After a Mass Overdraft, Why AI Entrepreneurship Has Become Even More Exhausting

From Frenzy to Burnout: AI Entrepreneurs Collectively Abandon Ship

Since 2024, the AI sector has witnessed an unprecedented 'Great Leap Forward' — from AI writing and AI art generation to AI customer service, AI companionship, and AI presentation tools, virtually every niche has been flooded with entrepreneurs. Yet as 2025 unfolds, a starkly different mood is spreading: a growing number of AI entrepreneurs are shutting down projects, disbanding teams, and even returning to corporate jobs at major tech companies.

When everything can be sold with an AI label, it also means nothing sells well anymore. After a mass overdraft on the sector's potential, AI entrepreneurship has become more exhausting than ever.

AI for Everything: A Collective Overdraft

Over the past year-plus, the rapid opening of large model capabilities has driven the barrier to AI entrepreneurship to historic lows. A small team of three to five people can call a few APIs, wrap them in a front-end interface, and launch an 'AI+X' product within a week.

This low barrier to entry has triggered an explosive surge in product supply. According to incomplete estimates, China alone saw the emergence of thousands of AI application products in 2024, spanning office productivity, education, healthcare, legal services, e-commerce, and nearly every other industry. Entrepreneurs scrambled to claim every possible 'AI-ified' niche, as if falling one step behind meant being left behind by the era.

But the problems followed quickly — when everyone is doing the same thing, differentiation becomes a luxury. A vast number of AI products are functionally identical, user experiences have become mediocre, and price wars erupt one after another. Even more fatally, many products' core capabilities depend entirely on upstream large model providers. The moment the underlying model is upgraded or a competitor directly integrates similar features, the entire business logic can collapse instantly.

This 'thin-wrapper startup' model is essentially a mass overdraft on the sector's dividends.

A Triple Bind: Why Entrepreneurs Can No Longer Hold On

The first bind: a death spiral of homogenization. When hundreds of AI writing tools and dozens of AI presentation products compete for users simultaneously, no one can build a real moat. User loyalty is extremely low, while customer acquisition costs keep climbing. Many entrepreneurs have discovered that users they painstakingly acquired are quickly lured away by the next free product.

The second bind: fragile business models. A large number of AI applications adopt subscription or pay-per-use models, but users' willingness to pay falls far short of expectations. On one hand, large model providers themselves keep rolling out free or low-cost official applications; on the other, users' 'novelty factor' with AI tools fades extremely fast, and they often cancel subscriptions after an initial trial. Many entrepreneurs candidly admit that their monthly active user numbers look decent, but their revenue figures are 'too painful to look at.'

The third bind: a sharply tightening funding environment. After the AI investment frenzy of 2023, the capital market is rapidly returning to rationality. Investors are no longer buying into a simple 'AI+' narrative — they want to see real revenue data, user retention rates, and sustainable growth potential. For most early-stage AI startups, these metrics are precisely their greatest weaknesses. Unable to raise money and unable to afford the burn rate, shutting down becomes the only option.

After the Shakeout: Who Will Survive

The industry's brutal shakeout is painful, but it's not entirely bad news. Once the bubble recedes, truly valuable projects become easier to spot.

Based on current trends, AI entrepreneurs capable of surviving the cycle typically share several common traits:

Deep industry integration. Rather than simply using AI to replace a single step, they deeply understand industry pain points and deliver truly irreplaceable solutions. Teams that have built data moats and industry know-how in vertical domains such as medical imaging, industrial quality inspection, and legal compliance are attracting greater capital interest.

Command of core technology. Rather than depending entirely on upstream large model APIs, they possess proprietary models, private datasets, or unique technical architectures in specific domains. While these teams require larger upfront investments, their moats run much deeper.

Self-sustaining revenue capability. From day one, they think seriously about their business model and generate cash flow through real business operations, rather than relying purely on funding to survive. In a capital winter, the ability to be 'self-sustaining' has shifted from a bonus to a survival necessity.

The Cooling-Off Period Is Where It Truly Begins

Looking back at every technology wave — from the internet to mobile internet to today's AI — the script of 'mass entry, collective overdraft, brutal shakeout, value recovery' plays out almost identically. After the dot-com bubble burst in 2000, the survivors were Amazon and Google; after the 'Thousand Groupon Wars' of the mobile internet era, Meituan emerged victorious.

AI entrepreneurship will be no exception. The current wave of collective abandonment is less a signal of industry decline than an inevitable stage in the industry's maturation. As the noise fades, the entrepreneurs who remain will have to answer the most fundamental question: What real problem does your AI product actually solve?

Those who can provide a clear answer will be the protagonists of the next cycle. And for the AI industry as a whole, this cooling-off period may have arrived at just the right time — only when the bubble is fully deflated can genuine innovation find room to grow.