The Verge55%

Netflix says around 300 titles used generative AI 73%

By Emma Roth43%

7/16/2026, 8:29:27 PM

BS Summary: This article contains 18 faulty reasoning types, including Post Hoc (False Cause), Recency Bias, and Confirmation Bias, with Appeal to Authority as the most egregious example at 31.7% saturation with 142 hits. Analysis detected 774 faulty-reasoning hits from 448 analyzed words, generating a BS Score of 65.6% and a BS Rank of 73% (5,718 of 21,168 articles). This article is worse (more manipulative) than 73.00% of the article peer group.

Netflix says roughly 300 titles on its platform used generative AI, most of which occurred in post-production. 
The streaming service revealed the news in its second-quarter earnings report released on Thursday, saying it’s “increasingly leveraging these tools to deliver higher quality output more quickly and at a lower cost.” 
It also provided some examples of titles that used AI, including The American Experiment, Glory, and Brasil 70: A Saga do Tri. 
These shows used the technology to “create highly complex sequences,” including “enhanced crowds, historical battle sequences, and worldbuilding establishing shots.” 
During Netflix’s call with investors, co-CEO Ted Sarandos said The American Experiment docuseries includes 17 minutes of “AI-enhanced footage,” which “were produced twice as fast and at half the cost of previous options.” 
“In many of the cases, productions would have left out those key shots because they just wouldn’t have been able to afford them,” Sarandos added. 
“They wouldn’t have been able to do them in the timeframes that they’re working on.” 
Sarandos similarly said last year that AI was used to create a scene in the sci-fi series The Eternaut to save time and cut costs. 
The streaming giant has begun to invest more heavily in AI as the technology becomes more advanced, with Netflix acquiring Ben Affleck’s AI startup and creating an AI animation studio. 
The service is also using the AI-generated voice of Gene Wilder in its new Wonka’s The Golden Ticket reality show. 
Netflix reported earning $12.56 billion over the past few months, and says it’s still on track to double its ad revenue to $3 billion. 
In its letter to shareholders, Netflix also addressed some concerns about engagement, which came up after a report from Bloomberg revealed that the streaming giant is struggling to keep viewers around for the second season of its shows. 
The service says “time spent is just one aspect of strong engagement,” adding that “quality and variety also matter.” 
It also highlights that its latest What We Watched report shows that subscribers watched over 97 billion hours, up 2 percent year over year. 
The company also announced that it will now switch to publishing this report just once per year, instead of twice. 
Netflix has started to introduce new types of content in a bid to compete with free-to-watch services like YouTube. 
In the past year, Netflix has rolled out video podcasts, TikTok-style clips, and most recently announced plans to stream videos created by digital media brands, like BuzzFeed, which would typically appear on YouTube. 
Earlier this month, The Wall Street Journal reported that Netflix is considering adding always-on channels. 
Update, July 16th: Added information from Netflix’s earnings call. 
Article reasoning-pattern comparisonThis article: 12.7%Emma Roth: 4.7%The Verge: 3.6%Confirmation Bias12.7%This article: 0.0%Emma Roth: 2.2%The Verge: 1.3%Anchoring Bias0.0%This article: 0.0%Emma Roth: 4.0%The Verge: 3.7%Availability Heuristic0.0%This article: 0.0%Emma Roth: 0.0%The Verge: 1.3%Representativeness Heuristic0.0%This article: 0.0%Emma Roth: 0.3%The Verge: 0.8%Hindsight Bias0.0%This article: 0.0%Emma Roth: 1.5%The Verge: 1.7%Overconfidence Bias0.0%This article: 8.5%Emma Roth: 11.4%The Verge: 6.0%Framing Effect8.5%This article: 0.0%Emma Roth: 1.4%The Verge: 0.9%Loss Aversion0.0%This article: 4.5%Emma Roth: 1.4%The Verge: 0.5%Status Quo Bias4.5%This article: 6.7%Emma Roth: 0.4%The Verge: 0.4%Sunk Cost Effect6.7%This article: 12.5%Emma Roth: 7.3%The Verge: 4.0%Optimism Bias12.5%This article: 5.6%Emma Roth: 0.9%The Verge: 2.3%Pessimism Bias5.6%This article: 8.5%Emma Roth: 8.0%The Verge: 9.4%Negativity Bias8.5%This article: 0.0%Emma Roth: 3.8%The Verge: 1.4%Self-Serving Bias0.0%This article: 0.0%Emma Roth: 1.0%The Verge: 0.7%Fundamental Attribution Error0.0%This article: 0.0%Emma Roth: 0.4%The Verge: 0.1%Actor-Observer Bias0.0%This article: 0.0%Emma Roth: 0.0%The Verge: 0.6%In-Group Bias0.0%This article: 0.0%Emma Roth: 0.6%The Verge: 0.4%Out-Group Homogeneity Bias0.0%This article: 4.5%Emma Roth: 3.0%The Verge: 2.9%Halo Effect4.5%This article: 0.0%Emma Roth: 0.0%The Verge: 0.2%Horn Effect0.0%This article: 0.0%Emma Roth: 0.0%The Verge: 0.0%Dunning-Kruger Effect0.0%This article: 12.9%Emma Roth: 2.6%The Verge: 1.8%Recency Bias12.9%This article: 5.4%Emma Roth: 1.2%The Verge: 0.4%Primacy Effect5.4%This article: 0.0%Emma Roth: 0.0%The Verge: 0.1%Blind-Spot Bias0.0%This article: 0.0%Emma Roth: 0.6%The Verge: 1.2%Ad Hominem0.0%This article: 0.0%Emma Roth: 0.0%The Verge: 0.4%Straw Man0.0%This article: 31.7%Emma Roth: 8.4%The Verge: 4.0%Appeal to Authority31.7%This article: 4.2%Emma Roth: 0.6%The Verge: 1.6%False Dilemma4.2%This article: 0.0%Emma Roth: 0.4%The Verge: 1.2%Slippery Slope0.0%This article: 0.0%Emma Roth: 0.3%The Verge: 0.1%Circular Reasoning0.0%This article: 7.4%Emma Roth: 3.4%The Verge: 6.7%Hasty Generalization7.4%This article: 0.0%Emma Roth: 0.9%The Verge: 0.2%Red Herring0.0%This article: 0.0%Emma Roth: 0.3%The Verge: 0.7%Bandwagon0.0%This article: 0.0%Emma Roth: 1.4%The Verge: 4.3%Appeal to Emotion0.0%This article: 0.0%Emma Roth: 0.5%The Verge: 1.1%Begging the Question0.0%This article: 21.2%Emma Roth: 4.5%The Verge: 2.3%Post Hoc (False Cause)21.2%This article: 0.0%Emma Roth: 0.0%The Verge: 0.2%Tu Quoque0.0%This article: 8.9%Emma Roth: 3.3%The Verge: 0.8%Burden of Proof8.9%This article: 0.0%Emma Roth: 0.0%The Verge: 0.3%Appeal to Nature0.0%This article: 5.4%Emma Roth: 0.3%The Verge: 0.3%Composition/Division5.4%This article: 4.9%Emma Roth: 2.0%The Verge: 3.6%Anecdotal4.9%This article: 0.0%Emma Roth: 0.0%The Verge: 0.1%No True Scotsman0.0%This article: 7.4%Emma Roth: 4.3%The Verge: 2.2%Ambiguity (Equivocation)7.4%This article: 0.0%Emma Roth: 0.0%The Verge: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Emma Roth: 0.0%The Verge: 0.1%Middle Ground0.0%This article: 0.0%Emma Roth: 0.0%The Verge: 0.1%Personal Incredulity0.0%This article: 0.0%Emma Roth: 0.8%The Verge: 0.2%Special Pleading0.0%This article: 0.0%Emma Roth: 0.0%The Verge: 0.2%Genetic Fallacy0.0%This article: 0.0%Emma Roth: 5.6%The Verge: 2.5%Unattributed Quote0.0%This article: 0.0%Emma Roth: 2.2%The Verge: 1.4%Quote-first Misdirection0.0%This article: 0.0%Emma Roth: 9.3%The Verge: 10.7%Biased Writer Voice0.0%This article: 0.0%Emma Roth: 0.3%The Verge: 1.1%Indoctrination0.0%This article: 0.0%Emma Roth: 0.0%The Verge: 1.7%Politically Left Leaning Bias0.0%This article: 0.0%Emma Roth: 0.0%The Verge: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Emma Roth: 4.4%The Verge: 4.2%Attempt to Sell a Product or S…0.0%

448 words analyzed.

Speakers

3speakers94%attributed speech29writer words
Voice mapSelect a segment to jump to its words
Netflix • 8 words • 0.0% coverageNetflix • 17 words • 0.0% coverageNetflix • 32 words • 0.0% coverageNetflix • 22 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageTed Sarandos • 33 words • 0.0% coverageTed Sarandos • 25 words • 0.0% coverageTed Sarandos • 15 words • 0.0% coverageTed Sarandos • 25 words • 0.0% coverageNetflix • 30 words • 0.0% coverageNetflix • 20 words • 0.0% coverageNetflix • 24 words • 0.0% coverageNetflix • 38 words • 0.0% coverageNetflix • 19 words • 0.0% coverageNetflix • 24 words • 0.0% coverageNetflix • 20 words • 0.0% coverageNetflix • 19 words • 0.0% coverageNetflix • 33 words • 0.0% coverageThe Wall Street Journal • 15 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverage
Selected voice

Ted Sarandos

100%flagged-word coverage
98 attributed words23% of attributed speech69% 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.

Loading…
Loading…
Loading…

Analysis

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