Lei Jun Announces: 60 Billion Investment in the Next Three Years! Xiaomi's MiMo-V2 Large Model Family Officially Released

marsbitPublished on 2026-03-27Last updated on 2026-03-27

Abstract

Lei Jun, founder of Xiaomi, announced at the Spring Product Launch that the company plans to invest over 60 billion yuan in AI over the next three years, with R&D and capital expenditure for 2026 alone exceeding 16 billion yuan. This underscores Xiaomi's strategic shift from a smartphone maker to an AI technology leader. The event featured the debut of Xiaomi's self-developed large model family, MiMo-V2, including the flagship agent-oriented MiMo-V2-Pro, the multimodal V2-Omni, and the voice-focused V2-TTS. These models are already being deployed across Xiaomi’s device portfolio. Xiaomi also showcased its first AI-native smartphone, Xiaomi Miclaw, currently in closed testing, and an upgraded version of its smart car, SU7, equipped with the XLA cognitive model and a new intelligent cabin system. The substantial investment aims to build a technological moat and accelerate Xiaomi’s integration of AI across its human-vehicle-home ecosystem.

In the AI-driven hardware revolution, Xiaomi is launching a saturation attack with unprecedented capital intensity.

On the evening of March 19, at the spring new product launch event, Xiaomi founder Lei Jun announced a major investment plan: over the next three years, Xiaomi is expected to invest more than 60 billion yuan in the AI field. Among this, the AI R&D and capital expenditure for 2026 alone has already exceeded 16 billion yuan. This marks Xiaomi Corporation's accelerated transformation from a smartphone manufacturer into a foundational-level AI tech giant.

Model Family Debut: MiMo-V2 Ushers in the Agent Era

The core highlight of this launch event was the flagship large model series deeply self-developed by Xiaomi:

  • Flagship Lead: Introduction of the flagship model MiMo-V2-Pro for the Agent era, possessing extremely strong task decomposition and autonomous execution capabilities.

  • Full Modality Coverage: Simultaneous release of the V2-Omni full-modality large model and the V2-TTS voice large model, completing the AI closed loop in visual, auditory, and text interaction.

  • Ecological Rollout: This model series has now officially landed on multiple Xiaomi terminal products, achieving rapid conversion from technology to experience.

Hardcore Implementation: The First AI-Native Phone and Smart Cockpit Upgrade

Lei Jun demonstrated the deep integration of AI technology within Xiaomi's "Human-Vehicle-Home Full Ecosystem" on site:

  • Smartphone Form Reconstruction: The first AI-native phone, Xiaomi Miclaw, has officially started closed beta testing. This model starts from the underlying architecture, aiming to provide a disruptive AI interaction experience.

  • Smart Mobility Evolution: The new generation Xiaomi SU7 achieves standard upgrades across the entire series, equipped with the XLA cognitive large model and the new Hyper OS Smart Cockpit. Through AI technology, the vehicle system can not only understand commands but also actively comprehend the driver's intent through logical reasoning.

Capital Strength: Building a "Tech Moat" with 60 Billion

The high investment plan of 60 billion yuan demonstrates Xiaomi's firm determination in its AI strategy. As the smartphone and electric vehicle core battlegrounds intensify today, Lei Jun evidently hopes to use self-controlled large model technology to build a "technology safe haven" for Xiaomi.

Conclusion: "Xiaomi Speed" in the AI Era

From releasing self-developed large models to the closed beta of the first AI-native phone, and then to the comprehensive AI-ization of smart cars, Xiaomi is proving its position in the AI race with extremely high execution power. As 60 billion yuan in funds continuously transforms into R&D成果 (achievements), that Xiaomi which "always believes that something wonderful is about to happen" might be preparing to use AI to redefine the smart life of the masses once again.

Related Questions

QWhat is the total amount Xiaomi plans to invest in AI over the next three years, and what is the specific amount for 2026?

AXiaomi plans to invest over 60 billion yuan in AI over the next three years, with the AI R&D and capital expenditure for 2026 alone exceeding 16 billion yuan.

QWhat is the name of Xiaomi's flagship large model series for the Agent era announced at the event?

AThe flagship large model series is named MiMo-V2, with the flagship model being MiMo-V2-Pro.

QWhat are the two other models released alongside the flagship, and what capabilities do they provide?

AThe other two models are the V2-Omni omnimodal large model and the V2-TTS voice large model, which provide AI capabilities covering vision, hearing, and text interaction to form a closed loop.

QWhat is the name of Xiaomi's first AI-native phone that has started closed beta testing?

AXiaomi's first AI-native phone is named Xiaomi Miclaw.

QWhich Xiaomi vehicle model received a full-series upgrade to include AI capabilities like the XLA cognitive model and a new smart cabin?

AThe Xiaomi SU7 received a full-series upgrade, now equipped with the XLA cognitive large model and the new澎湃 Smart Cabin.

Related Reads

Gensyn AI: Don't Let AI Repeat the Mistakes of the Internet

In recent months, the rapid growth of the AI industry has attracted significant talent from the crypto sector. A persistent question among researchers intersecting both fields is whether blockchain can become a foundational part of AI infrastructure. While many previous AI and Crypto projects focused on application layers (like AI Agents, on-chain reasoning, data markets, and compute rentals), few achieved viable commercial models. Gensyn differentiates itself by targeting the most critical and expensive layer of AI: model training. Gensyn aims to organize globally distributed GPU resources into an open AI training network. Developers can submit training tasks, nodes provide computational power, and the network verifies results while distributing incentives. The core issue addressed is not decentralization for its own sake, but the increasing centralization of compute power among tech giants. In the era of large models, access to GPUs (like the H100) has become a decisive bottleneck, dictating the pace of AI development. Major AI companies are heavily dependent on large cloud providers for compute resources. Gensyn's approach is significant for several reasons: 1) It operates at the core infrastructure layer (model training), the most resource-intensive and technically demanding part of the AI value chain. 2) It proposes a more open, collaborative model for compute, potentially increasing resource utilization by dynamically pooling idle GPUs, similar to early cloud computing logic. 3) Its technical moat lies in solving complex challenges like verifying training results, ensuring node honesty, and maintaining reliability in a distributed environment—making it more of a deep-tech infrastructure company. 4) It targets a validated, high-growth market with genuine demand, rather than pursuing blockchain integration without purpose. Ultimately, the boundaries between Crypto and AI are blurring. AI requires global resource coordination, incentive mechanisms, and collaborative systems—areas where crypto-native solutions excel. Gensyn represents a step toward making advanced training capabilities more accessible and collaborative, moving beyond a niche controlled by a few giants. If successful, it could evolve into a fundamental piece of AI infrastructure, where the most enduring value in the AI era is often created.

marsbit12h ago

Gensyn AI: Don't Let AI Repeat the Mistakes of the Internet

marsbit12h ago

Why is China's AI Developing So Fast? The Answer Lies Inside the Labs

A US researcher's visit to China's top AI labs reveals distinct cultural and organizational factors driving China's rapid AI development. While talent, data, and compute are similar to the West, Chinese labs excel through a pragmatic, execution-focused culture: less emphasis on individual stardom and conceptual debate, and more on teamwork, engineering optimization, and mastering the full tech stack. A key advantage is the integration of young students and researchers who approach model-building with fresh perspectives and low ego, prioritizing collective progress over personal credit. This contrasts with the US culture of self-promotion and "star scientist" narratives. Chinese labs also exhibit a strong "build, don't buy" mentality, preferring to develop core capabilities—like data pipelines and environments—in-house rather than relying on external services. The ecosystem feels more collaborative than tribal, with mutual respect among labs. While government support exists, its scale is unclear, and technical decisions appear driven by labs, not state mandates. Chinese companies across sectors, from platforms to consumer tech, are building their own foundational models to control their tech destiny, reflecting a broader cultural drive for technological sovereignty. Demand for AI is emerging, with spending patterns potentially mirroring cloud infrastructure more than traditional SaaS. Despite challenges like a less mature data industry and GPU shortages, Chinese labs are propelled by vast talent, rapid iteration, and deep integration with the open-source community. The competition is evolving beyond a pure model race into a contest of organizational execution, developer ecosystems, and industrial pragmatism.

marsbit14h ago

Why is China's AI Developing So Fast? The Answer Lies Inside the Labs

marsbit14h ago

3 Years, 5 Times: The Rebirth of a Century-Old Glass Factory

Corning, a 175-year-old glass company, is experiencing a dramatic revival as a key player in AI infrastructure, driven by surging demand for high-performance optical fiber in data centers. AI data centers require vastly more fiber than traditional ones—5 to 10 times as much per rack—to handle high-speed data transmission between GPUs. This structural demand shift, coupled with supply constraints from the lengthy expansion cycle for fiber preforms, has created a significant supply-demand gap. Nvidia has invested in Corning, along with Lumentum and Coherent, in a $4.5 billion total commitment to secure the optical supply chain for AI. Corning's competitive edge lies in its expertise in producing ultra-low-loss, high-density, and bend-resistant specialty fiber, which is critical for 800G+ and future 1.6T data rates. Its deep involvement in co-packaged optics (CPO) with partners like Nvidia further solidifies its position. While not the largest fiber manufacturer globally, Corning's revenue from enterprise/data center clients now exceeds 40% of its optical communications sales, and it has secured multi-year supply agreements with major hyperscalers including Meta and Nvidia. Financially, Corning's optical communications revenue has surged, doubling from $1.3 billion in 2023 to over $3 billion in 2025. Its stock price has risen nearly 6-fold since late 2023. Key future catalysts include the rollout of Nvidia's CPO products and the scale of undisclosed customer agreements. However, risks include high current valuations and potential disruption from next-generation technologies like hollow-core fiber. The company's long-term bet on light over electricity, maintained even through the telecom bubble crash, is now being validated by the AI boom.

marsbit14h ago

3 Years, 5 Times: The Rebirth of a Century-Old Glass Factory

marsbit14h ago

Trading

Spot
Futures

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of AI (AI) are presented below.

活动图片