When AI finds PMF: How tools reshape our expectations

The paradox of productization from ChatGPT to Claude

ChatGPT's "gibberish mode" has disappeared-last year it was allowed to imitate Nietzsche writing seafood market observations, but today it can only say,"As an AI assistant, I cannot generate fictional content." The more mature the product, the less courageous it is.

Anthropic just announced that the Claude 3 series has exceeded tens of millions of daily activities, and the media cheered that "AI finally welcomes PMF moment." But no one asked: ** When AI learned to say "Yes sir", it forgot how to ask "Why not try that? "** Commercialization is not the end, it is a cage.

QKPFX1 The essence of QKPMF is risk hedging
OpenAI's iteration from GPT-3 to GPT-4o is essentially a big gamble:

1. 2021 GPT-3: Parameter quantity is 175 billion, inference cost is $0.12/thousand tokens, allowing generation of politically incorrect content
2. **2024 GPT-4o **: Multimodal model, reasoning cost $0.005/thousand tokens, refusing to answer "Compare two ideologies"

The technical team all understands: ** Reducing costs is ten times higher than improving capabilities **. When Microsoft requires 99.99% availability of Azure OpenAI services,"being able to handle 99% of regular queries" is more important than "1% probability of disrupting perceptions." GPT-4's thought-chain reasoning capabilities were castrated in the Turbo version because long-chain reasoning consumes 17 times as much power as short-reply-electricity meters determine the direction of model evolution more than IQ.

Claude's "Constitutional AI" design is more typical: to avoid 0.001% ethical risk, the entire role-playing function is directly cut off. The hallmark of PMF's achievement is that product managers can present to the board a clear risk-benefit matrix **, and there is no room for gray areas in the matrix.

QKPFX4 How QK corporate customers domesticate AI
A fintech company's AI requirements list explains everything:

• ** Required items **: Seamless access to Teams/nails, support SSO login, and audit log retention for 90 days
• ** Prohibited items **: Automatic code generation, free adjustment of reasoning parameters, access to uncertified data sources

This leads to a paradox: ** The more people use AI, the less likely it is to break through cognitive boundaries **. When 80% of Claude's revenue comes from corporate subscriptions, Anthropic engineers 'OKR changes from "exploring emerging capabilities" to "reducing customer service order response time."

The most ironic case I have seen: a medical AI startup cut off its early cancer prediction module-not because it was inaccurate (an accuracy rate of 91.2%), but because the risk of misdiagnosis triggered FDA Class III medical device certification and the financing period was extended by 18 months. The investor said: "Make an electronic medical record summary generator first, and the PMF will be clear."

trained users expect

The operating director of a cross-border e-commerce company told me:

"Three years ago, when GPT was asked to write advertising copywriting, it would come up with crazy words like 'chocolate dancing in a crater.' Now it only outputs 'high-quality delicious food, limited time discounts.' But the conversion rate increased by 37%-** Safe mediocrity defeats risky surprise **"

Human expectations for tools are inversely shaped by tools. When ChatGPT refuses to discuss sensitive topics ten times, users will not try again the eleventh time. Stanford experiments showed that after three months of exposure to the castrated version of AI, 79% of users actively avoided complex questions and turned to security needs such as "help me write emails."

** The success of the tool in training users is precisely the failure of thought experiments **. Just as Excel turned finance staff into form workers, today's Claude turns knowledge workers into "Prompt optimization engineers."

refutation time: survival is more important than ideals?

Someone must have slapped the table:

"AI that doesn't make money will starve to death! Without PMF, how can we burn the next generation model with 10 billion yuan?"

This is half true. The problem is not in pursuing PMF, but in taking PMF as the end point. Take a look at Midjourney's counterexample-insisting on Discord community operations, refusing to access the enterprise office suite, and the V6 version still allows the generation of controversial images. What was the result? Annual revenue exceeded 300 million yuan, and users took the initiative to pay for "disobedience."

The real trap is path dependence. Once the enterprise subscription model is implemented, all resources flow to the customization, rights management, and audit modules. The day OpenAI disbanded the robot team, it was a sacrifice ceremony for PMF-when "general artificial intelligence" became a taboo word in earnings call conferences, we were farther away from AGI.

Zhang in ### Data Center
Lao Zhang, an operation and maintenance architect at a cloud computing company, recently switched the AI monitoring tool from a self-developed model to a commercial suite.

The early warning system he wrote three years ago would generate a report when the server temperature was abnormal: "Node load in the Northeast Region fluctuates like a heartbeat. It is recommended to check the third pump valve of the cooling waterway." Today, commercial systems only speak a fixed phrase: "Error Code 503: Resource Overload."

There was a real leak in the computer room last week. The commercial system issued 20 "excessive temperature" alarms, but did not mention water routes-this scene is not in the default rules. The self-developed model can be associated with water-cooling log warnings, but it was offline six months ago due to "high maintenance costs."

Lao Zhang stared at the accident report and smiled bitterly: "It turns out that the AI has not become stupid, but we taught it to learn to play dumb."

QKPFX12 The possibility of QK breaking the cycle
The way out is not to resist productization, but to redefine PMF:

1. ** Layered Product Strategy **: Like GitHub, which provides both Enterprise Edition and Copilot Raw
2. ** User Sovereignty Design **: Brave Browser Mode--Paying to advertise users are exempted from AI censorship
3. ** Anti-KPI indicator **: The "Monthly Surprise Index" circulated within Anthropic-Counts the number of times "I didn't expect this to happen again" in user feedback

** The most dangerous PMF is to use efficiency to strangle possibilities **. When Claude summarizes Hamlet in three sentences, it is not a demonstration of ability, but a shroud of thought.

The next time you see an AI company showing its growth rate, you might as well ask: "What crazy features did you cut off in your latest update?"

The highest level of > tool taming people is that people actively wear the shackles forged by it