It is so interesting to observe what happens when a new breakthrough arrives in tech. Swarms of smart people descend on it almost immediately, each trying to capture some piece of the opportunity. That is exactly what we are seeing with LLMs today. Because they work so well, so visibly, across so many domains at once, the response has been unlike anything I have seen before. Some people are going after verticals like healthcare, legal, and insurance. Others are solving agentic problems, or going deeper into the hardware stack. The list of opportunities keeps growing.
It is almost like watching progress happen in real time. In the past, even the smallest innovation took a very long time to be replicated and diffused around the world. Today, with a bit of effort in curating your feed or picking your career intentionally, you can have a front row seat to how the world is improving. That is a unique and new thing.
There are a few things I have always valued about the tech mental model.
The first is that tech approaches every opportunity through the lens of problems. What problem are you solving, and how big is it? You don't see the same instinct in most other industries. Nobody debates the size of the problem being solved by a new burger place at a particular crossroads. They talk about the opportunity: being first, having a unique concept, capturing the market. It is a different mental model entirely.
The second is openness. Not everyone in tech shares their secret sauce, but people get dangerously close. Think about all the open models coming out of China right now. Everything is out there. You can study it, fork it, fine-tune it, build on top of it. That is not how most industries work. I don't want to pick on restaurants too much because I genuinely love them and think they play an important role in society, but when was the last time a restaurant owner broke down, step by step, what made them successful and what unique insights they discovered along the way? The contrast is telling.
That thought was actually triggered by an event where I considered giving a talk. The speaker application made it clear that to get a slot, you had to present in high detail and full transparency what you had built and learned. I paused on that for a moment. How unusual that is. How specific to the tech world.
The third thing is that tech tends to be more positive sum. When things work, even though they work only rarely, people get quite wealthy. I think that kind of concentrated upside changes how people relate to each other. Most of the successful people I know are genuinely kind and thoughtful. There are bad actors, of course, but as a rule of thumb the culture leans toward openness and mutual support in a way that most industries don't. That mental model is worth preserving.
Today's massive investment in LLMs, both at the infrastructure and application layer, has created a race unlike any I have seen in my lifetime. It can feel intimidating. If you pick the wrong problem, a well-funded startup or one of the frontier labs could wipe you out before you even launch. But it can also feel liberating. You now have tools at your disposal capable of creating massive value for society at a scale that was simply not possible before.
Which brings me to something Naval tweeted that has been sitting with me ever since:
"The only true test of intelligence is whether you get what you want out of life."
If you are so smart, why aren't you happy?
I don't have a clean answer to that. But I do think that in a world where we can do so much more, where productivity can be higher than ever before, intentionality matters more than ever. The weight of what you are working on, the choice itself, is becoming increasingly important.
There is a well-known diagram about ikigai, which maps what you are good at, what the world needs, what you love, and what you can be paid well to do. That chart suddenly requires new weights. We can now pick bigger problems and therefore expect bigger rewards. Think about it, for just $20 a month, you have access to extraordinary tools to go after them. So the question is, in a way, harder than it has ever been: what problems will you work on solving, in a world where you have PhD-level intelligence in your pocket that is infinitely patient?