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I have built a solution for the Monetization of Custom ChatGPTs

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I have built a solution for the Monetization of Custom ChatGPTs

I have been sitting on this idea since the GPT Store launch in November. Over this time I was waiting for OpenAI to launch their Monetization program for ChatGPT Builders. Since they announced a Pilot Monetization Program at the end of March it became clear to me that they took a route of revenue sharing of the Plus Subs, at least for now. Unfortunately, a subset of ChatGPT Builders, particularly those operating very niche “high-value - small user base” chats, are excluded from monetization. These chats offer substantial value, justifying an additional charge for access and the extra friction of logging in on each conversation. The solution involves integrating a prompt-based paywall, which requires chat users to purchase a subscription via Stripe and validates the subscription by verifying the user’s email at the start of every conversation. While not the ultimate solution for monetizing custom ChatGPTs, it represents one of several potential approaches. Ideally, OpenAI will implement a more streamlined and reliable method for creating chat paywalls in the future. If you’ve created a valuable custom ChatGPT that users love and are willing to pay extra for, beyond ChatGPT Plus, and are considering monetization, I encourage you to visit and give it a try. The integration process involves creating subscription plans, setting up a Stripe account, and embedding a paywall prompt with an API key—a procedure that can be completed in minutes. Initially, the paywall may be somewhat porous and needs training, but after 10-15 training interactions, it keeps 80-100% of users from using chat unless they validate their subs. This is an experimental solution. I welcome and appreciate any feedback or thoughts you might have. If monetizing chats is not your goal and you’re just interested in experimenting with GPTs, or if you want to try breaking the paywall, please go ahead and share your experiences.

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86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, stripe, email · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, builder, users · Missing: platform, intuitive, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, subscription, training · Missing: arr, mrr, profit
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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