According to a report by Reuters, artificial intelligence giant OpenAI is preparing to launch an initial public offering (IPO) by the second half of 2026. This IPO could see the company valued at a staggering one trillion dollars, fueled by an ambitious capital raise of $60 billion.
Despite these ambitious plans, an OpenAI spokesperson emphasized that the company remains primarily focused on the development of artificial general intelligence (AGI). The company is striving to build a durable business model while ensuring that everyone benefits from advancements in AGI.
The potential IPO is seen as reflecting growing institutional interest in AI development, particularly for OpenAI. The company became the world's largest startup after reaching a $500 billion valuation in a secondary share sale last October.
During the share sales, OpenAI employees sold a cumulative $6.6 billion in stock to large corporate investors. This valuation surpassed that of Elon Musk's SpaceX, which had a valuation of $400 billion.
Despite its significant resources, OpenAI's flagship product, ChatGPT, has recently been outperformed by competing Chinese AI chatbots in a niche area: autonomous cryptocurrency trading. In an autonomous crypto trading competition, Chinese AI chatbots DeepSeek and Qwen3 Max briefly surpassed ChatGPT and Grok in trading performance.
DeepSeek achieved the only positive trading return, with approximately 9% as of October 22, while ChatGPT-5 slipped to last place after a 66% loss. These results came as a surprise considering DeepSeek was developed at a total training cost of $5.3 million, a small fraction of OpenAI's $5.7 billion spend on research and development initiatives during the first half of 2025 alone.
However, Nicolai Sondergaard, research analyst at crypto intelligence platform Nansen, suggests that the right prompts and improved training data could improve the trading performance of some of these AI models, particularly for ChatGPT and Google's Gemini.
"Assuming all models received the same prompts and instructions for trading, it can be assumed that the difference lies in the data each model has been trained on," Sondergaard stated.
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