<p>围绕人工智能的叙述已经从乌托邦的承诺转变为即时、可衡量的效率，这一转变定义了每个主要科技公司和新兴初创企业当前的运营任务。这一转变——从实现通用智能到提供可证明的投资回报——是最近的 Forward Future Live 讨论中探讨的核心张力。Matthew Berman 主持了此次会议，汇聚了来自企业软件层、深度人工智能开发、经济预测以及专业智能领域的多元视角。小组成员包括 Gauntlet AI 创始人 Austen Allred；Box 的联合创始人兼首席执行官 Aaron Levie；Harmonic 的联合创始人兼首席执行官 Tudor Achim；以及 Ramp 的首席经济学家 Ara Kharazian，大家共同剖析了经济环境如何重塑尖端技术的部署和货币化。</p>
<p>对于 Aaron Levie 来说，人工智能的挑战并不是概念性的，而是纯粹的架构和安全问题。企业界以谨慎的态度行事，并不在等待 AGI，而是在努力解决如何安全地将增强工具整合到庞大的受监管内容层中。这是一个消费者导向的人工智能开发者常常忽视的关键区别。Levie 强调，尽管模型强大，但实际实施需要解决数十年来的数据治理问题。讨论迅速聚焦于人工智能最大潜力在于释放被封闭的企业数据中的价值，这一任务远比运行简单的提示要复杂得多。“当我们考虑未来五年时，关注的重点不再是基础模型的突破，而是集成层、安全层和数据治理层，” Levie 指出，强调了理论能力与运营必要性相遇时的摩擦点。这一集成任务迫使企业参与者成为信任的管理者，确保大型语言模型所承诺的生产力提升不会妨碍核心的合规性。</p>
<p>这种对即时、安全集成的关注与 Ara Kharazian 所描述的当前经济逆风完美契合。对人工智能项目的无限制投机资金时代显然已经结束。Kharazian 的分析基于 Ramp 在初创生态系统中的全面支出数据，描绘了财政保守主义加剧的图景。公司仍在特定的人工智能基础设施上进行积极支出——计算和专业人才——但支出现在必须直接与短期收入生成或显著的运营节省挂钩。市场不再奖励未来颠覆的承诺；它要求具体的季度结果。Kharazian 指出，“资本成本根本改变了对长期人工智能投资的风险承受能力。创始人被迫在比两年前更短的时间内证明单位经济。” 这种压力环境将提供核心效用的工具与仅仅是新奇的工具区分开来。</p>
<p>Austen Allred 在构建 Gauntlet AI 的过程中，从一线提供了对企业讨论的重要反驳，专注于在这种高压环境中扩展人工智能原生公司的巨大困难。专业人工智能人才的竞争环境非常残酷，训练和部署专有模型所需的资本仍然是天文数字。对于创始人来说，建立可持续业务的路径涉及识别通用模型失败的细分市场，以及专业数据和迭代改进提供的防御性护城河。Allred 强调，当前市场奖励极端的专注和效率，警告不要犯过度承诺通用能力的常见错误。他的见解集中在所需的运营严谨性上：“如果你没有解决一个能为客户节省十倍于你收费的问题，你的资金链将比你想象的更快消失。” 这种不对称价值交付的强调是风险投资支持的人工智能公司的新基准。</p>
<p>讨论扩展到人工智能的地缘政治影响，这是一个风险不仅仅是经济的领域。Harmonic 的 Tudor Achim 提供了高可靠性、专业人工智能在情报和国防领域部署的背景。在这个领域，容错、幻觉或数据漂移的容忍度为零。尽管消费者级的大型语言模型令人印象深刻，但往往缺乏关键决策所需的可验证来源和确定性输出。Achim 强调，对于涉及国家安全或大规模基础设施分析的应用，重点必须完全从创造力转向精确性。技术必须快速、可靠，并基于可验证的数据源，通常需要较小的、高度策划的模型，而不是庞大的、通用的架构。这一专业要求突显了人工智能生态系统中日益扩大的鸿沟：高风险、特定领域的智能与主导公共话语的通用、面向消费者的工具之间的二分法。</p>
<p>这四种观点的汇聚——企业安全、经济约束、初创企业生存和专业可靠性——清晰地描绘了当前人工智能的拐点。炒作周期已经成熟为效率的任务。能够展示即时、安全集成的人工智能工具，直接影响底线的公司将会蓬勃发展，无论是通过自动化高度复杂的内部流程（如 Box 所寻求的），还是通过在专业领域提供关键的、无误的分析（如 Harmonic 所执行的）。市场已经发出了最终的命令：人工智能必须成为经济效率的引擎，而不仅仅是技术奇迹。</p>

<p>关于人工智能的叙述已经从乌托邦式的承诺转向对即时效率的关注，正如最近的 “前瞻未来直播” 小组讨论中所提到的。专家们强调了在企业中安全整合人工智能工具的必要性，涉及数据治理和合规性。经济压力导致对短期收入生成的需求，企业需要迅速证明单位经济效益。讨论突显了高风险、专业化的人工智能应用与消费级模型之间的分歧，标志着人工智能领域的一个关键转折点，在这个转折点上，效率和即时结果至关重要</p>

<p>The narrative surrounding artificial intelligence has shifted from utopian promise to immediate, measurable efficiency, a transition that defines the current operational mandate for every major tech firm and fledgling startup alike. This pivot—from achieving generalized intelligence to delivering demonstrable ROI—was the central tension explored during the recent Forward Future Live discussion. Matthew Berman hosted the session, bringing together diverse perspectives from the enterprise software layer, deep AI development, economic forecasting, and the demanding world of specialized intelligence. The panel featured Austen Allred, Founder of Gauntlet AI; Aaron Levie, Co-founder and CEO of Box; Tudor Achim, Co-Founder and CEO of Harmonic; and Ara Kharazian, Lead Economist at Ramp, all dissecting how the economic climate is reshaping the deployment and monetization of cutting-edge technology.</p>
<div class="lb-trans"><p>围绕人工智能的叙述已经从乌托邦的承诺转变为即时、可衡量的效率，这一转变定义了每个主要科技公司和新兴初创企业当前的运营任务。这一转变——从实现通用智能到提供可证明的投资回报——是最近的 Forward Future Live 讨论中探讨的核心张力。Matthew Berman 主持了此次会议，汇聚了来自企业软件层、深度人工智能开发、经济预测以及专业智能领域的多元视角。小组成员包括 Gauntlet AI 创始人 Austen Allred；Box 的联合创始人兼首席执行官 Aaron Levie；Harmonic 的联合创始人兼首席执行官 Tudor Achim；以及 Ramp 的首席经济学家 Ara Kharazian，大家共同剖析了经济环境如何重塑尖端技术的部署和货币化。</p>
</div><p>For Aaron Levie, the challenge of AI is not conceptual; it is purely architectural and security-focused. The enterprise world, which moves with deliberate caution, is not waiting for AGI, but rather grappling with how to safely integrate augmentation tools into vast, regulated content layers. This is a critical distinction often missed by consumer-facing AI developers. Levie emphasized that while the models are powerful, the actual implementation requires solving decades-old data governance issues. The conversation quickly focused on the reality that AI’s greatest potential is unlocking the value trapped within proprietary, siloed corporate data, a task far more complex than running simple prompts. “When we think about the next five years, it’s less about the foundational model breakthroughs and much more about the integration layer, the security layer, and the data governance layer,” Levie noted, underscoring the friction points where theoretical capability meets operational necessity. This integration mandate forces enterprise players to become stewards of trust, ensuring that the productivity gains promised by LLMs do not compromise core regulatory compliance.</p>
<div class="lb-trans"><p>对于 Aaron Levie 来说，人工智能的挑战并不是概念性的，而是纯粹的架构和安全问题。企业界以谨慎的态度行事，并不在等待 AGI，而是在努力解决如何安全地将增强工具整合到庞大的受监管内容层中。这是一个消费者导向的人工智能开发者常常忽视的关键区别。Levie 强调，尽管模型强大，但实际实施需要解决数十年来的数据治理问题。讨论迅速聚焦于人工智能最大潜力在于释放被封闭的企业数据中的价值，这一任务远比运行简单的提示要复杂得多。“当我们考虑未来五年时，关注的重点不再是基础模型的突破，而是集成层、安全层和数据治理层，” Levie 指出，强调了理论能力与运营必要性相遇时的摩擦点。这一集成任务迫使企业参与者成为信任的管理者，确保大型语言模型所承诺的生产力提升不会妨碍核心的合规性。</p>
</div><p>This focus on immediate, secure integration aligns perfectly with the current economic headwinds described by Ara Kharazian. The era of unrestricted speculative funding for AI projects has demonstrably concluded. Kharazian’s analysis, drawn from Ramp’s comprehensive spending data across the startup ecosystem, painted a picture of increased fiscal conservatism. Companies are still spending aggressively on specific AI infrastructure—compute and specialized talent—but the spending must now be tied directly to short-term revenue generation or significant operational savings. The market is no longer rewarding promises of future disruption; it demands concrete, quarter-over-quarter results. Kharazian pointed out the stark reality that “the cost of capital has fundamentally changed the risk tolerance for long-horizon AI bets. Founders are being forced to prove unit economics on a much shorter timeline than they were two years ago.” This pressure cooker environment separates the tools that deliver core utility from those that are merely novelty.</p>
<div class="lb-trans"><p>这种对即时、安全集成的关注与 Ara Kharazian 所描述的当前经济逆风完美契合。对人工智能项目的无限制投机资金时代显然已经结束。Kharazian 的分析基于 Ramp 在初创生态系统中的全面支出数据，描绘了财政保守主义加剧的图景。公司仍在特定的人工智能基础设施上进行积极支出——计算和专业人才——但支出现在必须直接与短期收入生成或显著的运营节省挂钩。市场不再奖励未来颠覆的承诺；它要求具体的季度结果。Kharazian 指出，“资本成本根本改变了对长期人工智能投资的风险承受能力。创始人被迫在比两年前更短的时间内证明单位经济。” 这种压力环境将提供核心效用的工具与仅仅是新奇的工具区分开来。</p>
</div><p>Austen Allred, speaking from the trenches of building Gauntlet AI, provided a crucial counterpoint to the enterprise discussion, focusing on the sheer difficulty of scaling an AI-native company in this high-pressure environment. The competitive landscape for specialized AI talent is brutal, and the capital required to train and deploy proprietary models remains astronomical. For founders, the path to building a sustainable business involves identifying niches where generalized models fail and where specialized data and iterative refinement provide a defensible moat. Allred highlighted that the current market rewards extreme focus and efficiency, cautioning against the common mistake of over-promising generalized capabilities. His insight centered on the operational rigor needed: “If you are not solving a hair-on-fire problem that saves a customer ten times what you charge them, your runway will disappear faster than you think.” This emphasis on asymmetric value delivery is the new benchmark for venture-backed AI firms.</p>
<div class="lb-trans"><p>Austen Allred 在构建 Gauntlet AI 的过程中，从一线提供了对企业讨论的重要反驳，专注于在这种高压环境中扩展人工智能原生公司的巨大困难。专业人工智能人才的竞争环境非常残酷，训练和部署专有模型所需的资本仍然是天文数字。对于创始人来说，建立可持续业务的路径涉及识别通用模型失败的细分市场，以及专业数据和迭代改进提供的防御性护城河。Allred 强调，当前市场奖励极端的专注和效率，警告不要犯过度承诺通用能力的常见错误。他的见解集中在所需的运营严谨性上：“如果你没有解决一个能为客户节省十倍于你收费的问题，你的资金链将比你想象的更快消失。” 这种不对称价值交付的强调是风险投资支持的人工智能公司的新基准。</p>
</div><p>The discussion broadened to the geopolitical implications of AI, a domain where the stakes are existential, not merely economic. Tudor Achim of Harmonic provided context on how high-reliability, specialized AI is deployed in intelligence and defense sectors. In this realm, the tolerance for error, hallucination, or data drift is zero. Consumer-grade LLMs, while impressive, often lack the verifiable provenance and deterministic output required for critical decision-making. Achim stressed that for applications involving national security or large-scale infrastructure analysis, the focus must shift entirely from creativity to precision. The technology must be fast, reliable, and grounded in verifiable data sources, often requiring smaller, highly curated models rather than massive, generalized architectures. This specialized requirement illustrates a growing chasm in the AI ecosystem: the bifurcation between high-stakes, domain-specific intelligence and the generalized, consumer-facing tools that dominate the public discourse.</p>
<div class="lb-trans"><p>讨论扩展到人工智能的地缘政治影响，这是一个风险不仅仅是经济的领域。Harmonic 的 Tudor Achim 提供了高可靠性、专业人工智能在情报和国防领域部署的背景。在这个领域，容错、幻觉或数据漂移的容忍度为零。尽管消费者级的大型语言模型令人印象深刻，但往往缺乏关键决策所需的可验证来源和确定性输出。Achim 强调，对于涉及国家安全或大规模基础设施分析的应用，重点必须完全从创造力转向精确性。技术必须快速、可靠，并基于可验证的数据源，通常需要较小的、高度策划的模型，而不是庞大的、通用的架构。这一专业要求突显了人工智能生态系统中日益扩大的鸿沟：高风险、特定领域的智能与主导公共话语的通用、面向消费者的工具之间的二分法。</p>
</div><p>The convergence of these four viewpoints—enterprise security, economic constraint, startup survival, and specialized reliability—paints a clear picture of the current AI inflection point. The hype cycle has matured into an efficiency mandate. Companies that thrive will be those that can demonstrate immediate, secure integration of AI tools that directly impact the bottom line, whether through automating highly complex internal processes, as Box seeks to do, or by delivering critical, error-free analysis in specialized fields, as Harmonic executes. The market has issued its final decree: AI must be an engine of economic efficiency, not just technological wonder.</p>
<div class="lb-trans"><p>这四种观点的汇聚——企业安全、经济约束、初创企业生存和专业可靠性——清晰地描绘了当前人工智能的拐点。炒作周期已经成熟为效率的任务。能够展示即时、安全集成的人工智能工具，直接影响底线的公司将会蓬勃发展，无论是通过自动化高度复杂的内部流程（如 Box 所寻求的），还是通过在专业领域提供关键的、无误的分析（如 Harmonic 所执行的）。市场已经发出了最终的命令：人工智能必须成为经济效率的引擎，而不仅仅是技术奇迹。</p>
</div>

The Efficiency Mandate Why Enterprise AI Adoption is Hitting the Economic Wall

- 人工智能叙事已转向即时可量化的效率，定义了科技公司的新使命。  
- 专家探讨经济环境如何重塑尖端技术的部署与变现方式。  
- 未来五年，企业需关注集成、安全和数据治理，而非单纯的模型突破。

效率任务：为何企业 AI 的采用正面临经济壁垒