The following recipe assumes you’re very ambitious.
Good filter. Must read this now.
The work you choose needs to have three qualities: it has to be something you have a natural aptitude for, that you have a deep interest in, and that offers scope to do great work.
Amazing checkboxes to eval new endeavors.
There’s a kind of excited curiosity that’s both the engine and the rudder of great work. It will not only drive you,but if you let it have its way, will also show you what to work on.
Love this analogy.
Knowledge expands fractally, and from a distance its edges look smooth, but once you learn enough to get close to one, they turn out to be full of gaps.
Beautiful example once again!
Four steps: choose a field, learn enough to get to the frontier, notice gaps, explore promising ones. This is how practically everyone who’s done great work has done it, from painters to physicists.
The steps to discover what great work is there for you to be done:
find something you’re excessively interested
learn enough about it to get you to one of the frontiers of knowledge
notice the gaps in the knowledge fractals.
Boldly chase outlier ideas
The big prize is to discover a new fractal bud. You notice a crack in the surface of knowledge, pry it open, and there’s a whole world inside.
Literal goosebumps reading this
you need to make yourself a big target for luck, and the way to do that is to be curious.
When in doubt, optimize for interestingness.
 fields aren’t people; you don’t owe them any loyalty.
P.G 🙏
I think for most people who want to do great work, the right strategy is not to plan too much. At each stage do whatever seems most interesting and gives you the best options for the future. I call this approach “staying upwind.” This is how most people who’ve done great work seem to have done it.
Try to arrange your life so you have big blocks of time to work in. You’ll shy away from hard tasks if you know you might be interrupted.
So very true this ^!
Try to finish what you start, though, even if it turns out to be more work than you expected. Finishing things is not just an exercise in tidiness or self-discipline. In many projects a lot of the best work happens in what was meant to be the final stage.
Very important.
Just as we overestimate what we can do in a day and underestimate what we can do over several years, we overestimate the damage done by procrastinating for a day and underestimate the damage done by procrastinating for several years.
Flow of time is brutal.
If you do work that compounds, you’ll get exponential growth. Most people who do this do it unconsciously, but it’s worth stopping to think about. Learning, for example, is an instance of this phenomenon: the more you learn about something, the easier it is to learn more. Growing an audience is another: the more fans you have, the more new fans they’ll bring you.
Doing work that compounds.
if you don’t try to be the best, you won’t even be good.
Resonated with this.
Have the confidence to cut. Don’t keep something that doesn’t fit just because you’re proud of it, or because it cost you a lot of effort.
Hard , hard truth to always remember. Sunk Cost Fallacy.
Originality isn’t a process, but a habit of mind. Original thinkers throw off new ideas about whatever they focus on, like an angle grinder throwing off sparks. They can’t help it.
Interesting take.
Power Law in Attention
Don’t divide your attention evenly between many topics though, or you’ll spread yourself too thin. You want to distribute it according to something more like a power law. Be professionally curious about a few topics and idly curious about many more.
plot-code
import matplotlib.pyplot as plt
import numpy as np
# Define a range of topicsn = np.arange(1, 21)
# Define different values of mm_values = [1.2, 1.5, 2, 3]
# Plot attention distributions for different m valuesplt.figure(figsize=(10, 6))
for m in m_values:
attention = (m -1) / (m ** n)
plt.plot(n, attention, marker='o', label=f"m = {m}")
plt.title("Attention Distribution Across Topics for Different m")
plt.xlabel("Topic Rank (n)")
plt.ylabel("Attention Allocated")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
Important Idea : Maybe, attention in transformers work this way too? Wondering if someone has explored this idea, i do know auto-regression implicitly echo this idea but maybe there are other ways to incorporate this into learning/inferences in “attention”.
Having new ideas is a strange game, because it usually consists of seeing things that were right under your nose. Once you’ve seen a new idea, it tends to seem obvious. Why did no one think of this before?
When an idea seems simultaneously novel and obvious, it’s probably a good one.
Seeing something obvious sounds easy. And yet empirically having new ideas is hard. What’s the source of this apparent contradiction? It’s that seeing the new idea usually requires you to change the way you look at the world. We see the world through models that both help and constrain us. When you fix a broken model, new ideas become obvious. But noticing and fixing a broken model is hard. That’s how new ideas can be both obvious and yet hard to discover: they’re easy to see after you do something hard.
GOLD. Amazing mind map here.
One way to do that is to ask what would be good ideas for someone else to explore. Then your subconscious won’t shoot them down to protect you.
Hack! This is why giving other people advice makes one sound like a genius to self. Rare case where not having skin in the game is useful!
One reason people are more conservative when choosing problems than solutions is that problems are bigger bets.
On the themes of “bets”.
You do need to work on important problems, but almost everyone is too conservative about what counts as one. And if there’s an important but overlooked problem in your neighborhood, it’s probably already on your subconscious radar screen. So try asking yourself: if you were going to take a break from “serious” work to work on something just because it would be really interesting, what would you do? The answer is probably more important than it seems.
Good Question.
The *second-system effect* or *second-system syndrome* is the tendency of small, elegant, and successful systems to be succeeded by over-engineered, bloated systems, due to inflated expectations and overconfidence. The phrase was first used by Fred Brooks in his book The Mythical Man-Month, first published in 1975.
Fun Trivia ^^^
Doing great work is a depth-first search whose root node is the desire to.
…
Never let setbacks panic you into backtracking more than you need to.
Corollary: Never abandon the root node.
…
Learn to distinguish good pain from bad. Good pain is a sign of effort; bad pain is a sign of damage.
P.G OG moment again!
The factors in doing great work are factors in the literal, mathematical sense, and they are: ability, interest, effort, and luck.
Well put.
So I’m going to pull a sneaky trick on you. Do you want to do great work, or not? Now you have to decide consciously. Sorry about that. I wouldn’t have done it to a general audience. But we already know you’re interested.
Don’t worry about being presumptuous. You don’t have to tell anyone. And if it’s too hard and you fail, so what? Lots of people have worse problems than that. In fact you’ll be lucky if it’s the worst problem you have.
Yes, you’ll have to work hard. But again, lots of people have to work hard. And if you’re working on something you find very interesting, which you necessarily will if you’re on the right path, the work will probably feel less burdensome than a lot of your peers'.
The discoveries are out there, waiting to be made. Why not by you?
Wow. ( Gripped by emotion goosebumps )
Like quite a few other PG Essays, I’m sure I’ll be back to re-read this again atleast once a year.
