Looking back at 2024

You accomplish less in one year than you want more more in 10 than you ever expect.

These words of wisdom seem attributed to Bill Gates. This year of any, I started to see their meaning. The past year represented a pretty stark departure from the last decade of my life. It is a return to helping build tools for computation. A return to my life’s work in academia, before started working on AI based startup companies. Let’s just say I’ve had some catching up to do, but having people quote my papers in meetings was somewhat surreal.

To back up a minute, in 2023, I joined NVIDIA as the product lead for CUDA Python. I left my academic post back in 2012 to join the founding team at Anaconda. In academia I was building compilers for scientific computing applications, e.g. ways to build hurricane simulations with hundreds of lines of code over millions using GPUs and other accelerators. I was literally told that my work had a “snowball’s chance in hell” at getting me a tenure track position, so I left. At Anaconda, I kept working with academics to produce better big data tooling for the Python data ecosystem. I was the PI on a few DARPA grants that funded some of our beloved PyData tools, e.g. conda, dask, numba, and bokeh. Making tools for academics and government was nice, but had the same feel of my previous work, i.e. not making significant impact.

From 2015 to 2023, I helped organizations use computing tools to build new and interesting technologies. This included virtual clothing sizing tools at Boldmetrics ¹, a fully online real estate system at REX Homes ², a cloud manufacturing system at Xometry ³, and an early film and television script evaluation system at StoryFit ⁴. I also kept helping with NumFOCUS which was a vestige of the last decade of work, i.e. building tools for scientists, but one that I felt uniquely suited to do. I did also run a side gig for CTO advisement for teams wear I helped folks like KindHealth build modern Health Insurance, Korbit.ai build AI education systems, and Sapling Learning build better math education. All this work had immediate impact to customers.

Near the end of 2023, a friend reached out about joining NVIDIA, right when I was looking at joining another startup. It represented an opportunity to take back a decade of learning to building tools for computation. I jumped at the opportunity even though it meant going from an executive role to an individual contributor. One year in it has probably been one of the most productive years of my life, but to see all that you need to come meet me at this year’s GTC where we are launching numerous Python products ⁵.

In 2010-2015, it was a different era of computing. Big data was the main hurdle to technical innovation at the largest companies. Python had already taken off as a great language for gluing and building computational pipelines, but during this time we sealed it as the data scientists preferred toolbox. Now we are focused on Generative AI systems and many of the same challenges exist. The community is more willing to accept compilers like numba and triton-lang. In the big data world, only the largest systems really needed to compile their computational kernels to something that would run 10-100x faster over the entire dataset, but in GenAI there is no small or medium datasets. Thus today we have the big data tools and compilers we wrote back in those early days that have enabled GenAI to move quickly and build just amazing models.

At NVIDIA, I’ve been pushing on numerous initiatives that combine the decade of learning in startups with the new computational of the GenAI stack. It is work I would not have been able to do without the previous decades of work. If there were any words I could whisper in my ears back in 2010, it would be these we started with.