ICL is a phenomenon separate from long-context performance degradation, they can coexist, similarly to how lost-in-the-middle affects the performance of examples in different positions just as fine.
It really depends on the task, but I imagine most real world scenarios have a mixed bag of requirements, such that it's not a needle-in-a-haystack problem, but closer to ICL. Even memory retrieval (an example given in the post) can be tricky because you cannot always trust cosine similarity on short text snippets to cleanly map to relevant memories, and so you may end up omitting good data and including bad data (which heavily skews the LLM the wrong way).
[1]: Coincidentally what the post author is selling
Built-in reasoning chain certainly helps in long-context tasks, especially when it's largely trained to summarize the context and deconstruct the problem, like in Gemini 2.5 (you can easily jailbreak it to see the native reasoning chain that is normally hidden between system delimiters) and DeepSeek R1-0528, or when you're forcing it to summarize with a custom prompt/prefill. The article seems to agree.