内容来源：https://techcrunch.com/2025/09/28/the-ai-services-transformation-may-be-harder-than-vcs-think/

内容总结：

近期，风险投资领域掀起一股新浪潮：多家头部机构正试图通过人工智能技术，重塑传统人力密集型服务行业的盈利模式。其核心策略是通过收购成熟专业服务公司，部署AI工具实现业务流程自动化，进而利用提升的现金流进行行业整合。

行业领军者通用催化剂（General Catalyst）率先投入15亿美元实施"创造型"投资策略。该计划专注于在特定垂直领域孵化AI原生软件企业，再以这些企业为平台收购同行业成熟公司。目前其布局已覆盖法律服务、IT管理等七大行业，未来计划扩展至20个领域。

该公司负责人马克·巴嘎瓦在接受采访时算了一笔账："全球服务业年收入达16万亿美元，而软件业仅1万亿美元。软件业的吸引力始终在于其高利润率——规模扩张时边际成本极低而边际收益可观。"据其测算，若能将服务业30%-50%的工作流程自动化，特别是在呼叫中心等场景实现70%的核心任务自动化，将产生巨大经济效益。

这一模式已初见成效。投资组合公司Titan MSP在获得7400万美元注资后，成功开发出能自动化处理38%常规任务的AI系统，随后收购知名IT服务商RFA。另一家孵化企业Eudia则通过AI驱动的固定费率法律服务，吸引雪佛龙、西南航空等财富100强客户，并完成对同业公司的收购。

然而斯坦福大学与BetterUp实验室的联合研究发出警示。针对1150名全职员工的调查显示，40%的受访者因处理AI生成的"表面光鲜但缺乏实质内容"的工作成果而增加额外负担，平均每个问题需耗费近两小时处理。据测算，这种"无效工作"相当于使万名规模的企业每年损失超900万美元生产力。

面对质疑，巴嘎瓦强调技术落地的复杂性正是其战略价值所在："如果所有企业都能通过咨询公司简单接入AI实现转型，我们的理论就缺乏立足之地。但现实是，需要精通各类AI模型的应用工程师与行业专家深度协作，这才是改造传统企业的关键。"

目前行业面临的核心矛盾在于：若按AI效率理论裁减人员，将缺乏人力纠正AI错误；若维持现有人力应对AI衍生问题，又难以实现预期的利润率提升。尽管存在这些挑战，硅谷投资者们仍在持续推进该战略。通用催化剂透露，其采用"创造型策略"的企业已实现盈利，只要AI技术持续进步，这套模式还将渗透至更多行业领域。

中文翻译：

风险投资家们坚信，他们已找到了下一个重要的投资风口：利用人工智能在传统劳动密集型服务业务中榨取软件级别的利润率。该策略包括收购成熟的专业服务公司，运用AI实现任务自动化，然后利用改善后的现金流兼并更多企业。

行业领军者General Catalyst（GC）将其最新募资中的15亿美元专项用于所谓"创造型"战略——专注于在特定垂直领域孵化AI原生软件公司，再以这些公司作为收购工具，兼并同行业的成熟企业及其客户资源。目前GC已在从法律服务到IT管理在内的七大行业布局，计划将覆盖领域扩展至20个行业。

"全球服务业年收入高达16万亿美元，"GC相关业务负责人马克·巴加瓦在接受TechCrunch专访时指出，"相比之下，软件行业全球规模仅1万亿美元。"他补充道，软件投资的核心吸引力始终在于其更高利润率。"当软件实现规模化，边际成本趋近于零，边际收益却极为可观。"若能将服务业自动化，用AI替代30%-50%的工作量（以呼叫中心为例甚至可实现70%核心任务自动化），其经济效应将变得不可抗拒。

优化后的现金流为高价收购更多公司提供了弹药，使其出价远超传统买家承受范围，从而形成支持者口中的"盈利飞轮"。

这一战略已初见成效。以GC投资组合中的Titan MSP为例，该企业分两轮获得7400万美元融资用于开发面向托管服务商的AI工具，随后收购了知名IT服务商RFA。巴加瓦透露，通过试点项目，Titan证明其可实现38%的典型MSP任务自动化。该公司现计划利用提升后的利润率，以经典并购策略收购更多MSP服务商。

无独有偶，GC孵化的Eudia专注企业法务部门而非律师事务所，已与雪佛龙、西南航空、Stripe等财富100强企业签订固定费用法律服务合约，以AI技术取代传统按时计费模式。为扩大业务范围，该公司近期收购了替代性法律服务提供商Johnson Hanna。

巴加瓦解释说，General Catalyst的目标是将所收购企业的息税折旧摊销前利润率至少提升一倍。

这种战略思维并非个例。风投机构Mayfield专门划拨1亿美元用于"AI同事"类投资，并领投了IT咨询初创公司Gruve的A轮融资。据其创始人透露，Gruve收购了一家年收入500万美元的安全咨询公司，六个月内将其营收提升至1500万美元，同时实现80%的毛利润率。

"如果80%的工作由AI完成，就能实现80%到90%的毛利润率，"Mayfield董事总经理纳文·查达今夏接受采访时表示，"综合利润率可达60%到70%，净利率维持在20%到30%。"

独立投资人埃拉德·吉尔三年来始终践行相似策略，支持那些收购成熟企业并通过AI进行改造的公司。"直接掌控资产比作为软件供应商能更快实现转型，"吉尔指出，"将企业毛利润率从10%提升至40%会带来巨大的价值飞跃。"

但早期预警信号显示，服务业这场变革可能比风投预想的更为复杂。斯坦福社交媒体实验室与BetterUp实验室联合对1150名全职员工的调研发现，40%的员工因所谓"AI垃圾工作"而负担加重——这些由AI生成的内容表面光鲜却缺乏实质，反而给同事制造更多工作量。

这种趋势正在对企业造成损害。受访员工表示，处理每项AI垃圾工作平均耗时近两小时，包括解析内容、决定是否退回，往往最终仍需自行修正。根据参与者报告的时间成本及薪资水平，研究者估算AI垃圾工作相当于每人每月征收了186美元"隐形税"。他们在《哈佛商业评论》文章中写道："对万名规模的企业，按AI垃圾工作出现频率估算...每年将导致超过900万美元的生产力损失。"

简而言之，单纯部署AI并不能保证效益提升。

巴加瓦反驳了AI过度炒作的观点，认为这些实施困境反而验证了GC策略的合理性。"这恰恰说明了机遇所在——将AI技术应用于这些行业并非易事，"他分析道，"如果财富100强企业只需聘请咨询公司，简单接入AI，与OpenAI签个合同就能完成转型，那我们的理论显然就站不住脚。但现实是，用AI改造企业确实困难重重。"

他指出，AI领域的技术复杂性正是最关键的缺失环节。"需要整合多种各有所长的技术，必须聘请来自Rippling、Ramp、Figma、Scale等公司的应用AI工程师，他们既精通不同模型的特性，又懂得如何将其嵌入软件。"他认为这种复杂性恰恰证明GC让AI专家与行业专家携手创业的策略具有前瞻性。

不过无可否认，AI垃圾工作正威胁着该战略的核心经济模型。更关键的问题在于其严重程度，以及这种状况会否随时间改变。

现阶段，若企业按AI效率理论裁减人员，将缺乏足够人力核查修正AI错误；若维持现有人手处理AI衍生的额外工作，风投期盼的巨额利润提升又难以实现。无论哪种情况，都可能延缓作为风投并购战略核心的扩张计划，进而削弱交易的吸引力。但现实是，仅凭几项研究并不足以让硅谷投资者止步。

事实上，General Catalyst表示，由于其"创造型战略"企业收购的都是具有现有现金流的业务，这些公司目前已实现盈利。

"只要AI技术持续进步，我们看到模型获得大量投资改进，就能在越来越多行业助力孵化企业。"巴加瓦对此充满信心。

英文来源：

Venture capitalists have convinced themselves they’ve found the next big investing edge: using AI to wring software-like margins out of traditionally labor-intensive services businesses. The strategy involves acquiring mature professional services firms, implementing AI to automate tasks, then using the improved cash flow to roll up more companies.
Leading the charge is General Catalyst (GC), which has dedicated $1.5 billion of its latest fundraise to what it calls a “creation” strategy that’s focused on incubating AI-native software companies in specific verticals, then using those companies as acquisition vehicles to buy established firms — and their customers — in the same sectors. GC has placed bets across seven industries, from legal services to IT management, with plans to expand to up to 20 sectors altogether.
“Services globally is a $16 trillion revenue a year globally,” said Marc Bhargava, who leads GC’s related efforts, in a recent interview with TechCrunch. “In comparison, software is only $1 trillion globally,” he noted, adding that the allure of software investing has always been its higher margins. “As you get software to scale, there’s very little marginal cost and there’s a great deal of marginal revenue.” If you can automate services business, too, he said – tackling 30% to 50% of those companies with AI, and even automating up to 70% of those core tasks in the case of call centers – the math begins to look irresistible.
The improved cash flow then provides ammunition for acquiring additional companies at higher prices than traditional buyers can afford, creating what proponents see as a lucrative flywheel.
The game plan seems to be working. Take Titan MSP, one of General Catalyst’s portfolio companies. The investment firm provided $74 million over two tranches to help the company develop AI tools for managed service providers, then it acquired RFA, a well-known IT services firm. Through pilot programs, says Bhargava, Titan demonstrated it could automate 38% of typical MSP tasks. The company now plans to use its improved margins to acquire additional MSPs in a classic roll-up strategy.
Similarly, the firm incubated Eudia, which focuses on in-house legal departments rather than law firms. Eudia has signed up Fortune 100 clients including Chevron, Southwest Airlines, and Stripe, offering fixed-fee legal services powered by AI rather than traditional hourly billing. The company recently acquired Johnson Hanna, an alternative legal service provider, to expand its reach.
General Catalyst looks to double – at least – the EBITDA margin of those companies that it’s acquiring, Bhargava explained.
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The powerhouse firm isn’t alone in this thinking. The venture firm Mayfield has carved out $100 million specifically for “AI teammates” investments and led the Series A for Gruve, an IT consulting startup that acquired a $5 million security consulting company and grew it to $15 million in revenue within six months while achieving an 80% gross margin, according to its founders.
“If 80% of the work will be done by AI, it can have an 80% to 90% gross margin,” Navin Chaddha, Mayfield’s managing director, told TechCrunch this summer. “You could have blended margins of 60% to 70% and produce 20% to 30% net income.”
Solo investor Elad Gil has been pursuing a similar strategy for three years, backing companies that acquire mature businesses and transform them with AI. “If you own the asset, you can [transform it] much more rapidly than if you’re just selling software as a vendor,” Gil said in an interview with TechCrunch this spring. “And because you take the gross margin of a company from, say, 10% to 40%, that’s a huge lift.”
But early warning signs suggest this whole services-industry metamorphosis may be more complicated than VCs anticipate. A recent study by researchers at Stanford Social Media Lab and BetterUp Labs that surveyed 1,150 full-time employees across industries found that 40% of those employees are having to shoulder more work because of what the researchers call “workslop” – AI-generated work that appears polished but lacks substance, creating more work (and headaches) for colleagues.
The trend is taking a toll on the organizations. Employees involved in the survey say they’re spending an average of nearly two hours dealing with each instance of workslop, including to first decipher it, then decide whether or not to send it back, and oftentimes just to fix it themselves.
Based on those participants’ estimates of time spent, along with their self-reported salaries, the authors of the survey estimate that workslop carries an invisible tax of $186 per month per person. “For an organization of 10,000 workers, given the estimated prevalence of workslop . . .this yields over $9 million per year in lost productivity,” they write in a new Harvard Business Review article.
Simply implementing AI doesn’t guarantee improved outcomes, in short.
Bhargava disputes the notion that AI is overhyped, arguing instead that all these implementation failures actually validate General Catalyst’s approach. “I think it kind of shows the opportunity, which is, it’s not easy to apply AI technology to these businesses,” he said. “If all the Fortune 100 and all these folks could just bring in a consulting firm, slap on some AI, get a contract with OpenAI, and transform their business, then obviously our thesis [would be] a little bit less robust. But the reality is, it’s really hard to transform a company with AI.”
He pointed to the technical sophistication required in AI as the most critical missing puzzle piece. “There’s a lot of different technology. It’s good at different things,” he said. “You really need these applied AI engineers from places like Rippling and Ramp and Figma and Scale, who have worked with the different models, understand their nuances, understand which ones are good for what, understand how to wrap it in software.” That complexity is exactly why General Catalyst’s strategy of pairing AI specialists with industry experts to build companies from the ground up makes sense, he argued.
Still, there’s no denying that workslop threatens to undermine the strategy’s core economics. The bigger question is how severe the problem is and whether or not that picture changes over time.
For the time being, if companies reduce staff as the AI efficiency thesis suggests they should, they’ll have fewer people available to catch and correct AI-generated errors. If they maintain current staffing levels to handle the additional work created by problematic AI output, the huge margin gains that VCs are counting on might never be realized.
It’s easy to argue that either scenario should perhaps slow the scaling plans that are central to the VCs’ roll-up strategy and that potentially undermine the numbers that make these deals attractive to them. But let’s face it; it will take more than a study or two to slow down most Silicon Valley investors.
In fact, because they typically acquire businesses with existing cash flow, General Catalyst says its “creation strategy” companies are already profitable.
“As long as AI technology continues to improve, and we see this massive investment and improvement in the models, I think there’ll just be more and more industries for us to help incubate companies,” Bhargava said.