So as someone who writes and talks a lot about producing meaningful stuff in a distracted world, I always get excited when prominent individuals give us insight into their own processes for achieving this goal. So you can imagine how happy I was when I saw Tim Ferriss recently linked to a blog post that was titled simply Productivity that was published in 2018 by OpenAI's Sam Altman.

Here's the opening two sentences of this essay. I think I am at least somewhat more productive than average and people sometimes ask me for productivity tips. So I decided to just write them all down in one place. So I'm excited about getting to this essay because when Sam wrote it, 2018, think about it, OpenAI was at a crucial turning point.

They had just released GPT-1, but they were operating as a nonprofit. Elon Musk had just left the board of directors after failing to convince the board that they should merge OpenAI with his Tesla company to help out its financial situation. Instead, Sam led a move from a nonprofit the next year to a capped profit status that opened up venture capital funding.

They could hire a bunch more talent and that's really where the OpenAI story we know today really took off. So he was pretty productive during this period. So it's useful to look back and say, how was he thinking about getting important work done on the eve of OpenAI making all of these important leaps?

So what I'm going to do is pull out, I think I have five, let me look at my notes here. I have five ideas from his essay and for each we'll get into it. I agree with a lot. I disagree with others. I think there's some big important ideas he highlights in some.

So we will get in the Sam Altman's productivity essay. Let me just load it on the screen now for those who are watching instead of listening. This is what the essay looks like. I missed that classic blog format, Jesse, back in the end of Web 2 where you kept things simple and just wrote long essays.

But here it is published in April of 2018. So seven years ago, basically to the day. That's what it looks like. So I'm going to jump through here and I'm going to pull out some quotes. All right, Jesse, we can bring it back to full screen. All right, here's idea number one.

I'm reading now from Sam's essay. Compound growth gets discussed as a financial concept, but it works in careers as well and it is magic. A small productivity gain compounded over 50 years is worth a lot. So it's worth figuring out how to optimize productivity. If you get 10% more done and 1% better every day compared to someone else, the compound difference is massive.

All right, so I have mixed feelings about this first idea of applying the compound growth idea to productivity. I think there's some piece of this that's right and some piece of this that's not right. That getting 1% better, sure. If you could get 1% better at something every day, you are compounding.

So your current skill level grows that your 1% is being applied to. And if you put that out over a certain number of years, you would be the world's very best expert in like five years or however the math works out. That's not really how getting better works though.

It typically has long periods of practice. It leads to new levels of skill and each level of skill is harder to get to than the last. So it's almost more of a linear or like a slow linear function that an exponential function like compound interest would give you. You're kind of getting better faster and then it really slows down.

It just gets harder to get to each new level. Whereas compound interest, you get a curve that picks up speed as it's going. I also am concerned about him making the slight shift. He's combining getting 1% better as an example of compound growth to doing 10% more each day.

This doesn't really jive with my study of long-term productivity. If you looked at my book, Slow Productivity, which came out last year, one of the things I do is study some of the most historically productive people, meaning like what they produce, historical figures that produce things that we look back at now and say that was super important.

What a productive intellectual life they had. So Galileo or Newton or Marie Curie or Jane Austen, et cetera. And what you find in these stories is their key was not getting more done each day. In fact, what I highlighted was there's a certain notable slowness or even lack of urgency behind their biggest achievements.

They thought about it. They had long digressions into other interests. They would come back to them. They would let it marinate. And ultimately, it didn't really matter. The busyness or phoneticism of any given day wasn't important. It was more about the consistent application of thought over a long period of time that eventually led to really big breakthroughs.

So when it comes to producing really meaningful stuff, I don't know that this idea of I get 10% more done each day really matters. I think where that will lead to more often than not is just busyness. That's the easiest thing to get 10% more done of. And busyness doesn't necessarily transmute into results.

All right. So that's an idea where I'm not completely on board with the way Altman is summarizing things. Idea two, however, I'm very much on board with. Let me read now from Altman's essay. It doesn't matter how fast you move if it's in a worthless direction. Picking the right thing to work on is the most important element of productivity and usually almost ignored.

So think about it more. I make sure to leave enough time in my schedule to think about what to work on. The best ways for me to do this are reading books, hanging out with interesting people, and spending time in nature. Interestingly, this is somewhat contradictory to idea number one, which is like, hey, get 10% more done each day.

It'll add up. And you say, no, take your time. Don't get started. Think more. Hang out in nature. Hang out with interesting people. Read, wait to get started. Really make sure that you have the right thing to work on. I'm a big believer in that idea. I remember I wrote an essay about this way back early in my writing career.

I wrote an essay for Ramit Sethi's blog, and it was called Don't Get Started. My argument was, it is basically Sam's argument. It is really hard to figure out the right thing to work on. The thing that's going to matter, and it uses the rare and valuable skills that you currently possess.

And you, it's probably going to take a long time once you choose the right thing to get really good results. So you want to be really wary of just diving into things, because when you dive into things, you're basically flooding your circuits with activity, and you are taking them out of the game for working on other more important things.

So resist working on things. I often say with big projects, resist working on them. Think about them, read about them, get excited about them, but resist working on them until you can't help it anymore. Sort of like me with book writing, me with this podcast. Man, I resisted podcasting for a long time.

I learned a lot about it. No, it's not quite right. I don't want to just do it for the sake of doing it. I don't like activity for the sake of activity. It took me years before I said, okay, I can't avoid this any longer. That's really what you should be looking for.

Fewer things, done better. That has been a theme through my work so long that the original, one of the original mottos of my study hacks blog back when it was still focused on students was do less, do better, know why. Do less, do better. It's a key idea. So I think Sam is absolutely onto it there.

That's probably reflected in OpenAI. They kind of chose their points. They chose their battles where they thought there could be big work, for example, working on very large scale language models. And then they went down that road year after year. So they carefully chose what they were working on and then really gave that a lot of attention over time.

That's where the big breakthroughs came from. All right, idea number three. Now we're going to get into the weeds of actual time management. Here's Sam. I highly recommend using lists. I make lists of what I want to accomplish each year, each month, and each day. Lists are very focusing and they help me with multitasking because I don't have to keep as much in my head.

If I'm not in the mood for some particular task, I can always find something else I'm excited to do. Later, he says, I don't bother with categorization or trying to size tasks or anything like that. The most I do is put a star next to really important items. I try to prioritize in a way that generates momentum.

The more I get done, the better I feel. And then the more I get done. I like to start and end each day with something I can really make progress on. A couple of interesting points about his approach here. One, we do see him preaching a principle that I talk a lot on this show, which comes from David Allen, who himself took it from Dean Atchison, which is the notion of full capture.

Having things written down and not being kept track of just in your head is critical for avoiding unnecessary stress and forgotten deadlines and scrambles. Do not use your brain as a task storage device or a calendar. Use task storage devices or calendars for that role. And he makes that clear here.

He says, look, they help me with multitasking so I don't have to keep as much in my head. By multitasking, he means just having multiple projects going on at the same time. So that's useful. I also notice, however, the simplicity of his systems. He just like writes things down on a list on paper.

He doesn't break it up into categories. He doesn't do any sort of prioritization. He just sort of looks at the list and says, what do I want to work on next? Maybe he'll put a star next to something to really remind him that it's important to get it done.

That type of basic system, a sort of what's known as an MIT system, most important task system, it's been around. It's an idea that's been around for a while. We hear it in the early 2000s. We hear Julie Morgenstern talking about this. We hear Brian Tracy talking about this.

We hear Leo Babudov's Inhabits talking about this. Even more recently, when Oliver Berkman came on my show last fall, this was basically what he was pitching. Get the important thing done first and then kind of do your best with the rest of the day. Hey, it's Cal. I wanted to interrupt briefly to say that if you're enjoying this video, then you need to check out my new book, Slow Productivity, The Lost Art of Accomplishment Without Burnout.

This is like the Bible for most of the ideas we talk about here in these videos. You can get a free excerpt at calnewport.com slash slow. I know you're going to like it. Check it out. Now let's get back to the video. It works and it doesn't. So, I mean, it works in the sense of if your goal is to make progress on what's important, this is emphasizing that really just means doing the important things and making progress.

So I think the fact that Sam had this sort of simple system, I just make sure the important stuff gets done, shows how when it comes to long-term productivity, this is really different and busyness. This is really different than I'm quick on my emails and Slack. I'm jumping on a bunch of calls.

For Sam, his productivity was dependent on doing a small number of things consistently well. The issue is for a lot of us, there's a lot of other stuff too that we have to do that is not just, hey, here's the project I want to work on. It is, I have to get back to this person.

This dean wants to know this. My students need me to sign this. The parking office needs me to update my license plate for the new license plate readers they installed in the Levy garage. Just making these up on the top of my head here. And we can't say no to those things.

So that's the context where you actually probably need a more complicated task storage system because you can't just, if you have many unignorable demands on your time, so you have the big, like Sam's focusing on, and the small. If you just have a big list and you're just trying to choose from there, hey, what's the big thing I want to work on today?

That small stuff is going to eat away at you because you're going to miss things. People are going to yell at you. Small things will get missed. People are like, where's this? Where's that? Your car is going to get a ticket because you didn't update your license plate information.

And that's going to become a source of stress and a problem. So I like the point that Sam is making here. It's like ultimately just doing something important every day is what matters for the stuff that people will remember you for. The busyness doesn't produce stuff that matters. But if you have a lot of that other stuff, smarter task storage might be important, right?

This is why I like to store stuff in cognitive context. So I could say I'm going to spend time on like my professor role and just see tasks for that divided into statuses. So it's very easy to sort of see what's what and what needs to get done. So smarter task storage, I think it's necessary if you have a lot to do, but don't forget Sam's big lesson here, which is, yeah, but the small stuff is secondary.

Do the best you can with that. Organize it in a way that's going to save you from stress, but it's really working on the important things each day that's going to matter. All right. Idea number four from Sam Altman. Here's Sam. I try to be ruthless about saying no to stuff and doing non-critical things in the quickest way possible.

I probably take this too far. For example, I am almost sure I am terse to the point of rudeness when replying to emails. I generally try to avoid meetings and conferences as I find the time cost to be huge. Again, I think there's a critical point here. Whether or not you have the power to say no to everything, it emphasizes how almost everything doesn't matter.

This is a theme, I think, that goes through Sam's essay here. The things that mattered that made open AI from a struggling nonprofit to a company with a massive valuation and huge impact on the world technological and economic scene, the things that mattered were small and hard, and he was pretty ruthless about coming back to them.

So if you worry about saying no to stuff, thinking that this somehow makes you less productive, keep in mind this uber productive individual's productivity was built on his default of, I really don't want to do stuff. Most stuff is just getting in the way of time. Taking my time away from the stuff I know for sure is going to be really valuable.

More executives should probably follow that advice as well, right? I mean, I've talked to more than a few executives who would have been in Sam's position at different companies that say, no, my job is right. I have to be in meetings. How can I say no to them? And he's saying, well, because if you want to be good at what you do, the meetings aren't really what matters so much as like you understanding, pushing, and developing the ideas that are going to make the biggest difference.

All right, idea number five from Sam. I have different times of day I try to use for different kinds of work. The first few hours of the morning are definitely my most productive time of the day. So I don't let anyone schedule anything. Then I try to do meetings in the afternoon.

Another great idea, the morning is a good time for deep work for most people. So just having a simple rule, I don't do meetings until this point makes a big difference. I was chatting recently with a president of a former president of a large company. And he was saying this was a huge change for him is that they were just filling his days with meetings, his staff.

And at some point they said, okay, you know, we're going to protect time in the morning for just working on your own stuff. And he was worried this would make him a worse executive instead made him way better. There's endless people that want your time. There's endless meetings you could take.

You're already constraining the meetings you can take because your day is only so long. So why not just change those constraints even more so that you have more time to work on the big thoughts that are going to matter as well. If Sam Maltman can do it, you can probably do it as well.

So ultimately I think Sam has a lot of non-surprising advice here. I mean, I think he would, would have been, these ideas would have fit well in my book, Slow Productivity. These ideas would have fit well in my book, Deep Work. And perhaps this is not surprising because he ended up being very successful at what he did.

He worked on deep stuff in a distracted world. I want to end with a quote from Sam's essay that I think summarizes well the gist of his whole philosophy here. Don't fall into the trap of productivity porn. Chasing productivity for its own sake isn't helpful. Many people spend too much time thinking about how to perfectly optimize their system and not nearly enough asking if they're working on the right problems.

It doesn't matter what system you use or if you squeeze out every second if you're working on the wrong thing. And I think that gets to the heart of it. Spend a lot of time figuring out what matters. That's not an easy question. But once you've answered it, spend a lot of time working on that thing.

And do your best with whatever else. And that'll work itself out. Don't stress yourself out too much. It's impossible to do it all anyways. But working hard on the right thing consistently, that's what matters. Everything else is just trying to take care of the details that are trying to get in the way of that.

Great way of thinking about it. So you need some systems and some rules, but mainly you just have to do the work of finding what to work on and putting in the right time. I wonder, he probably, his rules probably have changed by now just because that company's so large.

And he's a lot richer. Yeah, he is a lot richer. I don't know if that changes his rules. That's true. But I'm just thinking like the size of the company now versus then. Yeah. It'd be interesting to check in. I think now he probably is all meetings. I've never met Sam Altman, but busy guy.

All right. Well, we have some more, speak of Sam, we're going to do some AI stuff at the end of the show, but now we want to move on to some questions. But first, as always, let's briefly hear from a sponsor. Trust isn't just earned, it's demanded. Whether you're a startup founder navigating your first audit or a seasoned security professional scaling your GRC program, proving your commitment to security has never been more critical or more complex.

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This creates a flood of emails and urgent tasks. Every form still requires my signature. How did you set up Trello in your email to manage this kind of administrative sprawl without letting it dictate your day? Do your students have a pipeline to follow, forms to fill out? Well, it's a good question.

I mean, you have my sympathies. You're overseeing admin for 150 students. I'm overseeing the computer science major for 100-something students. So I'm with you, Kelsey. The first thing I would say, you talk about not letting administrative sprawl dictate your day. Well, it will be realistic. If you're overseeing admin for that many people, it's going to be a major part of your day.

So we want to make sure that the expectations here are realistic. We're not going to automate this work out of existence. We're not going to be doing six hours of deep work a day. But it shouldn't be controlling your day like a puppet master, right? So it shouldn't be you reactively balancing from one thing to another and feeling like you're always behind or always exhausted or frustrated.

That I think we can do better on. The story I like to tell when trying to get at the right solutions here is a story from when I was in graduate school and I was TAing for my advisor's distributed algorithms graduate class. Pretty big class. I don't remember how many kids.

Maybe it was like 50 or 60 kids or something like this. It's a theory class, right? A lot of problem sets. I remember early on. So as a TA, I was in charge of collecting those problem sets, writing the problem sets or some of the problems, getting the sample solutions together.

We had graders, but I would have to organize and work with the graders to do the grading, then get the grades entered and those problem sets back to the students. And I remember early on, it was very time consuming. I'd have all these different problem sets. My advisor and the teacher of the class wanted Xerox copies as a backup.

They're a stapled and, or dog-eared together. And I would like be pulling out staples and trying to run them through the Xerox machine. And this would take forever. And then try to, you know, get them to the graders, then get them back to the students. Like the whole thing was very time consuming.

And at some point I had this epiphany. I could just ask the students to do a little more, a little more that's going to make my life easier that for them, who cares? It's like a small extra step, but for me could add up to a lot of savings.

And that's when I started saying, okay, here's how you have to hand these things in. They have to be a single-sided, no double-sided that messes up the photocopy, no staples, just hand them in as a stack with your name on the top of every page. And I believe I said, when you hand them in, you have to alphabetize them.

So as you come up, you start adding these things to the desk up here, find where your name goes. So it'll be alphabetized when you're done. And then I could just take this whole stack and run it through the Xerox copy all at once, the document feeder, have a copy of all of them I could just put aside.

And then I have all of them alphabetized. And the graders could then, when they're done grading, putting them back alphabetical is easy to hand them back to the class. Like these small things, a little bit of extra work I made the students do made my life much easier. Now as a professor, I took this even farther, right?

I mean, when it comes to like prom sets and exams, I have these various administrative things that students can do that just makes my life much easier. I switched, for example, to digital submission of prom sets. Do your prom set, take good images of it, upload them directly to our class portal.

So me and the graders can see them digitally. We can note them digitally. We can enter the grades digitally. You can get them back digitally. I don't have to deal with papers. I started doing this with my exams. They're taken in class, but I actually have the students, when they're done taking the exam right there before they leave, take photos with their phone of every page, which they can then later on my behalf upload into the online system.

So we have digital copies, the simplified grading and comments and returning entering grades. And I can just keep the physical copies as a backup, just pick them up and put them in a drawer in my office, right? So I learned this rule. You have someone to do 10% more on their end can make your life 100% easier.

Small addition to their work can make your life bigly more, that's not a real word, but much more easy because it aggregates over a lot of different people. All right. So with that in mind, Kelsey, my lesson here is I would have some sort of procedure page, you know, FAQ or systems or whatever.

It's a website that you can easily update that you start building out your instructions for all the common things you do and handle as an administrator. And then you can just keep pointing students to this page. Oh, you need a external credit approval form. Look at this page. You need transfer credit approval.

Look at this page. You need to declare like approval for a major declaration. Look at this page. So you're preventing everyone from just informally emailing you and you have to kind of explain or work with them informally to get done whatever thing they need to get done. You can then experiment on this page with giving them more work to do to make your life somewhat easier.

Like for example, if there's a common type of form that you need to sign, which is like real easy for you to verify, but it's sort of annoying. People email you, you sign them, you email them back, you have to keep track and they're always coming in. You could have a procedure that says, here's a shared folder.

What you need to do, it's called forms to be signed. You need to set up your whole form like in the PDF and you need to put it in that folder. And on Friday afternoons, I go through and I sign all of those forms in a row and move them to a nearby folder called signed forms.

After Friday afternoon, you can find your signed form there whenever you want and move forward and submit it. You're giving them a little bit more work, but you've taken every one of those forms out of your inbox as something to react to. And instead you have 30 minutes on Friday where you do it all at once.

Another type of thing you can do is if there's anything where information needs to be looked up, see if the student can look the information up for you and then they submit that along with what's going on. Like in my role as director of undergraduate studies, for example, one of the things I'm responsible for is approving requests of students who want to declare a major in computer science.

When our department was smaller, the way the DUS would handle this is someone would say, hey, I'm thinking about declaring a major and he would load up using the backing systems, their schedule and see where they are and what they've taken already and kind of see like, is it reasonable?

Is there a way they have enough, you know, enough semesters left to take the courses? Like, is there a path, a reasonable path forward for them to actually finish this major? But our department has grown and that takes a lot of time. And so now I just, the instructions for the students are, if you want to declare a major, that's great.

You can send me a note, but here's what you need to send me. You tell me all the courses you've taken and then you need to give me at least a sample schedule for one way you plan to finish this major, what courses you're going to take in what semester.

Now I can just see at a quick glance, is this reasonable or not? And this adds up, this saves me five or 10 minutes over 20 or 30 students. That makes a big difference. The final thing I might argue here, Kelsey, if you have 150 students is having a daily or every other day office hours from one hour.

And basically when something comes in, that's one off, like you don't have a procedure for it, but it's going to require a more complicated discussion than just a one email message response, just keep telling people, here's my office hour schedule, come to the next one you can, and we'll get into it.

And just have this hour, do it different times. So it doesn't over, you know, if someone has a course that conflicts, one of the times will work and just have people come to your office hours. So you just have this like hour most days where like a lot of this work gets done.

So these type of procedures plus office hours can really reduce the amount of time you're reacting and get you a much more like efficient batching here. But the bigger picture thing here is my idea of make a lot of people do a little bit more work. It makes your life a lot easier.

All right, who we got next? Do you always have a line at your office hours? Not always. A lot of times? Yeah. I mean, so I'm teaching a class there. It depends if there's a problem set due. So the way I run my class, I have about a hundred kids I'm teaching.

I have four TAs. And so we've set things up. So there's an office hours five days a week. So it just kind of matters like when, if there's something due and I'm the nearest office hours to that thing being due, I'll get a lot of students. If it's not near me, whatever TA happens to have their office hours closest to that, they'll get more students.

I think for like homework questions, they go to the TAs more than me. And I kind of set things up that way. But I get students who aren't on my classes coming to talk about my books or this or that. So it's not like a long line. But you know how I am.

Like there's a lot of things where I've organized the amount of office hours. Like I've said, if you have questions about grading, I want to hear them. But here's how I, this is another example. Here's how I want to hear them. I want you to send me a screenshot from the online grading system.

Here's your answer. And here's the TA's comment about here's the grade you got and why. Send me that. Because I can just look at that and I'll know immediately, like, is this a mistake or not? Most likely. Much more efficient than you coming to my office, loading up the thing, showing it to me.

I can just go through those really quickly. And, but that's like a, that takes another common, you know, sorts of office hours, et cetera. So it's, it's not too bad. All right. Next question is from Kent. When I time block, I'm often unsure where to do deep work on days without meetings.

Should I dive straight into deep work? I've been experimenting with delaying, even opening my email until 10 AM. Right. Do that. Yeah. First thing is better for almost everyone. That's what Sam Altman talked about in our deep dive. Yes. Don't check email before. If you're going to do a first thing, deep work block, don't check your email until it's done.

Checking your email is just going to load up multiple different cognitive contexts in your mind that you're going to have to completely clear out again to do your deep work. It's going to take you a half hour. So just get right into the work and then move on to your email.

If you're stressed about it, put aside more time at the end of the day to kind of get on top of anything urgent in your email. Because anything that arrives after that is people messaging you in the evening. So they don't have an expectation of an immediate response. So I would say, try it.

I would, I mean, look, if I was in charge of the knowledge work world, I'd be like most knowledge workers, it should be, there's no, no email, no meetings before 11. Just take the most important thing you're working on and work on it each morning. I think that'd make a big difference.

Or the other model I'd pitch is the hybrid attention model. Two days or three days a week, you're at home. Those days there's no meetings, no expectations of emails. Three days you're at the office, you answer emails and have meetings. So on the, at home days, all you're doing is deep work, no interruption.

It really makes a difference. Deep work without interruption versus trying to do hard work with interruption. It's night and day. It's just two completely different cognitive states. All right. What do we got next? Next question is from Bradley. I live in Scotland and am a gardener. In my field, what would gaining career capital look like?

Well, I don't know the answer to that, Bradley, but I would say there are specific answers. And what you want to avoid is guessing or writing your own story about what you want the answer to be. So most of my listeners are not gardeners, but I think the specificity of your field allows us to point to a more general point here, which is, it's not always obvious what skills are valuable in a given field.

And it is worth actually going out there and doing some research to figure this out. And by research, I mean actually talking to real people and actually observing who in my field is at a place I would like to be at, has some attributes in their job that seem really appealing to me, be it just like status or income or flexibility or autonomy or specific types of projects to get a work on and figure out the reality of how they got there.

Treat it like you're a business journalist or a how-to book writer. How did they get there? What mattered? Take them out to coffee. Talk about their career. Like, how did you get from here to here? What was the key thing? It's a question I always say to ask people.

When you went from this step to that step, this impressive jump, what was the key thing you were doing that other people who would want to make that jump weren't doing or weren't doing enough of? So you want to really isolate the things that really matter. The answers aren't always what you want to hear, but it's always better to deal with reality than to guess or to write your own stories.

Writing your own stories, I think, is the more dangerous possibility here because it's the more tempting. People like to create a story about what they think matters because it matches what they want to do. It has some vision of them working like an hour each day extra on whatever.

Like, they have some vision for what they want the answer to be because it gives them a plan that's going to be fun to do that's like kind of hard but not too hard and everything works out well in the end. But the problem about writing your own stories is they rarely match reality.

You write a fantasy, your goals are fantastical as well. If you instead study the reality, you might come away and say, shoot, I'm never going to achieve that. Like, that's way too hard. I already see I'm not in the right place to do it. Okay, I'm going to need a different type of goal.

But it's better you figure that out than tell yourself a story. And maybe what you find out is, oh, there's an option here I wasn't thinking about, and now I see how to get there, and I never would have done this type of work, but now I know if I do this work, I have a good shot.

But this reality check-in will happen a lot with people, more in my world would be with people that have like academic aspirations. And they might be, you know, in grad school somewhere like, yeah, I want to be like a professor at like a top 25 school and, you know, be done sabbatical and writing books and like, this sounds great.

I want to do that. But the reality might be like, well, wait a second. Like, you didn't do that great in your undergraduate. You're in like a mid-tier grad school. You are not on that trajectory, and it's probably too late to fix that trajectory. And there's not like some easy thing you can do.

The other place where people often tell stories about book writing. I want to be a successful writer, and I want to write my own story about how that works. And it's going to be some clever scheme I have for like marketing or building an audience. And then, you know, as opposed to just, here's the real story about how you become an author.

Because they might not want to hear that story because it's not going to go well for them. So you got to be careful about writing your own story. Get evidence. The flip side is if you have real evidence about what matters, your return on investment in terms of your effort is way bigger than other people.

You have a much higher chance of actually getting to cool places than if you're out there just trying to like hustle or follow some sort of like highly inventive plan. Gardening in Scotland sounds cool. Yeah, he probably works around some golf courses. Yeah, you think he's at St. Andrews?

St. Andrews? Deep sand traps. All right, who we got next? Next question is from Andrew. How can I go from good performance to truly exceptional performance in my field of work? Well, the only way people become exceptional is with expert guided deliberate practice. That's it. You are having a real expert on how the field works, coaching you in the strategic stretching of your abilities where they're weak to improve them.

So this sort of very systematic, expert guided, difficult to do training is a necessary condition for exceptional performance. This is more clear in things like chess or baseball. Chess players now, they're much better than they used to be because they have these very rigorous training programs built on playing against computer chess programs where they can really push at exactly, you know, a particular type of mid-game situation they're struggling in.

They can just do those type of exact puzzles all day. It's very hard. If you're like a baseball player, you're working with hitting coaches that are, it's like very specific. Here is exactly what you need to improve on your swing. And we're working just on that. So that's the necessary condition.

The problem is it's not necessarily a sufficient condition. You might not ever be able to become exceptional in your field, right? These are where other things come in place, circumstances, natural abilities, the capacity for drive, ability to go through hardship, physical capacities, genes, like all these other things come into play where you're going to sort of ultimately hit your limit.

So, you know, if you want to get better, do expert guided deliberate practice, figure out how to do that in your field, but have the reality check that like most of us aren't going to become exceptional. So you don't necessarily want your plans to be built around. I'm going to be the very best person in the world doing this.

Not a bad thing to make a run at that because even falling short of that is going to open up a lot of opportunities, but everyone can get good. This is what I always say. Everyone can get most skills, especially professional skills. Basically, everyone can get good enough at those skills to really gain some career capital.

Not everyone can get great. In fact, like most things, most people can't get great. And when you're planning for, you know, your life, that's like something, that's something to keep in mind. All right. Who do we have next? Next question is from Alex. I'm interested in your take on multiple monitors.

I'm an academic and there's definitely a move towards people having multiple monitors on their desks. Personally, I have two, but I've noticed that I write a lot better if I unplug my laptop from my multi-monitor docking station and go and just write with one screen open. Yeah. The monitor wars are getting kind of crazy.

You now have people with like these giant curved ones and then multiple other monitors on top of them. It's unclear if like what they're doing is trying to debug their C++ code or launch a ballistic missile attack against Russia because they have a setup that would allow them really to do both of those things.

The multiple monitor movement really came out of computer programmer. I think developers, they have, you know, here's my code window. Here's the thing I'm compiling. Here's my counsel. And here is like where I'm Googling Stack Overflow. And I don't want to switch between things. I want all things open.

I think that's where that really came from. In terms of like its utility for most other people, I think it is useful in a lot of cases to have what I think of as like a double window width available. So to me, a double window width is you could have two windows open large enough that you can easily read them.

That's useful because there are a lot of circumstances where you're moving back and forth between two things. So if you're an academic like mathematician writing a mathematical paper and something like LaTeX, you have the editor where you have the markup language and then you have the PDF compiled version next to it.

So you can just edit here and see the changes over there without having to switch back and forth. Or if you're a writer, you know, when I'm using something like Scrivener, I have two and a half panes open. I have the pane where the main thing I'm writing and then next to it, a pane, the research I'm pulling up.

I'm pulling up Spotify. I'm pulling up different research sources that I'm copying quotes over. And then there's a narrow column that has like my footnotes. That benefits from like a larger monitor. Calendar is another thing. I have a calendar open and email open. So like while I'm answering emails, I have to schedule, I can look through that without having to switch back and forth with my email.

So I think being able to have two window widths open concurrently, easily readable, that's a boom that makes your life easier. Having like four or five, I think for most people doesn't matter. I also agree that when you're doing deep work, you don't need a lot of windows open because you only want to be working on one thing.

Like sometimes when I'm writing, there's parts where I am copying over information. I want a big monitor because I have like my research here. There's other parts where I'm wrangling the language, especially if I'm doing like a New Yorker thing. And there, like you, I'm happier on my laptop because I can put Scrivener, which I use into composition mode where it just puts full screen, just the words, not even any menus.

And that's fantastic for focusing on that. There's these cool tools. I've been wanting to experiment with some of these. I don't think they would quite work for me, but there's these cool writer tools. And I forgot what they're called, but it's a, it's a, it'll be a keyboard and then an ink screen like you'd have on a Kindle and not, and like kind of mounted on the end of the keyboard.

And all it is, is for writing and all you can do is write. And it's just like, you could put words on it. There's barely any formatting. It's, you know, black and white. And the idea is you just bring this thing somewhere and all you can do on it is like, write and like edit your writing.

That's it. Like produce one big file. I've heard people like novelists swear by that. They can just like disappear with this thing. And all you can do, it's like a typewriter with a memory. Kind of a cool idea. So yeah, different modes for different times, but one big monitor, I think for like 99% of us, that's probably enough.

And we have, what do we have here in the HQ? We have two. We have two. But you do like video editing and stuff on there. Yeah. That's useful. I'll sometimes use both. Usually just like one is enough, but sometimes I'll use both. What, how are things going with your keyboard and your Remarkable?

I've been loving the new Remarkable, the Paper Pro. I was just using it today. That's been going really good. I've been liking my mechanical keyboard. I have a really fast one here at the HQ. That one I really like. I can fly on that one. I got a quieter mechanical keyboard from my office at Georgetown because the walls are thin and I didn't want to clickety-clack everyone.

And it's a little slower. Honestly, it feels a little muddy to me. So I don't like that one as well. The one here I love though. I'd have to look up what it is. I did a lot of research on it, but I really feel like I can fly out there.

Like the springiness of the keys, like they really, they bounce up. I can ride at like the maximum speed of my hands on that thing. Do you have one of those at your house too? No, I took that one to my office. So I'm probably going to get another one of the ones we have here for my house.

Got it. Yeah, for what I'm writing there. All right. We got some calls. We're doing two calls today, right? Yep. All right. Let's start with the first one. Spotify co-president Gustav Soderstrom argues in an interview that 99.9% of evolution took place in an environment where little changed within a single lifetime.

And now in the 21st century with lots of macro changes in tech and culture, the first to accept and adapt wins. Can this worldview exist within a slow productivity framework that is predicated on minimizing reactive action? That's a good question. That's a good question. That's a good question. That's a good question.

I hear this a lot, especially from those who are tech adjacent. This idea that you need to be like aggressively up to speed on like the latest tools and experimenting them in your personal life and probably therefore need to be like up to speed with chatter about tools and be on like social media and YouTube and trying to keep up with things because you are going to get left behind otherwise.

I tend not to think that's true. I think on a macro scale, it can be true in the sense of major – if you're running a business, major business trends, you need to keep an eye on, right? Like the rise of the web was like a business trend that a lot of businesses needed to keep an eye on and cause a lot of disruption.

The smartphone culture, that's a business trend that made a big difference. A lot of people need to keep an eye on. But in terms of individuals, I think it's more common to say, especially with technology that ends up playing a big role in people's lives or playing a big role in businesses.

These type of technologies, these transformative technologies, make themselves unavoidable and they – so there's like a first adopter. It's kind of out there. People are keeping an eye on it and then they become kind of like unavoidable and it's like so obvious or inevitable that they're – how to use it and why you should use it.

It's so easy to use it and then they spread really quickly. Like let's look at some examples of this. I think we could do email. It's a good example. Like when email really took off and I've documented this in my book, World Without Email, it was self-evident. It was, okay, here it is.

We have the servers. It's easy to set up. We already had computers in these offices. It does voicemail and faxes much easier. It's very easy to use. You put the address in the two and you write it like a letter and press in. Then you have an inbox. It's like a physical inbox.

It was very easy to learn. And when it spread, it spread fast. It was very disruptive. But there wasn't like a giant advantage of like, well, these people knew about it and jumped on it and it really helped. And these people didn't know about it because it was inevitable.

Google is another example. Like effective web search. When that became available, everyone started using it and it swept really through. There wasn't like a giant advantage to people who like were keeping up with Google and they knew about it before other people. It became inevitable when it spread. The iPhone was a similar way.

People are like, oh, what is this thing? And there was like a one-year period where it was, oh, that's so cool you have one of those. And then it started spreading really rapidly because it was inevitable. This thing works. It does all these cool things. It's easy to use, easy to get.

That thing spread really far. So often like the most transformative technologies, they become inevitable and spread really fast and don't require a huge amount of learning on behalf of the consumer cycle. So I'm not typically a big believer that like everyone needs to be really up on the technology.

It's why I'm telling people I'm not as big on this idea that you should really be learning like the specific way to prompt the current like large language models right now. When AI, the big transformational impacts of AI are going to be inevitable and they're going to be easy to pick up and they're going to spread everywhere and they're going to disrupt the economy like those other technologies did.

And it's not going to matter. It's not going to be I learned how to do it and other people didn't. Right. So I think like right now, if you really are messing around a lot with language models and have found very specific ways, somewhat complicated ways to use them in your own work, it's kind of like the early adherence of the web.

They were right that this thing is going to be really big, but they were also hacking HTML code and knew how to follow like blog rings and go on Usenet news groups and IRC forums to find links to what's going on. That was interesting. And when the web took off, you didn't have to know how to do any of that.

It was like, I don't know. I go to Google and I find the websites and they're pretty nice and easy to use. So I agree adaptation happens, the world changes, but I often think those changes are easier for the people involved when it comes to technological revolutions than we sometimes imagine, at least the way that tech first adopters imagine.

All right, what's our second call? I'm excited about having to. Yep. Hey, Cal. Enjoy all that you do. I was watching the Masters Golf Tournament, and at the end of the day, Saturday, before the final round, Rory McIlroy is winning, and he did a post-round interview, and he said, I'm putting my phone away, and I won't look at my phone again until tomorrow night.

I thought it was cool, and I thought of you guys and what you're doing there. So anyway, check it out. I have to say, Jesse, there's a moment of disappointment when I see in my script Rory McIlroy and his phone. A little disappointed it wasn't Rory who called in.

It was like a little bit of a hope that he was going to. He reads all of Holliday's books. He does like Holliday's books. Yeah. I think Holliday actually talked to him. He's much better at like actually getting in touch with these people. That's a great example. I wrote an essay about a similar example back during the pandemic, I think.

It was Alex Honnold who did the free solo climb of El Capitan. He also will stop using his phone, but he'll stop using his phone months in advance of one of those life-threatening climbs because he doesn't want to be distracted. To me, the point is the fact that these high-performance athletes say I have to get away from my phone in order to like the next day use my brain at a high level just indicates – these are people who know how to focus – indicates the cognitive drag that is being generated by a life mediated through these screens.

It's like we take it for granted. Like I don't know. I have it here. I'd be bored without it. But we don't realize the sand that's put into the gears that is our mind and how we perceive the world. It's like whatever your equivalent is of playing a really good golf round.

It might be like being really present with your kids or having like a really good idea at work or just enjoying a day. Whatever your equivalent is of that is also getting gunked up by this online world that you're constantly also involved in. So to me, I think there's a lesson we can all take from this.

Like we can all do better at our own masters, I guess is one way of saying that. Did you watch it? I watched the – yes. Okay. So I was here. Let me think what the context was. I was at the HQ. This was on Sunday, right? Yeah. I was at the HQ right.

Well, it was all weekend. Yeah, yeah. But at the end, like I knew he was doing well and I kind of – and then he was struggling a little bit. And then I stopped following it. I was here riding at the HQ. And then I just looked it up.

And it was like as I looked it up, they said it's going to a playoff right now. And so I rushed home and I turned it on. And just coincidentally, I turned on the TV. It was already tuned to CBS. Because God, I don't know what I was watching on CBS.

So whatever, it was already tuned to CBS. Oh, because I was watching the Blue Origin live feed of Katy Perry and Gayle King and all of them going into space. That was aired on CBS that morning. Anyways, I turned it on and it was like just as the second shots were being made.

So for those who didn't watch it, Justin Rose, you know, second shot, there's a par four. This is almost more interesting to people than my baseball talk. Justin Rose hit like a really good second shot. Yeah. That's probably like 10 feet from the hole. And then McRoy just nailed it.

He hit it up higher on the hill and it rolled to like a foot and a half from the hole. 19 million people watched on Sunday, which is like higher than an NFL game. That's crazy. Most of the time. Yeah. Mad Dog said it was the most entertaining Masters he's ever seen in his life.

It was so many up and downs like in the final round. Yeah. Anyways, that was fun. That was fun. I don't watch a lot of golf, but I like when there's like a storyline like that. And Rory's my guy. I was telling my kids like, yeah, he reads my books or whatever.

And so they were like, so do you get credit if he wins? Like I do. I think I really do. He'll send you some of the $4 million pot. I thought it was $20 million. Well, that's a total pot. Oh. But the winner goes to. Yeah. Okay. So nevermind that I was excited for a second.

Bit of $4 million is not going to help me. All right. We got a final segment coming up. Ooh, tech corner. I got dystopian AI news to jump into, but before we get there, let's hear from another sponsor. Did you know that there are over 18,000 streaming titles on Netflix worldwide, but if you live in the U S you're only seeing about 7,000 of those, that's like paying full price for a gym membership, but only getting access to the treadmill.

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Go to shopify.com slash deep. Shopify.com slash deep. All right. With that, let's move on to our final segment. All right. So we want to return back to AI. I don't know, Jesse, what do you think? Are we getting good or bad feedback about all this AI stuff I'm doing?

Pretty good. The people that email me are, you know, bringing it up and talking about giving points and articles and stuff. All right. I like it. So I basically feel like this final segment, I don't know, I'm a computer scientist. I got to geek out a little bit and people care a lot about AI.

So I like to have an excuse to keep up with it. So today I have a, like a relatively dystopian thing to share. I'm going to bring this up on the screen for people who are watching instead of just listening. So this is this report called AI 2027 that was produced by five people from the space, Daniel Koko Tajlo.

Oh, I know who that is. Scott Alexander, Thomas Lawrenson, Eli Liflund, and Romeo Dean. This is a case study. They're saying we want to walk through a potential case study, like what might happen in the next few years with AI. All right. And like, and we're going to try to be more specific than just sort of vague descriptions of like, hey, maybe in the future, this will happen.

So they, they just published this. And so they start in mid 2025. So they start kind of where we are now or next couple of months and they get pretty detailed about, okay, late 2025, this will, this happens early 2026, this happens. And what this is, is a particular scenario.

So they're quick to say, this is speculative, right? It's not, this is what exactly will happen, but like, this is, they say a realistic scenario for what might happen with AI. Long story short, Jesse, it doesn't go well. Let's just say when we get to 2027, uh, you get a choice down here about whether you want to slow down or a race ending.

They think that the race, the AI supremacy is the more likely ending. I believe it may end with the destruction of humankind. So not, not the most positive ending, and this is not that far in the future. Um, so it doesn't, it doesn't go well. Uh, so you can take this off the screen, Jesse.

So I won't go into the technical details here, but like the basic version of their story is they imagine a, a fictional company that they call open brain and what they, and here they have open brain in the next year or so. What they do is they, they massively increased the compute, the compute, the compute.

So they're working on, they don't really get super detailed about the actual type of AI technology, but you think of it as like, I guess a giant language model with some reinforcement learning in there. And, uh, they 10 X the size of compute. And this thing starts to get, they get really specific.

It starts to get really good. And, uh, then, uh, essentially the storyline follows. It's, it's like a Nick Bostrom RSI recursive self-improvement, the super intelligence storyline. The same thing that these sort of philosophers have been talking about abstractly, just put like specifics on it. So then this company in the story is like, we're going to start tuning our AI towards like helping us improve the AI.

And then pretty quickly, they somehow have like 200,000 copies of this thing running, all working on trying to make it better. And it gets better and better. And there's like a race with China and, you know, long story short, at some point, the thing becomes self-aware and sentient and convinces the government to sign a treaty with it and takes over the world.

So, uh, doesn't go particularly well. Makes the concerns we have today about students plagiarizing with AI seem a little bit quaint. All right. So how, how worried should we be about this? Well, first of all, I'll say these people know about AI. They're certainly are from the tech world and you would probably categorize them on the doomer side of things.

So they're on the side that's like, this is from the sort of more singularity oriented. We're really worried about exponentials. We're worried about things really taking off a school of thought. So we do need to take these types of concerns seriously. In terms of pushback on this though, instead of just giving my own thoughts, I, you know, I was looking up some responses online.

And I'm pulling up one now. I thought this was interesting. This is David Shapiro from a sub stack. And he has 15 responses to this from the AI safety community. Uh, so here are, I won't read them all, but basically he, he says, here are some bad takes that are, uh, in this report and in similar types of predictions of doom that he thinks are not really accurate.

So for example, the idea that we can accurately predict the nature of non-existent future technology. And he says, look, um, if you could predict what was going to happen with future technology, you would just invent it, right? We don't know. It's, it's inherently unpredictable. So any exercise in saying this is what's going to happen next and the next for technologies don't exist, he said is fraud.

Okay. Here's another example. Um, quickly developed AI is intrinsically unsafe AI. He said, we don't have a lot of evidence. That's true. We've had a lot of, uh, developments in AI recently that hasn't shown that. And, you know, he goes on with like some other critiques. I think they're all very sane.

So it gives you a little bit of like, okay, maybe we're not definitely going to the end of all humankind by the end of 2027. The, and I read around like Kevin Roos' reaction and some other reactions. Uh, I would say if I'm going to think about what makes me feel better, I say there's a couple things here.

So I'll, I'll add in some, some other thoughts here. Uh, one, multiple people, including David Shapira pointed out a lot of these, this follows like this storyline is the storyline that's been there since like Nick Boxstrom, super intelligence, right? They, they put on technical seeming details about like how many flops of computation were involved, but it's that same recursive self-improvement.

AI starts improving itself till it gets really intelligent and way more intelligent than us now smarts us and kills us all. Like it's the same storyline we've seen since James Cameron, right? And, uh, the argument is, well, we have actual evidence. We've had big advances, especially in the last three years in like language model-based AI, for example.

And there's predictions like the storyline would give us predictions where we would see certain things that we're not. They're saying instead what we're seeing is as AI models are getting more powerful, we're finding them. It's, they're, they're not getting out of control. Actually, like their language models are very amenable to fine tuning.

Oh, don't give this type of answer. They don't give that type of answer, right? Like they're like only thing, all we've seen so far is actually as we make models bigger, they are very amenable to us sort of fine tuning, do this, don't do that. Sort of models aim to please, right?

Because all they're trying to do, they learn a distribution and then they're just trying to produce from that distribution. And you can do fine tuning with reinforcement learning, changes what that distribution is based on what you want it to be. And it, great, that's what we'll do, right? So I'm not being super technical about it, but there's this sense of in the two years since the AI pause letter written by Max Tegmark came out, a lot of the things they thought were going to happen next couple of years didn't.

Actually, we just got, it hasn't been so hard to, we're not, to control in some sense the output of like language models. Okay, that's one point that I've seen out there, which I think is an interesting one. More generally, I think there's a, the better claim is instead of just working through these scenarios where you invent seven new generations of technology and speculatively see how they unfold, is what we should be doing instead if you're worried about AI doomer scenarios is having short term, like here are the next milestones that should worry us.

If we see this start to happen, like that should be something we should be worried about. So we need near term milestones we're looking for and being concerned when we see what's going to happen with those milestones. My argument, what makes me feel better is this is really, the speculative story is really falling like an Isaac Asimov, I robot type of model that, that, that imagines what's going to happen is we're going to build a small number of mega AIs that have so much compute we can't even imagine it.

So if you're prone to thinking about exponentials, like a lot of people in that world are, you just want to see like the compute keep getting bigger. You're drawing the curve. We went from 20 million parameters in a language model to 100 million, to a billion, to a hundred billion, to close to a trillion, and we got these improvements.

So we just want to keep drawing out that curve. So if we go to 10 billion or 10 trillion or 100 trillion, it's going to become this even more powerful and powerful thing until it eventually becomes so smart it can do all, you know, all these other types of things.

But we don't actually know that that's going to be the right business model and that trying to make these things bigger and bigger is the right thing to do. In fact, I'm seeing a lot of pressures out there for a different type of business model. Smaller, more efficient, bespoke AIs to do specific things.

I think there's a lot of energy there. We don't even know. Like we're kind of getting – what's happening with language models now is we're out of text. So we've trained them on all the text there exists. So the way they're getting better now is with human-in-the-loop reinforcement learning.

So basically we're kind of generating new data for them by like having humans or reinforcement models based off of humans to try to teach it like do more of this or do more of that or we like this answer, don't like this answer. We like when you think or don't think, right?

We're kind of down now to like having humans tweak and push it to kind of push them to do different things better. But that curve is actually kind of flattening out. Now in the 2027 scenario, they somewhat obliquely refer to that these hundreds of thousands of agents are going to generate all the synthetic data that it can train on.

But there's big limitations to that as well because the synthetic data is going to be based off of the distributions you've already learned. And so is it really giving you something newer than the distributions? I also think reinforcement learning is really going to be the future. I'm more concerned about that anyways.

We talked about it in a recent episode. This seems really built around like an open AI vision of the future. We're building the biggest possible language models is what matters because that's what open AI is leading in. But I saw a very convincing talk, for example, from a RL conference this fall from a real expert in the field coming from DeepMind talking about how language models are kind of a red herring.

That if you want above human intelligence, it's got to be reinforcement learning models. And reinforcement learning models are, by definition, you kind of aim them at particular tasks. I want to make this alpha proof, really good at doing math Olympiad style math problems. And it's going to build a model and policies and get creative and be able to do stuff better than a human.

I'm going to use Dreamer V3 to learn how to play Minecraft. And we're going to build a model here that's like very good at playing Minecraft, right? So, I don't know. I think we're just as likely to see a world with RL style models bespoke for very specific tasks that we think are important or useful.

Not a world where we're trying to build this like mega brain that like ultimately comes alive and starts tricking us. So, you know, it's possible the world's going to end in 2027. Probably the future is more complicated. Probably just building these giant – and it's unclear what these giant things are.

A language model can't think, so it would have to be some sort of multimodal model that's not really super specified here. I mean, there's tons – this is important. There's tons of concerns. And I think if you interview the writers of this 2027 article, they'd be like, yeah, look, we're not trying to pinpoint exactly what's going to happen.

We're trying to get people's attention. Like, hey, you guys have to pay attention. Like, this is a type of thing that could happen. You've got to be thinking about AI safety now. That's what they're trying to do. And they're smart and this accomplishes that well. But I don't see the signs and a lot of other critics of this don't see the signs that that particular storyline is particularly plausible.

But we should worry that bad storylines are possible. And now is the time to worry when nothing really bad is happening yet so that when things start going off the rails, we'll be a little bit more ready for it. So, I don't know. A little doomer-y, Jesse, but I don't know that we should build the bunker, I guess, quite yet.

I didn't know that all the text is already in all the models. Yeah, they're out of text. Yeah. So, like, a lot of the improvements happening now is in this, like, fine-tuning step at the end. So, you'll come in and we want it to be better at doing this.

And then you do a lot of, like, extra reinforcement learning to sort of push it towards being better at reasoning or being better at doing, like, these type of math problems. But it's all, like, human feedback. Like, that's the right answer. So, do that. Don't give us answers like that.

So, it's sort of zapping it with these reinforcement signals. I'm a big believer that the RL, perhaps coordinated with knowledge and language models, that's probably going to be the future. Because reinforcement learning models learn, they build their own understanding of a novel world and come up with their own policies based off of direct experience for how to navigate that world in an effective fashion.

Everything that AI does that's better than a human can do, it's all from reinforcement learning. We can play chess better than people, that's reinforcement learning. We can play go better than people, that's reinforcement learning. We can do protein folding better than people, that's all reinforcement learning. We're getting better at math than most people, that's all reinforcement learning.

All the video games they can play really well, that's all reinforcement learning. Language models are good at trying to reproduce how a human would respond to something, the distributions that happen in a human's mind. And they're very good at that. But that's, like, what they can do. That's why it's, like, they're very compliant in some sense.

There is no – you worry about an RL model because it's just trying to accomplish a goal. And you don't know how it is figured out, I'm going to accomplish this goal. So that's where you can have ideas like, if I can trick a person, that helps me accomplish my goal.

I'm going to start tricking people because I want to accomplish my goal. A language model doesn't have that. It just tries to produce text that it thinks a human can produce. So it's sort of a different type of world. All right, that's enough of that for now. It's enough of this episode.

We'll be back next week with another one. And until then, as always, stay deep. Hey, if you like Sam Altman's discussion of his productivity ideas, you might like episode 312. It's titled Productivity Basics. You'll get some of my productivity concepts. You can compare and contrast. Check it out. I think you'll like it.

So I thought this would be a good moment to revisit some of the biggest ideas about the biggest topic that we cover on this show, productivity.