Core Insights - The article discusses the contrasting predictions of AI companies regarding their business growth by 2026, highlighting the uncertainty in whether AI can translate into tangible revenue growth [1]. Group 1: AI's Potential for Business Growth - Anthropic predicts that by mid-2026, AI models could autonomously work for a full 8-hour day, with at least one model expected to reach human expert levels in multiple industries by the end of 2026 [3]. - There is skepticism in the community regarding the success rates of AI models, with some arguing that a 50% success rate still necessitates human involvement for task completion and oversight [3][4]. - OpenAI's internal memo warns of a potential slowdown in growth, projecting revenue growth rates to drop to single digits (approximately 5-10%) by 2026, indicating a need for a "wartime" mentality among employees [4]. Group 2: Strategic Directions of Major AI Players - Anthropic's revenue model is heavily reliant on enterprise clients and APIs, which may allow it to surpass OpenAI in annual recurring revenue (ARR) without needing to replicate OpenAI's consumer-scale business [4]. - Google faces criticism regarding the performance of its Gemini product compared to ChatGPT, particularly in consumer-facing applications [5]. - Discussions around Meta's Llama 5 suggest potential changes in its release strategy, which could impact the open-source ecosystem in 2026 [5]. - Domestic players like Alibaba and ByteDance are also under scrutiny, with Alibaba potentially leveraging AI to integrate its various business units, while ByteDance's cloud services are gaining significant market share [6].

Core Argument - A leading AI researcher argues against the prevalent "AI bubble" theory, stating that skepticism towards AI's exponential growth is a serious misinterpretation of technological trends, similar to the initial underestimation of the COVID-19 pandemic [1][2] Group 1: AI Performance and Trends - AI models are doubling their ability to autonomously complete complex tasks at an exponential rate, with the latest models capable of handling over two-hour software engineering tasks [2][7] - The METR study shows a clear exponential trend in AI's ability to perform software engineering tasks, with models like Sonnet 3.7 achieving a 50% success rate for one-hour tasks seven months ago [5] - New models, including Grok 4, Opus 4.1, and GPT-5, have surpassed previous trends and can now execute tasks exceeding two hours [7] Group 2: AI's Competitiveness Across Industries - The GDPval assessment by OpenAI evaluates AI performance across 44 professions in nine industries, showing that top AI models are "astonishingly close" to human performance and even challenge industry experts [9][10] - The latest GPT-5 model has demonstrated performance that is nearly on par with human experts, indicating significant advancements in AI capabilities [10][13] Group 3: Future Projections - Based on current exponential growth data, it would be "extremely surprising" if improvements in AI suddenly halted, with predictions suggesting that by mid-2026, models will be able to work autonomously for an entire workday (8 hours) [12][15] - By the end of 2026, at least one model is expected to reach human expert performance across various industries, and by the end of 2027, models will frequently surpass experts in many tasks [15]