How to Become 'Irreplaceable' in the Age of AI, According to Former LinkedIn CEO Ryan Roslansky
In March 2026, LinkedIn CEO Ryan Roslansky and Chief Economic Opportunity Officer Aneesh Raman published their co-authored book, 'Open to Work: How to Get Ahead in the Age of AI.' Based on LinkedIn data, Microsoft research, and stories from over one billion members, the book reveals how work, careers, and companies are changing in real-time in the age of AI.
This article summarizes the core of career strategy in the AI era, based on an interview with Mr. Roslansky on the 'Young and Profiting' podcast. The words of someone who sees the professional data of one billion people every day carry a specificity that goes beyond mere inspiration.
1. 70% of skills will change—The risk of being a 'bystander'
1-1. The speed of change indicated by LinkedIn data
Mr. Roslansky observes structural changes in the labor market in real-time from the vast amount of data flowing through the LinkedIn platform every day—corporate job postings, applicant profile updates, and skill trends.
In the interview, he stated: 'The average skill set required for a job has changed by about 25% in the last few years. We expect it to change by 70% by 2030.'
According to LinkedIn research, 70% of the skills used in most jobs will change by 2030, with AI acting as the catalyst. This shift is also affecting hiring; LinkedIn analyzes that adopting skills-based hiring could potentially expand the global talent pool by 6.1 times.
1-2. Will AI take jobs or create them?
Mr. Roslansky added, 'It's not all pessimistic.' He noted, 'On the platform, 1.3 million new AI-related jobs have been created that didn't exist a year ago. Data center jobs, AI data annotator jobs, deployed engineer jobs—these are roles actually being created by AI.'
According to LinkedIn data, AI has added 1.3 million new AI-related jobs to the global economy in just two years. AI engineers and data-related roles are driving hiring. The growth of AI infrastructure is also contributing to employment, with over 600,000 new jobs created related to AI data centers.
However, caution is needed when interpreting these figures. Many of the 1.3 million jobs are not entirely new positions, but rather existing roles that now require an understanding of AI tools and workflows.
Mr. Roslansky's conclusion is clear: 'If you don't accept the fact that this labor market is changing, you will be left behind. This is not a change you can just stand by and watch. You have to participate in some way.'
2. 'Workshock'—Why this change is different from the past
2-1. The speed of uncertainty
Mr. Roslansky has experienced three or four major technological transformations in his own career, but he says AI is 'moving at a speed I've never seen before.'
'Every morning I wake up, there's something new in the headlines. A few weeks ago, it was all about ChatGPT. Then it was Claude. Then Claude Code. And then Open Claude comes out, and you think, "Oh, something new again." It's the craziest time in my career.'
2-2. The opacity of the technology itself
He described the unique difficulty of AI: 'It's as if this technology was intentionally made to be a bit hard to understand. No matter how smart you are or what you've studied, these models operate in ways that no one truly understands, and they are evolving in a way that is different from past technologies.'
People dislike uncertainty, companies dislike navigating through uncertainty, and markets dislike forecasting the future amidst uncertainty. This triple uncertainty is the essence of the phenomenon that Roslansky and Raman call 'workshock.'
3. Thinking in tasks—The '3-bucket framework'
3-1. Ditch your job title and break down your work
Roslansky asks his audience: "Imagine you are forbidden from using your job title. You won't lose your job, and you won't be fired. But you cannot describe yourself by your title. You must then explain what you do every day."
He says that thinking this way leads to two realizations. First, "you are actually doing more than what your title suggests." Second, "you understand what is truly important in your daily work."
3-2. Categorize into three buckets
He states that a typical job has 10 to 20 important tasks and recommends categorizing them into three buckets.
Bucket 1: Highly automatable tasks — Things that AI can completely replace.
Bucket 2: Tasks where AI boosts productivity — Things where you can achieve better results with AI support.
Bucket 3: Tasks that are difficult for AI to automate — Things that require human-specific judgment and creativity.
"If your job consists only of tasks in Bucket 1, frankly, you need to start thinking about finding a new job. Although most jobs are not like that."
4. The new 'Builder' role introduced by LinkedIn
4-1. Integrating five roles into one
Roslansky introduced a case study where LinkedIn changed its own approach to hiring.
"LinkedIn used to hire for four or five separate roles within the software development process: graphic designer, product manager, product marketer, front-end engineer, and back-end engineer. However, we found that if you equip them with AI tools, one person can perform many of these tasks."
So, LinkedIn created a new role called 'Builder.' As an entry-level 'Associate Product Builder,' it integrates traditional vertical skills into a single role.
LinkedIn launched a 'Full-Stack Builder' program last year. It is a training program to acquire skills that were previously divided between teams, regardless of job title. Aneesh Raman of LinkedIn explains it as 'entrusting an individual with AI tools to do what used to take days or weeks on a conveyor belt between design, product, and engineering.'
4-2. Education doesn't matter; look at 'what you have built'
Hiring criteria have also changed fundamentally. Roslansky states: "I honestly don't care if you went to college or what you studied to apply. I want to see what you can actually build using AI. The application process is: 'Show me what you've built using AI.'"
LinkedIn's careers page also clearly shows two paths: entry-level engineer and 'Associate Product Builder (APB).'
5. The 5 Cs: The most valuable 'human skills' in the AI era
5-1. Soft skills have become the 'most important skills'
Roslansky argues that as AI tools democratize hard skills, the true differentiator lies in human-specific skills.
"If many people have access to AI tools and can do much of what has historically been called hard skills, those skills become democratized. And what becomes more valuable and important are the human-specific skills on top of that. We have called them 'soft skills,' but calling them soft skills makes them feel 'not that important.' Ironically, they are now the most important skills."
The book argues that the future belongs not to those who resist change, but to those who adapt, and that the key lies in developing uniquely human skills such as creativity, curiosity, and communication.
5-2. What are the 5 Cs?
Specifically, the '5 Cs' that Roslansky cites are as follows.
Communication: The ability to converse with people, tell stories, and move teams forward
Compassion: The ability to understand others' perspectives and build trusting relationships
Creativity: The ability to think of new ideas and approaches that AI cannot generate
Courage: The ability to take action and take risks even in uncertain situations
Collaboration: The ability to achieve results as a team even when opinions conflict
LinkedIn data also shows that as AI literacy becomes a basic requirement for many roles, employers are placing even greater value on human capabilities like empathy and personal connection. LinkedIn points out that true competitive advantage comes from talent that combines AI fluency with these uniquely human strengths.
5-3. Challenges in the education system
He sounds an alarm regarding soft skills education: 'The education system was largely built for a different era and is probably past its prime. We haven't really prioritized the ability to teach these soft skills. If these are going to be the most valuable commodities in the future, it becomes essential for us as a society to find ways to ensure people possess these skills.'
6. 'Onlyness'—Finding your unique competitive advantage
6-1. What makes you stand out in a world where skills are democratized?
Roslansky advises not just entrepreneurs, but all professionals, to find their 'Onlyness'—their own unique individuality.
'In a world where you can build a website or a mobile app with the push of a button, what makes yours stand out? What enables you to win the next customer? Understanding that about yourself is the starting point.'
6-2. Everyone should think more 'entrepreneurially'
'Most entrepreneurs have a deep understanding of what makes them unique in order to survive. However, most people who are not entrepreneurs do not think that way. The labor market is going to shift significantly. Whoever you are, you will have to think more entrepreneurially about your career.'
7. Cultivating 'Personal AI'—The importance of investing time
7-1. Shallow usage does not lead to competitiveness
During the interview, the host pointed out, 'There is an overwhelming difference between someone who has spent two years training AI by feeding it all their emails, Slack messages, and skills, and someone who is just starting now.'
Roslansky strongly agreed with this, stating, 'Many people say, "I've used AI," "I've tried it a little," or "It's not very useful." Frankly, that's because they haven't put in the time and effort to set the context, train it, or help the AI understand what they are trying to achieve.'
7-2. Roslansky's Hack
He reveals his own practice: 'One of my best hacks is to open Copilot on my phone and just talk to it for 20 minutes. I provide a massive amount of context, like what my schedule is for the day and what's happening. Everything is a means to ensure that context is incorporated into the next question I ask.'
8. The Shift to Skills-Based Hiring: The End of the Degree Filter
8-1. The Problem of Narrowing 1 Billion People Down to a Few Thousand
LinkedIn's largest commercial product is 'LinkedIn Recruiter,' which millions of recruiters use every day. Roslansky points out the traditional problem.
'Three or four years ago, almost every recruiter would start by filtering for two things: "Where did this person go to college?" and "Where did this person work before?" If they went to Princeton, they must be talented; if they worked at Google, they must be smart. When you filter a member base of 1 billion people with these two criteria, you quickly end up with a very small group.'
As a result, an inefficiency was created where all companies were competing for the same small pool.
8-2. Toward Skills-First Search
'Recently, we switched our recruiter tool to search by "skills required for the job" first. When you do that, you realize two things. First, there is actually a massive talent pool on LinkedIn. Second, those people are often the most valuable talent you didn't even know existed.'
However, Roslansky also admits, 'Changing the tool is not enough; human instinct immediately reverts to "But, which university?" A mindset shift is essential.'
9. Signaling: Personal Branding in the AI Era
9-1. Three Levers
Roslansky lists three levers for being discovered by the market in the AI era.
First, establish your identity online.'You can't just create a profile, leave it alone, and expect good things to happen. You have to invest time and effort into your personal brand.'
Second, build your network.'Networking is often thought of as old-fashioned, but the way people find opportunities and get things done is often through people.'
Third, share unique knowledge and insights on the platform.'You don't have to be a great thought leader or a famous creator on LinkedIn. You can just answer questions in the comments on feed posts. By simply participating in the community, you can signal to the market who you are and what you can do.'
He particularly notes the phenomenon where people who share things they've created using AI tools on LinkedIn are actually receiving job offers. 'You might wonder, "Why share that?" but these people are actually being recruited because they are showcasing their ability to use AI tools innovatively.'
10. 'Open to Work' is a Mindset
10-1. The Effect of the Green Banner
LinkedIn's 'Open to Work' feature—the green circle that appears on your profile—is sometimes perceived negatively as implying that you are 'struggling to find work'.
However, Mr. Roslansky counters this with data: 'People who turn on the green banner are 27% more likely to get hired.'
10-2. 'Open to Work' as a mindset
To him, 'Open to Work' is not just a feature, but an attitude toward one's career in the AI era.
'Putting yourself out there, thinking differently, leveraging your network, utilizing your community, adapting, and learning new skills—these are all crucial for navigating the future of work in an AI world. Above all, Open to Work is a shift in mindset that says, "My career is in my own hands."'