The F1 broadcast could be a lot more entertaining

Here are some prototypes I made that might help

I love Formula 1, but the broadcast can be disorienting (and therefore boring) for newcomers. If you don’t know better, it just looks like cars driving around corners. The most interesting competitive dynamics are invisible.

To F1’s credit, they do have some data visualizations. Some of the new ones they introduced last year (like the ghost car) are amazing. But there’s still so much opportunity here for improvement and experimentation.

Over the years I’ve been collecting little ideas and have finally gotten around to prototyping them.

Here’s what I got.

Sense of Place

The first thing that bugged me when I started watching F1 was the feeling that I had no idea where the cars actually were on track. The commentators refer to turns by name and/or number, but unless you’ve been watching F1 for years, you probably have no idea what you’re looking at.

I would fix this by simply showing a little camera indicator on the track map, like this:

The F1 broadcast could show this as a mini-map in the bottom right corner, like this:

And then as the camera swivels left to follow the cars up through Eau Rouge (the name of the turn where this camera happens to be placed), you’d see the angle update in the mini-map in real time:

Speaking of “Eau Rouge,” I only know it’s called that because I’ve been watching F1 for several years now. If you’re new you probably have no idea which turn you’re looking at on the broadcast.

I’d fix that by occasionally putting in a graphic that just tells you the turn numbers and names, like this:

These are just two small things, but I think they’d go a long way towards helping viewers feel oriented, and like they can learn the tracks faster and understand what the commentators are talking about.

Sense of Pace

Almost every race in Formula 1, there are several moments when one driver is gradually catching up to another driver. And the question is if they will be able to overtake before the end of the race, or the next pit stop.

But it’s hard to get a sense of how quickly the gap between cars is shrinking. Anyone who’s watched F1 knows the feeling of glancing at the leaderboard every few seconds to see if a comically precise number (e.g. +1.489) seems to be growing or shrinking over time. It’s fun, but there are much better ways to present this information.

For example, F1 could simply show a line graph of “distance over time”:

Here the Y-axis is “distance travelled” and the X-axis is time.

The story the above graph tells is pretty intuitive, I think: Verstappen almost overtook Leclerc but couldn’t, then Leclerc pulled away.

F1 could put it in their broadcast like this:

Would be even better if the commentators had the ability to draw on the graph and explain it in real time like this:

I can hear it now, “Crofty, this was the crucial moment when Max had his chance, it seems his tires have gone off now though.”

One of the best places to use this kind of graph would be qualifying. Right now the experience of watching qualifying is mostly one of waiting until the car crosses the finish line to see what the lap time was. But what if we could see in real time how a car is doing compared to a reference?

One thing I love about this graph is it would make the impact of little mistakes like lockups so much more clear. You can see the point on the graph where they lost time, and have some sense of how bad it was.

Sense of Track Position

One of the most common “gotchas” in F1 is that the driver who is technically in 1st is not always actually in 1st. For instance, maybe the top 3 drivers all went into the pits, and the 4th place driver has yet to pit but is, for a brief shining moment, running in the lead.

F1 could do a lot to make this more clear, by showing us a “pit stop adjusted” leaderboard, like this:

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These are just a few ideas I had to improve the broadcast, but you could imagine many more! Maybe I’ll do a part 2 of this post if there is demand.

The Law of Shitty Moats

AI memory will be huge, but not a silver bullet. Moat diggin’ ain’t what it used to be.

Years ago, Andrew Chen published The Law of Shitty Clickthroughs, which states:

Over time, all marketing strategies result in shitty clickthrough rates.

Good ideas get copied, cheap channels get oversaturated, and audiences become desensitized. It’s a red queen’s race: to keep generating the same returns, you can’t stay still. You have to keep discovering new good ideas. This is capitalism doing its thing.

Of course, some businesses do escape the rat race, to some extent. Apple doesn’t really care what CPM TikTok influencers are charging these days, because they have many structural competitive advantages—collectively, a “moat”¹—that prevents their competition from being able to offer the same value they can at the same cost basis.

As startups grow and mature into large enterprises, executive teams increasingly become preoccupied with moats, for good reason. Their success is no longer a secret. Of course they want to innovate, but it’s easier to innovate when you’re not involved in cutthroat competition (n.b. Thiel’s Zero to One).

I would imagine OpenAI is in this situation now. ChatGPT is enormously important to OpenAI overall, how do we make sure it keeps growing? How can we put ourselves in a position to deliver value that no one else can? This is where memory obviously comes in. What LLM product would you rather use? The one that already knows everything about you? Or the new guy?

Predictably, tech pontificators (a group for whom I have much affection) have taken to Twitter and Substack to proclaim this the beginning of OpenAI’s era of true dominance for consumer LLM interfaces. And to be honest they’re probably to some extent right. But I don’t think AI memory will be the silver bullet a lot of people are making it out to be, because I have a different model of how moats get created.

To understand why, I propose a new law, similar to the Law of Shitty Clickthroughs, which I will creatively name The Law of Shitty Moats:

Over time, as a source of competitive advantage becomes common knowledge, it becomes harder for new entrants to capitalize on it.

Basically, nature abhors a vacuum, and a moats create a vacuum of competition. All the other players in the ecosystem (not just direct competitors but also suppliers, buyers, substitutes, etc—shout out Porter’s five forces) have zero interest in seeing one player dig themselves into an impenetrable position. If everyone can obviously see what you’re doing, it’s going to be harder (not impossible, but harder) to turn it into a big, long-term competitive advantage. In the early phases of a market’s maturation, before the moat is constructed, the other players have relatively more power and they will be able to do things to stop, slow, and/or mitigate it.

One great recent example is the AI system integration layer. You can imagine ChatGPT probably would have a much easier time building an integration ecosystem than Claude, because it has so much bigger scale. In a world where the value of an integration ecosystem was less well understood, maybe they would have run away with it. But instead Anthropic released MCP very early in the game, an open standard for integrating sources of data with LLM systems, thus massively neutralizing ChatGPT’s scale advantage. It’s not hard to imagine we may see similar open memory efforts take off.

The concept of “alpha” from investing here helps us understand what’s going on. Alpha is when you have knowledge that others don’t that you can use to profit. If you have real alpha, your actions probably look crazy to most onlookers. Billy Beane’s Oakland A’s looked ridiculous to most other managers; they were wrong. OpenAI introducing memory is not crazy. It’s obvious.

Switching costs are literally a textbook source of competitive advantage for software businesses. SAP, Adobe, Oracle, and more have been milking it since the 80s. But I would hypothesize that fewer modern businesses have been able to build as strong a moat based on switching costs, because we are more sensitive to getting trapped in proprietary systems. This is hard to prove or disprove, because there’s not an easy way to generate a counter-factual world where everything is the same except people have no idea about switching costs, but a world where the Law of Shitty Moats is real is more aligned with basic economic principles than a world where it isn’t.

To be clear, I’m not saying ChatGPT memory won’t be a popular feature, or that it won’t contribute towards OpenAI’s moat. All I’m saying is, other players in the ecosystem (including consumers) understand the idea of lock-in, and they don’t like it. So I don’t think this is game over for everyone competing with ChatGPT.

In fact, the real galaxy brained take is, if I were OpenAI, and if memory turns out to create as much value for users as it seems like it could, I’d want to make memory as open, accessible, and inter-operable as possible (modulo the obvious privacy constraints).

Why? I think OpenAI would rather be the operating system than just the one single killer app. To capture the full opportunity in front of them, they’ll need to behave more like a platform than an aggregator, to use Ben Thompson’s parlance. In other words they’ll ultimately make a bigger impact on the world and I think also be a bigger business that’s safer from regulatory scrutiny if they are more like Microsoft and AWS, less like Facebook / Google.

Who knows though! Will be fascinating to watch it play out.

1. Moats are the source of a bit of a major cultural divide in tech. The two camps are:

A) Business nerds — typically fans of Buffet, Christensen, Helmer, etc. Often MBAs, either literally or in spirit. A financial model hate to see them coming. Obviously they love moats.

B) Technology nerds — enjoyers of Hacker News, O’Reilly books, Mastodon, etc. Love elegant architectures. Alternates between hating moats and denying they exist. Often heard saying, “the only real moat is to innovate and build a better product.”

The business nerds are macro-right (moats are real, important, etc) but often micro-wrong. They get strategy drunk and make stupid decisions.

The technology nerds are macro-wrong and micro-right (great products matter most, execution and innovation are decisive, and moats are often less impenetrable than they seem).

Just Go

1

Imagine a world where superintelligent AI builds a way to send a message back in time to your younger self—but you can only send one, and it has to fit into a tweet.

What would you write?

What single kernel of an idea would most improve the trajectory of your life?

Here’s what I would say:

Stop co-founder dating.

Stop worrying if you are a ‘real’ programmer, or designer or whatever. Labels don’t matter. You are capable of much more than you realize.

Just go!! Allow yourself to do the damn thing. And have fun :)

2

Here are some of the limiting beliefs I’ve held:

• I thought it was bad to want to start a company “for its own sake.” Facebook was not meant to be a company, Zuckerberg told us. Founders were supposed to be reluctant heroes that were just trying to build nerdy fun things they were interested in, and the world was supposed to love it so much that it pulled the company out of you.

• I had a “Disney princess” romantic notion of co-founder relationships. I thought if I didn’t have a co-founder, my odds would be worse and my heart would be empty. I went looking for co-founders, but felt bad that it wasn’t happening “organically.” I held myself back from going too far on my own, and I was too quick to enter into partnerships that weren’t a good enough fit for me (or them).

• I was worried I didn’t fit in. Yeah, I can code, but do I want to be a programmer? Sure, I design things, but do I want to be a designer? In truth, I liked doing it all. From the inside, it felt natural to just keep building skills as needed. But I could have gone further, faster, if I was more comfortable not fitting neatly into a familiar box.

• I thought I had to do everything perfectly. It felt like walking on a high wire. One wrong move and the whole thing was doomed. Shipped a bad feature? Users will abandon you and never come back. Write a boring blog post? No one will read you again. Every decision had to be agonized over.

• I lost hope too easily. I paid too much attention to averages and not enough attention to the bright spots. Maybe most users or investors don’t get what you’re doing, but what about the ones who do? I missed opportunities by pivoting too early and too often.

Taken together, these form a pretty disastrous combination. I learned a lot in my 20s and early 30s, and built things I’m proud of. But I wasted a tremendous amount of time and stress on things that didn’t matter. I could have done much better.

It wasn’t until my mid-30s that I started feeling comfortable in my own skin as an entrepreneur.

3

So, what changed?

The main thing was, I quietly went through a crisis.

On the outside I seemed fine, I think. But really I was becoming increasingly exhausted and anxious. By the end I felt like I was stuck in first gear, operating at maybe 10-20% capacity. I made a lot of mistakes. My relationships suffered.

Eventually, it was obvious that I was cooked. My old paradigms had failed me.

And thus I became open to new ones.

4

If any of the above limiting beliefs resonated with you, here are some opposing ideas for your consideration, that I wish I could tell my 20-year-old self:

• It’s completely normal and healthy to want to build a business. The vast majority of businesses in human history were started by people who wanted to build businesses. You don’t need to be a reluctant hero. Just go for it. People who say they “didn’t set out to build a business” usually do this because it makes them seem humble and selfless, yet at the same time formidable. There’s less incentive to say, “business is a game, and I want to get very good at it,” but I assure you this is how most people who build businesses think.

• Co-founders can be great! But if you don’t have one, you’re better off building than co-founder dating. If you don’t already have an obvious co-founder, it’s probably best to just start making progress. Don’t let lack of a co-founder hold you back. The wrong co-founder is much, much worse than no co-founder. The honest truth, that few entrepreneurs talk about publicly, is that “co-founder” status is more often than not just a way to hire someone either A) before you can pay their salary, and/or B) who are otherwise unwilling to work with you. Consider carefully whether that’s a trade you actually want to make. It’s cool to see this idea gain more acceptance.

• Don’t worry about where you fit in. Now more than ever, skills are things you can tactically acquire to accomplish your immediate goals. They do not need to define you. You are capable of so much more than you or anyone realizes. The lines between various specialities are more permeable than they seem. Plus, if your goal is to accomplish something unique, it’s a good sign to have an unconventional skill stack.

• Nobody cares about your early failures. All of your heroes are guaranteed to have started their careers with a string of unremarkable performances that absolutely no one remembers. We’re defined by our ceilings, not our floors. Do your best, but never let perfectionism prevent you from shipping.

• Great things can take time. Just because something isn’t working immediately, or not everyone gets it, doesn’t mean it is doomed. Focus on the bright spots where people do care, and figure out ways to expand from there. It’s OK if it takes time. Some things happen fast, other things happen slow. Just don’t die.

5

In two words: just go!

Entrepreneurship is hard and can be painful, but you don’t have to suffer. We create our own suffering with the ideas in our heads.

https://x.com/nbashaw/status/1771562764936282311

Cultural Evolution

Hi friends! Been a long time. I’ve been fully focused on Lex for the past few years. And to be clear, I still am. But I do miss writing. So here’s something! Short, but important to me.

Hope you like it! I’d like to do this more often.

I have a very simple model of human behavior:

• We are thrown into the world pre-loaded with certain inclinations via genetics. One of these is to pay close attention to how the people around us do things.

• As we emerge from childhood, we notice what successful people tend to do, and we attempt to intelligently copy what worked for them.

• Some of us manage to become prestigious. We become leaders, and our groups thrive and grow. The next generation copies us. Our memes survive and reproduce. Yay!

• Others are less fortunate. At a certain point, they may become disillusioned with their old beliefs and search for new ways of doing and thinking. New models to copy.

Each step may seem obvious, but the second order effects are not: beliefs and behavior patterns are formed through a process of evolution and natural selection, the same way biological species are.

To be clear: this is not an idea I came up with! It’s called “cultural evolution,” and it has a rich intellectual lineage, although I think it’s desperately underrated. We’re all taught Darwin, Mendel, and Dawkins in school. But how many of us know about Henrich, Boyd, or Richerson? I’d argue that it’s even more important and useful for humans to understand the forces that shape their own behavior than it is to understand the forces that shape their genome.

In my view, cultural evolution explains almost everything, including:

• Scientific progress (n.b. Kuhn)

• Music and fashion trends and genres

• Accents, mannerisms, and turns of phrase

• VC (and, to be fair, founder) herd behavior

• Religion

• Food

• Politics

• Memes (“tremendous alpha right now in sending your wife photos of yall converted to studio ghibli anime,” anyone???)

• Parenting

Basically, anything related to the intersection of belief and behavior is governed by cultural evolutionary forces.

I hope to explore this more soon! To me the most fascinating thing is why this isn’t a more widely understood idea. But also I’m keen to see if we can apply the idea to unlock certain timely and topical issues, like maybe AI progress, global trade, and more. We’ll try to have fun with it and not over-think it.

See you again soon!

—Nathan

My new project: Divinations

A para-academic workshop for business strategy

Hey! Long time no post, I know. I’m emailing to say I launched a new thing and I would love love love for you to check it out :)

Here’s the TL;DR—

• Divinations is a newsletter on business strategy featuring interviews with founders, investors, execs, and academics. In it, they share stories from their experience crafting and executing company strategy.

• It’s written by me, Nathan Baschez. I’ve worked in product roles at Substack (yes, this website!), Gimlet, and General Assembly. I founded a company aimed at reinventing books, called Hardbound, that Fast Company named one of the top 10 apps of 2016, and Apple’s editors called “beautiful.” Also, I co-created Product Hunt.

• The free version gets you the weekly interviews (Thursdays).

• Full subscribers get the “thing of the week,” every Tuesday, which consists of: reviews of books, articles, and podcasts; strategy worksheets; templates; spreadsheets; business news analysis; etc. (I’ll keep all this nicely organized into a library, too, so it’s easy to find stuff in the future.) Also, you’ll be enabling me to spend time making this good, without selling out! To learn more about paid subscriptions, click here.

You can read the intro post here.

If you think you’re into it, subscribe!

(I won’t be posting much here, so see you over there!)