I went from almost being expelled and failing maths in high school to the top student of my maths class, co-founder of the first maths club and an elected leader looking after 300 students. At 16, I wrote my first neural net, sold software to clients, built a nuclear computer, gave talks to audiences of hundreds and mentored university-level courses.

I created purpose by creating useful things.

I wanted to study at MIT, but my family couldn’t afford it. After ranking in the top 3%, I enrolled at the University of New South Wales in Sydney to study computer science and engineering.

During COVID, whilst all my friends were playing video games, I built software for several companies and got hired as one of the youngest senior consultants at PwC. I built an ML platform serving 10k MAUs, served as tech lead for a payroll compliance platform that saved large enterprises millions of dollars and led the firm’s first applied research project, resulting in two first-author publications. All before I’d even graduated.

During that time I also worked on fusion research, simulating promising materials for tokamak reactors on a supercomputer. I enjoyed tackling the biggest problems, so I quit my job, wrote an 100-page undergrad thesis on post-training a robot I built, loved it, dropped everything and applied to study maths at NYU. After graduating I spent a year building companies in consumer (onexhq.com, capsol.ai, wherearepeople.com) and enterprise (shopstack.ai, axco.ai) working with great people like Fred Schebesta before moving to NYC.

All my work has centred on how humans interact with machines, culminating in the ultimate interface: a general-purpose robot. I left an amazing life in Sydney to realize this mission - at a scale only possible with America’s deep pools of risk capital.

In the words of Lao Tzu, “a journey of a thousand miles begins with a single step.” That next step is blue-sky research on the biggest bottleneck in robotics, scalable data collection.

Godspeed,

Lucas