AI 時代的韌性

OpenAI Foundation 的使命是確保通用人工智能惠及全人類。

我們一直不懈努力，務求跟上 AI 本身的飛速發展。四月，OpenAI Foundation 宣布推出⁠首輪 1 億美元的「生命科學與疾病治療」(Life Sciences and Curing Diseases) 資助計劃，目標是透過運用先進 AI，協助預防及治療阿茲海默症等疾病。上週，我們宣布推出⁠「就業與經濟未來」(Jobs and Economic Futures) 計劃，期望了解並塑造工作與經濟繁榮對未來世代的意義。

今天，我們正拓展下一項重大計劃的願景：確保隨著 AI 能力的提升，社會運用它的能力也能同步增長。我們稱這項工作為 AI 韌性：這是一種減輕 AI 風險所需的生態系統方法，能夠讓社會最大化其效益。

我們的工作已經開始。在展開工作後的短短幾個月內，OpenAI Foundation 正努力透過我們的 AI 韌性計劃，敲定超過 1.3 億美元的資助給各組織，相關資訊很快就會公開分享，後續亦會有更多計劃推出。^1⁠

變革性技術的模式

透過那些深刻塑造人類歷史的過往技術，最能體會 AI 韌性的重要性。

每隔一段時間，就會有一種技術出現，從根本上重塑社會。經濟學家稱之為「通用目的技術」。火。印刷機。電力。互聯網。每一種技術都遵循相似的發展軌跡：快速創新、真實風險浮現、相關機構急起直追。但每個例子亦顯示，要令一項強大的技術變得安全，需要付出巨大的努力。

火使人類文明成為可能。它讓我們保暖、煮熟食物，並保護我們免受食肉動物侵害。它也會把我們的城市燒成廢墟。隨著時間推移，社會逐步建立抗火韌性：耐火材料、消防栓網絡、專業消防部門，以及建築規範。一個生態系統，層層構建。

電力亦沿着相同的路徑。1882 年，愛迪生的珍珠街發電站照亮曼哈頓之後，電力亦帶來火災、觸電身亡事故和公眾恐慌。在沒有絕緣電線、斷路器和電力規範等防護措施的情況下，全國各城市都有工人和旁觀者觸電身亡。多個城市討論應否徹底放棄這項實驗。相反，隨着技術進步，我們設立了像保險商實驗室這樣的獨立測試機構，制定了像《國家電氣規範》這樣的業界標準，並進行公共投資，將電力帶到那些市場未能覆蓋的社區。每一層都令電力變得更安全、更易使用；時至今日，電力已非常安全，小朋友只要按一下開關，燈就會亮起。

這就是韌性發揮得宜的樣子。

AI 需要韌性生態系統

AI 正沿著與過往技術相同的軌跡發展，但速度前所未有。

我們仍處於其發展初期，但其益處已清晰可見：AI 正在降低創業門檻、擴大教育資源的可及性、加速科學發現，並革新醫療領域。

與此同時，風險也同樣快速浮現，而且與 AI 的益處呈現鮮明對比。催生新產業的同一股增長力量，亦可能顛覆現有產業，衝擊就業市場。能協助年輕人學習與創造的系統，亦可能引發不良行為。加速生物學研究的工具，亦可能降低製造有害病原體的門檻。而 AI 寫程式碼的能力，一旦落入不當人士手中，可能會威脅關鍵基礎設施。

OpenAI 早期團隊認為，要確保 AI 惠及社會，關鍵在於解決技術對齊問題。這一點仍然至關重要，並且是我們工作的核心，但我們現在相信，這只是整個拼圖中的一塊。隨着 AI 滲透至不同的行業領域和國家，社會亦需要獨立研究、公共基礎設施、業界協調，以及全新的專業領域。簡而言之，這一切都需要AI 韌性。

我們選擇將初期工作聚焦於四大領域[fn:2]，這些領域均處於重大近期風險與即時影響的交匯點：

1. 生物韌性，協助防範未來人為製造的大流行病；

2. 網絡韌性，致力確保全球關鍵系統的安全；

3. AI 模型安全，鞏固人類對我們所創造模型的掌控；以及

4. AI 對年輕人的影響，以使技術成為造福未來世代的正面力量。

我們的工作才剛剛開始。我們計劃分享更多有關各個領域的策略和首批資助項目的資訊，並逐步擴展至其他領域。

Bio-resilience

AI will enable biological research to move at unprecedented speed, helping develop new cures and public health improvements that enable us to all live healthier and longer. However, these same capabilities could also be misused by malicious actors, lowering the barrier to designing harmful pathogens.

The age of AI requires a renewed focus on biosecurity. Because advanced AI systems could be misused by bad actors to help create a wide range of biological threats, we will prioritize pathogen-agnostic biosecurity solutions. This will require investments across prevention, detection, and defense. We need to make it harder for malicious actors to access the expertise, equipment, and materials to create biological threats, improve our ability to identify and track novel outbreaks early, and strengthen the technologies—such as protective equipment, indoor air cleaning systems, and medical countermeasures—needed to respond quickly and effectively.

Cyber-resilience

AI has begun to rapidly reshape the cybersecurity landscape. The work that once required specialized teams can now be assisted or automated by capable models. At the same time, rapidly-improving AI capabilities can also be used to accelerate cyberdefenders, including by identifying and patching vulnerabilities and accelerating response.

Many large companies and private actors can spend heavily on cyber to secure their own systems, including with new advances in AI. We anticipate focusing significant resources on securing other important societal actors that are less resourced and will have a much harder time deploying AI-ready cyberdefenses as quickly as needed. In parallel, we are also focused on preparing for novel security challenges that artificial general intelligence will ultimately bring.

AI model safety

AI model safety focuses on the behavior of the systems themselves—whether they are truthful, reliable, and aligned with human intent. In a world where this goes awry, models can break out and behave in unpredictable ways, deceiving us or pursuing goals beyond their design. Getting this right becomes increasingly important as AI systems grow more autonomous and approach—and eventually surpass—human-level intelligence.

AI companies are investing substantial resources in model safety. However, the importance of this challenge calls for a broader, more robust ecosystem: independent institutions to evaluate model safety, public infrastructure to verify models’ safe deployment in practice, and continued advances in alignment science that advance the field broadly.

AI’s impact on young people

Young people are often the earliest adopters of new technologies, using them to learn, create, communicate, and explore the world. AI is no exception. But as these tools become an increasing part of young people’s daily lives, it is critical that we develop a stronger evidence base to understand its impacts.

Families, schools, policymakers, and community organizations are all grappling with questions about how and when young people engage with AI—including its impact on human connection, learning, and development. Our initial focus will be on advancing independent research to help guide those decisions—to better understand where AI can support development, the risks it may introduce, and the contexts that shape those effects.

These insights should drive broad safety standards and design principles that guide how any AI product is developed, how schools choose to deploy them, and if and how families decide to incorporate these technologies into their lives.

The work ahead

There is one critical difference between AI and the technologies that came before it: speed.

Fire resilience took millennia. Electricity resilience took decades. AI resilience is evolving in a matter of years. The systems that make it safe, reliable, and broadly beneficial must be built alongside it.

If we get it right, AI can become part of the foundational infrastructure of modern life—expanding access to knowledge, accelerating discovery, and improving lives at a global scale.

But that outcome isn’t guaranteed. No general purpose technology ever made itself safe.

Resilience is a permanent discipline that requires many people and institutions to build, invest, and collaborate. That is the work ahead, and it is one of the defining challenges of our time. We hope you’ll join us.