Recently, I ran a simulated car-buying scenario to test OpenAI's Deep Research. The setup was that in my younger days, I focused mostly on performance cars, but as I got older and more family-oriented, I realized there was a whole other category of vehicles that emphasize NVH (Noise, Vibration, and Harshness). I knew almost nothing about this space, so I wanted Deep Research to help me walk through a complete simulation of buying a car, from early-stage research to mid-stage selection to final negotiations, just to see how useful Deep Research could be in everyday life.
Note that I'm using OpenAI's Deep Research as just one example. My personal experience suggests that OpenAI Deep Research has much higher-quality findings than its competitors, but other similar tools like Perplexity Deep Research or Gemini with Deep Research might deliver comparable results.
The outcome was eye-opening. With Deep Research, my entire process of research, thinking, and decision-making was greatly enhanced.
The first and most noticeable benefit was that Deep Research itself significantly boosted my research efficiency. Take NVH as an example. Traditionally, if you want to learn about NVH from scratch, you would go online, search endlessly, and read numerous webpages and articles, then finally try to summarize everything and form your own viewpoint. But o1 Pro alone can already explain what NVH is, covering the basics. Deep Research then takes it a step further by providing concrete examples that make an abstract explanation more vivid. It tells you which manufacturers focus on NVH, how they achieve it, what their marketing strategies and selling points are, and so on. In other words, it automates the manual research process and produces a report of very high quality, comprehensively answering many of my questions. Deep Research let me take in a large amount of information quickly and in an organized way, giving me a basic or preliminary understanding of a new field, and saving me a huge amount of time.
Building on that, I discovered a second key advantage: because Deep Research took on the heavy lifting of data-gathering, reading, and note-taking, I had more mental energy for high-level thinking. Once I had a basic grasp of the NVH field, the next step was to narrow down my potential car choices. Before the AI era, I might have done an extensive search—comparing which car models emphasize NVH, listing each model's selling points and drawbacks, checking brand images, and reading both media and user reviews. Then I would compile all of that into a report. This alone would be an enormous amount of work—without AI, and given I'm not a car expert, I might have spent 5 to 10 hours scouring the internet just to gradually narrow down my options before doing a deeper dive. Achieving that level of diligence would already be considered very thorough.
However, with Deep Research, I felt like it completely redefined what a “good” piece of research looks like. After using Deep Research to gather the main points about typical cars that emphasize NVH, outlining their pros, cons, and reviews, the process took about ten minutes—work that would have otherwise taken five hours. This freed me up to consider other potential blind spots in my decision-making. For example, I ended up exploring four extra domains:
- Residual value. I quickly learned that residual values for electric vehicles drop quite a bit in the first few years. For instance, Lucid EVs and the Mercedes EQS often lose about 60% of their value in the first three years.
- Reliability. Different models have very different reputations for reliability. Land Rover, for example, sometimes sees used vehicles (after they've run smoothly for a while) selling at a higher price than new ones.
- Inspired by Deep Research, I also looked into inventory backlogs. For example, it found on the Edmunds site that there are more than a hundred Mercedes-Maybach GLS EQS680 models for sale, including some 2024 units that still haven't been sold [link]. This suggests there may be a significant discount opportunity.
- I did some research on car price negotiations as well. On Reddit, I found people mentioning that they managed to negotiate as much as 23% off that same EQS680 model.
That massively increased both the depth and scope of my research. Without Deep Research, limited time alone would mean I could never do such a thorough job.
The third benefit goes hand in hand with the second. Because we reached a breadth and depth of research that used to be out of reach, my mind was freed to think about areas I previously would never have considered. An example: in the past, after listing the pros and cons of each car, I would head to the dealer, test-drive, and haggle a bit—usually doing only minimal negotiation because my brain was wiped out by the research phase, and I wasn't all that interested in bargaining. Often, I ended up paying list price.
But after seeing all this data, even someone as slow on the uptake as me realized that the Mercedes EQS 680 might be worth pushing for a big discount, thanks to its heavy backlog and steep initial depreciation. Reddit offered similar success stories. So I discussed negotiation strategies with o1 Pro to see how I could get a good price. o1 Pro systematically analyzed it and advised me to avoid mentioning high-end brands like Rolls-Royce and Bentley. From the sales rep's perspective, once you bring those up, you get categorized as a customer who's not concerned about price. Then the conversation shifts from price to history, heritage, and the luxury experience—completely contrary to your goal. Instead, you should say that you're also considering lower-priced models and talk about how those might actually be superior in certain aspects. This creates a customer image: you have some money, enough to afford the car, but you also care a lot about getting the best value. If the salesperson offers a better deal, you're more likely to buy.
This salesperson's perspective opened my eyes. But then I was stuck, because I had no idea which other models could serve as good bargaining chips. Deep Research came to the rescue again, searching for targeted alternatives like the Cadillac Escalade IQ, the Porsche Cayenne Hybrid, and the Audi e-tron Q8. It showed me each model's performance advantages and pricing advantages compared to the EQS, plus relevant negotiation tactics. This was all enormously helpful—not just in saving time, but in letting me think at a depth that would have been impossible on my own. If the first two benefits helped me find answers more quickly, this one essentially redefined the boundaries of the question.
So overall, I believe OpenAI's Deep Research tool has delivered a massive productivity boost over traditional, human-only research methods. On one hand, it makes the research process itself highly efficient and straightforward. On the other hand, in both research and beyond, it also enables a depth and quality that used to be out of reach. It further proves that AI has tremendous untapped potential for our everyday lives, especially in scenarios where humans and AI collaborate. The AI does the data mining and hands us cleaned-up information. Our job is the “cognitive alchemy,” extracting insights, making decisions about the next step, and then pushing further with AI's support.
That said, remember we still have to fulfill our role as AI managers. We should open the links and verify crucial details. Tools like Deep Research make this verification easy—just click the link and you'll see the relevant passages highlighted. In my experiment, I didn't find any errors, but it's still an essential part of the process.
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