2026 AI Investment Trends: From "Hype" to "Real-World Impact" – Investors Are Becoming More Selective
Hello everyone, I'm your financial observer. Data released at the recent Shanghai Bund Conference has thrown a significant bombshell into the global AI community: In the first half of 2026, the amount of funding in the AI sector surpassed the total for the entire year of 2025 by a factor of 1.7.
What does this mean? It indicates that capital is flowing into the AI industry in a much more substantial manner than last year. However, if you think that people are still throwing money around recklessly and that any AI-related project will receive investment, you're greatly mistaken. The core message from this conference is that capital is undergoing a profound “rational return to reality.” Investors are no longer just looking at the fancy presentations; they are examining whether your customers are real, whether the money can be made back, and whether your team will survive the next five years.
Below, I will break down this news into five key aspects to help you understand the logic behind these changes in AI investment.
1. Shift in Investment Logic: From “Technological Showoff” to “Financial Viability”
Previously, investors would ask entrepreneurs things like, “How much better is your algorithm compared to OpenAI’s? How many parameters does your model have?”
Now, they ask, “Who are your customers? Have they actually paid? Can your delivery process be replicated? If the market cools down next year, can your team withstand it?”
This shift is quite dramatic. At the Bund Conference, over 500 investors and more than 120 AI startups engaged in intense discussions, and it became clear that the focus has changed. Technological leadership is just a ticket to enter the game; it’s no longer the core competitive advantage. The new key metric is the ability to create a sustainable business model.
It’s like when people used to watch car races and only cared about the engine power; now, they also consider whether the car can handle rough roads, how much fuel it consumes, and how expensive maintenance is. Investors are concerned about whether the delivery process can be replicated. In other words, if you can serve one excellent customer, can you maintain the same quality and profit with a thousand customers? If not, it’s not a viable business, just an expensive experiment.
Interestingly, startups are also making selective choices about their investors. In the past, it was the startups that sought funding; now, some entrepreneurs are more interested in whether the investors understand the industry and can bring in resources. For example, the founder of a smart pet care company mentioned that the industry insights and resources provided by investors are more crucial than the money itself. This shows that AI entrepreneurship has entered a deeper phase where money alone is not enough; you need experienced partners to succeed.
2. The Debate about “Bubbles”: “Intelligence Tax” or “Necessary Cost?”
When it comes to AI, the topic of “bubbles” is inevitable. The views of several industry leaders in the news represent two different perspectives:
View 1: Bubbles are harmful, but don’t be afraid.
Zhai Ning from Mingyisheng Capital believes that there are indeed bubbles, and many companies are not yet profitable. However, he argues that the value of bubbles lies in attracting the smartest people to solve real problems. Without bubbles and the influx of massive funds, those top talents might have gone into finance or the internet instead of focusing on fundamental research. So, bubbles are the “fuel” for innovation, but the key is whether they lead to “valuation illusions” (where prices are severely detached from reality).
View 2: Bubbles are a byproduct of innovation.
Feng Tian from BAI Capital is more optimistic. She says that bubbles and innovation are positively correlated. Without bubbles, there wouldn’t be such a large influx of capital and talent. The challenge for investors is to determine who can survive after the bubbles burst. She also points out that when dealing with new technologies, it’s inevitable for investors to pay a premium because no one knows the future, and quick decisions are more important than perfect ones.
My interpretation: These two views are not contradictory. Bubbles do exist, but they’re not necessarily bad; they’re just the “noise” in the market’s process of discovering value. The real risk lies in pseudo-innovation—companies that rely on hype without real practical capabilities. Only those that truly solve problems and have stable cash flows will stand out when the tide recedes.
3. Valuation Pitfalls: “Rising Valuations in the Primary Market, Diversion of Funds in the Secondary Market”
Jiang Haonan from Jiyuan Capital raised a very practical issue: Valuations in the primary market (before companies go public) are currently unrealistic. Some teams are getting valuations that are three to five times what they were three to five years ago.
It’s like housing prices; the houses haven’t changed, but the prices have skyrocketed because there’s too much money and too few good projects, driving up prices.
Jiang Haonan warns that in the short term, the secondary market (after companies go public) will face challenges:
1. Overseas giants going public: Companies like Anthropic, OpenAI, and SpaceX are about to or have already gone public, attracting a lot of capital.
2. Domestic leaders gathering funds: Several major domestic AI companies are preparing to go public, concentrating funds in their hands.
This means that the space for smaller companies is being squeezed. All the money is flowing towards these “star companies,” making it difficult for ordinary AI firms to raise funds through public offerings.
Advice for startups: Jiang Haonan’s advice is sound: “Each product should have a reasonable valuation that supports its business development.” Don’t expand blindly for a high valuation; be rational. Investors in the primary market should consider the company’s prospects over the next five years, not just the next round of funding.
4. Embodied Intelligence (Robots): Early “Money-Burning” Phase, Turning Point in 3-5 Years
Embodied intelligence (AI robots that can see, hear, and move) was one of the hottest topics at the conference. Institutions like Zhongke Chuangxing and Fengrui Capital are investing in this area, but the consensus is that we’re still in a very early stage, far from commercialization.
Why?
- Technology is not ready: Wang Chao pointed out that both the training methods and data are not yet adequate. It’s like teaching a child to walk; we’re still in the crawling phase, not running.
- Insufficient funding: Wang Chao also mentioned that companies in this field are not getting enough funding, indicating that this is not a bubble-filled area but one that requires long-term investment.
- Unclear application scenarios: Yan Qianhang made an analogy: It was like looking at autonomous driving in 2012; no one knew exactly where it would be used. The same is true for embodied intelligence; the technology needs to mature before we can see clear application scenarios.
Where are the opportunities?
- A turning point might come in 3-5 years: Zhao Fuheng and Yan Qianhang predict a qualitative shift in the industry within this time frame.
- Two types of companies are worth investing in: Those with strong systems and practical applications (e.g., capable of making useful robots) and those that can make breakthrough algorithms.
My interpretation: Embodied intelligence is a classic “long-term investment” area. Investing now is like investing in smartphones in 2010; the phones weren’t smart then, but you knew they would become popular. The competition is not about who earns the most quickly, but who has the right technology and patience.
5. The Ultimate Test: Who Can “Survive the Cycle?”
The term “surviving the cycle” appears repeatedly in this news. What is a cycle? It refers to economic booms and busts, and technological breakthroughs and bottlenecks. The AI industry is currently in a boom, but it will inevitably face challenges, increased competition, and possible market downturns.
Investors are now more interested in your ability to withstand risks:**
- Team strength: Can the team survive without funding? Can they quickly adapt if their technology fails?
- Business model: Is your revenue sustainable, or is it one-time?
- Respect for the rules: Zhai Ning emphasized that founders need to have a sense of respect for regulations, laws, and ethics. AI is not beyond the reach of the law, and compliance is a crucial aspect of survival.
In summary:
Investing in AI in 2026 is no longer about making blind decisions; it’s about making informed choices.
- For startups: Focus on customers and cash flows. Be rational about valuations and don’t let bubbles cloud your judgment.
- For investors: Don’t just chase trends; look for long-term value. Bubbles are not the problem; the real danger is losing the companies you invest in after the bubbles burst.
- For everyone: If you follow the AI industry, remember this principle: Focus on those that solve real problems, not those that just rely on hype. The former are worth investing in for the long term, while the latter could collapse at any time.
The future of AI is still bright, but the path ahead is rocky and full of challenges. Only those companies that are down-to-earth, financially sound, and capable of weathering the storm will succeed.