AI Services -Transformation can be harder than VCS seems

Venture capitalists have convinced themselves that they have found the next big investment edge: using AI to twist software-like margins out of traditionally labor-intensive service companies. The strategy involves acquiring mature professional service companies, implementing AI to automate tasks and then use the improved cash flow to roll up more companies.

The leading tax is General Catalyst (GC), who has dedicated $ 1.5 billion of its latest fundraise to what it calls a “creation” strategy focused on incubating AI-native software companies in specific verticals, and then using these companies as acquisition cars to buy established companies and their customers. GC has placed bets across seven industries, from legal services to IT management, with plans to expand to up to 20 sectors completely.

“Services globally are a $ 16 trillion turnover a year globally,” said Marc Bhargava, who leads GC’s related efforts, in a recent conversation with TechCrunch. “In comparison, software is only $ 1 trillion global,” he noted, adding that the lid of software investments has always been its higher margins. “When you get the scale software, there are very small marginal costs and there are a lot of marginal revenue.”

If you can also automate service business, he said – to tackle 30% to 50% of these companies with AI and even automate up to 70% of these core tasks in the case of call centers – math is starting to look irresistible.

The game plan seems to be working. Take Titan MSP, one of General Catalyst’s portfolio companies. The investment company delivered $ 74 million over two tranches to help the company develop AI tools for managing service providers, then acquired the RFA, a well-known IT service company. Through pilot programs, Bhargava said, Titan demonstrated that it could automate 38% of typical MSP tasks. The company is now planning to use its improved margins to acquire additional MSPs in a classic roll-up strategy.

Similarly, the company incubated Eudia, which focuses on internal legal departments rather than law firms. Eudia has signed up for Fortune 100 clients, including Chevron, Southwest Airlines and Stripe, which offer legal fees legal services driven by AI rather than traditional hourly. The company recently acquired Johnson Hanna, an alternative legal service provider, to extend the range.

General Catalyst seems to double – at least – the EBITDA margin of the companies it acquires, Bhargava explained.

The Powerhouse company is not alone in its thinking. Venture company Mayfield has cut $ 100 million specifically to “AI teammates” investments, including grinding, an IT consultant starting a $ 5 million security consultant and then grew 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 could have an 80% to 90% gross margin,” Navin Chaddha told Mayfield’s CEO, to TechCrunch this summer. “You could have mixed margins of 60% to 70% and produce 20% to 30% net income.”

Soloinvestor Elad Gil has been pursuing a similar strategy for three years, backing companies that acquire mature companies and transform them with AI. “If you own the asset you can [transform it] Much faster than if you just sell software as a seller, ”Gil said in an interview with TechCrunch this spring.

But early warning signs suggest that metamorphosis throughout the service industry may be more complicated than VCS predicts. A recent study by researchers at Stanford Social Media Lab and Betterup Labs, who examined 1,150 full-time employees across industries, found that 40% of these employees need to wear more work because of what researchers call “work slop” -i-generated work that occurs polished but lacks drugs, creating more work (and head characters) for colleagues.

The trend takes a toll on the organizations. Employees involved in the study say they spend an average of almost two hours dealing with each instance of Workslop, including to first decipher it, then decide whether to send it back, and often just to fix it themselves.

Based on these participants’ estimates of time spent with their self -reported wages, the authors of the study estimate that Workslop has an invisible tax of $ 186 per year. Month per month. Person. “For an organization of 10,000 workers considering the estimated spread of Workslop … this gives over $ 9 million a year in lost productivity,” they write in a new Harvard Business Review article.

Bhargava contested the notion that AI is overpowered and argued instead of all these implementation errors actually validating General Catalyst’s approach. “I think it kind of shows the opportunity, which is, it’s not easy to apply AI technology to these companies,” he said. “If the whole Fortune 100 and all these people could just bring in a consulting firm, clap some AI, get a contract with Openai and transform their business, then of course our dissertation is of course our dissertation [would be] A little 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. “There are a lot of different technologies. It’s good for different things,” he said. “You really need these used AI engineers from places like ripping and ramp and Figma and scale that have worked with the different models, understand their nuances, understand which are good at what, understand how to wrap it in software.” This complexity is exactly why General Catalyst’s strategy of pairing AI specialists with industry experts to build businesses from the bottom of giving sense, he argued.

There is still no one denying that Workslop threatens to undermine – to some extent – the core economy of the strategy. Even if a holding company is created as a starting point if acquired companies reduce the staff that the AI-efficiency dissertation suggests they should, they will have fewer people available to capture and correct AI-generated errors. If companies maintain the current staffing levels to handle the extra work created by problematic AI output, the huge marging gains that VCs count on can never be realized.

It is likely that these scenarios may need to slow down the scaling plans that are central to VCS ‘roll-up strategies and which potentially undermine the numbers that make these offers attractive to them. But let’s realize it; It will take more than one study or two to slow down most Silicon Valley investors.

In fact, because they typically acquire companies with existing cash flow, General Catalyst says its “creation strategy” companies are already profitable-a significant departure from the traditional VC playbook for supporting high growth, cash-burning startups. It is also probably a welcome change for the limited partners behind venture companies that have bank -rolled years with losses in companies that never reached profitability.

“As long as AI technology continues to improve, and we see this huge investment and improvement of the models,” said Bhargava, “I think there will just be more and more industries for us to help incubate businesses.”