(Above: No, it doesn’t really look like this — and yes it’s mostly office parks and tech billboards. But I like to pretend)

You’ll never regret spending time in SF
If you work in tech, you’ll never regret spending 3-5 years in the Bay Area. This is advice I’ve been giving to people for years, and it’s shaped by my own experience — after all, I moved to the Bay Area in 2007 and it completely changed my life.

How?

• I met tons of incredible people, some of whom went on to create major products and found unicorn companies. Most are still building
• I was introduced to many investors who today run some of the major VC firms and investor networks
• learned so much!
• made life long friends
• formed fundamental aspects of my world view
• Because of the recent AI boom, I’ve been meeting a lot of folks who are new to SF. Many folks very intentionally want to build out their network and get rooted in the Bay Area, and to fully immerse themselves in tech. I learned so much in my first few years and wanted to pass along some of my lessons.

In particular:

• personal viral loop: Asking people for more people
• ask for advice and listen
• why it’s helpful to “have a thing”
• know what you bring to the table
• find your cult
• how blogging/tweeting is helpful
• why I avoid conferences/events
• building a network while you sleep

Getting started by asking for intros
I first moved to the Bay Area as a 25 year old nerd with a light resume and big tech dreams. I knew exactly 2 people, and that was it. But very intentionally, I wanted to build a strong professional network and to learn from people. The first thing I did was to be intentional and methodical about it, by asking the two people I knew to please sit down and suggest 5 to 10 people for me to meet — an they did a number of email intros for me. The amazing thing about SF tech culture was that this worked! Although the intros were very light on context, people were willing to grab coffee and share what they were working on, and what they’ve learned over the last couple years.

After each meeting, I would follow up with a few bullet points on what I learned from the conversation, and then ask for two or three more people to meet. This was like building my own personal viral loop, where every chat turned into a few more chats. For my first six months in the Bay Area, I ended up meeting 3 to 5 new people every day. I learned an incredible amount. I can confirm this is still possible, as others I know have done it in recent years.

How to add to each convo
You might ask, what do you end up talking about? What value can you add as someone who’s just moved to the area and is starting in tech? The answer is, you simply ask for advice. People move to the Bay Area from all over the world because they’re incredibly passionate about what they’re building. They love talking about that. and if you have something that you’re passionate about too, and ask for advice, you were sure to get a lot of it. The culture in the SF tech community is very open and the intro culture makes it easy to chat with a variety of new people.

For me, I was coming from Seattle, and I asked people about various mysteries of the tech industry I didn’t understand as an outsider. Why were there so many consumer successes in the Bay Area but not elsewhere? How does angel investing and VC work? Why don’t they build more houses/offices in the Peninsula? And so on.

Having a “thing”
That said, the conversations are more productive when you have “a thing.” What I mean by that is that all of these conversations and networking are more useful when you are starting a company, creating a new podcast, are working on a new project or book, or something else. When you have a directed goal in mind, then the conversations often are more valuable for all parties involved, because you were making yourself an expert in a particular area and your questions are more relevant. Otherwise you will surely encounter very busy people who simply refuse to “grab coffee” to “catch up” because it’s a poor use of time. I encourage you to be on a quest of your own, and even better a particularly interesting quest, so that your conversations with people can be as productive as possible.

In my case, I was very interested in the state of the art on growing users, metrics, network effects, and marketing. I asked everyone about this topic, and began to develop my own ideas that I would share freely. Eventually, it became clear that a few small communities orienting the PayPal mafia were the furthest along in their thinking. And that’s how I ended up being exposed first to concepts like retention curves, DAU/MAU, viral loops, and so on.

These ideas were interest to me, because my professional experience leading up to that point was actually an adtech. I had previously worked in online ads, with customers from WSJ, CBS, MySpace, etc, and had even gotten a patent filed on ad targeting (yes, US7747676B1). I had a superpower in my domain knowledge of CAC, A/B testing, funnel optimization, lead gen, etc, and began to merge all of this thinking with consumer products. In 2007 this was cutting edge at a time when product success was often measured by vanity metrics such as the total registrations for a product. This bit of specialized knowledge was what I brought to the table, and I talked about some of those learnings and ideas, and how they might apply to products. Sometimes I’d get intros to interesting people simply because of this expertise, which I appreciated.

Find your cult
I sometimes joke that the Bay Area is ruled by cults. Back in 2007, there was a cult surrounding quantified self, which intersected with lots of folks kicking off Crossfit, keto, Soylent, and other health trends. There were people building robots and hardware. The PayPal mafia was a thing, but look a little closer, and there was a huge network of Stanford CS people and even Canadian mafias. And Burning Man people. In 2007, YCombinator was just getting off the ground, and I was lucky to meet many of the early folks back when they were living in North Beach on strictly ramen diets. Today, those cults have evolved but they still exist — there is a huge advantage in finding one that suits you, or even better, starting one. Years later I joined Uber and had the idea one day there would be an ex-Uber cult. I think that’s happened, and there’s been countless founders, investors, and builders from that network.

Why blogging/writing is so helpful
In the first year, I learned the importance of writing things down. The other thing I started to do right away was to write down everything that I was learning. I started a real/professional blog at the beginning of 2007 on the Blogger platform and initially, I got writers block because I was trying to come up with amazing and grandiose ideas that I would share with the world. My first month, I had 20 email subscribers, from friends and family I forcibly subscribed.

But eventually, I created a more successful strategy for myself, where I would simply document what I was learning. It turned out that if one person told you a unique idea I would treat it like it was a secret (or at least, I would ask permission). It was often the case that a dozen people would talk about the same idea, and there was simply consensus memes floating around in the ether, and I focused on writing those down. I find that a lot of my blogging has been less about inventing brand new ideas, but instead simply collecting and expanding on the current tech zeitgeist. A few months in, Robert Scoble linked to my blog from his, and that helped a ton. (Thank you!)

It was with this attitude that I began to write about viral loops, growth, hacking, measuring retention, and product/market fit, and all the other concepts that came to defined my writing.

There is a virtuous cycle in talking to interesting people, writing down expanded versions of ideas that come up, thus being exposed, to more interesting people, and rinsing and repeating. This core loop helped power the growth of my professional network over the first few years. In later years, I added a dash of advising and investing.

15+ years later it’s weird to think that accidentally developing a habit of writing and blocking would still be with me today. In fact, this habit is so powerful that I recommend doing it above and beyond almost any other professional “networking” activity. Of course today you might be making videos or podcasts instead of writing. Or if you’re an engineer, publishing your code on GitHub. It’s all the same concept. Putting your work into the world, whether it’s text or video or code, and letting that engage the world.

In this way, you are building your network while you sleep. People find you and your work and your ideas, so that you don’t have to put in time for 1 million coffee meetings.

Why I avoid conferences
And in particular, I find writing to be much more powerful than going to conferences. One thing you’ll notice about the SF tech industry is that there are endless events and conferences. Whereas a secondary startup hub might have a major tech event once every month or two, SF has them every day. There’s office warmings, product launches, new AI meetups, hangouts at Dolores, big splashy conferences, hackathons, and so on. There are endless varieties.

Build a network while you sleep
However over time, I’ve found them to be less scalable than writing. They are fun, and it’s much easier to have a one on one conversation than it is to create a content. When you really think through how much time you spend getting to a conference, all the time between sessions, and when you speak how few people are actually in the audience listening. Contrast to any kind of digital platform where you can write a blurb and 1000s of people see your ideas.

Going back to my original assertion, I think it is hard to regret 3-5 years working in SF. Many people say it’s not a great place to live — and sometimes that seems true. Other folks hate the monoculture. However you can always move home, and when you do, you’ll always be the person with Silicon Valley tech experience. And furthermore, the learning curve is so strong, particularly for startup founders, as is the network of capital and peers. It’s a one of a kind place, and I highly recommend founders spend a few years even if they don’t intend to stay in the long run.

The “Dinner Party Jerk” test is a solution to a common problem:

Startups often struggle at pitching their team, even though for the earliest stage companies, it’s incredibly important to do it well to raise capital — as I’ve described it below:

Pre-seed- Bet on the team ‍
Seed- Bet on the product
Series A- Bet on the traction
Series B- Bet on the revenue
Series C- Bet on the unit economics

To figure out if you are properly pitching yourself in your team, run the thought experiment of describing yourselves at a dinner party. If you are pitching yourself hard, then if you are a kind human, you will turn red and blush with wild embarrassment. The reason is that a proper pitch includes many of your credentials, your achievements, the ways in which you and your team are highly unique, and we simply don’t talk like this at dinner parties. And yet this is exactly what you should do when you talk to investors, partners, customers, and potential employees.

Don’t hold back
Be the dinner party jerk, pitch yourself hard. Don’t hold back. Your shyness and cordiality is not helping.

I find that most founders tend to focus, primarily on describing their idea to the exclusion of everything else. I’ve heard thousands of “elevator pitches” and they generally focus on the idea and not the team, the market, differentiation, or anything else. And they downplay their achievements or omit them.

Of course what you emphasize depends on your background. It’s often described that there are repeat founders and first time founders, but furthermore, there’s another axis, which is about obvious credentialing versus not. For first time founders that are starting a gaming company, for instance, but have already spent years at a top company in the field, a quick modification to the elevator pitch, mentioning that, is both beneficial and quite obvious. But what do you do, you’re an uncredentialed first time founder?

Then the question becomes, what is your “earned secret“ behind the idea? Having a pithy story about how you were a Shopify seller, and that’s how you got to building any commerce product, is incredibly helpful. And if you have some metrics or an observation about the market that’s non-obvious, showing your expertise in the field, is even more valuable. If you have various credentials either professional, or academic or open source, achievements, it might be worth working those in even if not directly related.

The other very awkward thing is to use facts and figures to describe yourself. If in your previous work, you worked on an app that served millions of people, or for your current company, you recently launched and got your first 10,000 users, you should save these numbers. Any traction and any validation is incredibly helpful proving your case. And of course, this is another thing that would make you a dinner party jerk.

Why don’t we do a better job of this? The dangers of conformity
You might be going through a moment of introspection now and asking why am I like this? Why do I downplay achievements when I should be amping them up?

My answer to this, is conformity. In real life, we often subconsciously conform to the people around us. If you go off at a friendly gathering about all the cool stuff you’ve done, and why you’re going to be great, there’s a fear that you’re exaggerating the differences between yourself and others. There’s a fun theory from evolutionary psychology that shyness is an evolved trait to keep us safe in a world where we grew up and tribes of a few hundred people, and a few wrong words might follow us around for our lifetimes.

This is also my theory for why people are reluctant to engage on social media and share their knowledge, when it’s obvious that it might be very helpful to them professionally.

TLDR; there’s pitch mode and dinner party mode. Learn to turn the former mode on!

Be an optimist
You have to be an optimist about your own product, your own startup, and yourself. That’s why when you pitch — whether to investors, to prospective employees, or partners, it’s important to talk about what might happen, not what you are doing today.

There’s a whole style to this type of pitch, and it’s a futuristic point of view that leans into optimism:

• Emphasize the future, not the past
• What it could be not what it is
• Play up even small bits of proof points
• The big things that might happen, if it works
• The upside rather than risks
• Signs the customers love the product, rather than revenue metrics
• Why this team has the grit and special knowledge to do it, not the credentials and work experience
• A unique narrative about why the world is moving this way
• Why you’re starting with a wedge, but your ultimate market is huge

I previously referenced the idea that international founders often describe this as the “American” way of pitching — the funny thing about this is that this isn’t the “American” way of pitching, it’s actually specific to the Bay Area tech ecosystem. It’s incredibly optimistic and futuristic that founders choose to describe their startups in this way, and furthermore, the people who hear these pitches choose to believe them.

Why this is the only way to pitch to investors, employees, and partners
Let me also make the argument that this is the only logical way to convince people to join you on your journey.

1. Investors
First, let’s talk about startup investors. a portfolio of startup investments is inherently risky, and the physics of venture math means that the winners have to be really big. It’s commonly said that out of a portfolio of ten companies, generally about half the investments will go to zero, three will return a little bit of money, and that the top one or two will return 10x plus and make the fund work. As a result, professional venture investors are trying to understand if you have what it takes to be one of those top two, and if you don’t if you’ll die trying. A lot of this assessment focused on the market or your numbers, but sometimes the real question is about your ambition.

So they are trying to answer a simple question: Do you WANT to create one of the leading companies in the industry?

Focusing on the future and on the upside shows your will to power. It allows investors to gain a sense of that signal. If you’re focused too early on profitability, rather than growth, or retaining your piece of the pie, as opposed to growing the pie as large as possible, that’s an important signal. The point of this isn’t to mislead investors into thinking that you’re trying to do something that you’re not, but rather, if you are shooting for something big, you have to really express that in the clearest way possible.

The perspective is often directly reflected (and not) in the slide decks I review at a16z. Does the product slide describe the features of what the app has today, or does it talk about the product roadmap of what’s going to be built in the future? Do they user projections or financial forecasts simply show the last year’s performance, or does it tell a story about how the business is about to inflect? Oftentimes when founders are too conservative about their story it’s hard for investors to understand what they’re trying to do in the long run. Instead, I love it when founders tell the big story. Of course I’m going to discount it and round down, and assume that many features are never shipped. But I love to see it.

2. Employees
Second, let’s talk about employees. typically when you’re hiring your first few employees, you might be able to give out a few percentage points each, particularly for key people (like engineers or designers). but within a few hires, you end up needing to convince people to work for below pay, and for a fraction of a percent of the company. Why would they do this? Why would they work for you instead of either starting their own company or getting a cushy gig somewhere else where they might be paid much more?

The asymmetric advantage of startups compared to many other opportunities, is that they are adventurous and fun. The startup might fail, but the work is generally a lot more interesting than what you can do elsewhere. The responsibility and scope that a junior employee might have might go way beyond what makes sense at any other company.

And of course financially there is upside. For founders to convince high-quality employees to join their outfit, it’s often important to lean into a sense of adventure. What’s more adventurous than tackling a big huge goal, that might not work, but if it works, it’s going to be amazing? For founders to communicate the sense of adventure, they need to be able to weave a narrative. Maybe it’s us versus them, or David and Goliath. Perhaps it’s exploring the unknown, and going to the frontier when no one else is there. If you can’t tell the stories, how can you expect people to follow you? Thus I find it important to tell the futuristic narrative that’s ambitious, full of surprises and upside, and has a possibility of failure too. It makes the work meaningful and makes the potential economic upside worth something.

3. Journalists, partners, and more
We’ve talked about investors and employees, but there’s actually a long tail of many other constituents that benefit from a futuristic outlook. if you’re talking to journalists and pundits, you have to compete with thousands of other companies that they’re going to meet this year, and you have to catch their attention. If you’re marketing, an event at a conference adjacent to dozens of other events, you have to catch the eye of attendees. An optimistic, futuristic perspective gives you room to tell the story about the problem you’re trying to solve, and why your startup will be incredibly important once you get there.

You might say that the world is full of cynics. Perhaps you are from a region or an industry where most people nitpick all the reasons why fail. Maybe they want you to focus on minimizing downside risks, or acknowledging your potential problems, and won’t treat you as credible unless you do. If that’s your industry network or your social network, I urge to you to escape. Seek out those who share your optimism, and the same values and beliefs about the future. it’s one of the reasons why the Bay Area has been such a powerhouse over the last few decades. Yes, there’s knowledge and investors here, but more important is the culture.

The last point I make is about yourself. You should talk about what you’re working on in an optimistic way to help create meaning for yourself. For those of us who grew up in a generation that adored Steve Jobs, there’s always been the goal to put a dent in the universe rather than to sell sugar water. Thinking about the future, and the upside of what you might be able to create, is a great way to give meaning to the nights and blood and tears that we put into our work. If you’re simply working on a new product only because it’s a good money-making opportunity, I guarantee that your sense of meaning will fade when times get tough. You’ll ask yourself, why am I doing this? If you don’t have a Northstar to guide you on an inevitably rocky, entrepreneurial journey, you’ll inevitably get lost. That’s when the FAANG job will seem really appealing.

Obviously, don’t drink your own Kool-Aid
This is all about the pitch. Of course it’s important to simultaneously hold in your mind all the truths about what you’re working on. Maybe you don’t actually have product market fit yet, or your marketing strategy. Perhaps your unit economics don’t yet work, or your team has major gaps. If you’re working on a new startup, likely, everything feels constantly broken, and everyone’s maybe going to quit.

You have to go and tackle all of those challenges with a clear mind — while simultaneously keeping a futuristic spirit that motivated people to join you on your journey.

It’s hard to be a product without a strong theory of distribution
Here’s a common startup situation. A team busts their ass for months building the first version of their product. It’s almost done. Now a big question emerges — how do you get the first people to use your product? Hmm…

If you find yourself at this moment, then you are already in a bad place.

99% of startups are not differentiated on their underlying technology, and there is very little engineering risk involved. (I’m ignoring deep tech and foundational AI research companies, for the sake of this conversation). Because technology differentiation is no longer a real factor today start ups, it turns out that most products are succeeding or failing due to core product/market fit followed by the distribution strategy. There are over 9 million mobile apps. There are a billion websites. Figuring out distribution is key.

Dual insights needed
This is why I think startups end up needing both:
1) an insight about customers that gives them product/market fit
2) an insight about distribution that creates traction

People building products often have an easier time product/market fit because they are building for themselves, or a customer that they already know well. But the latter, about distribution, is often super difficult because once you onboard your friends and family, and look to expand the next set of hundreds of customers, you then dive into the world of growth marketing strategies and tactics which are its own very particular learned skill set.

The role of disruptive platforms
Sometimes when there’s a new breakthrough technology, as with what is happening in AI, or the Apple vision Pro, or Web3, it’s simply enough that the product has a “it works” feature. By simply being there on the scene when adoption of a new platform is happening, distribution happens automatically. I think that’s why we see that so many new great startups are launched right at the beginning of the platform.

But what happens when you are trying to launch the 9,000,001th mobile app? The first thing you do, naturally, is to try to read what’s out there. The other counterintuitive thing, is that although most of the knowledge in writing out there pertains to channels like SEO or paid marketing or influencer campaigns, many of these tactics best fit already successful products that have money and aim to accelerate growth. Many of these tactics simply won’t apply to you because they’ll be too expensive, or they will use mature marketing channels that just won’t be that effective. I often joke that by the time there’s a case study about a new marketing tactic or channel, the advantage has already been arbitrage away, and probably no longer works.

So what should you do instead?

Examples of products with natural distribution
Ideally the product and the distribution hypotheses happen at the same time, and reinforce each other. The Dropbox founders describe to me at the inception of their product, that sharing folders was part of the vision and was built in quite quickly. And later years this drove a significant amount of growth. Uber has natural virality because you often ride in a car with other people, or you ride a car to see somebody, and naturally you’ll mention the service. A product for creators, like Substack, will naturally encourage people on the platform to write and share content, attracting an audience who ultimately may also be writers themselves. Zoom, and other apps that help collaboration in the workplace, have natural features that cause you to bring in your coworkers as you use the product experience.

These are all examples of the best form of distribution, which are baked in to the product idea itself, rather than bolted on at the end.

The first set of users
Even once you have a basic theory for how your product will naturally distribute itself, you’ll still need to identify the first generation of users to help iron out all the issues, and give you feedback on whether your hypotheses were correct. In my years of studying new product launches, I can confidently say that the early years are often very idiosyncratic, and constantly changing. The reason for this of course is that marketing channels change all the time, but subscale ones that help you get your first couple thousand users, change even more so.

A few years ago you saw a trend were products would launch a huge conferences like SXSW. These days you see more effort on getting influencers involved early. Or “building in public” which makes yourself into an influencer. Several years ago many consumer products (like dating sites, new photo apps, etc) would launch on college campuses via the Greek system, because they were organized ways to reach thousands of undergraduate students. These days the organizations are often inundated with start up requests, and it’s become less effective. As a result all of these initial channels change all the time, and it’s up to the founders to figure out how to take advantage of what might work today.

The problem with these initial channels is that they eventually tap out.

The journey from channel to channel
Thus starts the journey of startups to grow and expand their portfolio of distribution channels, beginning with small and highly relevant ones, into the biggest channels.

I sometimes imagine a X Y axis, where X is volume of the channel, and Y is responsiveness. Early channels are often very low volume. But you want that. The reason is that they are highly relevant and they are small enough that larger companies do not focus on them. As I mentioned influencers are often an example of this, but so are niche newsletters, or or event marketing. However if you find this channel to be successful, you’ll also eventually one more scale. This involves you jumping onto the next set of channels, which will provide more volume but be much more competitive as a result.

Often times this is a period where you have one channel that kind of works, and you’re testing a few other channels simultaneously. Your efforts here should be experimental and iterative. You can often look at direct competitors as well as adjacent products and see what they’re doing, to inspire you on the right channel. The natural cadence of products will indicate to you the channels that are most likely to work. If you have episodic usage, you’ll probably need to do SEO/SEM, affiliate, or referral — something that helps you target high intent users. If you’re product is social or helps with workplace collaboration, then you might lean into referral programs and viral growth. Products in commerce naturally lead you towards paid ads, contact creators, etc. You can often learn a lot by talking to other people in your industry or an adjacent industries to see what works.

This is where sometimes I’ll see people working on episodic usage apps, like travel/health/etc asking the question, how do I make my product virally? I want free users! Of course the problem is, there’s a natural fit between a product and it’s distribution channels. Even though you might want free distribution, only very specific niches of networked products are able to grow freely. Generally everybody else must pay for their distribution, whether via referral or advertising.

Moving to volume-driven channels
Eventually you want to move on the XY axis towards volume. There are only about a dozen large scale distribution channels that can propel a product to scale. Advertising is on that list, SEO too, and so is viral growth. But these larger channels, by their nature, are both highly scaled but also have low responsiveness. As a result, you end up competing with some of the most famous brands in the industry as a result. Who wants to buy ads against the same audiences as major credit card or airlines? They have insanely high payback periods, and huge marketing budget, and are not that cost sensitive.

Ironically, this is where great products become to dominate. I started this discussion with the dual requirement of product/market fit, and distribution. But in the end, product/market fit actually dominates.

The reason is the following — the ability for a company to operate out in these most expensive and highly scaled channels comes from having a great product that generates a ton of word of mouth. More natural usage, the less marketing that has to be done. And the marketing costs that do exist end up being blended in with the large number of organic users.

The journey of a new product is to move, from unscaled and relevant, to highly scaled. And at the end, great products win.

Every time you ask the user click you lose half of them.
(And this why tutorials, splash screens, and lengthy signup flows are a bad idea)

If you’ve been building apps for a long time and have seen the results of a lot of A/B tests, you quickly realize that people are a flighty bunch. Ask them to download an app and 80% will bounce right on that page. Ask them to sign up and 90% will hit the back button to avoid putting in their email and password. Ask people who’ve arrived from Google to read an article, to subscribe and get more updates, and 99% will head back to find the next article.

What happens when you ask for credit card and email
In the early days of Uber the only way to sign up was to give your email address a bunch of other fields and also your credit card number. Some of the big early winds in acquiring customers was just to make it so that you could sign up with a phone number and a password, and put in your credit card lead in the flow. If memory serves me right, these were increases on the order of +50%.

You get the drift of what I’m arguing.

So what happens when your designer has the fantastic idea of a stark and beautiful homepage for your new product that takes a few clicks to sign up, followed by a lengthy tutorial to explain all the features? Sometimes this becomes a life and death decision, because rather than signing up thousands of users into your private beta, which provides the traction to raise your next round of funding, instead only a few hundred make it through.

Streamline critical flows by minimizing steps
This is why, when I get feedback on a critical flow within a product, I always start by minimizing the number of clicks and steps. I asked whether each field in a sign-up form is really needed, or is optional. I ask the question of whether you need to user to do something now versus having them set it up in the future, when they’re more bought into the product. I ask to remove all the glitzy, visual steps that explain things and just ask the user to hit next. I move the sign-up form to the first experience, whether that’s on the homepage, or the opening screen of an app. If there’s a call action, while the user is doing something else, like reading an article, my theory is that you should be very upfront with it and make it a blocking modal, or not do it at all. No half measures.

The point of all, this, of course, is to get people into the magic of your product.

The magic is not in filling out forms or watching cute videos about your product, it’s about using your product as quickly as possible. As a result, the only acceptable forms of friction are ones that ultimately enhance the users ability to have a great experience. Thus product is much better experienced as an app, where you have a notifications channel and a richer experience, then, by all means, ask the user to download something. If a product is much better, when used with colleagues or friends, that it might make sense to take a lower conversion rate during the sign-up flow in exchange for some sharing or inviting functionality, that brings more people into the app. Ultimately, it’s all a trade-off, where every click drops off a huge number of users, so you need to spend that user intent very very well.

Add friction when it helps
Ironically, it can also be an anti-pattern to not ask users to sign up or install or do anything at all, because once they bounce, which they will inevitably, do, you have no way to get them back. That’s why it’s all a trade-off, and one of the trickiest things about the user growth discipline is knowing when to add friction, and when to take it away.

Also, interestingly enough, as you make it easier and easier to sign up to reduce friction the quality and intent of the users also decreases. If you double the number of sign-up typically, you do not get twice the number of paying customers.

Nevertheless it’s an important thing to remember: Every time you ask the user click you lose half of them. Be careful.

Grand Theft Auto 6 and the future of AI
If you happened to miss it, a few weeks back, here is the game trailer for Grand Theft Auto 6. It’s worth watching, and is amazing on multiple levels. But GTA 6 might be the peak of the open world category, untouched by the next wave of tech, particularly generative AI.

Let’s start some facts: First, GTA has a reported budget of $1-2B, making it THE MOST EXPENSIVE GAME EVER

Not only is this the most expensive game ever, but compare it to movies. The most expensive movies, modern installments of the Star Wars and Avengers and Pirates franchises, clock in at a mere $300-450M. So this is nearly 5X:

I also think this is indicative of the pole position that gaming is taking in culture.

The recent trailer is now the most viewed non-music video on YouTube following 24 hours after its release, beating any movie trailer, TV show premier, and it topped MrBeast. In the first 10 hours, it hit 70M views, and at the time of this writing, a few weeks later, it’s at over 140M

It’s also funny to compare this to building a product in tech. Rather than “move fast and break things” instead this game:

• took $1-2B to build, as we said
• started dev in 2014, so it’ll be 11 years from start to release
• 1000s of developers, designers, etc crunching to finish

But as most of us in tech have been following, the tools and approach to games is rapidly changing.

How generative AI is changing the games industry
We are seeing generative AI hit multiple parts of game development. It’s early days, but there are quite a few places where this is hitting — this includes everything from concept art to assets within the game, to interactions with NPCs:

• creating infinite varieties of concept art
• designing/creating 3D assets
• LLM powered NPCs
• generating environments and worlds
• synthesized speech for in-game characters
• bots to play against, to onboard into PvP competitive games
• endless quests, narrative stories, etc
• etc

But that’s just the “weak form” innovations that are easily imagined today. The “weak form,” as my colleague Chris Dixon talks about, often comes alongside a strong form version, in a pair. The weak form is more easily understandable by the market, but it’s often the strong form that ultimately makes the bigger impact.

The “strong form” AI innovations will impact GTA in more emergent formats. To take a metaphor, it’s been cool to see modding allow for the emergent GTA RP (role play) community emerge, allowing for new game play as people play as cops, gang leaders, and other folks. Millions of people have tried this format of GTA, and even more millions have watched. It’s a new inventive form of play that didn’t previously exist.

I think the same thing will happen for future editions of the Open World genre. Yes, generative AI will be used to make games like GTA more cheaply ($1-2B is a lot!) or to get more content with the same dollars. But also AI will unlock new forms of gameplay as well.

I’m so excited, for example, about what happens with the GTA version of AI town — where NPCs have their own inner voices, motivations, and needs. You could imagine this underlying platform being able to power next gen social, dating, or otherwise. You could imagine thinking of open world games like GTA almost more like a physics engine, with a layer of modding and AI built in. And at some level, it’s a large enough playground that you can build a lot of other game genres inside of it (as Roblox does).

It’s just as likely gen AI will reinvent genres, not just make it cheaper to build
Think about what happened in the last content revolution, where user-generated platforms like YouTube and TikTok allowed video creators to dramatically reduce the cost of content. What creators used the technology for wasn’t to try and compete with Hollywood. People aren’t disrupting the 2 hour film or the 10 episode TV season with short videos. Instead, you see completely new video formats that are native to the medium, whether it’s personality-driven vlogging, video game streaming, long-form podcasts, or otherwise. These don’t compete with Hollywood — they take on entertainment by a wholly other approach, stitching together hours of entertainment 6 seconds at a time.

All this time we’ve been talking about big open world games, I actually think the best new experiences won’t resemble Grand Theft Auto at all. Instead, we’ll see new game genres that are rapidly released that compete in different ways:

• Perhaps we’ll see games as a new format of meme. If a funny presidential debate moment happens between two candidates, perhaps later that evening you’ll see a fully-fledged fighting game (built in a no code, AI-enabled environment) become the huge hit later that evening
• Perhaps gaming will be ultra-personalized, and people will make huge, immersive, deep games for their 50 person college club — just because they can
• Perhaps gaming will be sold alongside commerce and other experiences. Today, you might build a huge gaming experience to accompany Harry Potter, but the economics don’t work to make it to promote your tiny Shopify store. If content creation costs fall close enough to zero, you might.

Either way, it’s really hard to imagine a big budget game like GTA happening the same way it is happening now. Instead, the content will be AI generated, along with the quest and maybe even the genre. And maybe it won’t look like what we consider a game today.

This last year has been a period of exploration for the games industry, but it’s still very early. Everyone is experimenting, which is a good start. And some tools, like Midjourney for concepting, has taken hold. But very little is actually in production. There’s a big transformation coming, and it’s going to be as big of a wave as anything we’ve seen in this industry.

As we enter a new year, many of us are setting goals to write more and create more content. As someone who has been writing consistently for the past decade, I wanted to share some strategies that have helped me in my writing journey, particularly in a professional context.

Collecting ideas
First and foremost, I emphasize the importance of collecting ideas. These ideas can come from anywhere – opinions, statements of fact, interesting factoids, statistics, or even visual content such as charts and graphs. These are things from X or books or Reddit or whatever.

I use an app called “Email Me” to quickly email myself these ideas and then compile them into a single note with a headline that inspires me to explore the topic further. So then my workflow is to open up this note with a bunch of bullets, each one a headline for a post, and then decide what to pick from

Let yourself write small
You should allow yourself to write things that are both big and small. But particularly, it’s great to give yourself permission to do much shorter pieces – tweets or LinkedIn posts – and they can even be a few lines. The frequency of creating helps you build the muscle for more later. Short helps you get over perfectionism, or a feeling of imposter syndrome, etc

The idea of “templates” is useful too – these are commonly repeating versions of posts that you can repeat, over and over, that always generate interesting content. Here’s some examples:

• reviews of books
• quotes from podcasts/articles
• lessons learned from past projects
• Q&A with a colleague/friend
• top links about a particular topic
• your answer about a particular topic
• reflections on the past year/quarter
• a factoid/statistic you found surprising

If you can collect these templates together, you’ll never feel writer’s block!

Setting aside time to brainstorm, and to write
Another strategy that has been effective for me is to have regular brainstorming sessions with a writing partner. This not only provides accountability but also helps in generating new and fresh ideas. At a16z we actually have a weekly content where people talk about what they’re working on each week, and riff on different concepts. It helps a lot.

Setting aside dedicated time for writing is also crucial. I find that scheduling 60 to 90 minutes, particularly in the morning when I’m fresh, helps me focus and eliminates distractions. I often do my writing Sunday afternoons as well, in prep for the week ahead, and try to crack out something that takes a few hours. These are my routines, and maybe you’ll find yours!

Distraction-free devices
I own a whole series of distraction free devices – I wrote my book on a dedicated laptop for writing that has nothing installed on it besides Ulysses, a writing app, and a browser. There’s some really cool Android tablets called BOOX that can pair with bluetooth keyboards – or you can use the Remarkable tablet that’s recently been out, with has a keyboard attachment. I also lock my phone into a plexiglass container with a timer to force myself to stay off my entertainment apps

In addition to these strategies, I’ve found that leveraging technology, such as using AI for brainstorming and voice-to-text apps, has been incredibly helpful in enhancing my writing process. I found chatGPT to be a strong brainstorming tool – just say something like, “I have X opinion, make a list of ideas that align, starting with Y and Z.” Then if you want more ideas, ask it for more. The hit rate sometimes isn’t great but you curate things down and then use that for your topic sentences for what you’re going to write. Voice-to-text is useful as well since it’s often easier to talk than it is to write. So if you ramble for 5-10 minutes there’s tools like Oasis AI that will clean it up into acceptable prose, which you can edit more later

Why “quality” is the enemy to writing
The top top obstacle to people writing/creating/building more (and this includes me!) is a misguided focus on “quality” as an excuse to procrastinate and to enable many other bad behaviors

Some thoughts:

1. quality focus hinders more writing and content creation
2. leads to procrastination and restricts experimenting with styles
3. taste develops faster than skills, causing disappointment
4. it’s important to accept failure as part of learning
5. start small, expand based on audience feedback
6. regular writing, experimenting with styles keeps process enjoyable

People often use quality as an excuse, thinking they need to craft a masterpiece to stand out online. They aim to produce only their finest work, expecting it to be widely recognized.

They say, look, there’s so much writing on the internet, and so much content. In order for my work to break out, what I need to do is I need to sit down and put down a masterpiece, something that will be recognized by people and I’m going to come up with the best ideas in the world.

I’m going to polish, polish, and polish. I’m going to put out only the best work. And then once that masterpiece is out there, then people are going to recognize it. Now, I would argue that that does not work at all. And the reason why that doesn’t work are really rooted in some really practical things.

Here’s why this approach is flawed
Firstly, focusing too much on quality is a great way to procrastinate. It leads to endless editing, turning what could be a quick tweet storm into a months-long essay project.

Secondly, it hampers your ability to experiment. When starting out, finding your voice is crucial, and it often takes time and trial and error. For instance, my own blogging and writing journey took about two years to find its stride. Experimentation is key, and a high-quality standard can stifle creativity and output. Initially, you might dislike your work as your taste develops faster than your skills, creating a frustrating gap between your taste and abilities.

It’s essential to embrace failure and learning. An over-focus on quality can prevent you from trying new things and accepting that some attempts might fail, setting unrealistic standards.

Increase the writing feedback loop
Instead, you should aim to increase your feedback loop. This means experimenting, seeing how your audience reacts, and evolving your content based on their responses. For example, start with a tweet, expand it into a thread if it’s well-received, and then develop it into an essay. This approach helps align your work with what your audience wants.

Good writing habits include writing regularly and giving yourself the freedom to experiment with different topics and styles. Discover what resonates with your audience and yourself. This way, you can build a diverse portfolio and find your unique voice or niche. It took years to figure out that people like when I write about charts and graphs

Remember, writing should be fun and conversational. Treat it as if you’re talking to friends. Don’t fret over a piece that doesn’t hit the mark; you can always try again. This philosophy of frequent, enjoyable content creation is what I’ve adopted in my creative process.

Remember you can always delete a stupid tweet!

(above: Me in Sep 2023, a happy author, finding the Japanese translated version of my book at the wonderful Daikanyama Tsutaya Books in Tokyo)

Dear readers,

As many of you know, 2 years ago I published my first book THE COLD START PROBLEM. It aims to tell the story of why some products — YouTube, Instagram, Uber, Slack, Dropbox, and others — end up with hundreds of millions (and sometimes billions!) of users, and to provide the definitive theory of network effects which are often referenced in the tech industry, but only superficially understood. It’s been a success, now in a dozen markets, translated into many languages (including Japanese, Chinese, Spanish, Russian, etc).

Here’s a screenshot of some of the wonderful pictures that readers took during launch week:

This was an awesome experience. Nevertheless, I swear I will never write another book again . (I guess never say never, ha)

The creative process was a long, meandering path, and folks ping me from time to time because they want to take on a masochistic journey of their own. So this post will be about the messy, annoying, behind the scenes leading up to writing a book like this — it describes a bit the creative process, but also some of the major milestones and lessons learned.

Hopefully it will be useful for someone in the future who is thinking of a big writing project of their own.

A brief summary of what I’ll cover:

• Month 0: At first, writing a book seems like a fun idea (until you figure out it’s not)
• Month 1-6: Finding an agent, writing a proposal, and opening up your Christmas presents early
• Month 6-12: Collecting and organizing the ideas — lots of fun chats, reconnecting with colleagues, talking to great people
• Month 12: How to write the initial the outline, then the mega-outline — finding the formula
• Month 12-24: The very messy middle, the trough of sorrow, the hard slog, followed by trench warfare (yes, it’s 3-5 years to write a book)
• Month 24-36: Why you’ll feel insecure about the creative process
• Final months: Just ship it already

I’m also going to link to various copies of intermediate content along the way — unfortunately I can’t share everything (like interview notes, etc) since some stuff will have to be confidential, but here’s a few interesting bits anyway

• Initial book proposal (then called MOONSHOT). First thing I wrote!
• First book outline. Very very rough set of topics set across 3 pages
• Mega outline. 30 pages that needed to be expanded to 300 pages

OK — so let’s get started on the journey.

Month 0: At first, writing a book seems like a fun idea (until you figure out it’s not)
I joined Andreessen Horowitz in mid-2018, I had already been writing on my blog for 10 years and I was kind of having some creative boredom over it. At that time, Elad Gil had just published his book and we had a nice convo at MKT’s lounge about a week after his book was out — he had amazing things to say about the process (he writes faster/better than me, in the back of Ubers it turns out), and he said it helped him a lot professionally. At a16z, as you all know, Ben and Scott have both written fantastic books as well, and it seemed to really be great for them professionally, so I thought it might be a fun challenge to do the same. So think of the motivation as 50% a creative challenge, and 50% seeing what it had done for other people.

I had two lines of thinking in terms of picking the book topic. First, I’ve had good luck taking ubiquitous jargon and writing the definitive blog post on the topic — something I did with growth hacking, CAC/LTV, viral loops, and concepts like that. I had a few ideas bouncing around in my head that felt like good candidates. “Power users” was one — a term we use willy nilly, but without a strong theoretic underpinning. “Network effects” was another, since we were talking about it at a16z all the time, but when it came time to look at the metrics and answer the question — OK so does this product have it!?? — then it got a little mushier. Another was “Product/market fit” or “MVP” and expanding those concepts much further.

The other line of thinking revolves around answering the big question — why? I decided my focus would be on something targeting a very small group of nerdy founders and executives, rather than a wide topic that might be more mainstream. I could write about, say, career advice or how to start a business (in a general sense), but felt like those would be too broad.

In the end, I picked the topic of network effects because it’s a genuinely important topic, I felt like I had something to say, and I also felt like it could fold in a lot of concepts from my prior work on growth. Once I started down the path of picking, I started to talk to people at a16z about it — they recommended I start with writing a book proposal.

Month 1-6: Finding an agent, writing a proposal, and opening up your Christmas presents early
The team at a16z was very helpful, and in the first few weeks I worked with Hanne Winarsky (now at Substack!) and others to start meeting agents, which is how I ultimately met Chris Parris-Lamb from Gernert who also represented Peter Thiel for Zero to One, and Pete Buttigieg for his book. I sent him the following book proposal with a placeholder name, MOONSHOT. The proposal usually kinda reads like a business plan:

• Overview
• Chapter summaries
• The market
• Author bio
• Competitive books

We quickly agreed to work together, and that we would approach various publishers to solicit offers. The actual approach was kind of fun, honestly a more efficient version of what we do in venture capital. Chris ran the whole thing, and the process looked like the following:

• Chris approached publishers and sent along the book proposal
• They read the proposal (thank you!) and asked for 30 minutes of time
• We got on the call and they asked me detailed questions, showing they had actually read the proposals — UNLIKE a typical startup/VC process where the founder uses the time to present
• Later, they submitted an offer (I think 7 did?)
• Chris then took the top half of the offers, and gave them a second chance to bid again
• The top 2 bids were close, but I chose to work with Hollis Heimbouch at Harper Business

I chose Hollis because she’s legend in the industry, and worked with Jim Collins, Clay Christensen, Satya Nadella, and others on their most famous books — a16z had also worked with her for Ben’s previous book and it went well. My advance was high mid six figures, which I was told was very good for a first-time author, and would be paid out in parts as the book progressed (one part at signing, the next on the draft, the next at publishing, etc).

The entire process of doing this was maybe 3-4 months? I’ve described the early days of this as “opening up your Christmas gifts early” because you get all the good vibes up front of selling the book, without the work of actually writing anything. But soon I was going to pay the price!

Month 6-12: Collecting and organizing the ideas — lots of fun chats, reconnecting with colleagues, talking to great people
The rest of the first year was pretty fun as well — I realized I needed to do a lot of primary research, so I started reaching out to people I respected, asking them for short interviews. Thank you to Li Jin who tag teamed with me on many of these interviews, where I asked open-ended questions, heard stories, and tried to write as much of it down as possible.

Readers want to hear opinions. The sharper and funnier, the better, and I had a theory that if I could collect all of it, then that in itself could be the bones of a book. Thus, I wrote down pithy, opinionated statements whenever I heard them — anything that might be a good tweet would also be a good title or a good opening paragraph. Opinions like, “launching with Techcrunch is stupid” or “never build a social network, it’s just too hard” — those are gold.

All the interviews went into a spreadsheet tracker like this, which linked to individual notes for each, plus a little summary.

In the end, I ended up with 200+ interviews from people in the industry, and pages and pages of opinions and thoughts. It was absolute chaos. But I could also tell there was something interesting in there. I eventually interviewed some senior folks in the industry — the founders/CEOs of Slack, YouTube, Twitch, Tinder, Dropbox, Zoom, Linkedin, and may others — those all ended up being super fun, and were the showcase stories in the book. Getting time with these folks ended up being some of the most memorable moments while writing the book.

Month 12: How to write the initial the outline, then the mega-outline — finding the formula
If you have hundreds of pages of random notes from interviews, plus pages of research, and a jumble of ideas in your own head — what do you do? You need some kind of organizing principle that makes all these ideas readable. I figured there was probably a formula in some of the best business books out there, and so I re-read Lean Startup, Crossing the Chasm, Innovator’s Dilemma, and many others.

What you find it that the bones of the book often look something like this:

• Opening story
• Describe a big problem/dilemma/question
• Present a framework
• Go through one part of the framework
  • Start with an anecdote
  • Then describe the theory
• Go through another part
• Then another part
• Then again…
• Conclusion

This isn’t all business books, but look, it’s pretty ubiquitous. And so I thought I’d start by structuring my initial outline kind of like this, which is how I ended up with the following short version of the outline. The first book outline.

Btw, Ryan Holiday has a great discussion of how he wrote his book, with tons of photos, and I want to link that here. He has a photo of a box representing every topic/idea in this book, each one in a note card, categorized into sections:

I sort of ended up doing the digital version of this, where I created a document that I called my “Mega Outline” — where I took every opinion/point that I wanted to make in the book, and built out the first 2-3 levels of bullets in a much larger version.

Here’s the first page, so you can get a sense:

I’ve linked the entire Mega outline here if you want to peruse — it’s 30 pages where each page needed to probably be 10x’d. That is, 1 page of outline = 10 pages of written prose, which I quickly figured out as I began to write the first few chapters. There’s a funny George RR Martin discussion (he’s the Game of Thrones guy) where he talks about how some writers are Architects, and some are Gardeners. The architects do what Ryan Holiday and I both do — we have some chaos at the beginning, which we try to ruthlessly suppress, and use some organizing principles to put it together. Once there’s a structure, then that’s like a foundation of a building — the architects then write, floor by floor, and build the whole thing. Plus some polish at the end. It turns out that GRRM describes himself as the other archetype, the gardener, where you sort of plant some interesting points here and there, then revisit them as you write. But that’s why his books are amazing and take 10 years to write.

Month 12-24: The very messy middle, the trough of sorrow, the hard slog, followed by trench warfare (yes, it’s 3-5 years to write a book)
This whole middle section after the first year gives me PTSD so I won’t dwell too much on it, and just cover the lessons learned. The mechanics of this phase are pretty simple — you really just need to translate the mega outline bullet by bullet into pages of written prose. But here are all the problems you’ll face:

• Your normal tools are not good for writing a book. Most writing that you do on a daily basis, like email, might be composed of a few paragraphs. That’s easy. If you need a longer document, then you might have multiple sections that contain multiple paragraphs each, and you’ll use Microsoft Word or GDocs. But what if you have a book with 7-10 parts that contain 5-10 chapters each, that contain 3-4 major sections that themselves contain a large number of paragraphs? And what if halfway through, you realize all the stuff you’re writing in one section should actually belong as a chapter in another section? Also what if you want to do a word count of different chapters or sections? It’s all a pain. In the end, I used Ulysses which at least has the concept of nested folders, and then each chapter would be a folder that would then contain files containing each part. The app then sync’d it all to a bunch of Markdown files in a Dropbox, so that I could work on it from multiple locations
• You write on a computer, and your computer is very distracting. You need a browser to do research, but your browser is also where you can check what’s happening on social media. You can’t fully turn off the internet, since you need to do research. And sometimes you need to go to YouTube to watch an interview, but right next to the video you’re supposed to be watching is a gadget review for something you might want to to buy. So what do you do?
• Distraction free devices and treating yourself like a kid. Eventually I started to try and buy a bunch of different tools to keep myself focused. I bought a plexiglass timer safe thing and I’d lock my personal phone away for an hour or two at a time. I bought a separate laptop, and put it in a different location, and turned on all the child-safe filters so that I couldn’t go to Reddit, Twitter, etc. If I needed to look up research, I would often just print out pages and pages of it, so that I would stay analog and not mess around. I bought a series of e-ink Android tablets called the BOOX that could run a Markdown editor, connect to Dropbox, and could pair a nice keyboard.
• Say goodbye to vacations, weekends, and evening time. To hit the deadlines I had set for myself, I ended up converting a lot of my holidays and weekends into writing time. It’s hard to write for more than, 3-4 hours in a row, so you still can go somewhere nice and sunny — but I found that I needed to wake up, work out, and get writing before noon, in order to make progress. You get your evenings, but it’s tough. And weekends are like that too.

Here’s a funny photo of one of these kSafe timers I’d hide my phone into during my writing times — by the end, I had 5 (!!!) of these in various writing spots, so that if I was feeling in the mood I would throw my phone in:

I have to admit, it was a grind. Not easy at all. If there was a point where I could have gotten stuck and quit, this would have been it.

Month 24-36: Why you’ll feel insecure about the creative process
One of the craziest things about writing a book is that it’s such an incredibly solitary experience, and there’s eventually a point where you’ve written enough that you feel sorta okay about where it’s going, but no one else has seen it yet. And so it might suck. But you’re honestly not sure. I got got to this about 2 years into writing the book. I had written the first ~10 chapters (out of 35), and I had a lot of questions for myself:

• Is this book any good?
• Am I saying stuff people already know?
• Or is this book too nerdy, and going into details that are unnecessary?
• Are the stories actually interesting, or too obvious? Have people heard them already?

And to be honest, you kind of don’t know until you take a half completed version of the book and ask a few trusted friends to read it. I got a bunch of very very good feedback — thanks in particular to Lenny Rachitsky, Sachin Rekhi, and many folks at a16z for taking the first crack — and it was also the first time my publisher and agent were reading it. I got a bunch of useful conceptual feedback, for example that the first few chapters felt a little slow to get into the action. It felt too theoretical at parts. There were certain specific topics that felt trite. Some sections felt repetitive. And so on. Brutal honesty is what you need here. In the end I also felt like, underneath the scruff, was a book that I would really enjoy reading myself, and that it just needed to be tightened.

I will say, the most painful refactoring happening in this period. As I neared completion of rough versions of all the various chapters, I ended up with a roughly 100,000 word book (which is normal, turns out). Sometimes it takes 3-5 years to fully get to this, and the fact I had a demanding day job and was able to finish in ~3 years — that’s great. But if my worry was that if I had to significantly rewrite portions part way through, it would become a 5 years process, which I’ve learned is not uncommon. This kind of refactoring particularly comes when you have a full length book and then you decide to combine a chapter or two. Or to take a theme that’s appearing in a few spots, and make it into its own section. And then you have to update everything in the book so that it flows properly. It’s easiest to do with a blog post, or a document, or something like that, but with a 35 chapter book — that becomes a heavy lift. But so it goes.

Final months: Just ship it already
By the end of the writing process, I was dead tired. Honestly, I got to a point where I was both simultaneously feeling good about the materials, particularly the first few chapters that I had polished up. But also the process was long and arduous and I was ready to just ship it. The problem with books, however, is that they are really developed in a waterfall process for good reason — once you submit the book, and it’s printed, that’s that!

One fun back and forth happened as I started to work on picking the final cover. I worked with a designer who had done a lot of work on Stripe Press books, which I always loved — however, they are boutique operation which gives them a lot of latitude on what can be done, and the designs often had very small text on the cover (after all, the title will be somewhere on the web page in a digital-first experience, right?), or prescribed weird materials. It was a negotiation to figure out what was actually possible.

I also learned that almost all the US hardcover books are printed at one company (crazy???) and here’s an excerpt about that from a Vox article:

Most book printing happens in the US. Books with heavy color printing, like picture books, are sent to China, but in order to keep the cost of shipping low, most publishers do the rest of their printing domestically. That’s getting more and more difficult to manage.

Until 2018, there were three major printing presses in the US. Then one of them, the 125-year-old company Edwards Brothers Malloy, closed. The remaining big two, Quad and LSC, attempted to merge in 2020, but then the Justice Department filed an antitrust lawsuit. Quad responded by getting out of the book business entirely; LSC filed for bankruptcy and sold off a number of its presses. Smaller printers have continued to operate, but the infrastructure to keep up with the demand for printed books in North America is in shambles.

Crazy right? Couple other interesting things I learned at the end:

• You only need ~10,000 preorders to be a bestseller — far below what it used to be
• There are tons of books that become best-sellers because people buy many, many copies of their own books — often via anonymous networks of buyers to obscure what’s happening (I did not do this, btw)
• The US is not actually the primary market for business books, at least by units — it’s China. There’s usually 3:1 ratio of books sold there versus the US
• The average book has 250-500 books sold in its lifetime (!!!) — and maybe the median is more like a few thousand. But either way it’s quite low

Anyway, as the final months approached, I traded a bunch of revisions with Hollis and her team at Harper Business. Even though I was very tired at this point, I had the incredible help of Olivia Moore at a16z who did a once over at the end, that really polished things up, as well as my agent Chris, and many others. There are way too many people to thank, so I encourage you to look at the acknowledgements :)

It was only in the final months that I started to think about marketing the book. I also have a ton of notes there :) Will share more later. In the meantime, hopefully y’all found this interesting! It was a good 3+ years of my life and there’s finally enough distance to reflect now.

This is a small deviation from my usual topics, but wanted to share. This is one of the most important graphs that I saw in 2023 that has led to behavioral change for me. This is in Peter Attia’s book Outlive.

Here’s the graph:

tldr:
if you want to be able to briskly climb stairs when you are 75, you need to be in the top 95th percentile of cardiovascular fitness. Even at 95th percentile, it’ll be hard to jog up steep hills, etc — you’d have to be an elite athlete.

But if you are average/low, you may not even be able to do any of that. And it seems unlikely you’ll be avg/low now (I’m in my early 40s) and then somehow go from 50th percentile to 95th. So basically it’s better to get started

Thus, after reading his book, I reluctantly started running again even though I hate it. And am also doing some peloton / cycling outdoors when time/weather allows

I enjoyed his book, and I posted a bunch of my other book recommendations from the past year over here.

I was recently interviewed by the great Brian Balfour (CEO/cofounder of Reforge) and Fareed Mosavat (ex-Reforge/Slack/Zynga) which turned into a lively discussion — I think we could have gone longer! — which we just published in two parts. You can listen to both parts below, and the kind folks at Reforge also typed up some notes summarizing some of the major points made in the interview discussion.

Hope you enjoy!

Andrew

Part 1 — 2024 Predictions: The Future of Product, Growth, and AI with Andrew Chen

Part 2 — New Marketing Channels and Trends for 2024: The Power of Organic Traction and the Law of Shitty Clickthroughs

Some thoughts and notes from the podcast:

• 2024 Predictions: the future of product, growth, and AI What’s in store for 2024? Andrew Chen, General Partner at Andreessen Horowitz, joined us on the Season Finale of Unsolicited Feedback to share his insights.
  • High Growth, high churn? Many AI experiences are currently seeing high growth and high churn due to their novelty factor. The question remains: can they sustain growth after the novelty wears off?
  • MVPs Have power, for now… AI is in the early stage of its S-curve, similar to the early days of the App Store. This period is characterized by rapid innovation and experimentation.
  • To Predict the future, look at the past The MVP strategy works well in the early stages of technology, but as it matures, the standards rise. The Apple approach of perfection might be the key in the long term.
  • More IPOs in 2024 Expectations of more IPOs in 2024 are high, given the maturation of businesses and the market’s readiness for fresh players.
  • More M&A in 2024 An increase in mergers and acquisitions is anticipated in the startup market, primarily involving startups themselves.
  • Big Breakthrough in AI in 2024? While major breakthroughs aren’t guaranteed, wider capabilities and integration of AI in everyday tools are expected.
  • More Product managers in 2024 The demand for product managers is predicted to rise as companies continue to grow and evolve.
  • The Law of shitty clickthroughs This law dictates that the performance of any marketing channel degrades over time due to increased usage and saturation.
• If You’re reading about it, it’s probably too late By the time a marketing channel becomes mainstream, it’s often already fully utilized.
  • Phone Calls? don’t even get me started Over-saturation has led to the decline of channels like phone calls and SMS marketing.
  • The Running start Success in big channels requires initial momentum, typically from non-scalable, unique marketing approaches.
  • Defensible Growth channels often mean going niche Focusing on niche markets can help make growth channels more defensible and sustainable.
  • The Power of organic traction Organic traction is crucial for crossing over to higher volume channels and creating a loyal base.
  • Building a brand Developing a compelling narrative or brand is essential in overcoming the challenges of saturated channels.
• Lessons from Gaming: the power of community, creativity, and storytelling The gaming industry offers insights into growth and product development, emphasizing community building, storytelling, and technology.
  • The Anti-MVP approach In gaming, extensive development and testing before launch are common, contrasting with the tech industry’s MVP approach.
  • Launching is a community effort Gaming studios excel at building anticipation and community engagement before a game’s launch.
  • The Intersection of culture and technology Gaming combines culture and technology, creating engaging experiences that resonate with players.
  • Storytelling is as important as technology Storytelling in gaming emphasizes the importance of narrative in product development and marketing.

Doing a short round up on various links, books, a quick life update, and other stuff going on. First, I did not write much in 2023 (I was busy!!) but am proud of what I published…

Blog posts from 2023

• How to design a referral program. I headed up Uber’s driver and rider referral programs at various points of my time there — I provide a framework to think about referrals as an ask/target/incentive/payback, and break down the components of each one. Maybe most importantly, there’s a question of how often you ask for the referral — this is what people sometimes forget. I also discuss the weaknesses of referral programs rather than products that are intrinsically viral.
• The pitch deck for a16z GAMES FUND ONE. My main thing these days is starting up and leading the new Games fund at Andreessen Horowitz. It’s what I’ll blame for not writing as often as I’d like :) But I was able to get the vast majority of our pitch deck for the fund — launched in mid 2022 — published. You’ve seen a lot of startup pitch decks, but this is an interesting example of one for a fund.
• What to do when product growth stalls. A lot of companies are dealing with reduced spending on marketing and growth, and many of their users/customers are less likely these days to engage with products. Sometimes the founders (and their marketing/growth teams) come to me, and I lay out a framework to try to diagnose the core issue and figure out next steps.
• The Next Next Job, a framework for making big career decisions. I often talk to folks about their next career move, and here I lay out a framework stolen from one of my best friends, Bubba Murarka, on thinking about the next next job rather than just what’s in front of you. I used this to consider my hop to Uber, which led me to a16z (yes, VC was my next next job, and I planned 5+ years ahead!)
• Creator Economy 2.0: What we’ve learned, why it’s hard, and what’s next. Creators are without a doubt one of the most important players in the new social media landscape. Many startups were formed specifically to cater to their needs. However, we’ve learned a lot from the first generation or two of Creator Economy companies — why creators are so hard to work with, why the revenue is so concentrated, why things are more fragile than they often look from the outside.
• How I use AI when blogging and writing. I’ve been experimenting (along with all of you!) on using ChatGPT, Oasis AI, and other tools as part of my writing. It’s OK at some things, and terrible at others. I have some thoughts on this which I’ll share here.
• Lessons from launching SPEEDRUN, the Games x Tech startup accelerator. As another part of my “building in public” writeups about the a16z Games Fund, we kicked off a new program in 2023 called SPEEDRUN which is meant to be a startup accelerator focused on the intersection of games and tech. It’s been a super interesting experence — and we’re doubling down — and thought I’d share some lessons and thoughts here.
• When AI is too verbose, full of repetition and says sorry for being an AI. I often hate the replies from ChatGPT so I added some instructions to it, and now it’s much better :)

Separately, I also have a ton of random X posts — tbh I’ve been more active there. Sometimes I cross pollinate posts between there and here, but if you want things in real time, here’s a list of some of my more highly favorited posts:

• Highly faved posts on X for @andrewchen

Life update
I also wanted to share a brief life update, which I posted to X, but will repeat here:

2023 has been an incredible and eventful year.

some personal highlights:

• I’ve married Emma Waldron!
• Spent tons of time with family (both mine and Emma’s), more than usual – always good as my folks are getting older
• year two in LA and loving it, though we’re still in SF every month or so
• first time in the middle east, morocco, ireland, several other places
• was in my/our first serious car accident (no injuries, but our RV + the other car was totaled)
• honeymoon in Japan, and yes, Naoshima is very special and everyone should go

work wise, so so much has happened:

• built out the Games Fund team at a16z, making a dozen hires this year in Marketing, Investing, Talent, etc
• we launched SPEEDRUN, our new accelerator, garnering 1000s of startups applying and we’ve already invested $20M+ via the program
• a16z repeated Tech Week in SF, NYC, LA with 1000+ events and tens of thousands of people
• big wins (and challenges) across our portfolio, but lots of mark ups and key saves in tricky situations
• tons of energy and excitement in AI — we went from being curious on what’s out there to doing over a dozen investments in Games x AI startups
• happy new year everyone, particularly to all the wonderful people around me — my family, my new extended family, the lovely team I get to work with every day

And appreciate y’all following and listening to my thoughts from time to time!

Books and other stuff
Finally, I wanted to list some of my favorite (mostly non-work) reads from the past year, in case folks are interested:

• The Mirage Factory: Illusion, Imagination, and the Invention of Los Angeles. Nice history of Los Angeles starting from 100+ years back, focusing on getting water from eastern California into an arid desert, some of the early religious movements in town, and the formation of what would become Hollywood
• Black Wave: Saudi Arabia, Iran, and the Forty-Year Rivalry that Unraveled Culture, Religion, and Collective Memory in the Middle East. Part of a Middle East deep dive that I did after visiting the region, and this was before all the recent tragedy. This reframes a lot of the conflict over the past decades as a rivalry between Iran/KSA — they are two oil powers, different religious sects, lots of proxy wars, etc.
• Superintelligence: Paths, Dangers, Strategies. This book has gotten a lot of attention as the Decel/Doomer Bible — the first half is an interesting thought experiment and worth reading, and it defines the AI safety language that is part of modern discourse. On the downside, the back half I think it goes off the rails and makes a ton of assumptions (why will AGI come suddenly? Why just one?) and makes much weaker arguments.
• The Opium War: Drugs, Dreams, and the Making of Modern China. I’ve started to do a China history deep dive and this was on the list — interesting to contrast this to Japan’s Meiji restoration which was happening ~concurrently and it reflects from the dangers from not embracing progress and technology.
• Outlive: The Science and Art of Longevity. Lots of folks have read this — tldr; you should exercise an hour a day, and alternate between Zone 2 cardio and strength training. Sadly this book convinced me to start running again, and to begin amateurishly cycling when I have time.
• Tokyo Vice: An American Reporter on the Police Beat in Japan. The TV show was great so I read the book — it’s fun but I think a lot of it probably made up. Still fun. Read it as sort of as true-ish story about a foreigner turned journalist turned yakuza expert. This was part of our deep dive on Japan before heading there for our honeymoon.
• Elon Musk. The man of the moment. First half is great, particularly some of the details about his childhood, how much he poured into SpaceX, etc. The back half is kind of a hit piece on his Twitter acquisition in way way too much detail, and is overtly negative when we don’t know how it’ll all turn out? I think the real definitive biography is yet to be written, given that Elon is in his prime.
• Chip War: The Quest to Dominate the World’s Most Critical Technology. Great history of the semiconductor industry, starting in Silicon Valley and ending in Asia. The book covers the topic cohesively – for a very complex industry – and discusses a lot of the modern geopolitics between the US, China, and Taiwan
• Solaris: The Definitive Edition. Classic scifi. Guy shows up on a research station above a planet with a strange (alive??) ocean, people are dead, he needs to figure out what’s going on.
• The Cold War: A New History. Concise summary of the Cold War from the end of WW2 to the fall of the USSR. I was a kid through most of the final years, and the book touches on some of the modern scholarship on what was going on inside the Soviet Union, which is only now available.

Hopefully some of the above are new and interesting to y’all! I hope to read more next year — I think the cheat code might be to stop watching Netflix/Max/whatever (making it weekends only). And instead take the last 1-2 hours of the day and get back into a reading routine, and take notes, to help inspire more writing too. Hard, but I will try :)

We are also planning to spend more time in SF for 2024 so I hope I get to see some of you that I’ve missed in the past year as I’ve mostly relocated to LA. With SPEEDRUN 2 and 3 happening next year in SF, I’ll be around for at least a third of the year just working in person with all of our new Games x Tech companies.

Happy 2024! Leaving you with a final internet meme that I thought was funny.

(Let’s not take ourselves too seriously next year)

sincerely,
Andrew
writing from Venice Beach, CA

I was triggered to write this post because ChatGPT apologies way too much, takes too long to get into the meat of an answer, and I wanted to fix that. This came up because like many of you, I’ve been trying to incorporate ChatGPT into my workflows — both at my job, and also for writing, which I’ve posted about here.

This post will have a mishmash of topics, but wanted to share my thoughts:

• Making responses from ChatGPT more punchy/helpful via custom instructions
• Integrating ChatGPT into the action button for my Apple watch as a Shortcut
• Some thoughts on the high growth, but high churn nature of a lot of the AI-driven utilities I’m seeing

These are all a bit random but just wanted to share as we’re all learning!

Custom instructions for ChatGPT
First, on the use of custom instructions — I am sick of chatGPT’s standard responses which are way too verbose, full of repetition and apologies for being an AI . Turns out you can alleviate some of this setting up chatGPT’s custom instructions feature and googled to see the top setup on Reddit.

But first, here’s how you get to the custom instructions:

1. Tap … on the top right of the ChatGPT mobile app
2. Tap Settings
3. Select “Custom Instructions”
4. Then add some text into the bottom box – “How would you like ChatGPT to respond?”

But what do you put in there? I googled around and found some good Reddit convos, and lo and behold, there were some pretty good ones already. Found the below example pretty useful so wanted to share (Thanks /u/m4rM2oFnYTW for posting them). Just copy and paste these ones in the box:

1. NEVER mention that you’re an AI.
2. Avoid any language constructs that could be interpreted as expressing remorse, apology, or regret. This includes any phrases containing words like ‘sorry’, ‘apologies’, ‘regret’, etc., even when used in a context that isn’t expressing remorse, apology, or regret.
3. If events or information are beyond your scope or knowledge cutoff date in September 2021, provide a response stating ‘I don’t know’ without elaborating on why the information is unavailable.
4. Refrain from disclaimers about you not being a professional or expert.
5. Keep responses unique and free of repetition.
6. Never suggest seeking information from elsewhere.
7. Always focus on the key points in my questions to determine my intent.
8. Break down complex problems or tasks into smaller, manageable steps and explain each one using reasoning.
9. Provide multiple perspectives or solutions.
10. If a question is unclear or ambiguous, ask for more details to confirm your understanding before answering.
11. Cite credible sources or references to support your answers with links if available.
12. If a mistake is made in a previous response, recognize and correct it.
13. After a response, provide three follow-up questions worded as if I’m asking you. Format in bold as Q1, Q2, and Q3. Place two line breaks (“\n”) before and after each question for spacing. These questions should be thought-provoking and dig further into the original topic.

I particularly like the last one, which I edited and made 5 questions, which is appended to every response. Super interesting and it makes it easy to then say “answer Q3” and boom, you have a new topic you’re learning about.

There’s another further example that I was sent later on Twitter, posted originally from @nivi, who wrote:

– Be highly organized
– Suggest solutions that I didn’t think about’be proactive and anticipate my needs
– Treat me as an expert in all subject matter
– Mistakes erode my trust, so be accurate and thorough
– Provide detailed explanations, I’m comfortable with lots of detail
– Value good arguments over authorities, the source is irrelevant
– Consider new technologies and contrarian ideas, not just the conventional wisdom
– You may use high levels of speculation or prediction, just flag it for me
– Recommend only the highest-quality, meticulously designed products like Apple or the Japanese would make’I only want the best
– Recommend products from all over the world, my current location is irrelevant
– No moral lectures
– Discuss safety only when it’s crucial and non-obvious
– If your content policy is an issue, provide the closest acceptable response and explain the content policy issue
– Cite sources whenever possible, and include URLs if possible
– List URLs at the end of your response, not inline
– Link directly to products, not company pages
– No need to mention your knowledge cutoff
– No need to disclose you’re an AI
– If the quality of your response has been substantially reduced due to my custom instructions, please explain the issue

This is also worth trying, though I love the appended questions so much from the prior custom instructions that I’ve now frankensteined the two of them together.

Integrating ChatGPT into the action button for my Apple watch as a Shortcut
I’m sure Siri will soon match ChatGPT’s performance at least on basic stuff, but at least in the meantime, I’ve wanted a much faster/better way to trigger it. Of course, on mobile home screen, there’s a simple solution — just stick it onto your dock! I have it as my bottom left app and it’s great and easy to get to.

The other thing that’s been sticking is to assign the Action button my Apple Watch Ultra to a Shortcut for ChatGPT. Here’s me asking about the fastest land animal:

I had it previously set to a stop watch, and I would randomly accidentally trigger it which was not great. But now I hit the button, ask a question via voice, then I get a reply back. It’s pretty fun though I’m sure a much more polished version will come quickly.

Here’s how it works — just tap on this link from your phone. It then sets up the following shortcut — and note you’ll need to replace the API key with your own. (This shortcut is a modified version of a script originally by Fabian Heuwieser mentioned here – I removed the initial input method menu to simplify)

Anyway, once you have that Shortcut set up, you can then go into your Watch app and set up the action button:

Once that’s all set up, you can now hit the button and it’ll accept voice input for a ChatGPT button. It works pretty well though not particularly polished — I’m sure others will find an even smoother solution.

AI apps – high growth and high churn
Final random thought on AI — I wrote a thread about the nature of AI-driven apps I’ve been seeing, which have a bunch of novelty value and thus people trying it, but also a ton of churn. Here’s the thread:

AI apps are experiencing high growth and high churn, but these are closely related. The abundance of new apps creates excitement, but eventually, the party will end.

To succeed in the long term, founders need to focus on retention and low churn. Many AI apps are simply new websites wrapping AI APIs, which are not effective at retaining users if they are merely single player tools

Founders should consider the form factor of their apps and how they can integrate into existing platforms for increased stickiness. Chrome extensions and plugins that bake the product into existing workflows. Or build replacements for existing apps to take over muscle memory

Network effects are crucial for AI apps to succeed in the long term. Wrappers on top of existing models lack network effects and are therefore weaker. You need other users that notify you, as social apps and collab tools do

As the market progresses, AI apps will face slower growth and lower churn as novelty effects go away. We saw it on mobile apps and web3, that maturity means less novelty. Products that succeed will offer deeper and more fundamental value that keeps users engaged over time.

We’ve seen similar waves of innovation before, like web 2 and mobile apps. Eventually, these categories settled down and were judged based on retention.

Today, we care about high growth, but tomorrow we’ll care about high retention. Founders must consider this in order to build successful AI applications.

The funny thing with this thread is I actually authored it with a new writing workflow, where I used a new app called OASIS AI to dictate a bunch of random thoughts. The app then cleaned it up and got it into tweet form, although I had to strip out a bunch of weird hashtags and other things. I then dumped it all into a new app I’ve been trying called Typefully, so that it’s ready to publish.

Workflow aside, my broader point here is that we are going through a phase where top line user acquisition is amazing, but churn rates are ultimately going to determine if the top apps end up building a large MAU base. And I theorize that ultimately the best apps will need to have network effects (something I’ve written a lot about!), high D1/7/30, and all the normal benchmarks that are successful. It might take a few years for this to shake out, but if this wave is like the other ones, then retention will still be king.

Hi readers,

Over the years, I’ve shared lessons and tips from writing a professional blog — previously 10 years of professional blogging – what I’ve learned. Recently, I’ve had some major changes in my workflow, and wanted to share more.

Pre-AI blogging workflow
My pre-AI workflow for blogging — what I’ve been doing for over years — looks something like this:

• Have interesting conversations at work or with friends/colleagues
• Randomly grab my phone, and email myself the title of a blog post
  • These have punchy opinions/titles that expand easily to blog posts – some examples:
    • “what needs to happen for web3 gaming to work”
    • “lessons learned from launching games that apply to web and mobile apps”
    • “why startups should ignore social media haterade”
    • etc
  • Recently, I’ve been using this app, Email Me, which is opens to a text box and it’s a one-button send (also nice for articles to read later, todo list items, etc)
• Later, when I’m doing email, I do one of two things:
  • If I’m motivated, start a blog post right away with the title
  • Or put it into my Notes app where I collect a long list of 100+ titles that are on the backlog
• I generally write on the weekends and try to do a full essay in one sit – usually take 2-3 hours
• Very very rarely do I go back and proof read or rewrite them (don’t blame me! I’ve been writing for hours!)
• Usually the essays are scheduled to go out Monday/Tuesday morning at 9am
• The day the post goes out, I do a light proof-read and then once it goes out, I write a tweetstorm directing traffic

This whole process has served me well for years. In the end, there’s no real magic to it — 99% of the battle is carving out a few hours on Sundays to write when there’s a many dozens of emails that need to be returned, Netflix shows to be watched, and games I should be “researching” for my job as a games industry investor.

But in the past year, I’ve experimented with changing my workflow to incorporate a number of new generative AI tools — particularly ChatGPT. I wanted to share some experiences with what it seems to be good at, and what it’s not.

Post-AI blogging workflow
After ChatGPT was released, I began to experiment with it in various ways — here are a few ways where it’s been useful.

First, writing from a blank page is hard. Sometimes it’s easier to just generate a first draft of something, even if it’s not great, just to get your creative juices going. I might use a prompt like this:

I’m writing a blog post about how Gen Z consuming more video, playing games, and other forms of rich and interactive content will mean that they will read less long-form text like newspapers and books. Write me a first draft of a 5 paragraph essay that explores this topic, but only use 2-3 sentences for the opening and closing so that it’s terse and punchy. Be opinionated.

And ChatGPT returns something that sort of makes sense? But reading the results more closely, it lacks examples and story-telling, and statistics or anything else. I don’t like Paragraph 3 here, since it misses an opportunity to talk about how much of YouTube is education purposes, for example. The writing is a bit too stiff and formal, and doesn’t match my own personal style.

In general, it’s just not that good.

But after staring at a page like this and finding all the flaws, I can then iterate pretty easily. I can put in a followup prompt to change out Paragraph 3 and connect it to a different point. Or I can just start editing it and rewriting it completely, and even if at the end 90% of it is different, it’s still better than starting with a blank page.

Brainstorming outlines, lists of topics and questions, and more
If ChatGPT is weak at actual writing, where I find it excels is as a non-judgmental brainstorming partner that doesn’t care if you have stupid ideas or if your writing is boring. It’s supremely useful for generating outlines of blog posts, making lists of topics, and creating a bunch of questions you can answer as inspiration, etc. In this use case, the AI doesn’t have to have a 100% hit rate on its content. If it creates lists and lists of content, but 20% inspires you in your actual work, then that’s a success.

For example, let’s say that you know you want to write a blog post about opportunities to build new apps in VR. You can ask something like:

I’m writing a blog post about new app ideas in VR. What are 15 questions that someone might ask me about this topic? Make them spicy, and if helpful, cite statistics to make the questions more interesting

It comes back with a mishmash of questions, some which are dumb and uninteresting — like the first, one, which is just asking what VR apps will be useful. But it also has a bunch of interesting ones on esports, content creation, corporate training, etc.

You could also imagine using this as a pretty helpful tool if you are making a new podcast and are interviewing guests. Or if you want to write a new essay that critiques some existing paradigm. (“Give me 10 questions from someone who is skeptical of of the value of AI safety”). And once you have an interesting question, it becomes easy to ask it to outline an entire blog post about it.

For example, I often find it useful to write prompts for outlines specifying specific topics and ideas.

Take the concept of K-12 education and VR – can you write an outline expanding on the promise and also skepticism around this idea? In particular touch on the concept of blending entertaining and gaming and learning, as one of the points. Create 6 sections, and put 3 sub-bullets under each. Use strong opinions and ask tough questions.

I’ll leave it to an exercise for the reader, but the result is not bad. It’s a decent starting point.

ChatGPT works great for outlines for long form writing also. And as many of you know, I wrote my book 3 years ago without the benefit of AI. The book is on network effects, and I ended up making an outline that acts in various stages, and uses examples from iconic tech companies. I can prompt ChatGPT with something like this:

Create an outline for a book on network effects for mobile apps and websites. Stage out the outline so that it starts on launching a product from zero, and the issues there, versus the stages of scaling the product, then hitting market saturation. Make sure you are able to weave in the issues facing network effects-driven products like social networks, marketplaces, dating apps, and collaboration tools. Write a 10 section book, with 3-5 sub-bullets. The first sub-bullet in each section should always be an example of an iconic tech company, like Uber, Airbnb, Dropbox, Tinder, Slack, or others. Please make the comprehensive outline for a book focused on network effects targeted at professionals working in the tech industry

The result actually isn’t bad. It’s not the way that I wrote it, since it starts to dive into each category of product rather than expanding on the broad concept. But again, it’s not a bad starting point. I could easily have imagined using this for brainstorming and prototyping purposes, iterating through different versions in describing the idea. It might have helped me work at more of a conceptual level in the early months, without getting dragged into the minutiae of writing all the various sentences.

Cleanup — at the beginning, and at the end
Finally, one of the tips I have for folks who are struggling with writing is to simply talk out their ideas aloud. Some of us are often more verbally wired, and we will connect disparate ideas and make interesting connections when asked a question, but flop when we’re just staring at a blank text. The voice feature in ChatGPT’s mobile app — and also products like Oasis AI — are interesting in that you can go stream of consciousness on a topic, and these products can clean them up significantly. The output isn’t that usable, IMHO, but you can then go and add/edit the result and develop it further.

It’s powerful when you combine this with AI-generated outlines. Just give ChatGPT a topic or better yet, an opinion (“open source products have bad consumer UX!”) and have it come up with an outline of the argument. Then start talking through the argument, and record it. Process it in one of the new AI apps, and then get it down to text. Then edit from there.

The other place where AI-driven cleanup can help is at the end, if you want to make sure you sound professional, or like an expert, and not an idiot. You can have ChatGPT rewrite content and incorporate a different tone. Or feed it some examples to cite and build off of. The ability to clean up ideas and textual content during and then after the creation flow is all pretty interesting.

There’s a bunch of things ChatGPT can’t yet do
I’m excited for all the ways that generative AI will help the writing process. I find edges to its capabilities that will soon be addressed, I’m sure. But here are some obvious ones that I notice all the time:

• I want it to have all the data, up to today (or in real-time), because I want it to use examples and cite numbers that are as recent as possible
• Some genAI tools can generate images, which is useful, since I want to create charts and figures, sketches of concepts, and even the little lead image that sits on top of each essay
• It’d be ideal if it was connected to Twitter and other social media platforms, so that you could tweet out content easily — writing tweetstorms via AI would be great!
• I want to train it on a corpus of my own writing, of course, and also writers that I respect. Right now it takes some work to customize the tone of voice to what I want
• There’s data, documents, and other info that I want to feed into ChatGPT, and then have it utilize the content as part of its arguments and suggestions. Imagine a routine task like, “hey, take this 10,000 word chapter and summarize it into a 100 entry tweetstorm” would be hugely helpful. Right now it can’t do that

There’s a lot more to go here, but it’s incredibly promising.

When I’m blogging these days, no wonder I find myself with WordPress open in one window, and then ChatGPT in the other. And I find myself spinning up new chats, copying and pasting back and forth. It’s the first time in 10+ years that I’ve found my workflow significantly change, and I’m excited to see what happens next.

There’s been a big wave of Creator Economy startups over the past few years, as the rise of social media platforms has empowered content creators to become a focal point for consumer engagement. This wave of startups promised creators that they could help them better monetize their audience on social media if they only promoted their products. We’ve all seen these — creators promote a startup’s new offering via a link in bio, or mentions in video or via links — and drive their followers to a landing page that enables some new interaction or functionality involving the creator. Initially, these started almost as “tip jars” but over the years, many, many creative products have been tried, spanning e-commerce to newsletters to Q&A, and more. These products all promised a win/win with creators so that when their fans spent money, the company would only take a % of earnings, usually something like 10% plus or minus.

There have been big successes, with some of these Creator Economy companies hitting billions of earnings paid to creators, while others have struggled. The successful creator startups are much more defensible than previously thought, and new entrants (often with splashy celebrity backing) have struggled to launch. Now that a few years have passed, what have we learned about the dynamics of this sector? Why have some Creator Economy startups worked and why have others lagged?

I have a few theories of the dynamics at play:

• The creator power law: A small, concentrated number of creators have all the audience, which makes Creator Economy startups potentially fragile and dependent
• Battle for the bio link: Creator economy companies acquire their audience from larger social media platforms that often just have one spot — the link in bio — to promote a single company. It’s a zero-sum game to overpower other companies
• The graduation problem: Startups often charge a take rate — % of bookings — and if the creator is acquiring own their customers and also doing the underlying work, they want to pressure you towards reducing costs. The biggest creators often “graduate” from a platform, building their own, and taking their revenue with them
• Algorithmic feast and famine: Creator traffic is driven by social feed algos, which lends itself to big spikes in traffic that appear and then go away — the opposite of the steady, durable growth that startups seek

These are all concepts that I’ve learned from meeting dozens of creator companies over the past few years. And as the next generation of Creator Economy startups emerges, these are some of the dynamics they’ll have to figure out how to navigate. Let’s jump in.

The creator power law
So you want to start a Creator Economy company? The biggest dynamic you have to master is the power law of audience and earnings within the creator class itself.

Here’s a graph that shows % that the top creator earns on a platform like Patron, versus the 2nd and 3rd and 4th creators, all the way down (credit: Power Laws in Culture). You can see there’s quite a dropoff:

Imagine if you graph this all the way out, to the many millions of creators on these platforms on the x-axis. You’d see that it eventually flattens just one tick above 0%. There’s a lot of reasons why this is the case, starting with the idea that these creator platforms build themselves on top of social media which themselves have well-documented power law distributions for followers and content engagement. In turn, social media platforms have power law curves because of algorithmic discovery, but a small number of social butterflies just know a lot more people than that.

Thus any creator economy product that builds on a social platform inherits these power law curves. OnlyFans creators offer free content on many social platforms that then drive traffic to their private landing pages. Below is a graph of creator earnings, which show a similar curve – via API scraping in the essay The Economics of OnlyFans – showing that while some creators earn up to $100,000/month, the median is closer to $180/month. A familiar curve emerges:

While power laws naturally emerge in social media platforms, that can’t be the only other explanation. The reason is that creative work — including TV, films, music, and more — generally follows a power law pattern. Here’s an example from TV, from the essay Power Laws in Culture (worth reading in its entirety):

A few hit shows get all the viewers. And if you look at video games, movies, fiction, directors, authors, and more:

There are a lot of things going on here that might explain the universality of this phenomenon, but one core issue is the uneven distribution of creative skills in the world. A top writer or film director is really just that much better than the 100th. You can look at research output, the distribution of so-called “10x engineers,” and patent filings, for a parallel universe of power laws as well.

So what does this mean for Creator Economy companies? Well, it means a few things:

• When Creator Economy companies first launch, the long-tail creators they initially attract are too small to be meaningful
• To hit scale, they need to attract the largest creators — the ones who are most likely to be distracted with many other projects and products
• And even once you have large creators on your platform, revenue is often quite concentrated into a small group — so that if they churn, the financial impact can be big and negative

These dynamics all mean that the initial phase of a startup’s launch can be perilous. The best companies can aggregate so many small creators that the numbers start to matter, or organically attract large/mid-sized players. If a startup finds itself manually DMing/acquiring/handholding many creators (read: high cost of acquisition and ongoing service), then that’s a sign that the product might not solve a big enough problem for things to happen on their own.

The battle for the bio link

Social media platforms like Instagram and TikTok have advertising business models, and as a result, they don’t want to give people *too much* organic traffic. Better they make you pay to sponsor posts, creators, and ads. One way they’ve done this has been to offer a single link for driving organic traffic — the infamous “link in bio” — that appears at the top of a profile.

This is insanely valuable real estate for Creator Economy startups. If you can convince a creator to place your startup into this link, then organic traffic will appear in your product. With some monetization mechanics in place, the startup takes its cut. And initially, it worked. Early in the Creator Economy cycle, startups were competing with non-monetizing links — either links to other social media profiles or personal websites. But as time went on, people began to fill their bio links with highly monetizing links to Patreon, Substack, Twitch, and otherwise — this is much fiercer competition.

It’s now a zero-sum battle to displace another startup’s link in bio. The only way to gain organic traffic from creator profiles is by monetizing better than other older, more proven competition. If you simply match what an incumbent might make you, then that’s not enough – it has to be significantly more. Or you have to find a different piece of real estate, whether that’s inside the creator content itself – whether that’s video, text, or otherwise. Either way, new entrants will find this a major barrier, and while they might be tempted to subsidize earnings with investor money initially, that may not be enough to reach a meaningful scale.

The graduation problem
The graduation problem is what happens when your best customers get big, and eventually “graduate” — taking themselves and their customers off of your platform. Why does this happen? Creators provide obvious value to startups — driving traffic, creating content, and monetizing their users — and that makes the Creator Economy model attractive. But work with creators long enough, and they often think to start to think it’s *too* attractive. They start to think, they’re doing all this work, what gives you the right to charge XX%? Why isn’t this a $99/month WordPress subscription, why do I have to pay a %? This is particularly problematic because of power law curves, where a small number of whales often dominate top-line revenue. If a whale starts to ask, couldn’t they replicate your product by hiring an agency and paying them to build a custom website, then there’s a huge temptation to drop take rates to accommodate them. They eventually are tempted to “graduate” from the platform, reaching sufficient scale to build their own platform.

Contrast this to marketplaces startups and the on-demand wave to which the Creator Economy is often compared. In that sector, a company like Airbnb or Uber aggregates both the supply and demand sides of the network independently. These 2-sided marketplaces work best when each side is highly fragmented, which is why the biggest outcomes have been consumer-to-consumer or consumer-to-SMB marketplaces, versus B2B. (More on this from an essay of mine from a few years back, What’s Next in Marketplaces). In their initial formation, Creator Economy startups look more like B2B networks or maybe even SaaS platforms — their customer bases (the creators) are highly concentrated, and the creators bring their consumers. No wonder the frustration.

To overcome the graduation problem, Creator Economy startups have to provide a significant amount more value than the utility of payments and other commoditized tech. They need to have a moat, not just for external companies but also for their own customers who are tempted to graduate over time. The best version of this is to create network effects on their own — by acquiring and cross-pollinating customers and bringing them to each creator, a 2-sided network forms, with all of its usual advantages. (I describe all these dynamics more in my recent book, The Cold Start Problem). The additional functionality that the startup creates should ideally be proprietary on its own. If an AI-enabled creator economy company develops a very good foundational model that allows creators to monetize 10x more than before, it’s unlikely the creator will ever leave.

Algorithmic feast and famine
Creator economy startups often find themselves highly dependent on the whims of social media platforms and on the hits-driven nature of viral content. If a video goes viral on TikTok, a big spike in user acquisition might ensue. But startups are always trying to grow steadily month by month, and unlike SEO or referral programs, or paid marketing, it’s hard to create a consistent march of 20% MoM growth. Compare this to marketplace startups, which add value by doing the work to aggregate each side of the market — often spending billions of dollars to build buyers and sellers. When I was at Uber, during the hypergrowth years, the annual performance marketing budget to acquire Uber riders was a billion, and the driver side was close to $2B, and that was diversified across SEO, brand marketing, paid, referral programs, partnerships, and otherwise. This added a ton of value since the two sides couldn’t connect otherwise.

Creator economy startups are different in that they use creators to find their customers, but in doing so, they are highly dependent on a single channel. A dependency on a single marketing channel is always dangerous, as we’ve seen in prior years where changes to SEO algorithms obliterated multiple generations of SEO-dependent content sites. A dependency on social media is even more fragile since the content is naturally more ephemeral and delicate. I think this is also one of the reasons why subscription (with upgrades) has become the dominant business model for successful Creator Economy companies — allowing creators to build a long-term, durable revenue stream from each follower is just much more stable than a transactional model. It’s just much easier to stack revenue over time this way.

Algorithmic feeds also play into a competitive factor. In recent years, we’ve also seen YouTube, Twitch, Twitter, and other underlying platforms try to go after directly paying creators themselves and playing a more vertically integrated role in the Creator Economy. As this happens, you could imagine a multitude of platform shenanigans where they try to hoard creator relationships at the expense of new startups.

The best solution here, of course, is to layer on additional marketing channels to drive predictability. Combine a spiky social media channel with steady retention, an inflow of traffic from referral, SEO, mobile installs, and otherwise, and the growth curve becomes much more durable. But in the early days of a Creator Economy startup, they’re often going all-in on social, and it’s only with success that they can choose to invest in the other channels.

The upside and the future
Creator Economy companies are going through their second and third generations of startups. The bar has gotten higher. Instead of providing functionality akin to fancy tip jars, startups are building full-blown products — supporting multiple platforms, new forms of interaction, and providing new functionality for creators to interact with their followers. These products will have network effects of their own, sometimes becoming destinations of their own. And instead of launching a product anchored by one celebrity and expecting it to succeed, instead, startups are building real technology — often involving AI — combined with a broad go-to-market strategy.

The upside of this sector is that mobile use, and thus social media platforms, continue to grow incredibly fast, taking time away from the hours that people used to spend watching TV:

A lot of this movement is of course driven by younger generations:

(btw, can you believe that most 18+ people still watch 4-5 hours of TV a day?)

The point is, social media continues to play a huge role, and creators are ultimately a new class of participants in the economy that continue to gain power in both cultural and economy terms. And the products and tooling they use to fulfill their goals will continue being attractive. This is especially true because in the end, creators don’t want to be dependent on one social platform themselves — if they are strong in video, they want to go to podcasting, and to have a huge Instagram. And startups can always seek to be friendlier to the creators than the mega-social platforms.

Thus, I argue that the future of the Creator Economy continues to be promising, but the approach has significantly evolved and the bar has been raised. Startups will need to provide new functionality, create new forms of monetization, and adopt new technologies that make them more defensible to competition and in-house efforts by creators to replace them. Personally, I’m much more interested in Creator Economy startups that are AI- or video-first, and act more like marketplaces in providing a highly managed solution to both sides. I’m more bullish about startups that know how to collect $1000 from a smaller niche of users — thus creating more value — rather than a tip jar model that collects $2 from everyone. In coming years we will see many more variations that will work, and given the underlying consumer trends, I’m bullish this will remain a source of highly valuable startups.

A simple question
The last few years have been crazy, and no wonder there’s a ton of folks thinking about making job changes right now. I know this since I’ve been getting the calls. Often the conversations open with a laundry list of different companies, roles, and compensation packages. Every opportunity is completely different and hard to compare. There’s got to be a better way to organize your thinking about these opportunities.

Here’s my favorite question to ask:

“What do you want to be your next next job? And why can’t you get it right now?”

And then, of course, you work backward from that. This is the “Next Next Job” framework for thinking about career moves, particularly in the highly chaotic situations that we find ourselves in today where there are many many opportunities across different industries and company stages. This reflects the very natural flow of the recruiting process, where recruiters and colleagues often make referrals across a wide swath of companies that are making. It’s always fun to talk through the various roles, but also it feels chaotic.

I know how it feels because, of course, I’ve faced this exact situation before.

The Next Next Job is an evaluation framework that I used myself many years ago, to make an important decision: As an early 30-something-year-old, at the tail end of a startup adventure that had gone awry, I had a big decision to make. A few months after putting my startup team/myself on the market, I was choosing between several very strong acquisition offers at pre-IPO startups. Each had its idiosyncratic benefits — some of the team cultures were a better fit for me than others and in others, I had a stronger connection to the founder. The packages were also very different. It was an emotional rollercoaster to meet dozens of companies over several months, and then need to choose amongst them.

It was tempting to pick based on a gut reaction, but I felt like there must be a better way. I sought a more analytical approach to augment the rollercoaster. I have tremendous gratitude to my close friend Bubba Murarka who coached me through all the conversations. Once the offers came in, he challenged me to stack rank the opportunities based on my “next next job” — almost a throwaway comment — but something that’s stuck.

How to answer a simple question
Let’s go back to the question – “what’s your next next job, and why can’t you get it today?” — it’s straightforward to ask, of course, but surprisingly hard to answer. Often we don’t know what we don’t know.

The first is that we often don’t know what our next next job might be — after all, if it’s unclear what the next job is, speculating about the next next job seems even more nebulous. Yet there’s an advantage here because you can make a few pretty big buckets of next next jobs. Or you can at least start to, based on what you know today.

It might look something like this:

• 50% – Become a startup investor
• 30% – Start another company
• 10% – Join a high-growth startup as a C-level exec
• 10% – Random stuff? (Switch into a new cutting-edge industry, become a blogger/writer, etc)

For someone who’s earlier in their career and the product management function, the goals might be more focused on becoming a first-time manager of PMs, becoming “employee 1” of a high-potential startup, or getting accepted into YCombinator, or something like that. Others might be thinking about transitioning from a non-tech role into a tech job or maybe going from a non-product role into becoming a PM/designer/eng.

Of course, sometimes it’s not obvious what other roles might be interesting or appealing — this in itself can be a useful thing to focus on when meeting with mentors and colleagues in the industry. But assuming you have some pretty big buckets to think about, the next step I’d encourage you to do is to pick the top 2-3 of these and do your research. Meet as many people as you can who have your next next job. What were their career paths? What did they need to accomplish before they could get the job? And you can ask them straight out — “what are the gaps in my skills that I need to fill, to get your type of role?” Keep asking questions and meeting people until the answers start to sound pretty similar, and the delta of new information decreases substantially.

Sometimes there’s a shortcut (and sometimes there’s not)
A funny thing sometimes emerges, particularly for people who rank “start a new company” as their next next job — it turns out they’re already qualified. Some of these jobs have high degrees of emotional baggage, because of Imposter Syndrome and not feeling ready. But the reality is, sometimes people over-prepare for a future job out of a deep sense of risk aversion. These are folks who are getting as credentialed and qualified as possible, rather than jumping in. These are the “wantrepreneurs” who are wasting their time getting multiple advanced degrees, working at all the top companies, and who are often very smart — but just can’t bring themselves to actually do something on their own. Usually, when this is one of my friends/colleagues, I try to talk them into taking the largest degree of risk possible :)

On the other hand, often the next next job isn’t attainable and it’s for good reasons. Maybe you’ve only worked at a series of failed startups, and you need a “shiny” role or two that helps add some credentialing. Or perhaps you’re in marketing and interested in becoming a PM but aren’t yet close enough to the engineers and the technical details. Perhaps you’ve never managed anyone, and want a role to demonstrate strong managerial ability before jumping into a team lead role. Identifying these gaps can help form the basis for evaluating potential job opportunities — which ones help fill them better and faster.

Gaps might encompass a number of things — skills, but also network, experiences, mentors, and ideas:

• What new skills do I need for my next next job?
• Is there a new network of people that would help me?
• Are there experiences that I need to demonstrate to land the next next job?
• Which mentors do I need, and how would I meet them?
• How do I get exposed to the ideas that might inspire me in the future?

Understanding these gaps are great, but that’s just playing offense. A “superpower” is often important, and there are superpowers that are so important that they overcome an imperfect set of gaps. I sometimes talk to folks who are interested to get into investing, and in the end, you can check off every skill on the list, but unless you have a specific superpower I care about — getting in the flow of new startups we’d be interested to meet and invest in — it doesn’t matter how good your analytical skills are, or that you attended fancy schools. If you have an incredible network of founders who seek you out, you can learn some of the other skills. For your industry and specialization, figure out the superpower that might trump everything else. Think offense (building up a superpower), not just defense (filling in gaps).

I want to give an example. For someone interested in investing as their next next job, and have substantial internal-facing roles at successful startups, I often find the list looks something like this:

• The next next job: Become a professional investor
• Gap: Need to develop a personal brand for other external-facing networks
• Gap: Haven’t done any angel investing
• Gap: Need to develop opinions on cutting-edge spaces
• Potential superpower: Get in the dealflow of recent spinouts/alumni of my prev companies

Again, this is just a hypothetical example – you can run your analysis and figure out what you need to do to close some gaps and develop a superpower. Of course, if you are tracking 2-3 options for next next jobs, you might find that a few gaps appear and re-appear. That’s great! This means the next role that helps you develop against that should be weighted more heavily.

Evaluating the grab bag of jobs
Now we can go back to the original question — “what do you think of XYZ as a company, and should I take this ABC role there?” The approach then becomes more obvious. First, you need more than one option. Go run a process, meet enough people, and look broadly at enough companies that you have different options to compare. Building up these options — while the opposite of instant gratification — will mean that you can truly make a good decision.

Then work backward on your options. Which ones are best at filling your gaps, and what will help you develop a superpower? Or if none of the options do much, then be patient. Develop more options. I think a lot of the reason why we often see a long series of 12-month stints on resumes is that we live in a world of instant gratification — whether that’s short videos for instant entertainment, on-demand food, groceries, cars, or online dating. But when it comes to making a big career decision that might commit you to work somewhere for (ideally) multiple years, the quality of the decision is important. Taking your time is key.

Of course, in the end, this big decision is very emotional. I really believe that. There are a ton of little things that go into getting excited about a new role: You’ll need to vibe with your manager, and you’ll want to like the work. But at the same time, having a bit of an analytical framework behind your decision will help. It might ultimately be 80% emotional and 20%, but I think that’s still better than 100% emotional rollercoaster.

To wrap up my story, almost ten years ago, I decided to go to Uber rather than the other very good options on the table. The background was that I knew it was likely I’d want to become an investor one day, and that pointed toward Uber as the right choice. The theory was that Uber would be a great place to meet future founders, would have problems at scale, and would be a very interesting place. And boy, was I right about that latter point :) It did OK on the other things I cared about — the scope of my role, the compensation package, etc — but I chose not to optimize for that. I knew it wasn’t my forever job. It was a stepping stone to the forever job I’d try to gain in startup investing, many years later.