The $20 Question: What Generative AI Subscriptions Actually Deliver
ChatGPT Plus costs $20 a month. Claude Pro costs $20. Gemini Advanced costs $20. What exactly are you buying for that money? The answer reveals a fundamental misalignment between what Generative AI companies promise and what they deliver.
The Promise vs. The Product
Generative AI companies market their subscriptions with language that implies personal improvement. Google says user feedback "accelerates improvements to our models." OpenAI encourages users to share feedback to "help improve the system." Anthropic positions Claude as becoming more helpful through interaction.
Users naturally interpret the marketing as follows: pay monthly, provide feedback, and get a personalized AI that learns your preferences and gets better at helping you specifically.
The reality operates on a different timeline. The systems do learn from user feedback, but the improvements appear in future model releases months or years later.
Your corrections today might improve GPT-5 or Claude 4, but they provide no benefit to your current experience. Meanwhile, the system forgets every conversation the moment it ends.
You are not buying a generative AI assistant that learns. You are renting access to a static model while providing free training data for future versions.
What Your $20 Actually Buys
Let me break down the real value exchange:
Computational access: Running large language models costs money, but industry estimates suggest $0.002 to $0.02 per query. Even heavy users generate perhaps $2-5 in actual computational costs monthly.
Priority access: You get faster response times and fewer capacity limits during peak usage.
Advanced features: You receive longer conversations, file uploads, and integration capabilities.
What you do not get: Any persistent benefit from the time you spend correcting, refining, and improving the AI's outputs for your specific needs.
Meanwhile, companies capture significant value from subscriber interactions. Anthropic analyzed over one million student conversations. OpenAI processes billions of user corrections. The feedback drives improvements in future models, but those improvements benefit new users, not the paying subscribers who provided the training labor.
The Enterprise Problem
The misalignment becomes more pronounced for business users. Companies pay thousands monthly for generative AI access while employees provide domain-specific corrections and refinements.
Legal firms train generative AI on contract language through corrections and feedback. Consulting companies refine analytical frameworks through iterative use. Marketing teams improve creative outputs through detailed feedback sessions.
The work creates valuable training data in specialized domains, but provides no accumulated learning benefit to the paying organization.
The Training Labor Problem
The arrangement creates an unusual economic model. In most subscription services, your usage data improves your experience, though it's often misused for other purposes too. Netflix recommendations get better over time. Spotify discovers music you like. Amazon suggests products you want.
Generative AI subscriptions work differently. Your corrections and refinements improve the general model for tomorrow's users, not today's. You bear the cost of training (time spent on feedback) while others capture the benefit (better models in future releases).
Consider a typical user session: You generate report or content, spend several minutes correcting factual errors, hallucinations, and improving the tone, removing excessive em-dashes, fixing passive voice, and eliminating unwanted emojis, then refine the structure through several iterations.
The work produces valuable training data that teaches the system about accuracy, style preferences, and content organization, but none of the learning transfers to your personal experience. The system forgets everything the moment your session ends.
Also,Users have remarkably normalized LLM hallucinations, Why? With no better alternative, expectations quietly adjust. We’ve learned to accept fiction with our facts, as long as it responds quickly.
Why GEN AI Companies Choose Forgetfulness
The technology for individual learning exists. Companies build sophisticated personalization for advertising, recommendations, and search. Generative AI systems could maintain user preferences, remember corrections, and adapt to individual needs.
They choose not to. Stateless architectures serve business interests:
Data collection: If the AI learned your style perfectly, you would provide fewer corrections and refinements. Forgetting forces continuous training data generation.
Cost efficiency: Personal models require more computational resources. Generic models serve everyone at a lower marginal cost.
Liability avoidance: Systems that remember can be held accountable for repeated failures. Forgetting provides plausible deniability.
Market control: Users invested in personalized AI would face high switching costs. Stateless systems keep customers interchangeable.
The above creates an industry-wide stalemate. Everyone stays forgetful because being first to remember means sacrificing the data extraction that funds R&D. It's a prisoner's dilemma where cooperation means continued exploitation of users.
The breakthrough will come from outside. A company with different economics, perhaps focused on enterprise customers willing to pay premium prices for genuine personalization, will force the industry's hand. When that happens, the current subscription model can collapse.
The question isn't whether this will happen. It's whether current players will adapt or be replaced by companies that put user value over data extraction.
What Subscribers Should Do
Understand what you're buying: Access to computational resources and general AI capabilities, not personalized learning or individual improvement.
Value your feedback: The time you spend correcting and refining AI outputs has economic value. Ask yourself: Are service improvements fairly compensating you?
Demand transparency: Ask AI companies for metrics on individual user experience improvement over time. If they can't provide them, they're not delivering personalized AI.
Consider alternatives: Evaluate whether the generic AI access justifies the subscription cost, or whether per-use pricing might better align with the actual value received.
Conclusion
The current Gen AI subscription model works because the technology is novel and the immediate utility is high. But as the market matures, the misalignment between pricing and value delivery will become increasingly difficult to sustain. Companies that solve the challenge, either through honest pricing or genuine personalization, will capture the next wave of Gen AI adoption. Those that do not may find their subscribers asking harder questions about what exactly they are paying for.
Cheers
Srikanth Devarajan