Why AI Professionals Get Trapped by Short-Term Thinking

Short-term thinking is silently derailing the careers of thousands of AI and tech professionals, according to insights from a veteran venture capital investor who has interviewed hundreds of candidates and founders across the technology landscape. The pattern is clear: most people optimize for their next paycheck rather than their next decade.

Kerkie Li Hao, a private equity investor at Kunshu Capital and author of the newsletter 'HaoHao Learning,' recently shared observations drawn from years of interviewing candidates and evaluating startup founders — offering a rare cross-section of how career decisions compound over time.

Resumes Are Source Code for Life Decisions

Li Hao argues that a person's resume functions like source code — it reflects not just past experience, but the underlying logic and mental models behind every major life decision. These thinking patterns, once established, tend to dictate future trajectories.

The distinction becomes especially stark after age 35. Before 30, career paths remain relatively open and flexible. But by the mid-30s, decision-making inertia becomes clearly visible in a candidate's history.

This observation carries particular weight in the AI industry, where the pace of change demands constant recalibration of skills and career strategy.

The Interview Trap Most Candidates Fall Into

One of the most striking patterns Li Hao identifies is that most candidates treat interviews as one-way transactions. They cycle through company after company without pausing to reflect on what the process reveals about themselves.

Key behavioral patterns he observes among tech professionals:

• Repetitive job-hopping without extracting lessons from each interview experience
• Failure to use interviews as mirrors — missing the chance to identify personal blind spots
• Overemphasis on immediate compensation rather than long-term skill positioning
• Ignoring macro trends in AI and technology that reshape entire job categories within 3-5 years
• Confusing busyness with progress — solving urgent problems while neglecting important ones

The result is a career that looks active on paper but lacks strategic direction.

Why This Matters More in the AI Era

The AI revolution is compressing career timelines in unprecedented ways. Skills that commanded $200,000+ salaries 2 years ago — such as basic machine learning engineering — are rapidly being commoditized by tools like GitHub Copilot, ChatGPT, and open-source frameworks.

Professionals at companies like Google, Meta, and OpenAI face a paradox: the industry's explosive growth creates abundant short-term opportunities, but the same disruption makes long-term planning harder than ever. According to LinkedIn's 2024 Workforce Report, the average tenure for AI professionals is just 1.8 years — suggesting many are chasing roles rather than building compounding expertise.

Li Hao's framework suggests that the most successful founders and professionals share a common trait: they regularly step back from immediate problem-solving to reassess whether they are even working on the right problems.

Breaking Free From Decision Inertia

The antidote, Li Hao suggests, lies in treating career decisions with the same rigor that venture capitalists apply to investments. This means evaluating not just the next role, but the trajectory that role enables over 5-10 years.

For AI professionals navigating today's volatile landscape, the practical takeaway is straightforward: stop optimizing for the job in front of you and start optimizing for the person you need to become. In an industry where entire categories of work can be automated overnight, the ability to think beyond immediate problems is not just a career advantage — it is a survival skill.

The professionals who thrive in AI's next chapter will not be those who solved the most problems. They will be the ones who consistently chose the right problems to solve.