The value of a contrarian startup hypothesis
You start a startup when you believe you can find a viable solution to a valuable enough problem for a big enough market. Whether defined explicitly or not, behind every startup is a hypothesis like this. Some startup hypotheses are low risk because they contain confident assumptions. When a startup contains high-risk assumptions, believing in the undergirding theory is challenging. If your target market is tough to define and access, perhaps because nobody has targeted this market before, it won’t be easy to sell to them. If the problems they face are challenging to identify and understand, finding the most crucial problem to solve will be difficult. If novel, untested, and poorly understood technologies are required to solve your chosen problem, there is a high chance that you won’t be able to deliver a solution.
|Conventional hypothesis||Contrarian hypothesis|
|Market||Easy to access.||Tough to define and access.|
|Problem||Obvious and common.||Emerging or difficult to pinpoint.|
|Solution||Obvious and achievable with off-the-shelf tech.||Requires novel technology.|
|Competition||Crowded markets; easy to copy.||Nascent markets; difficult to copy.|
|Payoff if correct||Mediocre.||Extreme.|
The less risky a startup strategy is, the more copyable it is. So, low-risk markets are crowded. Whether it’s the problem you tackle, the solution you offer, the market you target, or the technology you build, building a startup around a contrarian worldview will make your strategy look crazy to outsiders. They will think you are wasting your time; nobody will buy your product. For many startups, the outside perspective is correct, and they fail. But startups when these startups succeed, they succeed big. Peter Thiel claims that all great startups are founded around a secret:
Every great business is built around a secret hidden from the outside. A great company is a conspiracy to change the world; when you share your secret, the recipient becomes a fellow conspirator.
There are two kinds of secrets: those about nature and those about people. Natural secrets involve science, and their discovery can lead to important technological breakthroughs. Secrets about people are different – they involve things that people don’t know about themselves or what they want.
Startups with big secrets
Airbnb is the perfect example of a startup built on a seemingly ridiculous hypothesis. The “secret” hypothesis behind Airbnb is the realisation that people are willing to rent out their spare rooms or homes to strangers and that travellers are happy to stay in a strangers’ home. The founders of Airbnb discovered that there was untapped value in people’s unused spaces and that the conventional wisdom that people wouldn’t want to lodge in a stranger’s home was incorrect.
Of course, the idea behind Airbnb seemed ridiculous until it didn’t. And now, it is ubiquitous. Companies like Airbnb can thank their contrarianism for the following:
- More runway before others try to copy them. If people are skeptical of a business, they are unlikely to copy it. This runway can give startups time to achieve an advantageous lead in the market. If, like Airbnb, a company benefits from network effects, this lead will be especially tough to disrupt.
- Defensible technical or commercial moats. If a business is difficult to build because the market is hard to crack or depends on novel technology, those who copy it will have a hard time1.
- Surprisingly large addressable markets. When a startup enters an existing market, it is relatively easy to see how much opportunity there is. When one enters a previously neglected or undefined market, the market could become much larger than expected. Airbnb and Uber both had this2. Investors and analysts quantified their total opportunity based on the existing hotel and taxi markets, respectively. But, by reinventing how these jobs to be done could be achieved, both companies tackled untapped demand more than existing demand. This market expansion is why taxis and hotels still thrive in many markets.
- Impact on the world is more dramatic and unclear. When a startup optimises an existing solution, it incrementally improves productivity. When it deploys a truly novel technology, it creates new economic activity that can dramatically change business models and consumer behaviour3. The more a startup changes the world, the more value it can capture4.
Conventional wisdom machines
Information has rapidly become cheaper to distribute, store, and consume thanks to the printing press, the telegram, radio, television, cable television, the internet, search engines, and social media. The implications of these technologies have driven many of the major world events in modern history. Today, information costs are effectively zero. You can download the entirety of Wikipedia onto your smartphone. You can even run a large-language model on your smartphone if you’re savvy.
Now that the world’s information is at our fingertips, it is incredibly easy to become acquainted with conventional wisdom on any topic. Wikipedia, where users aggregate and summarise the contents of the internet, was the first great conventional wisdom machine; ChatGPT is the ultimate one5. It can essentially generate a Wikipedia entry for any topic you want, even if the existing content on the internet hasn’t presented knowledge in the way you’ve requested before. Wikipedia is artisanal ChatGPT — a quaint attempt to manually create what LLMs can create automatically.
These tools are helpful because, most of the time, conventional wisdom is what you want. When seeking medical or legal advice, the best answer is usually the generally accepted answer. General expertise is now free.
Value of differentiated thinking
When you build a startup, you need more than just conventional wisdom. Conventional wisdom never contains those secrets that lead to significant innovations.
When most of the world was information poor, you could create big businesses by copying other people in other locations. An entrepreneur inspired by the New York Times could copy the New York Times without competing with it by simply launching in new geographies. This duplication was done by leveraging privileged access to conventional wisdom. Now, everybody has access to this knowledge. As the internet destroys space and time, many companies are digital, and digital companies are increasingly global because customers are easy to access from anywhere.
The globalisation and democratisation of knowledge and markets, coupled with AI Copilots, means divergent thinking is more valuable than ever. When everyone can be an expert on the conventional view of any topic, ideas that go against convention (and turn out to be correct) are even more advantageous. When anyone can ask an AI for the best way to achieve something, people with a better answer are precious. Most contrarians are wrong, but when they’re right, they’re rich.
There are a few implications for this:
- Differentiated data is more valuable than ever. If your startup has privileged access to knowledge that commodity AI tools have not trained on, you can leverage this data to make correct contrarian decisions. A model is only as good as its data set, after all.
- Specialisation is once again wise for startups and individuals alike. AI tools empower everyone to be a generalist at nearly everything. Specialists are more likely to know what the consensus gets wrong.
- Even correct contrarians should embrace standard practices, and therefore AI tools. You differentiate your startup by doing things differently in the places that matter. But not everything matters equally. Sometimes, convention will suffice (especially if it is cheap).
- AI tools are a great way to strengthen contrarian arguments. The better you understand consensus, the better you can challenge it. ChatGPT is currently the best tool in the world for understanding conventional wisdom. Innovators should learn about conventions to find opportunities to go against the grain.
“All models are wrong, but some are useful.”
— George Box
The current generation of AI tools cannot run every aspect of your startup. But in areas where conventions are good enough, AI can run the show. In areas where differentiation is required, AI will remain a useful tool, but defiance of the convention should be the goal. One day, AI models might be advanced enough to independently uncover the secrets that begat great businesses. Until then, they have only increased the value of the most important natural resource for innovation: secrets.
This can backfire if you fail to recruit the best technical talent for your solution and competitors build even better technology. ↩︎
Many Airbnb and Uber use cases do not overlap with hotel or taxi use cases. This untapped demand is why introducing these products expanded the overall market. Even if the founders and investors believe their goal is to disrupt incumbents, truly novel technologies often create new markets more than they disrupt what exists. ↩︎
Another way of saying this: some technologies disrupt incumbents, while others disrupt whole industries and society. ↩︎
Many startups change the world but leave others to capture the value they create. ARM, for example, is a world-changing company in the semiconductor industry. But most of the value they’ve created has been captured by TSMC and Apple. With a better pricing strategy, they could be a more successful business. ↩︎
This is why articles written entirely by ChatGPT are not very compelling yet. They regurgitate information that readers can find elsewhere. For some purposes, that might be good enough, but differentiation is too valuable for many. ↩︎
16 April, 2023
Subscribe for updates
Subscribe for weekly advice covering product strategy, development operations, building teams and more.
Privacy and terms
I care about privacy as much as you do. I will only use your email address to send you this newsletter or to reach out to you directly, and you can unsubscribe at any time. I will not share, sell, or rent your email address to any third party, though I do store it the software I use to dispatch emails.
The information provided on this blog is for informational purposes only and should not be considered investment advice. The content on this blog is not a substitute for professional financial advice. The views and opinions expressed on this blog are solely those of the author and do not necessarily reflect the views of other organizations. The author makes no representations as to the accuracy, completeness, currentness, suitability, or validity of any information on this blog and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its use. The author may hold positions in the companies or products discussed on this blog. Always conduct your own research and consult a financial advisor before making any investment decisions.