Deep Research: A New Tool for Artificial Intelligence

Let Her (Him?) Rip!

I presented a query to ChatGPT’s new Deep Research tool to learn more about the controversy surrounding DeepSeek and its impact on Nvidia and other semiconductor providers. The query was as follows: “I would like some help writing a research report about the controversy surrounding DeepSeek and its impact on Nvidia and other semiconductor providers. Specifically, I watched a Smart Money episode yesterday where the talking head surmised that “nobody needs an H100″ anymore, much less a Blackwell. But Jensen Huang claims the inference of reasoning models, the future of conversational AI, requires 100 times the compute power of a simple ChatGPT search. Can you outline the cases, the growth of inference vs training, and perhaps provide some forecasts? Focus on high-level, with a few financial details as support. Focus on Nvidia, but include AMD and the Cloud providers’ ASICs which cannot (yet) run reasoning models well. Don’t focus on Deep seek, but rather the general market disruption over a 1-3 year timeframe.”

The Report

After 6 minutes, ChatGPT’s Deep Research read 42 sources and produced a well-laid-out report, covering 13 pages and 4720 words. The report is available on my website to avoid using AI to write a piece for Forbes.

Deep Seek Impact On Nvidia Overblown

The CNBC talking head knows a lot more about investing (theoretically) than AI and industry trends. While some may refute Deep Seek’s claims of the number and type of GPU’s used to create V3 and the reasoning chatbot R1, it will lower the cost of training and reasoning inference. We should see every foundation model builder adopt it or something like it. However, the six minutes ChatGPT required to create the report, and the outstanding content it produced, validate Jensen Huang’s assertion and the value of reasoning models.

So, How Good Was ChatGPT?

In short, it was amazing. The bot produced a report that includes:

* Training vs. Inference: Diverging Compute Demands in Conversational AI
* The Shift to Inference: From One-Time Training to Everyday AI Services
* “Nobody Needs an H100 Anymore”? The Push for Cheaper Inference
* NVIDIA’s Blackwell Generation: Upping the Ante for Training and Inference
* Cloud Providers’ ASICs: Google and Amazon Bet on In-House Silicon
* Outlook: Inference Growth, Market Forecasts, and Financial Implications

Each section was insightful and objective, and I did not detect any errors.

In Summary, What Did We Learn?

I learned that my intuition and experienced viewpoints on this controversy are the same as ChatGPT, for better or worse. I also learned that ChatGPT Deep Research can be an excellent research tool, the likes of which can help analysts learn more about topics and test hypotheses. Or it could replace us!

Conclusion

I hope this article provides insight into the potential of ChatGPT’s Deep Research tool and its ability to produce high-quality research reports. I also hope it provides a more in-depth understanding of the impact of DeepSeek on Nvidia and other semiconductor providers.

FAQs

Q: What is ChatGPT’s Deep Research tool?
A: ChatGPT’s Deep Research tool is a new feature that allows users to conduct research on a topic and receive a well-researched report in return.

Q: How did you use the tool?
A: I used the tool to research the controversy surrounding DeepSeek and its impact on Nvidia and other semiconductor providers.

Q: How accurate was the report?
A: The report was very accurate, with no detected errors.

Q: How much did the tool cost?
A: The tool cost about $2 to run, which is 200 times more than a simple inference query.

Q: What did you learn from the report?
A: I learned that my intuition and experienced viewpoints on this controversy are the same as ChatGPT, and that ChatGPT Deep Research can be an excellent research tool.