哈利·斯特宾斯（Harry Stebbings）邀请UiPath创始人兼首席执行官丹尼尔·迪内斯（Daniel Dines）就人工智能领域进行了一场“揭示真相、破除迷思的对话”。迪内斯最近在人工智能的帮助下撰写了一本书，他分享了对这项技术的局限性、其对劳动力的影响以及企业采纳未来的见解。 迪内斯对“数据中心里数百万个爱因斯坦”这一普遍观念表示怀疑。他认为，尽管人工智能可以拥有强大的推理能力，但它与人类智能有着根本的不同。人类通过“边干边学”，通过经验改变其内部“权重”，这类似于厨师多年来发展出独特的风格。然而，人工智能主要依靠记忆和检索，缺乏这种实时转变和持续自我提升的内在能力。迪内斯断言，这种区别是一个显著且持久的局限性。他还强调了人工智能的概率性本质，这使得它不适合需要跨越多个步骤的绝对“精确性”的任务，因此需要集成传统的、精确的计算工具。 关于Anthropic首席执行官达里奥·阿莫代伊（Dario Amodei）提出的“驾驭前沿”概念，迪内斯认为这是一种呼吁自我监管以避免失控行为，间接而言，也是针对开源人工智能的战略举措。他认为，在这种背景下，即使是中国人工智能实验室也是“好人”。他指出，企业对与前沿实验室合作持谨慎态度，主要是担心知识产权泄露给竞争对手，而非实验室本身成为直接竞争对手。 UiPath的战略以“工作地图”为中心，即对组织内部工作流如何运作的详细理解，包括所有流程、异常情况和系统。迪内斯认为，尽管人工智能，特别是“编码代理”，现在可以更轻松地生成运行企业的软件，但这些软件必须是精确、可审计和可预测的，而非概率性的。这造成了一种“不对称性”：人工智能使构建自动化变得更容易，但部署可靠的人工智能代理仍然具有挑战性。UiPath的“制图”产品旨在通过让代理访谈人类主题专家来记录这些复杂的流程，从而为人工智能创建必要的“手册”。 关于职位转型，迪内斯强调对其4,000名员工保持透明。他承认人工智能将带来改变，但强调目标不是大规模裁员。相反，公司必须理解人类贡献的全部范围——超越可衡量的成果——例如文化影响、指导和客户关系。人工智能可以自动化需要“凭证化专业知识”的任务，但在主动性、模糊性和人际连接方面表现不佳。他担心，基于传统的角色定义盲目裁员可能会淘汰对成功集成人工智能至关重要的人。他举例说，律师发现人工智能工具对于文档处理不可或缺，但也承认法律仍然需要人类的判断力和细微之处，这是一个人工智能擅长的明确“框架”。 迪内斯质疑对代币成本的关注，他表示为了更好的质量，即使更昂贵，他也会用机器取代人类，押注于未来的成本降低和错误减少。他认为，关于AI将取代所有定制软件的“末日论”（saspocalypse）被证明言过其实，因为将原型投入生产需要大量人力进行维护、安全和验证。 在讨论更广阔的市场时，迪内斯同意哈利（Harry）的观点，即黄仁勋（Jensen Huang）（英伟达首席执行官）受益于开源人工智能的成功。如果市场成为双头垄断，主要的人工智能实验室可能会开发自己的芯片，从而削弱英伟达的影响力。他支持开放模型以及企业拥有自己的“智能”而非租用它的理念，认为真正的价值在于每家公司拥有的独特“工作地图”。 迪内斯以个人反思作为总结。他现在每天投入一半时间使用Claude和ChatGPT等人工智能工具，增强他作为首席执行官的影响力。他承认欧洲的技术人才，但哀叹其目前在人工智能竞赛中的无关紧要地位，并建议年轻的欧洲企业家在美国寻找机会。他对未来保持乐观，尤其是在长寿和慢性病治疗方面，他个人每天服用60种经人工智能筛选的补充剂。

Harry Stebbings hosts Daniel Dines, founder and CEO of UiPath, for a "truth-telling, myth-busting conversation" on the world of AI. Dines, who recently authored a book with the help of AI, shares his insights on the technology's limitations, its impact on the workforce, and the future of enterprise adoption. Dines expresses skepticism about the common notion of "millions of Einsteins in data centers." He argues that while AI can possess strong reasoning powers, it fundamentally differs from human intelligence. Humans learn "on the job," transforming their internal "weights" through experience, akin to chefs developing unique styles over years. AI, however, primarily memorizes and retrieves, lacking this inherent capacity for real-time transformation and continuous self-improvement in the same way. This distinction, Dines asserts, is a significant, durable limitation. He also highlights AI's probabilistic nature, making it unsuitable for tasks requiring absolute "exactness" over many steps, necessitating the integration of traditional, exact computing tools. On the topic of "pacing the frontier," a concept proposed by Anthropic CEO Dario Amodei, Dines views it as a call for self-regulation to avoid rogue behavior and, indirectly, a strategic move against open-source AI. He believes even Chinese AI labs are "good guys" in this context. Enterprises, he notes, are wary of working with frontier labs primarily due to concerns about IP leakage to competitors, rather than the labs themselves becoming direct rivals. UiPath's strategy centers on the "map of work," a detailed understanding of how work flows within an organization, including all processes, exceptions, and systems. Dines argues that while AI, specifically "coding agents," can now more easily generate software to run enterprises, this software must be exact, auditable, and predictable, not probabilistic. This creates an "asymmetry": AI makes building automation easier, but deploying reliable AI agents remains challenging. UiPath's "cartography" product aims to document these intricate workflows by having agents interview human subject matter experts, creating the necessary manual for AI. Regarding job transformation, Dines emphasizes transparency with his 4,000 employees. He acknowledges that AI will lead to changes but stresses the goal is not mass layoffs. Instead, companies must understand the full spectrum of human contributions—beyond measurable outcomes—such as cultural impact, mentorship, and customer relationships. AI can automate tasks requiring "credentialed expertise," but it struggles with initiative, ambiguity, and human connection. He fears that blindly cutting staff based on traditional role definitions could eliminate the very people crucial for successful AI integration. As an example, he mentions lawyers who find AI tools indispensable for documentation, but acknowledges that law still requires human judgment and nuance, a clear "frame" where AI excels. Dines challenges the focus on token costs, stating he would replace a human with a machine for better quality, even if more expensive, betting on future cost reductions and reduced errors. He suggests that the "saspocalypse" of AI replacing all bespoke software proved overblown, as moving prototypes to production requires significant human effort for maintenance, security, and validation. Discussing the broader market, Dines agrees with Harry that Jensen Huang (NVIDIA CEO) benefits from the success of open-source AI. If the market becomes a duopoly, major AI labs might develop their own chips, reducing NVIDIA's leverage. He supports open models and the idea of enterprises owning their "intelligence" rather than renting it, believing the true value lies in the unique "map of work" each company possesses. Dines concludes with personal reflections. He now dedicates half his day to working with AI tools like Claude and ChatGPT, enhancing his leverage as CEO. While acknowledging Europe's technological talent, he laments its current irrelevance in the AI race, advising young European entrepreneurs to seek opportunities in the US. He remains optimistic for the future, particularly regarding longevity and chronic condition treatment, personally taking 60 AI-vetted supplements daily.