Moqi Intelligence Raises Over $1B in Angel Round; Alibaba and Tencent Rarely Invest Together as Embodied AI Sector Reshuffles

I came across an interesting funding news item today.

Morphi Intelligence (Morphi), an embodied AI company established only in August last year, raised over 1 billion RMB in its angel round series, with a post-money valuation directly breaking through 7 billion RMB, entering the unicorn list. This funding amount is the first-round record for the domestic embodied AI sector that is publicly verifiable.

What's even more worth discussing is the investor lineup—Alibaba and Tencent appearing simultaneously on the shareholder list of a startup's angel round. It's rare to see giants jointly investing in early-stage companies in recent years, especially since Alibaba and Tencent haven't been seen together in investment directions for a long time. Plus, top-tier institutions like BlueRun, Legend Capital, GSR Ventures, and Source Code Capital are also there, basically gathering all the capital players with a voice in the sector.

I'm particularly interested in the backgrounds of the two founders.

CTO Huang Qingqiu, born in the 90s, undergrad from Tsinghua, PhD from CUHK, joined Huawei in 2020 as a "Genius Youth," later becoming the head of the Intelligent Driving AI department, leading the algorithm iteration of ADS from 1.0 to 4.0, deployed on millions of vehicles. CEO Gao Wenli, spent 11 years at Huawei overseas, then co-founded iMile cross-border logistics, expanding business to over 20 countries with annual revenue of 4 billion RMB.

This combination is quite interesting—a technical lead from intelligent driving systems, and a commercialization operator starting from cross-border logistics. On the tech side, cutting into embodied AI using the data closed-loop methodology from intelligent driving; I approve of this route: the engineering commonalities in perception, decision-making, and control between the two systems are indeed significant, so rather than building from scratch, it makes sense to transfer validated engineering methodologies. On the commercialization side, instead of shouting about home robots right away, they adopted a "commercial-first" approach, starting with hotel and delivery scenarios, accumulating operational data in real environments, and gradually migrating to home scenarios. This landing rhythm is correct; those who started immediately with general-purpose home robots have mostly failed along the way.

Mass production is a tough nut to crack; how much of the million-unit quality control system can be transferred from cars to robots remains to be seen in actual deliveries. However, achieving this scale of funding and team configuration at least indicates that investors agree on the judgment of the technical route and commercialization pace.

I'd like to hear everyone's thoughts on this trend — will embodied AI end up being dominated by a few big players, or are there enough scenarios for companies with different technical routes to have opportunities?

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Reposted from Toutiao / Global Unicorn Enterprise Top 500 | Original article: https://m.toutiao.com/article/7659713944935760427/