Careers Used to Be a Ladder. Now They’re Becoming a Portfolio. Why? — Part I

Apparently, in some large tech companies, there is a growing group of people who look like ordinary full-time employees from the outside. They go to work every day. They have a title, a manager, a team. But behind the scenes, some of them are already running two or three companies or projects of their own.

Why do they stay employed?

One reason is that a large company is an extraordinary place to experiment. It gives them access to technology, talent, computing resources, real business problems, customer needs, and a huge amount of learning. Some people stay inside the company while quietly validating their own ideas. Once one of those ideas proves commercially viable, they may leave very quickly to build it full-time.

What is even more interesting is that some investors are actively looking for exactly these people: high-performing employees inside strong companies who are already testing business ideas on the side.

From the employer’s perspective, there are of course clear boundaries. If an employee uses company code, data, customers, computing resources, working hours, intellectual property, or confidential information to build a competing business, that is an obvious conflict of interest. No company should be expected to tolerate that.

But after working in HR for many years, what interested me most was:

“Why are employees less loyal than before? Why are more capable people no longer treating the company they work for as the platform for their entire career?"

I started to wonder whether what we are seeing is not simply a decline in employee loyalty. Maybe the logic of a career itself is changing.

We used to think of a career as a ladder. For decades, the standard career model looked something like this: Join a good company. Build expertise. Get promoted. Take on more responsibility. Move into leadership.Keep climbing. A career was a ladder.

**Career as a Ladder.**

In that model, the company mattered enormously. Organizations controlled most of the resources people needed to create economic value: capital, technology, software, brand, customers, distribution, talent, knowledge, and coordination.

Outside the organization, it was much harder for an individual to scale their capability.

That created a long-term exchange between employees and companies. Employees gave the organization their time, capability, creativity, and a meaningful share of their career risk.

In return, companies provided salary, career progression, professional identity, learning, income growth, and some degree of long-term security.

The implicit contract was: If you invest in this company for the long term, this company will invest in you for the long term.

So when people spent ten, fifteen, or twenty years in one strong company, it did not necessarily mean previous generations were naturally more “loyal.” It may simply have meant that the expected return from staying inside one organization was higher.

But that exchange is changing. The other side of “declining loyalty” is that companies no longer promise long-term relationships either. Modern organizations increasingly value flexibility.

Restructuring. Layoffs. Outsourcing. Automation. Performance differentiation. Role redesign.

From a business perspective, many of these choices are perfectly rational. Companies have to reallocate capital and labor as markets change.

But employees have learned something from this too: a company will not guarantee your next ten years simply because you gave it your previous ten.

That leads to a natural question:if companies are constantly reallocating human capital, why shouldn’t individuals reallocate their own career capital?

So what we often describe as declining loyalty may partly be a symmetrical response.

Companies increasingly treat labor as something that can be dynamically allocated.

Employees are beginning to treat their own time, capability, and experience the same way: as assets that can be dynamically allocated.

And then AI arrived.

AI is reducing individual dependence on organizations

In the past, spotting a business opportunity inside a large company and actually leaving to build it were two very different things.You might have had the idea. But you still needed engineers, designers, product managers, marketing, customer service, servers, and capital. Organizations could coordinate dozens or hundreds of people. Individuals could not. That meant even highly entrepreneurial employees often had little choice but to remain employees.

AI is changing that cost structure.

Today, one capable person can use AI to write code, design interfaces, conduct research, analyze customers, create marketing content, automate parts of customer support, and coordinate multiple AI agents to do work that previously required several people.

Servers can be rented. Software can be subscribed to. Distribution can begin on social platforms. A product that might once have required a ten-person team to validate may now be prototyped by one or two people in a matter of weeks.

So perhaps what is changing is not that: employees have suddenly become disloyal. It is that: the cost of becoming an independent producer has fallen. As a result, a formal job title increasingly tells us less about a person’s real economic identity.

Someone may officially be a Product Manager. But in practice, that same person may also be: Employee + Founder + Investor + Creator + Consultant.

Employee by day no longer necessarily means employee as an identity.

People who are simultaneously working at a large company and running several side ventures may simply be an extreme version of something broader.

We may be witnessing the emergence of an: AI-native workforce.

There is another question: who owns the value being created?

Imagine a highly capable employee working at a company worth hundreds of billions of dollars.

If she invests an extra 30% of her energy into the company, what might she receive? A stronger performance rating. A bigger bonus.More RSUs. A better chance of promotion. All of those things matter. But what if she invests that same extra 30% into something she owns? Most likely, nothing happens. Most startups fail. But if it works, she owns: Equity.

Those are very different payoff curves.

Historically, most people did not seriously compare the second option because the cost and risk of building independently were simply too high. But AI is lowering the production cost of entrepreneurship. And venture capital is lowering another part of the barrier.

The old equation looked something like: leave a large company = give up stability + give up access to organizational resources.

A new path is becoming more plausible: Salary + Side Project → Validate PMF → Raise Funding → Leave

That means a large company can unintentionally become an: incubator before incorporation.

This is what I mean by Career as a Portfolio

Under the traditional ladder model, people asked: what is my next promotion? How far can I go inside this organization? What is my next title? That is Career as a Ladder.

But increasingly, some people are asking different questions: where should I allocate my time?Where should I allocate my expertise? Which opportunities deserve my best ideas? What should earn me salary, and what should earn me equity? What should I own?

That begins to look much more like the mindset of an investor. Investors allocate financial capital. Increasingly, highly capable professionals are learning to allocate: Human Capital.

So perhaps employees have not suddenly become less loyal. Perhaps they are beginning to think about their time, skills, networks, and creativity the way investors think about capital: as something to allocate across different opportunities.

That is the shift from Career as a Ladder to Career as a Portfolio.

But this raises an even more interesting question. If someone becomes more capable, gains more AI leverage, and finds it easier to access capital and external opportunities, then: could the people companies most want to retain also become the people least dependent on the company?

And if even companies with the resources of Google, Microsoft, and OpenAI continue to see top talent leave to build independently, what will organizations need to offer in the future?

That is the question I want to explore in Part II.