AI Reality Bites #1: A Backoffice For The Green Transformation
Investment Insights into Climate Tech and AI
‘'AI Reality Bites' - Every day, new advancements in AI are announced - but what do they mean in practice?
The Imperative of Green Transformation
Climate change, one of the defining challenges of our time, is no longer a distant threat but a present reality. We will likely face devastating global environmental impacts, including extreme weather events, sea-level rise, and biodiversity loss. These changes threaten our ecosystems and pose significant risks to human health, food security, water supplies, and global economies. The urgent need for a sustainable global economy is apparent, and the scale of the challenge is immense.
To face these challenges and transform our economy, we must reinvent entire industries and create new ones. Crucially, these transitions must happen at an unprecedented pace and scale. A sustainable future requires an estimated $275 trillion in investments up to 2050. This enterprise is not just about mitigating environmental risks. It is about investing in the creation of new, green technologies, innovative business models, and low-carbon infrastructures that are resilient to future shocks.
Artificial Intelligence in the Fight
In this race against time, emerging technologies, including artificial intelligence (AI), have significant potential in shaping a more sustainable economy. With its capabilities for analyzing massive datasets, predicting trends, and optimizing processes, AI can be instrumental in redefining sustainable finance and regulation. These are the essential cogs in transitioning to a low-carbon economy. Harnessing AI's potential to drive change in these areas will require thoughtful integration with climate science, economics, and policy.
To better understand these topics, we talked to Samuel King, CTO and co-founder of Briink, a company that leverages AI to transform sustainable finance.
You can listen to an excerpt of our interview here, or scroll further down to read up on the highlights:
The current state of Climate Tech
Frontline Solutions
There are two kinds of solutions we need. On the one hand, we need frontline solutions to decarbonize our economy. And on the other hand, the transformation needs a sophisticated back office to be fast and financially efficient. One part of climate tech is constituted by what Sam calls "frontline solutions". These solutions generally include everything we need to decarbonize our economy, including transportation, manufacturing (concrete and steel), energy grids, innovations in renewables, nuclear and hydrogen.
Backoffice Solutions
The second part of climate tech and our focus in this piece is on the vast 'back office' mechanisms—those supporting solutions crucial for achieving large-scale transformation. A prime example is sustainable finance, which poses the question: How can we channel trillions of dollars towards eco-friendly solutions in just a decade?
Although a relatively recent addition to the economic landscape, sustainable finance is rapidly growing. It's a multifaceted domain, interweaving legal tech, regulatory tech, economics, finance, consulting, and specialized scientific expertise. Its primary objective is efficiently directing immense capital into sustainable initiatives with transparency and precision.
What needs to be done
The challenge described above is no small task. The process begins with how Limited Partners (LPs) amass and allocate their capital. Then there's the task that General Partners (GPs) and funds must identify fitting sustainable projects to invest in. Portfolio companies, on the other hand, must effectively manage these investments, demonstrating consistent progress against their sustainability metrics.
Adding to the complexity, there's an extensive network of law firms, consultants, and auditors, each with a crucial role. Law firms grapple with the complexities of Article Nine green funds and associated regulations. The booming field of ESG consulting assists in strategic decision-making while auditors ensure data validity. All these entities form a new era of sustainable finance knowledge work. This expansive ecosystem is akin to financial accounting, embedded deeply in every part of the economy and extending from individual accountants within companies to the banks, lawyers, and auditors involved. This complexity must ultimately be reduced, or a large share of the investments will be lost in the system.
Recent Trends
The landscape of sustainable finance has been changing rapidly over recent years. Previously, financial reporting and sustainability reporting were two parallel yet disconnected tracks. However, driven primarily by evolving regulations, especially in Europe with regulations like the Sustainable Finance Disclosure Regulation (SFDR) and the Taxonomy under the Green Deal, sustainability and finance have started to fuse together.
The implications of the new regulations are profound. Companies can no longer 'greenwash' their operations; their sustainability credentials must be tied to their actual financial reporting. Consequently, a company's sustainability has to be connected to the revenue and costs of its operations' sustainable and unsustainable parts. With investor pressure mounting, especially from Article Eight and Article Nine funds, sustainability is becoming an essential differentiator. This change has shifted sustainable finance from a niche area to a mainstream factor in capital allocation.
This focus on sustainable finance is not just about managing the necessary funds; it's about making the climate transition happen at scale.
The Role of AI
Machine learning, particularly the development of large language models (LLMs), is causing seismic shifts in the landscape of sustainable finance. AI excels in managing complex knowledge-based workflows, which lends itself exceptionally well to sustainable finance. The entire process of sustainable finance is a cycle of knowledge work, starting from understanding what's going on, setting targets, collecting and verifying data, reporting, analyzing data, implementing improvements, and monitoring them. At every single point of this cycle, there's an opportunity for AI to enhance and accelerate the process.
Use Cases for AI
For instance, AI can be used to screen supply chains or target investments at scale to understand their ESG credentials. This task would be time-consuming for human analysts, but it can be automated and scaled using AI. Another application is data collection and verification. AI can help automate document screening for ESG information, providing efficiency in a critical process stage.
Moreover, AI can assist with interpreting and understanding regulations. For example, Briink recently launched a chatbot based on large language models to help people interpret these regulations and how they apply to their context. Finally, once data has been collected and verified, AI can also be used to analyze it, identify areas where a company can improve its sustainability, and automate painful parts of the reporting process.
Large language models like GPT-4 have been a game-changer in this space, primarily because they reduce the need for labeled training data for new use cases. New LLMs accelerate the development of new features and make deploying these models much quicker. We can create a first use case in the ESG space within a week or so and then rapidly learn and improve it. This iterative process has revolutionized how quickly we can respond to new challenges and opportunities in the sustainable finance sector.
Privacy and Inference Costs
Data privacy is one of the challenges arising from using these models. ESG data is sensitive and private, and companies often are not willing to share this with third parties. Hence, Briink offers the option to switch between models, leveraging open-source alternatives when required.
n terms of costs, especially the inference costs of running large language models, this could be a barrier for some startups. But in sustainable finance, where the problems being tackled are tens to hundreds of millions of euros, these costs can be outweighed by the efficiency and savings generated. However, the holistic cost of inference, including infrastructure setup and maintenance, is a consideration. Utilizing platforms that provide managed services can mitigate these costs and enable a more agile approach, as Briink demonstrates.
In conclusion, the convergence of AI, machine learning, and sustainable finance is creating a robust 'back office' engine driving the green transformation of our economy. The interplay between technology, regulation, and finance is accelerating our ability to channel resources towards sustainable projects. The role of AI in automating, augmenting, and scaling sustainable finance operations is both a game changer and an essential ingredient in our global response to climate change. As technology advances and the urgency of climate issues persist, leveraging AI's capabilities in sustainable finance may prove vital in our collective efforts to build a resilient, low-carbon economy. The insights shared by Samuel King demonstrate not only the innovative applications of AI in this burgeoning field. The future of sustainable finance, intertwined with the technological revolution led by AI, will undoubtedly be a significant cornerstone in our journey towards a sustainable future.
Summary
The complexity of sustainable finance encompasses legal tech, regulatory tech, economics, finance, consulting, and scientific knowledge. New regulations and investor pressures have caused a profound shift, tying sustainability to financial reporting.
AI applications are transforming sustainable finance by screening supply chains, automating data collection and verification, interpreting regulations, and analyzing areas for sustainability improvements.
The convergence of machine learning and sustainable finance creates a back office engine driving green transformation.
Investment in AI-first startups in this space offers exciting prospects. The efficiencies, savings, and innovation provided by AI outweigh the costs, leading to new avenues for investment.
Key Challenges and Solutions
Allocation of Funding:
Challenge: Monumental investments are needed to achieve climate goals. Capital allocation is often inefficient due to greenwashing and a poor overview of true climate impact.
Solution: AI assists in verifying ESG metrics submitted by companies, combating greenwashing, and accelerating understanding of regulation, enabling quicker capital deployment.
Exemplary ventures: Clarity AI, Pachama
Shortage of Knowledge Workers:
Challenge: The climate sector is scaling at a rate the workforce cannot support. There's a lack of experienced professionals with the required knowledge.
Solution: AI can supercharge existing knowledge workers, closing the talent gap. AI copilots assist non-experts in navigating complicated regulatory fields.
Slow Moving Legislation:
Challenge: Urgent need for well-designed legislation and insufficient government incentives for climate progress.
Solution: AI improves access to transparent data for policymakers, accelerates risk assessment, and assists in policy modelling to inform decision-making.
Exemplary ventures: Sylvera
Thanks for reading, and please let me know what you think, what further opportunities in the market you see or what technological trends on the horizon you believe will make an impact. A special thanks goes out to Eduard Hübner, who was my great co-author for this piece.
- Rasmus
Connect with me on Linkedin or Twitter.
Incredible Insights Rasmus!
Great piece, chat and look forward to more to come - relevant, insightful and concrete.
Fully agree with this comment “ To face these challenges and transform our economy, we must reinvent entire industries and create new ones. Crucially, these transitions must happen at an unprecedented pace and scale…It is about investing in the creation of new, green technologies, innovative business models, and low-carbon infrastructures that are resilient to future shocks.”
It brings and often underestimated and yet transformational element if done right. The acceleration of such transformation and the speed of convergence amongst stakeholders will be enabled and determined by a new wave of effective, trust-based and innovative partnerships and sustainable alliances. The successful development and execution of such cross-sectoral alliances represents itself a challenge and transformational solution across emerging sectors that aim to address today’s societal and environmental challenges (climate tech, sustainable finance, life sciences / health care, supply chains, int’l development, etc)
Kudos again for such a thought provoking piece.