Investors Are Now Hunting for AI Projects on Bilibili and Xiaohongshu

marsbitОпубліковано о 2026-06-12Востаннє оновлено о 2026-06-12

Анотація

Investors Turn to Bilibili and Xiaohongshu to Source AI Projects The AI hardware boom is in full swing in 2025, with a surge in smart wearables like AI glasses, rings, toys, and companion robots. This frenzy has investors scrambling, not just sifting through business plans, but actively hunting for promising "under-the-radar" projects on youth and tech-enthusiast content platforms like Bilibili and Xiaohongshu. The logic is straightforward: for consumer-facing AI hardware, genuine user demand and potential pitfalls are often revealed earlier in public discussions, comments, and critiques on these communities than in formal pitches. As one industry insider notes, these products must ultimately be tested and understood by real people. This shift highlights a crucial challenge in the sector: user education. The success of AI hardware depends on moving beyond mere efficiency gains to fulfilling higher-order needs like "unleashing personal creativity." Products must convince users they are natural, unobtrusive additions to daily life. Early hype, as seen with devices like the Rabbit R1, often fades if the product fails to clearly solve real-world problems, leading to high return rates and market rejection. The market is now entering a shakeout phase. 2026 is seen as a year of commercial validation. Some projects have already stalled or been canceled due to market resistance, lack of differentiation, or financial woes. However, the long-term opportunity remains vast, with forec...

Author: Investment Community

"In the AI era, all hardware is worth remaking from scratch."

This statement might have sounded somewhat radical in 2024, but in 2025, its credibility has surged significantly—as we are witnessing, the wave of AI hardware entrepreneurship is rising, with financings following one after another. Across the ocean, the smart ring Oura Health has seen its latest valuation soar to a staggering 70 billion yuan.

This frenzy is sweeping up FOMO-stricken investors. This time, aside from sifting through thousands of pitch deck emails, they have strikingly turned their deal-sourcing gaze towards content communities densely populated by tech geeks and young people.

The underlying logic is rather simple: compared to spec sheets and roadshows, the real demand for a consumer-grade AI smart hardware product is often first exposed by whether it can be understood, discussed, and questioned in the public sphere.

Ultimately, all tech products aimed at the masses must go through the test of the crowd.

AI Hardware Explosion, Investors Scramble for Under-the-Radar Projects

We are experiencing a banner year for AI hardware.

Since 2025, extending from the wearable device track, more segmented smart hardware products such as AI glasses, AI toys, AI recording cards, AI rings, AI earphones, companion robots, and Agent Boxes have collectively seen an explosion.

Amid the fervor, certain details reveal the excitement in the investment circle: in the latter half of 2025, we frequently saw several top-tier institutions posting job openings to recruit AI/smart hardware investors, with the job descriptions almost invariably requiring the candidates to be based in Shenzhen, the hardware capital.

Good projects almost require 'repeated visits' and intense competition, especially those still under the radar with valuations not yet sky-high. Investors have even started approaching potential founders who haven't yet left their jobs. For instance, cafes near DJI are packed with VCs and FAs trying to 'persuade people to start a business.'

(Comment section of an AI sticky note product pre-sale video)

"After this content was posted, many technical discussion threads appeared in the bullet comments and comment section, and more users began sharing their similar needs: some mentioned frequently attending cross-border exhibitions, others talked about communication barriers when collaborating remotely with overseas teams, and some shared experiences where translation tools were inconvenient when accompanying foreign relatives for medical treatment or handling foreign affairs."

"These feedbacks not only helped us expand the direction for creator content but also directly influenced our thinking about the product line," Wu Zhen said.

(Comment section of UP主影视飓风's video)

Enthusiasts Gather

Remember January 2024, at CES.

A little orange square box called Rabbit R1, touted as 'the world's first AI hardware device,' stirred a frenzy in the tech world. The market hailed it as the iPhone of the AI era, selling 100,000 units upon launch.

But what followed was not success. On the contrary, skepticism soon arose with comments like 'actually using it is not as good as a phone.' At the time, the high-flying Rabbit R1 failed to adequately explain to users: what exactly can Rabbit R1 do now and in the future? What real user problems does this innovation actually solve?

Even today, user education remains the most critical challenge for AI smart hardware—when AI enters consumer electronics, the product consumption logic is no longer limited to 'improving life efficiency' but also includes higher-dimensional needs like 'unleashing personal creativity' and 'liberating the mind.' In this context, whoever can make users understand, discuss, and pay in real-life scenarios truly crosses the threshold from a tech product to a consumer good.

But this is not easy. Regarding user education for AI glasses, INMO's CMO Wu Zhen told us frankly: This is not simply about conveying product information; it also requires overcoming a psychological barrier. Users must first complete a 'virtual try-on' in their minds, confirming that integrating this device into daily life feels natural and not awkward, before they can proceed to the next step.

In Wu Zhen's view, the process of psychologically preparing users for AI smart hardware might be longer than for most consumer electronics categories. "The day users naturally put on AI glasses to go out without reminding themselves 'I need to use AI glasses today,' this behavioral change might be more convincing than many data points."

Shakeout Begins, Battle for Attention Heats Up

While the frenzy peaks, a shakeout is underway.

There is now market consensus that 2026 will be the year of commercial validation for AI hardware. This also means that trend-chasers and狂热资本 will face rationality and冷静.

A winnowing process is unfolding: At the end of 2025, the AI wearable device Friend AI Necklace stalled after encountering severe market resistance; after its peak appearance in 2024, Rabbit R1, unable to keep up with product expectations amid negative feedback, saw abnormally high return rates, severely damaging its reputation, and ultimately陷入欠薪与现金流枯竭的困境 (fell into困境 of salary arrears and cash flow depletion).

Meanwhile, savvy players are also开始审慎调整和优化决策 (beginning to prudently adjust and optimize decisions). In February 2026, market rumors emerged that the Doubao AI glasses project was suspended; at least within the visible cycle, this product line is no longer considered a direction to pursue. Earlier, in January, smartphone manufacturer vivo halted its AI glasses project, citing the difficulty of achieving differentiation at present.

Such developments are a reflection of industry differentiation.

Nevertheless, it is undeniable that in this trillion-dollar-value赛道 (track), entrepreneurs still possess enormous确定性机会 (deterministic opportunities). According to AICC predictions, by 2030, the global AI-related hardware market will easily surpass several trillion dollars. In China, the industry expects the market size for consumer-grade AI hardware (excluding phones and cars) to突破 (break through) 1.27 trillion yuan in 2026 and reach 2.56 trillion yuan by 2030.

Currently, opportunities for exit and entry coexist. OpenAI just announced it will launch its first AI hardware device in the second half of the year, and Meta plans to double the annual production capacity of its AI smart glasses to 20 million units by the end of 2026. The latest scene domestically: a batch of AI smart terminals from京东 (JD.com), including AI desk lamps, AI cooking machines, AI mattresses, and AI wheelchairs, are酝酿上新售卖 (being酝酿 for launch and sale).

People remain convinced that the battle for入口之争 (portals) behind AI smart hardware will not stop. The AI era is still an era of用户主权 (user sovereignty).

The热浪 (heat wave) will push everyone forward. But when attention, product power, and user perception begin to compete on the same table, the final battle is just beginning.

Пов'язані питання

QWhat new strategy are venture capitalists adopting to find promising AI hardware projects?

AVenture capitalists are increasingly looking for promising AI hardware projects on youth-focused content communities like Bilibili and Xiaohongshu. They analyze user discussions, comments, and product reviews to gauge genuine market demand and real-world product understanding before these ventures become widely known or receive high valuations.

QWhy is user education considered crucial for AI consumer hardware adoption?

AUser education is crucial because the value proposition of AI hardware often involves higher-level needs like 'unleashing personal creativity' or 'liberating the brain,' not just efficiency. Consumers need to understand and visualize how these devices fit into their daily lives without being intrusive. This mental acceptance and natural integration into routines are considered more important than just product specifications for driving adoption.

QWhat are some examples of AI consumer hardware that faced significant market challenges?

AExamples of AI consumer hardware that faced significant market challenges include the Rabbit R1, which suffered from high return rates and negative feedback regarding its practical utility, leading to cash flow problems. The Friend AI Necklace also encountered severe market resistance and stalled development.

QWhat is the predicted market size for consumer AI hardware in China by 2030?

AIndustry predictions estimate that the market size for consumer AI hardware (excluding smartphones and cars) in China will reach 2.56 trillion yuan by 2030. It is projected to first exceed 1.27 trillion yuan by 2026.

QWhy are some companies adjusting their strategies in the AI hardware market in 2026?

ACompanies are adjusting their strategies because 2026 is widely seen as the 'commercial validation year' for AI hardware. The market is entering a phase of rationalization and consolidation. Projects that fail to demonstrate clear product differentiation, genuine user value, or sustainable business models are being paused or canceled, as seen with Doubao's AI glasses and vivo's AI glasses project, indicating an industry-wide shakeout.

Пов'язані матеріали

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"PANews Column Registration and Submission Guide" provides instructions for users to register as columnists and publish articles on the PANews platform. Key application requirements are emphasized: content should focus on in-depth analysis within Crypto, Web3, blockchain, data, and viewpoints. Content primarily for brand/product introductions will not be approved, and heavily AI-generated content will be rejected. Promotional (PR/soft) content is directed to the business channel. **Registration Process:** * **Web:** Go to the official website footer, click "Apply for Column," and register with a phone number or email (login via verification code, no password). Fill in the column name, description, upload an avatar, and submit links to previously published work. * **Mobile:** Navigate to "My" -> "Contribute & Create" and complete the form. **Article Submission Tutorial:** 1. Log in to the PANews website. 2. Access the "Creator Center" from your personal homepage. 3. Use the editor to create and publish articles. **Video Upload:** The platform supports embedding videos from third-party sites (e.g., Bilibili). Copy the embed code from the source video, use the editor's "Insert/Edit media" button, paste the code under the "Embed" tab, and adjust the display size (recommended: width 100%, height 560px). **PANews Skills (AI Agent Tool):** PANews offers an official AI Agent skill set called PANews Skills, enabling AI tools to query platform content, track trends, and publish column articles directly. It includes three main skills: 1. `panews`: For tracking daily must-read lists, popular articles, and funding news. 2. `panews-creator`: For managing columns, publishing articles, and uploading images. 3. `panews-web-viewer`: For parsing PANews webpages into Markdown. These skills are compatible with various AI Agent tools (OpenClaw, Cursor, Claude Code, ChatGPT, Gemini, etc.). To use the `panews-creator` skill, users must obtain a specific authentication value from the PANews website after logging into their columnist account.

marsbit11 хв тому

PANews Column Registration and Article Submission Guide

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I Built Myself an Investment Workbench Using AI

For the past two weeks, I've been immersed in Vibe Coding—using AI to write code from natural language descriptions. This process has enabled me to quickly build functional tools that address long-standing personal ideas. Previously, I had many concepts but found execution too cumbersome. Key ideas included a unified dashboard for assets across US stocks, Crypto, HK stocks, and A-shares; a real-time alert system for price movements; an investment map visualizing sector relationships; and a tool to correlate prediction market bets with news and market data. Traditional development hurdles meant these often remained unrealized. Using AI (Codex, Claude Code, and DeepSeek API), I built four initial tools: 1. A **Cross-Market Asset Dashboard** showing total assets, daily P&L, and holdings by market, with added features for alerts and sector mapping. It's deployed locally for privacy. 2. A **Prediction Market (PM) Monitor** tracking bets on events (e.g., company valuations) and correlating probability shifts with news and market movements. I categorize bets by conviction to filter noise. 3. A **Simple Operations Backend** for managing my writing workflow (topics, progress, publishing). It's cloud-deployed for mobile access. 4. A **One-Click Formatting Tool** that automates converting drafts into various platform-specific formats, saving manual effort. While these tools are basic, they represent a significant shift: AI lowers the barrier to creating personalized systems. I believe individual investors can now feasibly build core systems for: * **Asset Observation** (tracking holdings and changes) * **Signal Monitoring** (watching for key market shifts) * **Sector Mapping** (understanding network relationships within a sector) * **Performance Review** (documenting rationale and outcomes) The power of Vibe Coding is its fast feedback loop. Ideas can be implemented, tested, and iterated on rapidly, turning "want-to-do" into "done." This marks the start of my new phase, where I'll share investment thoughts, tool tests, on-chain operations, and educational Web3 content.

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After Tokenization of Assets, How to Exit?

Title: How to Exit After Asset Tokenization? Author: Symbiotic Compiled by: Hu Tao, ChainCatcher Summary: Tokenization addresses how assets go on-chain but largely leaves the redemption question unresolved. While tokenized assets can settle instantly, the underlying redemption for assets like treasuries, private credit, or real estate can take from T+1 to 180 days. This gap hinders DeFi adoption of Real World Assets (RWAs). Three emerging models aim to provide instant exit liquidity, differing primarily in their capital structure and efficiency: 1. **Balance Sheet Model (e.g., Grove Basin):** A single entity (like Sky) provides immediate liquidity from its balance sheet, acting as a bridge during the settlement period. It offers simplicity and deep initial liquidity but is constrained by a single entity's capacity and risk appetite. 2. **Asset-Specific Vault Model (e.g., Upshift Clear):** Independent liquidity providers fund dedicated vaults for each supported asset, earning fees. It decentralizes capital sources but isolates liquidity and capital per asset, leading to potential fragmentation. 3. **Shared Liquidity Layer Model (e.g., Symbiotic Liquid Lane):** A shared capital pool supports multiple RWA types simultaneously. Funds remain productive between redemptions (e.g., earning yield in lending markets). Exits are settled via a competitive RFQ market. This model aims for higher capital efficiency, scalability across assets, and serves longer-duration assets like private credit. Key differentiators are: 1) Source of capital and risk bearer, 2) Redemption pricing mechanism, 3) Capital efficiency, 4) Scalability to new asset types, and 5) Composability. The shared liquidity layer model represents a move from piecemeal solutions toward scalable infrastructure, enabling T+0 exits by pooling capital, maintaining yield, and using competitive pricing, thus enhancing RWA utility in DeFi.

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After Tokenization of Assets, How to Exit?

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After Tokenizing Assets, How to Exit?

After tokenization, a key unresolved issue is providing holders with a reliable exit mechanism, as underlying asset settlement (taking days to months) lags far behind on-chain token settlement. Three primary models for instant liquidity have emerged, differing in their capital structure and efficiency: 1. **Balance Sheet Model (e.g., Grove Basin):** A single, well-capitalized entity (like Sky) provides immediate liquidity from its own reserves. This offers simplicity and deep initial liquidity but is constrained by that single balance sheet's capacity and risk appetite, limiting scalability. 2. **Dedicated Vault Model (e.g., Upshift Clear):** Independent liquidity providers (LPs) fund separate vaults for each supported asset. This decentralizes capital sources but isolates liquidity and capital, which becomes inefficient as the number of tokenized assets grows. 3. **Shared Liquidity Layer Model (Symbiotic Liquid Lane):** Independent capital providers fund shared vaults that can support multiple tokenized assets simultaneously. Capital remains productive between redemptions (e.g., earning yield in DeFi markets). Exits are settled via a competitive RFQ market where market makers bid. The article argues that the shared layer model offers superior capital efficiency and scalability. It transforms exit liquidity from an asset-specific patch into shared market infrastructure, allowing liquidity capacity to grow with overall market participation rather than being fragmented per asset. This is particularly valuable for longer-duration assets like private credit, where reliable T+0 exits can significantly enhance their utility in DeFi.

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