摘要：TMTPOST -- More and more developers will realize that today’s very good models are extremely affordable, making it possible for an

TMTPOST -- More and more developers will realize that today’s very good models are extremely affordable, making it possible for an era of AI-first applications to flourish. This will be the biggest change in 2025, Says Kai-Fu Lee, a 40-year veteran in AI research and innovation and the founder of Sino Ventures and 01.AI, on Saturday at the 2024 T-EDGE Conference and TMTPost Annual Economic Meeting.

Over the past year and a half, model capabilities have significantly improved, while costs have plummeted, with inference costs dropping approximately tenfold in one year. However, 01.AI aspires to achieve three to four times this rate of improvement—accelerating speed and cost reductions by 30-40 times the current pace—to outpace the industry and rapidly foster innovative applications, Lee said.

A year and a half ago, GPT-4 was the only available large model on the market, and GPT-3.5 could actually meet very few application scenarios because its model capabilities were not strong enough. But if application developers had chosen to integrate GPT-4, each call would have cost $1-$2. Who could afford to develop applications under those circumstances? It would have been easy to go bankrupt, Lee elaborated.

Lee noted that AI large model companies should focus on serving customers and co-creating value with them to achieve a win-win situation. Regarding AGI, he pointed out that one day, AI will be able to do more than humans, but it does not necessarily have to do everything that humans can do. He predicted that it may take seven years to achieve AGI.

"We firmly refuse to blindly burn money on unprofitable traffic or engage in loss-making ventures for mere publicity," Kai-Fu Lee repeatedly emphasized. "01.AI aims to build the fastest and most cost-effective models with exceptional performance, igniting the ecosystem of large models for both ToC (consumer) and ToB (business) applications," he added.

While scaling is a central theme, 01.AI shifts the focus from training time compute to inference time compute, achieving a complementary synergy that pushes the boundaries of model capabilities more efficiently, Lee pointed out.

Scaling laws remain relevant, but their efficiency has diminished for two primary reasons: the limitation of data, as the growth of text data is no longer as rapid as before, and the declining efficiency of adding more GPUs. The relationship between GPU quantity and training gains is no longer linear, Lee further explained.

To create faster and cheaper models, the core principle is optimizing memory usage and minimizing GPU reliance—storing when possible and calculating only when necessary. For Chinese AI startups, the focus must be on "effective innovation." Companies should avoid overly ambitious AGI experiments that risk high costs and limited practical application scenarios, Lee suggested.

Looking ahead to next year, global inference compute costs are expected to drop further, potentially driving the explosive growth of AI-first ToC applications. These applications, which require time to build user bases before monetization, could find new growth opportunities, he shared.

China’s AI 2.0 future holds key advantages: the ability to develop cost-effective models with extremely low inference costs, which are foundational for high-DAU (daily active user) applications. Additionally, Chinese teams possess strategies honed during the mobile internet era that can be leveraged to promote and scale AI applications. Together, these factors make Chinese teams well-positioned for success in the ToC space.

However, the industry faces a critical challenge: large-model startups must now prove their ability to achieve sustained revenue growth. As technical competition transitions to commercial competition, startups must evolve from academically driven ventures to entrepreneurially managed enterprises. Failing to make this shift will narrow their path forward.

01.AI positions itself with two key commitments: first, building the fastest and most cost-effective world-class models to ignite ToC and ToB ecosystems; second, refusing to blindly burn money on unprofitable traffic or engage in loss-making ventures for publicity.

Lee emphasizes that AI companies should focus on serving clients and creating shared value to achieve win-win outcomes. As for AGI, he notes that while AI will one day surpass humans in many tasks, it does not need to replicate every human capability. He predicts that we are about seven years away from AGI, estimating its arrival around 2030.

来源：钛媒体