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Staying relevant as AI improves is not just a matter of learning new tools or producing more. It means adapting to changing tasks while building the judgment, relationships, and perspective that help you decide what work matters. A September 24, 2026, essay by Marvin Liao offers that as a personal framework—not as a labor-market forecast or proof that any particular human capability guarantees a job.
What “relevance” means when AI can do more work
AI can change which tasks people perform without making every skill obsolete or predicting which jobs will disappear. A useful distinction is between automating activities and replacing an entire role: a technology may be capable of doing some tasks while people’s work shifts toward other tasks, oversight, coordination, or decisions.
In its November 25, 2025 report, McKinsey Global Institute estimated that technologies already demonstrated could technically automate about 57 percent of work hours in the United States. That is an estimate of technical potential, not a forecast of job losses. Realizing potential depends on adoption, workflow redesign, and organizational preparation.
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Two paths to staying relevant
Liao’s post reproduces a passage from Alex Oppenheimer contrasting visible career accumulation with slower, less measurable forms of development:
“The path that looked safe for a century – build skills, climb the ladder, scale headcount, accumulate prestige – has quietly become the long short path for almost everyone. The path that looked slow – deep relationships, judgment, taste, contemplation – is now the short long path. If we do it right, I think we can have the best of both worlds, and more importantly stay in control of our own unique path.”
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The phrasing is an argument about how to live and work, not evidence that relationships or taste will secure employment. Its useful point is that productivity and relevance are not the same thing. Producing more output matters only if someone can recognize what is useful, make sound choices, and work well with others.
Oppenheimer’s other lines, also quoted by Liao, sharpen the warning: “The deepest competitive advantage of the next decade is going to be the willingness to take the suit off.” And: “They’ll be efficient and irrelevant. The output will be enormous. The judgment will be threadbare.” These are rhetorical predictions, not established findings. Read them as a challenge to avoid mistaking polished, high-volume AI-assisted work for valuable work.
Practical habits for developing judgment and connection
Liao’s post recommends making room for activities that are not organized around immediate work output. They are suggestions, not interventions shown in the post to cause better performance or employment outcomes.
- Walk without earbuds. Leave some time without a stream of input or a task to complete.
- Have dinners without phones. Give conversation attention instead of treating it as background to notifications.
- Read beyond your field. Choose books unrelated to current work to encounter ideas and styles outside your usual professional frame.
- Make time for non-instrumental conversation. Talk with people without turning every exchange into networking, a request, or a transaction.
- Leave room for stillness. Protect some unfilled time rather than scheduling every gap for another task.
The intended connection is that attention, trust, and perspective can grow through practices that are not easily reduced to output counts. The post does not cite research testing whether these habits produce those outcomes, so treat them as a proposed way to shape a life and working style—not a guaranteed career technique.
What labor-market evidence can—and cannot—tell you
McKinsey’s report describes several changes relevant to workers, but its figures need to be read in context:
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match- Demand for AI fluency in U.S. job postings grew nearly sevenfold over two years, according to the report. This measures mentions in postings, not the actual skills of people hired.
- Its midpoint automation-adoption scenario estimates about $2.9 trillion in possible annual U.S. economic value by 2030. That is a scenario, not a guaranteed benefit; the report says capturing the value depends on workflow redesign and organizational preparation.
- The report projects that interpersonal skills such as negotiation and coaching may change less than highly automatable specialized skills, while widely used skills such as communication and problem-solving may evolve. These are model projections, not promises of job security.
The OECD’s discussion of information-processing skills adds an important caution: online vacancies can show changes in job content, but they do not represent every vacancy or job. Skill demand also depends on AI capabilities and adoption, training costs, regulation, labor-market frictions, and the choices of consumers, citizens, and policymakers. A trend in job ads is useful evidence, but it is not a complete map of work.
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A grounded way to apply the framework at work
Pair adaptation with discernment rather than treating either technical fluency or reflection as a complete answer:
- Identify the work that is changing. Break your role into tasks. Notice which activities AI tools can assist with, which require human decisions or collaboration, and where your organization is actually adopting the tools.
- Build practical AI fluency. Learn to use relevant tools and check their output. Fluency is increasingly visible in U.S. job postings, but the posting data do not show that tool use alone determines hiring or success.
- Strengthen transferable skills through application. Look for opportunities to use communication, problem-solving, negotiation, or coaching in changing workflows. McKinsey’s analysis suggests many skills span automatable and non-automatable work; it does not promise that any one skill will protect a role.
- Ask how the workflow should change. Individual tool use cannot by itself capture the value McKinsey associates with automation. Teams and organizations need to rethink how work is organized, trained, reviewed, and delivered.
- Make time for perspective and relationships. Try the post’s suggestions—unplugged walks, phone-free meals, wider reading, unhurried conversation, or quiet—because they align with the kind of judgment and connection the essay values, not because the post proves they guarantee a career advantage.
How to read the essay’s claims
The original post is a reflective essay, not a controlled study or labor-market forecast. It reproduces Oppenheimer’s words and makes a case for balancing conventional career-building with relationships, judgment, taste, and contemplation. The linked essay is not needed to understand the excerpts quoted in Liao’s post, and those excerpts should be attributed to Oppenheimer as quoted by Liao.
The broader evidence supports a more limited conclusion: AI has technical potential to automate many work activities, skills may shift across tasks, and adoption depends on organizational and social conditions. Neither that evidence nor the essay establishes that people are categorically irreplaceable. The framework is most useful as a reminder to adapt to changing work without making output volume the only measure of value.
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