Build Companies Around Work You Secretly Love

Dan Shipper believes organizations operating at the bleeding edge of AI are pioneering workflows that mainstream companies will adopt within three years. His company Every, with just 15 employees, runs multiple products, a daily newsletter, and a consulting arm by embracing AI as a fundamental multiplier of human capability rather than a replacement.

Empowering Teams Through AI Operations

Shipper advocates for dedicated AI operations roles within organizations to systematically identify and automate repetitive tasks. Rather than expecting busy employees to create their own AI workflows, Every has a head of AI operations who builds prompts and automation systems that help the entire team work more efficiently.

"We have a head of AI operations... she's just constantly building prompts and building workflows so that I and everyone else on the team are just automating as much as possible," Shipper explains. This dedicated role ensures automation actually happens, as employees focused on day-to-day work rarely have time to develop new systems themselves.

The practical implication is that companies should consider hiring specifically for AI operations rather than expecting AI adoption to happen organically across teams already at capacity.

Compounding Engineering: Making Future Work Easier

Shipper's engineering team practices what they call "compounding engineering" – the principle that for every unit of work, you should make the next unit easier. In a world where engineers increasingly delegate to AI agents rather than writing code directly, this means building better prompts and systems that improve future delegation.

"For every unit of work you should make the next unit of work easier to do," Shipper explains. His engineers maintain libraries of prompts and automation tools that continuously refine their ability to direct AI agents effectively.

For leaders, this suggests focusing less on immediate output and more on whether teams are building systems that accelerate future work. The most valuable engineers may be those who make everyone else more efficient rather than those who simply produce the most code.

The Allocation Economy: From Knowledge Work to Management

Shipper believes we're moving from a knowledge economy to what he calls an "allocation economy" where the primary skill becomes directing AI rather than performing tasks directly. This shifts the value toward traditionally managerial skills like evaluation, taste, vision, and knowing when to dive into details.

"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," Shipper notes. "They're human managers today, tomorrow everyone's a model manager."

For individual contributors, this means developing the ability to delegate effectively to AI, provide clear requirements, and evaluate outputs will become more important than technical execution skills alone. For organizations, it suggests flatter structures where more employees have management-like responsibilities.

Generalists Over Specialists

Shipper believes AI will reverse the centuries-long trend toward specialization, making generalists increasingly valuable. By handling specialized knowledge tasks, AI allows individuals to work across multiple domains effectively.

"AI is a little bit like having 10,000 PhDs in your pocket," he explains. "It's doing a lot of the specialized tasks that you might have had to spend ten years getting good at."

For hiring and team building, this suggests valuing breadth over depth and looking for people who can synthesize across domains rather than those with narrow expertise. Organizations may function better with smaller teams of versatile individuals rather than large specialized departments.

Building Companies Around Authentic Interests

Perhaps Shipper's most personal insight is that founders should build companies around what genuinely energizes them rather than following conventional startup patterns. When he stopped writing to focus on "running the business," Every stagnated. When he returned to writing as his central focus, both he and the business thrived.

"Every time I've leaned into something that feels like my hidden secret desire, it's actually worked a lot better," he reflects. "There's a huge tax to doing something every day that you don't quite like that much."

For founders and leaders, this means designing organizations around authentic interests rather than predetermined models, even if the resulting structure looks unconventional. The energy and insight that comes from doing work you genuinely love creates more sustainable businesses than forcing yourself into standard patterns.