# Open to Work in the Age of AI — or Open to Build?

> What LinkedIn's new book on AI and careers gets right, and what it misses for builders trying to redesign how work gets produced.

**Published:** 2026-03-31
**Canonical URL:** https://yihuisong.com/article/open-to-build

Over the past year, I've noticed a subtle shift in how people talk about AI and work. Most conversations are still framed around adaptation — how to stay relevant, what skills to build, how to use new tools more effectively. That framing makes sense. It reflects where most people are.

But the more time I spend building with AI, the less interesting that question becomes. The more interesting question is not how we adapt to AI, but how AI changes what kinds of work exist in the first place. That is why I wanted to write this article. Not simply to review *Open to Work: How to Get Ahead in the Age of AI*, but to use it as a lens to:

1. Understand how today's workforce is being guided to think about AI;
2. Reflect on what changes when the same shift is viewed from a builder's perspective.

## A Brief Note On What This Book Is

*[Open to Work: How to Get Ahead in the Age of AI](https://www.amazon.com/Open-Work-How-Get-Ahead/dp/0063486466)* is a new book written by LinkedIn CEO [Ryan Roslansky](https://www.linkedin.com/in/ryanroslansky/) and LinkedIn's chief economic opportunity officer [Aneesh Raman](https://www.linkedin.com/in/aneeshraman/). At a high level, it is a book about how AI is changing the world of work and how individuals should prepare for that shift. Its structure is fairly straightforward:

- It begins with the argument that AI is forcing a wake-up call around work,
- Then moves into what is structurally changing in jobs, careers, companies, and economies,
- And it finally ends with a practical path forward, including a 30–60–90 day plan for adopting AI and strengthening the skills the authors see as most distinctly human.

This book offers a clear expression of how a large part of the professional world is currently being encouraged to think about AI: not as a distant technical phenomenon, but as something that is already reshaping tasks, careers, and the definition of valuable work.

## Reading The Book From The Wrong Angle

I picked up *this book* expecting it to be a fairly standard one about how professionals should respond to technological change. In many ways, that is exactly what it is. It is thoughtful, well-structured, and clearly written for people who are trying to adapt to AI. It discusses: how should individuals stay relevant as work changes? How should they think about their skills, their careers, and the tasks that make up their jobs? That is a reasonable place to start.

But the more time I have spent building with AI, the less interested I am in the question of adaptation alone. What feels more consequential now is not just how people will adjust to AI, but how AI changes what kinds of work exist in the first place. That shift in perspective sounds small, but it changes the meaning of almost everything in the book.

## What The Book Gets Right

At a high level, the book makes a few arguments that are directionally correct:

- AI is changing work at the level of **tasks**, not simply replacing whole jobs all at once.
- The most durable human advantages will increasingly lie in **innovation**, defined by qualities like Curiosity, Courage, Creativity, Compassion, Communication (5Cs).
- Careers are becoming less like linear ladders and more like **nonlinear paths** shaped by change and adaptation.
- The right response is not passive observation, but **early experimentation with AI tools and new ways of working**.

In fact, this book is a very accurate reflection of how most professionals are currently being told to think about AI. It encourages people to separate routine execution from the parts of work that still depend on human judgment, context, and initiative. I especially appreciate its viewpoint of "the industrial age rewarded efficiency. The AI age will reward entrepreneurialism," which requires people to think about career development as if building a startup.

None of this struck me as wrong. To its credit, this book offers a coherent mental model for people who are trying to orient themselves in a changing labor market.

## Reframing The Core Ideas (From A Builder Perspective)

### 1. "Adapt to changing tasks" versus discovering new opportunities

The book emphasizes that jobs are really bundles of tasks, not fixed titles. Its practical lesson is that people should understand which tasks are vulnerable to automation, which will become collaborative with AI, and which remain more distinctly human. That is a useful exercise for anyone thinking about career resilience, because it shifts the focus from protecting a title to building capabilities that are harder to replace.

But from a product perspective, the same idea leads somewhere more unsettling. If a job is just a bundle of tasks, then the question is not only which tasks a person should keep. It is whether the bundle itself can be reorganized, absorbed, or replaced by software.

I had this reflection during an interview for a senior product manager role at a large tech company in 2025. They described a plan to build an agentic AI customer service platform with the goal of reducing the global human customer service workforce by 95% within two years. For technical roles, this logic is already familiar. AI is not only improving productivity; it is also accelerating layoffs. A brutal joke that went viral captured this well: "my colleagues who got laid off became skills in our company's code base."

That is the step softer discussions about AI often skip. We talk about acceleration and augmentation, but if the system works well enough, acceleration becomes substitution. And once that happens, the real question is no longer just how a person adapts to changing tasks. It is what newly defined work will remain valuable in the long run — work that AI still cannot fully absorb.

### 2. "Innovation (the 5Cs) as personal capability" versus scaling value to others

The book defines human's unique value as innovation, framed as the qualities of Curiosity, Courage, Creativity, Compassion, Communication (5Cs). It gives readers a way to think about themselves beyond efficiency at work.

Still, I find it slightly too comforting. In practice, the advantage is not simply in possessing those qualities, but in turning them into systems, products, and decisions that scale.

- Curiosity matters, but the deeper question is who can turn curiosity into a repeatable process that continuously surfaces better questions, explores solution spaces, and generates insights beyond what any individual would have time to pursue.
- Creativity matters, but the more consequential question is who builds tools that help others generate, test, and execute ideas more effectively.
- Communication matters too, but it takes on a different meaning when you can encode context, judgment, and response patterns into a system that handles thousands of interactions.

I have seen this most clearly during community efforts. We created a space where professionals could explore interests beyond their main jobs through conversations with people from very different backgrounds. The most inspiring moments were when those conversations led people to find new direction, take on work they had never imagined before, and eventually create far broader impact for others. The difference was that they turned those qualities into action, and then into something others could build on.

This is why I am less convinced by the simplistic contrast between efficient machines and creative humans. The real distinction emerges when humans turn innovation capabilities into a system others can depend on. That distinction feels much closer to where value will actually accrue.

### 3. "Start using AI tools" versus building with it

This is probably the most repeated advice today, and it's not bad advice. Most people are still early in AI literacy. Many have not yet developed even a basic intuition for what AI tools are good at, where they fail, or how they can fit into day-to-day work.

However, using a tool is only one layer of the story. The bigger opportunities are not in using AI tools for personal productivity, but in packaging AI into workflows that others rely on. For builders, the point is to reshape how work gets done across a product, a team, or an entire category. At that point, AI becomes part of the environment. Part of the workflow. Part of what users expect without necessarily naming it.

You can see this shift in product management itself. Traditionally, product managers improved output by coordinating people — gathering insights, writing requirements, aligning teams, and moving decisions forward. But as AI makes prototyping, validation, and synthesis dramatically faster, the most effective PMs will not just use AI to work faster. They will redefine how product output gets produced in the first place. The role becomes more about building systems others can rely on, to continuously transform user signals into insights, insights into prototypes, and prototypes into product decisions.

That shift matters because it is where markets move. People may debate whether they should adopt a tool. They do not debate in the same way once the tool becomes infrastructure inside a product they already rely on.

This is why I think the real divide over the next few years will not simply be between technical and non-technical people, or between those who use AI and those who do not. It will increasingly be between those who use AI to improve their own output and those who build with AI to redefine how output gets produced in the first place.

## A Different Question To End With

*Open to Work* is a helpful guide for a broad swath of professionals to navigate changes. It offers a sensible and humane way to think about careers in an uncertain moment. But I came away thinking of it less as a playbook and more as a signal. It reflects that many people are still approaching AI primarily as an individual career question, rather than as a structural product shift in how work is produced.

That is valuable information if you are building. Because designing products in this era requires understanding the starting point of the people who will use them — how they interpret change, what they fear losing, what they hope to preserve, and what kinds of language make new systems feel legible rather than threatening. In that sense, the book reveals the mental models users are likely to bring with them.

At the same time, it also clarified something more personal. The more I think about work in the age of AI, the less interested I am in becoming better at performing the traditional role of a product manager within an already defined structure. What excites me more is noticing where new behaviors are emerging, where existing workflows are becoming unstable, and where entirely new businesses can be built because the underlying constraints have changed.

If you're thinking about startups, products, or building something new, it's worth asking a different question:

> ***What becomes possible, and what should I build because AI exists?***