{"id":482140,"date":"2025-05-27T14:36:43","date_gmt":"2025-05-27T13:36:43","guid":{"rendered":"https:\/\/cryptoslate.com\/?p=482140"},"modified":"2025-05-27T14:36:43","modified_gmt":"2025-05-27T13:36:43","slug":"we-interviewed-gpt-3-in-2022-before-chatgpt-heres-what-it-got-right-and-so-very-wrong","status":"publish","type":"post","link":"https:\/\/cryptoslate.com\/we-interviewed-gpt-3-in-2022-before-chatgpt-heres-what-it-got-right-and-so-very-wrong\/","title":{"rendered":"We interviewed GPT-3 in 2022 before ChatGPT. Here\u2019s what it got right (and so very wrong)"},"content":{"rendered":"

“I believe that the all-time high of Bitcoin was around $20,000.” \u2014 GPT-3, April 2022<\/em><\/p>\n

“Bitcoin peaked at $68,990 in November 2021 due to a confluence of pandemic-era stimulus, institutional momentum, and speculative fervor. The misquote from GPT-3 highlights the limitations of pre-trained, non-updated models.” \u2014 GPT-4o, May 2025<\/em><\/p><\/blockquote>\n

Three years ago, I interviewed GPT-3<\/a> several months before the launch of ChatGPT. I had closed beta access to OpenAI's research dashboard and was fascinated by what this technology could do.<\/p>\n

I spent a week recovering from Covid, ‘chatting' late into the night with the AI about everything from Bitcoin<\/a> to consciousness. At the time, the AI responded in the first person and claimed to be able to move around the world, even claiming to be in the room with me.<\/p>\n

In the article, I treated it like an alien guest, curious, eerily articulate, but fundamentally limited. It was a historical moment: I was interrogating a machine that claimed to understand blockchain, investment logic, and even its own purpose. Looking back, the transcript reads as both charming and flawed, a document shaped by a young AI\u2019s eloquence and a journalist\u2019s skepticism.<\/p>\n

Today, the game has changed.<\/p>\n

What GPT-3 got right<\/h2>\n

Much of the framing in 2022 still holds. GPT-3\u2019s prediction that blockchain and AI could intersect in sectors like health data and voting remains prescient. The model described AI as a pattern-spotter, capable of parsing massive datasets, precisely the role many LLMs now play in analyzing on-chain activity or monitoring financial anomalies.<\/p>\n

Its explanation of how LLMs generate text, predicting the next most likely token, still serves as the go-to metaphor in 2025.<\/p>\n

And perhaps most crucially, its answers raised the big question: Could AI eventually replace human crypto journalists?<\/em><\/p>\n

What aged poorly<\/h2>\n

A lot of the piece now feels quaint.<\/p>\n