Leah, you have more experience – and especially more recent experience – in Venture Investing, AI Investing, Responsible Investing, and Sustainability than just about anyone Ive ever met.   What’s particularly strong about your background is your simultaneous focus on improving lives and acting at the frontier of excellence in investing and management.   Now you’ve brought AI into that domain.   What does AI demand of you as an investor and as an advocate for the poor?    Has AI changed your approach to investing?

Leah described how AI is now at the center of everyone’s attention in investing.  What we know is that it is evolving incredibly fast.   Not too long ago, when I was at Radical in Toronto, I was explaining what Machine Learning is.  The focus now is on GenAI.  But this is evolving and will continue to evolve.  The investing community has to keep up with that to stay relevant.   At the same time, AI is affecting everyone every day, regardless of whether you use ChatGPT or another tool in your own activities.   We all have to get comfortable with this, and to understand it personally.  

What Encode is about is reaching back further as a Venture investor into science itself.  We are investing through the UK’s ARIA – the Advanced Research and Invention Agency – in science projects. When something advances to the point where it might be commercialized through a company, Pillar gets a window on that.   In the meantime, Pillar can see the early science as it evolves.  For example, there are a ton of ramifications of AI for how new software is developed.  And that’s happening right now – it’s not only futuristic.  There’s a phrase that software is eating the world, and that AI is eating software.  That’s where we are now.   That requires organizing differently both in business and in investing.  We’re looking for mustard seeds like the protein folding problems that Google’s DeepMind solved through AlphaFold.

Anita asked Leah about potential applications in healthcare and education in low-income countries.

Leah said that the potential is tremendous, and yet the demands for reliable computing that underlie AI deployment create a whole new set of challenges for delivering services like this.  If she had a solution to the energy and computing problems, she would run for political office!

Encode and ARIA are addressing challenges even to conducting basic AI science, which can be risky and early-stage and out of alignment with the demands of a traditional scientific career, where publications are valued.  The timetable for advances is so fast.   We are seeing vast scientific discoveries on 12-month timeframes, which is so much faster than the scientific publication process normally accommodates.  So how do you get scientists to want to work on this?  That’s the front-door challenge that Encode and ARIA face.

Can you say a bit about what you’re learning as areas where next-generation AI is developing fastest?   What’s just over the horizon – just past GenAI?

One important area is in decoding the brain.  Neuroscience.  We think in ways that are unstructured and immediate.  When you throw a ball, you don’t do complex calculations on the arc of the throw.  You just throw!    How do you do that?  We really know very little about this.   And yet its so important to advances in AI.

Another area is soft robotics.  Using an octopus as a model, what if we could use AI to make robotics more flexible and natural?   How could overcoming the brittleness of a hard, jointed robot enable greater robotic intervention?

There are many others as well, of course.

The barriers to commercialization of many of these technologies are often not technical.  We have to worry about things like regulations and marketing and usability.  We have to think about things like manufacturability, analogously to the emergence of the assembly line in factories after the advent of electricity.  These are complicated processes through which new AI gets deployed.  So there are classical problems to solve as well as breakthrough science to achieve.

What advice would you give to your former self – a student entering into graduate studies in business and global affairs?

I’d push myself to start earlier, and to think more broadly.  I’d encourage myself to hang out  as a business and policy student with engineers.  To go visit the computer-science department.  Technology has always been important for solving social problems.  Understanding that better would have helped me along the way.