Deadline48%

Gregg Araki Found AI Useful For Editing This 'I Want Your Sex' Scene 67%

By Glenn Garner34%

8/2/2026, 3:40:52 PM

BS Summary: This article contains 16 faulty reasoning types, including Optimism Bias, Hasty Generalization, and Sunk Cost Effect, with Anecdotal as the most egregious example at 26.7% saturation with 122 hits. Analysis detected 641 faulty-reasoning hits from 457 analyzed words, generating a BS Score of 53.9% and a BS Rank of 67% (8,902 of 26,282 articles). This article is worse (more manipulative) than 66.10% of the article peer group.

With his first movie in more than a decade, Gregg Araki found himself working in a whole new era of Hollywood to bring I Want Your Sex to the big screen. 
Discussing his new erotic comedy thriller, now playing in theaters, the co-writer and director explained to Deadline how AI tools were particularly useful in post-production on one scene of dialogue filmed with stars Cooper Hoffman and Chase Sui Wonders on a mini golf course next to a freeway. 
“For me, one of the coolest things is the technology is so advanced now and it really makes it so filmmaker friendly,” he explained. 
“I remember talking to Rick Linklater about this; back in the old days, when I was making Three Bewildered People [in the Night] and he was making Slacker, it was really just fucking hard to make a movie, just technically hard with 60 millimeter and loading magazines and torn sprocket holes and tape slices, and all of the stuff that we had to go through. 
You had to be kind of a crazy obsessive person to even try to make a movie. 
Araki continued, “Whereas now, with the technology and the crazy sensitive cameras and iPhones and all of the software, all the things you can do—I’m not a big supporter of AI in terms of replacing actors and writers, but in terms of the tools you can do in post, there’s stuff that we did in I Want Your Sex. 
We shot that entire mini golf scene literally next to a freeway. 
It’s so loud in the rough cuts and stuff, but with tools that are available now in post, it’s like you can hear crickets whenever we’re mixing it. 
It was literally insane the way these things can work, and the same with the editing stuff, it’s amazing. 
The effects, all that stuff is really being kind of revolutionized in a way that makes all of that stuff accessible to indie lower budget filmmakers.” 
With the success of indie films like Obsession serving “shocks to the system” of a big-budget, IP-driven industry, Araki is “excited for the future” of Hollywood. 
“It’s just crazy times, everything’s so upside down. 
And for indie film too, it’s really hard to get distribution. 
It’s hard to get indie film finance, but it’s always been hard, so it’s always a struggle, different struggles,” explained Araki. 
“I’m personally kind of excited for the future, I think. 
I do think that a lot of the old formulas aren’t really working anymore. 
I think Hollywood’s really sort of scrambling—it’s kind of like the Titanic, people just kind of running around. 
So, the rules are really being rewritten.” 
Article reasoning-pattern comparisonThis article: 3.1%Glenn Garner: 1.6%Deadline: 2.1%Confirmation Bias3.1%This article: 0.0%Glenn Garner: 0.1%Deadline: 0.9%Anchoring Bias0.0%This article: 2.6%Glenn Garner: 1.4%Deadline: 2.5%Availability Heuristic2.6%This article: 0.0%Glenn Garner: 0.6%Deadline: 0.8%Representativeness Heuristic0.0%This article: 0.0%Glenn Garner: 0.5%Deadline: 0.6%Hindsight Bias0.0%This article: 1.5%Glenn Garner: 2.2%Deadline: 1.1%Overconfidence Bias1.5%This article: 5.7%Glenn Garner: 1.8%Deadline: 4.9%Framing Effect5.7%This article: 0.0%Glenn Garner: 0.6%Deadline: 0.5%Loss Aversion0.0%This article: 12.9%Glenn Garner: 1.6%Deadline: 0.6%Status Quo Bias12.9%This article: 14.2%Glenn Garner: 0.8%Deadline: 0.2%Sunk Cost Effect14.2%This article: 19.3%Glenn Garner: 5.1%Deadline: 2.5%Optimism Bias19.3%This article: 1.8%Glenn Garner: 0.3%Deadline: 0.6%Pessimism Bias1.8%This article: 8.5%Glenn Garner: 6.2%Deadline: 5.7%Negativity Bias8.5%This article: 0.0%Glenn Garner: 2.1%Deadline: 1.6%Self-Serving Bias0.0%This article: 0.0%Glenn Garner: 0.3%Deadline: 0.2%Fundamental Attribution Error0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.1%Actor-Observer Bias0.0%This article: 0.0%Glenn Garner: 0.6%Deadline: 0.6%In-Group Bias0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.1%Out-Group Homogeneity Bias0.0%This article: 4.2%Glenn Garner: 2.9%Deadline: 5.4%Halo Effect4.2%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.0%Horn Effect0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Glenn Garner: 1.0%Deadline: 1.1%Recency Bias0.0%This article: 0.0%Glenn Garner: 0.4%Deadline: 0.5%Primacy Effect0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.1%Blind-Spot Bias0.0%This article: 0.0%Glenn Garner: 0.9%Deadline: 0.6%Ad Hominem0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.1%Straw Man0.0%This article: 0.0%Glenn Garner: 1.7%Deadline: 3.0%Appeal to Authority0.0%This article: 1.5%Glenn Garner: 0.1%Deadline: 0.7%False Dilemma1.5%This article: 0.0%Glenn Garner: 0.2%Deadline: 0.2%Slippery Slope0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.1%Circular Reasoning0.0%This article: 14.9%Glenn Garner: 5.6%Deadline: 3.1%Hasty Generalization14.9%This article: 0.0%Glenn Garner: 0.8%Deadline: 0.4%Red Herring0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.6%Bandwagon0.0%This article: 3.9%Glenn Garner: 5.0%Deadline: 3.9%Appeal to Emotion3.9%This article: 0.0%Glenn Garner: 0.4%Deadline: 0.5%Begging the Question0.0%This article: 0.0%Glenn Garner: 0.7%Deadline: 1.6%Post Hoc (False Cause)0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.0%Tu Quoque0.0%This article: 0.0%Glenn Garner: 0.6%Deadline: 0.3%Burden of Proof0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.1%Appeal to Nature0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.1%Composition/Division0.0%This article: 26.7%Glenn Garner: 6.2%Deadline: 1.5%Anecdotal26.7%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.0%No True Scotsman0.0%This article: 0.0%Glenn Garner: 0.8%Deadline: 1.2%Ambiguity (Equivocation)0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.0%Middle Ground0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.0%Personal Incredulity0.0%This article: 0.0%Glenn Garner: 0.1%Deadline: 0.1%Special Pleading0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.1%Genetic Fallacy0.0%This article: 0.0%Glenn Garner: 1.2%Deadline: 2.4%Unattributed Quote0.0%This article: 6.1%Glenn Garner: 1.3%Deadline: 1.2%Quote-first Misdirection6.1%This article: 13.3%Glenn Garner: 4.6%Deadline: 5.7%Biased Writer Voice13.3%This article: 0.0%Glenn Garner: 0.7%Deadline: 0.7%Indoctrination0.0%This article: 0.0%Glenn Garner: 0.5%Deadline: 0.2%Politically Left Leaning Bias0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Glenn Garner: 0.0%Deadline: 1.5%Attempt to Sell a Product or S…0.0%

457 words analyzed.

Speakers

1speaker74%attributed speech118writer words
Selected voice

Gregg Araki

100%flagged-word coverage
339 attributed words100% of attributed speech22% writer coverage
0%10.0%20.0%Biased Writer Voice+18.0 ptsWriter: 0.0%Gregg Araki: 18.0%18.0%Quote-first Misdirection+8.3 ptsWriter: 0.0%Gregg Araki: 8.3%8.3%

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.