Why the Current Wave of AI Models Won’t Change the Game
Baidu recently released several large language models that directly challenge OpenAI’s dominance—and they’ve followed Deepseek’s lead by making them open-source. While U.S. tech firms frequently emphasize openness and transparency as core values, it’s intriguing to see Chinese companies strategically employ openness not merely as a principle but as a tactical maneuver aimed squarely at capturing market share from their American counterparts.
This strategy feels particularly insightful given prevailing Western skepticism regarding closed-source Chinese models due to the backdrop of China’s surveillance state. Baidu and Deepseek seem acutely aware of this trust deficit and have pivoted accordingly, transforming potential liability into a strategic advantage. It’s an unexpected yet brilliant play, adding to my conviction that we might be in the golden timeline.
However, despite these strategic moves, market reaction has been lukewarm. Deepseek exploded onto the scene and grabbed immediate attention, while Baidu’s arguably superior—and certainly cheaper—alternatives haven’t stirred the waters as deeply. High-profile AI influencers and key figures on X haven’t rallied around these new models as fervently as expected. This signals a significant shift: models quickly become a commoditized protocol layer.
This implies that differentiation in AI is moving up the stack—away from the models themselves and toward the services, tools, and user experiences built atop them. This new era will be dominated by companies and products that excel at training and fine-tuning models through real-world interactions.
Yet herein lies a critical challenge: training models through teachable, human-like moments—”Actually, you messed that up; next time, do it like this”—is harder than it seems. As more nuanced corrections accumulate, models often become overloaded, struggling to accurately parse edge cases and filter irrelevant context in dynamic real-world situations. This handicap suggests that the next frontier isn’t merely about building bigger or even smarter foundational models. Instead, it’s about creating adaptive, contextual feedback mechanisms that keep models sharp and aligned with human intent without overwhelming their internal logic.
The winners in the next AI wave won’t be those who simply build bigger brains. Instead, they’ll be the ones who master the art of efficiently teaching these brains gotdam common sense amidst endless complexity.
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