Aquiles Carattino

After a Failure, Revert to the Last Known Working State

2025-11-04T00:00:00+01:00

To give some context, I can use my personal experience as an example. At Dispertech, we are building a device to characterize nanoparticles. It is based on technology developed in a lab, and we must transform a rough prototype into a usable product. Our biggest failure arose when I could not detect the same particles detected in the lab years before.

If I could not succeed at detecting these specific particles, the project was likely going to end. Therefore, rationalizing such a failure had a very high emotional toll. Moreover, reverting the many failed attempts required some detailed understanding of the factors that brought me to this particular conjuncture.

The Multiparemetric Optimization Problem

One of the biggest challenges in many projects is to define what we are trying to achieve. In our case, the general idea of “user-friendliness” has many different approaches. Software to control the experiment must be accessible. The external housing has to look nice and include informative LEDs to show the status of the device. The optics must be designed to minimize user intervention.

We rely on disposable cartridges to hold a hollow optical fiber, the core component of our device that would allow us unprecedented accuracy. These cartridges have many different aspects to consider, from the cost of production to environmental friendliness. But we must also include the alignment precision (we are talking in the range of 1/100th of a millimeter) and the type of glue used.

Overall, around 20 different parameters can be changed, and each one will impact the outcome. Optimizing for an abstract idea such as user-friendliness already generates an uncertain goal. Using a different laser simplifies the electronics, but the user will force have to spend ten more minutes waiting for the device to be ready. How can you weigh the consequences of such a decision?

Moreover, when changing any of the parameters generates an impact on the others, we end up with an actual multiparametric optimization problem. We need to explore a multi-dimensional decision space to achieve what it looks like, at least, a local maximum. But these processes are time and money-consuming. Therefore we probably will try, consciously or unconsciously, to find shortcuts in the process.

The real challenge with multiparametric optimization problems is that the result we obtain does depend on the path we follow. And this is not related to the problem itself but our psyche. If we introduce a system change to make it better, we can fail, months later, to acknowledge that it is holding us back. We are set to go always up from where we are standing, even though perhaps we could reach higher if we first take some steps down.

Early progress is not a persistent indicator

When we start with a new project, there’ll likely be a lot of progress at the beginning. Perhaps it is fueled by motivation; maybe it is enabled by the number of things that must be done and have a relatively low difficulty. Early progress generates a fantastic feeling but making longer-term assumptions based on it can become risky.

Doing a linear extrapolation of what we expect to achieve in the coming six months based on how much we progressed in the first six months can give extremely biased outputs. The development stage is different, and we must adapt expectations also considering other factors. However, it is almost unavoidable to let our minds meander on those longer-term irrealities. Marathon runners know this better than anyone: the pace at which you run the first half-marathon can’t be extrapolated to calculate the time it’ll take to finish the race. Early progress is not a persistent indicator for future progress.

Once the output falls behind the projected progress, several feelings will emerge. Probably the most predominant is frustration generated by the apparent stagnation. These feelings can be self-motivated if we set expectations for ourselves based on what we have done up to a point. But it may also happen that others project their expectations on our company or us. When progress fuels motivation, instead of the other way around, lack of progress takes it away and gives space to the opposite feeling.

Stop and ask why

When building something no one else has made before, there is no reference to follow. We may relate to other products, but we are essentially on our own. Even after the initial phase, progress never stops completely. There are always some wins, either big or small, and plenty of learning opportunities. However, as deadlines pass by and the product is no closer to completion than before, it becomes a constant emotional struggle.

When we reach this point, we must realize there is a timeline that brought us here. We must be aware that there were plenty of smaller steps to arrive at where we are. It is the moment to stop, and ask why. It is a challenging exercise, but the more often we do it consciously, the better understanding we’ll have about the process.

When someone new starts to work on a project, they will likely ask why we are doing things in the way we do. Even random questions such as why is the fridge so far away from the kitchen. Perhaps there was no kitchen when we bought the refrigerator, and when we built it, no one thought about moving it. If we are receptive to what they have to say, fresh pairs of eyes can be truly enlightening.

Asking why means reflecting on the end goal of each step we took in the past. For example, I was struggling with building disposable cartridges. They are one of the aspects of making the device user-friendly. The entire ethos of what we were building was designed around those cartridges. Because of the cartridges, many parts of the device became too convoluted, hard to maintain and use.

The obvious question to ask ourselves was: why are we still using the cartridges? They were the source of most of our problems. We built for so long on the idea that cartridges were a must-have that it took us a very long time to step back and decide that we needed to tackle the problem differently.

When we work in projects with many different dimensions, not all orthogonal to each other, the paths we follow may prevent us from reaching the desired outcome. Now that we know where each decision leads, we can go back and check if they were the correct ones. By asking ourselves “why” retrospectively, we can understand at which point a decision pushed us down a road that would eventually lead to failure.

Revert to the last known working state

Decisions have a compounding effect, making the moment at which our path started to depart from our original plan almost imperceptible. Borrowing from version control systems, we can try to revert to the last known working state. It is not an easy task, and it requires a lot of thorough introspection. But if we do the exercise, we may find a specific moment in time in which we followed a particular path that eventually leads to the state we wanted to avoid altogether.

Bisecting a timeline can be a helpful approach but not always practicable. We look at the last time we know we were on track, and the first time we realized we were off track. We check at an intermediate point and decide if it belongs to the first or the latter cases. Then, we split the sub-interval again and continue until we find the moment in which the course of action was wrong. This approach can work great for software development but is much tougher to follow in projects with many moving targets.

Therefore, another idea to identify the crucial moment is to go backward and ask why as many times as needed. When we find a moment where the reasoning is flawed or poorly supported, it is likely a moment of departure from the path we should have followed. However, this alone is not enough. Poorly supported decisions do not mean they were the wrong decisions. Since we are analyzing retrospectively, we can use all the information we have now to support or invalidate the choices we made.

Even if we find a moment when we took a failed approach, we must be sure it is the first time it happened. A decision could have been taken based on wrong assumptions, and we could try to correct it. However, there’s no guarantee that decisions taken earlier were not problematic. There is no problem in going back to the project’s genesis to understand what happened. It just takes time.

After a period of introspection, we would gain confidence about the moment in which things derailed. Ideally, we would go back in time to the moment we identified and make a different choice. However, it is impossible to revert ourselves, our history. The new decision will be based on what we already know, on what we learned. Our resources are not going to be the same now than in the past. The temporal and budgetary constraints are not going to be the same as the context in which we were.

If we are lucky, we can revert some of the choices without invalidating the rest of the project. In our case, for example, we decided to revert the decision of working with cartridges. It didn’t mean we had to change the rest of the design, nor the electronics. It just required some geometric adjustments to the optical path. After some tweaking, things started working better than ever.

Dealing with failure is more complicated when alone

If there’s something that I’ve learned, when you feel frustrated because of a failure, the best you can do is to lean on others. The worst you can do is to keep everything to yourself. From people who can tell you: you looked happier one year ago, to business partners who have a deeper understanding of the processes. Even people who do not have a complete picture of what you do but can challenge your decisions are of great help.

Every bit of input we can gather about the path we followed is of great value to reconstruct our history. What a lot of people miss to realize is that in many decisions, there are emotions involved. Acknowledging a failure, deciding to revert to a previous state will have a toll. If more people share these feelings and the project’s responsibilities, it’ll be easier for each individual. A failure can become a learning opportunity only if we are equipped to seize it.

Do things that can scale

2025-11-04T00:00:00+01:00

One of the biggest hurdles for entrepreneurs is that they need to juggle short and long term planning at the same time. On the one hand, it makes no sense to worry about long-term if the short-term can’t be successful. On the other, if there is no long-term vision, we can better do something else.

In this continuous struggle between the vision and the day to day execution is when things can easily derail. Premature optimization can set a product in the path to failure. On the other hand, no long-term thinking can place the product roadmap in a dead end.

There’s a mantra I’ve seen repeated over and over that says “do things that don’t scale“, but very few people have understood what it actually means.

Paul Graham in of if his essays is perhaps the one who introduced the idea. Many people take his writings to heart, but few reflect on what thety actually mean.

Scaling services

There is a good example from Strava, a social network created around sports activities. Social networks are hard to scale, because the only reason for people to join is having their friends already in there. This situation creates a spiral hard to break. In its early days, Strava could work only with Garmin watches, so the founders made a bold move: they went out to buy Garmin watches and gave them as a present to friends and family. It was their approach to creating the initial user base.

Of course, giving watches for free does not seem like a good business model and, more importantly, it is not scalable.

However, the process that wouldn’t scale was user acquisition. The product they’ve built and wanted to commercialize had no scalability issues.

It is not surprising that many of the examples we find about things that don’t scale refer to the initial onboarding of users and customers. Knocking on the doors of every house to ask if they want to rent an air mattress does not scale. Having a person hand deliver shoes and ask each customer why they bought online will not scale.

Business models with a strong network effect, like Strava, or a double sided market, like airBnB, companies need to find ways to overcome the initial adoption friction.

For the companies discussed above, the things that wouldn’t scale were not core aspects of their business.

Things that really don’t scale

The history of Theranos is very relevant in this context, because it embodies the perfect example of embracing the Silicon Valley mantras while bringing them to the extreme.

Let’s tell the Theranos story from the perspective of a 20 year old who decided to follow the Silicon Valley pipe dream and dropped out of university.

Building hardware is hard. Building medical instruments is even harder. Therefore, before committing to developing new tech, you can focus on validating the market doing things that don’t scale. For Theranos, it meant performing tests using equipment from other companies.

As the market was being validated, and requirements of the users were honed in, the company could focus on building their instruments. They had a “guaranteed” product-market fit thanks to all the non-scalable things they did in the past.

That’s the story we would have been told if things would have scaled.

The fundamental flaw of Theranos was getting started from an idea that in itself wouldn’t have scaled. They had no evidence that what they claimed was possible at all. There’s a very large qualitative difference between software and hardware, and even more so between hardware and hard-tech.

Scaling the business model

Doing things that don’t scale has a different meaning when you focus on the core activities of your business. If you focus on things that can’t scale then you are in the path of failure.

Your user acquisition can scale if you just change the approach to an online form instead of knocking on doors. Shoe delivery can scale by using traditional mail instead of hand-delivering. However, there was no evidence that blood tests could scale in the way Theranos hoped for to justify their business model.

In software as a service (or SaaS) the business scaling factor seems trivial. There were little doubts regarding the ability of AirBnB to list 1 million houses on their website. I bet no one doubted that Strava could deliver their app to millions of users. Software is inherently scalable and distributable.

On the other hand, scaling product shipping businesses such as Zappos (or even Amazon) is not a given. Logistics, inventory management, and consumer demand prediction, are not necessarily scalable. They can easily be the source of issues that force companies to stop operating.

Handling scalability issues at the core of the business is where the skills of the founders to execute on strategy come at play.

Scaling Products

For people working on the development of products instead of services, the idea of scaling is different. Scaling the production of hardware-based products is not guaranteed. The leap forward is what may become the show stopper for a small company.

Without falling too far from the Theranos example, we can use of microfluidics as a technology which is hard to scale. Microfluidic chips are broadly employed in fields such as lipid nanoparticle fabrication because they allow to use small amounts of reagents. They are great for small batch production and formulation iteration.

However, a pharma company does not care about small volumes when they go into production. Microfluidics is a technology which does not scale in itself. We could put 1000 machines in parallel, but that’s only linear scalability. Most processes need a different approach to achieve their goals.

3D printing is another example. Although it enables rapid prototyping and manufacturing, it is a process that scales linearly. Fabricating 100 pieces takes 100 times longer than making a single one.

It is important to separate whether the business proposition revolves around non-scalable processes, or whether we are using them as stepping stones to learn and validate the market.

The need to scale

All the discussion we had up to now was on the premise that business need to scale. And this is where it becomes important to distinguish between intrinsic and extrinsic needs.

Not all businesses need exponential growth to be successful.

If you look around, you’ll see many companies that generate employment, create value, and are not growing. A restaurant is a prime example. Their space is limited, the kitchen has a finite size. Once they reach a given number of servings a day, they can’t grow anymore. And there’s nothing wrong with it, lot’s of people derive a livelihood from such businesses.

On the other hand, if the business case revolves around the “winner-takes-all” mentality, scaling is the only option.

By growing our market share we can lower costs and maximize profits. If we are into manufacturing, scaling production normally means using techniques that are much cheaper but require larger volumes.

If we built our business plan around competing on price, for example, we have no way but to scale. This is true for many business-to-consumer type of products, but not for luxury items, where scarcity is a value generator.

Financial scaling

Scaling becomes necessary when companies incur in debt, for example. Repaying with interests means that the output in the future needs to be larger than in the present. If the loan only enables a linear increase in productivity, it’ll be very hard to repay it.

Investment also comes with requirements for growth. Institutional investment (venture capital) will need a monetary return at least as large as what banks pay as interest rates. Increasing the valuation of your company comes with expanding business efficiency and market share in a non-linear way.

If your business model does not account for scalability, you’ll be in troubles sooner or later.

Bootstrapping avoids the scaling issues

If you start your business by chasing purely customer-driven growth, then you won’t need to focus on scaling exponentially.

If you grow only as much as your customers demand your products, and you built your business plan around it, then you can generate a completely different path. The overall idea is that if you can smartly leverage service contracts together with grants, you don’t need upfront investments.

You grow only if there is clear demand for what you are selling. And you don’t need an upfront injection of capital to achieve the first milestone.

Not all business ideas can afford it, though. If you are in the semiconductor industry, for example, there is no way around fabricating thousands of products at once, with the upfront costs they represent. If your business model requires competing on price, you will need to reach the economies of scale that only upfront capital allow.

Do things that can scale

If you are starting a new business, you should always be mindful of where do you want to go.

If you start with some unscalable approach, you need to develop a clear perspective on what it means in the long term. Keeping a long-term perspective is also important for your team. If the things that don’t scale become too ingrained in your products and processes, you may face show-stopper challenges later on.

Founders have to perform a balancing act between premature optimization and unscalable decisions. The trick is to find the sweet-spot somewhere in between where the path to success starts.

Effectual reasoning, a framework for aspiring scipreneurs

2025-11-04T00:00:00+01:00

Effectual reasoning, as opposed to causal reasoning, can be defined as the ability to chase goals based on resources available. It is about adapting to changing circumstances, and not focusing on trying to change the context in order to succeed.

The first time I read about effectual reasoning, I was well underway in my entrepreneurial journey. Once I fully grasped what it implied, I started seeing a very distinctive pattern that would split effectual from causal thinkers.

I’m not convinced you can learn how to become effectual, but it definitely helps to keep the idea in the back of your head.

Once you identify who are the effectual thinkers in your team, bring them closer to you, they are going to be a fantastic asset in the early days of the company.

Why effectual reasoning is so important for entrepreneurship

Let’s dive into effectual reasoning, a concept that’s a game-changer in the entrepreneurial world.

Every entrepreneur starts with a handful of tools: who you are, what you know, and whom you know.

That’s your starting point.

From there, you’re not just finding a path; you’re creating it. This approach is all about embracing the uncertainty and unpredictability of the business world and using it to your advantage.

Now, why does this matter in entrepreneurship? Well, think of traditional business strategies as a roadmap, trying to predict every turn and stop. Effectual reasoning, on the other hand, is more like a compass. You know your general direction, but you’re open to exploring, taking detours, and even forging new paths altogether.

This way of thinking is perfect for startups and new ventures where you often start with more questions than answers and more enthusiasm than resources. It’s about being scrappy, using what you’ve got, and leaning on your network. Remember, it’s not always about having the most resources; it’s about being the most resourceful.

One of my favorite aspects of effectual reasoning is iterative learning – learn as you go, adapt based on real-world experiences. It’s like building the plane as you fly it. You’ll make adjustments based on feedback, market changes, and even your own learning curve. This approach is dynamic, exciting, and incredibly practical.

And here’s the kicker: it’s not just about finding opportunities that exist; it’s about creating new ones.

Especially for scientists with complex tools and processes, in many cases you need to convince people that a change is required, that new things are possible.

That’s where true innovation happens. It’s not just filling a need; it’s creating a need that people didn’t even know they had.

So, in a nutshell, effectual reasoning in entrepreneurship is about starting with your means, embracing uncertainty, being resourceful, learning by doing, and creating opportunities. It’s a mindset that says, “Let’s see what we can make happen with what we have.” And honestly, that’s a pretty exciting way to look at the business world.

Contrast with Causal Reasoning in Academic Research

Let’s flip the script and compare effectual reasoning with causal reasoning, which is the bread and butter of academic research.

Causal reasoning is like being a detective with a clear case to solve. You’ve got a specific goal, and your job is to piece together the clues (or data) in a logical, analytical way to reach that goal. It’s methodical, it’s structured, and frankly, it’s pretty neat and tidy.

In the academic world, there’s a lot of emphasis on controlling variables, predicting outcomes, and following a linear process.

A big part of the job description is to lower uncertainty and risk. And for a good reason.

Scientists build knowledge, and knowledge needs to be reliable. You may have a hunch, but you need to pursue it in an extremely systematic way. You have to prove that your observations are statistically relevant and not an artifact. You need to push your hypothesis to the last consequences, trying to prove yourself wrong. It’s a radically different approach from entrepreneurship.

While causal reasoning starts with a clear goal and works backward to find the means to achieve it, effectual reasoning flips this on its head. It’s like starting with what you have in your kitchen and then deciding what you can cook. You’re not fixated on a specific goal; instead, you’re exploring possibilities based on your current resources and connections.

There is no right or wrong here. The two approaches are valid in different contexts and will yield different outcomes.

A very common pattern in academia is to collect resources to execute on a research plan. You gather some superficial insights, you write a proposal. Once you get the funds you hire people, buy equipment and move to prove your points. Not always, however, grants are awarded. At that point different PI’s will behave in different ways.

The more causal-oriented ones will keep applying to grants. Will spend more time on other tasks that may have been neglected, like teaching, writing a book, or accepting more invited talks.

Effectual-oriented academics will gather their network to keep trying. The grant failed but maybe a colleague has just received funding for a PhD and the topics overlap. Perhaps master students and your own time can push the project forward.

In the end, both scientists will be fully occupied, and eventually they both will get funds to keep doing the same research. It’s just the path each one takes that is different.

So, while causal reasoning is about prediction and control, effectual reasoning is about flexibility and creation.

It’s a shift from a world where problems have clear-cut solutions to one where you’re writing the problem statement as you go along. In that gray area is where the magic happens!

Practical Examples of Effectual Thinking in Business Decisions

Let’s get real and see how effectual thinking plays out in the wild, unpredictable world of business. It’s one thing to talk theory; it’s another to see it in action.

Starting with Who You Are: You have been grinding in a lab for the past several years. You are a tenacious person who is not let down when experiments fail. You also have a context where you grew up, perhaps you are sociable, and have plenty of friends. Perhaps you are an introvert with fewer but deeper connections. This is who you are, and will always be with you. Your values and principles that guide your future endeavors.
Adding what you know: During your PhD and postdoc you have learned new techniques, you’ve been to conferences. You have seen what others are working on and in what way your job fits their needs. You probably see yourself in regards to others just like yourself, grinding on the same topics. However, once you finish your PhD, you will be one of a handful of world experts on those topics.
Leveraging Existing Networks: PhD’s and postdocs are in a unique position when it comes to their network. Since you have been embedded in a strong community for several years, the amount of smart people you can reach out to is impressive. If you add the ones your supervisor can unlock, you have within reach a vast network of experts and leaders. A network, in itself, is invaluable.

I will give you a personal example on how these three aspects played a role when I started Python for the lab.

Right after my PhD, I knew I wanted to take a break. I wanted to be able to go back to Argentina for a while, do some traveling. I value working hard, but finishing a PhD is a natural break in your career, and for me it was worth taking it. That is part of who I am (or at least who I was back then).

During the last couple of years I got good at controlling instruments using Python. I was not necessarily a good programmer, but I was definitely an efficient one. I could quickly build solutions to automate setups, to acquire and analyze data. Programming instruments was what I knew.

But the reality is that I only got started when a former colleague, Sanli Faez, called me up and told me I could help him. He was the first, later I got calls from former colleagues and friends that were moving forward in their careers. That was my network.

I leveraged the three aspects that I had without being conscious, without making a plan. I just kept flowing, kept developing software and writing about what I was learning.

The three aspects of who you are, what you know, and whom do you know are fundamental to get started, even if we seldom reflect on them in a structured way.

How effectual reasoners start new ventures

Everyone has three valuable assets, and each one will leverage them in different ways. However, effectual entrepreneurs also use mental frameworks that help them decide how and if they will start a new venture.

In many cases they are not even aware of these mental models, that’s just the way they see the the world when they need to make decisions.

Once you become conscious about them, you can put them to use to your own advantage.

Affordable Loss Principle: While many people focus on what there is to gain by choosing a given path or career, entrepreneurs focus on what can they loose by starting a new venture. Once you define what is the maximum you are willing to lose, you can decide if the specific project is what you want to do. For example, professors never risk transitioning to a startup because their loss is their title and reputation, and many can’t afford the social downside.
Partnership with Early Customers: Here’s where you ditch the ‘build it and they will come’ mantra. Instead, you involve your potential customers from the get-go. Before you’ve even finalized your product, you’re in talks with the people who’ll actually use it. This collaboration can lead to invaluable insights and tweaks that make your product a perfect fit for the market. Academic startups have an even larger benefit with this approach. Many countries have special grants and incentives for private-public partnerships, where both sides get to benefit from the success of the execution.
Pivot Based on Learnings: Every entrepreneurial journey is full of uncertainty. Uncertainty is different from risk in the sense that it can’t be predicted. There are not enough data points to build a distribution of outcomes. As your path progresses, effectual reasoners don’t focus on trying to lower the uncertainty but on adapting to surprises. In other words, if life gives you lemons, make lemonade.
Transforming the event space: Finally, the last treat of effectual reasoning, relates to the interaction with reality under uncertain conditions. Effectual reasoners, instead of trying to lower the uncertainty by careful experimentation, they use uncertainty to shape the future to come. Instead of updating probability estimates, they create interventions in the event space, they reshape it and transform it.

One of the challenges that scientists face when they decide to follow the entrepreneurial path is that most information available is not tailored to their specific context.

In many cases, for example, the affordable loss closely relates to loss of income. If your contract ends and you decide to pursue an idea, you’ll be living off of savings. Each one’s runway will be different, and the question of whether you can afford to spend your savings in the project is a very personal decision.

Fortunately, many funding bodies have grants and options specifically tailored to give an income to young scientists exploring the entrepreneurial path. That’s a luxurious position compared to people from other industries.

Early customers are within reach if you look at collaborators. Probably you are starting from research you performed, tools you got good at building or using. If you ask around there is a large chance you’ll find someone who is willing to get your help. If you structure it as selling a “product” and not just “your time”, you are already way ahead compared to many other entrepreneurs.

Know more

To learn more about effectual reasoning for entrepreneurs, I strongly suggest you to read this pivotal work: Sarasvathy, S.; Kumar, K.; York, J. G.; Bhagavatula, S. An Effectual Approach to International Entrepreneurship: Overlaps, Challenges, and Provocative Possibilities. Entrepreneurship Theory and Practice 2014, 38 (1), 71–93. https://doi.org/10.1111/etap.12088.

Empowering Young Scientists: Rethinking ‘Academic’ Entrepreneurship

2025-11-04T00:00:00+01:00

Professors can have a crucial role by nurturing and supporting this career path, but from the sidelines.

How many professors have quit (or even just consider it) their position to join a spin-off they co-founded?

Creating spin outs can increase the visibility and impact of the original line of research of a group. It can create jobs for highly trained people in the disciplines that the local ecosystem is training. And as a company creates value, it increases the overall wealth of the people involved. However, many great ideas are never pursued because there is no one to bring them forward. Professors and academic scientists may not be the risk-taking type of people that are needed for new ventures. I would dare to say that it’s actually the opposite: risk aversion is the overarching characteristic across academic scientists.

But professors are good at building a self-congratulatory system. They give each other awards, grants, and tenured positions. That’s a system hard to change.

Spin outs follow a very different path, without ‘peer’ involvement. Customers are not peers, investors are not peers.

Creating safety nets for young scientists

If you ask me what would be the best way to increase the chances of translating science into technology, I would say: create a safety net that allows PhD’s and postdocs to focus on venture creation for 6 months to a year.

A safety net means paying people’s salaries, ensuring they won’t be unemployed the second their contracts ends. The biggest problem I’ve observed in scientists willing to start a company is time. PhD’s are not particularly well paid, which means personal runways are often too short. Plus, let’s be honest, who would sacrifice their own savings knowing the university will take a 40% stake, and the professor will be an equal shareholder to the person who made the sacrifice?

In order to give people time, there should be a quick review mechanism that ensures short application cycles, and with quick commencement dates. What PhD has time to sit down to write a proposal while they struggle to write up their thesis? Moreover, a stringent condition has to be to work in a different environment than their lab, an incubator-type of environment, where the entrepreneurial skills can be further developed.

I have seen some programs like this in countries such as Germany and France, but none in The Netherlands. It would be interesting to see what impact those programs had.

In the meantime, let’s give those PhD’s and Postdocs who decided to move companies forward the spotlight.

Entrepreneurial Mindset for Scientists

2025-11-04T00:00:00+01:00

When I started my first company, I brought with me my scientifically oriented way of thinking. I thought that objectivity and facts were enough to push forward. It took me a while to understand that scipreneurship requires a much more holistic approach to problem solving and opportunity identification.

Our accumulated set of experiences are almost as important as the formal skills we have developed.

Embracing different ways of perceiving the world is fundamental to understanding what it means to become an entrepreneur. Being able to understand processes at a technical but also emotional level is what I call the scipreneurial mindset. It’s the framework I developed over time to process inputs from many different sources.

Definition and Importance in Business

An entrepreneurial mindset is not just a fancy term. It’s a way of thinking that can help us decide which challenges are worth tackling head-on, making decisive choices, and owning the outcomes, whether they’re good or bad.

Sometimes this mindset is about seeing the glass as half full – finding those opportunities in the market that everyone else has overlooked, innovating, and not just taking risks, but smart risks.

If there is something that scientists have in common is that they are resilient. Experimental failures are common. Cleaning up, regrouping, and retrying is what we do almost every single day.

In business, the flow is similar. Things won’t always go your way, they will take longer, and it’s the ability to bounce back, regroup, and retry that counts.

A different mindset is crucial in business because opportunity recognition is a big deal.

You know those moments when you think, “Hey, why hasn’t anyone thought of this before?” That’s what I’m talking about. Being able to spot these gaps in the market can lead you to create something truly successful and needed.

And the ability to spot those gaps greatly depends on the accumulation of personal experiences.

PhDs and Postdocs are routinely exposed to people working in the exact same topics, with the exact same tools. But if you look at it globally, there’ll be only a handful of people in the world working this way.

That makes you already quite unique.

Add the personal history of each one, where were they born, what languages they speak, and you’ll understand your uniqueness.

As a scipreneur, sometimes, you’ll notice you have two halves of a puzzle. The ability to identify situations where this is obvious just to you, and why, is what sets you in the path to success.

I started building microscopes and tools back in 2009. I always worked with a special technique, called fluorescence microscopy. In 2019 when I co-founded Dispertech, I did a deep dive into Extracellular Vesicles, a specific niche in biology. One day it just became apparent what the field was struggling with, and how fluorescence microscopy could be of help.

It was one of these ‘eureka-moments’ when technology developments coalesce and allow creative thinking.

Innovation and adaptability are your tools for survival in the fast-paced business arena. The world changes quickly, and businesses need to pivot and evolve just as fast. What worked yesterday might not work tomorrow, so staying stuck in your ways is not an option.

For innovators, the shiny object syndrome is real. Pursuing always something new because it looks so much better than what we are doing. Changing focus too quickly can become detrimental. A new mindset will allow you to parse those ideas quickly, let them rest, and resurface when they make sense.

Scientists are not used to explicitly discussing risk management. Anticipate failure and have a contingency plan.

Entrepreneurs need to embrace risk management and uncertainty. But be aware, it’s not just avoiding risks – that’s pretty much impossible in business. Instead, it’s about understanding these risks, weighing them, and using them to your advantage. Think of it like surfing; you need to ride the waves, not run from them.

Lastly, and perhaps infinitely important, a proactive approach is what sets entrepreneurs apart.

It’s about not waiting for someone to hand you opportunities on a silver platter. You’ve got to be the one knocking on doors, making the calls, and sometimes, creating opportunities out of thin air. You need to learn to ask for help, move forward even if it’s not clear where, or how the path looks like.

The mind of an entrepreneur is always in tension between what the future could look like and what the present allows. We’ll take another look at this when we discuss about effectual reasoning.

Contrast with the Typical Mindset in Academia

Understanding the mindset of an entrepreneur also requires acknowledging and overcoming the patterns developed in an academic environment.

Don’t get me wrong, academia is fantastic for deep dives into specialized research. It’s all about digging deep into a subject, unraveling its mysteries, and adding to the pool of human knowledge. But here’s the thing: this approach has its own set of rules and norms.

In academia, there’s often a hesitancy to leap into the unknown. It’s more about sticking to proven paths and methods, rather than venturing into uncharted territories. This risk aversion makes perfect sense in the academic world where people is constantly judged by peers. There is limited tolerance for out of the box thinking and progress is done incrementally.

Entrepreneurs must take a plunge, even before knowing if there’s water in the swimming pool.

Academic projects can stretch over years, even decades. There’s a luxury of time to explore, refine, and ponder over details. Both PhDs and postdocs experience just a glimpse of these processes, but very quickly embrace the way of thinking. There’s a given context, long-term objectives, and a very narrow focus.

In business, however, time is a luxury you often don’t have. It’s about making quick decisions, sometimes on the fly.

Entrepreneurs can’t afford to focus on a single topic, they need to perform a balancing act between business, product, science, and strategy.

The aspect of working style, which is tightly related to how people behave, is also different. Academics often enjoy a degree of solitude in their research, focusing and valuing individual contributions. Creating a startup is a team sport. You’re constantly collaborating, networking, bouncing ideas off others, and sometimes, compromising to get the best results. First customers are closer to partners. The initial team in a startup is a living organism where everyone contributes across domains, aligned with the founders’ vision.

Let’s not forget about a crucial difference: access to resources.

In academia, even if you come from constrained environments, you have access to tools, people, space, and funds. Startups work with what they’ve got, stretching every dollar, and getting creative with their resources.

Transitioning from academia to entrepreneurship, requires that you brace for a change in pace, shift how you approach problems, and develop a new way of working with others.

It’s not just about what you know (our you think you know), but how quickly you can adapt and apply that knowledge in a fast-moving, ever-changing business landscape.

It’s an exiting and very fulfilling ride, with lots of ups and downs along the way. Hopefully the ups are much more powerful and give you the energy required for moving forward.

Exciting Vesicles – the field where biology meets technology

2025-11-04T00:00:00+01:00

All bodily fluids (urine, blood, saliva, milk, cerebrospinal fluid) contain extracellular vesicles at different concentrations and with different compositions.

In the past couple of decades, a lot of effort has been placed in studying how they are generated and how they transport valuable information across cells. It is believed that vesicles can be used to diagnose disease, since their presence is correlated with a plethora of ailments. There is building evidence showing that EVs can be used as biomarkers.

EV’s derived from cancer cells, for example, will expose specific proteins associated with them, becoming ideal targets of research for liquid biopsies. Some viruses and parasites exploit vesicles to bypass the immune system, or to favor cellular uptake. On the other hand, the same mechanisms that naturally favor communication, can be exploited deliver drugs specifically to certain tissues or cell types.

It is important not to forget that many basic biology questions regarding biogenesis, uptake, and the overall life-cycle of these particles are still open.

My Interest in the field

Coming from a purely physics background, my interest in extracellular vesicles is somewhat tangential to the tools used for their characterization. During the past 4+ years, I have been developing technologies to measure their concentration, size, refractive index, and protein content.

When Dispertech started, we focused on extracellular vesicles because we believed it was a booming field, with plenty of opportunities for new analytical tools (although we may have misjudged what the field needed.)

I joined online journal clubs, MOOCs, followed an endless number of researchers on Twitter (I compiled a list of interesting profiles) and LinkedIn, attended conferences, and embedded myself in the problems I perceived the field had.

The technological landscape

Biology in general, and the extracellular vesicles field in particular, can greatly benefit from technological advancements that allow researchers to gain new insights. There is a long standing tradition of translating tools developed in physics labs to increase the number of details that can be seen in biological samples: from super-resolution microscopy, to cryo-EM, from X-Ray diffraction to mass spectrometry.

Sometimes, however, the key to understanding biological processes lays not on how much you can see but on all the steps taken for preparing the sample before the measurement.

Super-resolution is not possible without specific fluorescence markers, antibodies, and buffers. Cryo-EM requires vitrification of very clean samples. X-Ray diffraction needs a degree of crystallization to be useful, etc.

Extracellular vesicles samples are not exempt of heavy workloads before each measurement. And this is rooted in the need of increasing the yield of the interesting particles.

Sample enrichment

Today, a broad pool of techniques has converged on the field of extracellular vesicles. On the one hand, sample preparation is mostly focused on enriching. This means increasing the concentration of interesting particles, and removing as much as possible of the background.

Techniques such as size exclusion chromatography (SEC), or tangential-flow-filtration separate particles based on their size. Separating EVs based on density through density gradient centrifugation is also possible, and there’s quite some enthusiasm on using free flow electrophoresis, which sorts particles based on mobility.

However, none of those methods is perfect. In biological samples there are always overlapping properties, therefore separation based on only 1 physical dimension seems almost impossible. That is why preparing samples requires a combination of steps, including ultra-centrifugation, filtration, etc. The hope is that the few remaining particles in the vial are going to be those of interest.

Imperfect techniques, however, do not mean they can’t be used. The challenge is then passed downstream, to the sample characterization step.

Sample characterization

The first question that most researchers try to answer when looking at EV’s is how big they are. This is rooted in the understanding that size can be associated with the biogenesis of the particles, and because it is the first check that one can perform to validate some of the sample enrichment steps.

Measuring size

The majority of the extracellular vesicles in the body are very small, with diameters well below

. This range is impossible to measure with conventional optical microscope, since they will all look like bright blobs. Measuring size, therefore, requires a more sophisticated approach.

NTA

Nanoparticle tracking analysis (NTA) is a smart way of using a conventional microscope to measure the size of colloids, particles that are free to diffuse in a liquid. By following the movement of individual particles, it is possible to determine their diffusion coefficient, and then relating that to each particle’s size.

The limitations of nanoparticle tracking analysis include that small particles are very hard to characterize. Bellow

particles diffuse too quickly and are very dim. This means that on the microscope’s camera they are very hard to localize, yielding inaccurate size measurements.

Sample confinement through the use of a hollow optical fiber, or faster cameras such as those used in iNTA can help overcome some of the limitations.

Coulter Counter

A Coulter counter works by applying a voltage across two liquid reservoirs separated by a membrane with an aperture. Once a stedy current is established, every time an object flows through the aperture it will create an electronic blockage. At small scales, the aperture becomes a nano-pore, and the method was branded as Tunable Resistive Pulse Sensing (TRPS).

The size of the particles that pass the nano-aperture can be derived by how much current they block.

Although the principle of operation is smart, the biggest limitation is that the implementation of the technique does not perform as accurately nor reliably as expected.

Biological samples are mostly complex fluids which can clog the nanopores, the current across the constrictions is heavily dependent on the medium used, the pore shape can change over time, altering the results of the measurements.

Flow Cytometry

In Flow Cytometry, the liquid containing the sample is passed through a special nozzle that creates a very narrow flow. A carefully aligned laser is then used to detect particles one by one as they flow through the focus spot.

Flow Cytometry was developed to work with cells, which are gigantic in comparison to an extracellular vesicle.

To detect small particles, the flow has to be much slower, the nozzles have to be re-engineered, and sensors have to become the best-in-class.

The idea of a nano flow cytometer is gaining a foothold in the community, but the control of the flow at the rates required is challenging, as well as the interpretation of the results.

The laser is scattered every time a particle is in the focus, but relating the signal strength to the size of a particle requires very careful calibration which is not yet possible in the current instruments.

SP-IRIS

SP-IRIS is an optical method that relies on a two step process. First, a micro-patterned substrate is prepared with some antibodies. This is similar to an ELISA test, but with control on the number of attachment sites and their location on a macroscopic area. The second step includes adding a fluorescent label, which adds an extra dimension to the detection events.

To count particles attached to a given site, the device uses an interferometric approach, not unlike iSCAT (used in the iNTA approach). The interferometric detection allows to calculate the size of the particles, and the fluorescence signal (which can be multiplexed) yields the extra dimensions.

The biggest challenge with this approach is overcoming the tyranny of the Langmuir binding isotherm. To guarantee that small populations of particles are detected, the user needs to wait many hours (up to 24) for all the particles to have enough chances of interacting with the substrate.

Bulk methods and Electron Microscopes

Other approaches to measuring size include transforming sample preparation tools into analytical instruments. Such is the case for analytical ultracentrifugation, that looks at the sedimentation properties of particles as the sample container spins, or A4F-DLS-MALS, which couples filtration with bulk size measurements (dynamic light scattering, multiangle light scattering).

Of course, tools like electron microscopes or cryo-EM are available, but they belong to a completely different league. They are very expensive, hard to operate, and require very delicate sample preparations that in most cases affect the results. The images they provide are very powerful, since they clearly show the structure of the EV’s, but they can’t be used to provide statistically significant results, nor can be used routinely to process hundreds of samples.

Beyond Size

Although size is the first physical parameter that was sensible to measure, biology relies on many other properties.

Surface proteins can be used to determine the origin of EV’s and therefore can hold great relevance when using them as biomarkers. The cargo (if any) of EV’s can be crucial to identify genetic disorders. Today, for example, there is more attention given to the corona of the particles: the surrounding proteins that not necessarily form part of the vesicles, but that may play a role in their behavior.

Understanding, first, and then using exosomes either for diagnostics or therapeutics, is a fertile ground for experimentation. It is an ideal domain for high-risk/high-reward biophysical tools that help gain completely new insights into the inner workings of intracellular communication.

Super Resolution Microscopy

A discussion on extracellular vesicles characterization wouldn’t be complete without including super-resolution microscopy techniques. Super resolution can be achieved through single-molecule localization, or through enhanced optical systems such as in STED. The latter is not yet very popular in the EV community, and I will not discuss it.

Single-molecule methods include strategies such as STORM or PALM, which became quite popular in the vesicle community in recent years. However, these methods are limited to a localization accuracy of around 20~50nm, which is not good enough to say much about the shape or size of particles below

in diameter.

Single-molecule techniques, on the other hand, allow the ultimate detection threshold: just one label is enough to generate a signal.

In this line of thought, other concerns appear, such as the labelling specificity and coverage. Quantifying the number of proteins on an EV based on the number of labels attached is risky and the entire process needs to be properly understood to derive accurate conclusions.

It is important, therefore, to understand what information researchers are after, and whether super-resolution or a device with a very low limit of detection is what will give them the results they need.

Where I think the field is going

I think in the coming couple of years we’ll see the emergence of diagnostic tests based on vesicles. In a first stage, these tests will be designed around existing tools and protocols. For example, an elisa test after some sample enrichment.

The biggest advantage of this path is that such tools are already validated and approved by regulatory bodies, therefore adoption can be rather quick.

To move forward, the main question is whether the techniques available today for sample enrichment and characterization, for example, are enough to find a correlation between particles and disease.

In order to reach the clinically-relevant stage, many exploratory tests need to be performed, without losing sight from the basic-biology driving questions. This is the stage where up and coming companies can help researchers: by understanding their underlying needs, they can design tools and services that match expectations. In turn, the better positioned solutions have much more to gain in the following stages.

As for the next steps, I am quite confident that the real breakthrough will happen thanks to much more complex analyses. Judging the samples in a binary fashion, for example by the presence/absence of a protein, will not be enough.

Multi-dimensional analysis, including physico-chemical attributes such as size, refractive index, charge, mass, and the protein or cargo content will unlock many more possibilities. Some that may even be hard to fathom today. Coupling sample preparation tools with analytical tools can speed up the process and give unique insights.

Machine learning algorithms able to analyze complex signals will naturally emerge as a necessary step.

Missing pieces and challenges

Extracellular vesicles are a complex object to study. Part of the complexity is given by the shear amount pf particles in bodily fluids. If one would be looking at EV’s from a tumor, for example, the chances of finding them amongst the myriad other EV’s and lipoproteins would be close to zero.

That leads to the need to use single-particle type of measurements, where even the smallest numbers can be detected.

The biggest problem with small-number detections is the number of false-positives. There is still a lot of work to be done in labelling and capturing EV’s to limit the amount of mistakes. Label-free methods such as SERS are very promising, but very hard to implement under the high-throughput conditions necessary to create statistically sound results.

Combining techniques in simple to use devices will be crucial for extending the understanding and use of vesicles. Things such as coupling electrophoretic mobility sorting with fluorescence detection on the same particles, will be fundamental to unravel multi-dimensional analysis. Performing measurements on different devices has limited advantages, since the particles observed are not really the same.

I think EV’s are going to be a catalyzer for many multi-disciplinary projects, bringing together people with a broad set of skills all the way from biology to engineering.

For companies operating in the field, there are many startups and scale ups that can risk experimenting with new approaches. However, most of them master one technique. Incentives to create strong partnerships could become essential tools to foster the creation of out-of-the-box solutions. It could also be, however, that only the biggest players in the field can pull through and incorporate a mix of techniques into a single measurement tool.

Training the next generation

I think the most important aspect is to acknowledge that science is multi-disciplinary. A biologist that can’t program, or a physicist that does not know cells exist, should not be the norm. Funding should allow such arrangements, especially because there’s a lot to gain at the intersection of fields but also quite some risks from a career perspective. Funding incentives should align with individual incentives to favor those collaborations and the healthy migration of people across domains.

In this line of thought, training people on the use of modern technologies is fundamental. And this idea is not limited to just machine learning tools. The more someone understands about the guiding principles behind a technique, the more chances they have to extend it to solve their own needs. That’s one of the things I like about open hardware, it incentivizes people to understand the underlying mechanisms that make a tool.

Black boxes are dangerous, not only because they can be misleading, but because they place too much power in external hands.

Next generation scientists should have a broad understanding on the applications of new algorithms for their own benefit. It is not simply speeding up the write up of a paper through a language model. It’s the way results are analyzed and how experiments are designed, without losing the perspective of the underlying biology, chemistry, or physics involved.

Exciting Vesicles

All in all, the field of extracellular vesicles is extremely exciting from many different perspectives. It is a field that emerged in a place where few devices were available for their study. The complexity of the field itself lead to very creative solutions to complex problems.

I think Extracellular Vesicles are an extremely promising field to work on, both from the technology perspective, as well as the basic biological questions being answered.

I am convinced that in the next 5 to 10 years we’ll see the first exosome-based vaccine for humans, allowing to move over many of the challenges derived from the use of lipid nanoparticles for drug delivery.

How I feel about quitting my own startup

2025-11-04T00:00:00+01:00

Creating a startup is an extremely personal endeavor. It starts with you, your computer, and perhaps one more person with whom to discuss. Companies can grow slowly, but during the first few years there is a large sense of ownership, you are the only one who can make things happen.

Moving away from the startup you created and sustained over years is a tough decision.

In my case, I’m departing a young company with lots of potential. Dispertech is, in many regards, still in its infancy: there were barely any customers. The first product, the NanoCET did not find product-market fit and we had to discontinue it. We developed a second product, the NanoQNT, which shows lots of potential, even at its minimum viable product level.

The closest feeling I have to describing how it feels leaving my own startup is that of a breakup.

You know it is for the better, but it still does not feel completely alright.

I really enjoyed working with the team that gathered around the fussball table after lunch every day. I liked overcoming challenges together, thinking about the future, solving problems in creative ways. One of the highlights of my journey has been seeing the team grow. They got better, and started doing things smarter every day.

The decision to move forward professionally was taken swiftly but not lightly.

Even if I had a lot of fun, and the company helped me grow in many different dimensions, over time I felt alienated. At a management level we didn’t build separation of concerns, nor clear communication strategies. I tried my best to setup a flow that would work for everyone, but I failed. Decisions involving team members were made without dialogue, and without concerns for consequences.

As the team grew, I couldn’t sustain feeling responsible for things that were completely out of my hands.

The entire situation became very taxing. It went from an empowering feeling, with a sense of achievement and purpose, to a balancing act. It prevented me from focusing on the things that mattered. My role within the team became unclear, and although I built a high degree of internal trust, authority is a strange phenomenon. I couldn’t focus on strengthening the skills I wanted to develop and, perhaps more importantly, I felt I was failing my team.

During almost 5 years, I placed all my energy into building tools and a company I thought were extraordinary (and I still believe they are). I spent a lot of energy into building a business and a team. I found immense pleasure at thinking about strategy, identifying opportunities, and brainstorming solutions. Now it is in the team’s hands to keep pushing forward, to bring the company to the next level.

Going away at this moment may like an irrational choice. I had to place myself and my peace of mind ahead of anything else.

I take with me a broad range of learnings, from product to business development. I think I got good at finding opportunities and creatively thinking about solutions. I developed both hard and soft skills. I’m still fascinated by technology, and the doors it opens.

I think I’m ready for new adventures. It’ll just take time to readjust and to collect myself.

How to incentivize scipreneurship

2025-11-04T00:00:00+01:00

Many PhD’s and Postdocs, sooner or later, will face some challenging career choices. The traditional “academia or industry” dichotomy has a third contestant: becoming an entrepreneur. How to incentivize scipreneurship is a common discussion.

While many people focus on the skills that need to be transmitted, I argue that having a context of practice is immensely more powerful.

I have experienced it myself: I was fortunate enough to have access to a professional coach during the last few months of my PhD. A coach I would have never been able to afford by myself. Through weekly discussions with him, it became clear that I wanted to pursue I highly challenging, intrinsically motivated type of job. Knowing what I wanted to pursue is very different from experiencing it.

I was lucky enough that my personal context allowed me to spend almost 2 years experimenting with ideas. I could count with the support of my partner, no family responsibilities, and some freelancing that would give me a basic income.

If I would have had a better support network, I wouldn’t have spent so long figuring things out.

This is article is my personal take on a very nuanced topic. I am of course biased to my own experiences, but I also spent a lot of time looking around and identifying the patterns that repeat.

Skills and practice

The first important point I want to make is that skills are developed, they are not taught. There is no way I will become better at sports by attending classes, I need to put the hours and effort at practicing. And the same is true for technical and soft skills.

Doesn’t matter how much I read about Python, I got good at it by spending hours solving problems.

More importantly, I got good at it by imitating others and implementing their lessons on my own practice.

The only way of becoming good managers is by having role models, try what we think is good, judge the outcome, and improve. Soft skills can be more challenging to iterate on because metrics are not unequivocal and people’s lives are involved in the process. Finding mentors, role models, and coaches is extremely important to speed up the progress. Of course, assuming we have the time it takes to implement practices into our daily lives.

During a PhD we have time to practice and get better in a range of skills.

Sadly we don’t always count with a support system that nurtures the personal and professional development of candidates.

It’s hard to become better by ourselves. Workshops, trainings, and coaching are important to speed up the process. A good workshop can shake some preconceptions, can give you a solid base to get started and can iron out some doubts. However, a workshop is only a tool, a moment in our lives. The rest is personal effort and continuous practice.

For example, we can attend a workshop on how to make compelling presentations. We can have fun, meet interesting people for few hours in the span of a couple of weeks. But without practice, we’ll never get better. And practicing does not mean just giving talks, it means consciously trying different approaches and understanding what works best for us, our audience, and our message.

In academia, this becomes a challenge. On the one hand, the system is geared towards helping bachelor and masters, with very specific needs and wants. Employees, such as PhD’s, postdocs, or even professors, are mostly left to themselves to learn new skills. It may be one of the reasons why PhD’s become so good at specialized techniques and methods but not so much at soft skills: Hours of practice plus guided development.

Entrepreneurial Practice for Scientists

And this brings us to the topic of scientists who want to become entrepreneurs.

I would like to make a distinction here between a scientist such as a PhD or Postdoc who is willing to pursue the opportunity of starting their own business, as opposed to a supervisor who wants to enable the creation of a new venture.

Becoming entrepreneurial is more of an attitude than a specific set of skills.

Entrepreneurs are risk takers, effectual reasoners, who act in highly uncertain conditions. They want to exert some change in the status quo by pursuing new opportunities.

Entrepreneurial practice has many aspects that depart from the daily practices of a scientist. From the identification of opportunities, to the definition of value. From understanding the basics of finance, to building a business model. From marketing to team building.

Time is the largest obstacle

Practice requires time. And one of the largest challenges in academia is the constant pursue of a different deadline. A PhD starts thinking about their career when it’s time to write down the thesis (or even worse, when the contract is finished!). A postdoc starts a position by marking in a calendar the deadlines for follow up applications.

Lacking time and requiring people to constantly think about their livelihood is an immense roadblock (not just for entrepreneurship.)

Giving people time is, I strongly believe, the single action that will create the largest impact to foster a scipreneurial career path.

Giving time is perhaps one of the most actionable ones as well. Many countries offer different types of grants for valorizing research. But virtually none of them target young scientists. Either they are barred from applying or the entire review process takes so long that it becomes unfeasible.

Universities are increasingly having investment branches, but the requirements to get funding are all over the place: from having a prototype to showing customer interests.

The resources are there, I’m just proposing to structuring them in a different way, and to make them accessible to the people who will execute the plans.

Investing in time is not a new idea. It is exactly is what Y-Combinator does (500kU$ gives 4 founders at least a year of runway), or what the Entrepreneur First program offers (24 weeks individual fellowships).

However, except for few examples, the approach is not actively supported by funding organisms nor universities that publicly claim they want to support healthier tech-transfer processes.

Career sacrifice is the second obstacle

Academia is a very peculiar industry because it does not hire people with different backgrounds.

You can only move out of academia, creating a large emotional barrier to those who want to explore other paths.

I’ve seen it first hand many times: people apply to two jobs, one in academia one outside. If the academic job is awarded there is little doubt, regardless of the conditions. For many, entrepreneurship falls in the same category: a plan B in lieu of an academic position.

Sadly, rearranging academic incentives and careers is not a matter of shifting resources from one place to another and it goes way beyond what a short article can cover.

Starting my own company was a way of precluding myself from an academic career. I was in a special place and the decision came at virtually zero cost. I was not interested in an academic career after my PhD and I knew I wanted to pursue an entrepreneurial journey, if possible. I was privileged enough to have access to a coach to helped me explore my ambitions and we laid out some alternative paths together. For most, the story will be extremely different.

I have seen many attempts at creating a spinout that simply collapses once the person in charge is offered yet another academic position.

How to use time

Having access to time does not guarantee its smart use. Especially for young scientists, the role of the supervisor has a large impact on the choices that each one makes. From papers that were not finished, to experiments that are not complete.

The first wise step is to set an independent path.

Professors may think they are good at everything. Sadly, I have seen them derailing companies just because they were not humble enough to acknowledge their limitations. Coupled with the influence they have on mentees who can’t stand up against them.

The second step is to focus on knowledge gaps.

Other entrepreneurs in the network can provide an immense value to those starting up. However, most incubators fail at securing long term commitment. Instead of focusing on setting up workshop after workshop on topics that can easily be found online, they could foster mindful discussions and mentoring sessions.

The third step is to embrace the entrepreneurial practice.

Entrepreneurs need to think multi-dimensionally. It’s not just developing a product, it is understanding whether customers want it. It is not just about what they can do, but building a team around an idea. It’s not just acting on the resources they have, but gathering those resources for the next step. And, most importantly, is realizing that for most of the journey, you will be chasing a moving target. Entrepreneurship requires constant adaptation to changing realities.

Those skills can hardly be taught. The only way to get better is by continuous practice. With a healthy support network, we’ll learn from examples, and will be able to make decisions faster, even in stressful and fast paced environments. It is a role very well suited for incubators, especially considering that entrepreneurship can be an extremely lonely path. Losing focus is rather common, and finding daily motivation can become a hurdle in the early days.

Modernizing incubation programs

Incubation programs sound like a good idea, but they work only if timelines are perfectly aligned.

As a PhD, I would have never had time to join one. Either I was writing my thesis, or my mind frame was in a completely different place. A rolling system would have made much more sense for me at the time, and especially if I would have had access to a short contract extension to explore entrepreneurship without other concerns.

Once you abandon the idea of “teaching entrepreneurship” and focus on mindful discussions, coupled with continuous practice, the process can become much more efficient. We could allow people to join, pause, and leave at different moments. Sadly, incentives for innovation are rather uncommon, resulting in programs that are copy/pasted from each other.

My first university incubation program was a copy/paste of the approach from Y-Combinator. However, there was no investment from the incubator to allow us time to focus on the project. More importantly, it was given by people that had never practiced what they were teaching. They had no empathy nor creativity to address the challenges I was bringing, as we were the only academic-spinout project of my cohort.

In the end, this resulted in shallow workshops, advice hard to relate to my case, and a large cultural mismatch: the Silicon Valley approach is different from what happens in Europe.

Not all ideas are worth pursuing

I believe that there is too little discussion about what happens when you realize your idea is not worth pursuing.

There is a lot of pressure to keep moving forward, keep grinding, pivoting, spending money which is not yours, raising more money to keep going.

At some point, it may become clear that it’s better to call a halt to the process. There are many reasons why it may be sensible to stop the creation of a company. From changes in the personal context, to misalignment between business plans and expectations. And, more importantly, perhaps the idea was not as good as originally believed.

Exploring ideas is a highly uncertain process. The context of academic spinouts adds more constrains, since pivoting may not be an option for founders. We can make the process more efficient, but we can’t increase the number of good ideas.

That is why I think that offering time is so important: there will be a deadline.

At a given point in time, there will be a realistic review, and people will have to judge if it makes sense to keep the direction, perhaps the team has to be restructured, or perhaps the idea has to be dropped altogether.

Judging ideas prima facie is very hard. We are biased, we build on lots of preconceptions around our activities. The only way to identify valuable ideas is to have to chance to pursue them and see where they go. There’s nothing like a quick reality check with potential customers or partners. And there is no way of pursuing ideas without time.

Acknowledging risks and costs

It is hard to avoid survivorship bias inherent to looking at examples of startups. Many people don’t perform a post-mortem (of companies or products) to avoid repeating the same mistakes. Even worse, they just place the burden on external factors alone.

Scipreneurs assume risks that are not explicitly acknowledged. It is crucial, therefore, to distinguish professors and young scientists.

The first risk a person assumes is financial. Many entrepreneurs start with non remunerated jobs. They spend countless hours looking for investment, prototyping, following workshops, without getting paid.

The risk in this case is to never recover their investment.

And even if an entrepreneur secures their own salary, there is the opportunity cost.

It’s very hard to put a number to it. In some cases the opportunity cost will be more emotional than financial. For example, abandoning an academic career to pursue a commercial idea may not have a financial cost, but it certainly is a higher-degree concern on people. Moreover, becoming an entrepreneur does not necessarily give access to competitive salaries. Long term, the question of whether a founder is more employable than a non-founder is open to debate.

In any case, whoever has decided to get a postdoc position is also assuming a large opportunity cost compared to finding a stable job. The reasoning regarding entrepreneurship should be equivalent, but scientists are not used to thinking in those terms.

Finally, the emotional costs of starting a business are seldom acknowledged.

Startups are a high-stress/high-reward environment. There are an infinite number of things to do, arising from various contexts. Entrepreneurs juggle between tasks of different importance and impact. They grow into that position without a plan. It’s easy to let work interfere with your personal life. Entrepreneurship as a career choice may have a direct impact on your relationships, and this is not a matter to overlook when making the decision.

Focus on people

A quick search online will immediately show that early investors invest in people, not in companies.

And I think that is the key to understanding how to incentivize the scipreneurial path among young scientists.

Academia is a very dehumanized industry. When you apply to a fellowship, or a grant, you don’t even get to see the face of the person rejecting you. They are “anonymous reviewers”. There is no single person who takes the responsibility of hiring the new tenure tracker, it’s always a “committee”. No manager can say: “You are doing a good job, I’ll offer you a permanent position.”

Focusing on people and their needs will be a key differentiator.

Many projects focus too much on companies, and what companies need. How to overcome the valley of death. Although these discussions are valuable at a macro scale, projects are are not born without people pushing them. If universities and funding organisms decide to incentivize scipreneurial activities, they can do so by focusing on the people who want to push ideas forward.

Moreover, it is time to give visibility to younger scientist. Not everything needs to revolve around professors and their needs, especially not when pursuing an entrepreneurial path.

Skills for scipreneurs

After joining two different incubators, interacting with tens of scientific founders, mentoring entrepreneurs, and helping professors setup companies, it became clear to me that skills are not a limiting factor for the creation of companies.

What helps the most are relatable role models. Inviting a professor to explain how they started a company will not help a PhD or postdoc.

Inviting the founder of a multi-billion company can be inspiring, but it can alienate those who are thinking smaller, as most starting entrepreneurs do.

The challenges scipreneurs face at the beginning normally revolve around understanding customers and their market. Planning and executing the development of an MVP. And, perhaps more importantly, breaking free of the academic approach to problem solving. These are challenges that are not solved by teaching skills, but they can be tackled with practice and guidance.

I have seen companies burning through 20M€ before understanding what they would do, and definitely before having a product. They didn’t lack skills: they hired a professional CEO and CTO, they build human resources to support the role of scientists and engineers.

What they lacked was people with time to think.

Is it worth it?

At a personal level, the scipreneurial path can be extremely rewarding. You get to work on a fast paced context, with lots of problems to solve. You need to step out of your comfort zone, stretch your abilities.

It is fascinating what happens when you manage to align people to work on the same project, with the same goals in mind.

It’s also fascinating to live a bit in the future, and a lot in the present.

At societal level it is a harder question to answer. Creating small companies can seem sexy, but it must be done in a way were benefits are fueled back appropriately. Innovation at small companies seems to be more efficient, but the resource constraints put a limit to how much it can be achieved.

As a whole, I believe in the importance of transferring knowledge and solutions out of academic labs and into society.

Whether the risks should be taken by society, or by venture capitalists, is a different discussion.

How can students, PhDs and postdocs present themselves efficiently in their CVs while looking for a job

2025-11-04T00:00:00+01:00

Writing a CV is challenging, and there are many different approaches. I will focus on the one that works for me, as the one who reviews CV’s.

In our case, the CV is half the information we receive, the other half is the cover letter. How to write a good cover letter for a job is a topic for another discussion.

What Is a Curriculum Vitae

A curriculum vitae is a summary of your life, not just of your academic achievements.

The real challenge is how to present yourself in a succinct way that allows the person screening it to determine whether you have the minimum required abilities and predispositions for the job. Therefore, The first step, always, is to carefully read the job description to which you are applying. LinkedIn makes it trivial to apply for a job, which means your application will be listed among many others. If you want to stand out: submit a custom CV where you at least hint that you read the job description.

The shape the CV will take depends on different aspects, including the position itself and where the candidate is in their careers. The rest of this article focuses on students, Ph.D.’s and postdocs of scientific careers who are giving their first steps outside of the academic world.

The CV of a student who just graduated

If you are a student just finishing a bachelor or a masters, the most important thing that must stand out is what have you studied.

Use the opportunity to quickly show what your skills are. Most study programs include graduation or thesis projects. It is very easy to get a sense of what your interests and skills are by stating your achievements in this section. Also, it’s a good place to set you apart from others. If a physicist writes that they are analytical, does not add anything. If they add that they were in charge of the weekly student newsletter, says a whole lot more.

New study paths pop up almost every year and it is hard to know what a master in sustainable energy, for example, prepares you to do. It is important to keep this in mind for multi-cultural applications. I, for example, an Argentinian living in the Netherlands, must review the CV of a German student. I won’t have a clue about all the careers available and what you are meant to learn in each one. Listing your skills is crucial.

Let someone else check your CV.

If you are giving your first working steps, ask someone to read your CV for you and give you honest feedback. A CV should always be read with a specific job in mind. If you are applying to become an astronaut, the skills you need to show are very different from those you need if you want to become an account manager in a high-tech company.

Ph.D.’s and Postdocs who are applying for jobs for the first time

If you are giving your first steps out of academia, there are several things you must un-learn.

For many jobs, the qualifications that set you apart are not the publications you have, nor how many. It is a matter of how do you perceive yourself from the perspective of the job to which you are applying. Papers are not the end-goal of your achievements, but simply stepping stones. Therefore, stop giving them more importance than what they actually have.

Since you already have experience, the question that must be addressed in the CV is what are you bringing to the table. You are not junior anymore, you should have learned plenty of things by now. It is important to transmit that feeling. Teaching is a good indicator of public speaking skills, supervising students, or applying for grants.

Stop listing the things everyone else has as major achievements.

Adding a collections of talks you’ve given does not make you a good speaker nor a good communicator. We’ve all given talks, and we’ve all listened to very bad ones. We have all written a thesis, but this does not make us good writers. Giving 10 invited talks does not even mean we enjoy presenting to an audience.

When you list achievements, do it not from a purely academic perspective, but also include the human side of things. For example, show whether you were part of a larger group, a collaboration, or if you’ve lead a project. Organising a conference, working on budgeting, they can all be excellent indicators for the person reviewing your CV.

Having a Ph.D. says very little about you, contrary to what most people think.

While I was doing my Ph.D. I was told that one of the challenges scientists face is that many HR departments do not know what skills are developed during a Ph.D., and that’s the first barrier to overcome. The reality is that Ph.D. programs are extremely individual, and there is absolutely no baseline for what it means having a Ph.D.

We are forced to present results, but that does not make us good speakers. We are force to write papers (and a thesis) but it does not mean we are good writers. We lead our own project, but it does not mean we are independent. Therefore, don’t assume that your shiny degree is all you need, especially not if on the other side of the table there’s someone who knows that having a Ph.D. says very little.

The specific details of your research may be a bit over the top for the person reading your application.

The CV must be short and sweet. You can explain the impact of your research without getting into the details. Do not use acronyms unless they are universal or you are sure about the context where you are using them. GCR may be the “gluco-corticoid receptor” or a “galactic cosmic ray”. If you are sending a CV for a position slightly outside of your comfort zone/field never use an acronym.

The CV of someone who already has work experience

For people who already worked in companies, it is sometimes hard to understand what they’ve done, what they’ve learned.

It is important to give it a thorough thought. Describing your current and past positions is the best chance to use keywords that may trigger a positive reaction. Being in charge of a team, or building a product from scratch, interacting with customers, regulators, or industrial partners. All this things can be extremely valuable. Did you receive trainings, or certifications? Have you learned about time or people management? All this things must be clearly stated.

Setting yourself apart

Job applications are, in many cases, a matter of finding a balance between standard accomplishments and out of the box ones.

Setting yourself apart, showing your true self is the best way of finding a job you care about. If you are applying for a technical position in a small company, you can show out of the ordinary things. Why are you different from your colleagues? What particular accomplishment sets you apart from the rest? The papers you publish are an academic metric that unless they are specifically relevant for the position, don’t add too much. They normally tell more about the context in which you worked than about yourself, and we care about you, not about your boss.

Regardless of your work experience, never under estimate your hobbies and side projects. During one interview, just by chance, the candidate mentioned they liked playing with Arduino’s. We are not specifically looking for someone with a background in electronics but knowing the person is able to set a goal, learn, and build something is incredibly important for us. Even if it does not count as professional experience, we do use Arduino in our prototypes, which makes it a relevant secondary skill.

The layout of the CV and the things that absolutely must be in there

Knowing what to write is the first step, knowing how to write is a different discussion.

There are many templates available for CV’s. Pick one that you think represents your personality. But don’t over stress about it. I tend to prefer single-column CV’s in which you can easily see the different blocks such as education, work experience, hobbies, languages, etc. It make parsing the information much easier. Use bold and italics wisely to highlight the things that really match the job description.

I have been asked about a one or two page CV’s. I think if you are just finishing your studies there’s a chance there won’t be much information about yourself to cover more than one page. If you already have experience, the challenge is to condense all you have done. In general, use the front page to capture the interest of the reader and use the second/back page for the extras. If your academic qualifications or work experience are good enough, I’ll flip the page, if they are not, probably there’s nothing on the second page that can make me change my mind.

To photo or not to photo is open for discussion, and I hope we normalise getting CV’s without one.

When I get a CV without a photo, I know I can base my decision without even having access to the looks of the person. However, I also understand that in some contexts this may work the other way around, generating some form of negative bias on the recipient of the application. I don’t have elements to give an opinion on this matter, I know I don’t mind getting CV’s without a photo and I think it would be a better idea to avoid them altogether, same as with marital status, for example.

In the XXI century, it is extremely important to make it easy to find you.

If you have a blog, a website, a profile on some social media that you think is relevant for the application, make it explicit. Although you can include links in a PDF, I strongly advice you to write down the full URL. In this way you guarantee that someone who printed your CV or is looking at your document without a mouse realises there’s something worth clicking.

In many cases, recruiters take the time to google you to create a more global picture of who you are. You can be proactive and lead the exploration towards the online spaces in which you are confident. On the other hand, don’t add links to shameful places. I’ve reviewed CV’s with a Github link to an empty profile. It was completely unnecessary to list it if you have no open source code in it.

Ask someone to read it

Never forget the value of early feedback.

Ask someone you trust and that may have experience reviewing CV’s (especially if they are in the same field where you are applying). There are some key elements in CV’s that are very easy to miss when it is your own, but very easy to spot on others’.

If you can, check how the CVs of others look like. Read it and think if they are transmitting what you think of them. Once you realise the patterns that people use to convey messages, or the pitfalls that must be avoided, you can start applying it to your own curriculum.

Once you are done with your CV, the next step is learning how to write a good cover letter for a job.

How to write a good cover letter for a job

2025-11-04T00:00:00+01:00

Cover letters are a complement to the curriculum vitae. In my case, when reviewing applications, I read the CV first and if that passes some basic thresholds, I read the cover letter while holding the CV next to it.

Cover letters, generally, must address two topics: why me and why here. Writing a good cover letter is not easy but it can get you much further than just a CV because you are in complete control of the reader. You can decide what and how to say things. Therefore, you must dedicate a healthy time to think about what to write when you are applying for a job. It’ll become easier later on, when many elements can be reused.

Don’t repeat your CV

If you are going to take only one advice out of this article, it’s this: don’t repeat in your cover letter what you’ve already said in your CV.

I have seen almost all cover letters repeating the information on the CV. Sometimes with identical words. This adds very little information about the candidate and should be avoided. I already know what and when you studied, or where you worked. No need to say, again, I studied this career in this university from this year to this year. The cover letter is the chance to express yourself beyond what the history of your life shows. It is your opportunity to preemptively answer questions that may arise while scanning your curriculum.

With that said, let’s explore the things that must absolutely be covered in your letter.

Addressing why me

Your CV essentially tells how others perceive you. It tells the grades you got in school, or who else decided to hire you. But it tells very little about how you see yourself. A good cover letter will show your skills, but also your passion and motivation. From a CV alone it is very hard to understand why you chose the specific path you are following. The cover letter is an ideal place to explain it.

It can be important to preemptively address issues that may be apparent from your CV. For example, if you had 5 different jobs in the last 2 years it’ll made me wonder about how you chose where to work, whether you know what you want in life. If you have an answer for these concerns, answer them as soon as possible.

Non-professional activities, such as hobbies, volunteering projects, something extraordinary that you’ve done such as taking a year off to write a book, are important features that could be included in the letter if you think they add to understanding you and why you would be a good fit for the position.

Do you enjoy writing and have a blog? Do you like taking photos and have an Instagram account? Not all hobbies are relevant for all jobs, but do not under estimate them. If someone has been experimenting with Arduinos or a Raspberry Pi, for example, they may be a better fit than someone who has a very specialized technical background in what we do. If you’ve managed to grow your personal following to thousands of people, it may be an indication that you are indeed a good marketeer.

The biggest challenge at this point is being able to convince the recipient of your letter to keep reading. Therefore, try to highlight what things you’ve liked in the positions you’ve held. For example, if you enjoy working in large teams or small teams. If you like working with clear goals or prefer to be able to pursue complex problems without clear step-by-step procedures. What gave you energy to come out of bed every morning.

Many people are afraid of stating what they prefer because there’ll be the chance of loosing a job opportunity. The reality is that if you are a person who likes working alone and you claim to be a good team player, you’ll end up in the wrong place. You won’t enjoy the work and probably that’ll reflect on your performance. Being open about preferences and ambitions only helps finding the best fit for you. Remember that at this stage, the credential aspects (degrees, past employment, etc.) were already checked in your CV.

Why here

Explaining who you are and what you think is simpler aspect of the two topics you must cover in the letter. You see the job description and try to determine what aspects of your personality are the most relevant for the job as you imagine it. However, addressing the why here requires a bit more of research. I’ve called it why here because it relates to how scientific proposals are structured. Perhaps it could be better called motivation for applying, and hence why it is called motivation letter as well.

This section of your letter is the best place to show that you have checked where you are applying and to what role. For example, most letters I’ve received stress how good candidates are at team playing. They completely neglect the startup nature of our company and the fact that most work is done essentially alone. Sure, we cooperate, but self-drive and determination are much more important than needing a team to succeed. Being a team player is not bad in itself but it is not always adding value to the profile we are looking for.

You can address what is it about the company that made you apply. Remember that people receiving your letter are smart. Empty sentences like I truly believe in the mission of the company, or I want to do something with an impact in society are automatically ignored in my mind and, probably, I will stop paying attention because I know they are copy/pastes generics. If you, on the other hand, talk about the product, how you would improve it, about the background of the company, etc. your chances of grabbing my attention are extremely higher.

Remember that if a company is trying to hire someone, they are looking for a person that can deliver value to them. If you focus exclusively on what you’ll learn working with us, I would be hesitant. It looks like a purely extractive commitment: you don’t want to do. Therefore, the idea is not to fall in either extreme. Be clear about what you can do and what you’ll need to learn. For example, we are an optics-based company. If you have a solid background in electronics it could be clear you can help us out with the hardware design, miniaturization, etc. but you’ll need to learn about optics.

If you share the vision of the company, don’t just copy/paste it. “I share the idea of empowering scientists” does not say much about what vision do you have. Explain how do you identify with our vision, where do you think we are going. Even if you are wrong and we have a different plan in our minds, it’s clear you’ve thought about the place where you could end up working. If you and your future employer have the same set of believes, the entire idea of working together becomes much smoother.

Avoid empty claims

The second biggest advice I can give anyone writing a cover letter (after not repeating what is written on your CV) is that it is better to make it short than empty. In some random cases I took the time to check whether the claims applicants were making were true. For example, people who claim to have been using different programming languages or modeling programs, but they don’t talk about either of them in their scientific papers, nor have any way of showing what they’ve done (such as portfolio website on Github or elsewhere, etc).

Again, remember that the person reading your letter is a smart person. Throwing keywords one after the other is meaningless. Be mindful about the message you want to convey and do it as a grown up. You are applying for a job, this is not a high school assignment in which you can get around by filling lines of text without adding meaning. The cover letter is not only a tool to pass to the next phase, it’ll also set the tone of the interview. Do it right and the interview will be on a positive tone, not on an inquiry tone.

Empty claims are not only those you can’t substantiate because you were overselling yourself. They can also be those claiming to share the vision of the company without saying what it is or what is your own vision. If you talk about leadership skills, for example, use the opportunity to give an example. Being in charge of a group of people does not correlate with having skills to do so. Take a random professor at a university and chances are they won’t have leadership skills and still manage groups of tens of people and millions in budget. Being elected for a board position, for example, is a whole other story.

Be mindful about multi-step processes

In my experience, applying to small or big companies is a completely different process. In small companies, your CV and cover letter will fall in the hands of the person making a decision very quickly. Therefore talking about technical or very specific topics can be an asset. In larger companies, however, this may be a drawback.

If the hiring process has many steps, the first person receiving your application will likely be the same person receiving the applications for any other job in the company. They can do some baseline checks on qualifications on your CV, and probably will skim through your letter. Be sure to provide enough general information so everyone can at least have a broad idea of what you are talking about.

If the company is looking for a Python programmer, for example, and you keep talking about numpy, it’s going to be hard for someone from the outside to judge that you actually have the skills required. When applying to larger companies, finding the balance between generalities and technicalities is very subtle.

Ask someone to read it

If you do care about the job, ask someone to read the cover letter for you. If you find someone in the same field, it’s even better. Ask how they feel after reading it. Probably they know you, they know your personality, and can decide whether you are conveying a fair picture of yourself or if you are failing at generating a correct image.

If you are applying to a larger company, ask someone who is not in your field to read it. Especially for technical positions, be sure you convey a clear message beyond the technicalities of the position to which you apply. The first person reading your letter will likely not be a technical person but they need to have some elements to judge whether your application should move forward or not. The easier you make it for them, the better it’ll be for you.

In any case, you have to remember that whatever a person says, it’s just feedback that you may or may not incorporate. The cover letter is yours, and it should reflect your character.

Another interesting read may be: How to present yourself in a CV

How to write a programming book

2025-11-04T00:00:00+01:00

I didn’t plan to write a programming book. This means I followed a very inefficient path, which led me to a position where I was forced to re-write the entire content. However, in this process, I have learned a great deal about what I should have done to be more efficient and to deliver more value to my readers. Some of the things I outline in this article are general to any kind of writing, not only technical books, and they also work for shorter pieces such as this one.

Why

If you ever start the path of writing a book, you should ask yourself why you are doing it. There are many reasons, and none is more valid than the other. You could write a book because you want to earn money by selling it. Perhaps a book is an excellent strategy to position yourself in a field. I have seen many people releasing free e-books to collect e-mails for their newsletters, or to try to become known thinkers in an area foreign to them.

Whatever your reason is, the best thing you can do is write it down, so you don’t forget it. The reason you embark on a relatively complex project such as writing a book should guide your decisions. If you know you want to sell your book, you must be sure you meet the market’s needs. If the book is mandatory reading for the students of a class, you should be sure you cover all the needed topics. If you are entering a new field, be sure you are up to date with the area.

The Outcome

Knowing why you write a book only sets the north. The next step is to think about your readers. Think about what message you expect to pass to someone who finishes the book. Depending on the subject and the type of book you want to write, you can think about a list of topics that you should cover. In my case, the book is a guide through an entire project. The outcome for someone who reads the book is a complete program.

I have seen many programming books that tackle different small projects. And with each project, there is a bit of new knowledge transmitted. Sometimes there is cohesion between the chapters, but often they can be read independently. Particularly, books aimed at more advanced programmers tend to be more atomic. Chapters are independent and focus on specific topics, without necessarily building upon each other.

The Base Line

The next crucial thing to know is where the book is starting once you know where the book is going. In my opinion, this may be one of the most challenging tasks. Is your book targeted at people who know how to program, but don’t know a specific language? Or they already know the language but don’t know about programming patterns? Are they comfortable using the command line?

There is always something else that needs to be explained earlier to understand what is coming afterward. It is possible to develop the feeling that your book should include everything. When this feeling appears, you should challenge it. Ask yourself the kind of readers you want to have. Are you targeting those who are just starting or those with few years of experience?

Once you fix the baseline and the outcome, you will have an idea of the size of the challenge you are up to. This is the moment to judge whether it is within your reach or better think about something else.

Outline of the Content

Once you know where the book starts and where it should finish, it is time to decide what goes inside. Most books are structured in chapters because they give a clear break to the reader. Once a chapter is finished, they can take a break before jumping to the next. For technical books, chapters should be cohesive and with a narrow scope. Remember that there is no limit to the number of chapters a book can have.

Ideally, if you already know the topic reasonably well, you can write the book’s outline as a series of bullet points. Each element on the list could be a chapter or a section. You can also be slightly more specific and write down some ideas for content, what do you want to transmit, or some keywords.

Sometimes, however, you need to do some research to write the book. Many times, when you decide to write a book, you do it based on what you already know. However, the way you acquired that knowledge may have been very unstructured. Writing a book is an excellent way of challenging that knowledge and filling your own knowledge gaps. If this is your case, the outline of the book becomes fluid.

When your book’s contents become fluid, you should always remember the two anchor points you defined earlier. Those are the only two places to keep you contained, preventing you from going off the rail. Once you start piling content, always check that every step brings you closer to the outcome you expect. If something off-topic appears along the way, store it safely for when you are looking for inspiration for your next book.

Execution

Once you have a project with a clear start, goal, and path, it all comes down to execution. Writing is not only a skill, but it is also a habit. The more you write, the easier it becomes. Writing a bit every day is better than writing a lot once per month. However, you must also acknowledge that writing is something that happens in between other things.

If you manage to spare some specific time per week for your book, it will be a great help. Part of the reasons for losing motivation is the lack of progress. If today your book is not closer to being complete than it was 6 months ago, it is hard to keep the spirits up. If that is the case, first remember why you started writing in the first place. Then, focus again on the outline and think about what is missing.

I, personally, tend to start writing until the point I realize there is something I am doubtful about. I start reading about it, which blocks my writing. While researching, I roam towards topics I didn’t need for clarifying my doubt. What was supposed to be a writing session ends up showing little progress. If you are writing a programming book, the research could be replaced by developing code. Developing while you write is a quick way of diverging from your real goal.

Before starting to write, try to think about the things you need to know. One thing is looking up, another is researching on a subject. When you start writing, just write. Perhaps you make mistakes, maybe some sentences could have been re-written more beautifully. If you keep typing without looking back, you will make significant progress. Remember, progressing is key to keep the motivation.

Once you are done with writing, it is time for editing. Re-read what you have written. Correct what you can, mark the things you are doubtful about, or you are not inspired to improve.

Separating research from the rest is more or less straightforward. It is such a different task that most people won’t really struggle. Separating writing from editing, however, is much harder and takes a lot of discipline. Especially when you see the red marks under the words you type, or the warnings that some text editors generate. Each one should find their own path. Some people prefer to spend time on each paragraph as they write. I prefer writing several paragraphs and going through them on another day.

Planning

If you are one of the lucky ones who can spend a fixed amount of time per week on the book, you can also try to plan for each chapter. If it is your first book, you won’t be able to calculate exact times. You can try, at least, to have clear objectives that you can keep updating as time passes by. You will quickly learn how many words you can write per hour and how many words are there in a chapter. From there, calculating is easier.

If you are not spending a fixed amount of time on the book, you can still plan but without dates. You can always add milestones such as finish researching for the chapters, writing X chapter, or editing. Having a list in front of you that gets shorter after every time you sit to work on something can give you a big motivation push.

Finishing

Obvious as it may seem, finishing the book is an important step. It doesn’t matter how much you correct it. Every time you read what you have written, you will find something to change. Knowing where to put the final dot and deciding that the book is ready takes a lot of courage. Publishing a book entails a degree of risk: what would happen if there is a mistake, or if people don’t like it.

You can always ask feedback from people you trust to gain confidence. The reality is that until you don’t make your book public, there is no way of knowing for sure. Mistakes can be corrected. Especially if you are distributing e-books, improved versions of the text can be shared very quickly with your readers. Especially if you are writing on a foreign language, chances of making mistakes that go undetected by automatic checkers are high. However, if the content offsets these issues, readers are still going to be happy.

Finishing a book also includes deciding what the cover will be, the typography, the page size. It can be a lot of fun to tweak these details until it looks exactly as you want. But you should always be practical. Everything is perfectible. It is up to you to decide when the result is satisfactory to make it accessible to others.

Conclusions

Writing a book can be a lot of fun. It can position you in a niche, or it can help you become known in an area outside where you are already known. However, writing a book is not a project that can be tackled without careful thinking. I made the mistake of not thinking ahead of time, writing happened organically. It wasn’t only a mistake, it was also a big one. I had to change the contents while writing, at some point, there were some inconsistencies in the finished book.

For the second version of Python for the Lab, I completely re-wrote the chapters, using what I learned from the first version. I also made a conscious plan and stuck to it. I was delighted with the outcome, to the point of sending it to print so I could actually hold it in my hands.

Mentor, Consultant, Coach, Partner: Who Do You Need Right Now?

2025-11-04T00:00:00+01:00

I always struggled to understand what a mentor, consultant, coach, or partner could do for me at different moments of my career.

Now that I have transitioned to all of them, I think I have a clearer picture of what to expect.

Mentor

It is a very personal relationship, non-transactional, that is built on trust. Traditionally, mentors were people a bit further ahead in their careers that could guide you in how to make decisions. Today, many people seek younger mentors to help them navigate changing landscapes. Mentors don’t always have the answers, but they can help you find them by yourself.

For me, the most important aspect is that mentors work with people, not with companies, and they keep the individual‘s goals at heart.

Currently, I am mentoring two people. We have informal meetings once or twice a month. Sometimes the topics of discussion are known beforehand, sometimes it’s just a touch base scenario.

In both cases, they are scientists (a PhD and a postdoc) who are considering starting their own companies. One is in a field very close to where I’ve always worked, so I have specific insights. The other is coming from a completely different path, but we have lots of shared experiences.

What is important for me, as a mentor, is that I am not supposed to have the answers for everything, nor should I expect people to do things my way. Sometimes just asking few questions is much more valuable than trying to fix an issue.

It took me a while to understand how to approach mentoring.

But once you see the power of asking questions from a fresh perspective, you understand how useful your role is.

Consultant

It is a contractual relationship, built on a very specific topic. When you work with consultants, you expect concrete outcomes. You specify at the beginning of the engagement what the scope is, and you expect the person to be an expert on those topics. A consultant can serve as a shortcut, when you need specific skills that your team lacks for a short time, or when you want a fresh perspective.

Consultants work for companies (sometimes they are companies themselves), and bear almost no responsibility on the outcomes of the project.

During my PhD and my first years after, I accumulated a lot of insight in a very specific topic of software development. As a consultant, I can very quickly help companies find the answers to complex problems. Mixed with my entrepreneurial background, I have a comprehensive view of what businesses may require.

Sometimes I have been asked to execute a solution, but most of the time they want me to validate their process, or they want me to address very specific concerns that may have an impact in their long-term.

I am not personally involved in the projects, once my allotted hours are over, it’s the company’s responsibility to execute on what I’ve delivered.

On the other hand, I get a lot of exposure to different problems, and I have the chance to think about smart solutions, which has a compounding effect.

Coach

It is a relationship similar to that of a mentor, but built around a specific theme and they work with individuals as well as companies. It is common to find leadership, management, performance, or transformational coaches. In many cases a coach will build relationships over a long time. They are expected to know the environment in which they work, and provide useful guidance in that context. It is very common for managers in companies to have the role of coaches to their teams.

A coach is a person that allows you or a company, reach new goals, tackle challenges, and become better at what you do.

Coaching is where I have the least experience. I was a coach to a startup for half a year. In that capacity, my role was to engage periodically with the founders and to challenge them. We had frequent meetings, and I would prepare each session to review what was done since last one, and to give new directions for the next meeting.

It worked because the founders were following my same exact entrepreneurial path, and I had a lot of empathy for them.

Coaching can be tiring, almost as much as it is to execute the plans, and it is extremely important to find people who are coachable. The dialogue is a two way street that must be nurtured as the relationship progresses.

Partner

It is a relationship built on doing businesses together, and it can become very intimate. Especially for young entrepreneurs, the role of a partner sometimes gets diffused into the role of a mentor or a coach, although I strongly advise against it. A partner is someone with whom you split responsibilities, you share the burden and the rewards of your activity.

Selecting partners must be done carefully. Collaborating can propel you forward, but the wrong partnership can jeopardize your ideas.

I started my first entrepreneurial adventure alone, and quickly understood that having someone alongside was infinitely valuable.

After my first experience, it became paramount for every new idea I had to first try to find someone with whom to do it.

A partnership is built on trust, shared values, and a shared vision. A partner for a business does not need to be a friend or a close colleague, and sometimes it helps if it’s people from different backgrounds, adding different perspectives.

It is a great challenge to understand who can be a partner, it normally takes time and, as most entrepreneurs know, time is always lacking.

My Story as an Entrepreneur: From Scientist to Founder

2025-11-04T00:00:00+01:00

Becoming an entrepreneur and a founder was definitely not in my plans when I started my PhD. During my studies I had come across only one professor who was building tools as a small side project, but no one else. Most non-academics with whom I’ve been in contact were working for large, industrial, companies.

When I joined the lab of Michel Orrit in Leiden, I very distinctively remember that in one of the first weeks he put me in contact with a former PhD in chemistry who had started his own company and was selling instruments that could be interesting for my project.

That small action was perhaps the seed of a very lengthy and slow train of thought that led me to where I am today.

My path is definitely not a linear one. I didn’t go from PhD to founder, to successful businessman.

My unfair advantage

My PhD was focused on building new tools to study poorly understood phenomena. I think that gave me a somewhat unfair advantage, because I learned how to make things. One of the early bottlenecks I had to overcome was developing software to control my setup.

Due to a mistake, a technician accidentally formatted the wrong computer in the lab. I was never able to run the original control software again.

That forced me to look deeply into how to solve my own problems, and triggered a complete new line of inquiry.

I was never the best at optics. I have to acknowledge that aligning microscopes requires a lot of patience. It is an iterative process of optimization, that very quickly becomes a painstakingly slow path to a signal in a detector. And all of this happens in a dark room, while days go by outside.

However, by automating my setup, I didn’t need to have a top-notch system. I could do experiments no one else could do simply because they would have taken too long, or the reaction time of the person wouldn’t be quick enough.

That serendipitous formatting of a computer pushed me to explore a more comprehensive approach to doing science.

I learned to build setups and the software to control them. And software is tightly coupled to electronics, so after 4 years of work, I had developed the skills to build complex instruments from the ground up.

The interesting thing of this story is that I didn’t realize what my skills were until much, much later.

It all felt so remote, so un-relatable: what can I learn from studying Uber’s business model? I want to build a microscope!

In the meantime, my personal deadline for finding a source of income was quickly approaching.

As romantic as starting a company can sound, each one of us has a different runway. In my case, I had given myself up to the end of the year to find a relatively stable source of income. I was dangerously approaching my savings limit, the threshold below which I didn’t feel comfortable going if it was not for an emergency.

Creation of Dispertech

By the end of 2018, almost exactly 2 years after I’ve finished my PhD, we had managed to secure a relatively small grant. But that grant had a multiplier effect: it was a pre-condition to convince an investment fund to come onboard and finance the first two years of the company.

In 2019, we established Dispertech. It had several shareholders, a reserve of shares for early employees, a reverse vesting strategy. The whole package: We became a real company.

We partnered with two experienced entrepreneurs who would become the managers of the company, while I would focus on the technical development.

More importantly, I managed to have a salary at the end of each month.

How did I start

During 2019 I was the only employee of the company, which meant that the execution of the plans we had laid down in the business plan were exclusively my responsibility.

The first thing I focused on was transforming the setup we had in the lab into an instrument I could have on a desk.

To be honest, I never thought it was possible to achieve what we achieved.

Transitioning from a well-financed lab, with optical tables, tools, lasers, technicians, to having just a desk, really pushed the boundaries of my creativity. I bought a 3D printer to speed up my own design iterations, learned enough of CAD design to get started.

That was the easy part, because I was on my own. I also had to find suppliers for the components we needed.

Interacting with suppliers can be both fun and stressful. Sometimes you find very cheap alternatives but wonder how well they are going to work. Some suppliers promise to deliver in 2 weeks, while others have a standard delivery time of 2 months or more. Some suppliers feel the urge to give you unsolicited advice on your project. It’s hard to balance how secretive you think you must be, and how open to external help is smart.

In the meantime you realize it is your responsibility to buy everything, from a screw driver to the nail polish you are using as glue.

In parallel to product development, you need to brush up what the incubator said about business development. I started building a massive library of papers from people working in areas I thought would be relevant. Some papers were very explicit regarding the limitations of other techniques.

Desk research only brings you so far. At some point the only valuable thing to do is talking to people.

While discussing with researchers, I understood that techniques claiming to be the holy grail were lagging behind in reproducibility and ease of use. Some others were claiming a resolution that was unrealistic. I knew that if we could tackle some of those challenges, we would have a compelling product.

By the end of 2019, the NanoCET was ready. Some people even liked the design, reminded them of Star Trek the original series.

It took around 8 months to go from registering the company in the chamber of commerce to having a minimum viable product that could be demonstrated with users, brought to conferences, and that would allow us to focus on the key differentiators.

And then what?

With a minimum viable product, a niche market to explore, and lots of energy, it was time to focus on growing the business.

We compiled a list of key opinion leaders to help us gain insights into various fields and their challenges. Moreover, we knew that getting these people onboard with a new technology was crucial to ease the introduction into new research topics. We identified conferences to attend, and started scheduling demos both in person as online.

The story on how to go from funder to manager, however, is a different one.

Professors can enable scipreneurs

2025-11-04T00:00:00+01:00

Many professors are slowly starting to wear a secondary hat as startup founders. Perhaps because of the pressure from funding agencies to justify the translation of research to society, perhaps because they understand the value it can bring to their careers.

However, I strongly believe the best path for professors is to become enablers instead of becoming entrepreneurs themselves.

One of the challenges is the ambiguous definition of co-founder. There is no black and white distinction between what are you supposed to do to be known as a founder of a company. Most people agree that co-founders are the people who make up the company since the beginning, sometimes even before there was a clear idea.

Co-Founders don’t need to be entrepreneurs

The context of scipreneurs is very specific, and is hard to find a one to one correlation with other entrepreneurial paths.

People working on research come up with ideas constantly. Many are not worth pursuing commercially but few can be.

Once we acknowledge that co-founders don’t need to be entrepreneurs, it lowers the threshold for people to engage in these discussions. A co-founder may be someone who enables the creation of a company even if they are not involved in the daily activities.

This view fits with the role of professors, and permanent researchers. They will seldom abandon their positions to join their startup, but they can be a crucial part in their creation.

On the other hand, adding the title of co-founder to an academic CV can help pull some strings. It definitely opens up new avenues for financing, and helps giving visibility to the research topics of the group.

Professors are ideal co-founders

Permanent academic scientists can be excellent startup co-founders. Professors have at their disposal a fantastic platform for launching companies:

They have access to money to de-risk innovation.
Their networks of colleagues, collaborators, and their 2nd degree network is extensive to different contexts and industries.
Their job description gives them flexibility in time allocation.
The institutions behind them (research institutes, universities, funding agencies) gives them a lot of credibility.

Many grants, tailored to incentivize valorization of research, can only be applied to by senior researchers. Even if it’s a PhD or Postdoc the one who will eventually push projects forward, professors are the only ones with access to this type of initial capital.

One of the struggles for startups is the creation of trust among their potential customers. Previous papers from the group and the existing network of collaborators are two fantastic stepping stones from which to get started. Moreover the professor can become the first user of the company’s products, adding even more credibility.

Professors are not good entrepreneurs

However, professors are very conservative and avoid risk-taking. The entire cycle of peer review, grant approval, and paper publishing, forces academics to play it safe. If they are too innovative, gatekeepers at every stage will make sure their ideas don’t progress.

Professors see the world from their permanent contract, and they are not used to career changes. Trying a new job is unfathomable to most academics.

And, lastly, most professors lack a crucial trait that makes entrepreneurs entrepreneurial: they are very good at causal reasoning instead of |effectual reasoning. They gather the resources needed to pursue an objective, rather than thinking how to reach their goals with what they already have at hand.

There are definitely exceptions, but we must be careful with exposure biases.

If we look at companies with an academic base, we will see that it’s extremely rare to find a professor who joins the company full time. It’s not just a matter of de-risking enough to make a safe professional transition, in many cases a company will never fulfill the personal ambitions of a professor.

I have discussed with many professors who lament they can’t hold more than a tiny percentage of shares in a company because of their contracts. They honestly believe that they need to own a larger piece as an incentive to do something and that’s the only roadblock.

I believe that focusing on share ownership is the wrong approach. If they really want to start a company, they are free to quit their job, and own as much shares as they want. That’s the reality of everyone in a non academic position.

Professors need to work on their motivations. If they have a fulfilling job, instead of trying to become an entrepreneur, they can help others to pursue that career path.

Professors can focus on being enablers

With all the tools at their disposal, professors can empower other members of their own group to push ideas forward.

Professors have a vast network of young people who are natural risk takers: PhD’s and Postdocs live on temporary contracts on the promise a better future down the road. Every contract they have access to is a risky move, and requires lots of personal sacrifices.

Scientist, especially young ones, are used to working on uncertain scenarios. They are probably running experiments without knowing where they would lead, without knowing IF they would lead somewhere at all.

In this context, professors who think valorizing their research is important and a path they want to pursue, can easily rely on their group.

The best thing a professor can do is to give time for whoever will take the project forward. Becoming a scipreneur requires a different focus: no more paper writing, no more exploratory experiments. And professors are empowered to make this concessions with their students. Open discussions among the group and the people involved can go to great lengths to prevent conflicts.

Finally, the last step of enablement is to let go.

Once a company gets its feet on the ground, the role of the professor needs to decrease until it becomes, at most, an advisory position. Supervisors are very influential on their mentees, and is very hard to separate their areas of expertise.

Once a supervisor who is only partially focused on the project, gives advise on ‘market size’, and ‘business strategy’, it is time to consciously step aside and let others push forward. I have seen many scientists who are reluctant at letting go, losing control. It’s important to show full support of their mentees, and to start trusting their instincts and worldview.

We should find ways to celebrate them

Something that always adds value is celebrating people who took risks and achieved new heights.

Since the idea of the professor/entrepreneur and the professor/co-founder is somewhat ambiguous, we need to find ways of identifying those enablers. The best way is to ask entrepreneurs themselves who do they think gave them the largest push in their careers.

Solve or remove a problem

2025-11-04T00:00:00+01:00

Last week I was traveling by train and the man in front of me had his legs stretched towards the corridor. The first time someone comes walking, they step on him. He complains and asks them to watch it. After a while, an old lady comes balancing herself, grabbing the seats as she moves forward. The man sees her, and braces to complain about being stepped on.

There are many ways of solving this problem. Still, it’s much simpler to remove it altogether: sit straight, like a normal human being.

Every entrepreneur has heard the mythical question what problem does your product solve?, and many have built entire business plans around value propositions that focus on solving customer problems. What many people seem to forget is that by solving a problem we introduce others. Switching from one provider to another, changing workflows, adding a new supplier to the list of vetted options.

However, there is another possibility: removing the problem altogether.

I see it happening in many companies that address market inefficiencies or convoluted government bureaucracy. When you explore the root cause of the problem, you’ll notice it’s completely made up by people and their rules, not by a natural phenomenon.

Scipreneurs are not immune to this logic either. Some opportunities exist only temporarily. Today, for example, many useful analytical techniques only work with a given range of concentrations, which forces scientists to either dilute or purify their sample.

Of course, one could focus on those processes as the problem to be solved and develop a new sample preparation instrument.

On the other hand, one could focus on creating an analytical tool that works in the natural regimes achieved at preparation time.

It’s exactly the same market, the same people, essentially the same opportunity size. However one is disruptive and the other is incrementally better. The caveat of removing problems is that in many cases doesn’t lead to a business case, just the adoption of good practices, a policy change, or the implementation of an idea.

If you think about the companies you know, which ones do you think have removed a problem?

The path to Surface Enhanced Raman for exosome characterization

2025-11-04T00:00:00+01:00

Using Surface Enhanced Raman Spectroscopy (SERS) to identify exosomes seems like a fantastic idea, but a very hard to realize one. The premise of label-free tools is that they shouldn’t introduce the type of biases that arise from the heavy manipulation required to label samples.

SERS relies on creating very small hotspots in which the Raman signal is greatly enhanced. The spectrum of the light that comes out of the sample, depends very strongly on some fundamental aspects of the molecules. Since each component has a unique spectral fingerprint, it is possible to use the signal to identify the composition of a sample.

The technique works very well for small molecules that can sit in the hotspot, and that have very clear spectral features. With larger objects, such as extracellular vesicles, it becomes harder (if not impossible) to get them close enough to the surface. On top of that, the recorded signal is too complex to quickly identify the components in the sample.

Signals in which identifying patterns is challenging, are an ideal target application for machine learning tools.

In the paper by Yeonho Choi et al on Nature Comms, they seem to overcome some of the challenges associated with SERS and exosomes, and move two steps further: they show they can train a deep learning model to identify EV samples from cancer patients and they can even define the tissue of origin.

However, the paper triggered me several questions.

When working with EV’s, one of the challenges is to get enough data to build good statistics. In the paper, they analyzed ‘100 signals’ which, based on the geometry of their chip and sample preparation approach, would mean they saw ~1000 particles per sample, or around 10 particles per analyzed spot.

What are the odds that at least one EV in that pool is coming from a cancerous cell?

My rough estimate puts the odds at around 1 in a million.

Even assuming the sample was enriched and the chances were in their favor, there is still the question of the size of the hotspot. SERS happens in very small gaps in between metallic structures. From that perspective, EV’s are relatively bulky objects. Even if they are sufficiently close to the hotspot, it has to be the differentiating protein the one that sits in the gap and that generates a meaningful signal.

With all these odds against, it was still possible to train an AI algorithm that can determine which samples came from cancer patients. The reviewers comments focused on the SERS aspects (and more superficially on the data analysis) and not too much on the exosome aspects.

Sadly, the data that was made available does not include the spectra, only the results of their trained algorithms, which allows only some partial reproducibility.

I am confident that spectroscopy and machine-learning analysis will open many doors in the coming years. Label-free methods like SERS can become crucial to get information out of complex samples. However, there is still a long road ahead to match the efforts put on extracellular vesicles research and clinical applications.

One of the more pressing challenges is throughput. A system designed for working with EV’s needs to be able to analyze at least tens of thousands or even millions of particles in order to identify the few ones that can be of interest. The “signal-to-noise” ratio will be extremely small, and the only way of improving it is by devising better sample isolation and enrichment devices and techniques.

I guess the real breakthrough will come from combining technologies either sequentially or directly in the same device.

In the meantime, fluorescence-based systems are the best candidates to get useful information out. The biggest challenge is to limit the number of false-positives. My gut-feeling tells me that the value of EV’s as diagnostic agents will arise from very small number statistics unless the sample preparation approach is radically improved. False-positives are the worst enemy for small number statistics.

I am still confident that fluorescence-based tools can build the ground knowledge that leads to the clinical application of small exosomes as biomarkers, and as therapeutic agents.

It is an exciting time for Extracellular Vesicles. Many tools are becoming available, many techniques are being adapted to work with them, which means there are many synergistic opportunities in the field.

The things I miss the most from my time as a PhD

2025-11-04T00:00:00+01:00

Many people, when faced with the struggle of stepping out of academia, ask themselves what are they going to miss after the transition. Sometimes, when it is an active choice, it is fair to ask if what we’ll get compensates what we leave behind.

In my case, there is one thing that made all the difference: access to people with interesting problems.

I didn’t value it enough while doing my Ph.D. I gave it for granted and, in hindsight, I didn’t interact with as many people as I should have. Many researchers are facing next-generation problems, they are posing questions aiming at very far fetched applications if any at all.

Outside of the academic environment, it becomes harder to meet people doing this type of cutting edge work, people who pose themselves hard problems. I miss the seminars and random talks by invited speakers.

During my Ph.D. I sometimes over estimated the value of being in the lab, of adding few more data points to a plot. I discovered way too late the value of attending random seminars for the pure pleasure of trying to learn something new. Maybe postdocs can be in the same situation, but I suspect that the pace of research in modern academia could preclude many PI’s from serendipitous learning.

If someone is starting their Ph.D. or postdoc, I would strongly recommend them to pay attention to every opportunity for random encounters. They won’t happen in different contexts.

What I miss the most, looking back, is going to seminars and talks. I know I won’t have them back naturally.

So, if you ask me today, I think one of the most valuable skills someone can have is being able to make connections through fields and problems from different disciplines. It’s at the intersection of worlds where there’s the most to win when thinking about solving the challenges of the future.

And the only way we have to develop the skills of being and interstitial person is by exposing ourselves to different realities: listening to people telling their stories.

There and back again 13 years apart

2025-11-04T00:00:00+01:00

Salzburg is a special place for me because it is the symbol of the most serendipitous thing that ever happened to me: winning a paper airplane competition in Argentina and qualifying for the world cup organized in Red Bull’s hangar at Salzburg’s airport. More than 10 years later I am back in the city, only that this time I came representing my own company at a conference.

There are moments in our lives, like that paper airplane contest, that serve as anchor points for who we were and what plans we had, but often we don’t give ourselves the time to reflect on those moments.

I remember the subjects I was taking at university the day I participated in the contest. I remember lengthy chats about building physics educational tools to replace the antiquated ones we were using. Some of the people with whom I was discussing 13 years ago are still my best friends, more than 10.000km away from where we started.

I remember the feeling of wanting to connect with people, understand our differences. I remember an overarching thought that today I would frame as creating something impactful.

I haven’t reflected about those ideas in many years. Time just flows and I seldom take the time to properly look back. When I entered the terminal at the airport in Austria, images started coming back: sleeping at the airport floor not to pay a taxi early in the morning, watching The Sound of Music with my Couch Surfing host’s boyfriend, a random art installation in a park that would make a vortex of water.

Having those anchor moments are an invaluable tool. They allow us to remember what we were doing, and how were feeling. Probably we can even remember were we wanted to go. If we get the chance to remember, we can see whether we are closer to where we wanted to be, and whether we are becoming the person we wanted to become.

These reflections are crucial for entrepreneurs, since they don’t have a predefined path to follow. It is up to each one of us to choose where we want to go and how to get there.

Not having a predefined path means it’s very easy to get lost. Small, incremental decisions pile up and can push us very far from where we wanted, or they can propel us faster than we ever thought possible.

For me, the most important thing to remember is that even when it feels the forces acting on us are immense, there is always a way to get closer to where we wanted to be. We just need to learn how to shape those forces in our favor.

A city visit was a trigger for me, but it has nothing to do with it. It was mostly about allowing me the space to reach back into old memories, ideas and ideals, and do a sanity check.

What does the creation of Altos Labs mean

2025-11-04T00:00:00+01:00

Altos Labs started after a scientific conference hosted at the US home of an Israeli venture capitalist (in California, nonetheless). The conference revolved around the topic of cell reprogramming to make people younger. A bit over a year later, these ideas managed to attract around 3 billion dollars as seed capital to kickstart a company focused on rejuvenation.

Although it’s the latest project, Altos Labs is not the first of its kind.

Years ago, in 2013, one of Google co-founders started another similar project called Calico Labs. A company focused on longevity research. In 2016, Zuckerberg and his wife started a biohub, a nonprofit focused on eradicating all disease by the end of this century. Neuralink, made famous by Elon Musk, is much more modest in funding, but still makes its splashy appearances on Twitter.

The first thing that shocked me is that some individuals have access to so much capital that they can easily sink billions in basic research. Secondly, the topics on which they focus do not belong to the core businesses nor backgrounds of the funders. These are ad-hoc projects to pursue some the idea of living forever, either rejuvenating, increasing the longevity, or eradicating disease.

Funnelling rich people’s money into research is definitely an attractive paradigm.

Taxes seem to be relatively low for the obscenely wealthy, which means they have excess resources to invest in whatever they please. For the past several decades, basic research funding has been almost exclusively dominated by public money (taxpayers’ money). And this trend of people directly financing research initiatives is definitely paradigm-shifting.

Or better said, it is closer to how it was in the XVII century, just that the people with money don’t do the science themselves. Private investment in research is definitely something to praise, but it is also something to reflect on. Especially when you start identifying patterns, such as geographical biases in where these initiatives appear.

The accumulation of wealth has been discussed for long, but little has been said about the accumulation of knowledge.

The four examples I’ve mentioned above are based in California. It’s not a coincidence, since many projects like these are funded by people who made money on the internet. The same process of accumulation of wealth in a small region of the world is being repeated with the accumulation of knowledge in the hands of few, geographically concentrated, initiatives.

Even if they are structured as non-profits, the know-how, the training, the partnerships, are all located and shared with a selected group of individuals: those who can afford to live in a place like California. And I don’t mean it only from the money perspective of affording, there are many reasons, such as visas or caring for family members, that prevent someone from living in a given place. The claims of diversity and inclusion should, at least, be taken with a pinch of salt.

Capital is half the requirement, the other half is talent, researchers, engineers, scientists, willing to work in such companies.

As soon as the creation of Altos Labs was announced, my Twitter timeline got full of messages of people saying they were joining it. Definitely, there’s a bit of a bias on who I follow, but it struck me how easy it seems for Altos to find candidates from different backgrounds, some with very high profiles. However, one has to consider that the counterpart is academia. It’s not a particularly levelled field for the job market.

I haven’t talked to anyone transitioning to Altos, but I can imagine that making the change is not even a matter of salary. For young scientists (i.e., younger than 40), job stability is already something that can’t be obtained in academia. Budget for running a lab, liberty for hiring candidates, performance evaluations not based on paper crunching. They all seem like very decent propositions without requiring any extra money to offer them.

I wonder what would happen once these initiatives are big enough to drive people away from academia altogether.

Universities, today, have the monopoly on degrees. A Ph.D. is awarded only by a university. And this is not minor, since the absolute majority of the research done in universities is carried on either by Ph.D.’s or postdocs. The positions are underpaid, under appreciated, and extremely vulnerable. In many countries they aren’t even recognised as employment contracts.

There’s an overall trend, especially in the internet-sector, to stop looking at degrees and focus on skills, wherever and however they were developed.

The monopoly on degrees will not be enough incentive to keep a stable supply of candidates. If I look in retrospect, the things I’ve done during my Ph.D. could perfectly well fit what it’s done at Calico or Altos. If I had to choose between the two options, I wonder if I would have judged that the value of a degree compensates for the employment context.

If this pattern continues, at some point universities will have to face a depletion of their workforce.

For the time being, private initiatives are negligible compared to state-based funding, but are useful to pinpoint the problems.

Spending 3 or 4 or 5 billion dollars over 5 years is nothing compared to what different states invest in basic research. However, private initiatives can be used as examples to identify and solve the problems of academic-based research, found all over the world. It’s not just money, it’s the working conditions that drive people away, independent of their skills.

Supervisors without any management training, endless hours in the lab without building skills required to move forward in one’s career, serendipitous assignment of funds to conduct research. All these problems are solvable, it just requires a lot of will. After all, they are the product of decades of movement in one direction, without any consideration on the people involved and left behind.

In the long term, societies cannot afford losing their scientists to the hands of private initiatives.

Although it’s interesting to see how research financed by private individuals develops over time, societies need to give them a thorough check. Publicly-funded research should focus on problems that extend human knowledge, or that benefit the society even through indirect paths. Privately funded research can focus on anything, and it seems that is targeting the billionaires dream of living forever.

The people who created these initiatives don’t have a particularly good track record on how they treat employees or the users of their platforms. It’s the same set of biases, abuses, and lack of accountability being translated to a completely new domain. Societies, at some point, will need to review how they finance research and whether they are willing to leave the bulk of it pursuing predefined interests.

Despite my concerns, I still congratulate those who made the switch to a private initiative, because they can be a force for a greater good.

Although I am weary of the current direction in which these things are moving, I think that scientists who moved to Altos labs (or Calico, or the Biohub, etc.) can exert a great force for improving everyone’s situation. Without the known academic performance problems, in these other contexts, scientists are free to explore alternative publication methods, they can strike different partnership opportunities and, of course, can contribute to pushing the boundaries of knowledge.

Scientists in private initiatives can explore and show how things can be done at a relatively low professional risk for themselves. Once they are in a setting with completely different incentives, the doors for creativity open from side to side. From focusing on low-citation tasks, such as outreach or open source maintenance, to proper mentoring of the next generation of scientists.

Exciting times are ahead of us. I am really curious to see how the interplay between old-fashioned academia and private institutions develops in the coming 5 to 10 years.