Eric Topol is an excellent example; he holds significant scientific prestige. The Scripps Institute, which he founded, aims to translate science into benefit. Not only do Topol and his institute publish papers, but they also lead innovation, seamlessly linking research and application.

People often confuse innovation with being innovative. Being innovative means doing something new.

Innovation is the act of creating something that improves others’ lives. So, innovators go beyond discovery and knowledge generation and drive towards practical implementation. Topol and the Scripps Institute are innovation leaders.

Innovation leadership

An innovation leader in science is someone who sees science through to the point that it provides benefits to others. They start with a vision of a better future and do what it takes to make it a reality.

Another impressive innovation leader is Michael J. Fox. Living with Parkinson’s, Fox envisioned detecting the disease before symptoms appear, enabling early intervention. While ambitious, his leadership has transformed the field of Parkinson’s disease. Fox commands significant scientific prestige.

We all cannot be as prominent innovation leaders as Topol and Fox. However, there is a way for you, no matter who you are, to become an innovation leader. Before we explore why innovation leadership matters and what innovation leaders do, let’s first explore why innovation leadership matters and what innovation leaders do.

Why innovation leadership matters

Innovation leaders in science don’t hand off their research results and hope others will use them.

Others don’t have the same vision driving them. They are more likely to give up on the research when the results don’t deliver right away. If a new target for a disease therapy and the first couple of trials fail, a company that has taken the reins from a researcher is likely to halt further development.

An innovation leader would ask why. Perhaps it was the wrong disease, or something else is needed to make targeting it effective. For example, only a subset of individuals with the disease may respond to a therapy targeting that target.

At the same time, innovation leaders have a deep understanding of the role of other disciplines. They understand that research and bringing a scientific result to the point where it is used require different skill sets. They don’t learn additional skill sets, but they do gain a degree of literacy in other disciplines to communicate with and work with them.

Innovation leaders make multi-disciplinary efforts possible.

Innovation leaders are persistent. There is a degree of faith that drives an innovation leader. It might then be argued that it is, in fact, better off to have someone else take the research findings forward. The innovation leader is biased. It could, however, also be that the faith in the vision is that her intuition, or subconscious, sees a pattern that is not easy to articulate without further research.

This was the case for Katalin Karikó. In 1990, she proposed mRNA as a therapeutic. The research community quickly soured on the idea; she failed to secure grants and was demoted. Yet she persisted. Developing mRNA therapeutics faced challenges, including inflammation. Karikó and immunologist Drew Weissman overcame this by altering the mRNA formulation. Her persistence paid off with the highest scientific honour: the Nobel Prize.

Most innovation requires changing complex systems, so new research findings are often constrained by prevailing paradigms and bottlenecks. Nevertheless, innovation leaders like Topol, Fox, and Karikó overcome these seemingly insurmountable barriers.

What do innovation leaders do differently?

Innovation leadership is not traditional authority-based leadership.

Harvard Business School Professor Linda Hill points out that leading innovation has become more about getting people to create with you. While you still need to have a vision, innovation leadership is also about shaping culture and capabilities so that you have a whole community of leaders working to create the future.

Hill describes three functions of innovation leaders: architects, bridgers, and catalysts. They build what is needed to move research into practical implementation. They work across disciplines, sectors and different types of stakeholders, and they promote the creativity and leadership of all of those around them.

Topol built the Scripps Research Institute, Fox founded the Michael J. Fox Foundation, and Karikó worked with many collaborators and served as a vice president at BioNTech.

The most important problems that any field faces usually have a wickedness to them. Wicked problems are problems with multiple interdependent factors and stakeholders that make simple solutions impossible. Think of world hunger as an example. There are also problems that require changing the prevailing system. Systems are themselves complex and adaptive and therefore resistant to change. The wickedness and systemic nature of important problems mean they are best addressed by building a system that influences and eventually replaces the existing one.

Building a system is a whole different level of leadership. The best systems are complex and self-organising. They are fertile ground for co-creation. They also require widespread awareness. Innovation leaders, therefore, communicate widely.

How to become an innovation leader.

Consortium projects foster innovation leadership. These projects unite leaders, and the best ensure that even junior members can step up and be creative. This inclusive structure unlocks nearly limitless possibilities.

I have come to think of consortium projects as the foundation for the complex system we need to build to achieve transformative change.

The multi-stakeholder and multidisciplinary nature of consortium projects makes them well-suited to advancing the process of building a system by turning a network into a community of practice.

When facing complex challenges that involve multiple disciplines and stakeholders, you not only need the collective intelligence of those disciplines and stakeholders, but also need to keep them motivated. The leader’s vision is not a good way to keep high agency people engaged. They need a degree of autonomy.

The key to innovation leadership is bridging daily routines and long-term impact, not simply crafting a vision. Ultimately, being an innovation leader requires a high degree of agency.

I recently asked Eleni Palpatzis, a neuroscientist studying cognition and ageing, and author of a newsletter on everyday brain function, about the relationship between goal setting, agency, and grit.

She is, by the way, an excellent example of an early-career researcher who demonstrates innovative leadership and skillfully communicates her science widely through her newsletter. Here is how she answered me.

Her description of the role of short-term and long-term goals I think provides a basis for a practical path for developing as an innovation leader.

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The plan

1. Set a long-term goal and vision.

2. Use that long-term goal to align with others who share it.

3. Set short-term goals together and build small projects, what I call tiny projects, that deliver in the short term.

4. Celebrate tiny project wins

5. Adapt those short-term goals based on the results of the tiny projects to build trust and momentum.

6. Apply for substantial funding using the achievement of your short-term goals as evidence for what is possible and your ability to work together.

This plan is for established scientists and all stakeholders in innovation.

If you are an early-career researcher, form a consortium with other early-career researchers.

If you are a biotech or med tech company, form a consortium with leaders in your field.

If you are part of a major pharmaceutical company, form a consortium with key opinion leaders and other companies.

If you are a patient, form a consortium with all the above, using your story and others like yours to bring everyone together.

If you decide you do want to become an innovation leader. There are many opportunities, and I would like to see if we can develop some of them for you. Reach out

Who is your favourite example of an innovation leader? Comment below.

References

1. The Scripps Institute

2. Ground Truths by Eric Topol

3. Michael J Fox Foundation

4. Katalin Karikó National Inventors Hall of Fame

5. How the Leaders Drive Innovation Linda Hill HBR podcast

Impactful consortium projects are highly interactive. All the problems that arise are fielded by the entire project.

Impactful consortium projects inspire. They inspire with their attendant vision and their ability to make progress.

Impactful consortium projects involve stakeholders as partners. If anything stakeholders help to keep a consortium project on track.

Impactful consortium projects are made up of tiny projects enabled by a psychologically safe environment. It’s not the quality of the plan you make at the outset that determines success. Its an experimental mindset that allows for adaptive implementation.

Impactful consortium projects build assets that help resolve innovation bottlenecks. That vexing lack of consensus, or resistance to new approaches can be overcome with enough people working together to show what is possible.

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Impactful consortium projects are the best source of more impactful consortium projects. Consortium projects have a way of revealing more opportunities because consortium projects create the assets that make pursuing those opportunities possible.

Impactful consortium projects are most impactful when they are sustained. The standard definition of a consortium project states that they are a limited time collaboration. Once you build trust and assets in a big project your rate of achievement goes exponential. Why would you stop?

Most importantly, we need impactful consortium projects if we want to reach the full potential of advances of research and technology. For how long have we been talking about personalised medicine? Digital health?

Knowledge and technology are not the limiting factors. Our inability to align and work together are.

What’s your experience with consortium projects that actually moved the needle? I’m curious what made them work—or what held them back.

It was one of those meetings where the discussion goes round and round. It was more like a ping-pong match than a discussion. Two sides of a debate just hitting the ball back to the other.

Out of frustration, I called on someone who had been quiet until then. “What do you think?” I asked her. I thought maybe I should not have done that. I was putting her on the spot.

But I was wrong.

She delivered a balanced, creative solution idea. The rest of the discussion faded away, and even though I was just listening, I could tell that the energy of the team had increased. That moment taught me something about teams.

Flat teams are more innovative

In a 2025 Nature commentary, David Budtz Pedersen makes the point that great science happens in great teams. This immediately raises the question - what makes for a great team?

A study in PNAS found that teams with more leaders and a flat structure are more innovative and have greater long-term impact. Teams that are structured in a more hierarchical way, a “tall team” with one or just a few leaders and everyone else in a support role, are not surprisingly best for the careers of the leaders but not great in delivering long-term impact.

That is a nice piece of research, but how do you make a team flatter, especially when it is a tall team?

After the call on that morning 15 years ago, this question, while not well formulated as precisely as above in my mind at the time, became a quest for me.

Meetings and working in a team often feel like frustrating wastes of time, but here was an example where a nearly infuriating meeting became a powerful moment of creativity that energised everyone in the meeting. A worthless meeting was made worthwhile.

My quest has been to figure out how to get teams to be creative, like on that day, both in terms of ideas and problem-solving. I thought that if we figure out how to repeat those kinds of moments and apply what we learned to big, ambitious projects, we could achieve the unexpected. Here is what I have found out so far.

Tiny projects

Encourage team members to propose and take on new projects. I have seen this work very well in consortium projects. Some of the most impactful outputs from a project have been led by junior researchers who took the initiative to do something no one else was doing.

One way to create such an environment is to create a process for creating what I now call tiny projects. These are projects that are deliberately not resource-intensive and serve only to test an idea. It is therefore easy to agree to let those who would otherwise be in just a supporting role have the autonomy to propose and engage in new projects. The risk is low.

When I was in training and working in the lab, my mentor, the head of the lab, encouraged such projects. We were allowed to gather preliminary data and then bring it back to the group. This meant that the lab meetings were always surprising and, therefore, energising. Some of the tiny projects did not work, and that was okay. Being able to test and then iterate is a great way to face the unknown. It is a great way to approach complex problems that have defied solution. It is not to say you should abandon the established project plan, but no one leads by following a project plan.

Tiny projects provide a sense of autonomy, which is one of the three needs defined in self-determination theory (SDT). Tiny projects develop new leaders.

Somewhat ironically, when a tiny project gains traction, it motivates those involved to complete the work outlined in the established project plan. Often, the project plan is to develop something such as a dataset that the tiny project will need.

When you allow such freedom, even in a hierarchical project, you begin to develop an adaptive system. Tiny projects function as feedback loops that can magnify the output of the bigger project. If they deliver positive feedback, it tends to compound into something bigger and bigger. This is a characteristic of complex systems, and this is desirable because it builds both capacity and resilience into your project.

Non-science skills

How to make best use of the overload of skill-building advice in books and online

The Successful Scientist

Transparency

Decisions that come from on high, in which those affected do not see the logic of the decision-making, do not promote a sense of autonomy. If, instead, those affected have been engaged or were at least aware of what was happening, you promote a sense of autonomy. No one wants to be in a collaboration where you have to give up your ideas. Transparency opens the door to collaboration. Maybe someone hears about a problem and, even though they are not directly involved, knows from past experience the perfect solution. In an environment that supports their autonomy, they will be more likely to speak up and propose working together to find a solution.

In this way, transparency helps to expose problems and ideas to a broader array of perspectives. It also encourages people with different perspectives to engage more.

“Serendipity comes from differences.” John Maeda

So, the interaction that comes from increased transparency is a key feature of an effective project. Limiting meetings to only those who need to be there is detrimental to the team’s creative potential. An effort should be made to push towards a project culture in which a diversity of perspectives is exposed to the problem or idea being presented.

Simply making opportunities for interaction, however, is not enough.

Facilitated dialogue

Even though I did not realise it, that day was the beginning of a practice of facilitating discussions, so that they become dialogues. A dialogue is more than a discussion. It is an exchange where you build on each other’s ideas. When there is someone in a meeting who is specifically focused on creating dialogue by asking questions like a did that day, it makes the setting safe for a diversity of ideas. Ideally, the facilitator should not have a stake in the discussion other than wanting to help find a solution. The facilitator should also have some knowledge of the topic. This can be anyone in a meeting who is not directly involved.

When I first started in a large consortium project, I attended up to 20 such meetings a week. I became very attuned to the interaction. Since it was mostly audio-only meetings, I became very attuned to people’s voice inflections. I could hear what was not being said. It was odd. I would show up at a face-to-face meeting and only recognise some people when they started talking. What I noticed is that the most senior people hated that. I suspect it was because, without the non-verbal signalling of the senior people in the project, those who were less senior felt more comfortable challenging ideas and proposing novel solutions. Audio-only meetings flattened the teams’ governance structure.

We can’t go back to audio-only meetings, but an attentive facilitator can work to counteract the effects of nonverbal communication that reinforce the hierarchy in tall teams.

Flattening your teams as an act of collaborative leadership

As Xu, Wu, and Evans illustrate in their research, flat teams have much greater innovation potential. Actively pursuing the flattening of your project structure increases the innovation potential of a team or a project.

The thing is that no matter what your role is in a team or a project by encouraging the development of tiny projects, transparency and dialogue, you can lead even the most hierarchical teams towards a flat structure. Doing so is an act of collaborative leadership.

Collaborative leadership is a distributed approach to leadership that operates through iterative cycles of collective sense-making, decision-making, and action-taking among interdependent actors in a system.

As our ability to address increasingly complex problems grows with greater access to data and AI, collaborative leadership becomes even more important. The great thing about it is that everyone can have a meaningful role in making the seemingly impossible possible.

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Pedersen, D. B. (2025). Great science happens in great teams—research assessments must try to capture that. Nature, 648(8092), 8-8.

Xu, F., Wu, L., & Evans, J. (2022). Flat teams drive scientific innovation. Proceedings of the National Academy of Sciences, 119(23), e2200927119.

Ryan RM, Deci EL. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol. 2000 Jan;55(1):68-78. doi: 10.1037//0003-066x.55.1.68.

Archer, D., & Cameron, A. (2013). Collaborative leadership: Building relationships, handling conflict and sharing control. Routledge.

The collision of love and imagination I intend to discuss is unusual because it occurs within the context of something vital to science, communication. Communication is more important than ever. Science is a creative process, but unlike many forms of ‘creative’ work, such as writing, in science, we don’t necessarily equate the creative process with communication. Communication occurs after something has been created. You communicate your results. However, there is a deeper way of looking at communication that brings it directly into the heart of the scientific creative process.

If you are a scientist, your next breakthrough may depend less on your isolated brilliance and more on how courageously you communicate with others.

A deeper view of creativity and communicaton

Maria Popova writes the Marginalian newsletter, which started out as a letter to a small group of friends where she would relate some of the ideas she found in old books she was reading. It has since blown up into a newsletter with a major following. The success of the Marginalian is because Popova tends to find interesting ways to connect the content of old books with the here and now.

In one of her newsletters, Popova connects the thoughts of three great thinkers to build the case for the art of unselfish understanding. She makes the case for what I like to think of as full-bodied communication.

She makes a connection between language and experience by relating the words of Hannah Arendt: “An experience makes its appearance only when it is being said, and unless it is said it is, so to speak, non-existent.” Science that is not talked about does not exist. More importantly scientific ideas that are not talked about don’t exist either.

Moving beyond the epistemologically obvious connection between language and existence she then quotes the physicist David Bohm on the paradox of communication:

“If we are to live in harmony with ourselves and with nature, we need to be able to communicate freely in a creative movement in which no one permanently holds to or otherwise defends his own ideas.”

Scientists often vigorously defend their ideas, creating paradigms that ossify an entire field in one way of thinking. It is therefore an imperative of innovation to have precisely the type of creative movements Bohm is talking about. What I have witnessed time and time again is that creative ideas and creative solutions to problems only happen when Bohm’s type of communication dominates.

The third and perhaps the most important aspect of communication that is important for the active of creativity is listening. Popova goes on to list out Erich Fromm’s six rules of listening:

• The basic rule for practicing this art is the complete concentration of the listener.

• Nothing of importance must be on his mind; he must be optimally free from anxiety as well as from greed.

• He must possess a freely-working imagination which is sufficiently concrete to be expressed in words.

• He must be endowed with a capacity for empathy with another person and strong enough to feel the experience of the other as if it were his own.

• The condition for such empathy is a crucial facet of the capacity for love. To understand another means to love him — not in the erotic sense but in the sense of reaching out to him and of overcoming the fear of losing oneself.

• Understanding and loving are inseparable. If they are separate, it is a cerebral process and the door to essential understanding remains closed.

In my experience, a freely working imagination allows for the divergent thought that is the first step in any creative process. However, divergent thinking is necessary but not sufficient.

Fromm’s point about overcoming the fear of losing oneself is an interesting fear to consider. We are often concerned about fear of failure or fear of the unknown, but fear of losing oneself is not a fear that I often consider. This is the fear that drives the need to get your point across and also drives you to think about how you are going to say what you want to say when you should be listening to what others are saying.

I was recently in a consortium meeting when one of the scientists who has led the field for decades turned to me during a presentation and said, “There are a lot of really good people in this consortium.” It made me wonder if his ability to lose himself in the presentations and the success of others was the secret ingredient of his own success. As Fromm put it, loving and essential understanding are inseparable.

In my experience, this is something I have learned is important to possess when facilitating a dialogue, whether in the context of developing a proposal or solving a problem. The imagination comes in when you have had discussions and you have listened well to everyone, so that you can connect what was being said in an unexpected way, or you can link what was being said to a current trend or a vision of the future. All of these require imagination and a bit of knowledge.

This is why I do not understand the people who say that facilitators should not have knowledge of the field they are facilitating, or should be somehow outside of the content part of the creative process that is happening in a meeting. In that case, the facilitator is reduced to someone who is just helping the group decide when it is time to take a break. You cannot even really understand the moods or subtexts of the dynamics between people if you don’t have some knowledge of the field and make use of it.

The important point is to understand communication not as a one-way broadcast and more than a flat exchange of ideas or updates. True communication is a dialogic process that is an act of creativity that is made up of love and imagination.

Having the courage to lose oneself opens the door to the convergence of ideas and thought.

Love and imagination to be thankful for 2025.

Here are some examples of how full-bodied communication showed up in my own year.

Our oldest daughter is now in her second year of studying biomedical sciences at the University of Leuven. She is already learning about the inexorable bond between science and communication, although in the more traditional way of writing an abstract or giving a presentation.

Our middle daughter continues her dance career. As part of a duet, she made it to the Youth American Grand Prix in Tampa, Florida. Dance is full-bodied communication. Below is a video her dance group made for me. You can also see an outdoor version on our website.

Our son plays the central defender for his football team. He used not to talk to the other players during the match, but is now intensely focused on the evolution of a match and communicates with his fellow defenders to organise a super-strong defence.

In the proposals and projects we have been working on, there have been lots of developments. Over the past few weeks, I attended three consortium meetings. Both the project imSAVAR and T2EVOLVE had their final consortium meeting. Even though I have been involved all along in both projects, seeing the results all put together over the course of a couple of days has been a communication extravaganza that illustrates all the love and imagination that have gone into these two projects. The most important news is that both consortia will continue to work together in the recently launched T2EVOLVE association. I plan to write about some of the more remarkable achievements of these projects in 2026, so sign up if you want to read more about these projects.

The INTERCEPT consortium has now reached its one-year mark. It is a really impressive team that has a real chance to truly transform the field of Crohn’s disease with a paradigm shift towards identifying at-risk individuals and intervening with treatment to prevent them from ever developing Crohn’s Disease. This is certainly a project you should follow.

A New Year’s resolution to spread love and imagination in the service of helping those who are suffering from disease.

Make it a point to build up your courage to lose yourself in a discussion by:

• Recognising the fear of losing ourselves in a discussion that we all have

• Understanding that fear will always be there, but losing ourselves by listening deeply is imperative for making meaningful breakthroughs. What you do in the face of fear is what matters.

• Think about the problem you can solve by leveraging the combined creativity of a group - what impact do you want to have? Could you achieve that on your own?

• Deploy some practical tactics

  • Always ask questions.

  • Repeat back what others are saying.

  • Build up a steel man case - argue the side of someone who an opposing view before you make your own perspective known.

If you are leading or joining a consortium, designing spaces where people can safely ‘lose themselves’ in each other’s ideas is not a luxury; it is a core innovation strategy.

One way to do this is to develop ‘tiny projects’, projects that don’t require lots of additional resources and that can be used to pilot or test an idea. Tiny projects are laboratories for full‑bodied communication: they force you to ask, listen, and build together at low stakes.

I would like to invite anyone reading this to consider making 2026 the start of a journey towards achieving major breakthroughs, beginning with consortium design and creative communication. I would love to be your guide.

May your 2026 be full of conversations so loving and imaginative that unexpected science becomes possible.

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A vision helps create a sense of shared purpose, which is important, but it is not the most important. In fact, if you insist too much on having a shared purpose too far, you could end up limiting your project’s potential to work across disciplines and stakeholders.

A vision on its own is nothing.

There is something much more important than vision. You can have a vision for your project, and it can be an important vision, but if that is all that it is, a vision, it is not inspiring.

Think about it. We have all encountered people with a strong vision. A world where any type of cancer is easily cured is perhaps one of the most common visions, but it is not a compelling vision. Why is that? It is certainly important and something we would all like to see happen. Still, at least when I hear a vision along those lines expressed, I am undecidely nonplussed.

A vision needs much more than an idyllic depiction of the future.

Perhaps the best example is John F Kennedy’s vision to put a person on the moon. On May 26th, 1961, Kennedy said: “I believe that this nation should commit itself to achieving the goal, before this decade is out, of landing a man on the moon and returning him safely to the earth.” Decades later, we still refer to this moonshot project. In fact, ‘moonshot’ has become a qualifier that defines a project as meaningful.

Research projects that get lasting nods of recognition

It’s also been more than a decade since I met someone in the respiratory research field who hadn’t heard of the very first project I helped develop, U-BIOPRED. It is always satisfying to see nods of recognition when U-BIOPRED is mentioned at a respiratory conference or meeting. Some even take credit for the project, though they were never really involved. What’s even more remarkable is that, among people I meet who are involved in other medical research fields, 50% also know about U-BIOPRED. You might think that such widespread recognition speaks to its impact, but I don’t think what we achieved with that project entirely explains it.

I recently participated in a panel for the Innovative Health Initiative, the successor to the EU funding body that supported U-BIOPRED, where we discussed consortium projects and what makes a good funding proposal. At one point, the conversation turned to having a purpose and, more intriguingly, to describing the project in a single sentence.

“Some of the best consortia that have approached me with ideas have been able to show me on one slide or one sentence what the purpose of the project is. I think that is the key. It is almost like you’re building a new business. You should be able to do the elevator pitch for what you want to accomplish so that you can sell that idea quickly and easily.” Maria Dutarte, Executive Director, EUPATI.

Maria’s point and the ensuing discussion led me to a realization: if you can articulate your project’s essence in just one sentence, you are on your way to creating a remarkable project, but then it struck me that it is not just being able to make a slide or a single sentence that matters. It is rather what makes it easy to describe a project in a single sentence that matters.

High concept

In Hollywood, there is a type of film known as a ‘high concept’ film. Jurassic Park is a high-concept film. High-concept films have an underlying concept that is both inspiring and easy to communicate. Something that is high concept can be communicated in ten seconds while evoking compelling possibilities.

The same goes for projects. You can have simple projects that are easy to communicate about, but they might not be very inspiring.

It turns out this easy-to-communicate/inspiring couplet is also considered essential to being a vision-driven leader. A vision that is not inspiring and communicated is pointless; the same should be true for projects driving towards a vision. High concept films and projects are ‘sticky’ ideas, which are described as simple, unexpected, concrete, credible, and emotional stories.

Any project can become a high-concept project if you identify and articulate its core purpose and compelling ‘sticky’ idea early in development. Ask yourself this: Can you describe a project in a single, memorable sentence? Better yet, challenge yourself now to distill your project’s essence into a ten-second pitch. This exercise not only clarifies your thinking but also transforms abstract insight into real-world understanding. If you can’t, the problem is likely not your use of language. It is the concept.

A special characteristic of high-concept projects

There is one characteristic I would add that high-concept projects have that high-concept films do not. That is, the concept needs to be compelling in regard to its feasibility. You tell me you are going to cure cancer. I don’t get excited. You tell me you are going to cure cancer by figuring out how to reprogram my immune system so that it functions like it did when I was 20, you have my attention. More importantly, you might even have my willingness to contribute.

For a project vision to be compelling, there must also be a clear path to achieving it. More importantly, the path to achieving that vision must itself provide a novel, easy-to-understand way to achieve it. It does not have to be easy to achieve. It has to be easy to understand.

Our natural tendency is to avoid or resist developing a high-concept project. We are in love with the familiar. What we do every day is familiar but likely not high-concept. We are afraid of the unknown. A high-concept project is likely to be different, creative, and ambitious. This is why we get excited when the concept of a project also conveys how it is going to get done.

The value of making your projects high-concept

Why should you be interested in making your projects high-concept?

First and foremost, a high-concept project attracts others. Thomas Kelley, in The Art of Innovation, points out that turning work into “wow” projects attracts attention and generates pride, drawing teams “out of thin air.” Seth Godin, the famous marketer/philosopher who describes communities and movements as ‘tribes’, points out that tribes form with a shared interest and a way to communicate. A high concept provides both of these.

This stickiness of a project is a good thing. We tend to overestimate what we can do on our own and underestimate what we can do with others. If you can simply state your project in one sentence, that helps. The trick is to make it inspiring. Imagine speaking less and achieving more; high-concept projects mean you spend fewer hours explaining and more hours building and innovating. Potential funders quickly grasp what they are supporting, allowing you to channel more energy into realization and less into persuasion.

This is important because nearly all major shifts, whether that be paradigm shifts or system-wide transformations, require that a critical mass of stakeholders is aware of and believes in the change. High concept projects are how you demonstrate what is possible. Malcolm Gladwell, in his book The Tipping Point, identifies three agents of change: Connectors (networked people) , mavens (information specialists), and persuaders. A high concept attracts all three of these types and gives them a good reason to apply themselves to your project.

The most important part of a funding proposal is not the content.

One thing I have noticed over the years is that once there is a strong concept, writing a proposal goes much, much faster. When I am asked to get involved in developing a project relatively late in the game, I often find myself in a cloud of confusion. It’s like walking into a dense fog, where numerous disjointed ideas swirl around like mist, obscuring clarity and direction. Rather than distinct landmarks, I encounter tantalizing glimpses of concepts that might fit together, yet lack cohesion. It’s only when we can thread those ideas together into a clear concept that writing becomes easier. Steven Pressfield in Turning Pro also points out that he won’t start any writing project until he is clear about the concept. Clarity about the concept upfront speeds downstream writing.

Perhaps one of the most important aspects of high-concept projects is that not only does the concept help you get funding in the first place, but once funded, they tend to attract more funding. As they are being implemented, high-concept projects tend to spawn new ideas and new opportunities.

High-concept projects are like a funding flywheel powering a neon sign. They attract more and more attention, and more and more people join in turning the flywheel, which then makes the neon sign burn brighter. So, making your project high-concept is not only important for the proposal you are writing now, but also a strategy for generating compounded returns on the effort you invest in developing a research proposal.

How do you make a project high-concept

To develop a high-concept project, you need a blend of elements.

You need to be aware of the trends affecting an entire field. For example, AI is perhaps the most prominent meta-trend right now. This requires having some degree of literacy about what they are about. By latching onto these trends, you are more likely to have a long-term impact. One thing to keep in mind is that impact is typically long-term change resulting from a combination of outcomes driven by project results. One way to create a project with the compelling, inspirational features of a high-concept project is to ground it in a pathway to impact.

You also require the ability to facilitate group thinking. Concepts often emerge and iteratively improve during group sessions. Good concepts are not just developed de novo. They emerge iteratively as ideas are discussed and an effort is made to connect them. For instance, in a recent Marie Curie proposal, the high concept became clear only when we started to think about the acronym and the logo. Up until then, it was just a vague idea. High concept projects are almost always collaborative and multi-stakeholder. You will always get a more inspiring concept by tapping into a group’s collective intelligence and creativity.

It is a process of divergent thinking, followed by a convergence into a high concept. A high-concept project developed in this way makes it easy to maximize collective intelligence and create a high-performance team. We know from multiple studies, most notably a 2010 study published in Science, that a distribution of contribution is one of the three traits of collective intelligence.

This is not only about the project’s concept. It is the beginning of building a culture, and that only happens when others understand what you are up to. A powerful way to embed this culture is through small, repeatable practices that reinforce the desired behaviors. For instance, by starting each meeting with the one-sentence concept of the project, you create a routine that not only clarifies intent but also aligns everyone on the core goals. The third thing you need is clear communication skills (writing), in which you convey the concept concretely. Fourth, it needs to be grounded in impact.

To remember these core elements easily, consider the acronym MAGIC:

• Meta-trend literacy,

• Ability to facilitate group thinking,

• Grounding in impact,

• Insightful communication, and

• Collaborative multi-stakeholder approach.

The most important element is the concept.

If we return to Kennedy’s speech, his sentence about the moon shot is short, clear, inspiring, and makes getting to the moon seem feasible.

When you are developing a project, particularly a big collaborative project, making it a high-concept project is a force multiplier. Doing so will help you secure funding. It will help attract and motivate contributors and secure even more funding when the project is up and running.

Mark Twain is often credited with saying: “I apologize for such a long letter - I didn’t have time to write a short one.”

If he were writing about a big collaborative project, he might have said: “I apologize for the long project description - The project does not have a good enough underlying concept to allow me to write a short one. “
I am passionate about developing high-concept projects, and with the recent advances in biomedical research and technology, there are many opportunities to develop high-concept projects and secure the funding to bring them to fruition.

If you have a project idea you’re passionate about and want to develop it into a compelling, fundable concept, I’d love to help. Get in touch to discuss how we can bring your vision to life and secure the support it deserves.

I just happened to see a link for the livestream on Linked In and watched it live.

I was in the motions of launching a new newsletter focused on the transition from a successful scientist to becoming a leader in your field.

So, I sped up the process.

After having working with hundreds of scientists who have made that transition I wanted to create a newsletter focused on that topic.

I will be covering topics like how to work with multiple different disciplines, the importance of having an impact orientation and more.

The fist post is about what you can learn from the Nobel Prize presentation that happened today, check it out:

The Successful Scientist Substack

This newsletter will remain, but I plan to focus it more on big projects and will be doing a series of interview/workshops with big projects to understand what they are doing and why it matters, as well as what makes the project work.

I will also be guiding a discussion at the end of the session on the future of the project’s field of interest.

I have come to realize that project design begins with anticipating the future.

By positioning your research and your projects in the adjacent possible space of multiple different domains you are more likely to achieve a breakthrough.

If you are part of big project that you would like to profile (free publicity), just let me know.

The EU is poised to nearly double its research and innovation funding, increasing from €95.5 billion to €175 billion over the next seven years. If you are a researcher, this is, for once, good news. It is not only good news for European researchers. For health-related topics, US researchers are also eligible for funding.

The challenge is that EU funding rounds are highly competitive, and that is likely to become even more the case. There are two aspects that make developing EU funding proposals particularly challenging:

1. The format, tone, and focus of an EU funding proposal diverge significantly from what you write in a scientific paper.

2. A large proportion of the funding goes to consortium projects, which means having to bring together leaders across multiple disciplines and getting them to align and agree on the design of a project.

The second aspect, however, also points towards an opportunity. Consortium projects bring in substantial funding, but more importantly, they also give you access to the collective research resources of the entire consortium. By research resources I mean data, protocols, preclinical models, and expertise.

The opportunity becomes even more substantial when you consider that your best source of further funding, and of more consortium projects, is an existing consortium project.

Over the past 19 years, I have helped design and develop more than 60 medical research consortium projects, most of them for EU funding. I see the same mistakes made repeatedly.

It takes a lot of time and effort from everyone involved to develop an EU funding proposal. I see the same mistakes over and over again—mistakes I myself made. If you can avoid these mistakes, your chances of winning funding and multiplying your research resources go up dramatically.

Here are the 10 mistakes I see most often and how to avoid them.

10 Mistakes in EU Proposals

1. Not positioning the research

Something that took me a long time to understand when I transitioned from academia is that there is a spectrum of research and development along what is called the innovation value chain. The innovation value chain progresses from discovery to near commercial implementation.

While researchers often work, at least to some degree, across the whole spectrum of the innovation value chain, at any given time their focus may be on different ends of the spectrum. The problem is that the funding programs and the call topics have a particular range of the value chain in mind. A project that is taking a concept into the market has a different structure, set of activities, and outputs than one focused on discovery.

You have to include partners whose research is positioned for the particular call topic. The best type of partners are those who have positioned their work across the innovation value chain. With those types of partners as part of your consortium, you can target multiple funding opportunities.

I had a group of leading researchers come to me with a nearly completed proposal. They clearly had made an effort to include partners and activities that would allow for the rapid implementation of the project results. In my experience, it is often the case that a proposed project has no clear pathway to impact. However, in this case, the funding programme was focused on radical innovation. With some concentrated design sessions, we were able to make the proposal more radical—but the whole process would have been much more efficient if they had positioned their research first.

Get my free White Paper on positioning for EU funding, by subscribing:

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2. Not defining the problem

It is very easy to forget about the problem you are working on, or at least let it drift away into a vague and hazy concept. However, when you pitch your research in a funding proposal, in my experience, those proposals where the problem is very clear are the ones that do best.

Often this is about granularity. In medical research, everyone is ultimately working on the problem of improving the lives of individuals suffering from a disease. I have found that it is best to go at least one level deeper in terms of granularity than what you initially think.

Getting clarity on the problem is even more important when the project is to be a consortium. The mistake I see all the time is that researchers start with the methods before defining the problem. The consequence is that you have many methods or activities planned, but they do not all address the same set of problems.

3. Work package titles that are too complicated

When I have examined the titles of work packages from successfully funded projects, they are mostly short and very clear: Management, Clinical Study, Genomics, etc.

When I first started helping develop proposals, I tried to create long, intriguing titles for each work package. It did not work. This led to an unavoidable expansion of the titles as I tied myself in knots trying to fit everything in. It is nearly impossible to capture the complexity in a concise title.

4. Ignoring risk planning

Risk planning is often an afterthought: “Oh, yeah, we’d better list out some risks.”

When people do put effort into risk planning, it is often focused only on mitigating risks. Very little thought is given to how to respond if the risk becomes a reality. However, knowing how you plan to respond is more important than your mitigation plan. Reviewers are more interested in how you will adapt to the inevitable problems that arise.

I have spent way too many informal conversations in project meetings perseverating with others about whether a certain bad outcome was going to happen. It would have been better to be thinking about what would happen if it did happen.

Even better is when you can link your risk response plans to particular milestones while designing a project.

5. Having deliverables that do not align with the objectives

Often the writing of the deliverables for a project comes towards the end of a development process. The consequence is that the deliverables sometimes don’t seem to line up with the objectives.

I have had multiple people who review EU grants tell me that the first thing they do is flip through a proposal until they reach the deliverables table. If they can’t make sense of the project just by reading the deliverables, they downgrade their opinion of the proposal.

Deliverables are a strategic aspect of your proposal, and if they are not clear and achievable they can make projects difficult during the implementation phase.

6. Ignoring exploitation planning

EU funding is about excellent science, but it is also about increasing economic competitiveness. Hence, the exploitation planning section of a proposal should be given time and effort. The more specific an exploitation plan is to a project, the more realistic it appears.

When generic platitudes about exploitation make up the exploitation section, the assessed impact of a project is diminished.

7. Light touch on data management

In the past few years, data management has become an important feature of EU projects. There is real recognition that when data is not reusable, it represents a substantial lost opportunity.

Often the tendency is to allocate only a small amount of budget, or to make data management an afterthought. A rule of thumb is that 10% of the budget should be dedicated to data management.

Without a data management plan that describes how you will structure the data and apply standards, the credibility of your plan suffers. It is especially important to include how you will sustain datasets after the project funding has ended.

8. Not involving stakeholders as partners

The desired mode of stakeholder engagement is to engage them as partners, not just as advisers. In other words, they do more than provide advice or review clinical protocols—they are part of the team.

Often the engagement of stakeholders is tokenistic at best. Reviewers see through this, and when we relegate stakeholders (such as patient representatives) to tokenistic input, we miss a big opportunity to get inspired and to benefit from different perspectives.

For example, in the imSAVAR project, patient stakeholder input uncovered a blind spot in preclinical toxicity testing—sex differences.

9. Imbalance in the budget

Particularly in Horizon Europe consortium projects, budgets need to be balanced in terms of distribution across work packages and across partners. If the budgets are put together without balance, the credibility of the work plan is diminished.

A balanced budget is one that stays within the expected range, is distributed across the work packages in proportion to effort, and ensures fairness across partners.

10. Confusing Gantt or timing chart

Gantt charts were not meant to be constrained to a single page. They are designed to depict dependencies and often need multiple pages to be clear.

Faced with this reality, the common move is to limit the information on the Gantt chart—only work package numbers, for example—or to make it overly simplistic, with every task stretching across the entire project.

I assume most reviewers realize this and don’t penalize a proposal for it, but it is clearly a missed opportunity. There is no better way to convince someone of the feasibility and clarity of your plan than a timing chart that communicates effectively.

Instead of following the strict rules of Gantt charts, design a timing chart optimized for communication. For example, end-to-start tasks do not need to be on the next row down. If you label the task bar itself, related tasks can run along the same row.

I have spent many long nights working to fit a big, detailed Gantt chart into a proposal while keeping the text readable. No more—I now take a communication-first approach to timing charts and try to ignore as many formalities of Gantt charts as possible.

Final Thoughts

EU funding can be a powerful flywheel for your research—each successful project generates momentum and resources for the next. Most importantly, a funded consortium project provides access to far greater research resources than the funding alone would allow.

The process all begins with positioning your research correctly.

➡️ If you’d like a deeper dive, download my free white paper on positioning your research for EU funding.

For example, I noticed that the concept of interception, treating a disease before it manifests clinically, was part of the Innovative Health Initiative’s (IHI) strategic research agenda and some call topics before people knew and accepted of the concept.

I would bring interception up as an idea for a project and would be met with dismissive uncertainty.

A few months later, it seemed to be what everyone was talking about. Of course there were pioneers. During that time I met the leaders of the INTERCEPT consortium. They had been wanting to develop the concept of intercepting Crohn’s Disease for decades. We worked together and developed a successful IHI funding proposal.

So, starting in the second half of September I want to hold online workshops where I will use some design thinking approaches to gather perspectives on a specific trend in life science that is reflected in the current Horizon Europe 2026-2027 work programme. We will then identify the opportunities for EU funding.

These workshops will become one of the benefits of the paid levels of this newsletter, but at least the first one will be open for free subscribers as well.

When you register for the meeting you will also have to opportunity to pick your top three trends and I will use that to decide upon the topic for the first workshop.

Register

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Fear paralyzes us in subtle ways.

We were driving to Orly Airport in Paris, France at 6:45 AM on a misty Sunday morning. This was for a big vacation. We were taking our children to the Caribbean, the first time since we were married there 20 years ago. So, a more important vacation than most.

Driving through Paris was the riskiest part of the trip. If there was traffic or an accident, it would delay us perhaps to the point that we would miss our flight. I was full of fear that would happen. I kept paying attention to the time. Okay, if we make it through the first half hour and the GPS destination time does not become later, then there is less risk that we will hit traffic. During the holiday periods in Europe, particularly France, when you travel on a weekend day there is always a risk that you end up in a multiple-hour traffic jam. I recalculated the risk in my head for every 30-minute block, then 15-minute block, and then 5-minute blocks.

Fear is why ambitious projects fail.

Whenever we fear something, we become more cautious, more risk-averse. I see this all the time in project co-design sessions when an ambitious idea emerges. Inevitably, someone levels "This is too ambitious" criticism followed by: "We need to make sure that what we are planning is feasible." This is actually fear masquerading as rationality.

The fear of infeasibility makes sense. Project failure can be damaging financially, socially, and psychologically.

The problem with fearing infeasibility is that the more ambitious a project is, the less we are able to judge its feasibility. With an ambitious project, there is no precedent. You can only truly judge feasibility by trying. We also overestimate what we can do on our own, and underestimate what we can do together.

Simply being bold enough to ignore the feasibility criticism and start an ambitious project is not enough. Fearing and thinking about potential risks over and over again takes time and energy and usually, as was the case that Sunday morning, does little to change the outcome.

When innovation progress gets stuck, it is because otherwise would-be game changers embrace the feasibility criticism and temper their ambitions. They are also blinded by their need for certainty and miss new opportunities that make the infeasible, feasible.

Jason Crawford, the author of The Techno-Humanist Manifesto, points out that humans are problem-solving animals and that we have a history of solving unsolvable problems.

"Our problem-solving ability is based on two deep and powerful facts. The first is that reality contains a vast space of possibilities in which to search for solutions. This is because the possibility space is combinatorial: new possibilities are created from the combination of simple elements. And combinations grow very quickly."

He backs this up by pointing out that if you are engineering proteins or simple organisms like yeast, there are10³⁷⁹ and 10^577,584 functional possibilities respectively. In other words, it is highly likely that there are solutions to the problems that make ambitious ideas seem infeasible.

Ambitious projects are a profound act of creativity happening in a vast space of possibility. Creativity cannot be planned. It requires the willingness and ability to react to opportunities. Embracing the creative nature of ambitious projects is how we avoid ceding our ambitions to the feasibility trolls and the uncertainty hobgoblins of our minds. However, we still need a way to cope with the fear of the uncertain; otherwise, we will put too much energy into worrying.

Coping with our fear of uncertainty

In project design, we attempt to cope with uncertainties by quantifying them as part of risk planning. It is a common practice to give each risk a score, a combination of probability and magnitude of impact. We then proceed to plan how to mitigate, or prevent, the risks with the highest scores.

However, this exercise tends to fan the flames of uncertainty. This is especially the case when there are risks driven by external factors we cannot control. To mitigate a risk, you have to have some control over the factors that feed into the risk. What are we then supposed to do? Hope for the best? Just live with the fear of uncertainty?

The experience of driving through Paris made me wonder if there are better ways to handle fear, a way that would reduce the waste of energy that accompanies fear. Then I remembered a process I learned about a few years ago.

Tim Ferriss practices what he calls fear-setting. Instead of goal setting, you set your fears. You think about what will happen if the fear you have comes true. What will you do, or what will be the possibilities if what you fear comes true? It is about deciding how you will deal with the consequences a priori.

You could look at fear-setting as a means of being prepared. A way of mitigating risk, but it is more than that. It is about giving ourselves the confidence to proceed in the face of fear, in the face of uncertainty.

Had I thought about this earlier that day we were traveling through France, I might have done some fear setting and realized that if we got stuck, there would be multiple flights each day to our destination from that airport.

It turned out there was no problem. We made it to the airport on time and I am now writing this looking out over the Caribbean.

Applying fear-setting to ambitious projects

There is a part of risk planning that is similar to fear setting, but is often overlooked.

We need to plan a response in addition to planning mitigation. When the biggest risks become reality, it often means we have to reduce our ambitions. However, with a good response plan in place, the project will still be worthwhile.

We can go even further and design our ambitious projects in a way that makes them more able to respond. A response-able project is one that is designed in such a way that the effort will not be a waste even if the biggest risks become reality.

For example, if a project is to identify a biomarker that allows us to select out the patients who would respond to a particular therapy, it is both ambitious and risky. What if no biomarker provides enough separation between different subgroups to allow us to predict who would respond? A response-able project design would be to make sure that the samples and data you collect can easily be put to use even if the primary goal of finding a predictive biomarker fails. If it fails, your response could be to use the samples and the data to understand some of the mechanisms that underlie the disease process.

Knowing that even in the face of failure the project will deliver something of value makes it easy to engage in an ambitious project without wasting energy on worrying about risks. That energy can be redirected towards being curious. Making a project more response-able also makes a project more adaptive, more able to pursue those nearly infinite possibilities when they arise.

As I describe in Assembled Chaos, when we started the U-BIOPRED project we were told multiple times that integrating different types of high dimensional data, “omics”, was not feasible. We did not know how to do it. It remained part of our ambitions, be we figured even if we could not integrate multiple types of omics datasets, having a well phenotype cohort of individuals with sever e asthma would in and of itself be highly valuable. Today, integrating omics datasets is part of many projects in the life sciences. U-BIOPRED was part of the beginning of a wave of innovation.

How to build response-able ambitious projects

1. Create a counterweight to fear—a compelling vision. One of the best ways to create a compelling vision is to give it a face. Have stakeholders tell their story. Having a deeper understanding of the problem and its human impact is always motivating.

2. Conduct a premortem. Develop a future scenario where the biggest risks of the project become reality. What could you do instead?

3. Build the ability to respond into your project. Include efforts to make the assets, i.e., datasets, such that they can be repurposed. Plan to build relationships with other initiatives that can provide alternatives. Put effort into making the protocols and standard operating procedures reusable.

4. Structure your milestones to include decision points where you will implement your responses. This helps to remind everyone about the response-able nature of your project.

5. Get curious about failure. Don't just ask how, ask why, and then identify new opportunities.

Resilient ambitious projects

David Krakauer, President of the Santa Fe Institute, considers science as a collective wave, not a collection of discrete particles. Build your project to be able to respond so that it will contribute to the collective wave of innovation in your field regardless of what happens.

The Big Project Collective is a newsletter where leaders in their field learn how to build and deliver resilient ambitious projects.

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When you read it come back here and provide some of your thoughts or topics you would like to learn more about, or suggest a fundable frontier in medical research that you would like to review.

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Some of the most important projects in any field are large-scale collaborations or consortium projects. It's pretty much a given that if you want to move a field forward, it has to be through a large-scale collaboration. Silos or smaller-scale collaborations are too novelty-focused and incremental to make a real difference.

The proposals that secure funding for these types of projects require a collaborative writing process. Perhaps we could better describe it as a collaborative design process. Thinking of it as just writing a proposal assumes that the writing is the hardest part—it's not.

The hardest part is figuring out what you are going to do in the project and how it integrates with what everyone else is doing. One of the biggest factors that makes this difficult is that we do not know how to provide input.

A common approach to giving input is to read the proposal and make edits as you go. This approach is wrong for many reasons. First, editing is something that comes toward the end of the writing process. Often I see people immediately jumping in with text edits right from the first draft. This is also problematic because the piece will lack any voice as multiple people choose a style and choose words that sound best to them.

A funding proposal is a logical argument, a pitch for funding, and that requires a clear-to-follow line of logic that is hard to achieve with too many voices. This style of providing input is worsened by the curse of knowledge. When we know something and what is in front of us relates to it, we want to add what we know, even if it disrupts the logical flow. The result: obfuscated and confusing text.

What many people don't realize is that when it comes to a collaborative writing project, there are four different types of input, and the best ways to provide each of them are not directly obvious.

Four Types of Input

1. Verbal

Yes, the most important input to a collaborative writing process is verbal.

Merging different perspectives and meeting diverse needs requires rapid and real-time feedback. If anything, a good discussion helps to build mutual understanding. For collaborative writing, the discussion is part of the writing process. Once you have worked out the concept of what you are writing about, the actual writing part becomes much easier.

2. Strategic

This is the input you give in the early phases of writing together. It consists mostly of comments or questions on the written text. This can be very helpful. If anything, it tells the person who drafted the text that more could be done to explain the point he or she is trying to make.

3. Content

You may be asked to contribute content. The key for this type of input is to lose your attachment to perfection. When you are asked to provide content, it is because your expertise or knowledge of you and your group are needed. It is not a request for polished text.

The problem is when this content is delayed, it becomes a bottleneck for the whole effort. Often, what you are really thinking to do becomes more detailed when you write it down. Writing is a thinking process. Thus, it is more important to produce a rough draft or even a copy and paste from an old document quickly than a refined piece of content later.

What would meetings be like if everyone took the time to polish and refine their thoughts before speaking? Collaborative writing thrives on the rapid cycle iterative process of providing input before it is perfected.

4. Editing

First, edit for logical flow. Does the argument of each sentence, each paragraph, and each section make sense to you? Is there something missing? Then edit for spelling and grammar, and unless you are the primary writer, don't change the voice of the writing.

The last thing you do is edit for grammar and spelling.

When the Input Comes Matters

It was a Friday evening, and usually I don't do any work on Friday evenings, but the deadline was the next Tuesday. So, I opened my email inbox. There they were, the work package write-ups we had been waiting weeks to get.

I opened the first attachment and scanned the document.

Then the next two.

Uh oh.

I sagged in my chair.

The work package descriptions were off. In this funding program, the call topics were very specific. What we were submitting was not answering the call. It was doing what everyone did routinely for their research.

I consoled myself—at least we had something. Maybe the reviewers wouldn't notice?

The next day, when the sun was still rising, I looked at them again. It dawned on me: I can shape what they sent to fit the call topic. It was going to take some bold changes.

I called the researcher who was coordinating the proposal and explained what I was thinking. He agreed.

Some scrambling and some late nights. We restructured the work packages and explained it to the rest of the researchers.

It turned out to be one of the highest scores we have ever seen a consortium get on a funding proposal.

Don't Write in Silos

Our role in a collaborative proposal, or any type of collaborative writing, is not to produce our own siloed version of what you would like to do, or what you alone think. It's to be part of a process of collective thinking.

Writing is thinking, and writing is leadership. Collaborative writing is collective leadership.

We all know how to think on our own; we also know how to think together. Little attention is ever given to the skill of writing as a form of thinking.

Most writing advice is about clarity or building a story. Sure, there is a lot of advice about journaling, which is clearly a type of thinking, but that is writing or thinking in a silo.

Collaborative writing is a process of highly structured thinking. Like any structured process, it is important to know how to optimize that process and to be clear about the inputs needed.

"When a person reads a piece of writing, it changes the way they think. When you change the way someone thinks, you may change the way they act. Writing, therefore, is leading." —Nathan Baschez

A great collaboratively written document is a work of art. It also is likely something that we will refer to and serve as a guide for a long time. It will definitely change the way people act.

We should double down on our commitment to collaborative writing and hone our skill in providing input:

• Prioritize attending meetings where the concept is being developed

• Give strategic input—ask questions and pose new ideas

• Use a prototyping mindset when providing content—it is just a draft or first thoughts

• Avoid changing the voice of the writing when asked to copy edit—read the piece of writing through, then go back and provide suggested edits

• Read and re-read the entire piece, even the parts you think don't concern you

If you believe as I do that collaborative writing is a powerful form of leadership, support the Big Project Collective and learn how you can make changes that matter with big projects.

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We become dissatisfied with work because we keep chasing what's new.

Satisfaction and meaning come from mastering the familiar.

"Satisfaction lies in mindful repetition, the discovery of endless richness in subtle variations on familiar themes." George Leonard

What most do not realise is that meaningful work is not just about finding the right kind of work; it’s about how you approach your work.

Medical science is an inherently meaningful type of work, but there are many medical scientists who are dissatisfied and seek to find more meaning in what they do.

In this article, I will show you how switching from a novelty bias to a mastery mindset will increase both your sense of satisfaction and meaningfulness.

Understanding mastery

Mastery is the achievement of comprehensive skill, deep understanding, and effortless competence in a particular domain through sustained practice and learning. It represents a level of expertise where complex tasks become intuitive and performance appears natural rather than forced.

Several key elements characterise mastery:

Deep Knowledge: Masters possess not just surface-level skills but profound understanding of underlying principles, patterns, and connections within their field. They grasp the "why" behind techniques and can adapt their knowledge to novel situations.

Automaticity: Through extensive practice, masters develop the ability to perform complex skills with minimal conscious effort. A master pianist's fingers find the right keys without deliberate thought, while a master surgeon's hands move with practiced precision.

Pattern Recognition: Masters can quickly identify meaningful patterns and relationships that novices miss. They see the wood rather than just individual trees, allowing them to make rapid, accurate judgements.

Continuous Learning: True mastery involves recognising that learning never ends. Masters remain curious, seek feedback, and constantly refine their understanding even after achieving high levels of competence.

Creative Application: Masters can transcend rigid rule-following to innovate and create within their domain. They understand the fundamentals so deeply that they can bend or break conventional approaches when appropriate.

The problem is that if we do not have the patience and perseverance to let mastery develop, we are limited to inferior performance, inferior creativity, and inferior strategies. This is supported by what we know about neuroscience.

Neuroscience of mastery

There are two types of learning: fast learning and slow learning. Studies have shown that in fast learning, existing neural circuitry is repurposed. In slow learning, new neural circuits are formed.

Once we have built the new neural circuits, we can do something that seemed impossible before.

Josh Waitzkin, in his book The Art of Learning, discusses how the masters of the sport of Tai Chi push hands are often thought to possess magical abilities because their opponents seem to fly across the mat without being touched.

When in reality, the masters have laid down neural circuits so they can move very quickly and in ways we don't expect. Waitzkin goes on to explain how, once he achieved mastery, he was able to defeat one of these magical Tai Chi masters who seemed to anticipate every move he made by thinking of a different move until the very last moment. Waitzkin's mastery allowed him the space to think of and implement this strategy in the course of a match. The routine actions and reactions of Tai Chi push hands were automatic for him.

If we constantly default to fast learning in pursuit of novelty, we never develop mastery and all the benefits that come with it. We are stuck with assembling and connecting together existing neural circuits, and that does not promote one of the most sought-after states of mind.

Don't stop at the plateau.

In the process of developing mastery, there are always plateaus. The first one is after what we typically call 'beginner's luck'. George Leonard points out that those who achieve true mastery learn to love the plateau.

For the past 18 years, I have been a consultant by accident. When I moved to Belgium, the idea was to continue working as a physician-scientist. However, due to cross-border work permitting issues that was not possible. So, I started to help researchers prepare proposals for EU-funded consortium projects. It was hard.

Bringing together multiple different researchers and other types of stakeholders, getting them to align, decide and contribute to 100-page proposal was not simple. But then we had some success, and I have now helped develop more than 60 consortium projects. However, there have been times when I was not in love with the plateau. I assumed that the problems that arise in a collaborative proposal development process were just inherent to the process. I tried different solutions, and they did not seem to help much.

I then began to chase shiny objects: different types of consulting that were not about proposal development, in hope that I could walk away from the brain-busting problems of a collaboratively developed funding proposal.

A few years ago, I was managing a small team doing this different kind of work. I realised that I missed the scientifically creative aspects of developing a proposal. So, I reconfigured what we did as a company, and now I am starting to see some of those thorny, persistent problems resolve as I have focused more on proposal development. I was not in love with the plateau. I had abandoned my path to mastery. This raises an important question.

How do we fall in love with the plateau?

Mastery and flow

Mihai Csíkszentmihályi is credited with defining a mental state that we as humans crave - flow.

Flow happens when the task we are doing is just beyond our abilities.

Flow requires a degree of mastery.

When the task is too hard, we have to think too much about what we are doing. In a flow state, mastery enables you to get to that point of being on the edge of constant improvement. Mastery also allows you to be nearly automatic about the aspects of the skill you mastered. Like what Waitzkin describes, this allows you to be strategic while performing at a high level. It is then that we lose the sense of time and develop a deep sense of satisfaction.

Focusing on flow states allows us to at least tolerate the plateau.

However, mastery and the flow it enables do not lead to a sense of meaning. The virtuoso pianist who masters her art does not just play in empty concert halls. She gives performances and touches the souls of those who listen to her. Those moments on stage give meaning to her skill as a pianist. The thing is mastery-fuelled meaning is not something only available to the small percentage of people who achieve virtuoso status. It is available to all of us.

Collective Mastery

Human intelligence is the result of 3.5 to 4 billion years of evolution. That is a time scale that is hard to fathom. The upshot is that we grossly underestimate what is possible with billions of years of evolution. I believe that in that time period the human brain has evolved to be the best possible manifestation for what the human brain is good at. In other words, if we were to build something that matches human intelligence, we would end up creating human beings, including what we think of as flaws or limitations.

You would be right to dismiss this argument straight away with the logic that "ChatGPT is already smarter than me." Therein lies the point. Human intelligence is a generative intelligence made up of 8.2 billion nodes that are infinitely rearrangeable. What's more, the problem of powering these nodes is largely solved.

This means we can think of humanity as one big collective intelligence. The challenge is getting all those nodes to communicate and work together. It also means that, like fast and slow learning in our own individual brains, there are both fast and slow collaborations. Fast collaboration is the small team that quickly makes decisions. It’s the origin of move fast and break things. It relies mostly on experience and hierarchy. Slow collaboration is the deliberate collective thinking. It is about challenging norms and developing something new. It’s solving a problem that, when you first meet with a group, you have no idea how it is going to be solved. However, slow collaboration can be fast. Slow collaboration, just like our brains, is about building new circuits and relationships.

I have witnessed both slow and fast collaboration in consortium projects. Sometimes decisions just need to be made and a few leaders make them. On the other hand, the ability to achieve something can be slow to develop. This is particularly the case when the collaboration is about bringing disciplines together. Once those circuits are formed, a multi-disciplinary collective can solve difficult problems quickly. Slow collaboration is also about investing effort in developing assets such as datasets, which can then be used repeatedly.

Consortia, like individuals, can create group flow. This makes the many meetings that take place in a consortium project energetic and enjoyable. As the relationships build, the ease of challenging each other and building on each other’s ideas rises. The meetings then become engaging whether you are part of the agenda or not.

Consortia also go through periods of low achievement or plateaus. This is particularly true at the start of a consortium project.

Consortia that invest the time and effort in building relationships and exhibit patience to move through the inevitable plateaus are the most powerful force for collaboration. This makes the work we invest in collaboration meaningful. While very few of us will ever develop virtuoso-level mastery in a skill like piano playing, by working with others we all can develop collective mastery. We can stand in awe of what we have achieved together, and our sense of dissatisfaction will give way to a deep sense of meaning in our work.

Chasing new things limits our potential.

The problem with chasing new things is that you may end up not developing mastery on an individual or a collective level. For example, the bias towards novelty is what stalls progress in medical science. Instead of doing the hard work to collectively push scientific findings forward into the clinic, a lot of promising medical science languishes.

The one caveat is that in collective mastery, the focus is built around a vision and doing what is necessary to realise that vision. It means that the vision is a sort of a framework for developing multiple new opportunities. The key is that they are all aligned under a common vision. Collectively everyone is moving towards towards that vision.

Turning away from the new and refocusing on the familiar is how you get to the meaning that is hiding in plain sight. This means developing individual and collective mastery:

1. Decide on your mastery pathway.

A mastery pathway should include both individual and collective mastery.

What have you been developing?

Do you feel stuck?

Decide if it is a plateau on a much bigger trajectory.

Everyone should have a skill or asset they are developing on an individual or local team level, and everyone should have at least one project where they are part of a collective mastery effort. Develop your individual skill or asset for use in the collective mastery project.

2. Learn to love the plateau.

a. Recognise the plateau for what it is

Just realising what is happening helps motivate your perseverance. In a collaboration, focus on the collaboration - help each other solve problems.

b. Reflect back on how far you have come.

Remember what it was like when you started. When I started, I was clueless about project development. I had to look up what a 'deliverable' was. To keep a consortium motivated, communicate progress.

c. Cultivate flow

Flow states in their own right are satisfying. Make it a point to get into flow in your work. Enjoy the flow. For a collaboration, invest in facilitating the interactions. Good facilitation can turn dreadful meetings into meaningful experiences.

d. Commiserate with others

Sharing your challenges is a great way to form connections, and connecting in and of itself is worthwhile enough to help you persevere on your current plateau. A consortium project is a great forum for commiserating. It becomes superpowered when you stakeholders that are affected by the problems you are addressing, like patient stakeholders. Their stories leave everyone inspired and motivated.

e. Remember that the new thing you are considering will come with plateaus as well

Keep in mind that when addressing a complex problem in a collective setting, you will need to continually develop new projects, new opportunities, but they should contribute to resolving the problem or the bottleneck that you are focused on.

Your meaning is already here.

The meaning you seek isn't waiting to be discovered in some distant, novel pursuit, it's already present in the work you're doing now, waiting to be unlocked through mastery. When you shift from chasing what's new to deepening what's familiar, you transform routine tasks into opportunities for flow, plateaus into periods of integration, and collaborative challenges into shared triumphs.

Stop looking elsewhere. The meaning is already here, hiding in plain sight.

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We have optimised medical research for the wrong metric, and it is sabotaging our own success.

It is now, more than ever, imperative that we do something about this. Medical science has come under attack, and the reality is we have made it easy for them.

The reproducibility Achilles’ heel.

I just read the other day that the reproducibility crisis in science was used as a justification to cut NIH funding. It’s hard to make a sound counterargument.

The primacy of publishing novel findings in high-impact publications as a metric for academic success blots out any concern about the lack of reproducibility. Yet reproducibility is not a new problem. It’s been a topic of discussion for decades. When a system has a simple positive feedback loop that promotes novelty over everything else, the system becomes very difficult to change.

The long-overdue shift to treating biology, not disease

Making use of the exploding ability to characterise all of the molecules that our cells are producing, has led to an exponential expansion of our understanding of the complexity of biology. This has led many to suggest that we are on the verge of a new era where we abandon old definitions of disease to focus on the characteristics of biology.

However promising that might sound, we have been constrained by arbitrary definitions of disease for too long. Our approach to health and disease has calcified into a system optimised for novelty that resists change.

We have known for some time that processes and even the cells that orchestrate those processes are the same throughout the body. Take fibrosis, for example. It is a critical feature in many different diseases.

Developing anti-fibrotic therapies is certainly happening. Yet the challenges are that fibrosis is a complex process and likely needs to be attacked in multiple ways, and that anti-fibrotic therapies often have side effects that are hard to tolerate.

This highlights the challenge. Even though fibrosis is a process involved in many different diseases, the concept of fibrosis is itself a bucket of underlying biological processes. For example, here are the known fibrotic processes in the liver:

It is likely that each one of these processes can also be broken down into a similar diagram. Many, if not all, of these molecular components are involved in other disease processes.

The future could be that we go to the doctor, he or she does not just prescribe an anti-fibrotic. Instead, he or she draws blood or collects samples, and our molecular processes are characterised. Then we get a personalised treatment regimen of multiple medicines and even a modified version of our own cells that tunes up the processes that have gone awry.

Aside from oncology, the practice of medicine remains bound to existing disease definitions which lump people together into artificially defined groups. The focus on novelty, not impact, has not helped resolve what is an unbelievable translational gap of 17 years from research findings to clinical implementation.

What is holding this shift back?

It is the optimisation for novelty. Researchers who find new molecules or pathways are rewarded.

What we need is more work to understand how these processes all connect together.

More work to know how we can embrace the complexity of what we know and develop therapies that nudge our underlying biological processes back to a healthy state.

This is how we get to treating biology and not disease.

For example, a basket trial where, based on molecular profiling, a custom anti-fibrotic and anti-inflammatory therapy regimen is delivered. We also need increased efforts to understand the molecular interactions and what they mean to developing new therapeutic strategies.

I should disclose at this point that I am an adviser for the company TopMD, which is creating pathway biomarkers by incorporating the complexities of biological processes into a topological map. It is analyses like this that will help enable a new paradigm of treating biology and not disease.

Crisis as an opportunity

The diminution in the valued position of science is very troubling. It’s troubling not just for the US, but the entire world. Many of the scientists I work with are the leaders in their field in Europe, this is important for them too. The drastic cut in science funding is a symptom of a much deeper disease. Simply restoring the funding in the US would be a missed opportunity.

Restoring the funding does not address the underlying problem - the incentive to be novel, not confirmatory, not synergistic.

Novelty is the wrong performance indicator.

Maybe at one point, when our understanding of biology was much simpler and there were low-hanging target molecules, a novel finding would equate with a rapid pace of new therapy development. That is no longer the case. We need to shift from a scientific mindset that values novelty to one that values impact.

The system, at least in the US, has experienced a shock, and when there is a shock it is a good moment to consider making changes. The system right now is less capable of resisting change. The damage is done, now is the time to rebuild better, but how?

Valuing collaboration

Europe has a long history of valuing collaboration over individual performance. Probably because the amount of funding available for medical research in Europe is a fraction of that in the US, even with the cuts to the NIH. EU funding bodies put a premium on large collaborative projects and student exchanges, both of which are ways of doing more with less by banking on the added value of combining different disciplines and different perspectives.

Does it work?

The evaluation of the one of the bigger programs that the US has sought to emulate, Innovative Health Initiative (IHI) suggests that it does.

The IHI is a public-private partnership that has funded more than 220 consortium projects. I know from experience that these projects are a fantastic opportunity to experience all of the good and all of the bad of large-scale collaborations.

When you look at field-normalised citation impact, all of the very first IMI call projects were above the field-normalised norm (the IHI was call the IMI at that time). In later calls some projects were 5x the norm. The U-BIOPRED project, a IMI first call topic, was a project which was 3x the norm is a project I helped develop. That project formed a lot of my thinking about how to make a consortium project highly interactive. This metric is, of course, anchored in publications which are biased towards novelty; nonetheless, it validates the potential of the collaboration through a big consortium project approach.

The reality is that many of these types of collaborations are still constrained by the dominant novelty focus of science. The novelty focus fuels silo thinking even when you are supposed to be collaborating.

Many of the IHI projects have been focused on improving our ability to have a finer-grained understanding of the biology of health and disease. This positions them well to drive the transition from treating disease to treating biology. What is more important that most do not realise is that consortium projects are where stakeholders come together to break the bottlenecks that are holding back a field. It is this innovation implementation focus that makes consortium projects our best hope for rebuilding the degrading trust in biomedical science. One of the best ways to deal with the whims of politics is to focus on delivering unmistakable value. Unmistakable value defangs politics.

Now is the time to make a change.

The opportunity we have before us is to move beyond the shock that the defunding of biomedical science in the US has created and push forward with more funding of collaborative projects that move us toward a paradigm where we treat biology and not disease. Such a move is likely to open up many new ways to treat disease.

It is also likely to open up deeper personalisation of medicine, which with the new forms of treatment has become more possible than ever before. A really exciting opportunity is that it will make the process of intervening in the pre-disease phase possible.

It is inherent that we understand the biology that leads to a disease in order to intercept a disease before it manifests. Otherwise what are you treating? Here focusing on the biology enables us to treat to biomarkers before anyone has to suffer from a disease.

The reality is that the slow pace of translating medical research into meaningful impact has put medical science at risk. It is not that medicine has stagnated. No, indeed there are new forms of treatment. There is also a whole lot of potential that goes unfulfilled.

Potential to deliver the right medicine to the right patient at the right time.

Potential to deliver treatment regimens that achieve nearly 100% effectiveness.

Potential to interrupt disease processes before they produce disease.

At the root of a lot of lost potential is the inability to shift to treating biology, not disease. This stagnation was inevitable. When we optimise for novelty and not impact, the system will be weighted towards producing novel insights that are not reproducible.

Consortium science as the solution.

To break free of this stagnation, more emphasis should be placed on consortium science. When we work in a consortium project, it is almost a given that we will share techniques and it’s a great format for reproducing models and results. Most importantly, consortium projects endow us with collective agency.

Agency to solve the problems that arise in the delivery of a project.

Agency to create the critical mass that can have the influence needed to shift paradigms.

Agency to drive forward the impact we are all waiting for.

This means that NIH funding should be restored with an emphasis on funding consortia that break the bottlenecks holding back innovation. Even in the US, there is a good track record of this with the Cell Atlas projects and, of course, the Human Genome Project.

It also means that rewards in science should be focused on what has been done to move towards impact, not novelty.

On an individual level, it means choosing to invest time and effort in big consortium projects. It means grounding even your most basic science work in impact.

This newsletter is about the type of big projects we need to make medical science even better than it was before. Show you support by subscribing.

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We abandoned the concept of personalised medicine in favour of precision medicine 15 years ago; that was a mistake.

The problem with precision medicine is that it still lumps people together; it’s just smaller groups.

What most do not realise is that truly personalised medicine is becoming increasingly feasible.

It will be the future, unless we fail to do the work that needs to be done to change the current system.

Why we walked away from personalised medicine.

It was a grey autumn morning 15 years ago when I sat leaning forward towards the desktop speaker. T

We were debating how to frame a grant funding proposal. Specifically, we were having a dialogue about the term "personalised medicine".

"I just can't imagine we would ever be able to have truly personalised medicine. There just wouldn't be enough money to fund the clinical trials you would have to do, and how could you make a medicine that takes years to formulate and manufacture personal?"

I could hear, or at least imagine, the nodding of everyone on the call.

This was not just our small group; this shift happened across many different fields. The thinking that personalised medicine was not possible became codified when the National Research Council asserted that personalised could be misinterpreted to mean developing treatments for each individual.

Instead, the idea was we should aim for precision medicine. Instead of treating, for example, all of asthma the same, you work to define different sub-groups of asthma. However, it would no longer be a misinterpretation to think we can develop treatments for each individual.

The future of medicine should be treating the individual, not disease.

Engineered cell therapy could make truly personalised medicine possible. Engineered cell therapy such as CAR-T involves removing our own cells, modifying them to better target a cancer, and putting them back into our body to do the work. There is also growing evidence that CAR-T or a similar approach can be used for other diseases as well.

CAR-T represents a shift. A shift away from discovering new medicines to engineering them. As such it represents the fuzzy border between science and engineering. With CAR-Ts it should be feasible to make modifications rapidly - days and maybe even hours. The biggest implication of this is that truly personalised medicine is no longer impossible.

Will the need for randomised controlled trials hold us back?

As the number of targeted drugs and the number of biomarkers grow, it becomes more difficult to run randomised controlled trials (RCTs). It becomes increasingly challenging to find patients with a particular biomarker.

When we talk about complete personalisation, we will need to have biomarker profiles unique to each individual. RCTs not only become difficult; they become impossible.

Are we willing to accept new forms of therapy without RCTs?

Without extensive toxicity testing?

The rising concerns about vaccines, which are some of the most tested interventions we have, should give us pause that moving away from RCTs for most therapies will be straightforward.

A placebo-controlled RCT is our best way of knowing in an objective manner if an intervention is working or not. Even so, RCTs are inherently flawed because they do not represent the real world. The real world, however, is messy, and it is difficult to know if the effect you see is due to the intervention or some other variable.

Let's say you have a cold and you have heard that a raw egg in a milkshake cures the common cold. You try it, go to bed and the next morning your cold symptoms are gone. You might logically conclude that an egg in a shake works. However, the common cold tends to resolve rather suddenly. You may have awoken with no symptoms, egg in a shake or not. A randomised controlled trial would reveal this false positive result when the majority of the study participants saw no effect with an egg in a shake. Individual experience is too compromised by confounding variables.

True personalisation would require us to have faith in our understanding of biology, real world data and the ability to predict the effect of a particular customised intervention without having the luxury to study it for ten years.

Maybe AI can save the day.

There is a lot of hype that AI can cure everything. This might be true, but what any AI needs is data upon which to train. It even goes deeper; it needs knowledge upon which to train. Considering that we are still uncovering new dimensions to measure and consider in biology, such as spatial relationships, single cell expression, as well as the impact of the environment, etc., we don't have the knowledge to train the AI. More troubling is that even if we had the knowledge, we would need to have a way to organise and share that knowledge or data. Right now, the practice of medicine and the practice of collecting complete data varies widely, introducing bias into datasets.

Furthermore, we will need to have the ability to study those molecular processes on an ever-increasing scale. Personalisation requires more molecular real-world data. It requires that such data is available and usable to build knowledge.

Progressing towards truly personalised medicine is a wicked problem.

There are too many features of the current system and too many interdependent stakeholders to easily progress towards truly personalised medicine. It is what is known as a wicked problem.

I believe we need to lean into multi-stakeholder problems while not expecting them to get dramatically better anytime soon. The only way they will get better is if a critical mass of stakeholders adopts a new paradigm.

To make this shift, there has to be an evolution.

First, we must build awareness about how the current system is not working. Then we to demonstrate the feasibility and the value of the change. The third step, which is often missed, is diminishing bottlenecks or barriers to change. The last step is developing the concept into a broad community that is ready to take over from the old system.

Big projects lead the way.

To move towards truly personalised medicine, there are several concepts that big projects need to develop:

1. Organising, structuring and molecular data available.

2. Developing and maintaining cohorts that include molecular profiling.

3. Advancing the engineering of cells and other effectors to manage diseases

4. Achieving a sufficient level of evidence for regulatory approval without a RCT.

5. Identifying biomarkers to guide treatment in the pre-disease phase

6. Developing generative AI to support medical research and clinical decision-making

7. Adaptive studies where treatments are tailored to the individual.

8. Building an understanding of stakeholder perspectives on personalised medicine

There are many large projects already working on these concepts. Here are a few examples:

P4O2: Building cohorts of lung disease (at risk, COPD, Long COVID, pulmonary fibrosis) that includes molecular profiling

T2EVOLVE: Developing preclinical models, stakeholder perspectives, and influencing the regulatory environment to drive the development of engineered cell therapy forward.

GOE: Organising the genome data for 500,000 people in Europe.

Please add your ideas for other concepts or projects working towards truly personalised medicine

Leave a comment

What you can do.

No matter who you are, there is a role for you in reviving truly personalised medicine.

If you are a well-established researcher and you perceive a barrier to the realisation of truly personalised medicine:

1. Convene a consortium around the barrier/problem that you perceive. Do this even if there is no immediate funding opportunity.

2. Start a simple project together, a prototype project.

3. Seek funding for large projects

If you are a researcher with a technique or asset that you would like to develop further,

1. Develop a description of that technique and asset.

2. Contact relevant big projects and join as community members.

3. Contribute and build relationships to become involved in the next project.

If you are an early-career researcher involved in a big project

1. Step forward and volunteer to lead efforts in the project that are directly part of the project plan and require leaders.

2. Deliver more than expect to demonstrate your ability to lead

If you are a member of the public,

1. Seek out and get involved in projects - many will have educational videos and stakeholder groups you can join.

2. Offer to contribute your expertise regardless of what it is; problems are solved more quickly with a multidisciplinary effort.

What you achieve in big projects is what will be remembered. It is also what you will enjoy remembering the most.

I am creating a roadmap for guiding different stakeholders on what they can do to help drive forward the kinds of big projects that will make a real difference.

To get a copy when it is done, subscribe to this newsletter.

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The deliberative component of democracy declined in 27 countries in 2024. This erosion of our ability to reason together isn't just a political crisis, it's a fundamental threat to human problem-solving capability.

At the heart of this crisis lies a set of cognitive viruses: logical fallacies. These flaws in reasoning limit our potential as humans, and we often succumb to them because of their deceptive simplicity. It is also easy to ignore or simply accept them.

Many logical fallacies tend to hide in plain sight. By knowing how to identify logical fallacies and how to call them out, we can do a lot to limit their damage.

The straw man logical fallacy

17 years ago, in the drab confines of an academic meeting room, I heard the term 'straw man' used to describe what was really a prototype. It was a diagram sketched out with simple boxes on a slide.

The prototype was the start of what ended up being a productive way of working in that particular consortium project. I became enamoured of the straw man approach. Fortunately, I did move on from the term 'straw man' to other terms such as 'fast mover', 'low resource', and now I am more fond of just calling them all prototypes.

Recently, I came across an article that mentioned 'straw man' in the key takeaways. One groggy Sunday morning, after waking early to make forgotten bread, I read the article. While it clearly dispelled my wrongly conceived conception of the term 'straw man', I learned that the straw man fallacy has a particularly corrosive effect, but there is something we can do about it. But first, let's clarify what a straw man is and what it is not.

A weak form of rhetoric

Wikipedia quotes Stephen Downes for the definition of a straw man.

"A straw man fallacy (sometimes written as strawman) is the informal fallacy of refuting an argument different from the one actually under discussion, while not recognising or acknowledging the distinction."

An example of a straw man argument would be if someone says a restaurant is too expensive for a casual dinner and the response is, “So you are cheap and never want to spend money on anything nice.”

Another straw man argument is saying that a scientific paper received money from industry; therefore, it must be disregarded. While undue industry influence is a real concern when you are analysing a scientific paper, that is not what you are debating. A scientific paper should be evaluated based on whether its findings hold up under scientific scrutiny.

That it was funded by industry is irrelevant to that argument. It may mean that you should be particularly vigilant, but you should be able to judge the scientific merit of the study based on its methods, results and analyses.

I am certain you can think of a lot of straw man arguments that are flying around in this currently hyper-charged political climate.

Once you have a name for the fallacy, it is easy to call it out. "That's a straw man argument." Of course, you may need to clarify what that means. It does, however, seem like you could find yourself doing that a lot on social media and a lot of conversations.

What's great about being aware of straw man fallacies is that you can do a lot more than just calling them out.

Man of Steel

One writer who I think of as a Superman of philosophy is Jonny Thomson. The article he wrote that I read on that Sunday morning was 'Daniel Dennett's 4 Rules for a Good Debate.' He makes the point that using a straw man is not a good way to debate.

"The opposite of a straw man is a steel man. This is where you not only represent someone's arguments faithfully and with respect, but you do so in the best possible light."

This is similar to Keith Sawyer's concept of improvisational innovation, where you always build on each other's ideas. It is also a form of empathy and emotional intelligence, which are really what you need if you want to be a high-agency achiever.

By respecting and truly trying to understand the person you are having a discussion with, you create an environment where there is trust and safety to open up and put bold ideas forward. It is in that combinatorial space that the greatest form of creativity takes place. We know that collective creativity outperforms individual creativity and the biggest determinant of that gain in performance is the social dynamic within a group, not the IQs of the members of a group. So, what we want and what we need is a way to create steel men, not straw men.

Rules that turn any discussion into a productive use of your time.

A dialogue is when you build on each other's ideas. Thomson reminds us of the four rules of debate as defined by Daniel Dennett:

“First, and most important, is that you should attempt to express your target’s position so clearly, vividly, and fairly that your target says things ‘I wish I thought of putting it that way.’

Second, you should list all of the ways in which you and your partner agree on things.

Third, you should recognize the ways in which your partner has taught you something new.

Fourth, only after all of this can you go on to try and rebut or criticize their position.”

Following the first three rules often eliminates the need for rebuttal or criticism. In fact, I think if we go even further and faithfully ask those we are debating why they believe what they are saying enough times in succession (5 whys), we will always come to point where there is common ground between us.

When you find common ground with someone, you can then build something on that common ground that you both agree upon.

Usually, before you can get to Dennett's first point, you need to build up a level of understanding. It is important to note that Dennett's rules are for philosophical debates, which tend to have lots of detailed background texts you can read and re-read and therefore represent an opponent's position.

In real-time discussions, you likely will not have the prior knowledge to go into so much detail. This is where a questioning strategy like the 5 Whys is particularly helpful.

I have a confession to make.

When you look at Dennett's four rules, it is basically a way to compliment first and then criticise.

My confession is that despite spending a lot of time facilitating the process of combined thinking, I always cringe when someone starts out a response with an overbearing compliment. In fact, in some people I know that when they do that, they actually have a low opinion of what was just said. I have had people tell me as much.

But that is exactly the point; yes, you can gain something by the formalisms of being nice, but if you really want to be kind, you will take a genuine approach and first own your opponent's point of view before expressing your own.

"Seek first to understand, and then to be understood." Stephen Covey

Why this matters, Elon.

Elon Musk is famous for eschewing meetings. He finds emails much more efficient means of communication. Emails are also a flat and easily disrespectful way of thinking together. They work well when all you want to do is tell someone what they should do. When you do that, you limit the thinking to just your own.

Reflecting back what you have just heard is like a communication protocol for human interaction, for collective intelligence. When we limit our chances to interact and we limit the way we interact, the depth of our creativity becomes limited.

If the only form of argument everyone uses is a straw man argument, we will lose our ability to access our collective capacity to create. The strawman fallacy is like a computer virus that targets the collective intelligence of humanity.

Mindful meetings.

One of the things that makes meetings dreadful is that most people are thinking about what they are going to say next, not what is being said now.

So, if you really want to benefit from the brilliance of thinking together when you are in a meeting, ask yourself what you are thinking about. If you’re thinking exclusively about what you are going to say, use that as a prompt to refocus your attention on what is being said.

One way to do this is to think about a question you would like to ask.

• What would you need to know to clearly, vividly, and fairly represent what someone is saying?

• What would you need to really learn something from the person you are listening to?

The strawman antivirus we are born with.

The antivirus that will help combat the straw man fallacy mental virus that risks fractionating humanity is a well-timed question. This assumes that your well-timed question is aimed at increasing your understanding of what is being said.

This not only keeps on track to follow Dennett's four rules, it is also a great way to increase connection, which is one of the main components of what Joe Hudson will replace knowledge work - wisdom work. In essence, the way to prevent falling into straw man logical fallacies is curiosity.

The antidote to this collective intelligence virus isn't just individual awareness, it's collective action.

At the Big Project Collective, we're building a community of leaders who understand that the skills needed to combat logical fallacies and enhance collective thinking are crucial for tackling humanity's biggest challenges.

This newsletter draws from my experience working with biomedical research leaders, revealing how the principles of effective collective intelligence apply universally across fields. Whether you're leading a research team or building a company, these insights will help you create environments where genuine dialogue and collective wisdom can flourish.

Subscribe to join a community of more than 1.000 people dedicated to strengthening humanity's collective intelligence, one conversation at a time.

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We all have a tendency to fear meetings where a lot of different people contribute to the discussion. Keep meetings small. When they get too big we imagine that we will get stuck in a morass of conflicting viewpoints. Where this becomes particularly worrisome is when we have to make a decision together, like you do when you are developing a big collaborative project. You come to a point where you have to decide what you are going to do together.

How do we best bring together people with different perspectives, especially when they all have their own ideas and their own interests?

This is an important question if you are hoping to do something meaningful by developing and implementing a big project. The whole effort could die a whimpering death, before it even starts.

It would be hard to quantify, but I would bet the challenge of bringing people together is why some of the most important and challenging problems go unaddressed.

When people with different sets of expertise do work together well, the advancements that are possible are stunning. So, it is well worth solving this problem of too many cooks in the kitchen. We can address it on a few different levels.

Let them feel heard.

People need to feel heard. It is the essence of empathy. Feeling heard is not the same as getting your way. They want to be relevant. It may be that what they had to say was heard, duly considered and then discarded or molded into something different. That's okay, as long as they felt heard. The thing is, often the ideas and concepts they want you to hear improve our thinking and the quality of the ideas and plans that emerge. When developing a big project we should be eager to hear and feel what as many people as possible are saying.

While making sure that everyone feels heard gives you the latitude to either merge and even eliminate some ideas, it is still likely that you will be stuck with a multitude of possibilities.

The four-day pivot

I was brought into their funding proposal development project late.

It was late on a Friday night and I thought, what the hell, I’ll look through the proposal. As I read through it, it was indeed clear and well written, but I had the sense that something was wrong.

The methods section had details and there was a nice set of objectives and I liked how they had included a way to quantify the objectives. What was it that was missing?

They had built a very nice, close to implementation project building a platform that could shortly after the project be introduced into the field. I was about to reach up and turn off the computer when I realized what was wrong.

The programme they were applying to aims at radical innovation. In other words, new ideas that had not been tried before. High risk high gain projects. Their project was highly credible incremental innovation, building on something that already exists. Proposals sent to this particular programme are routinely rejected for being just incremental.

With four days left until the deadline, I was not sure I was going to be able to help them. I started to send them ideas for what they could add to the project to make it more radical. They were simple three-line descriptions.

The first two weren't feasible. The third one sparked interest.

I then sketched out how I thought it would all fit together with simple text boxes and arrows. That was not completely correct, but it communicated the concept well enough. Then through the process of iterating the drafts with me pointing out sections where the new concept could be more prominent, the result was a radical innovation project that was high risk, but feasible.

Most importantly, there was now a palpable energy in the consortium.

Prototyping is not just for physical products.

We are all familiar with the concept of prototyping. We associate it mostly with the design of physical products. The iconic story is that of the designers of IDEO going to a grocery store and using a plastic butter dish as the first prototype of the Apple mouse.

We think of prototyping as being how a product is refined over time, and that is the most common use of prototyping, but what most do not realize is that prototyping is also useful for:

• Communication

• Exploration and

• Active learning.

In designing a big project, prototyping can have a particularly important role in exploration and deciding on what the project should be. In the example above, putting out some quick and simple ideas or 'possibilities' led rapidly to a possibility that resonated well with the group. When exploring, a prototype should be simple and rough. If we put in too much effort, the perception becomes that it is a final product, something to be refined not something to consider and potentially discard.

A key to using prototypes for ideation is that the prototype is low fidelity and easy to produce. In the design of a project, that is typically a box diagram showing how things interact or a diagram with swimlanes which show how the project will unfold over time without the rigid formalities of a Gantt chart. Not only does this help by being fast it also means you will be less attached to the prototype.

It is very easy to get lost in a rabbit hole of making a nice figure. Putting too much time in finding the right icons, the right colors and the right types of arrows. The first version you present should be a simple box and text version. This can be something that explains how what your product will work, or it could be how the project elements will interact.

What if the idea is relatively straightforward? Let's take for an example identifying a new biomarker to better target a therapy. In these types of settings it may be better to describe what the future will be like for the different stakeholders involved. You can do this by creating what are called zygotic or science fiction narratives. These are immersive narrative scenarios that help to understand what will be needed to realize the full potential of what the project is producing. When you can infuse scenarios with real-life perspectives, it will reveal aspects you did not anticipate.

When you are creating together, the the thinking of a group goes through divergent and then convergent phases. This is the so-called double diamond model of creativity. Prototypes are used at the peak of the second diamond. They help kick off the process of divergence. They also let people know that they have been heard. Being commissioned to create a prototype means that you were listened to.

Master the art of collaborative innovation.

If you want to be someone who brings ideas together and thereby focuses efforts into projects that can really make a difference, here are some actions you should take and priorities you should have.

Actions

Build your skills for making people feel heard

• Practice curiosity by thinking up go-to questions that dig deeper.

  • Five whys - asking five times why in a series

  • Just summarizing with an open-ended question.

  • "How would you make that happen?"

  • "Tell me more about..."

• Listen better, pause before responding.

• Listen to what is not being said.

• Respond with "Yes, and..."

Develop your prototyping skills

• Develop go-to type of graphic for sketching ideas. Keep it simple - boxes, stick figures.

• Narratives - learn to write future narratives

• An online tool such as Canva and practice sketching while others are talking. Real-time visualization helps others feel heard.

Prioritize

• Creating in a diverse group - the larger the better. Prioritize settings where a group with diverse expertise is creating together.

• Meetings where there is a dialogue. "No Dialogue. No meeting." A dialogue is where you build on each other's ideas.

• Prototyping. When you get stuck, create prototypes or better yet, ask everyone involved to create prototypes. When a meeting draws near its end with no resolution - task everyone with creating prototypes.

• Paying attention to the phases of a dialogue. When it gets too abstract, introduce some concrete prototype ideas. Shift from divergent to convergent thinking.

Tackling problems, that others can’t

The most ambitious projects die not from technical impossibility, but from the inability to harness collective intelligence. When we master the art of making people feel heard while rapidly prototyping possibilities, we unlock something powerful: the ability to tackle problems that single perspectives simply cannot solve. The next time you're facing a complex challenge that requires diverse expertise, resist the urge to keep the group small or delegate the thinking to a few. Instead, lean into the apparent chaos of multiple viewpoints. It's often where breakthrough solutions are hiding.

The techniques I describe above work, but they take practice, especially when you're in the middle of a heated discussion with strong personalities and competing priorities.

If you're working on or planning a significant collaborative project, a big project, I'm running a cohort course inside the Learning Cohort Member Paid level of The Big Project Collective. Starting in June where we'll practice these skills and the skills of developing a big together in live sessions. Think of it as a safe space to prototype your facilitation and project development abilities before the stakes get high.

For the month of June join at the annual paid member level and get 1 year of the Learning Cohort Membership at no extra cost (200 euro discount).

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Even if you're not ready for the course, start practicing one technique this week: ask "tell me more about that" before offering your own ideas. Small changes in how you facilitate conversations can unlock surprisingly big results.

When a team of scientists hit a decade-long dead end trying to solve the structure of a tricky viral protein, it wasn’t a new algorithm or a bigger research grant that broke the impasse; it was the effort of thousands of ordinary people.

For years, scientists struggled to crack the structure of a stubborn viral protein, the structure of the Mason-Pfizer monkey virus (MPMV), a simian AIDS-causing retrovirus, protease that would turn out to be key to developing treatments for HIV. It was a problem that resisted even the most advanced lab techniques and the resources of major research initiatives. The challenge was so complex it seemed insurmountable, highlighting the limits of both big, collaborative science and focused, hypothesis-driven research.

The Human Genome Project, a quintessential big science endeavour, laid the groundwork for understanding genetic mechanisms underlying diseases like HIV/AIDS38. By mapping the human genome, it created a shared knowledge infrastructure that enabled targeted drug development. For MPMV, resolving the protease structure was critical for designing inhibitors to block viral replication. Despite its importance, the protein’s monomeric crystal structure resisted traditional methods like molecular replacement and NMR spectroscopy.

Biochemists focused on retroviral proteases epitomise little science: hypothesis-driven, technique-oriented research. Their work relies on incremental advances in X-ray crystallography and computational modeling. However, the MPMV protease’s unusual monomeric conformation defied existing algorithms.

The solution came from 57,000 players of an online game, Foldit, that allowed individuals to experiment iteratively and personally with 3D models. Solutions emerged in weeks, a stark contrast to years of stalled academic efforts. This personal science approach generated models accurate enough for molecular replacement, enabling the protease’s structure determination.

It was a 15-year timeline:

• 2000–2011: Little Science efforts stalled on the MPMV protease10.

• 2011: Foldit players solved the structure in 3 weeks1017.

• Post-2011: The solved structure informed inhibitor designs targeting the dimer interface, advancing antiretroviral drug development.

In this case, Big Science enabled Little Science, and Personal Science provided the solution. As Derek de Solla Price predicted, the transition from Little to Big Science isn’t a replacement but an integration, one now enriched by grassroots participation.

Big science is beautiful, but it is not a swan.

When large flagship initiatives have been announced, such as the Human Genome Project and the War on Cancer, there has been scepticism that such large efforts draw away from more focused and creative individual investigator efforts. Critics criticised the Human Genome Project because it had no hypothesis. Yet now efforts such as the Human Genome Project and the War on Cancer are heralded as the drivers of important breakthroughs. The same was true for the effort to put a man on the moon.

So, looking retrospectively we can clearly see that big coordinated structured efforts can and have produced. They do so mainly by helping to resolve bottlenecks in research and innovation that are exacerbated by competition. Great examples are the lack of standards or sharing of data, both of which limit the extent of data available or data that is comparable enough to answer big questions. However, the question remains if that kind of work is science, or is it something else. Is it a swan masquerading as a duck? That argument can be supported by the initial criticism of the Human Genome Project that it had no hypothesis.

I can myself say from experience that trying to fit a single hypothesis onto a large multi-partner collaboration with multiple objectives is at best highly arbitrary. That is the hint to the answer to our question. A project like the Human Genome Project is a collection of smaller science projects. When you come down to it, forming hypotheses is a way to structure focused scientific efforts. If we consider that you only learn something and advance our knowledge when a hypothesis is proven wrong, making a large overarching hypothesis does not make sense. It should be a number of focused hypotheses that can be tested and rejected or further tested.

What Big Science projects do is allow those individual efforts to test hypotheses to be much more efficient and much more powerful. They also allow for much bigger experiments than would ever be possible. So, it is not Big vs Little science. They are parts of a whole. They are like three Russian dolls, separate but each of the small ones fitting nicely into the largest one.

Each of these types of science has features that are a distinct part of their nature.

1. Big Science

Challenging (wicked) problems

The problems big science works on are difficult, almost defying a solution. Wicked problems are problems that have many stakeholders and many interdependencies. Think about world hunger as an example. Getting to the Moon is another example.

Impact-oriented

A Big Science project, on the other hand, should be framed and linked to an impact. It’s how we make our science useful. This is not to be confused with high-impact publications. No high-impact publication has ever relieved the suffering of an individual patient.

Collaboration

Collaboration is central to a big science project. While collaboration is often essential for Little Science and personal science, the scale of the collaboration in big science is greater. This leads to a natural aversion to Big Science because we know that coordinating multiple different voices is very difficult. However, this is also the strength of Big Science as people with many different perspectives, when herded properly, produce the most powerful form of creativity.

Complex

Big science is a complex solution to complex problems. Big Science is about achieving transformation and making changes. Change only happens as you build an idea from early adopters into a majority. So, to a degree, the more people you can get on board, the more likely you are to achieve the change you seek. In scientific terms, these are paradigms, and paradigms only shift when there is evidence and belief that they need to shift.

Multi-disciplinary

All fields are moving towards a sort of singularity. Medical science, for example, now routinely involves data scientists, computational engineers, economists, statisticians, chemists, and so on. Working in a multi-disciplinary project requires a lot of effort, but it can be worth the investment.

Multi-stakeholder

Creating change requires that you get stakeholders on board. Each stakeholder will have a different perspective and often the ability to resist or even block the change that you are seeking.

Pitfalls of Big Science

The pitfalls of Big Science are that it can lead to a type of groupthink where paradigms are ossified, forming a barrier to paradigm-shifting breakthroughs.

Big Science can also end up being Little Science in Big Science clothing. Scientists are used to working in their small groups, their silos. They often fall back into that pattern when a Big Science project starts. The problem with that is you do not get the full advantage of Big Science, which is the creativity and problem-solving that comes with interacting deeply with a wide group of collaborators. Big Science projects where that happens are just a collection of Little Science projects with a whole lot of extra bureaucratic burden.

2. Little Science

Approachable problems

Little science is about experiments and studies that are feasible and achievable. It is about testable hypotheses. So, the vague and complex nature of wicked problems is to be avoided if you are going to have success with little science.

Curiosity-oriented

Little science is about following curiosity. It is the unexpected finding that reigns supreme. Pursuing unexpected findings is the essence of curiosity. Even before you have findings, little science proceeds from the standpoint that any testable question is of interest because it might lead to an unexpected finding. Our minds are feeble when it comes to anticipating the true nature of things around us.

Knowledge generation-oriented

Little science is highly focused on knowledge generation. It is where something new becomes known. This is its value. The challenge is that knowledge gets stuck as just being knowledge and not translate into something valuable.

Smaller collaborations

The collaborations in little science tend to be smaller. The caveat is that big clinical studies often have multiple investigators; nonetheless, I would still classify it as little science because it is focused on a tractable question and a testable hypothesis.

Simpler

Little science is simpler. If something is too complex and has too many variables to control, it’s not a good topic for little science.

Techniques

The currency of little science is the techniques that people develop. New techniques increase the chance that you will find something new and novel, which is the glory of little science.

Testable hypotheses

Little science is focused on testing hypotheses and, as such, requires that they are testable.

Incremental

Little science is incremental. Even the major breakthroughs are followed by lots of additional work, not only verifying but extending the findings.

The false peak pitfall

False peaks are the pitfall of Little Science. Publications can become sort of a false peak. Instead of combining efforts and creating the amount of data necessary to definitively answer a question, scientists opt for a smaller amount of data or the use of a unique model in order to get their names first on the publication. This is largely why great concepts languish in the translational gap between research and innovation.

3. Personal science

Personal science is both individuals working in a crowd sourcing way and the process of personal improvement. Thinking of that as science is something that only became clear to me by reading and participating in challenge groups by Anne-Laure Le Cunff. It is about using a scientific approach to improve yourself. It begins with curiosity and the design of what Le Cunff calls Tiny Experiments.

For example, in a 30-day challenge as part of Le Cunff's Ness Labs, I wrote at least one permanent note on my Roam Research Knowledge Graph. A permanent note is a note on a topic you are reading about that is meant to stand alone. I had tried multiple times to become consistent with writing permanent notes but failed. By taking it as an experiment and iterating my process, I was able to write 46 permanent notes over 30 days. I now use permanent notes as part of my writing practice, including this article.

Individual curiosity

Personal science is the height of curiosity. Literally, you can follow it anywhere you want to go. Creating tiny experiments provides a framework and a structure for being curious.

Tiny Experiments

Designing and testing Tiny Experiments are two key activities for personal science. The personal aspect makes the concerns and constraints about designing experiments less important. Tiny Experiments are a way to indulge your curiosity and to take action in the face of uncertainty.

Rapid iteration

Like with the Foldit example, personal science is about rapidly trying out different things. It is about not getting hung up waiting to develop a plan. It’s action-oriented. Just try and see what happens, and then try again.

Shifting your own mind-set

Personal science is great for shifting your own mindset. A mindset is a strategy for thinking, and we can be very rigid about our thinking. Giving ourselves permission to experiment with different ways of thinking or different habits helps us gather evidence that a different mindset is better or worse.

Habit formation

Personal science is also a great way to form habits because you have adaptation built into the process.

Journalling

One of the most useful tools for personal science is journaling. You are working with your mind, whether that is to learn something or to test if a different mindset or habit is valuable. Capturing your thoughts as you go is the data capture of personal science. Here is an excerpt from my experience of experimenting with writing permanent notes.

The ‘shiny new experiment’ pitfall

The challenge with personal science is that it can become a means unto itself instead of a way to increase our ability to do more with Little Science and Big Science. This is what happens when we procrastinate by doing more learning.

An operating system for meaningful work

These three types of science are not mutually exclusive. They are, in fact, integral to each other. Together, they form a sort of operating system for meaningful work.

If you are not experimenting and trying to improve your own abilities, you are unlikely to have the agency to do what it takes to do little science and are almost certainly not going to gain the equanimity and the insights needed to operate in a big science environment.

The Little Science is generating the assets, the knowledge, and the techniques that big science seeks to move forward to be applied in creating a "permanently better life."

Is beauty or sexiness more important?

Big Science is beautiful because it is about solving long-standing problems or bottlenecks that enable more research or the translation of science into impact. Little science and personal science are sexier because they are where the stunning breakthroughs are first identified.

So, if we want to do meaningful work, we need all three of these types of science, and they need to be operating at full capacity.

It is a worthwhile exercise to stop and ask yourself if you are optimised on all three levels.

It is very easy to gravitate to one or the other of these three components. We can become addicted to self-improvement, yet if we do not use that improvement to generate new knowledge, it is not going to be that meaningful. Similarly, if we generate new knowledge but no one uses it, the meaning is also drained. If you stay focused on the big science-type projects, its highly social nature and the big vision can be captivating, but that alone is likely to leave you frustrated at the slow pace of progress.

It’s not just for scientists.

Up until this point, I have been referring to this meaningful work operating system in the context of doing what is traditionally thought of as science. However, this operating system is relevant to any line of work.

Ash Maurya advocates for a scientific approach to lean startup development of new enterprises. You have lean experiments to validate ideas, value propositions, and business models. You also have the larger context, the Big Science, of strategy for the company, the mission if you will. The essence of the operating system is that there is a bigger, fuzzier science that you need to push what has been created into a form where it will benefit others; you have a more concrete little science component where you are creating something new, and you have personal science which helps you have the agency to achieve the little and big science.

For myself, I think I use this operating system for the writing I do here on Substack. The personal science is my practice to improve my writing and, more importantly, learn what is the most valued content. The Little Science is the articles themselves. The Big Science is putting this work into the context and the service of getting people to engage and work together more in Big Science projects.

How to put this into practice?

When you think about the three types of science as an integrating operating system for the meaningful, the implication is that all three sciences should be optimised and interacting with each other.

• Review each of the features for each type of science.

• Jot down what you are doing in relation to each feature or how you perceive the benefits of each feature.

• Are you in balance across all three areas?

• Does your little science feed into your big science and vice versa?

• Do you feel you are as good as you could be at accomplishing things in the little science or big science context?

• Read Tiny Experiments and then design your 1-2 experiments to increase the area of your operating system that is most out of balance. Do these over the next two weeks.

• Journal the results.

• Repeat.

Do more meaningful work

Its not difficult to decide you want to do meaningful work. The real challenge however is continuing to take action towards meaningful work. This post is my first attempt at formulating this science inspired Operating System for Meaningful Work. I would love to have your feedback, even if it is just a thumbs up.

Leave a comment

Big Science and big projects are the pieces that are most often missing from our approaches to meaningful work. Start your big project journeying by booking a call to explore how to increase your big project footprint.

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There is one question that is the wet blanket of ambitious projects.

It dampens enthusiasm for collaboration.

It's an age-old question that sits at the heart of all conflict.

If you can answer this question many times over, you have the power to achieve unimaginable change.

The question?

What’s in it for me?

While looking out for your own needs is important, “What’s in it for me?” becomes super powerful when it’s being asked by others-and answered by you.
It is the essence of empathy.

When I started out as a project manager I knew empathy was important. I had previously cared for patients as a physician and had come to appreciate that empathy for others was one of the most satisfying aspects of being a clinician.

Running projects? That seemed to have little room for empathy. Then as I started into my first big project we started to have patients involved. They produced artwork as part of an asthma art contest, an they told their stories.

The effect on the researchers was powerful. Now after more than 60 big projects I have come to have a much deeper understanding of empathy.

Empathy isn’t just a simple emotion. It is a skill that can be mastered. What most don’t realize is that there are many different types of empathy.

Here are seven differnet types of empathy and how you can make them actionable in developing and delivering big projects that have the potential to change the world.

1. Empathy for Those With the Problem

Don’t assume, discover

Most projects start with a solution. That’s a mistake.
The real work begins with understanding the problem-deeply, from the perspective of those who live it every day.

I’ve watched rooms full of scientists go silent when I say, “Let’s start by defining the problem.” Everyone thinks they know it. They rarely do. The truth is always more complex, and what matters most to one person may be irrelevant to another.

Actionable insight: Start with your own take, but don’t stop there. Convene a group. Listen. Let the problem reveal itself.
Related: How to Run a Problem-Defining Workshop

2. Empathy for Your Collaborators

Heed the hidden drivers

Here’s the brutal truth: pure altruism doesn’t sustain big projects.
Everyone at the table-academics, industry, government-has their own “what’s in it for me?” That’s not selfishness; it’s reality.

In public-private partnerships, for example, academia wants high-impact publications. Industry wants products that sell. If you ignore these drivers, your project will stall.

Actionable insight: As someone who wants to develop a big project your task is to truly understand the goals and needs of other partners. The tendency will be to dismiss their ideas or expression of need when it is different than your own ideas. Often this is in the name of focus. Successful big project leadership requires serving multiple different goals and aspirations within the same project.

3. Empathy for the Implementers

Pay tribute to the gatekeepers of reality.

You can design the perfect solution, but if the people who have to implement it aren’t on board, it will fail. Worse, they’ll let it fail quietly.

Implementation isn’t just about compliance-it’s about enthusiasm. When implementers are excited, they’ll adapt, improve, and champion your solution. When they’re not, they’ll poke holes and walk away.

Actionable Insight: Bring implementers in early. Ask: “What would make this work for you?”

4. Empathy for the Affected

Manage ripple effect.

Your solution will touch more lives than you think.

Maybe you’re building a new therapy. But if it requires specialized labs, will hospitals adopt it? Will clinics pay for it? What about the patients who never see the benefits?

Stakeholder empathy means looking beyond the obvious. Do a stakeholder analysis: who’s affected, what do they value, and how does your solution fit (or not fit) into their world?

Actionable Insight: Identify every group touched by your project. Aim to deliver to the values of each and every type of stakeholder. Involve them in your thinking.

5. Empathy for the Funders

Interpret their language.

You’d be amazed how many proposals ignore what funders actually ask for.
Funding calls are Frankenstein monster like patchwork of conflicting needs. Your job is to read, re-read, and clarify. If possible, talk to the funders. Understand their criteria and motivations. Tailor your pitch to what matters to them.

Actionable insight: Read calls for proposals until you can recite them. Where allowed, talk to the funders.

6. Empathy for those running the current system.

Respect the Status Quo, but don’t bow to it).

Most solutions require changing the status quo.

Sometimes, the people running the current system will have to implement your new approach.

Sometimes, your project threatens their entire way of working.

Understand why the system works as it does. If it’s “just the way we’ve always done it,” there’s room for change. If there’s a real reason, address it directly.

Actionable insight: Study the current system. Ask, “Why is it done this way?”

7. Empathy for the Resisters.

Turn Opposition Into Insight

This is the hardest empathy of all.

Resisters challenge what gives your work meaning. Still, you must listen. Understand their objections, and design your project to address them. Sometimes, the best strategy is to build enough community support that resistance becomes a minority voice.

Actionable insight: Listen to the resisters. and then make them the minority.

An empathic leadership operating system

To build these seven types of empathy, use this loop:

• Listen: Not just for your turn to speak-listen to understand which types of empathy matter most.

• Ask Questions: Use assertive inquiry. The “five whys” technique is your friend.

• Design: Use what you learn as design criteria. Group design sessions leverage collective intelligence.

• Communicate: Turn empathy into a compelling story and vision.

• Collaborate: Involve all types of people, at every stage. As you may have noticed for most of the types of empathy the action step is to bring people with different perspectives into your big project. The smaller the project the more difficult it is to make it multi-stakeholder.

  The most complex and most meaningful projects require the involvement of multiple stakeholders and multiple different disciplines.

The defining trait of those who change the world.

If you want to make a big change, you need all seven types of empathy and you need to put them to work in making a big project happen. It is how you become a high-agency achiever.

It’s not about more effort-it’s about a new mindset. Empathy isn’t just a warm feeling; it’s the defining trait of leaders who change the world.

Sharen your empathy along with other skills you need to develop and lead big projects. Join the big project collective.

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Being an expert seems important, but its meaningless.

Its your ability to work with others that makes all the difference. Multi-party collaborations are where the greatest and most meaningful achievements happen.

• The Apollo moonshot involved more than 400,000 skilled workers

• The Human Genome Project was 20 universities and 1,000's of researchers

• The Apple iPhone requires parts and resources from 50 countries

• The Strive nonprofit in the US bringing together 300 community organizations to make substantial gains in public education where others have failed

Small and simple collaborations are just not that powerful.

Multi-party collaborations, while potentially much more powerful, are however constrained by collaboration’s evil step-sister, competition.

Merging ideas and aspirations

It requires skill to merge the ideas and aspirations of multiple people, particularly the high-powered people you need to do truly meaningful work. If you don't merge interests well, your multi-party collaborations will end up mired in a swamp of competitive acrimony.

There is one thing that makes it much easier to get high-powered people to collaborate. Knowing something about their fields of expertise. We tend to like and trust people who have knowledge we can relate to. Plus, effective collaboration only happens when we understand each other.

I have seen many misunderstandings and sometimes outright conflict when high-powered people try to collaborate.

It’s because communication between people with different expertise is like trying to get directions from someone in a foreign country but they don’t even realize that you are speaking a different language. Of course, there is frustration and conflict.

Mixing researchers and bioinformaticians

One of the best examples comes from discussions between medical researchers and bioinformaticians.

A concept that is seemingly simple always leads to a great deal of confusion. That is the concept of a data model. Often researchers don't know what is meant by the term data model. You will get responses like: "You mean the variables?" The variables certainly are a component of a data model, but it is also how the data is structured.

For example, if you have a study with just one point of measurement the model is different than if you have follow up visits through time in which case a particular variable needs to be represented in such a way that it can be analyzed in comparison to other time points. Another feature of a data model is what parameters do you have describing each data point.

The data model matters because when you go to run the code that will analyze the data, the structure of your data model will make certain types of analyses more or less difficult.

It is a classic problem of merging disciplines because the researchers who understand how the data will be used need to help define the data model, but when they don't understand what a data model is you can go around in circles.

Science’s lost in translation problem

This lost in translation problem is particularly true for science. Scientists are well known for their use of obscure words and difficult concepts. Scientists do this because they are trying to not leave out any subtle and not so subtle complexities.

However, complexity and clarity have an antagonistic relationship. A concept cannot be clear to a non-expert and complex at the same time. It is a sort of clarity-complexity uncertainty principle.

Does that mean that to effectively collaborate we need to become experts in the fields of all of our collaborators?

Fortunately, no. Researchers do not need to become bioinformaticians and bioinformaticians do not need to become researchers.

Different types of knowledge

What most people do not realize is that there are different types of knowledge.

For a breadth of knowledge it is more about being literate, but not necessarily an expert. We have to understand the issues and know why they are important.

Guiding the design, development and delivery of big medical research projects across multiple different research areas, I have seen this problem over and over again. In fact, the above data model example comes directly from my experience.

I started out simply deferring to the experts, trusting that they would find a way to understand each other and merge their interests.

But then I noticed after I had learned something about bioinformatics in a project, it was very useful in helping to merge the interests bioinformaticians and researchers in other projects. I was better able to be a bridge between the researchers and the bioinformaticians. I was also better able to understand what was feasible and where connections could be made.

So, now, the first thing I do when I start to support a group in the design of a big project is look to gain boundary-spanning literacy. It does mean having to have a process for learning what is most relevant in an efficient manner. To do this I created an approach to learning, but first I had to understand something about knowledge.

The T model of knowledge

There is a model of knowledge called the T model. You have some people who have deep expert knowledge, which is likely the vertical part of the T. Others have knowledge that spans between experts - the horizontal top of the T. In reality, it is like a bunch of Ts combined together with one long horizontal top of the T stretching between multiple areas of deep expertise.

In network theory, this is called boundary spanning. It would be easy if we could just have a few boundary spanners and that would be enough.

But what I have seen over the past 19 years is that the need to have people with diverse expertise talking and working together in very substantive ways has increased markedly. With the rapid pace of new technology becoming available this is likely to just continue to increase. Some would say, particularly with the emergence of AI that can provide the in-depth expertise that now the role of humans is to be connectors. The rapid pace of knowledge expansion means that to stay relevant and achieve something meaningful we must have both depth and breadth to our knowledge.

You now understand that by developing more breadth in your knowledge you will be more equipped to effectively bring together people with different expertise. Next, I will show you how to build up the breadth of knowledge you need 10 x faster than you might otherwise do it.

This will make the power of multi-party collaborations available to you. Those types of collaborations are how you will be able to achieve something meaningful.

How does one build up the breadth of one’s knowledge, expanding your boundary-spanning literacy?

It is simple - learn.

But, of course, we all know how long it has taken us to become experts in what we know. However, this type of learning does not need to take that long or require that much effort as long as we take the right approach.

Here is a easy to implement 7-step process for learning a topic efficiently based upon a few principles of knowledge and learning that anyone can apply.

Be humble in what you know and bold in your curiosity.

The Dunning-Kruger effect is that the less we know about something, the more we think we know. So, if you feel particularly confident about a topic, it's a sign you don't know enough.

You can build a habit that will help prevent you from falling victim to the Dunning-Kruger effect.

That habit is curiosity.

Default to always asking why.

Ask yourself, as your favorite LLM, and ask others around you.

There is no better way to infuse energy into a meeting than by asking a stupid question. Those that readily know the answer will be happy to rattle off an explanation. It is more likely to make them feel good than it is to annoy them.

Often those types of questions mean that experts have to go back to the underlying assumptions of their field, which can serve as a hook for others in adjacent fields to latch onto.

If anything, the time used to explain the basics to the curious interjector can serve as a moment for the experts to think.

Outside of live interaction, your curiosity can serve as cue to go off and learn about a topic.

Precisely, Mr. Feynman

“We can’t define anything precisely. If we attempt to, we get into the paralysis of thought that comes to philosophers... one saying to the other: you don’t know what you are talking about! The second one says: what do you mean by talking? What do you mean by you? What do you mean by know?”

Richard Feynman

There are ways to be more efficient about your learning. I use a variation of the Feynman technique. Richard Feynman was famous for his ability to explain complicated physics for anyone to understand. His learning approach consisted of reading about a topic, then writing out a summary for someone with a 6th grade reading level.

By forcing himself to explain a new concept at such a level, he would reveal gaps in his thinking. Those gaps were more prompts for deeper learning.

In this way, we can create an approach that is active learning. Learning with a purpose. This is a type of high agency learning. Instead of just learning what is put before you, it is a way to focus your learning.

In fact, it is best to start with a set of questions. What do you want to learn? Then refine those questions when you make your first simplistic summary of what you have learned.

The first thing I do is ask myself what do I need to know about this topic that will help structure the discussion we are about to have.

I then find a source, read through it with the question in mind and then step away and start to write about what I just read. Writing is thinking and by doing this you shift your interaction with what you have just read from a passive absorption of knowledge to that of a sort of dialogue. As you write you are thinking.

Mental models

When I am doing this for gaining a degree of rapid literacy, I try to build up mental models of what is important. A mental model is a concept, framework or a worldview that you carry around in your mind.

I am not talking about the generic mental models, but rather a customized simplification as a framework. Importantly, it does not have to be precise. For example, when learning about AI foundation models recently, I made the mental model that a foundation model is able to learn for itself.

To test if that mental model was true, I asked Perplexity AI if that was the right conception of foundation models.

The response was that it was partially true.

The self-learning happens when a model is being initially trained. It looks at the data and runs its own tests and validations, which is similar to the process of building your own mental models and then testing them.

This process does not need to be with an AI, it can even as good or not better to do it with the very experts whose field you are trying to learn about.

Build up your literacy in big projects

One of the best ways to build up literacy around a topic is to be in a project where people with literacy in that topic are also engaged and busy doing what they do as part of the project. There is no better way to learn than to see the types of problems that experts encounter and how they work to solve them.

This type of experience is great for building up your mental models of a particular field of expertise. This is how I learned a great deal through the years.

It is also again another opportunity to take notes and make this part of your active learning process.

The final step is applying your knowledge. The way to do that is to aim to transform discussions into a dialogue. A dialogue is where you build upon the ideas of others. Use your boundary-spanning literacy to build connections and synthesize new ideas.

Summary

If you want to do meaningful work, you will need to bring together multiple high-powered people and get them to work together.

Being able to do that starts with increasing the breadth of your knowledge so that you can connect your ideas with theirs and the ideas of others.

Increase the breadth of knowledge by having a disciplined process for learning.

1. Write down what you think you know about a topic.

2. Form questions on what you don’t know.

3. Read.

4. Summarize what you read in your own words.

5. Form mental models.

6. Test your mental models by asking LLMs and more importantly your collaborators.

7. Apply your knowledge to transform discussions into dialogue.

Building up your breadth of knowledge is the first step to harnessing and focusing the competitive energy of high-powered people to build and deliver big meaningful projects.

I have focused on doing this for the past 19 years. I can help you design, develop and deliver your big projects. Book a free call.

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