Why Flexible Skills Are a Game-Changer in Startups Today

The idea that it takes 10 years to master a skill has roots in research from psychologists like Anders Ericsson, whose studies formed the basis of the “10,000-hour rule.” But in today’s technological economy, fueled by access to information, open-source tools and AI-enabled workflows, the time to build meaningful skills is irreplaceable. What used to take ten years can, in some cases, happen in three. And in some cases, it takes 10, but not for the reasons we previously imagined.

This question is more important than ever. As startups shift across sectors – edtech to fintech, SaaS to AI, Web2 to Web3 – the ability to redeploy learning across environments is critical. But what exactly transfers? And what should businesses keep in mind when they rotate parts or force learning time?

Learning loops and phase motion

The past ten years have produced a wave of successful founders and users who have shown that while part of the division is important, muscle killing is transferable.

Take the example of Elon Musk. After building PayPal in the early 2000s, he moved into the automotive sector with Tesla and space technology with SpaceX, industries where domain expertise was considered non-negotiable. But what Musk brought was not deep technical knowledge of rocket propulsion or battery chemistry (at first). He brought first-principles thinking, team-building and the ability to raise and deploy capital efficiently. The process of execution, not being immersed in a part, gave him a way to build skills on the job.

Closer to Southeast Asia, consider the SEA Group approach. Initially best known for its gaming arm (Garena), the company expanded into ecommerce (Shopee) and digital currency (SeaMoney). That kind of vertical expansion isn’t possible without a leadership team that understands how to take business intelligence from one industry and adapt it to another, often in real time.

What these companies share is the ability to organize learning loops. Every product that is shipped, introduced to the market, or a customer segment that is analyzed adds to the flight’s functional muscle. This is no accident. It’s systemic. They hire teams with a strong sense of curiosity, build a culture of experimentation and often design their organizations to be fast and iterative, not just shallow.

Institutional vs. personal skills

Another question that often comes up is whether it is the individual or the company that builds skills over time. McKinsey’s “Three Horizons of Growth” framework provides one lens. Companies that live and measure over 10 years tend to compare short-term performance (Horizon 1) with medium-term growth rates (Horizon 2) and long-term vision rates (Horizon 3). But what is less discussed is how skills are transferred to these horizons.

Amazon is an example. What started as an online bookstore became a logistics giant, cloud computing leader and AI infrastructure developer. Each expansion required new skills, but Amazon didn’t just hire outside. It has invested heavily in internal mobility, document culture (popular, six-page memos) and cross-functional leadership development. In short, it has set the art of transportation.

Compare that to small startups or sole proprietors. The ten-long period of time can still hold if you are building a corporate memory from scratch. But even here, macro trends are changing the curve. Access to shared knowledge (through GitHub, X, or open-source platforms), the rise of low-code/no-code tooling and generative AI assistants allow individual workers to enter new domains faster than ever.

This is seen in sectors such as DeFi and AI infrastructure, where founders are running new businesses with little direct background, but quickly rise to speed through community contribution, protocol design templates and hyperactive Discord-based information transfer.

The role of money and talent is increasing

Another factor that is not considered in the time pressure of technology is capital leverage. In the past, building capabilities required years of bootstrapping and expensive trial and error. Today, a skilled team has access to component experts, growth consultants and async tools that can often scale up information quickly.

OpenAI’s startup fund strategy is a modern example. By investing in founders who may not be AI veterans but have a strong empathy for the user or industry context, OpenAI accelerates market understanding while lending its expertise to the food chain.

Similarly, in the tech-to-fintech transition taking place across Southeast Asia, we are seeing finance and hiring systems being used as a bankruptcy weapon. Companies that started with a deep understanding of user behavior in online learning are now applying that insight to financial literacy products, personal finance apps and digital lending platforms. They don’t just go around. They are pollinating each other.

The important thing is that the companies have a balance sheet, or a table of contents, to support these extras. Without capital leverage, sector transformation still takes years. With it, you can drop learning degrees, hire the right consultants, or get smaller teams to hire, and drive products faster than legacy players can.

Expectation: Focus on flexible opportunity

The benefit of today’s user does not mean how long they have been in the field. That’s how fast they can use lessons from one domain to another. As technology becomes more innovative and industrial boundaries blur, flexible skills such as systems design, product-driven growth and market storytelling often outpace narrow domain mastery.

Founders and teams navigating this transition need to ask: What part of our technology is strong, and what part is in shape? Can we transition our hiring engine from SaaS to fintech? Can our product testing book from edtech inform our go to market in AI tools?

Finally, the ten-year rule still applies, but not in the way it used to. It’s less about spending 10 years in one vertical and more about spending 10 years getting a lower-class education itself.

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The idea that it takes 10 years to master a skill has roots in research from psychologists like Anders Ericsson, whose studies formed the basis of the “10,000-hour rule.” But in today’s technological economy, fueled by access to information, open-source tools and AI-enabled workflows, the time to build meaningful skills is irreplaceable. What used to take ten years can, in some cases, happen in three. And in some cases, it takes 10, but not for the reasons we previously imagined.

This question is more important than ever. As startups shift across sectors – edtech to fintech, SaaS to AI, Web2 to Web3 – the ability to redeploy learning across environments is critical. But what exactly transfers? And what should businesses keep in mind when they rotate parts or force learning time?