AI and the nature of work
The next wave of AI
AI and the nature of work
The next wave of AI
Founder went from pressure washing to AI
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AI and the nature of work
Six months ago, the AI sector was looking pretty bubbly. Companies were plowing hundreds of billions of dollars, much of it borrowed, into building new data centers, but had no clear path to profitability. Experts and journalists, myself included, were comparing the AI build-out to the railroad bubble of the 1800s and the dot-com bubble of the ’90s, in which speculation led to overinvestment that eventually crashed the stock market. Even OpenAI CEO Sam Altman voiced public doubts. “Are we in a phase where investors as a whole are overexcited about AI?” he said last year. “My opinion is yes.” Today, however, we’re in a very different world. Software developers are adopting AI tools en masse and reporting astronomical productivity benefits.
This is where a debate superficially about finance turns out to hinge on deeper philosophical questions about the nature of human work. A separate school of thought holds that most knowledge-work tasks share the same basic structure, and thus can be automated. As a group of analysts at SemiAnalysis recently argued, all knowledge work, including coding, is made up of four basic components: consuming information (“Read”), applying existing knowledge (“Think”), producing a structured output (“Write”), and checking that output against some standard (“Verify”). Coding might have certain qualities that make it easier for AI to perform this basic four-step process—such as more data to read and objective standards to verify an output—but that doesn’t make the field unique.
For instance, even if no objective standard for a “good” academic paper or legal brief exists, experts in those fields tend to have a clear sense of better or worse. Perhaps AI systems could develop such a sense if given enough high-quality examples to learn from. “There’s clearly a spectrum here, with coding on one end and things with really hard-to-judge outputs, like short-form fiction writing, on the other,” Mollick, the University of Pennsylvania professor, told me. “But a lot of knowledge work—law, finance, consulting, marketing—falls somewhere in the middle. And many of the tasks in those jobs are probably closer to the coding side of things.”
AI companies are investing even more money into chips and infrastructure in anticipation of even more demand. But if the current boom turns out to be limited to coding, then by the time the new data centers are built, there won’t be enough customers to pay for them. Instead of turning a profit, the AI companies—not to mention the chipmakers, data-center builders, and cloud providers—will be stuck with huge losses on their books....
The next wave of AI
Here’s a real-life small business AI application with a high ROI: a showroom assistant. Picture a salesperson in a showroom walking around with a customer. But instead of taking notes, the salesperson has an app running on their phone that’s listening - and transcribing - the entire back-and-forth conversation. At the end of the visit, the application takes all that it’s learned and – acting as a layer on top of the company’s ERP and quoting system - generates a quote. It’s more accurate and significantly less time-consuming for the salesperson. But more importantly it frees up the salesperson to do what they were hired to do: generate more sales.
This is actually happening. This is just one of the AI applications being developed by Canals, an AI software development firm. It’s not replacing a company’s existing ERP system. It’s making these systems better.
“Many companies are still burdened by highly manual processes, particularly in sales order entry, accounts payable, accounts receivable, and warehouse operations,” said Michael Delgado, CEO of Canals. “Many salespeople spend most of their day re-keying customer information rather than serving customers or solving problems.” AI isn’t replacing ERP systems. It’s becoming a productivity layer that sits on top of them. Canals - and a growing number of other software companies - are developing applications that are using AI to automate all the manual process that come with customer emails, supplier information, sales orders, invoices, remittance notices, packing slips and all the other documents that employees are manually entering into their ERP system like Epicor, Infor and SAP. Serving approximately 100 distributors, Delgado says that the company’s aim is not to substitute as an ERP system but to help employees use these systems more efficiently.
For AI to prove its value, there has to be quantifiable ROI. There must be a measurable level of speed and accuracy that’s driving results. But for Delgado, the ROI isn’t labor savings, it’s increased sales. For him, the faster that quotes turnaround, the more business can be won. He references one customer of theirs that saw their win rate on quotes tripling because of their automation. Most AI vendors talk about cost reduction efficiency headcount. Delgado is talking about revenue. “It’s sales first,” he said. “You get a sales lift due to speed and accuracy.”....
Founder went from pressure washing to AI
The 24 year-old spent six summers pressure washing houses in upstate New York with his dad. Now he has a Wharton degree, and just raised $40 million from two of the most competitive names in venture capital to fix blue-collar and home services problems at scale. Probook, his New York-based startup.
The pitch starts with a grievance. Over the last few years, home service operators were sold an explosion of AI tools—voice agents, chat widgets, follow-up bots. They ended up with five vendors and bills growing faster than the revenue those tools promised. According to Eliadis, none of these products were built for dispatch—the part of the business where an operator decides which of 40 technicians goes to which of 150 jobs, in what order, with what expected ticket size.
“Dispatch is the brain of every home service business,” he told Fortune. “That’s where customer experience is made or broken.” So Probook built the scheduling brain first, then layered on everything else—answering calls, cleaning up job data, sending customer updates—in one connected system. The early numbers are promising: an Indiana customer with 14 locations and 260 technicians booked 2,542 jobs in its first month without a single human touching the booking. A Florida operator cut its dispatchers from 22 to 10. A Kansas shop did the same and grew its average job revenue by 20%. Probook’s sweetest customer right now is the private equity-backed home services rollup—think investment firms that have been quietly buying up local HVAC and plumbing shops across the country, bundling them under one roof, and squeezing out efficiencies. That acquisition pace hit 88% growth year-over-year through mid-2025. And Probook sells directly to owners who are laser-focused on profit margins.
The harder question is what happens with ServiceTitan. The $6.3 billion publicly traded company is the dominant software platform for home service businesses—and it already has its own AI scheduling product. For now, Probook is listed as a ServiceTitan partner, meaning the two technically work together rather than against each other. Eliadis calls them complementary. A16z GP David Haber called it “a years-old structural moat,” in a written statement. Still, ServiceTitan has $960 million in revenue and its own engineers. Eliadis was the company’s only salesperson until February of this year. He’s been to customers’ weddings. He’s slept on their couches….