A developer friend recently said “If I was 20 now I’d drop everything and jump into AI.” But he’s spent over a decade building expertise, network, and reputation to be at the very top of his field. So he’s staying put for now.
Another, older, college friend is a high flying exec at a now-publicly-listed tech startup. He’s good at what he does, has the perfect resume, the rest of his career could be easily extrapolated toward enviable positions. Yet he’s pivoting because, as he told me, “life is short” and he doesn’t want to end it wondering “what if?”
I’ve had many similar conversations with both technical and non-technical friends in recent days. As much as I’d like this newsletter to be about concrete technical developments and soaring SOTA advancements, I think it’s worth spending one issue on the fuzzy wuzzy topic of career pivots, because this is one topic that I’m coincidentally uniquely qualified to offer commentary.
Pivoting in my thirties
I remember how scary my first career pivot felt, also at age 30. I was 6-7 years into the finance career I had wanted since I was 16, jet-setting around the world, grilling CEOs and helping to run a billion dollars at one of the top hedge funds in the world. Externally I was hot shit but I knew deep down it was unsatisfying and not my endgame. Making some endowments and pensions a bit richer paled in comparison to making something from nothing. I decided to pivot from finance to software engineering (and devrel). The rest is history.
6-7 years later, I am again pivoting my career. I think a SWE → AI pivot is almost as much of a pivot as going from Finance → SWE, just in terms of superficial similarity while also requiring tremendous amount of new knowledge and practical experience in order to get reasonably productive. My pivot strategy follows the same playbook as last time; study nights and weekends as much as possible for 6 months to get confidence that this is a lasting interest
where I can make meaningful progress, then cut ties/burn bridges/go all in and
But that’s just what works for me; your situation will be different. I trust that you can figure out the how if you wished; I write for the people who are looking to get enough confidence about their why that they actually decide to take the leap.
I think there’s a lot of internalized ageism and sunk cost fallacy in tech career decisionmaking. So here’s a quick list of reasons why you are not too old to pivot.
Reasons you should still get into AI
More broadly, the way that both incumbents are startups across every vertical and market segment are embracing AI is showing us that the future is “AI-infused everything” – therefore understanding foundation models will more likely be a means to an end (making use of them) rather an end in itself (training them, or philosophizing about safety and sentience). Perhaps it might be better to think of yourself and your potential future direction less like “pivoting INTO AI”, and rather “learning how to make use of it” in domains you’re already interested or proficient in.
My final age-related appeal is a generic one – Challenging yourself is good for your brain. Neuroplasticity is commonly believed to stop after 25, but this is debated. What is much more agreed upon is that continuous learning helps build cognitive reserve, which helps stave off nasty neurodegenerative diseases like dementia and Alzheimer’s. This 72 year old congressman is doing it, what’s your excuse?
Are you working on anything nearly as challenging as understanding how AI works, and figuring out what you can do with it?
How I’m learning AI
there is a method to this madness; this is a quick process dump but the actual structure of how I recommend learning will be fleshed out in a future post. Subscribe!
I’ve done the fast.ai course content, but also am following my curated Twitter list, and adding notes to my public GitHub AI repo and to the Latent Space Discord. Important new papers get read the week they come out, and I try to run through or read the code of highly upvoted projects and products. We’re also about to release “Fundamentals 101” episodes on the podcast where we cover AI basics, which has forced me to read the papers and understand the history of some of the things we take for granted today
I’ve also recently written down the podcasts and newsletters I’m using to keep up – share and add suggestions if you have them!