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Anthropic has once again come forward calling for a "pause," this time warning about the risks of AI "self-improvement." It sounds noble, but you know how it is—when a company valued at hundreds of billions is raising funds while advocating for the entire industry to slow down, the picture always seems a bit ambiguous. They might genuinely fear losing control, but more likely, they’re worried that before they’re ready, newcomers will flip the table.
Analysis
Anthropic has once again come forward calling for a "pause," this time warning about the risks of AI "self-improvement." It sounds noble, but you know how it is—when a company valued at hundreds of billions is raising funds while advocating for the entire industry to slow down, the picture always seems a bit ambiguous. They might genuinely fear losing control, but more likely, they’re worried that before they’re ready, newcomers will flip the table.
Looking at the recent wave of AI developments, it feels like a chaotic party. Doubao, ByteDance's AI assistant, saw its monthly active users plummet by 6.1 million after introducing paid features. The number is shocking but expected. Users who were content with free services run faster than anyone else when asked to pay. The question is, how can AI products actually make money? Monthly active users gained through burning cash subsidies are no different from bubbles. Doubao had to introduce fees; otherwise, it would forever be "running on love." But once paid, users aren’t biting—it’s a vicious cycle. Even more ironic is that at the same time, there are headlines like "Doubao isn’t responsible," implying that AI mistakes can be blamed on algorithms. Hey, the code is written by humans, and so is the responsibility.
Anthropic hasn’t been idle either, urgently halting its Mythos project due to internal employees selling API access. Another "family scandal." AI companies talk every day about safety and ethics, yet their own backyards are on fire. Data leaks, API misuse—these risks they’ve known for a long time, but in the rush to make progress, security modules always feel like afterthought patches. The Mythos incident isn’t isolated; it’s a microcosm of the industry’s common illness: technology runs too fast, but management is like a toddler stumbling along. Regulations haven’t caught up, and companies are already fighting internally.
On another front, the news about "setting rules for AI" claims AI has solved an 80-year-old math problem, and mathematicians are panicking. Panicking about what? Those who thought AI could only chat and paint should be the ones worried. Mathematics is the ultimate battlefield of logic, and AI’s breakthroughs here mean it’s closer to true general intelligence. But the phrase "setting rules" carries a sense of helplessness—humans always want to put reins on things before they spiral out of control, but once the technological singularity approaches, rules might just be pieces of paper. The mathematicians’ anxiety is real because their field was once seen as a fortress of human intellect, and now AI is breaking through effortlessly. This impact goes far beyond chess or poetry.
Zooming out, these events pieced together feel like a rebellious show during AI’s adolescence. Companies shout "responsible AI" while brutally competing in the market. Anthropic’s appeal might be sincere, but on the battlefield of business, slowing down equals suicide. Look at Doubao—its paid strategy was clearly a last resort, forced by unsustainable costs. But users are used to free services, and this gap is enough to make any product manager崩溃. The Mythos leak exposed the flaws beneath the glossy exterior: AI companies haven’t even learned how to manage their own tools.
The most biting part is the timeline: on the same day, we see industry leaders calling for calm, products losing users, internal security collapses, and technological breakthroughs causing panic. The AI field is like a spoiled yet anxious child, trying to show maturity while exposing its immaturity. Where are the regulations? Where are the policies? Right now, apart from sporadic after-the-fact reactions, there’s almost nothing. This allows companies to run freely but also means risks are accumulating. When AI starts solving math problems, we’re still debating whether to charge fees or how to prevent API theft—this contrast itself is absurd.
Perhaps what the AI industry truly needs isn’t more appeals, but more practical consensus: technology can move fast, but responsibility must keep pace. Doubao’s example proves that without a sustainable business model, even the hottest products are fleeting. Anthropic’s warning has value, but unless the entire industry is willing to pause (which is almost impossible), it will remain just PR statements. Incidents like Mythos should be alarm bells for the industry, but they’ll likely be drowned out by the next hot topic.
Ultimately, these developments point to a core contradiction: the speed of AI development is severely out of sync with humanity’s ability to adapt. We’re using 19th-century laws to govern 21st-century technology and 20th-century education to train future generations. The mathematicians’ panic is justified—AI isn’t just solving problems; it’s redefining "intelligence" itself. When AI can crack a century-old math难题, are we ready to face a world of intelligence no longer centered on humans? This question is vast, but Doubao’s pricing dilemma and Anthropic’s safety appeals are small footnotes under this grand issue. The real challenge isn’t what AI can do, but how we coexist with it—and for now, it seems we’re still groping in the dark, with a mix of arrogance and fear.
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