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Venture capitalists have convinced themselves they\u2019ve 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.<\/p>\n
Leading the charge is General Catalyst (GC), which has dedicated $1.5 billion of its latest fundraise to what it calls a \u201ccreation\u201d strategy that\u2019s focused on incubating AI-native software companies in specific verticals, then using those companies as acquisition vehicles to buy established firms \u2014 and their customers \u2014 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.<\/p>\n
\u201cServices globally is a $16 trillion revenue a year globally,\u201d said Marc Bhargava<\/a>, who leads GC\u2019s related efforts, in a recent interview with TechCrunch<\/a>. \u201cIn comparison, software is only $1 trillion globally,\u201d he noted, adding that the allure of software investing has always been its higher margins. \u201cAs you get software to scale, there\u2019s very little marginal cost and there\u2019s a great deal of marginal revenue.\u201d <\/p>\n If you can automate services business, too, he said \u2013 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 \u2013 the math begins to look irresistible.<\/p>\n The game plan seems to be working right now. Take Titan MSP<\/a>, one of GC\u2019s 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.<\/p>\n Similarly, the firm incubated Eudia<\/a>, 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.<\/p>\n GC looks to double \u2013 at least \u2013 the EBITDA margin of those companies that it\u2019s acquiring, Bhargava explained.<\/p>\n Techcrunch event<\/p>\n \n\t\t\t\t\t\t\t\t\tSan Francisco<\/span> The powerhouse firm isn\u2019t alone in its thinking. The venture firm Mayfield has carved out $100 million specifically for \u201cAI teammates\u201d investments, including Gruve, an IT consulting startup that acquired a $5 million security consulting company, then grew it to $15 million in revenue within six months while achieving an 80% gross margin, according to its founders.<\/p>\n \u201cIf 80% of the work will be done by AI, it can have an 80% to 90% gross margin,\u201d Navin Chaddha, Mayfield\u2019s managing director, told TechCrunch this summer<\/a>. \u201cYou could have blended margins of 60% to 70% and produce 20% to 30% net income.\u201d<\/p>\n Solo investor Elad Gil has been pursuing a similar strategy for three years, backing companies that acquire mature businesses and transform them with AI. \u201cIf you own the asset, you can [transform it] much more rapidly than if you\u2019re just selling software as a vendor,\u201d Gil said in an interview with TechCrunch this spring<\/a>. <\/p>\n But early warning signs suggest this whole services-industry metamorphosis may be more complicated<\/a> 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 \u201cworkslop\u201d \u2013 AI-generated work that appears polished but lacks substance, creating more work (and headaches) for colleagues.<\/p>\n The trend is taking a toll on the organizations. Employees involved in the survey say they\u2019re 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. <\/p>\n Based on those participants\u2019 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. \u201cFor an organization of 10,000 workers, given the estimated prevalence of workslop . . .this yields over $9 million per year in lost productivity<\/a>,\u201d they write in a new Harvard Business Review article.<\/p>\n Bhargava disputed the notion that AI is overhyped, arguing instead that all these implementation failures actually validate GC\u2019s approach. \u201cI think it kind of shows the opportunity, which is, it\u2019s not easy to apply AI technology to these businesses,\u201d he said. \u201cIf 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\u2019s really hard to transform a company with AI.\u201d<\/p>\n He pointed to the technical sophistication required in AI as the most critical missing puzzle piece. \u201cThere\u2019s a lot of different technology. It\u2019s good at different things,\u201d he said. \u201cYou 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.\u201d That complexity is exactly why GC\u2019s strategy of pairing AI specialists with industry experts to build companies from the ground up makes sense, he argued.<\/p>\n Still, there\u2019s no denying that workslop threatens to undermine \u2014 to some extent \u2014 the strategy\u2019s core economics. Even if a holding company is created as starting point, if the acquired companies reduce staff as the AI efficiency thesis suggests they should, they\u2019ll have fewer people available to catch and correct AI-generated errors. If the companies 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.<\/p>\n Arguably, these scenarios should perhaps slow the scaling plans that are central to the VCs\u2019 roll-up strategies and that potentially undermine the numbers that make these deals attractive to them. But let\u2019s face it; it will take more than a study or two<\/a> to slow down most Silicon Valley investors.<\/p>\n In fact, because they typically acquire businesses with existing cash flow, GC says its \u201ccreation strategy\u201d companies are already profitable \u2014 a marked departure from the traditional VC playbook of backing high-growth, cash-burning startups. It\u2019s also likely a welcome change for the limited partners behind venture firms, who have bankrolled years of losses at companies that never reached profitability.<\/p>\n \u201cAs long as AI technology continues to improve, and we see this massive investment and improvement in the models,\u201d Bhargava said, \u201cI think there\u2019ll just be more and more industries for us to help incubate companies.\u201d<\/p>\n<\/div>\n
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\n\t\t\t\t\t\t\t\t\t\t\t\t\tOctober 27-29, 2025<\/span>\n\t\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n