from the not-with-a-whimper-but-a-bang dept
Although the field of artificial intelligence (AI) goes back more than half century, its latest incarnation — generative AI — is still very new: ChatGPT was launched just three years ago. During that time a wide variety of issues have been raised, ranging from concerns about the impact of AI on copyright, people’s ability to learn or even think, job losses, the flood of AI slop on the Internet, the environmental harms of massive data centers, and whether the creation of a super-intelligent AI will lead to the demise of humanity. Recently, a more mundane worry is that the current superheated generative AI market is a bubble abou…
from the not-with-a-whimper-but-a-bang dept
Although the field of artificial intelligence (AI) goes back more than half century, its latest incarnation — generative AI — is still very new: ChatGPT was launched just three years ago. During that time a wide variety of issues have been raised, ranging from concerns about the impact of AI on copyright, people’s ability to learn or even think, job losses, the flood of AI slop on the Internet, the environmental harms of massive data centers, and whether the creation of a super-intelligent AI will lead to the demise of humanity. Recently, a more mundane worry is that the current superheated generative AI market is a bubble about to pop. In the last few days, Google’s CEO, Sundar Pichai, has admitted that there is some “irrationality” in the current AI boom, while the Bank of England has warned about the risk of a “sharp correction” in the value of major players in the sector.
One element that may not yet be factored in to this situation is the rising sophistication of open source models from China. Back in April, Techdirt wrote about how the release of a single model from the Chinese company DeepSeek had wiped a trillion dollars from US markets. Since then, DeepSeek has not been standing still. It has just launched its V3.2 model, and a review on ZDNet is impressed by the improvements:
the fact that a company — and one based in China, no less — has built an open-source model that can compete with the reasoning capabilities of some of the most advanced proprietary models currently on the market is a huge deal. It reiterates growing evidence that the “performance gap” between open-source and close-sourced models isn’t a fixed and unresolvable fact, but a technical discrepancy that can be bridged through creative approaches to pretraining, attention, and posttraining.
It is not just one open source Chinese model that is close to matching the best of the leading proprietary offerings. An article from NBC News notes that other freely downloadable Chinese models like Alibaba’s Qwen were also “within striking distance of America’s best.” Moreover, these are not merely theoretical options: they are already being put to use by AI startups in the US.
Over the past year, a growing share of America’s hottest AI startups have turned to open Chinese AI models that increasingly rival, and sometimes replace, expensive U.S. systems as the foundation for American AI products.
NBC News spoke to over 15 AI startup founders, machine-learning engineers, industry experts and investors, who said that while models from American companies continue to set the pace of progress at the frontier of AI capabilities, many Chinese systems are cheaper to access, more customizable and have become sufficiently capable for many uses over the past year.
As well as being free to download and completely configurable, these open source models from Chinese companies have another advantages over many of the better-known US products: they can be run locally without needing to pay any fees. This also means no data leaves the local system, which offers enhanced privacy and control over sensitive business data. However, as the NBC article notes, there are still some worries about using Chinese models:
In late September, the U.S. Center for AI Standards and Innovation released a report outlining risks from DeepSeek’s popular models, finding weakened safety protocols and increased pro-Chinese outputs compared to American closed-source models.
And the success of China’s open source models is prompting US efforts to take catch up:
In July, the White House released an AI Action Plan that called for the federal government to “Encourage Open-Source and Open-Weight AI.”*
In August, ChatGPT maker OpenAI released its first open-source model in five years. Announcing the model’s release, OpenAI cited the importance of American open-source models, writing that “broad access to these capable open-weights models created in the US helps expand democratic AI.”*
And in late November, the Seattle-based Allen Institute released its newest open-source model called Olmo 3, designed to help users “build trustworthy features quickly, whether for research, education, or applications,” according to its launch announcement.*
The open source approach to generative AI is evidently growing in importance, driven by enhanced capabilities, low price, customizability, reduced running costs and better privacy. The free availability of these open source and open weight models, whether from China or the US, is bound to call into question the underlying assumption of today’s generative AI companies that there will be a commensurate payback for the trillions of dollars they are currently investing. Maybe it will be the realization that today’s open source models are actually good enough for most applications that finally pops the AI bubble.
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Filed Under: ai, allen institute, artificial intelligence, bank of england, bubble, china, customizability, genai, olmo, open source, open weight, openai, privacy, safety protocols, startups, sundar pichai, white house Companies: alibaba, chatgpt, deepseek, google, nbc