(This architecture diagram may look boring, but it actually demonstrates the operating model of a “digital sweatshop”: a planner assigns tasks, dozens of crawler agents dig in parallel, and finally, a chief editor summarizes. Simple, crude, yet extremely effective.)
Today, we’re not talking about those chatbots that only know how to write acrostic poems or sweet-talk you.
Let’s talk about something boring: the art of “grunt work.”
Have you noticed that current AI is like a drunken encyclopedia salesman? It’s brimming with confidence and can spin a pile of seemingly flowery nonsense in three seconds. But if you ask it, “Please help me compile a capacity distribution table for the global solid-state battery industry chain in Q1 2024, with data sources attached,” it immediately starts dodging eye contact, or even invents a few factories that don’t exist.
Why? Because while its “brain capacity” is large, its “eyes” are blind. The knowledge of most LLMs (Large Language Models) cuts off the day their training ended. Even when they do connect to the internet, they only scratch the surface, daring to draw conclusions after merely glancing at the top three results on Google.
This is the background behind the birth of GPT Researcher. This thing isn’t built to chat with you; it’s more like a tireless, emotionless foreman holding a Python whip, directing a group of digital interns to dig frantically through the ruins of the internet.
Brute Force Aesthetics: From “Solo Combat” to “Saturation Attack”
This is actually quite counter-intuitive.
We are used to the elegance of ChatGPT’s “one-question-one-answer” style. But GPT Researcher tells you: elegance is worthless in the face of hardcore research.
Its core logic comes from a paper called “Plan-and-Solve”. Simply put, when you throw a question at it, it doesn’t answer immediately. Instead, it “stops to think” (Generating Research Questions).
For example, if you ask: “Is Tesla’s Cybertruck a failure?”
It won’t just blabber. Instead, it breaks this question down into a pile of sub-questions:
- “What are the Cybertruck delivery figures for Q1 2024?”
- “What represent the major quality complaints from owners?”
- “How do sales compare to the rival Rivian R1T during the same period?”
- “What are the latest ratings from Wall Street analysts on Tesla’s pickup business?”
Then comes the key part—Parallelization.
It instantly splits into over 20 “crawler agents” that rush simultaneously to every corner of the internet. Some go to read financial reports, some go to flip through complaint threads on Reddit, and others check authoritative news.
This isn’t a conversation; this is a “saturation attack.”
(Don’t be scared by the complex process. Essentially, this accelerates the painful human research loop of “search-open page-copy-paste-summarize” by 1000 times using code.)
The “Solvent” for Bias
There is an interesting blind spot here: We always assume the way to eliminate bias is to “find the single truth,” but GPT Researcher’s logic is to “drown out noise with more noise.”
Humans are prone to bias in research because we are lazy. We find an article that aligns with our views, think “Yup, this is the truth,” and close the browser. But machines aren’t lazy.
The reference materials mention a core statistic: each study aggregates over 20 web resources.
The logic of the author, Assafelovic, is simple and crude—if you only look at 1 source, the probability of it being wrong is 50%; if you look at 20 sources and they are independent of each other, the probability that everyone is colluding to lie to you is extremely low.
This reminds me of the “Law of Large Numbers” in statistics. It doesn’t seek the absolute authority of a single source but pursues the cross-verification of multi-source information. It even specifically uses the Tavily Search API. The advantage of this over the Google Search API is that it doesn’t throw a mess of HTML tags at you, but directly extracts the “meat” (plain text content) from the webpage to feed the LLM.
To put it plainly, it feeds the AI refined fodder, not grass roots covered in mud.
Who is Swimming Naked? Perplexity vs. Stanford Storm vs. GPT Researcher
Looking at the industry, this is actually a war regarding “Deep Search.”
- Perplexity AI is like a consultant in a suit. The experience is silky smooth, and it can even draw charts for you, but it’s closed-source and costs you $20 a month.
- Stanford Storm is the honor student of academia. What it writes looks like Wikipedia—rigorous but a bit pedantic—and it’s a bit heavy to deploy.
- GPT Researcher is like a Swiss Army knife in the hands of a geek. Open source, based on LangChain, supports one-click Docker deployment, and most importantly—it’s cheap.
Let’s do the math: A deep research session takes an average of 3 minutes and costs about $0.10 in tokens (approx. 0.7 RMB).
This is awkward. If I were a junior analyst at a consulting firm earning $50 an hour, the materials I spend a day compiling might not be as comprehensive as what this $0.10 script runs.
This is not just a victory for tools, but a “dimensional strike” (devaluation) against the value of white-collar labor.
(In this battlefield, the winner isn’t the one with the most search results, but the one that feeds the LLM most comfortably. Tavily wins on “digestion” capability, not “swallowing” capability.)
The Ouroboros Concern
Of course, this isn’t without its bugs.
If we extrapolate immaturely: When GPT Researcher starts scraping content on a massive scale, what exactly is it catching?
The current internet is being rapidly filled with AI-generated content. If 15 out of those 20 sources in GPT Researcher are garbage content generated by other AIs (like water-cooler blogs written by ChatGPT), what happens?
This becomes “Ouroboros” (the snake eating its own tail).
It turns hallucinations manufactured by AI into a kind of “consensual hallucination” through “cross-verification.” When the whole web is spreading the same fake news concocted by AI, GPT Researcher will follow the logic of “majority rules” and write this fake news into the report as ironclad law.
Quantity can dilute bias, but quantity can also solidify fallacies.
This is also why the project emphasizes support for long-context models like gpt-4o and prioritizes filtered search engines like Tavily as the core—essentially, this is panning for gold in a landfill; the quality of the filter determines the baseline of survival.
Don’t Fire the Researcher Yet
Back to the end.
GPT Researcher is strong, strong enough to send shivers down the spine of someone like me who writes for a living. But what it can currently replace is merely the “information collection” and “preliminary organization” loop.
It can tell you “what happened in the past ten years,” but it can’t tell you “what this implies.”
It can list the conflicts of 20 viewpoints, but it can’t sniff out the scent of commercial opportunity within those conflicts like a human can.
So, don’t rush to be anxious. Instead of worrying about being replaced, treat it as your “external brain expansion.”
Hand over all that menial labor of manually opening Google, right-clicking to copy, and switching windows to paste.
That $0.10 isn’t buying an answer; it’s buying you time to think.
In this era where answers are becoming cheaper, the ability to “ask good questions” is the most expensive luxury.
