人工智能将在 2025 年重塑世界，但大多数人都没有意识到

如果 2024 年是人工智能融入主流的一年，那么 2025 年将是人工智能开始彻底重塑它的一年。

2024 was the year AI went mainstream. But just wait until 2025.

2024 年是人工智能成为主流的一年。但要等到 2025 年。

Most people are still blissfully unaware of just how different the world is about to look. But for those immersed in the tech space, the pace of change is exhilarating—and overwhelming.

大多数人仍然幸福地没有意识到世界即将变得多么不同。但对于那些沉浸在科技领域的人来说，变革的步伐令人振奋，而且势不可挡。

OpenAI’s latest models, o3 and o3-mini, exemplify this transformative moment. These models aren’t just smarter chatbots; they’re problem solvers, excelling at reasoning tasks like complex coding, advanced mathematics, and even scientific challenges. O3 represents a new breed of AI that not only processes data but thinks—a shift that pushes us closer to artificial general intelligence (AGI).

OpenAI 的最新型号 o3 和 o3-mini 体现了这一变革时刻。这些模型不仅仅是更智能的聊天机器人；他们是问题解决者，擅长复杂编码、高等数学甚至科学挑战等推理任务。 O3 代表了一种新型人工智能，它不仅能处理数据，还能思考——这一转变让我们更接近通用人工智能 (AGI)。

The o3 models achieve breakthroughs in reasoning through test-time compute, a mechanism allowing the AI to ‘ponder’ over complex problems before delivering a response. This innovative leap sets the stage for AI to tackle questions that demand deeper understanding, moving beyond rote predictions to reasoning capabilities that challenge the boundaries of what’s possible with machines.

o3 模型通过测试时计算在推理方面取得了突破，这种机制允许人工智能在做出响应之前“思考”复杂的问题。这一创新飞跃为人工智能解决需要更深入理解的问题奠定了基础，超越死记硬背的预测，发展到挑战机器可能性界限的推理能力。

o3 is better than 99.95% of programmers Source: X

o3 优于 99.95% 的程序员 来源：X

From Transforming Industries to Challenging Geopolitical Norms

从转型产业到挑战地缘政治规范

But these advances aren’t happening in a vacuum. They’re part of a broader narrative of AI-driven disruption, from transforming industries to challenging geopolitical norms.

但这些进步并不是凭空发生的。它们是人工智能驱动的颠覆的更广泛叙述的一部分，从行业转型到挑战地缘政治规范。

As AI becomes increasingly integral to our daily lives, its infrastructure demands have skyrocketed. AI is no longer just a software story. It’s about infrastructure—massive, tangible infrastructure. To make AI work, we need chips, electricity, and interconnects at an unprecedented scale.

随着人工智能越来越融入我们的日常生活，其基础设施需求也随之猛增。人工智能不再只是一个软件故事。它与基础设施有关——大规模、有形的基础设施。为了让人工智能发挥作用，我们需要前所未有规模的芯片、电力和互连。

Consider this: OpenAI’s ambitions include building data centers that demand as much power as entire cities. We’re not just talking about a few racks of servers; we’re talking about infrastructure projects on par with major metropolitan utilities.

考虑一下：OpenAI 的雄心包括建立与整个城市一样需要电力的数据中心。我们谈论的不仅仅是几个服务器机架；我们谈论的是与主要大都市公用事业同等的基础设施项目。

This is a CAPEX boom that doesn’t just benefit Silicon Valley engineers but electricians, construction workers, and local businesses. AI is making atoms great again, driving wealth creation across a broader swath of society than any previous tech wave.

这是资本支出的繁荣，不仅有利于硅谷工程师，还有电工、建筑工人和当地企业。人工智能正在让原子再次变得伟大，推动社会财富的创造比之前的任何科技浪潮都要广泛。

While social media enriched a small subset of programmers and influencers, AI’s hunger for physical infrastructure is creating opportunities for the concrete mixer, the electrician, and the local HVAC shop. This isn’t just a digital revolution; it’s a physical one, with economic ripples that extend far beyond traditional tech hubs.

虽然社交媒体丰富了一小部分程序员和有影响力的人，但人工智能对物理基础设施的渴望正在为混凝土搅拌机、电工和当地的暖通空调商店创造机会。这不仅仅是一场数字革命，更是一场数字革命。这是一种物理性的影响，其经济影响远远超出了传统科技中心的范围。

Test-Time Compute

测试时计算

But this isn’t just a story of growth. It’s a story of constraints—and cracks are already forming. Scaling laws, once gospel in the AI world, are facing practical limits.

但这不仅仅是一个成长的故事。这是一个充满限制的故事，而且裂痕已经形成。规模法则曾经是人工智能世界的福音，但现在正面临实际限制。

Pretraining these massive models requires staggering amounts of data and power, pushing current infrastructure and resources to their breaking point. The hyperscalers like Google, Amazon, and Microsoft are scrambling to secure energy sources, from renewables to nuclear, to keep their data centers running.

预训练这些庞大的模型需要大量的数据和电力，将当前的基础设施和资源推向极限。谷歌、亚马逊和微软等超大规模企业正在争先恐后地确保从可再生能源到核能等能源的安全，以保持数据中心的运行。

OpenAI’s o3 introduces a promising workaround: test-time compute. This allows models to ‘ponder’ more complex questions, using more resources for tougher problems and optimizing performance. It’s an elegant solution, but it’s only the beginning.

OpenAI 的 o3 引入了一种很有前途的解决方法：测试时计算。这使得模型能够“思考”更复杂的问题，使用更多的资源来解决更棘手的问题并优化性能。这是一个优雅的解决方案，但这只是一个开始。

The implications of these advancements are staggering. AI’s reach will extend into every industry, from medicine to agriculture, manufacturing to science. It’s poised to create orders of magnitude more wealth than previous technologies by impacting everything, everywhere, all at once.

这些进步的影响是惊人的。人工智能的影响力将扩展到各个行业，从医学到农业，从制造到科学。通过同时影响一切、无处不在，它有望创造比以前的技术多几个数量级的财富。

Critics might call this another tech bubble, but AI has the two critical ingredients to prove them wrong: massive capital investment and unparalleled generality. It’s not just a new tool; it’s a General Purpose Technology (GPT) on the scale of electricity, reshaping the fundamental fabric of human productivity.

批评者可能会称之为另一个科技泡沫，但人工智能有两个关键因素可以证明他们是错误的：大量的资本投资和无与伦比的通用性。它不仅仅是一个新工具；它是一个新工具。它是一种电力规模的通用技术（GPT），重塑了人类生产力的基本结构。

“This chart is pretty insane, it’s the current AGI progression in passing the ARC test. o3 already exceeded the average human decision-making scores, where will OpenAI’s o4 be in 6 months?” Source: X

“这张图表相当疯狂，它是当前 AGI 通过 ARC 测试的进度。 o3已经超过了人类决策的平均得分，6个月后OpenAI的o4会在哪里？”来源：X

Still, key questions loom large as we enter 2025. Are we hitting walls in pretraining? How do we solve reasoning at scale? When will the nation-state race to AGI begin?

尽管如此，随着我们进入 2025 年，一些关键问题变得越来越突出。我们在预训练中是否遇到了困难？我们如何解决大规模推理问题？民族国家的 AGI 竞赛何时开始？

OpenAI’s breakthroughs, like o1’s chain-of-thought reasoning and scaling inference compute, offer glimpses of answers. But the road ahead will test the limits of innovation, infrastructure, and human coordination.

OpenAI 的突破，例如 o1 的思想链推理和扩展推理计算，提供了一些答案。但前方的道路将考验创新、基础设施和人类协调的极限。

The U.S. is leading the AI race, with regulatory frameworks emerging and partnerships between AI labs and defense sectors strengthening. Meanwhile, other nations are lagging, though that’s unlikely to last.

美国在人工智能竞赛中处于领先地位，监管框架不断涌现，人工智能实验室与国防部门之间的合作关系也得到加强。与此同时，其他国家正在落后，尽管这种情况不太可能持续下去。

Europe’s regulatory-driven approach and China’s puzzling hesitation in prioritizing AI could shift as the race heats up. The geopolitical implications are profound.

随着竞争的升温，欧洲的监管驱动方式和中国在优先考虑人工智能方面令人困惑的犹豫可能会发生变化。其地缘政治影响是深远的。

National strategies around AI are emerging as critical differentiators, with some countries already positioning themselves as AI superpowers. In the U.S., regulatory efforts are balancing innovation with control, while strategic partnerships between AI labs and defense sectors signal an acknowledgment of AI’

围绕人工智能的国家战略正在成为关键的差异化因素，一些国家已经将自己定位为人工智能超级大国。在美国，监管工作正在平衡创新与控制，而人工智能实验室和国防部门之间的战略合作伙伴关系则标志着对人工智能的认可。