Indeed, I realized it's hard to compete with PhD students for grants, and subsidising my work with content marketing does not fit my style, and I prefer owning my work and choosing my own research direction. I also want people to use my work, and create solutions that are cost-effective.
So the most logical way was to bootstrap an AI start-up in the area I'm interested, so that's what I'm doing. Unfortunately, it's hard to publish or contribute to open-source, since it becomes too easy to copy, which cuts my margins and ability to fund my research and compute.
Now I spend most of my days doing AI research, and outsource most other parts, really enjoying it :)
I think you'd attract a more sympathetic audience if you reframed Emil's Story as "How to get people to leave you alone and let you think." E.g. half of your AI autodidact degree boils down to 'make money with what you\'ve learned', and it's always interesting to see people unpack their approaches to this idea. Your thinking around target numbers would be valuable.
I agree, it was written some time ago, and the language is hyperbolic, but many of the key points still hold true. Building a skillset to reach $100/hour consulting gigs was key to work 2-3 days per week, have time for research, and saving money for an ML rig, while living in Paris. Around $5K MRR is sufficient to live off, $10K MRR is more comfortable and allows renting a few extra A6000 Ada and start outsourcing, and $20K MRR to afford full outsourcing to focus on research and renting 8xH100s. That's a good goal to balance research freedom with other responsibilities. After that, it's optional to trade research freedom for higher MRR and more responsibilities.
Sure, so it seems fair for him to offer advice on how to apply AI or start a company based on it, but doing AI research means generating new knowledge about AI itself, and I don't see any evidence of that.
> doing AI research means generating new knowledge about AI itself, and I don't see any evidence of that.
Wouldn't colorization count as research? In vision domain there are a lot of papers like this. Just arranging and rearranging known blocks and getting SOTA result on some datasets. ;)
So the most logical way was to bootstrap an AI start-up in the area I'm interested, so that's what I'm doing. Unfortunately, it's hard to publish or contribute to open-source, since it becomes too easy to copy, which cuts my margins and ability to fund my research and compute.
Now I spend most of my days doing AI research, and outsource most other parts, really enjoying it :)