Startup ARR is less secure than ever, new research shows

最新研究显示：初创公司的年度经常性收入（ARR）正变得前所未有的不稳定

AI has ushered in a lot of never-happened-before moments, but one of the most transformative is its impact on enterprise IT. Companies that have historically been cautious and committed long-term to what they buy are on pace to spend $4.25 trillion on technology in 2026, market researcher IDC predicts. It’s almost all driven by AI. 人工智能带来了许多前所未有的时刻，但其中最具变革性的影响之一是对企业IT领域的影响。市场研究机构IDC预测，那些历来谨慎且倾向于长期采购的企业，到2026年在技术上的支出有望达到4.25万亿美元。而这几乎全部是由人工智能驱动的。

New research from venture capital firm Madrona shows that 74% of 150 enterprise IT professionals it surveyed plan to expand their AI budgets in the next 12 months, and the rest plan to hold spending steady. Yet these same enterprises say that fewer than half of their AI pilots ever make it into full production. That’s actually an improvement. Last year, MIT famously reported that 95% of enterprise AI projects had failed in terms of ROI. Fewer than half succeeding is a pretty low bar, but it’s better than a 5% success rate. 风险投资公司Madrona的一项新研究显示，在其调查的150名企业IT专业人士中，74%的人计划在未来12个月内增加AI预算，其余的人则计划保持支出稳定。然而，这些企业同时也表示，他们只有不到一半的AI试点项目能够最终投入全面生产。这实际上已经是一种进步了。去年，麻省理工学院（MIT）曾发布一份著名报告称，95%的企业AI项目在投资回报率（ROI）方面均以失败告终。虽然“不到一半的成功率”门槛依然很低，但这确实比5%的成功率要好。

But the most telling finding from Madrona’s report is that, even when an enterprise does roll out the AI tech, it doesn’t commit to it long term. Some 77% of enterprises reevaluate their AI vendors every six months or even on a rolling basis. “This creates a ‘fast in, fast out’ dynamic that is fundamentally different from traditional enterprise SaaS, where multi-year contracts provided a moat of inertia,” Madrona writes in the report. “In enterprise AI, switching costs are lower and the re-evaluation cadence is relentless.” 但Madrona报告中最能说明问题的发现是：即使企业确实部署了AI技术，他们也不会进行长期承诺。约77%的企业每六个月甚至以滚动方式重新评估其AI供应商。Madrona在报告中写道：“这创造了一种‘快进快出’的动态，与传统的企业级SaaS有着本质区别，后者通过多年合同构筑了惯性护城河。而在企业级AI领域，转换成本更低，且重新评估的频率非常高。”

This has widespread implications for all those fast-growing annual recurring revenue (ARR) numbers startups report. Enterprise trial budgets are what fueled the initial AI boom of 2025. This year was supposed to be the year these big customers settled in and started committing long term to AI startups. Enterprise contracts are what allow so many AI startups to claim astronomically fast revenue growth — think the phenomenon of startups going from $0-$10 million in three months. Yet, for the first time ever, enterprise revenue remains insecure, even after a startup’s AI product graduates out of a pilot phase and gets adopted by a company. 这对初创公司所报告的那些快速增长的年度经常性收入（ARR）数字有着广泛的影响。企业试用预算是推动2025年初AI热潮的动力。今年本应是这些大客户稳定下来并开始对AI初创公司进行长期承诺的一年。企业合同是许多AI初创公司能够宣称实现天文数字般收入增长的原因——想想那些在三个月内从0美元增长到1000万美元的初创公司现象。然而，这是有史以来第一次，即使初创公司的AI产品度过了试点阶段并被企业采用，其企业收入依然处于不稳定的状态。

Part of the issue is that many AI startups haven’t fully landed on a good way to price their AI wares for enterprises. New research from VC firm Andreessen Horowitz that surveyed 50 technical AI buyers found that more than half of them want AI fees tied to the work produced or other outcomes, rather than to usage like the number of tokens consumed. Charging for usage like tokens is basically a SaaS-era business model. Once an enterprise knows it needs email, or HR software, or cloud storage, it’s merely a matter of how many employees or how much data it must pay for. 部分问题在于，许多AI初创公司尚未完全找到一种适合向企业销售AI产品的定价方式。风险投资公司Andreessen Horowitz（a16z）对50名技术AI买家进行的一项新研究发现，超过一半的买家希望AI费用与产出的工作成果或其他结果挂钩，而不是与消耗的Token数量等使用量挂钩。按Token等使用量收费基本上是SaaS时代的商业模式。一旦企业明确了自己需要电子邮件、人力资源软件或云存储，剩下的只是需要为多少员工或多少数据付费的问题。

For AI, pricing “around the recognizable work” is what helps the startup prove its worth to the customer. When the fees revolve around, say, how many reports are processed, or tickets closed, or leads generated, this makes the product “economically valuable to both sides,” writes a16z partners Tugce Erten and Sarah Wang. All of this means that AI has potentially ushered in a new era of enterprise experimentation. That opens doors to startups — enterprises are more willing to try their tech — but it also means an enterprise contract no longer secures long-term revenue. When or if enterprises will revert to their long-term buying habits remains to be seen. 对于AI而言，围绕“可识别的工作成果”进行定价，有助于初创公司向客户证明其价值。a16z合伙人Tugce Erten和Sarah Wang写道，当费用围绕着例如处理了多少份报告、关闭了多少工单或产生了多少潜在客户时，这使得产品对双方都具有“经济价值”。这一切意味着，人工智能可能已经开启了一个企业实验的新时代。这为初创公司打开了大门——企业更愿意尝试他们的技术——但也意味着企业合同不再能确保长期收入。企业何时或是否会回归其长期的采购习惯，仍有待观察。