Get Ready… to Not Be Ready

How to prepare for change in tech and data science jobs

By Luis Serrano and Rosaria Silipo

You can’t truly prepare for what you don’t know. You don’t know if the next challenge will be a water sport, an endurance sport, or a tactical sport. The only thing you can do is stay fit and learn a variety of basic techniques, like kicking, swimming, or running, so that you’re as prepared as possible. In other words, get ready… to not be ready.

The same principle applies to technology. The next big technological shift will catch us all off guard. It will be built around something new, something we can’t yet predict. The best strategy is to keep your mind sharp: maintain your math skills, keep programming, stay up to date with AI concepts, and remember the computer science fundamentals you learned in college. The more tools you have in your toolkit, the better equipped you’ll be to handle whatever comes next.

If you’re skilled in one sport, you can often pick up another more easily—if you’re fit, you’re adaptable. The same goes for technology: if you’re strong in classic data science, you’ll be able to master new AI paradigms as they emerge.

The Question

With the rise of AI, many white-collar jobs, including data analyst and data scientist roles, are under threat. Predictions range from Dario Amodei’s estimate of 10–20% unemployment to Goldman Sachs’ forecast of 300 million jobs lost or changed worldwide. With AI automating everything from coding to reporting, data professionals are understandably concerned about the future of their work.

We believe that data analysis can never be fully handed over to AI. However, the tools we use for this analysis will change greatly in the near future. Training a machine learning model from scratch may soon be obsolete, replaced by downloading and fine-tuning large language models. Writing Python scripts without AI assistance may soon be a thing of the past. Change is inevitable for today’s professionals, but what about students currently studying data science? They’re learning about decision trees, random forests, and linear regression. How will these skills help them in an AI-dominated job market?

We heard this question repeatedly, especially from students: What new subjects should they study to avoid a dead-end career?

This isn’t a new question. Every disruptive technology brings uncertainty. Ten years ago, new data science algorithms changed the field, and the same will happen again. Back then, people asked, “What do I need to study to become a data scientist or ML engineer?”

Hosting a podcast has many benefits: meeting expert guests, learning about new topics, and discovering fresh perspectives. Our September AMA session set the stage for a new perspective on this exact topic.

The Sport Analogy

Let’s put this in perspective with a sports analogy. Imagine a new sport will be invented in ten years, and you want to be the best at it. How can you prepare?

You cannot. You do not know it. You do not know if it will be a water sport, an endurance sport, a tactical sport. You just do not know enough. The only thing that you can do now is to keep fit, learn a lot of basic sport techniques, like kicking, swimming, or running. Just be as ready as you can. Just get ready … not to be ready.

The same happens in technology. The next technology shift is going to throw all of us off. It is going to be built around something new that we do not know yet. Then, your strategy can only be to keep mentally fit as much as possible. Keep your math skills sharp, maintain your programming experience, hone the AI concepts you are learning today, keep at hand the computer science experience you accumulated over the years. Basically, make sure to have as many tools as possible in your toolkit. Then, you will be ready to tackle the technology shift.

If you are good at one sport, can you quickly learn to play another sport? If you are fit, you can. It is the same thing with new technology like AI. If you are good at classic data science, you can easily master the new AI paradigms.

Just as athletes cannot predict exactly which sport might rise to prominence in the next decade, students today can’t foresee every technological shift. Instead of fixating on one set of skills, it’s more important to develop a broad athletic, or in this case intellectual, foundation.

This analogy helps illustrate that continuous learning, adaptability, and building core competencies are the best ways to prepare for an uncertain future, whether in sports or technology. Staying ‘fit’ both mentally and physically, prepares you for change better than specializing in a single current tool.

How to Stay Fit

How can we stay “fit” for the AI-driven data professions of the future? There are two sides to this answer. This analogy shows that continuous learning, adaptability, and building core competencies are the best ways to prepare for an uncertain future—whether in sports or technology. Staying mentally and physically fit prepares you for change better than specializing in a single tool.

Just as athletes can’t predict which sport will become popular next, students can’t foresee every technological shift. Instead of focusing on one set of skills, it’s more important to develop a broad foundation—athletic or intellectual.

Adaptability is more important than knowledge. Knowledge becomes outdated every few years, but adaptability lets you learn new techniques, apply your skills to new domains, and fill the gaps that come with disruptive technologies.

On one hand, studying the basics—math, statistics, data engineering, and machine learning algorithms—lays the groundwork for future growth. Neural networks will remain relevant for a while, given their recent advances. Concepts like regression, perceptron, gradient descent, Hessians, linear algebra, probability, and clustering will not be forgotten. They may be reinterpreted, but they’ll still be the building blocks of future technology.

On the other hand, you need to shape your mindset: get ready not to be ready, learn how to learn, and stay curious. Since you can’t predict what will change next, practice being flexible and adaptable.

Adaptability as your Mindset

This sports analogy, comparing AI to a sport that hasn’t been invented yet, has reassured us and our student listeners, about the future of data science jobs. We can rebuild data professions by stacking traditional math, statistics, and machine learning “bricks” to support new AI-driven technologies.

To do this, you need a well-stocked toolkit of skills and enough adaptability to choose the right tool for any innovation.

Watch Luis’ video on YouTube “How to stay relevant in AI” where he presented this sport analogy