When to Push Through and When to Pivot Your Career

There's a question that lives rent-free in the head of almost every working person at some point: Am I failing because I haven't tried hard enough, or am I failing because this was never going to work?

Most career advice doesn't touch this question. It either screams "never quit" or whispers "know your worth." Neither is particularly useful when you're actually in it—on your 17th job application with no callbacks, or 18 months into a side business that feels like it's running uphill in sand.

In a recent conversation on Chris Williamson's Modern Wisdom podcast, entrepreneur Alex Hormozi took a direct run at this problem, and what he laid out is worth sitting with—not because it's a clean answer (it isn't), but because it at least gives you a better question to ask yourself.

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The actual distinction between pushing and pivoting

Hormozi's framework is deceptively simple. The question isn't "am I struggling?"—struggle alone tells you nothing. The question is whether a foundational assumption you started with has been proven false by real feedback.

His example: imagine you build a product for dog owners because you believe they'll pay for it. You talk to a hundred dog owners. None of them want it. That's not a push situation. That's a pivot situation, because the premise—that demand exists—has been disproven.

But if the response is "I'm not sure what you're selling, can you explain it differently?"—that's an execution gap, not a dead end. The assumption (demand could exist) hasn't been invalidated; you just haven't demonstrated the product clearly enough yet. That's when pushing makes sense.

The distinction is subtle enough that most people miss it. When things aren't working, the instinct is to treat all bad outcomes as the same signal. They're not. A market saying "no" is different from a market saying "what?" The first tells you to change direction. The second tells you to get better at the thing you're doing.

What makes this genuinely hard is that both signals feel the same in the moment. Frustration is frustration. The emotional texture of "this will never work" and "I haven't figured this out yet" is almost identical from the inside, which is probably why Hormozi is willing to call this "one of the harder lines to know."

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Losing always teaches you something. That's the problem.

Here's where things get interesting—and where Hormozi's thinking diverges from the typical grit-and-grind script.

He's not particularly concerned about whether you learn from failure. He takes it as given that you will. What concerns him is what you learn.

"Losing teaches you stuff and we just need to make sure that we learn the right thing. Teaching will occur. Whether you take it away or not is up to you."

The example he uses is instructive: a manager who hires three bad employees in a row and concludes that all employees are untrustworthy. The lesson that actually landed—people can't be trusted—is a generalization built on a small and potentially unrepresentative sample. But it now functions as a worldview. The fourth employee, who is competent and trustworthy, walks into a system wired against them before they've done anything wrong.

This is not a small problem in workplaces. It's a structural one. Managers who've been burned by one underperformer become hypervigilant with everyone. Leaders who've had one bad direct report micromanage the next five. The behavior is internally consistent with their experience—and corrosive to everyone who comes after.

Hormozi's prescription is to make early decisions as carefully as possible, specifically to avoid building compensatory habits on bad data. The reasoning is behaviorist in its bones: changing a behavior with a long history of reinforcement is harder than changing one with no history. Scar tissue is harder to work with than fresh muscle.

There's something worth interrogating here. The implication is that bad early experiences carry a heavier cognitive tax than later ones—that the grooves carved at the beginning of a career or business are deeper, harder to redirect. That tracks with what psychology has documented about habit formation and cognitive entrenchment, but it also raises a question: what do you do if your early experiences were bad? The framework is better at prevention than cure, which is useful advice for people who don't yet need it.

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Description versus explanation, and why most career confusion lives in the gap

The more philosophically interesting part of this conversation is Hormozi's distinction between describing reality and explaining it.

He uses a classroom-logic example: "Johnny stole because he's dishonest." Most people nod at that sentence. But unpack it and it collapses into circularity—"dishonest" is just a label for someone who does dishonest things, including stealing. So the sentence means: Johnny stole because he's the type of person who steals. That explains nothing.

The actual explanation, in Hormozi's view: Johnny stole because he was previously reinforced for stealing, or because he observed someone who was rewarded for it and modeled that behavior. That's a mechanism, not a label. It tells you something actionable.

Apply this to the workplace. "Sarah underperforms because she's lazy." What does lazy mean? It's a label we apply to someone who produces less than expected. The underlying question—why is the output low?—is what actually matters. Is the expectation clearly defined? Is the feedback loop functional? Has she been reinforced for coasting, or punished for taking risks? Without that level of inquiry, "she's lazy" is just a description wearing an explanation's clothes.

This is connected to one of Hormozi's more practical points: articulate expectations in behavioral terms. Not "be a team player" but "respond to Slack messages within two hours during business hours." Not "be more proactive" but "bring one proposed solution when you flag a problem." The gap between what people mean when they use abstract words and what they'd actually need to see is where most workplace resentment is born.

"Unspoken expectations are premeditated resentments."

That line comes from writer Neil Strauss, cited by Hormozi mid-conversation, and it's one of those observations that's slightly uncomfortable because it's accurate. The majority of workplace conflict I've watched play out—between managers and reports, between colleagues, between leaders and teams—has its roots in expectations that were never stated clearly enough to be evaluated. Someone is frustrated that a thing isn't happening that they never clearly asked for. Someone else is blindsided by a performance review that reflects standards they didn't know existed.

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The complaint as diagnostic tool

Near the end of the conversation, Hormozi makes a point that lands differently than it first appears: chronic complainers, he argues, aren't morally deficient—they have an inaccurate model of reality.

A complaint, structurally, is a gap between what you expected and what you got. If you're shocked that there's traffic on your morning commute, that's not really about traffic—it's about a prediction you made that turned out to be wrong. Reality didn't fail you. Your model of reality did.

This reframe has obvious applications to careers. The person who's perpetually shocked that promotions are political, that job searches take longer than expected, that managers don't always recognize good work—they're not wrong that these things are frustrating. But the frustration is partly fueled by a model that treats these things as aberrations. When they're actually just... how it works.

"Reality is undefeated. It's a million and zero."

There's a fine line between accepting that reality operates on its own terms and just telling people to suck it up. Hormozi doesn't quite land on either side of that line cleanly—the conversation is more interested in mechanics than in justice—and I think that's worth naming. The framework is best suited to people operating with real agency in their situations: entrepreneurs, managers, people with some runway. It doesn't account as neatly for situations where the feedback loop is broken by factors outside someone's control.

But as a diagnostic lens—as a way of asking is my model of this situation accurate, or am I being repeatedly surprised by the same things?—it has genuine utility.

The harder question it leaves open: once you've identified that your model is wrong, how do you build a better one without mistaking the next round of bad data for good data?

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By Vanessa Torres