Summary of Key Points
AI has not dispelled the importance of effort, but it has completely rewritten the “price structure of effort”: The old model, where high returns could be achieved through overtime, memorizing knowledge, executing tasks, and following trends, is no longer effective. Instead, abilities that were once underestimated, such as making informed decisions, defining problems, taking responsibility, and maintaining a solid foundation, are becoming increasingly valuable. In the future, rewards will not be based on how much time you have invested, but on how many correct choices you have made.
Detailed Analysis
1. Hard work is becoming less valuable; choosing the right thing to do is more important than working harder
In the past, working overtime was valuable because people’s time was limited—working an extra two hours meant handling more clients or writing more code, directly linking time to value. However, AI is not tired or needs rest. What used to take a team a week to draft, AI can complete in minutes. Speed is no longer a competitive advantage; what really makes a difference is choosing the right tasks: Is this client worth pursuing? Should this feature be developed? Was the direction wrong from the start? In the past, mistakes could be corrected over time, but with AI, mistakes can be amplified quickly (for example, a wrong project could lead to significant losses in just half a year). Therefore, the most valuable skill in the future will not be executing tasks, but deciding which tasks are worth undertaking.
2. Knowing the answer is not as important as knowing how to ask the right question; asking questions is more valuable than memorizing knowledge
In the past, knowledge was valuable because it was difficult to obtain—researching industry data required visiting libraries or networking with experts. Now, AI can provide answers instantly, making knowledge widely available. However, the challenge is knowing what questions to ask: If AI provides ten marketing strategies, can you determine which one is suitable for your client? Can you spot logical flaws in its suggestions? Or even realize that the question itself is flawed? Experienced individuals are not just because they remember a lot, but because they have seen many problems and know where to avoid pitfalls. While AI can provide answers, asking the right question remains a human skill.
3. AI can handle execution, but responsibility is the key to a stable job
AI can write emails, prepare meeting notes, draft contracts, and analyze data, but who will sign off on these actions and take responsibility? For example, AI may analyze investment opportunities for a boss, but the boss will sign the documents and bear the consequences of any losses; AI may suggest treatment plans for doctors, but the doctors will confirm them and be responsible for any issues; AI may draft legal documents, but lawyers will verify their content and be held accountable if there are legal issues. Execution can be outsourced to AI, but responsibility cannot be transferred. The core of future jobs will not be about knowing how to use tools, but about being willing to take on consequences: The closer you are to decision-making, the harder it is for AI to replace you.
4. Don’t blindly follow trends; maintaining a solid foundation is crucial for long-term success
With new AI models and tools emerging every day, it’s easy to feel anxious about falling behind. However, the faster things change, the more crucial it is to have a stable foundation:
- Cash reserves (enough for half to a year’s living expenses) to avoid making hasty career decisions for money;
- Transferable skills (such as communication and problem-solving) that can be applied in different industries;
- Professional credibility (a good reputation in your field) that allows you to start new endeavors without starting from scratch;
- Low debt to avoid being overwhelmed by short-term fluctuations.
Following trends may seem like embracing change, but quitting jobs without a plan, taking on debt to start a business, or frequently changing careers essentially means betting your livelihood on the uncertainty of technology. True resilience means being able to stay in the game even if your judgment is wrong and wait for the next opportunity.
Final Conclusion
In the AI era, effort is still important, but the focus should shift: from doing more to choosing the right direction, from memorizing knowledge to asking the right questions, from executing tasks to taking responsibility, and from following trends to maintaining a solid foundation*. In the future, the rewards will go to those who know what is worth investing in**.