Concept Hype is Over, AI Startups Must Deliver Practical Applications
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This year, it's rare to see investors offering help in times of need—most prefer to add icing on the cake. In the AI field, securing funding has become increasingly difficult, and the entrepreneurial journey is growing tougher.
However, new trends reveal that the barriers to AI entrepreneurship are actually lowering. Solo entrepreneurs are flourishing in the wave of large models, appearing in every imaginable scenario—AI plugins, AI cameras, and more. More individuals are leveraging the convenience of large models to launch small-scale ventures.
What’s evident is that AI entrepreneurship is shifting toward strong, practical, and application-driven development. This aligns with observed trends: multiple AI entrepreneurs have emphasized that the prerequisite for starting a business is delivering tangible results.
For Chen Zheqian, founder of Yizhi Intelligence, who ventured into human-computer interaction back in 2017, practicality is a key reason their products are favored by many brands.
When discussing brand trends with Jianshi, Chen also noted that brands are increasingly focused on results, with ROI as a strong driver. Accordingly, their AI outbound calls and newly launched digital humans have adapted. More AI applications are emerging in marketing scenarios.
Jianshi: Last year, the metaverse concept was hyped intensely. Is this year’s AI just another hype cycle like last year?
Chen Zheqian: Actually, AI didn't suddenly emerge today. On the contrary, it has already gone through several waves of development. From my perspective, I've witnessed three major AI booms.
In 2012, facial recognition technology became a hot topic, and people realized that artificial intelligence was worth researching.
Then, in 2016, the emergence of AlphaGo revealed even greater potential for AI. Human-computer interaction became a trend, and products like smart speakers rapidly appeared during that period. However, the technology wasn't mature enough at the time and didn't achieve widespread adoption.
Our company, Yizhi Intelligence, was propelled by this wave because AlphaGo marked the true starting point of AI's boom in the entrepreneurial field.
So, the rise of large language models at the beginning of this year represents the third wave in my view. Of course, if we consider the entire AI industry, it might be the fifth wave.
Jianshi: From 2016 to now, what do you think is the most critical factor in AI entrepreneurship?
Chen Zheqian: My strongest feeling is that AI indeed has certain technical barriers—it's not something just anyone can do. If a high school student claimed they wanted to research AI, it would be highly unlikely.
However, AI is not so lofty that it is out of reach. Some large companies are eager to prove the sophistication of AI and promote the idea that AI can transform human life. But at least based on their financial reports, revenue is clearly disproportionate to the scale of financing and the speed of burning cash.
Large corporations still have the financial resources to invest in AI, but for entrepreneurs whose businesses are solely AI-focused, this is not a company that can sustain itself or create real commercial value for society.
I have always believed that AI entrepreneurship must be grounded in reality. As I often tell my colleagues, we are essentially a business company—we just use AI technology to create a product. The key is to find a way to sell the product, and the profits generated must be sufficient to support subsequent R&D and sales.
Over the years, AI has periodically been hyped up, and with each wave of hype, some investors become willing to invest. As a result, some technically skilled individuals start businesses before their products are even ready, playing with concepts first.
But judging by this year's trends, the likelihood of investors funding AI concepts is very low. Everyone is being cautious and starting to focus on the practical applications of AI. They won't offer help in times of need but will prefer to add icing on the cake. Therefore, for entrepreneurs seeking financing, grounding their work in reality has become even more critical.
Conclusion: There's no need to be pessimistic about AI—what matters most is having something tangible in hand.
Chen Zheqian: Yes, I think a crucial point is that there’s no need to either overly criticize AI or hype it up as some grand banner. AI is, in essence, just a technology.
Trying to inflate a technology’s hype without building real revenue means even the most advanced technology holds little value. Additionally, technology has its limitations. For example, large models excel at image generation today, but they are slow in real human-machine conversations. If you use them for dialogue, they generate responses one sentence at a time, and you might have to wait two or three seconds.
In phone call scenarios, people expect AI to respond within 500 milliseconds, not wait two seconds for an answer.
Technology will undoubtedly grow stronger in the future. But as AI entrepreneurs, our task is to find Product-Market Fit (PMF) and truly commercializable scenarios within the current technological landscape.
Interviewer: How did you come up with the idea of developing AI outbound calls?
Chen Zheqian: From the start of my entrepreneurial journey, my direction was to work on intelligent dialogue systems. Dialogue systems are somewhat like the brain—they need a medium to deliver value. Some apply dialogue systems to smart speakers, TVs, or cars.
Back in 2017, judging by the overall level of the intelligent dialogue industry, smart speakers, autonomous driving, and AI customer service were still largely gimmicks. The robots at the time weren’t advanced enough to convincingly mimic real humans.
As I mentioned before, when starting a business, feasibility is key. That’s why I chose AI outbound calling—because the telephone itself is a strong medium, and the brief duration of these calls (just a few minutes) allows the AI to convincingly mimic human interaction.
After further studying outbound calling scenarios, we found that consumer brands particularly benefit from this AI functionality. As a result, we’ve created significant value for these clients, leading to rapid revenue growth and a solid market share.
Interviewer: Why do you prioritize human-machine interaction in AI?
Chen Zheqian: Human-machine interaction brings businesses closer to their users, fostering stronger brand recognition.
Technological advancements inherently shorten distances. In the past, communication relied on beacon fires or urgent messengers. Later, movies could be watched from 5–10 meters away, TVs from 2–3 meters, and now smartphones are used within half a meter. With Apple’s Vision Pro, the distance is reduced to just a few centimeters between the glasses and the eyes.
Hardware interaction devices are getting closer to human eyes. Similarly, marketing has evolved—from one-way interactions like newspapers to the era of social media, where users can comment and engage with brands directly through text.
Now, for example, you can watch live streams of celebrities and potentially get a reply by sending bullet comments. AI outbound calls create a more interactive experience with brands. The reason private domains are so popular is that they shorten the distance between users and brands.
We have always used technological means to bring businesses closer to users, thereby enhancing users' commercial value or increasing their LTV (Lifetime Value).
Jianshi: Has there been any change in brands' requirements for your AI capabilities during outbound calls?
Chen Zheqian: Definitely. This year, for instance, many brands failed to meet their KPIs during the 618 shopping festival. Brands are increasingly focused on direct conversions, with ROI as a strong driver, rather than planting seeds and waiting for them to grow slowly. Previously, we might have done some phone-based 'grass planting' (brand awareness campaigns), but now brands want faster conversions—like seeing whether a user makes a purchase right after a call.
So now, brands aren’t just asking how much AI can reduce costs; they care more about how much it can increase revenue. This year, we launched a digital human tool in response to these shifts in brand priorities.
Currently, brands are also very interested in large models and digital human live streaming. It’s not just about saving costs for live streaming—it’s also a revenue-generating tool.
The reason is that with the same budget, what might have previously only funded one livestream room can now support multiple rooms through digital human livestreaming. The difference in potential traffic between operating one versus ten livestream rooms becomes significant.
Jianshi: Have any new positions emerged within the company?
Chen Zheqian: Yes, we've created a new role called Digital Human Livestream Operator in our digital human division. Their task is to control digital human hosts and ensure successful livestreaming events.
Unlike traditional livestream operators who review sessions with human hosts, these operators now analyze sessions with digital humans, researching areas for improvement in dialogue and response mechanisms within the livestream environment.
The market is seeing many new positions like AI Training Engineers. Beyond AI entrepreneurs seeking funding, there's a growing number of grassroots AI entrepreneurs leveraging major tech companies' large models to create niche startups.
From the perspective of super individuals, starting businesses in the AI industry is becoming easier rather than more difficult. This suggests that AI technology will enable more super individuals or one-person companies in the future.
Jianshi: It's clear you're a technology optimist.
Chen Zheqian: Because you must believe that technology will ultimately bring about unparalleled power. Just like when we look back over a decade ago, before the era of Baidu and Gaode Maps, it’s hard to imagine how people navigated without them. Similarly, ten years from now, we might not even recognize how we live today.
Technology will undoubtedly change many things—it’s just that we can’t fully envision it yet.
Jianshi: From an entrepreneur’s perspective, what challenges might arise with the future trend of AI?
Chen Zheqian: I think there are three main ones.
First, cash flow is the biggest challenge. Cash flow depends on initial investors. Companies that rely on investor funding rather than generating their own revenue will find it increasingly difficult to operate in the future.
The second source of cash flow lies in customers. However, today, many clients’ revenues fall short of expectations, leading to cost-cutting measures. This means the ongoing challenge is figuring out how to sustain oneself!
Don’t count on external support. In such tough conditions, companies must fully mobilize their products, internal workflows, services, R&D, and efficiency—ensuring not only survival but also maintaining high-quality offerings.
Second, competition among peers will inevitably become increasingly fierce. Market competition is like a battlefield. As the economy slows down, the competition intensifies—everyone wants to survive, and no one wants to be the loser.
Startups already face significant challenges. Every startup strives to survive, and survival requires solid capabilities. However, some companies resort to unethical tactics, such as price wars, when they can't compete fairly.
This puts businesses in a dilemma: choosing between price and quality. The challenge for everyone is: how do you stand out in such a market and make users remember you?
Third, internal management must be prioritized. In the past, rough management might have sufficed, but in the current climate, refined management is essential. Salaries are one aspect—balancing employee morale is crucial. Additionally, employees need spiritual motivation, ensuring they feel they are growing and gaining something beyond just their paychecks.