What to do with your career

Knowing what to do with your career (and your life) is one of the hardest quests that silently wait for everyone.

Nobody escapes it. And there is no universal answer.

What’s sure is that you should steer away from your uncle's unsolicited advice. Or anyone else’s advice for that matter.

Nobody is a pro at figuring this out. You are undoubtedly the most qualified person to make decisions. You are the ultimate CEO of your life.

So this is not career advice.

But if you are looking for one here it is:

Be wary of presumptuous people who know what’s best for you!

This is a framework that can help you bring clarity to your decisions.

Your life so far

Our childhood is a series of events that happened because of where we were born, our parents and a bunch of other circumstances.

Like cycling on a countryside road, we have almost no choice (unless we clearly want to end up in the bush)

During college, we experiment with different hobbies, meet people who have completely different stories and wonder what our career could be.

We end up on a highway where we can see a lot of exits but everything happens so fast that we hesitantly approach the least worst of the bunch.

And after university, we are promoted to pilots and given a plane to figure out which path to go in a directionless sky.

Too bad nobody told us how to fly.

This is when having a framework is the first lesson in the flying school of life: it’s about learning some ground rules to figure out which kind of pilot you are.

Get ready:

1. Identify your values

Without knowing what your values or beliefs are, it’s impossible to know where you are going and whether the direction is truly your own or not.

List down 10 values you feel are important to you.
Here are some common ones (but you can use others too):

• Money - Do you accrue a significant amount of wealth?

• Fame - Do you become more popular because of your work?

• Relationships - Do you prioritize time for family & friends?

• Knowledge - Are you constantly required to learn new concepts/skills?

• Power - Do you increase your influence via resources/ connections?

• Meaning - Do you move closer to your ultimate goal?

• Flexibility - Are you in total control of your schedule and your time?

2. Prioritize

Before moving forward, trim down any value that is either redundant or that you already know is not as important as others. Aim for 7 to 8 values.

Now rank them by placing them in one of these 3 buckets: top shelf (you’ll prioritize these no matter what), middle shelf (you’ll make sure not to completely fail at them), bottom shelf (nice to have but really an afterthought).

3. Reflect

When approaching career decisions, many of us are torn between a few options: stay in the current position/path, explore X or take this offer and move to another country.

Reflecting on your top-shelf values is the best way to guide your decision.

The goal is not to make the perfect decision but to make the best decision you can with the information you have by following a process you can rely on.

Are you choosing knowledge over money?
Then a repetitive job that pays well is not going to suit you.

Are you prioritizing fame?
Then you have regularly to put yourself out there.

Is flexibility not as important as you thought?
Then you might choose an office job that offers you knowledge and money.

4. Avoid common pitfalls

Not everything can make it to your top shelf. Prioritizing is one of the hardest tasks but it’s the only way to make this framework effective.

Knowledge is not just about reading. It’s the foundation of personal growth and this is how you avoid the feeling of stagnation that comes from many jobs.

Understand what role money plays in your life.
It was shown that overoptimizing for money does not meaningfully improve life quality. In fact, making over $75’000 per year does not make you any happier. But security and peace of mind are priceless.

Now it’s your turn: open a new tab and go through this framework.

Once you are done, let us know if it helped you get more clarity by replying to this email (we reply to every message!)

And if you want to dig deeper into this topic, check out how a variation of this framework helped Julian Shapiro decide between startups and writing.

Deep Dive: AI in medicine

AI has been promising to solve all sorts of medical challenges for many decades and with the recent hype, even more attempts are being made.

But history has no shortage of major downfalls. Very recently Babylon, the UK digital health darling went from $272.50 to just $0.77 per share and will soon delist via a merger with the Swiss MindMaze.

But why is it still so difficult to solve medical problems at scale using AI?

Even when capital is not a major constraint, there are some things to consider:

AI predictions vs information retrieval

There seems to be a blurred line in many medical environments between generating answers from data inferences vs retrieving knowledge from scanning large datasets in real-time.

Both can be effective depending on the application but they don’t scale in the same way and ultimately they generate very value for users and payers.

Not everything is ChatGPT.

Small and biased datasets

Many companies focus on specific edge cases and applications (from ICU to rare diseases), which is not intrinsically a problem (and could actually be used to fast-track some niche solutions) but in order to scale (and chase those VC returns 💸), they tend to generalise their claims for other use cases.

A huge problem arises when the data they gathered is not representative of any population and it's over-indexing on a very small cohort of patients (for example algorithms detecting sepsis).

For example, Epic’s faulty sepsis prediction algorithm.

Deciphering doctor's notes

Whether they are already digitalised or still written by hand (praying to the NLP gods for good extractions 🙏), there are still questions if those notes can be used at scale and for which purposes.

Quality over quantity

Too many companies claim they have the largest medical datasets in the world and that they can use them to draw conclusions on many different topics, from predicting diseases to clinical trial recruitment.

But making sense of data covariants is not a trivial aspect and large EMR datasets are not the solution to all problems.

(for example, eligibility for clinical trials requires information usually not included in EMR and relying on correlations is a dangerous path)

What do we need then?

More data: data collection at scale using point-of-care systems and/or high throughput approaches.

New types of data: data obtained with new imaging techniques or different protocols to leverage standard methods that lead to new data dimensions.

Contextual data: predictive data only works in its natural environments.

Multi-input data: integrating multiple data types, usually obtained from different modalities into a single system that can infer causality from the lowest to the highest level of the system.

Why is this all about data?

Because with AI becoming a commodity and many open source models, data is what sets apart great products that solve large-scale problems.

This week's top reads

1. Netherlands first in the EU to allow pre-approval tastings of lab-grown food (Silicon Canals)

2. The CERN Venture Connect is a new initiative to invest in CERN technologies with startup potential

3. EMEA Life Sciences Cluster Outlook 2023 (JLL)

4. Suddenly, It Looks Like We’re in a Golden Age for Medicine (NYT)

Latest funding rounds in health & bio

• Umed raised $12M to automate clinical studies and medical evidence through prospective research 🇬🇧

• Neko Health, a platform for preventive health through body scans raised a $65M Series A 🇸🇪

• Cobea, the company behind the Alpine White brand of oral care products raised 3.75M CHF 🇨🇭

• Avea, one of the pioneers in science-backed longevity supplements raised $2.5M for market expansion and human studies 🇨🇭

• Berlin Heals raised 6M CHF to advance heart failure therapy with its implantable C-MIC (Cardiac Microcurrent) device 🇩🇪 🇨🇭

• Reyedar raised €3M to commercialise its degenerative disease screening technology based on eye-tracking and deep learning 🇳🇱

• Eaze raised €1.7M for its personalised sleep coaching app that delivers a data-driven online treatment online by qualified sleep experts 🇩🇪

Today was a long read and I’m super pumped you made it to the end!

You are the best!

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