Let's begin with a telling scene: on the eve of the National Day holiday, news about ByteDance's Doubao staff working overtime went viral. A blogger on a social platform revealed that the internal team would not rest during the Mid-Autumn Festival, launching a high-priority emergency project to rapidly rebuild a new product benchmarked against Meta Muse.
The pace was reportedly frantic: joint debugging on September 27, a demo version required by September 30 for internal testing, with a total development window of only about six days, a truly lightning-fast build. This meant multiple upstream and downstream teams had to cancel their holidays and go full throttle. Once again, we felt the intense speed race among big tech firms in AI. As the National Day holiday unfolded, many projects chose the long break to concentrate their firepower for a sprint. This is one of the most vivid scenes of the AI era.
Overtime During Holidays: A Reality for Big Tech
This year's long holiday was far from peaceful. Behind it all, Muse's explosive popularity is undeniable. After launch, it quickly topped the free charts on North American app stores, surpassing 2.5 million downloads within ten days, growing faster than the original ChatGPT at the same stage, and directly driving a sharp rise in Meta's market value. What truly set it apart was not a leap in model capability, but turning a personal AI Agent from a lab concept into a consumer-grade product that ordinary users could pick up and use right away.
The market's feedback was swift. Information circulating on social media indicated that to catch up with the competing project, Doubao's entire product line was mobilized, coordinating multiple supporting teams across algorithms, engineering, product, and design, with some employees starting overtime from the Mid-Autumn Festival. A viral post on social platforms read: "Working overtime at ByteDance for triple pay for the first time, absolutely amazing." More details emerged: reports suggested that in April this year, Doubao launched an internal beta project codenamed Spell. After Muse went viral, ByteDance upgraded the Spell project's capabilities and accelerated development, planning to strip the capabilities from hardware limitations and turn them into a standalone consumer-grade personal assistant app usable on all device models. The internal name is rumored to be "Xiaodou," directly targeting Muse.
As AI enters a high-speed iteration cycle, concentrated sprints during holidays have become a realistic industry norm. This brings to mind the AI battle during this year's Spring Festival, when employees at big tech firms collectively worked overtime in a similar fashion. Around the Spring Festival, a person involved in overtime work at Tencent Yuanbao told the media: overtime was needed to support Yuanbao's Spring Festival activities, and on the other hand, to wait for the DeepSeek V4 model update. Rewinding to late January, Pony Ma announced at the company's annual meeting that its AI application Tencent Yuanbao would launch a Spring Festival campaign giving away 1 billion yuan in cash. No one dared to be careless. ByteDance was also part of the large group working overtime during the Spring Festival, with out-of-town teams dispatched to Beijing to support Doubao during the holiday. AI swept through, and the fiercest Spring Festival battle in history began. There was also Alibaba. As Alibaba (BABA) Chairman Joseph Tsai previously revealed, after DeepSeek released the R1 model, Alibaba realized it had fallen behind in AI. The company quickly decided: "Cancel the Spring Festival holiday, everyone stays at the office, works overtime, sleeps in the office, we must accelerate development." Within weeks, Alibaba rolled out the Qwen series of models. After a fierce collective "war," we witnessed the arrival of the AI era right after the 2026 Spring Festival.
Big Tech AI Departments Go All In
The AI competition is intensifying. Looking around, AI has entered an ultra-fast iteration period. After more than two years of adoption, user growth for general-purpose conversational large models has gradually entered a plateau, and simple Q&A interaction struggles to build a sufficiently strong product moat. An industry consensus is shifting: the market no longer needs just AI that is good at chatting. The next generation of AI competition is no longer about conversation experience, but about digital agents that can autonomously handle complex tasks. Simply put, it's not about casual chatting anymore, but about getting things done for you.
Thus, a new arms race has begun. Recently, big tech firms have noticeably ramped up their moves in personal AI Agents. Especially after Meta's Muse went mainstream, it forced tech giants to accelerate their own answers, urgently launching projects, and another round of encirclement swiftly arrived. For instance, Tencent quietly launched its cloud-resident personal Agent LightVela. This is a cloud Agent hosting platform built by Tencent Cloud's Lightweight Cloud team, featuring 7x24 uninterrupted operation, long-term memory, scheduled tasks, and installable skills. It is said that in venture capital circles, Tencent has earned something of a "Chinese version of Muse" reputation with LightVela. A few days ago, Manus released version 2.0 for overseas users and launched Cue, an intelligent assistant for personal life scenarios, equipping the Agent with an independent email, virtual identity, and the ability to handle personal affairs within a user-authorized budget. Manus, which has been operating overseas for over a year, is currently preparing a domestic version of its product and team, and cooperation with domestic model manufacturers is also advancing. Alibaba (BABA) officially announced its Qwen Personal Agent plan at the Yunqi Conference, relying on consumer ecosystems such as Taobao, Alipay, and Amap, hoping to create a dedicated digital assistant for ordinary people, connecting personal consumption and life data with user authorization. Overseas tech giants are also pressing ahead with product iterations. For example, Elon Musk's xAI launched Grok Bot on August 12, nearly a month earlier than Muse, but only for paying users and without breaking into the mainstream. However, none have fully closed the commercialization loop. Just as Muse achieved phenomenal mainstream success, it encountered API blocks from third-party platforms, the dedicated virtual machine brought substantial computing costs, and the failure rate for complex tasks remained high. Each is a hurdle on the path to personal Agent adoption. Amid the fierce fighting, manufacturers at home and abroad still face common practical challenges.
Every Long Holiday Is a Battle for Users
For big tech firms, holidays are also battlefields. In the mobile internet era, holidays were mostly about operational campaigns to drive traffic; in the AI era, they are crucial windows for backend R&D teams to do version iterations, tackle technical challenges, and close the gap in the race. When the outside world enters vacation mode, teams can detach from their busy daily schedules, concentrate firepower on key technical bottlenecks, and push forward key projects. As a result, we see that from Spring Festival to Mid-Autumn Festival and now to National Day, big tech firms that like to strike first are sprinting at full speed during holidays. Giving up vacations combined with high incentives reflects the industry's anxiety over fleeting market windows. Today, the iteration cycle for AI products has been greatly compressed. Once a new product form validated by the market emerges overseas, the time left for domestic players to respond and catch up is very limited. If they miss the precious positioning opportunity, by the time user habits and industry patterns are preliminarily set, catching up often requires multiples of manpower, time, and resource costs.
But everyone is also clear-headed: high salary incentives and holiday sprints are means for big tech to seize the initiative, but not the winning card. The game in the personal AI Agent track is never just about the speed of development and iteration. Even if the surface functions of overseas products can be quickly replicated, the numerous real-world tests cannot be bypassed. Zooming out to the competition across the entire AI track, it's not just about development speed, but also ecosystem integration, risk control capabilities, and long-term product commitment. As holidays slip away in code and debugging, in this AI race without a pause button, the real hard battles are still ahead. Everyone is rushing forward with all their might.