6 Lessons on building deep tech that lasts: reflections from Malmö to Barcelona
What I learned in Malmö and Barcelona about the pace of deep tech, community building, and patient capital.
Two weeks, two cities, one lesson: deep tech doesn’t scale on hype. From Malmö’s warehouses to Barcelona’s biotech cafés, here’s what I learned about climate, community, and patient capital.
In this reflection from The Drop in Malmö to Impact Week in Barcelona, Alice shares what two weeks of climate, health, and deep tech conversations revealed about the future of impact investing. From Physical AI and biomanufacturing to Europe’s manufacturing bottlenecks and data centre energy use, the journey connects how technology, funding, and community are evolving together. The takeaway: building real impact takes time, patience, and investors willing to back science-driven innovation early, a mindset that underpins maze impact’s second pre-seed VC fund.
From Malmö to Barcelona: What Physical AI, Biomanufacturing and Deep Tech Scale Really Require
In the span of ten days, I went from a warehouse full of climate founders in Malmö to a café overflowing with biotech investors in Barcelona. The events couldn’t have been more different in tone or focus, yet the same questions kept surfacing. How do we scale impact without getting lost in hype? Where are the real adoption bottlenecks? And who’s actually building the connective tissue between climate, health and technology?
This is what I learned along the way (besides discovering I love Swedish cinnamon rolls/tapas and vermut):
Lesson one: climate is no longer a category
At The Drop, Hampus Jakobsson from Pale Blue Dot opened with a line that stuck: “Climate is not a sector or a thesis. Climate is everything.”
It wasn’t just a slogan. Every session and hallway conversation made that point clearer. The same technologies that help decarbonise supply chains are also reshaping manufacturing, food systems and logistics. Climate has stopped being a niche investment theme and started becoming the baseline condition for innovation.
Even the community events reflected that shift. I co-hosted the Junior Impact VC meetup with other analysts and associates, and it drew our biggest group yet. There’s something powerful about early-career investors comparing how they evaluate the same climate logic across sectors, from batteries to biotech. It felt like the next generation of impact investors growing up together.
Lesson two: physical AI is moving from theory to throughput
A side event hosted by Planet A on Physical AI brought me back to hardware. Founders from Stær and Yaak described a world where progress depends less on raw compute and more on how models hold up in the real world. Robots no longer fail because of chip limits. They fail because they cannot sustain precision when the environment stops cooperating.
The discussion went deep into what that means. In controlled settings like factories or warehouses, robots already perform well, but those spaces are exceptions. The harder test is what happens when light changes, a surface shifts or an object behaves unpredictably. That is where most systems still break.
The speakers argued that hardware has already caught up to most industrial needs. Processors such as Qualcomm’s mobile chips or Nvidia’s Orin NX can handle multi-camera stacks in real time. The constraint now is model intelligence: the ability to combine navigation, manipulation and task planning under imperfect data and still make good decisions.
The next generation of robotics, they said, will rely on multiple small, specialised models that talk to each other rather than one all-purpose model. Each module handles a distinct function, from movement to perception to reasoning. This modular structure is what allows robots to adapt faster and reuse shared infrastructure instead of rebuilding from scratch.
Integration still matters, but the stack is becoming more open and efficient. Real-world data now matters more than synthetic training. Many teams deploy early, not for revenue, but to capture the messy edge cases that make systems robust. In that sense, test deployments are data engines as much as pilots.
The big takeaway was that speed and reliability, not sophistication, will decide who wins in Physical AI. The goal is no longer to build smarter robots in theory, but to build consistent ones that outperform human benchmarks in practice.
Lesson three: biomanufacturing is finding its moment…cautiously
In the session with Alexander Hoffmann and Michael Luciani, the tone struck a balance: optimism around falling costs, but cautiousness around scaling. The promise is real: automation, AI, and lab tools are compressing the gap between design and execution. To test that promise, I dug into recent data.
For example, the NHGRI (National Human Genome Research Institute) reports that the cost to sequence a human genome, which was tens of millions of dollars in the early 2000s, had dropped to about USD 525 by 2022 (Our World in Data). That level of decline shows what exponential progress looks like in tools that support biotech. Meanwhile, in DNA synthesis, companies like Twist Bioscience now offer gene fragments using silicon-based, high-throughput platforms at costs starting around USD 0.07 per base pair (7¢) for small fragments (twistbioscience.com).
But falling cost curves don’t guarantee business success. In the session, investors emphasised that founders must show process validation, not just a drop in theoretical cost. A biomanufacturing platform only becomes investable when it can deliver consistent yields, low variance, and economic predictability at scale outside the lab. The real differentiator will be who can run many design–build–test–learn loops faster and smarter than anyone else.
Lesson four: even data centres can be impact infrastructure
The session hosted by Burhanuddin Pisavadi asked a simple but uncomfortable question: are data centres really the biggest problem? AI workloads are growing fast, and so is their energy footprint. Afterwards, I looked into it. The International Energy Agency estimates that data centres consumed between 300 and 380 terawatt-hours (TWh) of electricity in 2023, roughly 1.5% to 2% of global demand (IEA report). The U.S. Department of Energy projects that figure could climb to 6.7% to 12% of national electricity use by 2028 (energy.gov). The direction of travel is clear: the world’s appetite for computation keeps rising, and so will the pressure to power it cleanly.
Instead of treating that trend as a dead end, the conversation turned toward opportunity. Data centres could become platforms for energy innovation, not just energy consumption. Some operators are already repurposing server “waste heat” into district heating networks, turning what used to be a liability into a local resource (Inc.com). Disclaimer: Location is indeed still a challenge, though. Others are pairing facilities with geothermal or renewable energy systems to stabilise demand and reuse thermal output more efficiently.
The main takeaway for me was that data centres sit at a rare intersection of technology and infrastructure. They are massive consumers, but they are also fixed assets with long lifespans and predictable energy flows. If capital and engineering can align around circular use models, data centres could evolve from part of the climate problem into part of its solution. The question is whether the sector can move fast enough before regulation forces it to.
Lesson five: Europe needs manufacturing fluency
By the time I got to Impact Week in Barcelona, the conversation had shifted from science to scale. In a panel with Rick Hao from Ruya Ventures and Yan Zhao from Breathe Battery Technologies, the mood was clear: Europe has plenty of deep tech talent but still struggles to build things at scale.
Startups often stall at around 20 million euros raised. Manufacturing capacity is limited, and founders lack the playbook to move from prototype to production. The panellists were blunt: Asia is not a backup plan, it’s a strategic partner.
Breathe Battery used the Chinese ecosystem to iterate quickly and land a top-five smartphone client in under two years before returning to Europe with traction. That’s what manufacturing fluency looks like. Knowing when to go east for speed and when to come back for market positioning is now a founder skill, not a luxury.
Lesson six: Barcelona reminded me that community still matters
“The Future of Human Health at Impact Week” came together almost overnight. Fiorela Kapllanaj from Nina Capital, Gökçe Gün from We Venture Capital and I organised it remotely with support from BITS in Bio, and within days, the word spread. On the morning of the event, Anís Café near Casa Norrsken was overflowing with founders and investors from across digital health, biotech, and diagnostics.
The feedback afterwards was unanimous. People were excited to finally be in the same room, to exchange experiences and find shared ground between subfields that rarely overlap. What stood out was how easily the conversation moved between research, commercialisation and patient outcomes. The collective sense was that the health innovation ecosystem in Europe is ready for more collaboration if someone simply creates the space for it.
Closing thoughts after recovering from all the travelling
Travelling between Malmö and Barcelona felt like following the same conversation through two different lenses. In Malmö, the dialogue revolved around systems, hardware, and throughput. In Barcelona, it shifted toward people, collaboration, and how ecosystems form. What united both was a simple truth: progress takes time. It requires patience, learning, and structures that allow experimentation to breathe.
What I came home thinking about was the role investors play in shaping that timeline. We often expect founders to move fast, deploy, and iterate, but few funding structures are designed to support the reality of deep tech or science-based innovation. These companies do not follow a software cycle. They build physical things, run biological processes, and depend on testing that takes months, not days. The traditional pace of venture capital often works against that rhythm.
If we want to see real impact, funds need to evolve. That means taking earlier risks, providing flexible capital, and staying long enough to let technology cross the valley between promise and proof. It means being comfortable with ambiguity while still demanding rigour. As impact investors, we are not here only to chase growth curves but to design capital that gives new science and engineering ideas a fair chance to prove themselves.
At maze, that belief is guiding our next chapter. As we raise our second fund focused on pre-seed deep tech founders, our goal is to back teams who are building the foundations of future industries in climate, health, and sustainability. We want to be the first call for founders who need patient partners, not just fast capital. Because the truth is that transformative ideas rarely fit short timeframes. They need early believers, and that is the kind of investor we choose to be.