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.

Trending Cryptos

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

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit48m ago

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit48m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit52m ago

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit52m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit53m ago

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit53m ago

Trading

Spot

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