43 chunks · 14 episodes
Insights from Rob Dodson: "Ultimately the value of a second brain is not to take notes but to help you think better. Doing so means having a criteria for what a valuable note should look like and a process for how those notes evolve into something useful. Otherwise the note taking is just a form of
In the 90's it was hard to imagine a world of infinite[bl] content. Twitter, blogs, Facebook, all only make sense in that world. Very hard to imagine ahead of time. Now we have infinite thinking. Infinite cognitive labor. What kinds of weird new types of value are now possible that previously were u
Everyone should be able to marshal abundant cognitive labor on their behalf.
Electricity is to physical labor as LLM is to cognitive labor. After electricity became widely distributed, physical strength mattered much less than before. Electricity replaced a lot of jobs but also created many new ones. If you replace steam with electricity nothing changes. It took us a few dec
Don't Repeat Yourself (DRY) is a useful rule of thumb mainly because cognitive labor is expensive. But if cognitive labor is abundant, it becomes much less important.
...ded value. Not by pushing models to be 10% better. How do you take advantage of abundant cognitive labor to make compounding value? AGI will come not from the models but from the emergent use of them.
Data has potential energy It used to be hard to unlock that energy. Abundant cognitive labor blows it wide open.
...LMs are additive for consumers and possibly subtractive for employees. LLMs are abundant cognitive labor. Consumers never had the resources to dispatch cognitive labor on their behalf, so extra labor is fully additive. Companies, however, have long paid ...
LLMs can be made to be default-converging. Every input to them, if scoped small enough, it will do what a reasonable person would with that information. So if you make the structure clear enough, they can auto-converge. If you have just the right amount of meta-structure then LLMs can be default-con
In a world of abundant cognitive labor, serendipity gets more important. It's easier to plant seeds, tend to them, and judge them. That implies the balance is moving away from exploit and ...
Great distillation from HBR: AI Doesn't Reduce Work—It Intensifies It. By taking away the cognitive labor, work becomes more pure. Focused on the parts that are where judgment is deployed. High leverage. Intense.
When you have abundant cognitive labor at your fingertips, it's overwhelming. It's giddy and exciting, but also a lot. Suddenly you realize that your opportunity cost is orders of magnitud...
...last century or two we've gotten used to abundant mechanical labor. Now we have abundant cognitive labor. This will change even more than the Industrial Revolution did. Infinite software is downstream of cognitive labor being abundant. But it's just the ...
Orchestration gets more onerous as you can outsource more. AI allows outsourcing more cognitive labor. So now you need more cognitive labor to orchestrate. Orchestration requires a view that can span all of your relevant contexts.
Great piece from Rob Dodson: My Second Brain Never Worked. Then I Gave It a Gardener. The hard part of maintaining a second brain is the cognitive labor. LLMs can do the cognitive labor part.
A position of "It's OK to write code with AI but it has to be reviewed by humans" is untenable. Code Review is excruciating cognitive labor. If you try to review every line of AI code you will go crazy. It's not possible. LLMs can produce code so quickly the only way to tackle it is to use LLMs to r
The components of the ground truth can exist in a group but still be obscured by various weeds. Weeds like empathy issues, personality mismatches, power dynamics, etc. A system to do the cognitive labor to clear those weeds can make the truth discovery process much more effective.
The hardest part about a knowledge graph is maintaining it. You need to garden it constantly. But what if you had a gardener who did the cognitive labor for you? That would unlock the power of having a knowledge graph. LLMs can do cognitive labor without getting bored.
When you have all of your ingredients set up just right, you can do magic when you cook. A mise en place. Effortless, focused just on the act of creation, not the labor of getting to that point. Imagine an AI system that did that for you. That automated the cognitive labor of creating the right mise
Don't use Claude to think for you. Use it to do cognitive labor for you. You're doing the thinking, and it's helping you lever your thinking farther.