Latent Space: The AI Engineer Podcast
00:34:31 · Fabian Theis
[00:34:31] S0: Welcome. Let me know if you come come by Munich. So I think traditionally, Europe... But I would say definitely Germany is is quite bureaucratic, and processes are far from as digital as you would expect in different places. Not that I'm saying that US is maybe the best example for fully digitized processes, but stuff is definitely faster. The thing is people have realized that now, and I think the the particular startup creation and implementation is, I think, more than competitive in Germany versus Europe. It's actually sort of the the the prime number, and in particular, like some hotspots, Berlin, but in particular, Munich sort of the... I I think is is the capital for for an... Highest number of startups. And I think even nowadays, if you look into European Union, there's equally as many startups being created as in US. The thing is where Europe and particularly also Germany drops off is at the scaling part. So, you know, early VC and also sort of non dilutive type of money, this is there. But then when you go into scaling, these big VCs that sort of take these bigger risks are, I I think, not not closely as present. So I've I've I've saw this again and again that, you know, if you wanna sort of really go into the the sort of bigger tickets sort of series A and so on. The series B, you... People start going more into US also for that. But maybe I'm sort of simplifying here, but this is at least my current learning, Brenner.
00:35:44 · Brandon Anderson
[00:35:44] S2: Okay. You are on the board or I guess part of several local startups. Right? What what does it take to go from, you know, idea to creating a, you know, to creating a new startup in Germany?
00:35:56 · Fabian Theis
[00:35:56] S0: I I don't if if if you heard that the European Union tries to regulate a whole bunch of stuff, and that can be sometimes an advantage. You know, it's kind of cool, like, you know, all across Europe we can now go with a mobile phone. You might have heard that everyone needs to have USB C, things now for their mobile phones. This kind of kind of cool stuff. So there's some some good stuff coming. But then of course there's something like things like the AI act and so on that make things a tad more complicated. Luckily, not yet for research, but you know, for data access across hospitals and so on, there's some, you know, and we were talking about about health deep tech type of stuff, right. So there's some, systems that I think fantastic in Europe, and I'm thinking about Finland, you know, Finjan, Denmark, and so on with this sort of very detailed description of the population in terms of sampling, getting medical records connected far better than... I mean, of... US is also similar to MS, like like in terms of sort of getting all the systems sort of to talk to each other. Like, when patients, you know, sort of go to to different states in US, it's similar as in Germany when they go to different places. It's like very hard to build patient trajectories. Right? But there are some places that know how to do this, and there's a thing like the European Union tells you, it's called the European Health Dataspace, where in... Back maybe next two years, these things need to be sort of going across boundaries. You need to sort of start building these these health record type of combinations. So I do think divisions are starting to happen, but the scale is is not as fast. So I think what you need for a tech startup, what I noticed is is always the biggest thing is you need to have the best team for that. And you know, we have professors and and PIs that really think about this. So for example, for for the department that I called it at, I said, guys, every every PI of a group we don't expect you to at least have one one one spin off in in in the next three years. Let's see how that works. Right? But you sort of you sort of get people a bit a bit on that track. I But think this is happening very much from a bottom up nowadays anyway, where the students are actually pushing for that.
00:37:43 · Fabian Theis, RJ Hanaki
They do realize that there's a whole bunch of opportunities at the moment, particularly with with this sort of computational angle getting... Yeah. I I would say pushed even more on the forefront that we can now start actually guiding experiments. We haven't really spoken so much about it. Right? But once you have these perturbation world models, you can actually go back and drive the experiments. Right? So I do think computational startups can be very good also data generation, and I think this is what will be also turned not just into nice papers, which is sort of mostly our business, but also I think it's next generation of startups. And you do see a few of these popping up where you actually get the lab and the loop really implemented. [00:38:18] S1: What do you see as some of the the the most promising areas for commercialization of sort of these Virtual Cell adjacent ideas?
00:38:28 · Fabian Theis
[00:38:28] S0: It's interesting, right? Like, if if you looked at earlier, and of course, this is what people always reflect to what OpenAI, Anthropic, and so on wanted to do. Like, they didn't really know how to make money, and some people might argue they don't even know nowadays, right? And there's a few startups that, you know, got really quite strong investment that also said we built a foundation model of biology, we decode biology, and so without actually saying what you wanna do with it. And I've been seeing this again and again that these platform type of ideas are super attractive. You know, they sort of fit into what we know. Mean, you spoke about some of the tools and platforms that we build like ScanPy and SEvers and so on, right? But turning this actually into a monetary thing is not as obvious. I mean, there might be some sort of consulting business, but if you say you build a Virtual Cell platform, typically what investors understand, and this is maybe even more so in the more conservative conservative VC VC ecosystem ecosystem in in Europe Europe than than in in US, is that at some point you develop a drug, and then you know it's a clear thing. So I saw so many of these platform companies pivoting at some point to we make a drug, and yeah, decide, of course, we have our our sort of Virtual Cell, whatever business, but, you know, we have two drugs that we found and now this... So if we put a lot of money in, maybe you have some other accounting assumptions, there's a few companies that sort of still stick with the scaling up of of the AI business, but it's not as clear how you would sort of directly turn this into a viable sort of really scalable business model, and if there's even room for many of these. And of course, then there's also this angst that at some point, like Astra two point zero might be doing parts of this already anyway.
00:39:54 · RJ Hanaki, Fabian Theis
[00:39:54] S1: Yeah. Mean, I think that it's actually this has been a thesis of mine that up until recently, every single company that builds a model and builds something ends up just building drugs, because that's the path to monetization over time. And that, like, whereas you you have to, like, land sales with pharma that's very difficult or some other big big pocket spender. If you're a tools company, if you say I'm gonna build a port... Like a... An asset, and then I can sell that off. That's really attractive to an investor. [00:40:27] S0: Yeah. [00:40:27] S1: But we've seen actually the opposite happening this year. Right? So you have like, you have instructional, you have a couple, you have bolts, you have Brandon will know them better, but there's a couple of those. And then, Tai is one that we talked to. And then we've interviewed a few that are doing this, doing reinforcement learning environment plays as well. I would love to see the activity level in structure that that we see in structural also doing things like what Noetik is doing.