It's not clear if AI will take our jobs. But someone _using_ AI might. Right now, there’s a little bit of advice out there about [what you should be doing](https://www.youtube.com/watch?v=mv50AczvLqE) to avoid being replaced by AI. But then I [came across an article](https://80000hours.org/agi/guide/skills-ai-makes-valuable/#31-skills-using-ai-to-solve-real-problems) by [Benjamin Todd](https://benjamintodd.org/), who started this really cool project called [80,000 Hours](https://80000hours.org/), which got me thinking about the **_bigger picture_** of orienting your career in today’s world of AI. And even forgetting AI, how to think about your future work. _But first! A word from our sponsor._ > Building the future takes people willing to take big bets on long timelines. [E1 ventures](https://e1.vc/) backs founders pushing the edge of what’s possible in science and technology. > > If you’re building something ambitious and looking for investors aligned with your vision, reach out to E1 Ventures. >  ### **How Good Is AI Really?** In order to begin this conversation, Ben thinks we should look at the current AI landscape. What is AI really good at right now? Modern LLMs can translate natural language requests into working code in various programming languages and applications. Adoption is widespread: 84% of software developers report using or planning to use AI tools in their workflow (up from 76% the year prior), and over half of professional developers now use AI assistance daily. Every time we think we’ve hit the ceiling of what AI can do… the ceiling moves. It has moved faster than many people would have predicted. > Back in like 2015, we were telling everyone to learn to code and do data science… those types of skills, especially coding, is what AI is best at now. > > It seems much more ambiguous whether that’s the right thing to focus on in terms of skill development. > > _Benjamin_ ### **How to Account for AI’s Rapid Improvement** So if AI keeps leaping forward like this, how do you build a career that doesn’t get blindsided every two years? > What is best is always going to be changing as the technology changes. So you should be more in a mindset of, how do I ride the wave? > > _Benjamin_ That starts with a single question: “What problems are going to become _more valuable_ as AI advances?” Benjamin breaks it down into four kinds of skills that will rise in value: 1. Skills that are hard for AI to do. 2. Skills needed to deploy AI effectively. 3. Skills that create things the world wants more of. 4. Skills that are hard for others to learn. Benjamin is not alone in [emphasizing the importance](https://www.youtube.com/watch?si=zpccLEBbz3RW9Vt-&t=726&v=CRraHg4Ks_g&feature=youtu.be) of learning to deploy AI effectively. > Learning to deploy AI to solve real problems is likely to become one of the most valuable skills just because it’s complementary with AI. So as AI gets better, there’ll be still these remaining parts of the process that AI can’t do that are needed for the process. And then if you have those remaining skills, those remaining gaps, those become more valuable as AI gets better. > > You want to focus on things that will be hard for AI to do, but that are complementary to AI being deployed and that we could use way more of. And then from there, you can kind of apply that to more specific skills. > > _Benjamin_ Three years ago, AI couldn’t produce anything close to a photoreal or even coherent image. Today, we’re getting [photoreal video](https://www.youtube.com/watch?v=HK6y8DAPN_0). > I think that’s a really good illustration of firstly, just like how hard it is to figure this out. But also that message of you’re never going to be able to find this single answer. It’s about like constantly adjusting into the thing that is the key bottleneck at that time. > > _Benjamin_ In my opinion, Benjamin’s fourth point is especially important: **Focusing on skills that are hard for others to learn.** Deep specialization and rare expertise give you a bigger advantage. There is an example that Naval Ravikant [uses about a deep-sea diver](https://nav.al/rich?utm_source=chatgpt.com). > Let’s say that you’re the best person in the world at deep sea underwater diving. You’re known to take on deep sea underwater dives that nobody else will even attempt to dare. Then, by sheer luck, somebody finds a sunken treasure ship off the coast. They can’t get it. Well, their luck just became your luck, because they’re going to come to you to get that treasure. You’re going to get paid for it. > > The person who got lucky by finding the treasure chest, that was blind luck. But them coming to you and asking you to extract it and having to give you half, that’s not luck. You created your own luck. You put yourself in a position to be able to capitalize on that luck. Or to attract that luck when nobody else has created that opportunity for themselves… we don’t want to leave it to chance.” > > _Naval_ That’s the idea: you specialize in something rare, something valuable– thereby creating a moat for yourself. But Benjamin’s point goes even deeper. ### **A Mental Model for A Life’s Work– In An AI World** And that takes us to the whole vision behind 80,000 Hours: building a mental model of a career of purpose and adaptability in today’s world. > 80,000 Hours helps people find careers tackling the world’s most pressing problems. And yeah, we have an online guide, a job board, podcast, and free one-on-one advice to help people find more impactful, fulfilling careers. > > _Benjamin_ Benjamin started 80,000 Hours in 2011. It was originally an internet side project, but today it’s transformed into a London-based nonprofit that conducts research on high-impact careers and provides evidence-based career advice. > We were trying to figure out what to do with our own careers and just felt like none of the advice, the normal careers advice you could get seemed like it was really based on much research. So we just started thinking for ourselves, if we wanted to have an impact and also find careers that we enjoyed too, which paths would be best and actually trying to compare different paths. > > Most career guides are just about how to get a certain job, like how to write a CV or something, but what jobs are actually worth aiming for in the first place? > > A very common advice is follow your passion. But we think that kind of gets things backwards rather than like, start with whatever you happen to be interested in as like a 16 year old, which is typically stuff like sports and music, which are like the hardest areas to make a career in. > > Start by asking like, what does the world need? What’s actually valuable? The research supports a picture where passion is something that you can develop from building valuable skills that you find engaging and using them to do meaningful things. > > When it comes to how to make an impact… really think about which problems are most pressing in the world. And then we break that down more into which problems are biggest, most tractable… most solvable, and most neglected by others. > > _Benjamin_ So once you understand which problems actually matter, the next question becomes: which of these are you uniquely suited to help solve? > Fit would kind of come next… once we have a bunch of, like a short list of impactful things, then we say, focus on, generally focus on the one where you have the best fit. > > _Benjamin_ Benjamin’s advice is pragmatic: Stay adaptable. Keep learning. Save more. And– stay close to people who actually understand what’s going on.  > One of my top pieces of advice is seek out people who have some clue on what’s going on. Start to develop your own mental model of it. I think starting to think about this stuff is one of the most crucial things. > > _Benjamin_ > You can’t future-proof your career forever. So you should be more in a mindset of How do I stay one step ahead? > > _Benjamin_ That’s the game now. Not competing with AI… but learning to build the future with it. Because the future isn’t going to reward the people who bet on one safe job. It’s going to reward the people who update fast, follow the actual problems, and learn to work _with_ these new tools as they evolve. Thanks for watching, and as always, until next time, keep on building the future. _Until next time, keep on building the future!_ –Jason