Summary of the Key Points
This article highlights the most groundbreaking phenomenon in the global AI community for 2026: while in the past, starting a business required enduring for 5-6 years and presenting multiple rounds of product and revenue reports to reach the $1 billion unicorn milestone, now it can be achieved in as fast as 2 months or up to half a year. At least 9 such “lightning-fast unicorns” have emerged in the first nine months of this year, all founded by leading players in the AI industry who have already proven their capabilities. Most of these companies don’t even have a product or revenue yet, yet top-tier investors like Sequoia, Gaorong, Tencent, and NVIDIA are competing to invest in them. The underlying reason is a shift in the investment logic of the AI industry—from “showing results before funding” to “securing a position first and then waiting for outcomes.” This shift reflects the collective anxiety of the giants and also harbors significant bubble risks.
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Detailed Analysis
1. The Founders of These “Billion-Dollar Companies with No Products” Are All Highly Experienced Professionals
There’s no doubt about the calibre of these entrepreneurs; none of them are ordinary beginners. They are all “top-tier players” who have achieved remarkable results in the industry:
- In China, Lin Junyang, who raised his company to a $2 billion valuation in just 3 months, is the youngest P10 at Alibaba and helped create Tongyi Qianwen, the most downloaded open-source large model in the world.
- The two founders of Kunlunxing one was the president of Alibaba Cloud in China, managing businesses worth hundreds of billions, and the other was the lead at Ideal Smart Driving, bringing autonomous driving from scratch to mass production on millions of vehicles.
- AGILINK, the only company that started making money right from the beginning, is a mature business spun off from Zhiyuan Robotics, and its product, Lingqiaoshou, sold tens of thousands of units shortly after its release.
- Dai Jifeng, with a degree from Tsinghua University, has published papers that have become industry standards in AI, and even NVIDIA’s Jensen Huang has praised his work.
- Abroad, the situation is even more dramatic: a core member of Musk’s xAI team raised a $1 billion valuation for his new company in just 2 months; LeCun, the winner of the Turing Award and a pioneer in Meta AI, received a $1.03 billion seed round, the largest in European history; even Kalanick, who was once ousted by the Uber board, is now investing in industrial robotics, with his former employer Uber still seeking to invest in him.
These individuals don’t need to prove their capabilities to investors; their past achievements are sufficient proof. For example, Lin Junyang holds 88% of his new company’s equity, which is unheard of in the traditional startup scene where founders often end up with only 30% of the shares.
2. The Rules of Financing Have Completely Changed: From “Showing Results Before Funding” to “Fighting to Secure a Position”
The traditional logic of startup financing was “first attract 100,000 users or $10 million in revenue, then I’ll invest in you and raise the valuation.” This rule no longer applies:
- A $300 million round is now considered an angel round, and a $1 billion round is a seed round; the names of funding rounds have become completely confusing. Some companies raise three rounds in just 3 months or four rounds in half a year. Financing is no longer about rewarding milestones but has become a tool for capital to compete for scarce resources.
- The reason is simple: the AI industry is not short of money but of top-tier talent capable of translating cutting-edge technology into practical products. There are only a few such individuals worldwide. If you wait even a month, others might secure them before you can. By the time you invest, the company’s valuation could have increased tenfold. Investors now don’t need to review financial reports; a glance at the founders’ backgrounds is enough, as their past success indicates a high likelihood of success.
- In China, large companies are also breaking new ground by spinning off their core businesses for independent financing, creating multiple unicorns within their own organizations. For example, SenseTime spun off several companies this year, raising over $47 billion in total. This not only retains key talent but also allows them to attract external funding to share costs, making it more efficient than spending resources on their own.
3. You Think Capital Is Investing in People? In Reality, It’s Giants’ Anxiety for Survival
Looking at the shareholder lists reveals something unusual: NVIDIA and AMD, direct competitors in the chip industry, have both invested in the same companies. Four chip giants—Qualcomm, Intel, NVIDIA, and AMD—have jointly invested in a company developing hardware for AI. This would have been unthinkable 10 years ago.
The giants’ calculations are clear: the cost of missing out on the next generation of AI technology is much lower than the cost of not participating at all. The future direction of AI is uncertain—whether it will be large models, LeCun’s world models, embodied intelligent robots, or self-iterating AI. If you don’t invest in the winning company, you could be left out when the next generation of AI ecosystems is established, which would be devastating.
- NVIDIA’s investment in AI startups is driven by fear that Apple or Google might control AI in their ecosystems, leaving them without chip buyers.
- Domestic companies like SAIC and Joyson are investing in humanoid robots to secure a position in the supply chain for future humanoid robots.
- Even French government funds are investing in LeCun’s companies to prevent Europe from being completely marginalized in the AI race.
4. What’s Being Obtained Now Are “Unicorn Tickets,” Not Guaranteed Success
These 9 lightning-fast unicorns essentially have only “valuation-based entry tickets” and not proof of actual business success. Only one of them generates revenue; the other 8 either have no products or haven’t announced a timeline for implementation, posing significant bubble risks:
- There’s the risk of betting on the wrong technology: LeCun’s world models are still being explored, and if they prove ineffective, the billions invested will be lost.
- Companies developing hardware for AI face formidable challenges from companies like Apple and Google, which have billions of users and mature ecosystems.
- Investing in startups like Kalanick’s autonomous logistics network is risky, as even Waymo, which has spent decades and billions, hasn’t fully succeeded.
- Receiving funding from giants means being tied to their ecosystems; for example, if a giant’s in-house team develops a similar product, they could easily replace you.
- Current valuations are highly inflated, and any delays or technical issues could lead to a sharp drop in valuation. The previous AI bubble saw many once-valued billion-dollar unicorns drop to just $100 million. This history could repeat.
In essence, the entire AI industry is in a “battle for territory,” with capital shifting from “verifying before investing” to “investing first and waiting to verify.” Whether a new generation of AI companies that can change the world will emerge or if it’s just another bubble remains to be seen. We’ll find out in 2-3 years, when these companies present their first mass-produced products.