What I Learned from Zero to One

A book on tech startups published 10 years ago
14 August 2023
6 min read

Zero to One is one of the books I wanted to read forever but never got around to it. Until a few weeks ago.

I assumed the title referred to building something from scratch. However, as the second paragraph in the book explains, 0 to 1 refers to inventing something new.

Published almost a decade ago in 2014 — the year that saw an iPhone app called Yo raise $1.5m in venture funding — the book focuses on projects with the typical Silicon Valley levels of ambition to alter the course of humanity (ideally with something that you can build in JavaScript). My goals for Writing Analytics are decidedly a few levels below that. Still, I got a bunch of great insights from the book.

Here's what I learned.

"What valuable company is nobody building?"

Great question. One or two people may have tried answering that one before. The authors make an interesting point, though:

This question is harder than it looks because your company could create a lot of value without becoming very valuable itself. Creating value is not enough—you also need to capture some of the value you create.

This includes established businesses serving millions of customers at a loss or with minimal profits. You can raise prices and cut costs, but won't that come at the expense of the value you provide?

Let's say you raise capital to sell iPhones at half the price. You're creating plenty of value for your customers. You have a great product-market fit. Growth will likely be off the charts (all word-of-mouth—never ran an ad in my life™). But as soon as you increase your prices, you'll be out of business because all the value you used to provide disappears.

The example above is silly, but it isn't that difficult to fall into a similar trap with a "real" business.

In the context of SaaS, misguided lifetime deals or unsustainable free tiers come to mind—particularly when incurring ongoing costs per-user (bandwidth, storage, etc). A generous free tier is a great way to grow, but people signing up for free stuff doesn't mean your paid product has a product-market fit.

You want a great product with a free tier, not great freebies with upsells that nobody cares about.

The Ideology of Competition

The authors have an interesting view of competition:

Competition means no profits for anybody, no meaningful differentiation, and a struggle for survival. So why do people believe that competition is healthy?

Being a monopoly does make your business easier to run, I guess. That's why people keep creating new ones. To me, the interesting point from this chapter was this:

Rivalry causes us to overemphasise old opportunities and slavishly copy what has worked in the past.

I can relate to that. Watching a competitor and deciding they're ahead because of this thing or that feature is a trap. You waste time copying their mistakes instead of doing what makes sense for your business.

Seeing the opportunity through the frame of you vs them limits your ability to build a better product by looking outside. Perhaps the correct answer is differentiating your product away and pivoting to a different market rather than fighting to the death.

Winning is better than losing, but everybody loses when the war isn't one worth fighting.

Last Mover Advantage

It's much better to be the last mover—that is to make the last great development in a specific market and enjoy years or even decades of monopoly profits. The way to do that is to dominate a small niche and scale up from there, toward your ambitious long-term vision.

Reading this in the context of the whirlwind around AI, web3 and VR, people are trying to get in early on the latest 1000x opportunity. But is being early in these cases a long-term advantage at all?

VR seems to be in a pretty deep trough of sorrow. Are there any real winners in the web3 space? And if the current predictions hold, you could replicate most of these AI companies soon by chatting with GPT5 for half an hour.

Perhaps the real winners are still yet to be founded, just in time to take advantage of the opportunity when Meta and Apple have poured trillions down the VR R&D drain; when the web3 regulation is finally settled; and when the current AI businesses are raising their Series G to carry them over until the models are finally good enough.

Follow the Money

It doesn't matter what you do as long as you do it well. That is completely false. It does matter what you do. You should focus relentlessly on something you're good at doing, but before that, you must think hard about whether it will be valuable in the future.

The above made me think about the value of software engineering as a vocation in the future. Engineers have been able to command incredible salaries—partly because of the market conditions but also because building software is a difficult and valuable.

We've been through the outsourcing scare. Now, we're going through the AI scare. It looks like the next few years will tell us how "predictable" our work is.

Software is a form of leverage. The value comes from applying it to a problem. Going back to the difference between value creation and value capture: the best way to ensure your long-term viability is to learn to apply some of the leverage yourself. Learn to find valuable problems that you can solve with software.


Startup messed up at its foundation cannot be fixed.

Agree with this 💯.

Companies are like countries in this way. Bad decisions made early on—if you choose the wrong partners or hire the wrong people, for example—are very hard to correct after they're made.

As the authors pointed out, changing the US Constitution now is nearly impossible. What went into it at the beginning turned out to be pretty important.

If You Build It, Will They Come?

Even though sales is everywhere, most people underrate its importance.

Count me in.

But customers will not come just because you build it. You have to make that happen, and it's harder than it looks.

We've all heard this one before, but perhaps you have to experience it yourself to understand what people mean.

I liked the authors' take on sales:

In engineering disciplines, a solution either works or it fails. You can evaluate someone else's work with relative ease, as surface appearances don't matter much. Sales is the opposite: an orchestrated campaign to change surface appearances without changing the underlying reality.

Maybe it's because I'm more familiar with engineering than sales, but this makes a lot of sense.

If anything, people overestimate the relative difficulty of science and engineering, because the challenges of those fields are obvious. What nerds miss is that it takes hard work to make sales look easy.

Anything that looks like selling is someone doing a poor job at selling.

Man and Machine

A chapter on AI, written in 2014—that should be interesting...

The most valuable businesses of the coming decades will be built by entrepreneurs who seek to empower people rather than try to make them obsolete.

Despite the prognostication of the past few months, I agree with the authors here. It comes down to whether you believe that the human brain is just a more advanced and insanely optimised version of a computer, or they are fundamentally different.

When a cheap laptop beats the smartest mathematicians at some tasks but even a supercomputer with 16,000 CPUs can't beat a child at others, you can tell that humans and computers are not just more or less powerful than each other—they're categorically different.

A great way to put it. My 18-month-old is just figuring out his first words, but he sure can recognise a dog when he sees one. I didn't have to train him on 50 million dog photos.

Even if you can model both the same way from systems theory perspective, it doesn't mean there has to be a pathway for one to simulate the other perfectly. My chair and I both have legs, but it doesn't mean I can make it walk.

As I said, this is a belief of mine. It could well be that the opposite is true. Perhaps the recent progress in superconductors makes quantum computers widely available and provides the paradigm shift that I believe is necessary.

As computers become more and more powerful, they won't be substitutes for humans; they'll be complements.

AI complementing humans is the only bet that makes sense at the moment. Even if it all leads to a capable general AI, the disruption of reaching singularity would be so widespread that it won't matter that you were early on it anyway. Lots of zeroes in your bank account may become meaningless during a massive upheaval.

Better technology in law, medicine, and education won't replace professionals; it will allow them to do even more.

I'm a little more sceptical here. AI is reshaping professions as we speak. While it certainly won't get rid of lawyers any time soon, it will, for sure, change how many will be needed to cover the current level of demand. Some markets might be able to absorb the surplus. In other markets, people will be out of a job or business.

Final Thoughts

Zero to One is an eclectic collection of notes on startups and building the future. The book lives up to its subtitle pretty well. The content ranges from rants about competition and green energy, macro economy and history tangents to lessons-learned and actionable advice.

I can't say I agree with the political views of the authors, but I found this book and the advice helpful. It's a pretty good read, even though you're not building a startup to change the world.

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