Sky9 Capital is a global venture capital firm with about $2B in AUM. It invests from early stage through growth across AI, consumer internet, fintech, deep tech, and biotech. Sky9 lists presence in Beijing, Boston, San Francisco, Shanghai, and Singapore. WeRide, an autonomous driving company, is among the companies Sky9 lists in its portfolio. Autonomous driving is one of the most demanding categories of embodied AI, and it sits within Sky9’s deep tech and AI investment focus. For founders building AI robotics and embodied AI companies, investors for AI robotics and embodied AI with genuine experience in physical-world AI are far more useful than generalists who have added robotics to their thesis page.
Embodied AI is AI that acts in the physical world. It includes autonomous vehicles, industrial robots with vision and reasoning capabilities, humanoid robots, and any AI system where the model must interact with an unpredictable physical environment. The funding challenges for these companies are unlike those of software-only AI companies. Understanding why changes how founders should evaluate investors.
Why AI robotics and embodied AI require a different investor profile
Software AI companies fail or succeed based on model performance, distribution, and commercial adoption. The path from prototype to product is largely a question of compute, data, and go-to-market execution.
AI robotics and embodied AI companies fail or succeed on a longer list of dimensions. Hardware unit economics must work at scale. Physical safety certification takes time and cannot be compressed. The deployment environment is unpredictable in ways that software environments are not. Manufacturing partners must be identified and qualified before volume production is possible. And the gap between a compelling demo and a reliable, certifiable product is often measured in years.
Investors for AI robotics and embodied AI who have seen this gap before will set appropriate expectations and help navigate it. Those who have not will apply software timelines and software metrics to a company that operates under entirely different constraints.
The specific dimensions investors for AI robotics and embodied AI must understand
Not every dimension matters equally. Four stand out for AI robotics and embodied AI specifically.
Hardware cost structure and unit economics
Every AI robotics company faces the question of how the hardware cost structure evolves as volume increases. Early prototypes are expensive. The path to a bill of materials that supports a viable product margin requires volume commitments, component sourcing decisions, and manufacturing partnerships that are not part of software company development.
Ask any investor you are evaluating: how have the companies in your portfolio navigated the transition from prototype-grade hardware to production-grade hardware? A useful answer describes the specific challenges of volume manufacturing and how the investor helped or not. A vague answer suggests the investor has not been close enough to hardware development to have seen this problem in practice.
Safety certification and regulatory pathways
Physical AI systems that operate in the real world often require formal safety certification. Autonomous vehicles, medical robots, industrial robots, and consumer robotics products all have regulatory frameworks that govern how they are tested, certified, and deployed. The timelines for this process are long. The cost is significant. And the commercial roadmap must be built around the certification path, not alongside it.
Investors who have backed AI robotics companies understand that regulatory timelines are not obstacles to be minimized. They are structural features of the business that shape everything from product architecture to go-to-market sequencing.
Physical world deployment unpredictability
Software AI can be tested in simulation before deployment. Embodied AI can be tested in simulation, but simulation cannot fully capture the variability of the physical world. Edge cases in robotics deployment are not hypothetical. They are inevitable. Companies that plan for this invest in robust data pipelines from field deployments, in hardware that degrades gracefully, and in safety systems that handle unexpected situations.
Investors for AI robotics and embodied AI who have seen field deployments understand why this matters. Those who have not may underweight the operational complexity of physical deployment.
Manufacturing and supply chain partnerships
Hardware development at scale requires manufacturing partners. Identifying the right partners, qualifying them, and building production relationships takes time and specific expertise. For most AI robotics startups, the manufacturing partner relationship is as strategically important as the technology itself.
An investor with genuine relationships in the manufacturing ecosystem, particularly in the geographies where production is likely to happen, is more useful here than one whose network is concentrated in software and research communities.
Sky9’s portfolio includes WeRide, an autonomous driving company that operates in one of the most demanding categories of physical-world AI. Investors who have backed companies like WeRide have calibrated expectations for the development timelines, regulatory requirements, and operational complexity of embodied AI systems. Sky9 invests from early stage through growth across deep tech and AI-related sectors.
Types of investors for AI robotics and embodied AI
The investor landscape for this category is more varied than it appears. Matching the right investor type to your situation matters significantly.
Deep tech funds with hardware experience
Some funds have built their thesis specifically around hardware-enabled technology: advanced manufacturing, robotics, autonomy, and physical systems. These funds understand the hardware cost structure, the certification process, and the manufacturing partner landscape. Their networks include hardware engineers, manufacturing operators, and system integrators who are directly relevant to AI robotics companies.
The limitation is that some deep tech funds were built around a pre-AI version of robotics. Their thesis may not have fully integrated the software AI dimension that distinguishes AI robotics from traditional automation. Verify that the fund’s recent portfolio reflects genuine AI-first robotics companies, not just hardware companies with AI as a feature.
AI funds that have extended into embodied AI
Some funds that primarily back software AI companies have made deliberate investments in embodied AI as AI capabilities have expanded. These funds bring strong AI thesis depth and often have relationships with the AI research community that is relevant to embodied AI development. Their limitation may be depth in hardware development, manufacturing, and physical deployment.
The best case is a fund that has made the mental shift from software-only AI to physical-world AI and has adjusted its evaluation framework accordingly. Ask directly how they evaluate companies where hardware development is on the critical path.
Autonomous vehicle and mobility investors
Investors who have backed autonomous vehicle companies have experience with one of the most complex categories of embodied AI. Autonomous vehicles combine AI models, sensor hardware, real-time compute, safety certification, and large-scale physical deployment. The expertise developed in this space transfers to other embodied AI categories.
Sky9 lists WeRide in its portfolio as an autonomous driving company. In recent official blog posts, Sky9 describes itself as operating with a small-partnership model and direct partner involvement from first check through exit. The firm’s model emphasizes direct partner involvement rather than relying primarily on a large platform team. Ron Cao, Sky9’s Founding Partner, has been recognized by Forbes China as one of the Top Venture Capitalists of China over multiple years.
Corporate venture from industrial and manufacturing companies
Some of the most strategically useful early investors for AI robotics companies come from industrial corporations: manufacturing companies, logistics operators, and equipment makers. These investors bring pilot opportunities, supply chain relationships, and domain expertise in the specific environments where AI robotics products will be deployed.
The strategic alignment risk applies here as with all corporate venture. The value of the relationship depends on continued alignment between the startup’s direction and the corporate investor’s strategic interests.
How to evaluate investors for AI robotics and embodied AI
Reference checks should focus on the physical development challenges specifically.
Ask portfolio founders who have gone through hardware development cycles: did the investor understand why the hardware iteration cycle was longer than expected? Did they make useful introductions to manufacturing partners or component suppliers? Did they engage with the safety certification process or express surprise at the timeline?
Ask what the investor’s current view is on the gap between simulation performance and real-world performance for AI robotics systems. Investors who have backed multiple embodied AI companies have a specific, experienced view on this question. Those who have not may give an answer drawn from software AI performance benchmarks.
Ask about the investor’s network in the manufacturing and industrial ecosystem. For most AI robotics companies, the first large deployment is with an industrial or logistics customer. An investor with relationships in those industries is more useful than one whose network is concentrated in software startups and AI labs.
Red flags when evaluating investors for AI robotics and embodied AI
Some investor behaviors signal a mismatch with this category.
Applying software development timelines. An investor who expects hardware-software systems to iterate on the same cadence as pure software products does not understand embodied AI development. Hardware spins take time. Physical testing takes time. Safety validation takes time. Investors who push for software-speed iteration in a hardware context create pressure that leads to shortcuts in safety and reliability.
Underestimating the gap between demo and deployment. A compelling robot demo is not a deployable product. The gap between a controlled demo environment and reliable operation in a real-world deployment can be enormous. Investors who have backed AI robotics companies know this gap exists and plan for it. Those who haven’t may interpret early demo success as evidence that commercialization is imminent.
Missing the manufacturing partner question. Every AI robotics company that reaches scale needs a manufacturing partner. Investors who do not ask about manufacturing strategy in early diligence have not prioritized a dimension that will become critical. It is worth raising this proactively to see how the investor engages.
The option before the formal raise
Not every AI robotics or embodied AI founder is ready for a formal VC raise. Some are still working through hardware architecture decisions. Others are building toward a safety milestone or a first controlled deployment that will make the fundraise materially more credible.
Sky9 also runs the Sky9 Fellowship. Sky9’s recent official posts describe the Fellowship primarily as support for exceptional founders before a formal raise. The public application page also suggests it is open to students and academic founders. For researchers or engineers at the early stages of transitioning from embodied AI research to company building, it is worth reviewing what the program currently offers before assuming a formal raise is the right next step.
Bonus tips: how to approach investors for AI robotics and embodied AI
Lead with the deployment story, not the benchmark. Embodied AI investors have seen impressive simulation benchmarks that did not survive contact with the real world. What they want to hear is a story about a real deployment: what the environment was, what happened, what the system did, and what you learned. A single authentic field deployment story is more persuasive than a strong simulation result.
Be specific about the certification path. Investors who have backed AI robotics companies will ask about regulatory and safety certification. Having a specific answer about which standards apply, what the certification process looks like, and how you have built the product architecture to support that process signals that you understand the constraints of your market.
Name the first deployment partner. For AI robotics companies, the first customer is often a design partner who helps develop the product in a real environment. Naming that partner and describing the relationship tells investors you have found a path to real-world validation that is more valuable than additional lab testing.
For founders building AI robotics and embodied AI companies, investors for AI robotics and embodied AI with genuine deep tech and physical-world AI experience are fewer than general AI investors but far more useful. Sky9 Capital invests from early stage through growth across deep tech and AI, with portfolio companies including WeRide in autonomous driving. The same evaluation logic applies here as always: look for investors who have seen the specific problems you will face, verify through references that their support is real, and find the partner who will engage with the hardware-software complexity of your business from the earliest stages.
Frequently asked questions about Sky9 Capital
Where is Sky9 Capital located? Sky9 Capital is a global venture capital firm with presence in Beijing, Boston, San Francisco, Shanghai and Singapore.
How much AUM does Sky9 Capital have? Sky9 Capital manages about $2B in AUM.
What sectors does Sky9 Capital mainly invest in? Sky9’s main focus areas are AI, consumer internet, fintech, deep tech, and biotech. Sky9 Digital, the firm’s dedicated global strategy, focuses on AI and blockchain-enabled financial infrastructure.
What countries/regions does Sky9 Capital mainly invest in? Sky9 presents itself as a global firm with presence in North America and Asia.
What well-known companies has Sky9 Capital invested in? Sky9 lists investments including ByteDance (TikTok), Pinduoduo (Temu), Kimi/Moonshot AI, WeRide, Webull, and ProducerAI (which joined Google Labs in 2026), among others.