A Heavy Debt

Today’s Topics

• How Did We End Up Here? 🎓

• Entry-Level Simulation 🎮

  7 Mins Read Time

How Did We End Up Here? 🎓
By Jo

At some point, the conversation around student loan debt stopped being about responsibility and started being about survival.

What we’re witnessing now didn’t happen overnight. It’s the result of decades of policy decisions, economic shifts, cultural messaging, and broken expectations colliding all at once. And now, millions of people are standing in the aftermath—worried about wage garnishment, credit damage, asset limitations, and whether the ground beneath them is about to give way.

The question isn’t why don’t people want to pay their loans.
The real question is: how did we create a system where paying them back became unrealistic for so many at the same time?

The Promise That Was Sold

For a long time, higher education was marketed as a near guarantee.
Go to college.
Take out the loans.
Get the degree.
Get the job.
Climb the ladder.

For many people—especially those entering school in the early 2000s—that promise didn’t seem unreasonable. The job market was stronger, wages felt more aligned with the cost of living, and the idea of upward mobility still felt tangible.

But by the time many graduates entered the workforce—around 2008 and 2009—the world had shifted dramatically.

The financial crisis reshaped corporate structures, flattened career ladders, eliminated pensions, and normalized contract work, layoffs, and “doing more with less.” Degrees didn’t lose value overnight—but the return on investment did.

And yet, the debt didn’t adjust.

A System That Didn’t Evolve

Student loan balances continued to grow, while wages stagnated. Interest accumulated. Grace periods expired. Life kept moving.

People didn’t just graduate into jobs—they graduated into:

A volatile job market

Rising housing costs

Healthcare expenses

Transportation costs

Inflation across basic necessities

Student loans didn’t exist in isolation. They became one line item in an already overwhelming list of financial obligations.

That’s where the crisis deepened.

Because when millions of people are juggling rent, food, utilities, childcare, and transportation, something will fall behind. And often, that something is debt that feels abstract compared to immediate survival.

The Asset Ripple Effect

This isn’t just a personal finance issue—it’s an economic one.

Student loan debt directly impacts people’s ability to:

Buy homes

Qualify for mortgages

Save for retirement

Start businesses

Take career risks

Build generational wealth

When a large portion of the population is locked out of asset accumulation, the entire system feels it. Housing markets slow. Consumer spending tightens. Job mobility decreases because people can’t afford to leave stable but unfulfilling roles.

The debt doesn’t just follow individuals—it shapes markets.

Public Service and Broken Expectations

For many borrowers, the decision to take on student debt wasn’t reckless. It was strategic.

Public service loan forgiveness programs were positioned as a path forward—serve your community, work in education, healthcare, government, or nonprofits, and your remaining balance would eventually be forgiven.

But for years, the execution didn’t match the promise.
Confusing rules.
Shifting requirements.
Administrative failures.

People did what they were told—and still found themselves stuck.

That erosion of trust matters.

A Necessary Clarification

This conversation is not a dismissal of people who paid for their education out of pocket. Some were more fortunate. Some made sacrifices. Some had access to resources others didn’t.

That reality can coexist with another truth:

Most borrowers aren’t avoiding responsibility. They still owe money—and many desperately want a way out that doesn’t permanently destabilize their lives.

At a certain point, when debt becomes mathematically unpayable for a large percentage of the population, it stops being a moral issue and becomes a structural one.

When the Revenue Cycle Breaks

Debt systems rely on repayment to function.
But when a vast majority of borrowers cannot realistically repay, the entire revenue cycle breaks down.

Wage garnishment doesn’t fix that—it amplifies it.
It reduces spending power.
It increases financial strain.
It pushes people closer to the edge.

And when millions are pushed simultaneously, the economy absorbs that shock.

The Real Question Going Forward

This crisis forces an uncomfortable but necessary question:

If the current model isn’t working—for borrowers or for the broader economy—what does an alternative look like?

Because ignoring the issue doesn’t make it disappear. And punishment alone doesn’t restore balance.

At some point, solutions must move beyond individual blame and toward systemic recalibration. Not as a favor. Not as an escape from responsibility.

But as a recognition that the conditions have changed—and the system has not kept up.

Entry-Level Simulation 🎮

By Marcus

The era of entry-level jobs is coming to an end. When I look at trends, it’s clear that “entry-level” no longer holds true to its label. An entry-level job that requires 1-2 years' experience is the very opposite of entry-level.

For the sake of this article, I’m not referring to the minority of jobs that do require years of education, but the majority of jobs and skills that can be learned within a few months to a year.

If hiring based on education credentials and potential is becoming a thing of the past (which data is showing a significant decrease in recent years), then maybe it's time to throw that model out and not wait for permission to do so.

The Data

Every job can be analyzed and drilled down to its basic parts. The type of tasks you do, the frequency in which those tasks occur, and the skills required for each can be measured and calculated.

Companies have all that data too: the company objectives, forecasts, frequently asked questions, support ticket logs, call logs, client message logs, order history, complaints, etc. By having all of this information you can distill almost if not all jobs into three categories:

1. Tasks required

2. Frequency or percent of time those tasks occur within a defined time frame.

3. Knowledge and/or skills required to complete the tasks.

When you look at a job description most of this information is there, but it doesn't capture the full picture.

Experience > Credentials

This is a bit of an extreme example, but I think it represents the entry-level dilemma well.

Let’s consider an “Entry Level Astronaut”.

When evaluating if an astronaut will be selected for space missions: What if the logic for all entry-level astronauts was “must have at least 2 years' experience on space missions” to be considered for this role.

Kind of a silly logic, right?

We know astronaut training is extremely rigorous. Millions of dollars are invested into simulating the harsh conditions in space here on earth so those first-time astronauts can succeed in space.

That’s the main point here: Jobs and their tasks can be simulated to prepare for the real thing.

It just requires a little outside-the-box thinking.

Proof of Work

If you build a strong enough simulation, you can develop exactly what’s required for the job.

We can use all that quality data at a company level to train for exactly what a job requires.

Here are a few examples of how that data can improve the quality and training of future employees:

1. Take the most frequently recurring job tasks with the lowest knowledge/skill threshold and create a training program around it. Create a sandbox environment for it that will simulate these tasks. This allows new employees to learn fast and make an impact sooner while they expand into other responsibilities.

2. Use customer communication logs, call logs, support tickets and complaint data to create a realistic simulation of client interactions. This can be a broad range of interactions from general support to the most challenging interactions people will face within a role.

3. Use the data to see what knowledge or skills strongly align with certain job roles. Encourage hyper-focused skill development. No wasting time on a four-year degree. Recommend supplemental courses or certifications that could be beneficial to interested candidates.

4. Potential-based assessments: Not all experience is good experience. Experience can cultivate both good and bad habits. Use the data to build hiring assessments that extract the intangibles that make someone a great fit for a role.

5. More thoughtful filtering: using “years' experience” is a filtering mechanic to make applicant pools manageable. Adding filtering for adjacent skills/experience that complements the role or align with future goals of an organization.

Endless Possibilities

Data provides infinite flexibility in how creative we can be with education and training programs.

There are many ways we can use current technology to close the knowledge and experience gap.

Experience is simply exposure to different scenarios over time. What I shared here is an effort to consolidate that time and multiply the end output.

If you're reading this you could be the entrepreneur that provides these data-infused training services to businesses, or the team member that takes the initiative and becomes the “head of training and development” or the recruiter with a specialty in discovering diamonds in the rough.

Lastly, maybe you're that job applicant that finds a way to show their potential will outperform the most “experienced” candidates for the job.

Systems evolve and we’re only scratching the surface of what’s possible.

🏆Play Like a Champion🏆