AI Is Coming for “Boring” Industries—but Is It Innovation or Just Another Tech Bubble?

This month, Peter Rudegeair and Berber Jin published an article in The Wall Street Journal highlighting Silicon Valley’s latest target: unglamorous, clerical-heavy industries like accounting, property management, and customer service. Venture capitalists, eager to find AI’s next big frontier, are pouring billions into rolling up these businesses and infusing them with automation.

AI-driven rollups aren’t a new concept—Wall Street has long used consolidation strategies to streamline industries like car washes and HVAC services. But the difference here is the aggressive belief that AI can make these businesses significantly more profitable by automating administrative workloads. Firms like Long Lake Management Holdings and Crete Professionals Alliance have already raised hundreds of millions of dollars, acquiring dozens of companies across various service sectors. According to The Wall Street Journal, Long Lake alone has raised over $600 million and now manages firms employing around 1,400 workers.

The appeal is clear. In a venture environment where capital deployment has slowed, AI startups remain a beacon for investors. Major backers like Thrive Capital, General Catalyst, and Andreessen Horowitz are not only funding headline-grabbing AI ventures like OpenAI but are now betting on less flashy sectors. As Marc Bhargava of General Catalyst put it, AI could transform service businesses that traditionally “just barely break even.” The firm has already allocated $1.5 billion to the strategy, cutting checks of at least $100 million per project.

But while venture firms see efficiency and scale, skeptics see hubris. The comment sections of these reports are filled with voices unconvinced by the premise that AI alone can turn thin-margin businesses into gold mines. One commenter, Joe J, put it bluntly: “Essentially, you have people who believe buying a bunch of same-type businesses that are marginal, or marginally successful, and believe that software will solve all their problems and make them tons of money? What could possibly go wrong?”

Others worry about AI’s fundamental limitations in handling human-centric work. Jim Scales lamented his experiences with automation, stating, “In all my years on this planet, I have NEVER had a positive experience with an automated, robotic, AI system that solved an issue.” Jim Norton raised another critical concern: “How does a given AI application, model, know when it’s made a mistake? Then who is liable?” AI’s inability to fully grasp context, nuance, and human emotion remains a glaring weakness, especially in service-driven industries where trust is paramount.

Of course, early-stage skepticism is nothing new in tech investment. Disruptive innovation often follows a pattern: bold promises, fervent investment, industry skepticism, and, in some cases, long-term success. The fundamental question isn’t whether AI can improve these businesses—it likely can—but whether the efficiencies will be enough to justify the massive capital infusion required to roll them up and integrate them successfully.

The other looming issue? Cost. As commenter Steve D noted, “Prices will be going up in those industries.” AI-driven efficiencies don’t always translate to consumer savings, particularly when investors expect high returns. If service prices rise without a corresponding boost in customer experience, these investments may struggle to live up to their promise.

Ultimately, venture firms are placing a massive bet that AI can do more than streamline tech companies—it can overhaul and consolidate industries that have operated the same way for decades. Whether this push results in a new class of AI-enhanced enterprises or another cautionary tale of overhyped disruption remains to be seen.

References

• Peter Rudegeair and Berber Jin (2025, 26-Jan). Now Wanted in Silicon Valley: Ho-Hum Businesses With Thin Profit Margins. The Wall Street Journal. Available online. Accessed 15 February 2025.