The New Physics of Skills: From Friction to Flow
When everyone can do everything, who does what?
Look at any product team today and you’ll notice something odd. The org chart hasn’t changed. The product manager still sits at the center. The designer still owns the interface. The engineer still writes code. The marketer still runs campaigns. Same titles. Same Slack channels. Same reporting lines.
But watch what actually happens in the meetings. The PM shows up with a wireframe she built in twenty minutes using AI. The marketer is running his own A/B tests without waiting for analytics. The designer is generating front-end code. The engineer is drafting product copy.
Nobody announced a reorganization. Nobody changed anyone’s title. But the boundaries between these roles have become porous in ways that would have been unthinkable a few years ago.
I have been thinking about this shift through a metaphor borrowed from physics. For decades, skills inside organizations behaved like solids. A skill belonged to a role. A role belonged to a function. A function belonged to a department. Movement across these boundaries was possible but slow, expensive, and rare. Crossing into another team’s domain was like walking through a wall. If a marketing manager wanted to run a complex data analysis, she needed months of training or dedicated help from an analyst. If an engineer wanted to do interface design, he needed to go to design school.
AI has changed the temperature. And when the temperature rises enough, solids become liquids.
The Phase Change
AI does not make everyone an expert at everything. That is simply not possible, and in fact it would be dangerous. What AI does is dramatically reduce the friction involved in acquiring and applying skills in adjacent domains. The cost and effort to stretch into a neighboring discipline has collapsed.
Research that once required a dedicated analyst can be synthesized in minutes. Designs can be mocked up from a text prompt. Code can be prototyped without a formal engineering background. Data analyses can be run by people with no SQL knowledge. The barrier hasn’t disappeared, but it has dropped from a wall to a curb.
This is why skills are becoming fluid. They no longer stay locked inside functional silos. They flow across roles, across teams, across the old boundaries that once kept everyone in their lane.
The implications are significant. LinkedIn’s workforce data suggests that by 2030, roughly 70% of the skills used in jobs will have changed, with AI as a primary catalyst. An Atlassian design leader, after the company ran an all-hands AI experimentation week, put it this way: what could have taken months of self-learning happened in days.
Anchor and Radius
When skills flow freely, we need a new way to think about professional capability. I propose a simple framework: anchor and radius.
Every professional has an anchor. This is their core domain of deep expertise and judgment, built through years of learning, practice, pattern recognition, and tacit knowledge. The anchor is what allows someone to make good decisions when information is incomplete, to exercise taste in their field. AI does not replace the anchor. If anything, anchors matter more in an AI world, because they guide the effective use of all the new tools. A weak anchor paired with AI leads to confidently wielding tools in foolish ways. As the saying goes, a fool with a tool is still a fool.
What AI does is extend the radius of competence around that anchor.
Think of each professional as having a circle of capability centered on their core expertise. AI increases the diameter of that circle. The product manager’s anchor might still be product strategy and orchestration, but her radius now extends further into design, analytics, even light engineering. The marketer’s anchor remains customer insight and creative storytelling, but his radius now reaches into data analysis, growth experiments, and user experience.
Without an anchor, a larger radius is dangerous. With a strong anchor, a larger radius is a force multiplier. Careers in the AI era will be defined not just by the depth of your anchor but by how far your circle extends around it.
Here’s the observation that changes how organizations work: as everyone’s circle expands, the circles inevitably overlap.
What Happens in the Overlap Zones
The PM’s circle now overlaps with design. The marketer’s overlaps with analytics. The engineer’s overlaps with UX. These adjacencies create very real tensions inside organizations.
Who owns the early user experience work when product managers can generate wireframes and user flows themselves? Who owns insight generation when a marketer can run experiments and queries without analyst support? Who defines technical feasibility when AI can generate a plausible code prototype for a non-engineer?
These aren’t turf battles or ego fights. They are structural consequences of reduced friction.
Consider what Airbnb did. When Brian Chesky announced in 2023 that they had merged product management and product marketing, it was widely misinterpreted as eliminating PMs. It wasn’t. It was a recognition that the overlap zone between building the product and marketing the product needed a single integrated owner. “You can’t develop products unless you know how to talk about the products,” Chesky explained. The skill sets had overlapped so much that maintaining separate roles was itself creating friction.
When designers at the Figma conference cheered Chesky’s announcement, they revealed the tension. Designers felt some PMs were encroaching on their terrain in unproductive ways, and the old role definitions weren’t resolving it.
The key managerial question is not whether overlaps exist. They do, and they’re growing. The question is: who prevails in the gray zones? I see three forces that determine influence in these overlap areas.
Anchor depth. When a decision truly hinges on deep craft or specialized mastery, the domain specialist retains authority. AI might help a PM create a draft design, but information architecture and interaction design nuances still require a seasoned designer’s judgment. Paradoxically, AI can increase respect for deep expertise by automating the easy parts and highlighting the hard parts.
End-to-end ownership. When decisions involve balancing trade-offs across the whole product or value stream, the person with holistic accountability prevails. Product managers typically own the end-to-end outcome, which naturally positions them to integrate across domains. AI-augmented breadth makes them even more effective here, because they can personally explore each side of a trade-off before deciding.
Decision frequency. Influence accrues to whoever makes the most frequent, iterative decisions. In a fast-paced, AI-powered workflow, there are dozens of micro-decisions every day: tweaking a prompt, choosing which experiment to run, adjusting copy based on feedback. The person consistently in that loop gains authority over time. High-frequency decision-making, compounded over dozens of iterations, beats a slow deliberative process that routes through hierarchy.
These forces suggest that overlapping skills won’t produce a free-for-all. We’ll see a new balance of power based on who brings deep craft when it matters, who integrates across functions, and who drives fast iterative cycles.
The Rise of the Full-Stack Product Builder
No role illustrates this shift more clearly than product management. Even before AI, great PMs were defined by their breadth. They sit at the intersection of customer needs, business strategy, design, and engineering. Their value has always come from synthesis rather than deep craft expertise in any single area. AI amplifies that inherent breadth dramatically.
LinkedIn saw this early and acted on it. In 2025, they sunsetted their Associate Product Manager program and replaced it with the Associate Product Builder program. The name change is telling. Participants learn coding, design, and product skills together, leveraging AI tools throughout. LinkedIn even created a formal “Full-Stack Builder” job title with its own career track, enabling anyone from any function to take a product from idea to launch.
The rationale, as LinkedIn’s Chief Product Officer Tomer Cohen explained it, was that the traditional model of highly specialized teams was too slow. Organizational bloat meant even small features took six months, whereas AI tools allow leaner teams to deliver faster. The application process reflects this philosophy: no resume required. Candidates submit a 60-second demo of a product they built and answer questions about how they used AI in the process.
Shopify has moved in a similar direction. Their PMs don’t just write specs. They use AI to generate prototypes and analyze data independently. Atlassian ran an internal “AI Product Builders Week” where over a thousand designers, PMs, and engineers set aside their normal tasks to experiment with AI together, producing dozens of production-ready prototypes in days.
The full-stack archetype integrates three capabilities: strategist (market understanding, customer insight, product vision), builder (hands-on creation, prototyping, technical execution), and growth driver (adoption, experimentation, metrics, iteration). The anchor remains judgment. The expanded radius shows up in the building and growth dimensions.
This isn’t just a PM story. Marketing is converging from the other direction. Today’s marketers segment customers with AI-driven clustering algorithms. They generate and test content variations at scale. They build personalized user journeys that start to look like product UX flows. They run A/B tests and attribution models that were once the domain of data scientists. At HubSpot, 74% of marketers were using at least one AI tool at work in 2024, up from just 35% the year before.
The pattern is the same across functions: T-shaped professionals, deep in one area with broad ability in others, are increasingly valuable. The horizontal bar of that T can now stretch much further than before.
The Risks of Expanding Your Radius
I want to be direct about the dangers, because the full-stack archetype has failure modes.
The first is the jack-of-all-trades trap. Expanding your radius without maintaining your anchor depth makes you broadly mediocre rather than distinctively valuable. Organizations still need deep specialists. Not every product manager, marketer, or engineer will become full-stack, and that is perfectly fine.
The second risk is burnout. Because full-stack people can do so much, organizations tend to pile responsibilities onto them. Just because your star PM can also do great design work and run analyses doesn’t mean she should do all of it all the time. The new bottleneck is cognitive bandwidth. No matter how talented an individual is, there are limits to how many domains they can integrate in a single day. Full-stack humans need protection from their own capability.
The third risk is starting-point bias. In complex work, whoever produces the credible first draft of an artifact wields outsized influence on the outcome. If AI allows PMs to generate the first draft in domains that used to be out of their reach, they increasingly set the direction before the specialist formally gets involved. A PM who comes to a meeting with a plausible wireframe is effectively steering the design conversation from the outset. This can accelerate progress. It can also marginalize expertise if not managed carefully.
What Leaders Must Do
The changing physics of skills places new demands on the C-suite.
Redefine roles around outcomes, not tasks. Rigid job descriptions that specify “you do X, then hand off to Y” are becoming obsolete. Define roles by the outcomes they own and let the tasks be fluid. Some companies are already dropping traditional titles in favor of “product builder” or “growth owner” to signal this shift.
Protect your integrators. Your best full-stack people are force multipliers. They are also the first to burn out if you load everything onto them. Ensure they have support. Monitor their workload. In performance reviews, check in on how they are managing breadth, not just depth.
Clarify decision rights in the overlap zones. As overlaps proliferate, ambiguity in decision-making creeps in. Define who has the call on what and when, even as collaboration increases. The PM might have final call on priority and scope, the designer on UX quality, the marketer on brand voice. Make it explicit. Overlaps without governance become stalemates.
Reward cross-functional value creation. If your compensation and promotion criteria only celebrate individual functional excellence, you are sending the signal that staying in your lane is safe. Highlight team wins that were only possible because someone ventured beyond their role. Create career paths that allow growth in breadth, not just depth.
Treat AI fluency as foundational literacy. Don’t leave this to chance. LinkedIn created an entire culture program around being AI-native in product development. Atlassian’s company-wide experimentation week gave every team member hands-on education. If you institutionalize it, you get a company-wide leap. If you leave it to individual initiative, you get pockets of innovation surrounded by inertia.
The Management Crossroads
We stand at a choice point. One path is to continue managing skills with the old assumptions, treating overlaps as anomalies or problems to be squashed. The other is to redesign organizations for skill liquidity, encouraging learning, experimentation, and collaboration across boundaries.
The companies already taking the second path are reporting not just faster output but higher employee satisfaction. Top talent wants to grow and contribute in multiple ways. If you provide the platform for it, they flourish. If you don’t, they leave for organizations that will.
Your organizational chart is a lagging indicator of how work actually happens. It describes the past, not the present. When skills become fluid, leaders who will win will let the skills flow and wisely guide the current.