Silicon Valley GirlLinkedIn CEO: These 3 Jobs Will Explode in the Next 5 Years | Ryan Roslansky
CHAPTERS
Davos with LinkedIn’s CEO: creators arrive on the world stage
Marina introduces Ryan Roslansky at Davos and frames LinkedIn as a real-time lens on the global labor market. Ryan highlights how creator influence has surged—so much so that it’s now visible even in traditionally corporate/policy spaces like Davos.
What LinkedIn’s labor-market data says about AI and hiring right now
Ryan explains that sluggish hiring is largely driven by macroeconomic conditions rather than AI. At the same time, LinkedIn data shows substantial net-new job creation tied to AI infrastructure and adoption.
Entry-level hiring is down—but not because AI (and not uniquely entry-level)
The conversation zooms in on entry-level opportunities. Ryan notes entry-level hiring is down about 12% globally, but the decline mirrors broader market sluggishness rather than being an entry-level-only collapse driven by automation.
Two emerging alternatives: micro-entrepreneurship and a return to trades
Ryan describes two pathways gaining momentum as traditional hiring slows: people taking careers into their own hands via creator/micro-entrepreneur routes, and increased interest in trade/first-line roles viewed as resilient in an AI era.
‘Career paths are dead’: the shift from ladders to skill-based navigation
Ryan challenges the idea of a linear career ladder, noting LinkedIn data doesn’t show a single ‘typical’ path to most roles. Instead, careers increasingly evolve through horizontal moves and continuous skill accumulation, especially as roles flatten and generalists become more valued.
Top skills to build now: AI literacy + the human advantage
Ryan outlines a two-sided skills strategy: develop baseline AI literacy regardless of profession, and strengthen the “human skills” that differentiate you as work becomes more automated. He argues these aren’t “soft” in importance—they’re increasingly decisive.
Using LinkedIn to get hired: demonstrate expertise through content
Marina shares how her team hires based on candidates’ posts, using content as a proxy for thinking, depth, and personality. Ryan reinforces the idea that a LinkedIn profile is extended by what you publish—turning knowledge into visible proof of skill.
How to grow on LinkedIn: optimize for opportunity, not vanity metrics
Ryan explains LinkedIn’s feed is designed to create economic opportunity, not just entertainment-driven reach. The platform rewards authentic, identity-based content that reaches a high-quality professional audience, even if raw views are lower than other social networks.
Is college still worth it? ROI concerns, but social learning still matters
Ryan shares sobering stats about graduate underemployment and debt, suggesting parts of the system are broken. Still, he argues college can be valuable for social development, networks, and communication skills—while hiring is increasingly shifting toward skills and demonstrated knowledge over school brand.
‘Open to Work’: a practical guide to careers in an AI-first world
Ryan introduces his book as a framework for reducing uncertainty about AI’s impact on jobs and skills. The goal isn’t prediction, but helping people make clearer decisions by combining LinkedIn insights with actionable guidance on what to learn and how to position themselves.
The ‘5 Cs’ AI can’t replace (and how to invest in them)
Ryan highlights five human capabilities he believes will matter most as AI becomes ubiquitous: curiosity, courage, creativity, compassion, and communication. He argues these can be learned and practiced, and points to training resources (like LinkedIn Learning) to develop them.
Top jobs likely to surge in the next 3–5 years (plus an honorable mention)
Ryan names three fast-growing job categories tied directly to the AI ecosystem: data annotators, data center buildout roles, and forward-deployed engineers who bridge AI and business needs. He also gives creators an informal “fourth” spot as a rising career path.
Which jobs may disappear: think in tasks AI can automate
Rather than naming specific titles, Ryan proposes a task-based lens: break your role into tasks and assess which are automatable. Work centered on AI-strong tasks—summarizing, rewriting, translating—faces higher disruption unless workers add complementary skills.
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