New contenders are emerging on the scene, but true game-changers are still waiting in the wings.

When OpenAI unveiled GPT-4, Silicon Valley investor Andreas Hoffmann proclaimed, "We stand at the dawn of a computing revolution." Three years on, that prophecy has materialized.

However, when we take a step back and view this transformation through a historical lens, it resembles more of a "belated awakening." In 2012, AlexNet burst onto the scene in the ImageNet competition, quietly sowing the seeds of deep learning. By 2016, AlphaGo's victory over Lee Sedol showcased AI's "extraordinary prowess." And in 2022, ChatGPT captured the public's imagination, sparking a frenzied rush of capital, talent, and computational resources into the field.

This year, in particular, has marked a historic turning point in the venture capital landscape: AI startups have, for the first time, captured 51% of global venture capital investments, surpassing all other sectors combined. This data, sourced from CB Insights' latest report, underscores the unprecedented enthusiasm for artificial intelligence. The United States leads this wave, contributing 85% of AI financing and 53% of deal volume.

Data indicates that the global AI market investment scale is nearing $200 billion, yet no enterprise has emerged with the "Apple-level" disruptive power. Reflecting on Apple's ascent: in 1976, when Steve Jobs founded Apple, personal computers were the domain of engineers. The Macintosh redefined human-computer interaction with its graphical interface and user-friendly experience. In 1998, the iMac shattered the "industrial design shackles" of colorful electronics. In 2007, the iPhone transformed phones from mere communication tools into gateways to the mobile internet. Each disruption saw Apple complete a closed loop from technological breakthrough to ecosystem construction.

Today, AI players are attempting to replicate this path: large models as the "Macintosh," hardware as the "chip," and applications as the "App Store." The question remains: in AI's "Cambrian explosion," who can strike the right balance between technology, ecosystem, and commerce to become the new era's "Apple?"

01

AI Entrepreneurship: A Tale of Two Halves

Looking back at the past three years of AI entrepreneurship, we can identify three distinct stages.

The period from 2022 to 2023 marked the foundational era for large models. ChatGPT's emergence ignited the generative AI craze, with global tech giants and startups alike betting big on underlying model development.

In 2024, we entered the application exploration phase. As technologies matured, tools like Cursor, Midjourney, and Perplexity rose rapidly, signaling AI's shift from technological demonstration to practical value creation.

2025 ushered in the era of vertical integration. AI startups began embedding themselves deeply across industries, seeking commercialization paths in specific scenarios. At YC's Summer 2025 Demo Day, over half of the 169 startups featured AI agents as their core direction. These companies abandoned generic platforms, opting instead to dive deep into verticals targeting tasks that are "unwilling, poorly executed, and expensive" for humans.

For instance, Solva automated insurance claims with AI, achieving $245,000 in annualized revenue within just 10 weeks of launch. Autumn resolved complex billing issues for AI companies, finding adoption among hundreds of AI applications and 40 YC startups. In healthcare, Perspectives Health monitored doctor-patient conversations, generating real-time medical records and forms, saving doctors half their documentation time while maintaining 25% weekly growth during trials.

However, beneath this prosperity lie underlying concerns. AI entrepreneurship exhibits stark polarization: application-layer companies thrive, while infrastructure faces high barriers and resource concentration.

Indeed, data reveals that the global number of newly added AI unicorns has actually declined by 12.50% year-on-year and 6.67% quarter-on-quarter, indicating a market restructuring. The domestic market mirrors this trend: from the early "AI Six Little Tigers" to Hangzhou's "AI Six Little Dragons," most firms lack robust ecosystems and sustainable operations, with few achieving scalable revenue.

Capital markets have also become more rational. Investors now prioritize user retention, unit economics, and computing costs over mere technological novelty. AI entrepreneurship is transitioning from a hype-driven frenzy to a value-validation restructuring phase.

02

What Does AI Entrepreneurship Need to Become the Next Apple?

Apple's success stems not just from the iPhone or MacOS but from a "counterintuitive" underlying logic.

First, strategic resolve: from the 1998 iMac to the 2007 iPhone, Apple spent nine years elevating "consumer electronics" to a "lifestyle brand." Second, ecosystem closure: the "hardware-software-service" triad of the App Store, AirPods, and Apple Watch creates formidable barriers. Third, organizational resilience: Jobs' "obsessive" culture and Cook's "operational philosophy" complemented each other, balancing innovation and profitability.

In contrast, the AI industry faces three bottlenecks preventing "Apple-level" firms from emerging: 1) Technology-commerce disconnection: large model developers and hardware vendors lack ecological synergy, hindering efficient productization. 2) Organizational deficits: most AI firms remain stuck in "engineer thinking," neglecting user experience and branding. 3) Capital misallocation: venture capital overchases short-term trends while neglecting long-term infrastructure.

In essence, most AI startups remain "tool providers" without forming true ecological closures.

Domestically, AI entrepreneurship has shifted from the "Six Little Tigers" to the "Six Little Dragons." The early "AI Six Little Tigers" fell into losses due to over-reliance on B2B scenarios. Newer entrepreneurs target B2C sectors like AI writing and code generation platforms, yet face challenges surviving amid tech giants' ecological blockades and open-source model disruptions.

Gartner statistics reveal that in 2023, 62% of global AI startups iterated products over three times within 18 months, but only 17% achieved positive commercial cycles. This exposes a harsh reality: AI entrepreneurship is a "computing power leverage" game—survival depends on optimizing model performance, data quality, and cost control.

Investment market shifts also reflect AI's transitional phase. In Q3 2025, global venture capital reached $95.6 billion, but deal volumes hit their lowest since 2016. Investors are becoming more selective, channeling larger funds into mature, high-potential projects.

03

A New Cycle, A New Opportunity

Historically, AI has undergone three waves: the 1980s expert systems collapsed due to data and computing scarcity; the 2000s machine learning relied on manual feature engineering, failing to break the "black box" dilemma; the 2020s large models achieved general intelligence through self-supervised learning and massive data, yet face deployment challenges.

Compared to previous waves, the large model revolution drives more profound change with a "double-helix structure" of simultaneous technological breakthroughs (large models) and industrial demand (digitization). IDC data shows China's AI IaaS market surged 122.4% YoY to $19.87 billion in H1 2025, with GenAI IaaS growing 219.3%.

Moreover, domestic and overseas AI development exhibit distinct traits.

Overseas markets are innovation-driven by foundational models, with firms like OpenAI and Anthropic pushing model capabilities. China, on the other hand, prioritizes application deployment, leveraging its vast user base and diverse scenarios for commercialization.

Industry analysis reveals the core issue lies not in business imagination but in supply-demand dynamics. On one hand, computing supply diversifies as domestic and foreign cloud providers develop proprietary chips, stabilizing resource availability and pricing. On the other, demand structures reshape as enterprises seek AI integration into business processes for value closure.

Against this backdrop, emerging forces are quietly rising.

In chip design, Hygon Information's revenue grew 54.65% in Q1-Q3, while Cambricon's surged 2,386.38%, showcasing domestic AI chip potential. LiblibAI, focused on AI visual creation, secured $130 million in Series B funding—the largest domestic AI application deal—indicating renewed capital appraisal of application-layer firms.

In other words, AI is transitioning from "resource supply-driven" to "innovation-empowered-driven."

Apple's birth resulted from Jobs' 1976 "garage madness" and Cook's 1997 "rationality." Who will become the "Apple of the AI era?" The answer may lie in keywords: long-termism, ecological thinking, and user-centricity. Just as the 1998 iMac broke electronics' "gray tradition" with rainbow colors, future AI firms must find their "breakthrough point"—not by competing on model parameters but by redefining human-world relationships through technology.