Anthropic has publicly stated that a substantial share of its paid traffic came from other AI labs attempting to copy Claude. The company turned away trillions of tokens worth of that traffic. On Monday it posted that a new defense reduced the amount of Claude’s thinking copycat labs can pull by 90 to 99 percent. It banned tens of thousands of accounts, many of them within two hours of starting, and named a number of labs coming out of China. One network operated more than 20,000 fake accounts at the same time using stolen payment details and fake identities. Anthropic has acknowledged that the action meant turning away meaningful revenue.
How Distillation Works and Why Anthropic Hid Claude’s Thinking
Distillation means taking a large, expensive model such as Claude, sending it millions of questions, and saving every answer along with every bit of its reasoning. A smaller model is then trained on that collected material. The approach functions as a shortcut that skips years of work and billions in compute by copying someone else’s homework.
Anthropic’s own tests illustrate the strength of the shortcut. A model trained on Claude’s full reasoning reached 80 percent on a popular coding benchmark. When the same model received only a summary of the reasoning, the score fell to 71 percent. That difference is why Anthropic began hiding Claude’s full thinking process. Paying customers do not see it either.
Scale of the Activity and Named Labs
Anthropic described the activity as covert industrial-scale campaigns. One example cited is Xifu, which distilled cyber capabilities of leading U.S. models during development of GLM 5.3. The same company tried to distill Claude Fable 5; after Anthropic’s cyber safeguard degraded those attacks, it switched to an older Claude model. Other references point to Zaipu and Moonshot.
The new measures control where reasoning can go, block and patch extraction attempts, and make repeated attempts difficult. A graph shared by Anthropic showed average attempts dropping sharply once the safeguard was fully applied, with some distillation attacks falling to zero. The company expects the contest to remain a cat-and-mouse game that will require continued iteration of defenses. Distillation is framed as a problem for every developer platform. Despite the tradeoffs, Anthropic said the measures are clearly worth the cost for reasons of national security and ensuring power.
Revenue Questions and the Timing of Charges
Because many of the accounts used stolen cards and fake identities, part of the usage growth that had been celebrated appears to have come from copycats. Anthropic is seeking to go public this year, so the reduction in traffic raises questions about the numbers that supported earlier projections. Since September 24, Anthropic has charged customers for certain requests that are blocked before Claude even responds, meaning payment can occur for a refusal. The new classifier carries a false-positive rate of 0.01 percent and is described as roughly as robust as the company’s safeguards against bioweapons assistance.
One AI skeptic asked directly how much of Anthropic’s revenue had been coming from distillation. The accounts in question were not free sign-ups; they were API customers routing substantial volume through the service. Turning that volume away is expected to affect the bottom line. Average weights on some days dropped from 8,000 to 135 times, and the remaining leak became too small to measure.
Effects on Other Models and Industry Projections
The reduction in accessible reasoning raises questions about how models such as GLM 5.3 and those associated with Moonshot and Kimmy will continue to perform if distillation becomes far harder. Earlier market reactions to inexpensive Chinese models had been linked to the use of distillation accounts. Anthropic stated that it had exposed foreign labs conducting industrial-scale extraction of Claude’s reasoning through hundreds of thousands of fake accounts.
Broader projections for AI spending have been cited as context. Americans may need to spend around $3.5 trillion a year on AI services by 2032—five years from now—to justify current data-center build-outs. That figure equals 8.8 percent of GDP. Present spending is described as not even at 1 percent and possibly not half a percent. The build-out itself is projected to cost $10 trillion from 2025 to 2032, a larger share of the economy than the railroad, highway, or telecom booms. Demand for the underlying hardware continues to climb, yet the requirement for spending at the scale needed to sustain current pricing ratios is viewed as unlikely to materialize fully.
Open-weight models have begun to grow. The prediction offered is that Anthropic’s revenue will table off rather than continue the doubling and 10x growth seen over the last 18 months. Whether the company can complete an IPO on the basis of earlier growth assumptions remains an open question.
Local Infrastructure as an Alternative Approach
Startuphakk builds custom software solutions and has used Open Mono Agent, an open-source repository designed for local AI. The preference is to avoid sending data into the cloud. Local setups eliminate a usage meter and keep infrastructure under the owner’s control—the same principle applied for 30 years. Current testing fits models into 12 gigabytes, allowing use of graphics cards consistently available for $300 to $400 rather than the $2,000 to $3,000 range. The platform emphasizes local-first operation, unlimited tokens, sandbox by default, and open source.
Most companies are described as facing failed integrations and AI investments that deliver nothing rather than pure technology problems. Starterpack’s work focuses on database architecture, API design, system integration, and scalable infrastructure built the way it has been done for more than 25 years. When AI is included, it is designed into the architecture running in the client’s environment rather than as a wrapper around someone else’s API or pricing schedule. The openmonoagent.ai platform is a terminal-native coding agent that runs entirely on local LLMs with zero API costs, zero telemetry, and full ownership.
The reduction in accessible Claude reasoning and the associated revenue impact illustrate how quickly the rules can change when a business depends on a model that others can copy. Integrations can break, and the meter can continue to run even when no answer is returned. The episode leaves open how both the targeted labs and Anthropic’s own growth trajectory will adjust.
Conclusion
Anthropic’s new defense cut the amount of Claude’s reasoning that distillation campaigns can harvest by 90 to 99 percent and turned away trillions of tokens of paid traffic. Tens of thousands of accounts were banned, often within hours. The company accepted the loss of meaningful revenue and has begun charging for certain blocked requests.
The move raises questions about both the future strength of models such as GLM 5.3 and Anthropic’s own revenue path ahead of a potential IPO. Rules can change quickly when a business depends on a model others can copy, and some responses focus on local infrastructure that removes the meter and keeps AI under the owner’s control.
