Why Quitting NOW Could Be the Smartest Move Before AGI Takes Over Your Job—and Your Future
Ever caught yourself wondering if quitting your job before AI does it for you is just plain common sense—or borderline reckless? As the gears of artificial intelligence start grinding faster than ever, the old playbook on career longevity is getting tossed out the window. We all know AI isn’t just another buzzword; it’s rewriting the rules at a pace that makes yesterday’s advice obsolete overnight. But here’s the kicker: instead of battling machines at their own game, maybe it’s time to rethink where your true value lies—beyond the algorithms and automation. This isn’t just about survival; it’s about carving out space for uniquely human strengths—judgment, trust, leadership—that no AI can replicate (at least not yet). So, before the digital cavalry arrives and makes your role redundant, why not take the reins and decide your next move on your own terms? Tough decisions ahead? Absolutely. But the alternative might just be letting AI decide your fate—and who wants that? Dive into a practical, no-nonsense strategy to navigate the upcoming AGI transformation with clarity and grit. LEARN MORE
Key Takeaways
- Quitting can be a rational strategy when a career or business no longer deserves your future time and attention.
- Sunk costs should not determine where you invest the next stage of your career.
- The safest position in an AI-driven economy may be creating value through human judgment, trust, relationships, networks, and leadership.
- Instead of competing with AI at tasks it can automate, use AI as leverage to solve valuable problems for specific people.
- The most practical response to technological uncertainty is to choose a purpose, start small, and take responsibility for creating value.
Artificial intelligence is changing the rules of work faster than most people are prepared to acknowledge. But the answer may not be to work harder, cling tighter to an existing career, or try to become better than machines at tasks they are increasingly capable of performing.
This article is a further discussion of the ideas explored in the Steven Bartlett‘s Diary of a CEO (DOAC) podcast conversation with Seth Godin, particularly his thoughts on quitting, resistance, purpose, systems, and the coming transformation brought by AI. The conversation offers a useful framework for thinking about an increasingly AI-driven world: instead of waiting for technology to decide what happens to us, we can decide what we want our work to be for and where human beings still create unique value.
The provocative idea behind this article’s title is simple: if AI is likely to make parts of your current job obsolete, why wait until AI forces you to quit?
The Future of Work May Require More Quitting
We are often taught that quitting is a character flaw.
The familiar message is that successful people never give up. But Seth Godin challenges that idea by arguing that quitting can be a powerful decision when it allows someone to stop investing in the wrong path.
The real problem is not quitting. The real problem is being unable to recognize when something no longer deserves your time.
This becomes especially important as AI changes industries.
A job that once seemed stable may gradually become less valuable as software becomes capable of performing more of its routine tasks. A profession that once required years of training may eventually be transformed by automation. A business model that once worked may become economically difficult to defend.
In that environment, stubbornly refusing to quit can be more dangerous than quitting.
The question becomes:
If I were starting today, knowing what I know now, would I choose this path again?
That question cuts through the emotional attachment we have to our previous decisions.
Don’t Wait for AI to Tell You When to Leave
The most uncomfortable possibility is that many people will not voluntarily leave obsolete roles.
They will wait until the market leaves them no choice.
That is the opposite of what Godin’s broader philosophy of “picking yourself” suggests. Instead of waiting for a company, industry, institution, or gatekeeper to decide your future, you can identify a problem worth solving and take responsibility for solving it.
The technological environment makes this increasingly possible.
Godin describes AI as potentially the biggest change in the world since electricity. His comparison is important because electricity did not simply eliminate a handful of jobs. It changed what businesses could do and created entirely new ways of creating value.
AI may operate in a similar way, except much faster.
That means waiting for the old system to return to normal may not be a particularly good strategy.
There may be no “normal” to return to.
The Donkey Problem Is Coming for Knowledge Workers
One of the most useful concepts in the conversation is the story of the donkey caught between food and water.
The donkey wants both the carrot and the water, but moving toward one means moving away from the other. Because it cannot accept the trade-off, it remains stuck.
Humans do something similar.
We say:
“I want a creative career, but I don’t want rejection.”
“I want to build a business, but I don’t want financial uncertainty.”
“I want more freedom, but I want the security of a traditional job.”
“I want to use AI, but I don’t want my old skills to become irrelevant.”
“I want to become an entrepreneur, but I don’t want to sacrifice anything.”
The word that keeps us trapped is but.
Godin’s solution is to identify the contradiction rather than pretending it does not exist. Sometimes two desirable things simply cannot be maximized simultaneously. The way forward begins when we acknowledge the trade-off.
AI will make this lesson more important.
You may not be able to preserve every part of your old career while simultaneously adapting completely to a new technological environment.
Something may have to be abandoned.
That is not necessarily failure.
It may be the decision that creates room for the next chapter.
Sunk Costs Are Not a Reason to Stay
One of the strongest arguments for quitting is also one of the hardest psychologically: your previous investment does not determine your future investment.
People become trapped by sunk costs.
You spent years getting a degree.
You spent ten years building a career.
You invested money in a business.
You developed expertise in a particular technology.
You built a reputation in an industry.
You may therefore feel that abandoning the path means admitting that all of that effort was wasted.
But the investment has already happened.
The relevant question is not:
“How much have I already invested?”
It is:
“Knowing what I know today, is this still the best place to invest my next five or ten years?”
Godin describes previous investments as gifts from your former self. You can appreciate what they gave you without being obligated to continue using them forever.
That is an extremely useful mindset for an AI transition.
Your old career was not necessarily a mistake.
But the fact that it worked for you yesterday does not mean it will be the right vehicle for tomorrow.
Don’t Compete With AI at Being AI
This may be the most important lesson from the conversation.
Godin argues that there are two broad possibilities: you work for AI, or AI works for you.
The first scenario involves allowing AI-driven organizations to progressively automate the valuable parts of your job while leaving you with the tasks machines cannot yet perform.
The second involves using AI as leverage.
The distinction is profound.
If your competitive advantage is simply that you can perform a predictable set of tasks, you are vulnerable.
If your value comes from knowing which problem to solve, whom to solve it for, what matters to those people, and how to build trust with them, your position may be considerably stronger.
Godin specifically points toward human networks as an area AI cannot simply substitute for. He argues that people who use AI to connect, lead, build networks, and create trust can create value in ways that are difficult to reduce to a software procedure.
This suggests a practical rule:
Don’t try to become the world’s best machine at a machine-friendly task.
Instead, become the person who knows what the machine should help accomplish.
The More AI Advances, the More Human Value May Matter
AI is exceptionally good at activities that can be described as rules, procedures, patterns, and repeatable processes.
That creates an uncomfortable question for knowledge workers:
What happens when everything you do can be written down as instructions?
Godin gives the example of bookkeeping. If the rules governing the work can be documented, they can increasingly be encoded into software.
But consider something different: judgment, taste, relationships, trust, context, leadership, and understanding people.
Those things are much harder to reduce to a simple instruction manual.
This does not mean AI will never become capable of sophisticated judgment.
It means that competing purely on procedural competence is likely to become increasingly difficult.
The opportunity may therefore move toward the human layer surrounding technology.
Instead of asking:
“How can I protect my job from AI?”
Ask:
“How can AI make me dramatically better at serving people?”
That is a much more productive question.
Don’t Become a Junior Version of OpenAI
There is another trap in the AI economy: assuming that the only way to benefit from AI is to build something that looks like the biggest AI companies.
You do not need to become the next OpenAI.
You may not even need to build an AI startup.
The conversation gives a powerful example of a person with family responsibilities who wants to start an AI company. The answer is not to pursue the biggest possible company at any cost. Instead, the person can identify a smaller audience, use AI to solve a specific problem, and build a business around that value while honoring other commitments.
This is an important correction to the startup mythology promoted by social media.
You don’t have to become a billionaire founder.
You don’t have to raise venture capital.
You don’t have to employ hundreds of people.
You don’t have to become famous.
You need to solve a problem for someone who values the solution.
That can be enough.
Find the Smallest Audience You Can Help
One of Godin’s recurring ideas is that meaningful work begins with a specific person or group.
Who is this for?
That question can dramatically simplify a career decision.
Instead of saying:
“I want to build an AI business.”
Ask:
“Who has a problem that AI can help me solve?”
Instead of:
“I want to become a content creator.”
Ask:
“Which 20 people would find this useful?”
Instead of:
“I want to leave my job.”
Ask:
“What specific problem could I solve independently?”
This approach also makes experimentation easier.
Godin describes how people often avoid starting podcasts, blogs, and creative projects because they imagine the enormous final destination. His solution is to reduce the challenge to its smallest possible unit: interview the neighbor rather than Michelle Obama; start with five readers rather than a million.
The same logic applies to AI.
Don’t ask how to reinvent your entire career overnight.
Ask what you can build, test, sell, or improve for a handful of people this month.
Resistance Will Make You Want to Stay Stuck
Technology is not the only obstacle.
Often, the biggest obstacle is psychological.
Godin calls the force that prevents people from doing meaningful, creative work resistance.
Resistance can look like procrastination.
It can look like perfectionism.
It can look like endless research.
It can look like waiting for the perfect business idea.
It can look like waiting for someone important to give you permission.
It can even look like constantly learning about AI instead of actually using it.
The AI era creates an almost perfect environment for this kind of resistance.
There is always another model to test.
Another tool to learn.
Another newsletter to read.
Another expert to follow.
Another prediction about AGI.
Another reason to wait.
But technological change does not reward endless preparation.
At some point, you have to act.
Embarrassment Is the Price of Reinvention
Leaving an established career or starting something new often involves embarrassment.
You may look inexperienced.
Your first product may be mediocre.
Your first customers may be few.
Your first attempt may fail publicly.
Your friends may not understand what you’re doing.
Godin describes embarrassment as a kind of cost of entry. The solution is not necessarily to eliminate embarrassment but to make the first step small enough that you can tolerate it.
This is particularly relevant when building something with AI.
You can experiment privately.
You can start with five customers.
You can create a prototype.
You can solve one problem.
You can publish something under your own name—or even test an idea anonymously.
The objective is not to eliminate fear.
It is to make fear small enough that you can move anyway.
Pick Yourself Before the System Picks for You
Traditional career systems were built around waiting.
Do well in school.
Get into college.
Get the degree.
Get hired.
Get promoted.
Follow the corporate ladder.
Wait for the next opportunity.
AI challenges this model because the tools required to create things are becoming increasingly accessible.
Godin describes the transition from microphones, cameras, and publishing tools to AI: the gatekeepers are disappearing, yet people continue to behave as though they need permission.
This may be one of the biggest psychological adjustments of the coming AI era.
The question will increasingly be:
What are you going to do with the tools you already have?
Not:
Who is going to give you permission?
That does not mean everyone should quit their job and become an entrepreneur.
It means people should recognize that they have more agency than the traditional career system taught them to believe.
Purpose Matters More When Machines Do More
As AI takes over more tasks, simply being busy will become a weaker measure of value.
That makes purpose more important.
Godin does not frame purpose as something you necessarily discover at birth. Instead, he describes it as something you can choose for the work you are about to undertake: What change are you trying to make, and for whom?
That distinction matters.
You don’t need to discover your one grand purpose in life before doing anything.
You can decide:
“The purpose of this project is to help 100 small businesses save time.”
Or:
“The purpose of this business is to help parents find reliable childcare.”
Or:
“The purpose of this newsletter is to help entrepreneurs understand AI.”
Purpose becomes practical when it identifies a person and a desired change.
And once you know who you are serving, it becomes easier to decide what to quit.
Don’t Maximize Everything at Once
Modern culture constantly tells us to optimize.
Make more money.
Have a bigger company.
Get more followers.
Travel more.
Build a personal brand.
Become healthier.
Be a great parent.
Become an entrepreneur.
Master AI.
The result is often the donkey problem again.
You cannot maximize every dimension simultaneously.
Godin’s example of the parent who wants to launch an AI startup while raising three young children makes the point clearly: the answer is not necessarily to abandon either responsibility, but to acknowledge the constraints and choose a project that fits within them.
This is especially important in an AGI-shaped future.
If AI makes it possible to accomplish more, that does not mean you should fill every available hour with more work.
Technology can increase productivity.
It can also increase expectations.
The choice of what not to do may therefore become increasingly valuable.
Quit the Work That AI Makes Cheap, Not the Work That Makes You Human
The title of this article is deliberately provocative.
“Quit before AI makes you” does not mean everyone should resign tomorrow.
It means you should identify the parts of your professional identity that depend on work becoming scarce.
If the most valuable thing you offer is producing routine reports, generating predictable copy, processing standardized information, or following well-defined procedures, AI may eventually do much of that work faster and cheaper.
That is a signal to evolve.
But perhaps your real value lies elsewhere.
Maybe you understand customers unusually well.
Maybe people trust you.
Maybe you have exceptional taste.
Maybe you know how to bring the right people together.
Maybe you can lead a team through uncertainty.
Maybe you can persuade.
Maybe you can teach.
Maybe you can create experiences people remember.
Maybe you know a particular community deeply.
These capabilities become potential foundations for an AI-enhanced career.
The goal is not to outrun AI.
The goal is to move toward the work where AI makes you more capable without making you unnecessary.
The Common-Sense Strategy for an AGI World
Nobody knows exactly what an AGI world will look like, how quickly it will arrive, or which occupations will change first.
But we do not need perfect predictions to make better decisions today.
The common-sense approach is to start with the principles raised in the Seth Godin conversation:
Quit what no longer serves your purpose.
Stop treating sunk costs as commitments.
Name the resistance keeping you stuck.
Accept that some goals cannot be maximized simultaneously.
Choose who you want to serve.
Start with the smallest viable audience.
Use AI as leverage rather than treating AI as your competitor.
Build relationships, trust, judgment, taste, and networks.
Pick yourself instead of waiting indefinitely for permission.
Pay attention to what is changing without allowing every technological development to dictate your priorities.
Most importantly, remember that your attention is finite.
The transcript makes this point particularly well: as AI changes faster and faster, it is easy to spend all your attention watching the next model, the next announcement, and the next disruption. But attention spent worrying about change cannot be recovered. The alternative is to return to your purpose, identify the change you want to make, and get back to work.
Conclusion
The coming AI transition may destroy some jobs, transform others, and create entirely new categories of work.
But perhaps the biggest mistake would be to think the only choice is between keeping your current job and being replaced by AI.
There is another option.
You can quit the parts of your work that no longer make sense.
You can abandon assumptions that belonged to an earlier economy.
You can stop defending sunk costs.
You can learn to use AI as leverage.
And you can move closer to the things that make you valuable precisely because you are human: trust, judgment, relationships, leadership, empathy, taste, context, and the ability to identify problems worth solving.
The question for the next decade may therefore not be, “Will AI take my job?”
A better question is:
“If I were starting again today, what would I choose to become—and how could AI help me get there?”
That is the decision worth making before AI makes part of it for you.
FAQs
Should I quit my job before AI replaces it?
Not necessarily, but you should evaluate whether the skills and tasks that define your current role are becoming increasingly easy for AI to automate and begin building alternatives before you are forced to change.
What does “pick yourself” mean?
Picking yourself means taking responsibility for creating something valuable rather than waiting for an employer, institution, publisher, platform, or other gatekeeper to give you permission.
What kinds of work may become more valuable in an AI-driven economy?
Work involving trust, relationships, leadership, judgment, taste, context, community building, and human-to-human connection may become increasingly valuable as AI handles more predictable and repeatable tasks.
How can I start preparing for an AI-driven career?
Start by identifying a specific problem you can solve for a small audience, then use AI to increase your ability to solve that problem rather than simply learning AI tools without a clear purpose.
Does this mean everyone should become an entrepreneur?
No. The central lesson is about agency and adaptability, not entrepreneurship; you can use the same principles inside an existing company by identifying valuable problems, using AI effectively, and building expertise that remains difficult to commoditize.
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