AI Will Reshore American Jobs

Dan Shipper believes AI will dramatically reshape how companies operate, with organizations that embrace AI-first principles gaining significant advantages. As CEO of Every, he's building a company at the bleeding edge of AI adoption, where his team of just 15 people runs multiple products, a daily newsletter, and a consulting arm by maximizing AI leverage.

AI as a Force Multiplier, Not a Replacement

Shipper rejects the common narrative that AI will simply replace jobs. Instead, he sees AI as making expensive services more affordable and accessible while enabling workers to serve more people efficiently. "What cheap intelligence does is make expensive services affordable for small companies and individuals, stimulating demand," he explains. This creates opportunities for workers who can effectively use AI tools to multiply their impact.

The practical implication is that teams should focus on using AI to expand capabilities rather than just reduce headcount. At Every, they've found that AI allows small teams to accomplish what previously required much larger organizations.

Compounding Engineering: Making Each Task Easier Than the Last

Every's engineering team practices what they call "compounding engineering" - for each unit of work, they make the next unit easier. Rather than repeatedly performing similar tasks manually, they invest time in creating prompts and automations that streamline future work.

"You could spend a little bit of time being like there's a platonic ideal of a PRD, and what I'm gonna do is write a prompt that can take my rambling thoughts and turn that into a PRD," Shipper explains. This approach creates exponential productivity gains over time.

For leaders, this suggests prioritizing investments in AI workflows that compound, rather than just solving immediate problems. Teams should dedicate time to building reusable assets that make future work more efficient.

Dedicated AI Operations as an Organizational Function

One of Shipper's most actionable insights is having a dedicated head of AI operations who focuses on identifying repetitive tasks and building prompts and workflows to automate them. This role helps overcome the natural resistance to changing established work patterns.

"It's really hard if you're working in a job all day, fighting fires... am I going to do this in the way that I know how or am I going to do it in the new way that might not work?" Shipper explains. "Having an AI operations lead lets you identify those things and have them solved without people who are doing the work having to take time to do it."

This suggests organizations should consider creating dedicated AI operations roles rather than expecting teams to transform their workflows while handling their regular responsibilities.

The Allocation Economy: Management Skills Become Universal

Shipper believes we're moving from a knowledge economy to an "allocation economy" where management skills become increasingly valuable and widespread. Using AI effectively requires the same skills as good management: communicating problems clearly, gathering the right information, providing feedback, and having a vision for success.

"When you look at what skills are gonna be valuable in the AI era, one big group of skills are the skills of managers today," he explains. "Right now management skills are not broadly distributed because it's very expensive... it's now going to be much cheaper to manage, so more people are going to have to do it."

For individuals, this means developing management skills will be crucial for success in an AI-powered workplace, even for individual contributors. For organizations, it suggests investing in management training for all employees, not just those with direct reports.

The CEO's Role in AI Adoption

When consulting with companies on AI adoption, Shipper has found one factor predicts success above all others: whether the CEO personally uses AI tools. "If the CEO is in it all the time being like 'this is the coolest thing,' everybody else is gonna start doing it," he observes.

Leaders who use AI themselves develop realistic expectations about capabilities while demonstrating commitment. The most successful companies have CEOs who lead by example, openly sharing how they use AI in their own work.

For executives, this means personally engaging with AI tools rather than delegating exploration to others. For teams without executive buy-in, this suggests focusing initial efforts on use cases that might appeal to leadership.