# Why LLM performance degrades after two weeks of release? _Published: 2026-06-30_ We have all seen it. A new language model comes out. Everyone rushes to try it. The first few days are full of screenshots showing amazing reasoning, better coding, smarter conversations, and people calling it the best model ever released. Then something strange starts happening. Around two weeks later, people begin saying the model feels... different. - It is not as sharp anymore. - It used to solve this. - It feels lazier. Whether the model has actually become worse or whether our expectations have changed is a different discussion. But the pattern appears often enough that it deserves attention. I don't know if this has an official name, so for now let's call it the **Two-Week Smartness Problem**. After looking at how these systems are built and deployed, I think there are a few reasons why this keeps happening. ## 1. Models are trained in a controlled environment During training, companies like OpenAI and Anthropic work in a very controlled setup. The model learns from carefully prepared data. Reinforcement learning is done with clear objectives, clean evaluation tasks, and predictable conditions. Everything is designed to teach the model what good behavior looks like. **But users are nothing like that.** Once the model is released, millions of people start asking questions that nobody inside the lab expected. Some ask vague questions. Some intentionally try to jailbreak the model. Others combine five different tasks into one prompt. The model suddenly moves from a controlled classroom into a chaotic fish market. It isn't that the model forgot what it learned. It is simply being tested in situations that never existed during training. ## 2. Real users always find the edge cases No internal testing team can compete with millions of users. Within hours of release, people begin trying things that nobody thought of before. - Someone discovers a strange reasoning bug. - Someone finds a prompt that causes incorrect code. - Someone else discovers a conversation that confuses the model after twenty turns. **Every new release goes through this process.** The internet becomes the biggest testing ground for these labs, and it finds problems much faster than any company can. **Many people mistake these newly discovered weaknesses for the model becoming less intelligent.** ## 3. The world around the model keeps changing Modern LLMs and AI tools are much more than a language model. They depend on retrieval systems, APIs, search tools, databases, memory systems, browser tools, and many other moving parts and tools that people keep building daily. Even if the model itself stays the same, one of these surrounding systems can change. Like: - A retrieval pipeline may start returning different documents. - An API might change its response format. - A search system could rank results differently. - A database may contain newer information than what the model was originally designed around. Small changes like these slowly create a gap between what the model expects and what the external production system actually provides to the LLMs. From the user's point of view, it simply looks like the AI became worse. ## 4. Product updates slowly stress the model out A model is rarely left alone after launch. Developers keep adding new features, new safety checks, business rules, memory systems, tool routing, and longer system prompts. Each change may improve one area while making another area slightly worse. The longer the instruction stack becomes, the harder it is for the model to balance every requirement. > A coding request now has to follow formatting rules, safety rules, product rules, business rules, memory rules, and tool selection rules before it even starts solving the user's problem. > > None of these changes are necessarily bad. But over time, they can change how the model behaves compared to launch day. ## 5. Economics and cost-cutting Running the newest models is expensive. During launch week, companies allow generous usage because they want developers and users to experience the model at its best. It creates excitement and shows what the technology can do. This is marketing at play. That period does not last forever because compute is not free. As usage grows into millions of requests every day, cost becomes impossible to bear for a company. Companies begin optimizing inference, changing routing logic, adjusting rate limits, or deciding when a smaller model is good enough. **We have already seen examples** of products using different models depending on workload or subscription level. Like with the release of Fable 5. None of this is surprising. Every company has to balance performance with cost. --- ## Can this problem be solved? **Yes and no.** Some parts of it can and some can't. Problems like controlled environment training can be addressed by exposing models to more real users before a general release. Larger beta programs, early access releases, and collecting feedback from a different group of users can help uncover issues that simply do not appear inside a controlled testing environment. The more real-world prompts a model sees before launch, the fewer surprises it will face after release. **But not every problem has a straightforward solution.** - You cannot completely predict how millions of people will use a model. - External systems will continue to evolve - Developers will continue improving the product - The cost of serving SOTA models will always influence engineering decisions. In other words, some parts of the Two-Week Smartness Problem can be reduced. Others are simply part of running AI systems at large scale. ## Is the model actually getting worse? Sometimes yes. Sometimes no. Many of the complaints are caused by changing expectations. When a new model launches, people focus on what it does well. Two weeks later, everyone has already accepted those strengths and started looking for weaknesses instead. ## Final thoughts I don't think there is a single reason behind the Two-Week Smartness Problem. It is usually a combination of problems, as we have discussed earlier.