August '23: More (than) human

Hello and welcome to the August newsletter.

This month we’ve got a feature piece about how AI is transforming business intelligence and making data work more human, a podcast about the six most crucial substances in history, and a book on how digital twins will usher in truly personalised medicine. Enjoy.

We’re also looking forward to speaking at the ETH AI Center on 14 September for ETH Zurich's first seminar on venture capital for the next generation of mission-driven tech entrepreneurs and investors. Let us know if you’re going to be there!

And, finally, we’re delighted to announce our investment in Beyond Work, which is designing the future of work with LLMs. More below.

Mattias and the Moonfire team.

🌓🔥

AI is the new BI

It’s often said that data is the new oil – valuable, abundant, and easy to store. Businesses build their data stacks and stockpile data without really knowing why they’re doing it or what they plan to do with it. But does that really matter?

AI’s capability, powered by vector search, to sift through unimaginable mounds of data and find – and generate – what you’re looking for is changing the way businesses approach their own data, infrastructure, and how they build data products for their customers. The key lies not in why or how the data was collected, but in having the data and properly orchestrating it.

The early stages of this are already playing out, with AI assistants being able to convert natural language prompts into SQL queries the user can then run, and the largest SaaS enterprises in the world releasing English language APIs. This new layer of abstraction allows data teams – and eventually anyone – to query data through natural language and other low-code approaches.

A host of startups are building in this space, each focusing on specific use cases and workflows, with a collective ambition to make data handling more intuitive for every individual, regardless of their technical background. Lightdash does exactly that, turning dbt projects into full-stack BI platforms where analysts can write metrics and the whole team can answer their own queries with no SQL needed. Latitude’s simple visual UI and AI assistant allows startups to explore, visualise, share, and collaborate on their data, without countless hours of dev time spent building and maintaining data pipelines. Shape automates ad-hoc data queries by allowing users to get AI-generated – and analyst approved – answers to data questions on Slack. And Calliper focuses on helping leaders at high-growth startups make faster, better decisions, with automated customer insights and alerts.

Next will come the GitHub Copilot for every kind of data practice – scaling capabilities with humans in the loop. Not just being able to query your data, but train on your analytics codebase to help navigate dbt and orchestrate your data models and pipelines more effectively. Startups are already building for that future. Dagster has created a cloud-native orchestrator for the whole development lifecycle – from local development and unit tests, all the way up to production – with integrated lineage and observability, a declarative programming model, and best-in-class testability. And Orchestra allows users to define, control and monitor their data pipelines through a no-code / low-code UI, further democratising and simplifying data orchestration and observability. Alongside, the data stack will have to evolve to become more AI-native, focusing on the optimal ways of pre-processing, embedding, retrieval, and inferencing.

Looking much further ahead, as models and controls improve, we will see more accessible, democratised business application development. Rather than ever-better BI visualisations to help direct business or product strategy, you ask the model how to solve a business problem and it advises on, and perhaps partially designs, the software and data models you need to do it. If you can imagine it, you can build it – if you have the data to power it and the constellation of models to handle it. As Beyond Work is setting out to do, it’s about using AI as the core engine for a new way of working, and eliminating a lot of the tedious labour that is in every digital job today along the way.

The edge at all these inflection points is data. And not just proprietary data. In an API-first world, data calls are easier than ever before, and the ability to amalgamate and probabilistically weigh external and synthetic data alongside your own will play a big role. A lot of perceived competence comes from the models themselves, but an extraordinary amount of untapped perceived competence will come from giving AI models access to the right data and allowing those models to take interesting actions.

Of course, in an increasingly interconnected digital world, businesses will need to think about what data they’re sharing, how data is being stored, and how models are being trained. Different levels of data sensitivity will necessitate a nuanced orchestration of both internal and external data and models. All rich earth for new startups to mine.

This latest AI wave has ripped up the playbook for product development in data and beyond. Where something like robotic process automation was built from the ground up, LLMs are top down – making them a lot more flexible in orchestrating and creating knowledge. It's a clean slate, an opportunity to take new approaches to data that shape the future of work. If you're a founder building in this space, we'd love to talk.

– Mattias and Akshat 🌓🔥

Beyond Work closes $2.5m pre-seed to make work more human

We’re delighted to announce our investment in Beyond Work. We led the $2.5m pre-seed, with co-investment from MIT’s E14.

Work is one of the most universal parts of the human experience — but, sadly, so are its frustrations. For the history of computing, humans have had to learn to speak machine to build and get their work done. With large language models (LLMs), we can now make software that interacts with humans as humans.

This is the premise Beyond Work is taking and running with. They are building a human-AI work platform from the ground up, with LLMs at its core, hoping to change the way enterprise teams work.

‘[LLMs] can replace over-designed, sprawling user interfaces with something much simpler and more human. Just tell your computer what you want it to do. Not with a keyboard and mouse, but in the way you interact with everything else in your life.’
– Christian Lanng

Chairman Christian Lanng, who also serves as CEO and Co-founder of Tradeshift, heads up a superstar founding team including Joakim Recht, former Distinguished Engineer at Uber, Mikkel Bo Schmidt, former Head of Design at Tradeshift, Ekaterina Kharchenko, former Customer Success Manager at Stack Overflow, and Malte Højmark-Bertelsen, former Head of AI and Data Science at KMD.

This team has an opportunity to free up human energy and attention in a way that simply didn’t exist before. We can’t wait to see it, and we can’t wait to start using it.

More on this soon.

Curious Worldview: 144: Ed Conway | The Unbelievable Efficiency of Globalisation – Material World

This podcast with Sky News’ Economics Editor Ed Conway digs into the subject matter of his new book Material World: A Substantial Story of Our Past and Future, namely our rising appetite for raw materials and the “the six most crucial substances in human history”: sand, salt, iron, copper, oil and lithium.

It’s a great exploration of both the efficiency and fragility of the supply chains at work, and there are some great anecdotes: how the UK’s drinking water security relies on one room in Cheshire, why steel stock – not GDP – per capita might be a better benchmark for how well off a country is, and how Steve Jobs discovered his hippie rebellion through copper mining magnate Robert Friedland.

'Virtual You: How Building Your Digital Twin Will Revolutionize Medicine and Change Your Life' by Peter Coveney and Roger Highfield

In this book, professor in Physical Chemistry at UCL Peter Coveney and Science Director of the Science Museum Group Roger Highfield survey the efforts by scientists around the world to build digital twins of human beings.

These virtual copies could usher in a new era of personalised medicine, one in which your digital twin can help predict your risk of disease, participate in virtual drug trials, shed light on the diet and lifestyle changes that are best for you, and help identify therapies to enhance your well-being and extend your lifespan.

From multiscale modelling to new forms of computing, they explain what it will actually take to make this “virtual you” a reality, and consider the ethical questions in realising truly predictive medicine.

You can listen to the authors talk about the book on a recent Talks at Google.

That’s all for this month. Congratulations again to the Beyond Work team! 👏 Welcome to the Moonfire family.

Until next time, all the best,

Mattias and the Moonfire team

🌓🔥