Congratulations to KTH, and to the other Swedish universities that have secured new resources through the Strategic Research Areas initiative. For SCI, this is particularly meaningful through our engagement in quantum technology and polar research.

At the same time, the emerging VR-Vinnova excellence-cluster initiatives point in a similar direction: towards stronger and more visible research environments built through collaboration across disciplines, institutions, and sectors.

This also brings to the surface an issue that is not always discussed openly: coordination has a cost.

In academia, we rightly value academic freedom. The freedom to define our own questions and pursue our own ideas is fundamental to research. At the same time, major strategic initiatives are built on a broader logic. They aim to create environments that are not only excellent in parts, but strong, coherent, and internationally visible as a whole.

That requires more than outstanding individual researchers. It requires cooperation across boundaries, the willingness to build consolidated groups, and sometimes also the readiness to engage in efforts that go beyond one’s own immediate research interests or local priorities.

This is not always easy, nor should it be taken for granted. Coordination takes time, attention, and flexibility. It can create friction, and if handled poorly, it can easily become more process than purpose.

But when important opportunities arise, especially when substantial resources are connected to broader national priorities, it becomes essential to ask not only what each of us wants to pursue, but also what we can build together.

That, perhaps, is the real challenge. Not to weaken academic freedom, but to complement it with a culture of cooperation strong enough to meet ambitions that are larger than any one researcher, group, or institution. In that sense, alignment should not be understood as uniformity. It is better understood as a collective capacity: the ability to gather strong competences around shared goals when the moment calls for it.

As many new alliances are now taking shape, this seems particularly timely. The question is not whether coordination is desirable in itself, but when it creates real value, and whether we are prepared to invest in it when it matters. If we get that balance right, coordination becomes more than an administrative necessity. It becomes part of how strong research environments are built.

I am pleased to share SCI’s Verksamhetsplan 2026. A plan is not a goal itself, its value is whether it helps us focus, coordinate, and follow through. SCI is a large and diverse school, with world-leading research environments, ambitious educational programmes, and a community that spans many disciplines and backgrounds. In that setting, clarity matters: we need shared priorities that help us make good choices, and we need a common language for how we evaluate progress over the year.

For 2026, I want to highlight three main focus points:

1) AI in education: competence and shared practice.
Generative AI is already part of our students’ learning reality and of many colleagues’ teaching practices. Our task is to ensure that AI becomes a resource that supports learning, not a shortcut that undermines it. In 2026 we will strengthen competence for both faculty and students and, just as importantly, facilitate structured sharing of experiences, teaching materials, and course experiments across SCI so we learn faster together.

2) Makerspace: a place to build, test, and meet.
As an engineering school, we benefit enormously from environments where ideas can move from concept to prototype. A Makerspace is not only about tools; it is about creating a shared arena where students and researchers can meet across programmes and disciplines, build prototypes, test ideas, and learn by doing. Developing this environment is a concrete way to strengthen both education and innovation culture at SCI.

3) Quality and inclusion: the foundation of long-term strength.
Quality in education and research is inseparable from the environment in which we work and study. In 2026 we continue to embed quality, equality, diversity, and inclusion in everyday leadership and decision-making, particularly in recruitment, competence supply, and how we support colleagues and students in their development. A sustainable work and study environment is not an “extra”; it is a prerequisite for excellence.

We will also continue the transparent reporting of financial resources that we have built over the last years. It has become an important part of how we create shared understanding and enable good planning at all levels.

I encourage you to read the Verksamhetsplan 2026 as our shared direction for the year ahead, and as an invitation to dialogue. The plan will guide priorities at school level, but it is only through the work in departments, units, programmes, and research groups that it becomes real. Thank you to everyone who contributed to shaping it, and thank you in advance for the collective effort that will turn priorities into outcomes.

AI is now at the center of how we talk about teaching, learning, and employability, at KTH in general, and very concretely at a school level within SCI. The question isn’t whether generative AI will be used; it already is. The real question is how we integrate it so it becomes a resource rather than a shortcut that quietly erodes learning.

Our SCI faculty breakfast on AI in education highlighted both strong engagement and a clear need. Faculty explicitly asked the school to facilitate structured sharing of experiences, teaching materials, and course experiments so we can learn faster together. That kind of open exchange is a powerful tool, one we are in the process of building. It also reinforces something I feel strongly about: we will not navigate this transformation by writing policies and documents alone. Policies can set boundaries and provide a shared vocabulary, but practice is built in the everyday, in assignments, seminars, feedback, and assessment design.

A term that is often used is “AI readiness.” But AI readiness is not about becoming a prompt expert; it is about judgment, i.e. the ability to evaluate outputs, verify claims, recognize uncertainty, and decide when AI supports learning and when it replaces it. That kind of judgment rests on deep disciplinary knowledge and critical thinking, and it’s part of a university’s responsibility to teach it explicitly. Research comparing ChatGPT with human tutors points in this direction: AI can be helpful as a structured dialogue partner, but human guidance remains essential for nuance and tailored feedback (Frontiers | Socratic wisdom in the age of AI: a comparative study of ChatGPT and human tutors in enhancing critical thinking skills).

At the same time, we should be honest about risks. If AI becomes an “answer engine,” students may produce polished outputs while thinking less, a concern raised clearly in STEM education discussions about (Gen)AI and learning design.  And this is also an equity issue: students differ in access, confidence, and literacy, and a “GenAI divide” can widen if universities don’t provide structured support and shared expectations (The GenAI divide among university students: A call for action – ScienceDirect). KTH’s decision to provide Copilot access to all students and staff is an important step. The next question is how we align tool choices, whether Copilot or other platforms (free or paid) with our learning goals and with students’ development of judgement.

This is why, at KTH (SCI), as at many other universities, we are treating AI integration as ongoing academic development, not a one-off compliance exercise: raising a shared baseline of AI competence among teachers and building on-site platforms, workshops and recurring arenas, where we can share materials, compare course designs, and learn from each other as practice evolves.  If we keep the focus there, on judgement, critical learning, and collegial exchange, I genuinely believe we can navigate this transformation, and do it in a way that serves our students best.

At the risk of repeating myself (I wrote a similar blog just a few months ago), I felt it was important to return to this issue in light of the latest VR grant outcomes. The trends continue, and they demand our attention.

Here is the VR project grant data for Natural and Engineering Sciences (NT), 2021–2025:

The total funding has remained relatively stable. What’s changed is the number of applicants, up by nearly 200 in five years, and the resulting success rate, which has dropped steadily. This isn’t a case of declining quality, but of a system stretched too thin.

Rather than restating why fundamental, curiosity-driven research is important, a point that is, by now, broadly understood, I want to raise a different question: Are we simply too many researchers competing for the same pot of money?

My answer is no. The system doesn’t suffer from an excess of researchers, it suffers from structural misalignment. We are, rightly, growing our research capacity in strategic areas like AI, quantum technology, and nuclear energy, where special initiatives and targeted investments exist. But these researchers may also apply for general project grants, which means the competition in that shared pool intensifies, while the size of the pool remains largely unchanged.

This imbalance creates pressure not because the science is weaker or less relevant, but because the system hasn’t adapted to its own strategic expansions.

If we continue expanding our research ambitions without recalibrating the funding architecture, we risk cultivating frustration instead of innovation. Excellence is no longer enough, luck and timing increasingly decide who gets to move forward. This is a dangerous signal to send to our most ambitious minds, especially early-career researchers.