Samuel King is co-founder and CTO of Briink, one of Merantix’ younger ventures. After finishing his education at Cambridge University, Sam gained extensive experience in Data Science and Machine Learning, working across organizations in London and Berlin. While he was working, he got heavily involved in the climate tech community. In 2021, he joined Merantix to co-found Briink, a company that uses Natural Language Processing to empower the transformation to a carbon-neutral economy. In our interview, we talked about Briinks' vision for a green future and how Machine Learning can help accomplish it.
Eduard: With Briink, you are building a company in climate regulation. There is a lot of funding going into the field and political pressure urging companies to lower greenhouse emissions faster. What do you think? How will the area evolve in the near future?
Sam: There certainly is a lot of funding going into this field - and rightly so! I believe the green bond market grew to just over half a trillion USD in 2021, not to mention these massive impact and climate funds being raised in the VC world - World Fund launched last year here in Berlin, with a $350m fund exclusively for climate tech investment. So yes, lots more capital is one way and the field is evolving for sure.
At the same time, governments are starting to make bold moves to support this. Europe is leading the way on this with flagship sustainable finance regulations like the EU Taxonomy, which provides a framework to direct capital to the right place at scale. However, all of these regulations also lead to another major trend: exponentially more complexity. We are constantly having to broaden our picture of what really falls under "sustainability". The EU Taxonomy for example is now expanding from an initial focus just on greenhouse gas emissions and climate change, to also covering water, pollutants, recycling and biodiversity!
That is why technology has such an important role to play. Extracting, structuring and interpreting the deluge on data needed in order to assess companies against this increasing scope is becoming impossible to do manually. At Briink we are building technology that automates the process of identifying and assessing sustainable business activities in line with the EU's regulatory framework in order to help direct capital to the right projects faster!
Eduard: Your company is using machine learning to help accelerate climate policy. Could you maybe elaborate a little bit on this vision and what you are doing?
Sam: In order to have a good chance of becoming a truly sustainable global economy by 2050, we must reach our 2030 goals. That is less than eight years away, which is not much time if you think about the scale of transformation required in almost every industry! One of the most important things we can do to make sure this happens is to get capital into suitable projects which will drive this transition as fast as possible. Yet the process for assessing these projects currently is far too slow! From collecting the necessary environmental metrics, to extracting and transforming this data in order to disclose under the myriad of environmental standards and green financing options - most work is still done manually at the moment. This is precisely what machine learning is good at: automating the boring stuff!
To give a concrete example, at Briink we have built machine learning models using Natural Language Processing (NLP) which are able to read through millions of documents (such as sustainability reports and life cycle assessments) automatically to extract only the relevant sustainability information related to conducting an EU Taxonomy assessment. Actually I wrote an article about some of the ways Briink is using NLP to specifically help accelerate EU Taxonomy adoption for anyone interested to learn more.
These sort of machine learning powered tools can not only save users weeks of headache, but also help organize this mass of unstructured data into a machine-readable format that can be easily organized and audited; an increasingly important requirement as sustainable finance regulations get stricter.
Essentially ML can help us assess companies' environmental credentials and move faster in this space! In that way, I really see ML as a critical enabling technology that acts as a catalyst across industries as they transition to a net-zero future.
Eduard: Within the last two years, we heard a lot about Large Language Models, like GPT-3. How can current trends in Natural Language Processing enable the transformation you speak of?
Sam: Developments in NLP, and in particular these large pre-trained language models based on Transformer architectures, are crucial to the timing of Briink. Some years ago, we would have had to spend millions of dollars and hire a large team of PhDs to collect all the labeled data and design the model architecture we need to power the ML use cases we’ve built at Briink. Today we can leverage these pre-trained models and partially retrain them with a much smaller corpus of data to fine-tune them for our specific use cases in sustainable finance while achieving state of the art results.
Additionally, I would say that the maturing of the ML ecosystem in general, helps a lot too here. For example, open-source MLOPs tools like MLflow make it much quicker for us to set up a robust experiment tracking pipeline. Open-source tools like this are wonderful and help accelerate leveraging ML for practical use cases like ours.
Eduard: Could you tell me something about your ideation? When and how did you develop the idea of founding Briink?
Sam: I've worked in AI/ML, and in particular NLP, for nearly a decade, mostly on commercial applications. However, I've always seen climate change and sustainability as the defining challenge of our generation. In particular I was really interested in the question of how AI could help accelerate the green transition. That led me to get involved with the climate tech community in Berlin and London through groups like Climate Change AI and Work on Climate; which are amazing communities and well worth checking out for anyone interested in this space. That is also where I met my co-founder Tomas, a lawyer by trade but also an early employee at a start-up in the States that pioneered NLP for Case Law.
Combined, we have a real wealth of experience with applying NLP to real world uses-cases and asked ourselves: where could this technology make a real difference to the climate transition. We also spoke to an obscene number of people in this space in the run up to deciding to start Briink to help answer that question. Together these experiences brought us to the hypothesis that climate regulation will be the single biggest driver of transformation over the following decade as governments implement increasingly bigger levers to try and stimulate the green transition under the short time frames we have. Yet it was also clear to us that the manual and laborious way these regulations are currently being adopted is a critical bottleneck to actually reaching our 2030 goals. We founded Briink to solve that.
Eduard: I would love to get your opinion on something rather operational as well. For young startups, hiring is usually one of the bottlenecks. It is hard to find top talent, and early hiring decisions have a long-lasting impact on the company. How do you approach this challenge at Briink?
Sam: Yes, it's a massive challenge and, honestly, something I underestimated in terms of the time you need to get it right. But you are right. It is crucial. My top tips for founders: Firstly, get a HR intern or working student early on to help manage the pipeline and admin side. That way, you can concentrate on really engaging with candidates and searching for rockstars. We have fantastic support now, and it has made a world of difference. But there is a hell of a lot of bureaucracy running a successful hiring process, and if there is no one else to do it, it will be the founders' job, and that is time that you can spend much better.
Secondly, it is also worth saying that notice periods, at least in Germany, can be long - which is kind of antithetical to the time frames for an early-stage startup. You may, therefore, want to start looking early for roles that you think you will need 3-6 months from now! It seems crazy but starting the search early helps give you the space to find the right person rather than whoever is available at that moment.
Finally, look for people who are ‘drivers not passengers’. Who cares about the mission and wants to help push it forward! Technical problems can always be solved, but being on the same page about why you are doing what you're doing is foundational!
Eduard: Thank you, Sam! I wish you all the best for Briink!