In October 2025, OpenAI announced a $6.6 billion financing and set a record for the largest single fundraising by a startup globally, with its valuation breaking through $157 billion, leaping to the world\u2019s third-largest unicorn. The news quickly ignited the global tech and investment circles and again pushed the \u201cAI bubble\u201d controversy to a climax. From ChatGPT\u2019s sudden emergence in 2022, to Sora stunning the world in 2024, and to the AI Agent concept sweeping the globe in 2025, artificial intelligence has undergone a rapid leap from concept to capital frenzy in just three years. However, behind this seemingly prosperous feast, the doubts about whether AI has fallen into a bubble have grown louder. This article, drawing on multiple media reports and industry data, will objectively dissect the real picture of today\u2019s AI industry and explore whether this technological revolution has morphed into a capital game.<\/p>\n\n\n\n
OpenAI\u2019s fundraising record is not an isolated case. According to Caijing<\/em> magazine, global AI financing totaled $125 billion in 2024, up 42% year over year\u2014far outpacing overall global venture capital growth. The US and China alone accounted for nearly 80% of AI startup funding. In China, there were 1,023 financing events in AI in 2024, totaling more than $45 billion, up 38% from 2023. Top players such as Moonshot AI, MiniMax, and Zhipu AI saw their valuations double within half a year; some firms even joined the \u201cunicorn\u201d ranks before their products were commercialized.<\/p>\n\n\n\n The speed of these valuation surges has far outstripped the growth logic of traditional tech companies. Take Moonshot AI as an example: when it completed a $1 billion round in March 2024, it was valued at $2.5 billion; by October 2024, its valuation had skyrocketed to $5 billion, doubling in just seven months. This \u201clightspeed valuation\u201d phenomenon has sparked broad concern. In early 2025, GSR Ventures partner Zhu Xiaohu publicly stated: \u201cOver 90% of AI companies are overvalued and will face brutal consolidation.\u201d He noted that many AI projects can fetch hundreds of millions of dollars in valuation based on nothing more than a demo or a concept\u2014eerily similar to the scenes of the 2000 dot-com bubble, driven by capital fervor detached from fundamentals.<\/p>\n\n\n\n Even more alarming is the severe disconnect between revenues and valuations at some AI firms. According to Yicai<\/em>, in 2024 fewer than 5% of Chinese AI startups posted annual revenue exceeding RMB 100 million (about $14 million), yet there were over 30 companies valued at more than $1 billion. This structural imbalance of \u201chigh valuation, low revenue\u201d has become a key piece of evidence for the AI-bubble thesis.<\/p>\n\n\n\n II. The Technology Illusion: The Gap from \u201cDisruptive\u201d to \u201cMiddling\u201d<\/strong><\/p>\n\n\n\n AI\u2019s actual ability to land in real scenarios has become the key yardstick for testing whether there is a bubble. Although large models have shown astonishing capabilities in text generation and image recognition, their limitations have become increasingly prominent in commercialization. For AI coding assistants, for example, a 2024 GitHub survey showed that only 17% of developers believed AI tools \u201csignificantly improved efficiency,\u201d while more than 60% said AI-generated code required extensive manual correction and in fact increased workload. Microsoft\u2019s 2025 internal assessments similarly found that the code adoption rate of its AI-assisted programming tools in real projects was under 30%, far below market expectations.<\/p>\n\n\n\n In healthcare, the accuracy of AI diagnosis faces challenges as well. A 2024 study in Nature<\/em> showed that AI\u2019s misdiagnosis rate for rare diseases was as high as 34%, far higher than human experts\u2019 12%. In China, after a Class-A tertiary hospital introduced an AI-assisted diagnosis system, medical disputes due to misdiagnosis rose 22% within a year, and the hospital ultimately had to suspend some of the system\u2019s functions.<\/p>\n\n\n\n More awkward still, some AI products have fallen into a loop of \u201cdazzling demos, weak deployment.\u201d An \u201cAI salesperson\u201d released by a leading AI company in 2024 could converse smoothly with customers onstage at the launch, but in actual rollout, it was heavily complained about for failing to understand dialects and handle complex contexts, and was taken offline just three months after launch. This huge gap between \u201ctechnology illusion\u201d and business reality is draining market trust in AI.<\/p>\n\n\n\n III. The Profitability Dilemma: An Unbridgeable Chasm Between Burn Rate and Returns<\/strong><\/p>\n\n\n\n The ferocity of AI\u2019s cash burn has become jaw-dropping. According to OpenAI\u2019s 2025 financials, its annual loss reached $5 billion, mainly due to compute costs and R&D. The daily operating cost of ChatGPT alone exceeded $7 million, while annual revenue was only $3.7 billion, leaving a huge deficit. To keep running, OpenAI had to rely on financing \u201cblood transfusions\u201d; of its $6.6 billion round in 2025, explicit use-of-proceeds was earmarked for \u201ccompute procurement over the next three years.\u201d<\/p>\n\n\n\n Chinese AI firms are in similarly dire straits. At one leading large-model company, R&D investment in 2024 reached RMB 1.5 billion (about $208 million), but annual revenue was only RMB 80 million (about $11.1 million), meaning R&D was 18.75\u00d7 revenue. In an internal letter, the CEO admitted: \u201cIf we cannot achieve a commercialization breakthrough within the next two years, the company will face capital-chain risks.\u201d This business model of \u201chigh input, low return\u201d has trapped the AI industry in a vicious circle of \u201cthe more advanced, the more loss-making.\u201d<\/p>\n\n\n\n More brutally, the \u201cMatthew effect\u201d is intensifying. CB Insights data shows that in 2024, 90% of global AI financing went to the top 10% of companies, leaving the remaining thousand-plus AI startups to split just 10% of the funds. Many small and mid-sized AI firms, unable to secure follow-on financing, have already seen waves of layoffs and bankruptcies. In the first half of 2025, more than 200 AI startups in China deregistered, up 150% year over year.<\/p>\n\n\n\n IV. Historical Mirror: The Cycle from the Dot-Com Bubble to the AI Frenzy<\/strong><\/p>\n\n\n\n Today\u2019s AI shows striking similarities to the 2000 dot-com bubble. Between 1999 and 2000, the Nasdaq surged from 1,500 to 5,048\u2014an increase of over 230%\u2014with internet firms taking half the stage. Back then, any company with \u201c.com\u201d in its name could see its valuation multiply. Pets.com, founded in 1999, saw its stock soar 70% on listing day despite never turning a profit, with market cap once hitting $300 million, but it declared bankruptcy just 268 days later.<\/p>\n\n\n\n History rhymes. Since 2024, global AI concept stocks\u2019 average P\/E has exceeded 200\u00d7, far higher than the 15\u201320\u00d7 average of traditional sectors. In China, one AI-chip firm posted net profit of just RMB 50 million (about $6.9 million) in 2024, yet its market cap once broke RMB 100 billion (about $13.9 billion), implying a 2,000\u00d7 P\/E. Such valuations detached from fundamentals mirror the \u201cprice-to-dream\u201d ratios of the dot-com era.<\/p>\n\n\n\n Equally noteworthy is that today\u2019s \u201cconcept speculation\u201d playbook in AI resembles that period: from 2023\u2019s \u201cYear One of LLMs,\u201d to 2024\u2019s \u201cAI Agents explosion,\u201d and to 2025\u2019s \u201cAI+Everything,\u201d every year a new concept is hyped by capital. As one VC partner revealed: \u201cWhen investing in AI projects now, we don\u2019t look at the tech or revenue; we look at whether the story sounds compelling, whether it can tell a \u2018disrupt-the-industry\u2019 narrative.\u201d This story-driven logic is a textbook sign of a bubble.<\/p>\n\n\n\n V. The Road Ahead: Finding Hard-Core Value Amid the Foam<\/strong><\/p>\n\n\n\n Despite conspicuous bubble signs, AI is not value-less. Unlike the 2000 dot-com bubble, today\u2019s AI already has real application scenarios and commercial cases. For instance, AI\u2019s substitution rate in customer service has reached 30%, significantly reducing labor costs; in film and TV production, AI-assisted generation has cut VFX costs by 40% and shortened production cycles by 50%. McKinsey forecasts that by 2030, AI will contribute $13 trillion to global GDP, with a 42% compound annual growth rate.<\/p>\n\n\n\n The key is how to sift out companies with real technical moats and commercial flywheels amid the foam. History shows that after a bubble bursts, 90% of companies disappear, but the remaining 10% grow into the next generation of giants. Amazon and Google emerged precisely after the 2000 collapse. The current AI industry likewise needs a \u201cdefrothing\u201d process to weed out firms that live by concept alone and keep the players that truly create value.<\/p>\n\n\n\n For investors, it is time to return to business fundamentals and focus on hard indicators such as revenue structure, customer retention, and technical barriers, rather than being seduced by \u201cdisruptive innovation\u201d narratives. For entrepreneurs, abandon \u201cvaluation-driven\u201d thinking, focus on real pain points, and build sustainable business models. As one AI-unicorn founder said: \u201cOnly when AI is no longer an \u2018AI company,\u2019 but becomes infrastructure like water and electricity, will this industry truly mature.\u201d<\/p>\n\n\n\n Does an AI bubble exist? The answer is likely yes. But \u201cbubble\u201d is not a pejorative\u2014it is the growing pain of every technological revolution. From railway manias to the internet bubble, history repeatedly proves: after the bubble bursts, real value emerges. Today, the AI industry stands at a crossroads\u2014leftward lies the abyss of capital revelry; rightward, the long march of technology landing. The ultimate winners will be the few who can stay clear-headed amid the clamor and hold to essentials within the foam.<\/p>\n\n\n\n [Disclaimer]<\/strong>: The above content reflects analysis of publicly available information, expert insights, and BCC research. It does not constitute investment advice. BCC is not responsible for any losses resulting from reliance on the views expressed herein. Investors should exercise caution.<\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" On November 20, a single remark from Bridgewater\u2019s Ray […]<\/p>\n","protected":false},"author":256241814,"featured_media":4765,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"{title}\n\n{excerpt}\n\n{url}","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[132868,132842,132846,132860,132866,132844],"tags":[],"class_list":["post-4762","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-big-data","category-business-economy","category-global-markets","category-market-trends","category-startups-innovation","category-technology"],"jetpack_publicize_connections":[],"jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/pg46U3-1eO","jetpack-related-posts":[{"id":5940,"url":"https:\/\/bccmedianews.com\/?p=5940","url_meta":{"origin":4762,"position":0},"title":"Unitree Rings the Bell First, Agibot Splits Into a Full Industry Chain: Humanoid Robots Face Their Real-World Commercialization Test","author":"revieweditor","date":"2026\u5e7406\u670805\u65e5","format":false,"excerpt":"2026 is widely recognized by the industry as the d\u2026","rel":"","context":"\u5728\u201cAI & Big Data\u201d\u4e2d","block_context":{"text":"AI & Big Data","link":"https:\/\/bccmedianews.com\/?cat=132868"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/bccmedianews.com\/wp-content\/uploads\/2026\/06\/1744274556005-e1780639678116.jpg?fit=1176%2C662&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/bccmedianews.com\/wp-content\/uploads\/2026\/06\/1744274556005-e1780639678116.jpg?fit=1176%2C662&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/bccmedianews.com\/wp-content\/uploads\/2026\/06\/1744274556005-e1780639678116.jpg?fit=1176%2C662&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/bccmedianews.com\/wp-content\/uploads\/2026\/06\/1744274556005-e1780639678116.jpg?fit=1176%2C662&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/bccmedianews.com\/wp-content\/uploads\/2026\/06\/1744274556005-e1780639678116.jpg?fit=1176%2C662&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":4740,"url":"https:\/\/bccmedianews.com\/?p=4740","url_meta":{"origin":4762,"position":1},"title":"$57 Billion! 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