For weeks, I've been watching Claude suddenly choke on fully specified engineering tasks it used to crush. I called it exactly what it felt like: Anthropic was silently lobotomizing their models.
When a non-deterministic system degrades overnight, developers naturally blame their own prompting.
I didn't have on-the-ground telemetry, but I did have a very rigorous workflow that had been tested and validated to create hundreds of thousands of lines of production code using Claude Code. Suddenly, those workflows completely fell apart—both in my own work and on customer projects. It happened exactly at the same time I posted my initial blogs, and right when everyone else started seeing the exact same signals. Of course I blamed myself and went to work trying to find the underlying issues.
When I first shared those notes, it clearly hit a nerve. That initial post racked up over 100,000 views on LinkedIn in less than 48 hours. We all knew something was broken.
The Receipts: 43,000 Calls Later
Now, someone has brought the receipts. According to a recent post on r/ClaudeAI, a developer published a 65-day analysis across more than 43,000 Claude invocations. Their data claims to prove that Anthropic has been quietly nerfing the model's reasoning budget while marketing it as the exact same model.
Their breakdown confirms what many of us experienced in production. According to their metrics, the model's allocated thinking budget dropped anywhere from 18% to 50% between July and August, at one point hitting a median of literal zero. In fact, the author notes that nearly 40% of Fable 5 calls currently receive zero thinking tokens, leaving the median invocation clocking in at just 123.
The post contrasts this with Anthropic's public benchmarks, which boast performance numbers achieved using reasoning budgets between 16,000 and 128,000 thinking tokens. The analysis argues that the underlying weights haven't lost capability—Anthropic is simply withholding the compute customers pay for, throttling consumer tiers to cut operating costs while promising "full model access."
The Cost of Rented Intelligence
You cannot trust static vendor benchmarks, and you cannot assume the model you paid for on Monday is the same one answering your queries on Friday.
This is the central vulnerability of building on rented intelligence. If you run mission-critical engineering pipelines or multi-agent orchestration, you cannot rely on vendor claims or historical evaluations. You have to monitor the actual inference regime delivered in the wild turn by turn.
I documented the specific task failures that tipped me off to this shift as they happened in my Claude Code 2.0 Recap and my breakdown of how Claude Code broke down on a fully specified planning task.
If you want to review the full dataset, methodology, and statistical breakdown from that developer, read their complete 43,000-invocation analysis on r/ClaudeAI.
Jason Vertrees is the founder of Heavy Chain Engineering, which helps lower middle-market vertical SaaS companies and PE firms turn scattered AI usage into measurable delivery leverage — 85% faster feature velocity, six-to-eight-week projects shipped in days. If you want help building an AI-native engineering organization, book an AI Delivery Assessment or email jason.vertrees@gmail.com.