Prediction for Next Hot Thing in Q4 2025 / Q1 2026: someone will make the Nobel prize-worthy discovery that you can stuff results of your deep search into a database (vector or otherwise) and then use it to improve the ability to compile a higher-quality report from much larger amount of sources.
We'll call it DeepRAG or Retrieval Augmented Deep Research or something.
Prediction for Q2 2026: next Nobel prize awarded for realizing you may as well stop treating report generation as the core aspect of "deep research" (as it obviously makes no sense, but hey, time traveler's spoilers, sorry!), and stop at the "stuff search results into a database" and let users "chat with the search results", making the research an interactive process.
We'll call this huge scientific breakthrough "DeepRAG With Human Feedback", or "DRHF".
One of the implications of OpenAI's DR is that frontier labs are more likely to train specific models for a bunch of tasks, resulting in the kind of quality wrappers will find hard to replicate. This is leading towards model + post training RL as a product, instead of keeping them separate from the final wrapper as product. Might be interesting times if the trajectory continues.
PS: There is also genspark MOA[2] which creates an indepth report on a given prompt using mixtures of agents. From what i have seen in 5-6 generations, this is very effective.
[1]: https://x.com/_philschmid/status/1896569401979081073 (i might be misunderstanding this, but this seems a native call instead of explicit)
[2]: https://www.genspark.ai/agents?type=moa_deep_research