Why Deep Research?
When a question comes to mind and rapid scaling is required, Deep Research is currently the fastest way.
Why Visualization?
Humans tend to get bogged down in details when processing textual information; visualization is an effective way to focus quickly and identify the next question.
Can Deep Research and Visualization Become a Final Product?
Yes and no, depending on how they are used. However, they serve as the best efficiency tools: quickly identifying issues and pinpointing where more time and effort need to be invested.
For example, following the post-earnings plunge of Oracle and AVGO (Broadcom)—though the reasons differed—Oracle's decline was purely due to missing expectations and concerns over cash burn, while for AVGO, it might be due to anticipated margin compression or simply being overpriced. But in reality, what lies beneath is a synthesis of the series of issues we've discussed previously, which is essentially a liquidity problem: how massive Capex will align with future revenue.
Deep Research can establish a reference framework, gathering enough data (which might be wrong) to perform calculations (which might also be wrong) and reach conclusions. Visualization helps people orient themselves rapidly.
Take the infographic below as an example: the analytical framework and mathematical model at least seem to make sense, and some conclusions appear generally reliable. The specific numbers, of course, undoubtedly contain errors.
The question is, what do we need? A perfect framework, precise calculations, or the factors that haven't been accounted for?
Everyone has a different answer, and I have mine.
Today, for me, this workflow addresses at least two "pain points": efficiency, which is obvious; and the issue of conclusions and recommendations. Independent media is not suited to provide specific investment conclusions or advice. Using entirely AI-generated results without human intervention can achieve at least two things: first, "AI generation may contain errors and is for reference only"; second, because it is AI-generated, people naturally harbor skepticism, leading them to focus more on the process rather than the result. Is this a disguised form of "investor education"? Just a little joke.
Therefore, the slides below are all generated, with content sourced from Gemini's Deep Research. The following content serves two purposes: first, as an exploration case for AI applications; and second, to provide a potentially viable line of thinking.
