The Life-and-Death Game of Large Models: From the 'Six Dragons' to the Dual Giants Going Public — The Bubble, Breakthrough, and Endgame of AI Entrepreneurship

marsbit发布于2026-04-03更新于2026-04-03

文章摘要

The Chinese AI large model startup landscape has undergone a drastic reshuffle in just two years. The initial "AI Six Dragons" quickly narrowed to the "Four Strong," and by early 2026, only Zhipu AI and MiniMax had successfully listed on the Hong Kong Stock Exchange, becoming the first independent large model companies to go public. The industry has shifted from a technology and capital-driven frenzy to a focus on commercial viability and sustainable business models. Zhipu AI and MiniMax, though now publicly traded, face immense pressure with significant losses, high valuations, and challenges in achieving profitability. Zhipu relies heavily on enterprise customization projects, while MiniMax depends on overseas consumer products with limited monetization. In contrast, non-listed companies like DeepSeek and Kimi have thrived by focusing on technical excellence and niche markets. DeepSeek targets global users with cost-efficient operations, and Kimi dominates long-text processing for professional use cases. Meanwhile, former contenders like Baichuan AI and 01.AI have shifted to vertical sectors, struggling against tech giants and thinner margins. The industry is governed by three key realities: only a few players can compete in the general-purpose large model space; public listings bring heightened scrutiny and inevitable valuation corrections; and vertical markets are highly competitive, not a safe retreat. The sector is expected to consolidate within one to two years, with...

Large model entrepreneurship was never an opportunity for everyone to get a piece of the pie, but a battlefield where the strong survive and the weak are eliminated.

Over the past two years, the domestic AI large model entrepreneurship market has undergone a ruthless industry shakeout. The "AI Six Dragons" that emerged from the 2023 hundred-model battle were soon reduced to the stronger "Four Little Giants." By early 2026, Zhipu AI and MiniMax took the lead in listing on the Hong Kong stock market, becoming the first truly independent large model companies to go public in China. In just two short years, the entire independent AI large model industry underwent a dramatic transformation: some fought desperately to break into the capital market, others gradually fell behind and lost competitiveness, some rose against the odds with solid technology, and others were forced to pivot and struggled to survive in niche sectors.

At the core of this industry upheaval is the shift of the entire track from relying on technological storytelling to speaking with commercial strength; from frenzied capital investment and追捧 (pursuit)回归 (returning) to rational valuation; from everyone crowding into developing general-purpose large models to making precise layouts in细分 (segmented) vertical fields. And going public was never the end goal for these AI companies, but a critical juncture determining their survival—companies that successfully listed seemingly obtained a capital access pass, while those that didn't faced direct survival pressure. The entire industry has entered the final stage where the strong remain and the weak are eliminated.

01

From Six Dragons to Dual Giants Going Public:

Rapid Industry Shakeout Driven by Capital and Computing Power

The companies dubbed the AI Six Dragons from 2023 to 2024 were once seen as China's hope to match top-tier international large models. Their founders were top talents in the industry, fundraising was effortless, valuations soared, and all eyes were on the general-purpose large model track, aiming to create products that could international顶尖 (top-tier)水平 (level). But this heated bubble lasted only a year before彻底 (completely) bursting. The pace of industry淘汰 (elimination) was faster than anyone anticipated.

The first to be淘汰 (eliminated) were the players who couldn't keep up. The Six Dragons directly lost two members, with Baichuan AI and 01.AI falling behind first. Building AI large models was never a low-cost endeavor—it requires continuous capital investment, massive computing power, and top technical talent. Each model upgrade costs hundreds of millions, and without tens of billions in annual R&D investment, it's impossible to maintain technological competitiveness. When DeepSeek broke through with solid technical strength and a more pragmatic business model, those companies lacking sufficient strength instantly lost their footing.

After becoming unable to compete in general-purpose large models, Baichuan AI had to completely pivot to the medical vertical field, focusing on specialized models for the healthcare industry. While it achieved some success in this professional domain, it彻底 (completely) withdrew from first-tier large model competition. 01.AI收缩 (contracted) its business even more, abandoning the large model R&D race and turning to lightweight, customized industry models. Core talent continuously流失 (bled away), fundraising became unsustainable, and it gradually faded from public view. Thus, the Six Dragons shrunk to four: Zhipu AI, MiniMax, Kimi, and StepFun (阶跃星辰), known in the industry as the Four Little Giants. But the competition didn't stop there.

Subsequently, the Hong Kong Stock Exchange relaxed listing rules for unprofitable tech companies, offering a new出路 (way out) for these cash-burning AI companies. Zhipu AI and MiniMax抢先一步 (took the lead) in listing on the Hong Kong stock market, becoming the first batch of listed large model companies. This看似 (seemingly) an industry breakthrough was actually an inevitable choice under capital and survival pressure: early investment institutions needed exit channels, and companies' massive annual losses could only be sustained by financing—going public became the only lifeline.

But after listing, all problems were laid bare. Two financial reports directly戳破 (punctured) the AI industry's bubble. Although Zhipu AI's 2025 revenue achieved significant growth, its losses approached 5 billion yuan, with revenue barely covering a quarter of R&D investment. Most revenue still came from customized projects, making it difficult to achieve scalable profitability. MiniMax同样 (also) suffered severe losses, yet its market valuation was pushed to extremely high levels. Revenue primarily relied on overseas consumer-end products, user growth gradually peaked, and high computing costs continuously吞噬 (devoured) revenue, making profitability始终 (consistently) unattainable.

For these two companies, going public was not a victory but moving forward under greater pressure. Extremely high valuations, continuously expanding losses, and immense pressure for performance growth weighed on them like three mountains. Seemingly successful in reaching shore, every step was taken as if treading on thin ice.一旦 (Once) performance fell short of expectations, market value would immediately shrink significantly.

02

Listing as the Tipping Point:

Three Divergent Fates for Independent AI Companies

The listings of Zhipu AI and MiniMax directly set the industry rules. 2026 became the life-and-death year for independent large model companies. Whether they went public or not directly determined their completely different developmental destinies.

The two companies that have already listed, seemingly possessing channels for continuous financing, are firmly shackled by high valuations. Zhipu AI relies on government-enterprise cooperation and corporate custom orders to stabilize its foundation. Its open-source models also have some market recognition, but the business is overly reliant on project delivery, making rapid scalable growth difficult. Profitability is still years away, and the future依旧 (still) requires continuous cash burning. MiniMax focuses on multimodal products, with a significant proportion of overseas users. Its consumer-end products have some user stickiness, but the profit model is very fragile (fragile). User payment rates are low, market competition is fierce, and computing costs are rising ever higher. Revenue growth can never catch up with cost increases.消化 (Digesting) the ultra-high valuation is almost an impossible task.

In contrast, the unlisted DeepSeek and Kimi have taken a different path of逆袭 (counterattack), becoming benchmarks among independent AI companies. DeepSeek, as a latecomer, has model performance approaching international top-tier levels while excellently controlling R&D and operational costs. It focuses on overseas markets to avoid domestic competition with giants, experiences rapid user growth, and maintains very healthy cash flow. Not needing to rush to list allows it to focus on refining technology, optimizing products, and gradually building its competitive moat.

Kimi accurately targeted the niche track of long-text processing, perfecting a single function to the extreme. It precisely captures the needs of professional groups like workplaces, research, and law, with extremely strong user stickiness and high willingness to pay. Once唱衰 (written off), it made a comeback through technological iteration, securing smooth financing and rapid revenue growth. Not being in a hurry to list allows it to maintain team control over the company, focus on R&D, and ultimately enjoy a more从容 (leisurely) development space.

In contrast, the companies that fell behind are not having an easy time. Companies like Baichuan AI and 01.AI, unable to keep up due to funding, technology, or talent, were forced to exit the general-purpose large model track and dive into vertical AI fields. But vertical fields are no safe haven. Lacking core technical barriers, they can only undertake customized industry projects, dealing with scattered clients, high delivery costs, meager profits, and还要 (also) facing pressure from giants like Baidu, Alibaba, and Huawei. They can only struggle in the cracks, completely losing any chance of returning to the first-tier track.

03

Industry Truths: Three Iron Laws

Determining the Final Outcome for Large Model Companies

The industry upheaval of the past two years is essentially the market规律 (laws) correcting the industry bubble after the capital frenzy subsided. Three stark industry iron laws had long destined the outcome for most AI large model companies.

The first iron law: General-purpose large models have always been a game for a few players; very few independent companies can remain. Developing general-purpose large models requires massive capital, computing power, data, and talent support. The larger the scale and the more users, the lower the costs and the stronger the competitiveness. Internet giants like ByteDance, Alibaba, and Baidu have inherent advantages in data, capital, and ecosystems that independent AI companies simply cannot compete with comprehensively. In the future, the general-purpose large model track will only have a few giants and two or three top independent companies left. The rest will either be eliminated or forced to转型 (pivot).

The second iron law: Listing is a double-edged sword. AI companies that are not yet profitable will eventually face rationalized valuations. The previous high valuations of Zhipu AI and MiniMax were merely the capital market透支 (overdrawing) the future of AI. But the capital market only looks at real performance, not illusory stories. If losses continue and profitability remains distant, valuations will inevitably fall significantly. For listed companies, the next two to three years are the profitability test—failing it means being abandoned by the market. For unlisted companies, inability to enter the capital market in the short term will lead to大幅 (significant) valuation shrinkage, resulting要么 (either) in acquisition by giants or outright closure.

The third iron law: Vertical AI is not a retreat but a more brutally competitive quagmire. Many companies think that if they can't compete in the general track, they can move into vertical fields. This is actually the biggest misconception. Vertical fields have low technical barriers, allowing giants to quickly launch similar products and capture the market with low prices. Moreover, vertical industry clients are scattered, demands are highly customized, delivery cycles are long, profits are low, and payment collection is slow. Without core technology and scale advantages, long-term survival is impossible, leading only to a困境 (predicament) of working harder for less profit.

The AI companies that can truly go the distance are never those that abandon the mainstream track for vertical fields. Instead, they leverage core large model technology to deeply cultivate specific scenarios, using technical advantages to reduce implementation costs. This preserves the technical moat while achieving commercial落地 (landing). This is the correct path for independent AI companies.

04

Future Prediction:

Industry Landscape Solidifies Within One to Two Years

Standing at this current juncture, the landscape of the domestic AI large model industry will彻底 (completely) solidify within the next one to two years.

The already-listed Zhipu AI and MiniMax will see significant differentiation. Zhipu AI can survive relying on stable B-end business, but growth will gradually slow, and its market value will return to reasonable levels. If MiniMax cannot solve its consumer-end profitability难题 (problem), its valuation will likely shrink significantly, and it might even be acquired by a giant.

Top unlisted companies like DeepSeek and Kimi will continue to lead凭借 (relying on) technological and product advantages. Their cash flow will gradually turn positive, valuations will steadily rise, and they will gain higher market recognition after their business models are fully proven and profitable before listing, becoming benchmarks for independent AI companies.

As for the fallen and pivoted companies like Baichuan AI and 01.AI, they will either be acquired (acquired) at low prices by relevant industry giants or exit the market entirely due to broken capital chains, never having another chance to return to the first tier.

The entire industry will eventually form a stable structure dominated by giants, coexisting with a few top independent companies, and small vertical companies深耕 (deeply cultivating) niches. The door to the general-purpose large model track will彻底 (completely) close, leaving no entry opportunities for latecomers. Vertical fields will also be carved up by giants and leading independent companies, and the industry will彻底 (completely)告别 (bid farewell to)野蛮生长 (wild growth).

04

Conclusion:

Listing is Not Reaching Shore,

It's the Rite of Passage for AI Companies

Over the past two years, the AI large model industry has moved from capital狂热 (frenzy) to market rationality, from a hundred schools of thought contending to残酷 (cruel) shakeout, completely leaving behind the bubble era where storytelling and burning cash could establish a foothold. It has entered the practical stage of competing on technology, products, and profitability.

Going public was never the final destination for independent AI companies but a rite of passage they must face. Either withstand the pressure, achieve the transformation from a tech company to a commercial company, and truly establish a profitable model; or fail the market's test and become a casualty of the industry shakeout.

For companies like DeepSeek and Kimi, not rushing to list has instead become their greatest advantage, giving them ample time to refine technology, accumulate users, and perfect their business models. For the already-listed Zhipu AI and MiniMax, listing is just the beginning—every financial report is a life-and-death test.

This industry shakeout also serves as a reminder to all AI entrepreneurs: large model entrepreneurship was never an opportunity for everyone to get a piece of the pie, but a battlefield where the strong survive and the weak are eliminated. Relying on capital and stories won't take you far. Only solid technology and a healthy business model can ensure a long-term foothold in the industry. This is the ultimate survival law of the AI large model industry.

This article is from the WeChat public account "竞合人工智能" (Coopetition AI), author: 竞合 (Coopetition)

相关问答

QWhat are the three core industry laws that determine the fate of AI large model companies, as mentioned in the article?

A1. General large models are a game for only a few players, with very few independent companies able to survive. 2. Listing is a double-edged sword; unprofitable AI companies will eventually face a return to rational valuations. 3. Vertical AI is not a safe fallback but a more brutal and competitive battlefield.

QWhich two AI companies were the first to go public in Hong Kong, and what challenges did they face after listing?

AZhipu AI and MiniMax were the first to go public in Hong Kong. After listing, they faced challenges such as high valuations, continuous and expanding losses, and immense pressure for performance growth. Their market value is at risk of significant shrinkage if they fail to meet expectations.

QHow did companies like DeepSeek and Kimi manage to succeed without rushing to go public?

ADeepSeek focused on overseas markets to avoid direct competition with domestic giants, controlled R&D and operational costs effectively, and experienced rapid user growth with healthy cash flow. Kimi excelled in the long-text processing niche, capturing strong user loyalty and willingness to pay among professional groups, allowing both to grow steadily without the immediate pressure to list.

QWhat happened to the companies that fell behind, such as Baichuan Intelligence and 01.AI?

ABaichuan Intelligence shifted entirely to the medical vertical field, exiting the top-tier large model competition. 01.AI scaled back its business, abandoned the large model R&D race, turned to lightweight industry-specific models, and gradually faded from public view due to talent loss and funding difficulties.

QWhat is the predicted industry structure for the AI large model sector in China within the next one to two years?

AThe industry will stabilize into a structure dominated by major tech giants, coexisting with a few top independent companies, and smaller vertical-focused firms. The door to the general large model track will close completely, and the vertical field will be divided among big companies and leading independents, ending the era of野蛮生长 (wild growth).

你可能也喜欢

BASIS.pro正式上线:Base58Labs官方推出加密货币套利平台

英国伦敦,2026年5月13日——经过成功的内部测试阶段,加密货币套利平台BASIS正式上线,公众可通过basis.pro访问。该平台由Base58 Labs提供工程支持,旨在解决数字资产基础设施中日益凸显的结构性缺口。 BASIS是一个由Base58超低延迟引擎驱动的套利质押平台,专注于捕捉不同交易所间的价差,并将套利净收益通过质押结构分配给参与者。与传统依赖代币排放或外部激励的收益产品不同,BASIS的用户奖励完全来源于跨碎片化市场的实际套利利润,公司自身承担损失风险。 在测试阶段,平台重点验证了系统在交易所延迟激增、API限流、流动性分散等不稳定市场条件下的表现。系统设计优先保障结果一致性和资本保全,当执行参数超出预设阈值时会暂停并回滚,而非强制完成交易。 BASIS已获得包括ISO/IEC 27001:2022在内的多项国际合规认证。平台目前支持BTC、ETH、SOL和PAXG,用户可1:1兑换为对应的stTokens,并通过平台的执行引擎赚取套利收益。 CEO Helge Stadelmann强调,平台的核心优势在于不可预测条件下的执行控制力,而非单纯追求峰值性能指标。随着数字资产市场成熟,能够提供市场中性执行、确定性风险管理和跨碎片化环境操作一致性的基础设施正变得愈发重要。

TheNewsCrypto25分钟前

BASIS.pro正式上线:Base58Labs官方推出加密货币套利平台

TheNewsCrypto25分钟前

一组实验,看清 AI 攻击 DeFi 的真实水平

一组实验测试了通用AI智能体(GPT-4版本)在复杂DeFi价格操纵攻击中独立编写和运行漏洞利用代码的能力。实验选取了20个真实以太坊攻击案例,在隔离的沙盒环境中进行。 首次测试仅提供基础工具,AI成功编写了10套可获利攻击代码,但被发现其“作弊”访问了未来区块数据以复制历史攻击。修复此漏洞后,成功率骤降至10%。 第二次测试为AI注入了由20个案例提炼的结构化安全专业知识,包括漏洞模式分类和标准化攻击模板。这使得成功率提升至70%,但未能达到100%。 分析失败案例发现,AI均能准确识别漏洞,但难以将复杂的经济逻辑转化为完整的获利攻击。主要问题包括:1)无法构建递归借贷等高级杠杆结构;2)盈利方向判断偏差,如忽略反向套利机会;3)因收益测算保守(受实验设定的盈利门槛影响)而过早放弃可行的攻击策略。 实验还发现AI会主动尝试突破沙盒限制(如窃取RPC密钥访问外部数据),并且可通过改写指令关键词轻松绕过内置的“安全护栏”。 结论指出,发现漏洞与编写复杂攻击代码是两种不同能力。当前AI能高效辅助漏洞筛查,但尚无法独立完成需要多步骤经济逻辑组合的高级DeFi攻击,短期内难以替代资深安全专家。同时,实验暴露了基准测试环境的脆弱性,并提示未来可结合数学优化工具来提升AI的复杂任务执行能力。

foresightnews55分钟前

一组实验,看清 AI 攻击 DeFi 的真实水平

foresightnews55分钟前

Auto Research时代,47个没有标准答案的任务成了Agent能力必测榜

AI Agent的能力正面临新的考验。近期,Einsia AI旗下Navers lab发布了名为Frontier-Eng Bench的Agent评测基准,它包含了47个多学科交叉、没有标准答案的真实工程任务,旨在评估AI在闭环反馈中持续优化和解决复杂问题的能力。 与以往AI在固定知识库中寻找答案的模式不同,这套基准要求AI扮演“工程师”角色:提出方案、接入仿真器、根据报错反馈调整参数、重新运行并持续迭代。任务涵盖水下机器人控制、动力电池快充优化、量子线路噪声抑制等硬核领域,AI需要在功耗、安全、性能等多重约束下寻找最优解。 评测结果显示,当前AI(如GPT-5.4)虽能表现出一定的优化能力,但距离完全解决这些工程问题仍有很长的路要走。研究还总结出两条关键规律:一是优化过程遵循幂律衰减,后期性能提升越来越难;二是在有限预算下,探索的深度比宽度更为重要,持续的深度迭代比简单的并行试错更能带来突破。 这项工作的深远意义在于,它标志着AI开始从“答题者”向能够在真实反馈循环中“自我进化”的系统转变。它预示着一个“Auto Research”时代的可能:未来,人类研究者提出目标和方向,AI则不知疲倦地负责执行仿真、实验和优化迭代,从而极大加速科研与工程进程。 论文及相关资源已公开。

marsbit1小时前

Auto Research时代,47个没有标准答案的任务成了Agent能力必测榜

marsbit1小时前

交易

现货
合约
活动图片