equivocation)10.6%This article: 0.0%Matt Sledge: 0.0%The Intercept: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Matt Sledge: 0.1%The Intercept: 0.1%Middle Ground0.0%This article: 0.0%Matt Sledge: 0.1%The Intercept: 0.1%Personal Incredulity0.0%This article: 0.0%Matt Sledge: 0.1%The Intercept: 0.1%Special Pleading0.0%This article: 0.0%Matt Sledge: 0.4%The Intercept: 0.4%Genetic Fallacy0.0%This article: 20.1%Matt Sledge: 2.6%The Intercept: 2.1%Unattributed Quote20.1%This article: 0.0%Matt Sledge: 2.0%The Intercept: 1.3%Quote-first Misdirection0.0%This article: 40.4%Matt Sledge: 8.3%The Intercept: 10.5%Biased Writer Voice40.4%This article: 0.0%Matt Sledge: 1.4%The Intercept: 2.1%Indoctrination0.0%This article: 0.0%Matt Sledge: 4.8%The Intercept: 6.1%Politically Left Leaning Bias0.0%This article: 0.0%Matt Sledge: 0.3%The Intercept: 0.3%Politically Right Leaning Bias0.0%This article: 0.0%Matt Sledge: 0.1%The Intercept: 0.5%Attempt to Sell a Product or S…0.0%

601 words analyzed.

Speakers

8speakers29%attributed speech428writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 14 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 31 words • 100.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 10 words • 100.0% coverageWriter's voice • 25 words • 100.0% coverageMyla Rahman • 26 words • 100.0% coverageBrad Garlinghouse • 32 words • 0.0% coverageBrad Garlinghouse • 19 words • 0.0% coverageWriter's voice • 31 words • 100.0% coverageWriter's voice • 8 words • 0.0% coverageRipple Labs • 5 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 26 words • 100.0% coverageWriter's voice • 10 words • 0.0% coverageSolana Policy Institute • 20 words • 0.0% coverageColin McLaren • 4 words • 0.0% coverageWriter's voice • 9 words • 100.0% coverageWriter's voice • 29 words • 100.0% coverageWriter's voice • 32 words • 100.0% coverageWriter's voice • 39 words • 0.0% coverageSecurities and Exchange Commission • 31 words • 100.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 17 words • 100.0% coverageWriter's voice • 21 words • 100.0% coverageChris Larsen • 19 words • 0.0% coverageChris Larsen • 15 words • 0.0% coverageWriter's voice • 15 words • 100.0% coverageWriter's voice • 17 words • 100.0% coverageWriter's voice • 14 words • 0.0% coverageBrad Sherman • 2 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverage
100%flagged-word coverage
31 attributed words18% of attributed speech88% writer coverage
0%50.0%100.0%Unattributed Quote+79.0 ptsWriter: 21.0%Securities and Exchange Commission: 100.0%100.0%Biased Writer Voice-50.7 ptsWriter: 50.7%Securities and Exchange Commission: 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.