AI 時代的韌性

OpenAI 基金會的使命是確保通用人工智慧造福全人類。

我們一直持續不懈地努力，想跟上 AI 本身的快速發展。今年 4 月，OpenAI 基金會宣布⁠在生命科學與疾病治療領域提供首批 1 億美元補助金，期望透過運用先進 AI 協助預防與治療阿茲海默症等疾病。我們上週宣布⁠了「就業與經濟未來」計畫，希望能了解並形塑工作與經濟繁榮對未來世代的意義。

我們正進一步擴大對下一個重大計畫的願景，確保隨著 AI 能力提升，社會駕馭 AI 的能力也能以同樣的速度成長。我們將這項工作稱為 AI 韌性：這是降低 AI 風險的生態系統方法，旨在讓社會能將效益最大化。

我們已經著手行動。在計畫啟動短短幾個月內，OpenAI 基金會已著手落實透過「AI 韌性計畫」向各組織提供總計超過 1.3 億美元的資助金；首批名單將於近期公開，後續也將持續投入更多資金。^1⁠

轉型科技模式

唯有透過過往深刻改變人類歷史的技術變革為借鏡，才能最切實地理解 AI 韌性的核心重要性。

每隔一段時間會有新科技問世，徹底重塑社會。經濟學家稱這類科技為「通用技術」。火。印刷術。電力。網際網路。每一項都循著相似的發展軌跡：快速創新、實際面臨風險，以及各機構急起直追。但每個例子也都說明，要讓強大的技術變得安全需要付出哪些努力。

火讓人類文明成為可能。火讓我們得以保暖、煮熟食物，並保護我們免受掠食者攻擊。火也會讓城市付之一炬。隨著時間推移，社會逐步建立起韌性：耐火材料、消防栓網絡、專業消防部門以及建築法規。一個生態系，層層建構。

電力也走過相同的路。1882 年，愛迪生在珍珠街發電站點亮曼哈頓後，電力也帶來了火災、觸電身亡事故與公眾恐慌。在沒有絕緣電線、斷路器和電氣規範等防護措施的情況下，全國各城市都有工人和路人觸電身亡。各城市都能討論是否應該完全放棄這項實驗。相反地，隨著科技的進步，我們成立了保險商實驗室等獨立的測試機構、制定了美國《國家電工標準》等產業標準，並透過公共投資，將電力輸送到那些被市場遺忘的社區。每一層都讓用電變得更安全、更普及；如今，電力已安全到連孩子都能一扳開關就開燈。

這就是韌性建構得宜的樣子。

AI 需要韌性生態系統

AI 正走上與以往科技相同的發展軌跡，但發展速度卻是前所未有的快。

我們仍在發展初期，但效益已經清晰可見：AI 正在降低創業門檻、擴大教育機會、加速科學發現，並改變醫療領域。

與此同時，風險也正以同樣的速度出現，與 AI 帶來的效益如同一體兩面。推動新產業誕生的同一股成長力量，也可能顛覆既有產業並衝擊職涯。能協助年輕人學習與創作的同一套系統，也可能導致不良行為。加速生物研究的工具可能會降低製造有害病原體的門檻。而 AI 撰寫程式碼的能力一旦落入不當人士之手，可能會威脅關鍵基礎設施。

早期的 OpenAI 團隊相信，確保 AI 造福社會主要取決於解決技術和期望是否一致。這點仍然至關重要，也是我們工作的核心，但我們現在認為 AI 只是整個拼圖中的其中一塊。隨著 AI 擴散至各個產業與國家/地區，社會也將需要獨立研究、公共基礎建設、產業協調，以及全新的專業領域。簡言之，AI 韌性必不可少。

我們選擇將初期的工作重點放在四大領域^2⁠，這些領域兼具「巨大的近期風險」與「立即影響」：

1. 生物韌性，用於協助預防未來由蓄意製造的疫情；

2. 網路韌性，用於確保全球關鍵系統的安全；

3. AI 模型安全，用於鞏固人類對我們所創造之模型的掌控；以及

4. AI 對年輕人的影響，協助讓科技成為造福未來世代的正向力量。

我們的工作才剛起步。我們計畫分享更多關於各領域策略與首批補助的資訊，並隨著逐步擴展到其他領域。

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.