With the explosive emergence of tools like Claude Code, AI is evolving from a chat tool into an Agent. As models begin to genuinely execute tasks for humans, token consumption is poised for exponential growth. Whoever enables AI to truly integrate into production workflows will secure the most stable and sustained token usage. Following the collective surge and subsequent rapid correction of AI concept stocks earlier this year, major domestic model companies are now seeking new growth narratives. Having capitalized on trends like vibe coding and the "lobster" boom, the domestic model player MiniMax, having tasted success, is eager to expand its network and hunt for the next goldmine. On May 11th, MiniMax launched a new collaboration initiative called the "10x Team." Beyond its already established partnerships in verticals like industrial software, game engines, chip design, finance, and accounting, this initiative sees MiniMax publicly inviting experts from fields with greater global potential for deep integration with large models—such as economics, life sciences, and materials chemistry—to co-create. Simultaneously, it listed "10x Team Researcher" positions on recruitment platforms. The underlying ambition is clear: to replicate the "tenfold efficiency leap" witnessed in programming across more industries. This could be a win-win, allowing MiniMax to enhance its general-purpose intelligent base capabilities while driving deeper model penetration into more industrial scenarios. In fact, "general-purpose large model + co-creation with industry experts" has become a consensus among leading companies. Anthropic consistently absorbs academic and industry researchers; its Economic Index further incorporates the model's impact on various sectors' economic activities into its evaluation framework. OpenAI introduced HealthBench for healthcare and prioritized legal and financial scenarios for GPT series optimization. Google DeepMind has long championed "scientific breakthroughs" as its banner, with projects like AlphaFold (structural biology) and GNoME (materials science) demonstrating that collaboration between top domain experts and foundational research teams can yield "domain-level leaps." In late 2025, Baidu also launched a similar "ERNIE Mentor" program, engaging experts from industry and academia to guide large models in knowledge transfer, quality assessment, and professional calibration. Over the past year, programming became the first phenomenally successful scene for large models to demonstrate "tenfold efficiency." Tools like Cursor and Claude Code have fundamentally reshaped software development processes, and the related infrastructure competition is largely settled. Following Claude Code's explosive popularity, the entire AI industry quickly reached a consensus: AI's most critical capability is no longer just "answering questions" but "completing tasks." Once AI enters real production systems, it becomes a necessity. Programmers invoke it daily, businesses run it daily, team collaboration requires continuous integration, and inference chains grow steadily. Model calls transition from sporadic needs to sustained consumption, naturally leading to exponential growth in token revenue. However, such certainty attracts competitors to share the pie. Eighteen months ago, AI programming was a market dominated by Copilot. Today, overseas players like Cursor, Windsurf, Cline, Claude Code, and Aider are fiercely competing, while domestic contenders like DeepSeek TUI, Kimi Code, MiniMax-M2.5, ByteDance's Trae, Tongyi Lingma, ERNIE Fast Code, Zhipu's CodeGeeX, and Alibaba's Qoder are all vying for market share. As the programming红利 enters a bottleneck phase, the question "What is the next field to be 10x'd?" becomes one all companies must answer. MiniMax's answer is: to sink model capabilities into fields characterized by high professional knowledge density, complex workflows, and a lack of standardized approaches. This is precisely what cannot be solved by model teams optimizing behind closed doors alone. It requires the involvement of top domain experts to define problems, co-build evaluation systems and workflows, and then have models drive industry transformation in reverse. Industry knowledge inherently presents significant barriers. Chip design involves complex verification processes, industrial software encompasses vast engineering systems, finance has its own risk control logic and regulatory frameworks, and life sciences are filled with tacit experimental experience and specialized knowledge structures. This knowledge does not naturally exist in publicly available internet corpora. The challenge for a truly usable industrial Agent lies not in the model's reasoning ability, but in whether it understands the industry workflow. This is causing large model companies to increasingly resemble hybrids of research institutions, industrial organizations, and consulting firms. MiniMax's "10x Team" represents, to some extent, the first time a domestic large model manufacturer has explicitly brought this "scientific collaboration model" to the forefront. In MiniMax's view, this resembles an industrial research partnership mechanism. The model team is responsible for foundational capabilities, industry experts define problems, construct workflows, and establish evaluation systems, and then the Agent enters actual production scenarios. Because when AI's goal shifts from "answering questions" to "completing tasks," the importance of industry experts is rapidly amplified. Looking back, the most critical talent in the internet era was the product manager, as they defined user needs. In the Agent era, the truly important individuals may become those who understand industrial processes best. Programming is merely the first industry being reconstructed by Agents. What all large model companies are truly seeking now is the next scenario capable of generating massive token consumption while genuinely creating industrial value. Over the past year, the valuation growth speed in the large model industry has increasingly reminded many of the internet bubble around the year 2000. Recently, economist Ma Guangyuan pointed out that upstream infrastructure like computing power, optical modules, and hardware indeed have orders, revenue, and profitability because the world is frantically stockpiling computing power. However, midstream large models and downstream applications, such as humanoid robots, general AI, and ToC/ToB implementation scenarios, remain largely in the conceptual and storytelling phase, lacking large-scale commercialization, sustained profitability, or a real demand explosion. Yet, all these future expectations are already priced into current valuations. The entire industry is well aware that if AI fails to truly enter industries and help businesses continuously improve efficiency and profitability, this capital game may be difficult to sustain long-term. Only when AI genuinely starts working for enterprises, participates in production processes, and helps industries make money can the entire industrial chain truly operate. This is why leading global AI companies are now aggressively pushing into the deep waters of industry. Anthropic no longer emphasizes just model capabilities but also how Claude integrates into enterprise workflows. OpenAI continues to strengthen vertical scenarios like healthcare, law, and finance. Google DeepMind has long positioned "scientific breakthroughs" as a key strategic direction. Because everyone knows AI must truly start helping industries make money, improve efficiency, and reduce costs for the entire industry narrative to move forward. Otherwise, the bubble will eventually burst. And once it bursts, the impact won't be limited to a few model companies. From GPUs to cloud providers, from data centers to AI startups, from primary to secondary markets, the entire AI upstream and downstream could experience a severe chill. Therefore, all large model companies today are essentially racing against time to prove one thing: AI is not just a concept, but genuine productive force. MiniMax's "10x Team" is, in essence, an industrial positioning move within this context. It aims to preemptively engage industry experts, truly embed model capabilities into complex industrial processes like chip design, industrial software, financial analysis, and life sciences, and gradually build its own data moats, workflow barriers, and commercialization advantages. Because when AI's goal shifts from "answering questions" to "completing tasks," industry knowledge becomes the new scarce resource. Programming is just the first industry being reconstructed by Agents. What the entire AI industry now truly wants to prove is whether the next one will be the entire industrial world.