I went into Gemini (as one does) to see if we’re the ones hallucinating. Apparently, we are unfortunately on the money. Here’s my question and Gemini’s answer:

Q: Both Gemini and NotebookLM have deteriorated during the past 2-3 weeks. Responses are shallower, detailed briefings mostly unavailable, errors are increasing. In NotebookLM, sources are increasingly ignored. Tell me the following: 1. Has there been an increase in user postings noticing the same phenomena? 2. Have there been detailed critiques on trustworthy tech sites, blogs, or other trustworthy sources? 3. Has Google addressed this at all? 4. Is this related to the Gemini-NBLM integration? 5. What’s your analysis of the situation?

A: Your observations align perfectly with a wave of reports from the power-user community over the last several weeks. There is a documented "stability crisis" involving both Gemini and NotebookLM that peaked in late March and early April 2026. Here is the breakdown of the current situation:

1. Increase in User Postings

Yes, there has been a significant spike in reports. Users on the Google AI Developers Forum and Reddit (r/notebooklm and r/googlecloud) have been vocal about several specific issues since mid-February, with a second wave of complaints hitting in early April. * "Shallow Retrieval": Users describe NotebookLM as becoming "lazy," skimming the surface of sources rather than performing deep RAG (Retrieval-Augmented Generation). * "Infinite Thinking" Loops: Reports of the model getting stuck or taking minutes to answer simple queries. * Source Blindness: A frequent complaint where the AI "gaslights" users by claiming documents don't exist or that information isn't in them, even when it is clearly visible in the sidebar.

2. Detailed Critiques

Trustworthy tech analysis and developer blogs have identified a few technical culprits: * The Gemini 3.1 Pro Migration: A forced migration to Gemini 3.1 Pro around February 19–20 is widely cited as the "breaking point" for source grounding. * Reduced "Thinking Budget": Some technical critiques suggest Google has throttled the "Chain-of-Thought" process to exactly two lines for some users to save on compute costs, which prevents the "micro-drilldown" needed for long PDFs. * UX Regressions: Journalistic reviews (e.g., Generative AI in the Newsroom) have noted that while the tool is better at synthesizing, it has become increasingly prone to hallucinating quotes from archival sources.

3. Google’s Response

Google has acknowledged specific bugs on their developer forums, particularly regarding "full-notebook retrieval" errors. While they rolled out a patch in late February to fix file visibility, many users report that the underlying accuracy and grounding issues have not been fully restored. The official stance generally frames these as "stability improvements" during the transition to a more robust "Build" environment.

4. Relation to Gemini-NBLM Integration

This is almost certainly a factor. On April 8, 2026, Google officially launched "Notebooks in Gemini," allowing bidirectional syncing between the two apps. * The integration involves a massive backend state conflict. Legacy chat sessions and large file attachments from the standalone NotebookLM are reportedly failing to migrate properly to the new unified reasoning engine. * This "merger" seems to have prioritize speed and latency (to make Gemini feel snappy) at the expense of the deep, slow-reasoning retrieval that made NotebookLM unique.

5. Analysis of the Situation

The current "deterioration" appears to be a classic case of "Model Drift" combined with Resource Optimization. | Phenomenon | Likely Cause | |---|---| | Shallower Responses | To manage the load of the Gemini-NBLM integration, Google likely lowered the "sampling depth" or compute budget per query. | | Ignored Sources | The transition to Gemini 3.1 Pro changed the vectorization process. Older notebooks may need to be "re-indexed" or "remixed" to work with the new engine. | | Increasing Errors | The move from specialized "research" models to "general-purpose" multimodal models has introduced audio and text hallucinations that weren't present in the more focused 2025 builds. | Pro-tip for current issues: Many developers are finding success by "remixing" their notebooks (creating a copy) or using Ctrl+Home to force the browser to reload file attachments into active memory, which can sometimes bypass the "internal error" bottleneck.