Quick Read
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Each of Dan Shipper's companies increased its workforce by double in the last year even with significant internal use of artificial intelligence.
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AI automation needs human monitoring to guarantee proper functionality, which may increase rather than decrease the amount of work required for supervision.
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This goes against the Wall Street story that businesses can easily swap employees for AI to boost profits.
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Dan Shipper, the founder of Every, an AI-focused media and software company, is challenging the traditional Wall Street viewpoint that businesses can easily boost profits by substituting human workers with AI. Dan explained his reasoning why hedoubled his headcountin the last year on a recent episode ofLenny's Podcast.
The Headcount Paradox
A shipper shared an outcome that even his host found surprising: Each team doubled its workforce over the last year while relying extensively on AI tools internally. The host referred to the decision as "quite unconventional" and pointed out that hiring at such a rate "is not what people would have anticipated from a company so focused on AI."
A shipper's personal experience indicates that, even with AI, he is working harder: "We have so much automation, so much AI, and"I also work significantly more," he said. The overall labor data presents a comparable situation. Total nonfarm payrollsclimbed to 158,736 thousand in April 2026, the top level in the BLS dataset, even as the use of enterprise AI increased during the same time period.
Why Shipper Calls Automation a Fraud
Shipper didn't hold back on this. "Automation is a lie. In the sense thatevery time you set up an automation to ensure it is functioning properly"you need a human on top of it," he said. His analogy is the modern manager. "Managers actually spend a lot of time working. Most managers are not like on the beach," he said. Overseeing AI processes, in his opinion, is the same role.
The statistics from his Senior Engineer Benchmark clearly illustrate the situation. GPT-5.5 achieved a score of 62 out of 100, whereas human engineers scored in the high 80s to low 90s. Shipper compared it to "a human driving a car versus another human driving a car," indicating that the human engineers were also utilizing AI tools. This represents AI competing against AI-enhanced humans and falling behind by a considerable amount.
The difference, according to Shipper's interpretation, boils down tojudgment instead of raw ability"Every coding model available in the market will take that instruction seriously," he stated when asked to address a reported bug. In contrast, a senior human engineer would "examine the codebase and say, this is a mess," and challenge the assumption altogether. Knowingwhen not to obey a commandis something the models have yet to understand.
Bank of America has recently reduced its ratingSalesforce(NYSE:CRM) to Underperform due to what it described as an "AI-powered structural reset" linked to possible seat-model contraction, while consensus analysts maintained a mean Street target of $263 on Agentforce growth. Shipper's argument supports the positive side: automation increases the scope of tasks requiring oversight instead of reducing it.
The Application That Crashed and the Bursitis
The shipper's most intimate demonstration of his idea was also his most challenging. He created an application named Proof alongside his main work, utilizing AI-enhanced coding, but it experienced frequent failures following its release, resulting in what he called "a lot of embarrassment." The coding sessions were so demanding that he ended up with bursitis in his elbow. A founder focused on AI, equipped with all the latest tools, still launched a faulty product, as the AI failed to identify issues that a human reviewer would have noticed.
His conclusion is pragmatic. "In any real-world scenario, there's always a human, pretty much nearby, ensuring it's functioning properly," he stated. "Even though the models are improving at automation, I still employ engineers." When the host suggested the idea that "SaaS is the future of AI. This B2B SaaS," Shipper's response was quick: "Hashtag send tweet."
Investors considering the labor-replacement story may find Shipper's experience to be a contrasting viewpoint in an ongoing discussion. Business models that rely on anticipated reductions in staff numbers might be making unrealistic claims. Firms that focus on human oversight of automation, such as B2B software providers offering this supervision, could prove more resilient than the fully automated solutions suggest. Readers can access the complete discussion onLenny's Podcast.
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