How to make good decisions

Judgement is the most important skill

As we enter an age of AI native startups, it’s easier than ever to build, which only increases the importance of good judgement and decisions.

Most startup advice focuses on speed of execution, but much less on what decisions matter, and how to make them well.

What decisions are important?

At Prolific, only a few important events and decisions really made the difference to success.

Resources, capital, talent, and risk should not be allocated evenly in startups, but concentrated where they can create exponential impact. The tricky bit is to see which decisions might have those asymmetric consequences, and make them well.

I’m a fan of this framework from Patrick Collison of Stripe.

The decisions which are important to consider carefully are

High Magnitude/Hard to Reverse

Easy to Reverse/High Magnitude

High Magnitude/Hard to Reverse decisions require the most robust decision making process.

Easy to Reverse/High Magnitude require a good level of confidence, and importantly a process for tracking and adjusting.

It’s also easy to miss whether decisions are reversible or not. Some choices feel like big commitments, but could be reversed with some friction, upset, or perhaps embarrassment. For example a building lease, supplier contract, or setting up a new team.

Some decisions feel trivial, but could become critically embedded, with 2nd order effects, such as technology choices for an MVP, or work from home guidance.

How to build an autonomous decision making culture

The decision making culture at your startup will influence this, who gets to make specific decisions, how democratic are they?

“No Rules Rules” which deals with the culture at Netflix, emphasizes giving employees the freedom to make decisions, coupled with the responsibility to act in the company's best interests

However this doesn’t really just mean there being "No Rules". This is where in my opinion, the book gets misinterpreted.

At Netflix there are certainly “cultural norms” - you are expected to optimise for certain outcomes, and to make decisions in certain ways - aren’t these implicit rules? If you don’t you’ll get a reminder of the “non-rules” through feedback, and performance review. Looser rules can only go in hand with strong accountability for results. At Netflix you’ll be let go for poor results of your decisions, a feature which too many leaders especially in the UK struggle with.

In building a decisions making culture, consider what kinds of decisions you are truly incentivising (re risk taking). Do you really reward your team taking smart risks and failing, or for playing it safe?

When to make a decision - Hunter S Thompson v Angela Merkel

There are 2 ways to think about delaying decisions:

“A man who procrastinates in his choosing will inevitably have his choice made for him by circumstance.” Hunter S Thompson.

Angela Merkel, the former German Chancellor, was known for delaying important choices until the last possible moment - "merkeln"

Kicking the can down the road can be effective, because a lot of problems will go away by themselves. Sometimes paths become obvious, or options reduce, making contentious decisions politically easier. But this is really a form of deciding which decisions are actually high impact, and which don’t matter. It can help with politics, but this shouldn't be your main concern.

Optionality should be maintained only when it’s valuable , and decisions should be made as soon as it’s possible to make them confidently - there should always be a bias towards speed and clear planning in startups.

Not deciding is often deciding because inaction will have consequences. Often apparent when not addressing cultural or performance issues.

Being “data driven”

I feel like leaders claiming to want to always be "data driven" covers up for lacking a rigorous framework and mental models, for how to make good decisions, and when data matters.

Early only Founders can often work around this due to their intuitive feel for what customers want. As you scale, you need to figure out what decisions matter (for many too much time is wasted on them - the bike shed fallacy), and how to make them.

People also often underate the opportunity cost of getting useful data, it might be the best way to make a decision, but waiting might not be worth it. Particularly early on, when your user numbers are tiny.

Wittgenstein's Ruler, a concept introduced by Taleb, states that when measuring something, you may actually be learning more about the measuring tool than the object being measured.

"Unless you have confidence in the ruler's reliability, if you use a ruler to measure a table you may also be using the table to measure the ruler" -  Nassim Nicholas Taleb (Fooled by Randomness)

Startup metrics and attribution models can act as unreliable rulers. When measuring business performance, the data might tell you more about your measurement biases than your actual business growth, so be wary of taking data at face value when making decisions.

If you want to get good at decisions and prediction you need to practice. Texas road gamblers would hone their skills by constantly making wagers and predictions on everything they saw, such as when is the next train coming, which fly will crawl up a window. You can do the same, and as we saw in the “teaching a horse to sing example” it’s useful to log key decisions, predictions, and outcomes in a decision log. To learn, for transparency, and as a tool in building an aligned decision making culture around risk, ethics and goals.

Methods and useful models for making decisions

In Principles Ray Dalio explores some interesting ways to formalise decision making frameworks in organisations.

For example "Believability-Weighted Decision-Making" suggests giving more weight to advice from people with proven track records in the specific area of decision-making. This approach helps filter out less reliable opinions and focuses on expertise.

You might find it impractical to implement such structured methods, but some key elements like controlling emotions, root cause analysis, weighting advice, measuring results, surfacing principles, long term visualisation, 2nd and 3rd order effect, and most importantly tracking results should be incorporated.

There are many useful mental models that can help with making decisions. Charlie Munger’s “Poor Charlies’ almanack” is a great exploration of some of these, and the biases we are prone to.

His "lollapalooza effect" concept (when several factors or forces work together in the same direction, creating a result that is more powerful than the sum of its individual parts) has become a favourite of mine in trying to explain, or cause large effects - and due to the power law, large effects are what you are looking for in startups.

There are many useful models highlighted here in particular I like to use first principles thinking, and inversions. What's your favourite?

Rules, razors, and heuristics can be helpful, but you need to be careful about leaning on them as a substitute for really thinking. I think they are best aligned to operational principles you can use in your team. For example you might have "if in doubt go with the fastest, least complex option" or whatever is appropriate to your team culture.

What startups can learn from Nassim Taleb

I was first introduced to Taleb’s “Fooled by Randomness” by a fellow poker player, poker being a excellent education in startup risk concepts.

The mix of probability concepts and ancient wisdom struck a chord with me. Taleb’s Incerto series, which expands on this theme, has had the biggest influence on my thinking of any book. It addresses the problem of how to live in an uncertain world, that we can’t predict, and contains many lessons that I think are valuable for startups.

Taleb himself is an abrasive character, and not everyone’s cup of tea. I am on the long list of people he’s blocked on twitter, although at some point I was forgiven. But there’s much to learn from contrarians, and “bullshit detectors” on the search for truth.

I know I irritated people by using terms like “ergodicity” at Prolific, but I think some ideas about risk of ruin, anti-fragility, asymmetric outcomes, and via negativa rubbed off on the culture. Below are some useful ideas from Taleb’s books for startups;

Should you join a startup or become a dentist?

Taleb presents dentistry as an exemplar of a stable profession that operates in what he calls "Mediocristan," where outcomes are more predictable and less subject to extreme randomness.

He contrasts this with professions in "Extremistan" like becoming a rock star, or joining a startup, where success is highly unpredictable and follows power law distributions.

You need to be aware of your exposure to risk in joining a startup, how ruinous would failure be for you? How do you feel about swapping predictable income for the possibility of an extreme outcome

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However, there is no reward without risk (if you don’t see the risk, look harder). Rewards without putting you own “skin in the game” are empty.

“The three most harmful addictions are heroin, carbohydrates, and a monthly salary.”  -  Nassim Nicholas Taleb

A monthly salary creates a dependency, and corporate life can become numbing, preventing you from taking risk and experimenting.

"What matters isn’t what a person has or doesn’t have; it is what he or she is afraid of losing." - Nassim Nicholas Taleb (Skin in the Game)

Taleb tells a fable in Skin in the Game of a wolf encountering a domestic dog, who enjoys regular meals, treats, and affection. Upon noticing the collar around the dog's neck, the wolf chooses to reject this comfortable life in favor of his freedom but “Freedom is never free” - there are real downsides and risks.

Taleb also emphasizes the importance of having direct exposure to risk "skin in the game". For startups, this might mean founders keeping significant equity, or ensuring all team members have meaningful ownership stakes.

Venture capital and ergodicitity

Taleb's concept of ergodicity focuses on the difference between individual outcomes over time versus group outcomes at a single point in time. This is a confusing sounding concept, but bear with me, as it’s very powerful.

N people gambling once in a casino (ensemble probability) is fundamentally different from one person gambling N times (time probability). In the latter case, the individual faces an eventual risk of ruin that stops the game entirely.

Non-ergodic systems contain absorption barriers - points of no return like ruin or death - where the process stops completely. Once these barriers are hit, there's no recovery or continuation.

Russian Roulette demonstrates non-ergodicity clearly: while a group of 100 people playing once has a 16.7% death rate, a single person playing multiple times eventually reaches certain death.

VC portfolios are non-ergodic systems, individual outcomes can significantly differ from ensemble averages. So your VC investor is playing a different game to you, and their incentives and risks, are different from yours.

For the VC, many failures are acceptable to maximise the potential payoff of the best bets. But for the individual startup failure is much more costly, the game ends.

Of course startup failures don't mean actual death, failure might be quite different for a 20 year old in SF, for an early employee etc. They are also aren't just about losing money, are you risking reputation, relationships, opportunities?

You can also think of capital allocation to asymmetric bets within the company as a similar “portfolio approach”. You can afford to have some bets fail in order to capture the largest outcomes.

Innovation advances by tinkering

Taleb argues that innovation, of knowledge and technology, primarily advances through "stochastic tinkering" rather than planned, top-down research. This approach involves making small experimental adjustments while remaining open to unexpected discoveries and serendipitous outcomes.

A pretty good analogy for a startup getting to product-market fit.

The overall innovation system, or capitalism, is good in so far as it harnesses these incentives, and rewards risk taking (not skills or hard work). To do this it must also allow for failure, avoid cronyism, and lack of "skin in the game" which leads of hiding risk and systemic failures.

We are all fooled by randomness, especially investors

"Mild success can be explainable by skills and labor. Wild success is attributable to variance” -  Nassim Nicholas Taleb (Fooled by Randomness)

Successful startups owe significant portions of their success to luck, though founders often create post-hoc narratives attributing their success to deliberate choices. This narrative fallacy leads to overconfidence and misunderstanding of what truly drives success.

"Remember that nobody accepts randomness in his own success, only his failure” - Nassim Nicholas Taleb (Fooled by Randomness)

Investors in startups are particularly fond of trying to “pattern match” when what worked before won’t necessarily work again. Startup founders should be sceptical of advice from successful entrepreneurs who may be exhibiting survivorship bias. This is why first principles thinking is so valuable.

As noted in my first post, the biggest mistake I saw from new hires coming into Prolific, was trying to apply the pattern of success from their previous startups, when it wasn’t appropriate to the context. And consistent hiring mistakes I made were overrating previous success in a different context. Now I think about Taleb’s surgeon paradox when hiring.

Antifragility

Antifragility is a property of systems that grow stronger and more capable when exposed to stressors, shocks, volatility, or disorder. Unlike robustness, which merely resists change, antifragile systems actively benefit from disruption and chaos.

Typically startups are harnessing an inflection point, perhaps a new market, platform shift, or even regulatory environment. Startups should seek to benefit from disruptions and market shifts, even those they haven’t yet predicted.

This might be through organisational design, strategy, business model, risk management or culture - all of which should seek to embrace change and adaptation.

As an aside here, Taleb notes the intangible benefits of network effects in big cities.

"Collect as many free nonlottery tickets (those with open-ended payoffs) as you can, and, once they start paying off, do not discard them. Work hard, not in grunt work, but in chasing such opportunities and maximizing exposure to them. This makes living in big cities invaluable because you increase the odds of serendipitous encounters - you gain exposure to the envelope of serendipity." -  Nassim Nicholas Taleb (The Black Swan)

This is why it’s easier to build a unicorn in SF than Oxford.

Whats the point of getting rich?

Nassim Taleb defines "fuck you money" as having enough wealth to gain independence without the burdens of excessive wealth. His key insight is that there's an optimal range - too little money makes you a slave to wages, while too much money makes you a slave to your net worth.

Too much wealth leads to a risk of preference capture:

“When people get rich, they shed their skin-in-the game driven experiential mechanism. They lose control of their preferences, substituting constructed preferences to their own, complicating their lives unnecessarily, triggering their own misery. And these are of course the preferences of those who want to sell them something.” -  Nassim Nicholas Taleb (Skin in the Game)

Beyond optimising for freedom of how to spend your time, what do you actually want to use money for? Beware of thinking you've developed a taste for Michelin star restaurants.

Via Negativa

Taleb explores the concept of via negativa (removing rather adding) both in knowledge and decision making, and with regard to complexity.

For example early stage products begin to lose coherence as you add more and more features in. Occasionally a reset, and deletion of features is needed, and improves the overall experience. Or as Gerry McGovern once put it “websites don’t poop”

The same goes for organisational complexity, it tends to build up over time to fit new needs, but more rarely are processes or redundant teams removed or reassigned.

Learning to say no to almost everything is a key to effective focus, a defining factor of success in startups. Steve Jobs famously said that innovation comes from saying no to 1,000 things, taking pride in the things Apple didn't do as much as what they did do.

Via negative is also connected to my favourite mental model - inversion. Avoiding stupidity is easier than seeking brilliance.

Wittgenstein’s ruler

Wittgenstein's Ruler, a concept introduced by Taleb, states that when measuring something, you may actually be learning more about the measuring tool than the object being measured.

"Unless you have confidence in the ruler's reliability, if you use a ruler to measure a table you may also be using the table to measure the ruler" -  Nassim Nicholas Taleb (Fooled by Randomness)

When venture capitalists reject startups, their feedback often reveals more about their own investment biases and past experiences than the actual potential of the startup.

Startup metrics and attribution models can act as unreliable rulers. When measuring business performance, the data might tell you more about your measurement biases than your actual business growth, so be wary of taking data at face value when making decisions.

Lessons I learned at Prolific - part 2

This is my second post on lessons I learned at Prolific. This time it explores, decision making, the factors that drive success, scaling challenges, and risk.

Some of these will be topics to dive into in further posts, let me know what you find interesting, or would like to hear more about.

At some point there might be a part 3 around hiring, people, and growing teams. and culture.

Business models are as important as product

Sometimes you hear people say “ideas are worthless, execution is everything” - this is wrong.

Execution is important, but succeeding in a startup, is a combination of fast execution, good decision making and good ideas. The two skills are in tension, the best founders need to sit at the right place on the spectrum for execution versus smart decisions. And the right place to be depends on the company and context.

Another phrase you hear is “first time founders think about product, second time founders think about distribution”. I might also tack onto this “investors think about differentiation and defensibility”.

YCombinator is heavily focused on executing on the product fast - “talk to your customers, build something they want, keep shipping fast.”

It makes sense, because this comes first in the process, without some kind of product-market fit, you are going nowhere. Their risk profile is also different, they are investing in huge batches of companies. Markets and models can be figured out later to capture whatever momentum emerges.

You’ll hear a lot less discussion about pricing or business models at YC. But business models can be powerful from the start, and getting them right can be the highest impact decisions you will make as a startup.


I mentioned in Lessons part 1 - the powerful growth driver for Prolific was the network effects of the marketplace platform. Because we focused on individual researchers rather than institutions first, we had the ability for customers to use the platform without any sales process. We continued to making tweaks to the way the pricing and revenue model worked as we grew, with big compounding effects. In combination this allowed us to grow entirely from revenue, in the early years.

Most early stage startups are not very defensible. Does it matter?

At the start it’s often very unclear how a startup might become defensible, or how it’s different from others.

Many forms of defensibility arise from making good choices about the business model. Examples are network effects, platform effects, partner integrations, regulatory, sales/services lockin, proprietary data etc.

Simply continuing to move fast is a form of defensibility and differentiation in itself. and in the unpredictable future market of new AI startups, it might be the only approach you have. Still you want to be wary of rapidly building a commoditised, low margin business, or you’re not going to like where you end up.

One way to start ahead is to begin in an unattractive (perceived as small) market, like academic research data in 2014. Niche markets can be off putting, but gaining strong product-market fit in a niche, can often be a great springboard to wider adjoining markets.

Few things matter a lot

Only a few important events and decisions really made the difference to success, surrounded by a lot of noise. This was hard to see at the time, but hopefully these posts might help you spot some of those inflection points ahead.

Outcomes for startups follow a power law distribution.

The power law fundamentally shapes how startup founders and investors should approach decision-making.

Resources, capital, talent, and risk should not be allocated evenly in startups, but concentrated where they can create exponential impact.

The tricky bit is to see which decisions might have asymmetric consequences, and make them well.

I recommend reading the Black Swan and spending a lot of time at underground poker games. You can also practice using a decision making framework like this one favoured by Patrick Collison to learn what decisions might fit this profile. If you develop any edge at decision making, then making more good decisions will move you ahead of the competition.

Learn to get a sense of the few decisions that need a lot of time spent on them, and avoid getting bogged down by those that don’t.

At Prolific, among thousands of choices, only a few made the difference to direction of the company. - about business model, product features, which markets to focus on (embracing AI), how to fund the business (bootstrapping then YC, and later a big series A).

Unfortunately what tends to happen is the opposite, in what’s know as “the bike shed fallacy”

"A nuclear power plant proposal receives minimal discussion and quick approval because of its complexity. A simple bike shed proposal triggers lengthy debates about materials, colors, and design since want to contribute opinions"

Time spent discussing an issue becomes inversely proportional to its monetary value or importance

The decision making culture at your startup will influence this, who gets to make specific decisions, how democratic are they?

You don’t have to get very big before the answers to this are not clear. You’ll experience a subtle shift as you go from a tight small initial team, where a lot of strategic alignment is implicit (around ~20) and some hierarchical structure begins to emerge. For any RACI framework there will also be subtle cultural political framework of consensus and alignment. Startups can feel like they’ve run into mud at this point.

Pain can't hurt you, but what you can't feel might kill you.

A lot of things that are on fire should be ignored for the few things that matter. People will get very upset about some of this, but you need to remain focused. You also need to keep realigning your team's focus, while building a strong decision making culture.

Are you even seeing the decisions that are implicitly being made, and drifted into, or are you intentionally surfacing them? One practice we introduced at Prolific was intentional decision logs for major decisions.

Most people are familiar with the concept of technical debt, but startups also accumulate organisational debt.

You need to decide which fires need to be left to burn. You are going to have to get used to the discomfort of ignoring most non-existential risk yourself. You will also have to convince your team it’s fine, to keep them executing. Otherwise the lack of confidence and alignment can itself lead to chronic culture problems.

As companies grow, and more experienced people join, there’s also a natural growth of process, and formality. Unless you continually assess what’s actually helping at the current stage of growth, there’s a danger this turns to bureaucracy and stagnation, that will be fatal. It’s a dangerous siren song, because at the time if feels like what a “grown up” company run by experts should be doing, and hence a sign of success.

Brian Chesky described something similar in his now famous Founder Mode talk.

The reaction to this, where a Founder seizes more direct control, for a hard reset, can also lead to a painful cycle of destructive evolution.

Early decisions can live with you a long time, and have severe consequences. Some are inevitable and conscious, some create invisible fragility, some can be avoided.

Technical shortcuts and manual processes might be the only viable way to get a product in front of your initial customers. “Doing things that don’t scale” is rational as you work towards product-market fit.

Hiring decisions, and cultural shifts (intentional or not) will stay with you and echo on much longer than you might think. The effects get bigger the larger your company grows, something I’ll explore in a later post. You need to make sure your team aren’t learning the wrong lesson, and continuing to “fight the last war” when a new threat lies ahead.

Company formation and governance decisions can have long term consequences that may even become existential. Look at the challenges OpenAI is having fixing these right now.

Where to set up your company, how to arrange your cap table, and shareholder/board agreements are critical. This may not seem very relevant when starting out, and isn't always predictable. This is an area where you need a really strong reason if you choose to diverge from standardised mainstream approaches. Standard legal structures have evolved through an process that killed other startups, so take advantage of their lessons.

Lessons I learned at Prolific - part 1

After some time to reflect on my experiences at Prolific, I wanted to write about some of my reflections on building companies. Partly to help myself think this through, and to share some of the things I learned that might be useful for others.

These are some of the biggest lessons, that have stuck in my mind from Prolific. One is that only a few really important events and decisions really made the difference to success, surrounded by a lot of noise. This was hard to see at the time, but hopefully this might help you spot some of those inflection points ahead.

I think this topic is going to turn into 2 or 3 posts, and then I’d like to dive into some of these areas and others in more details

Let me know what’s interesting here that you’d like to hear more about

Why join a startup?

Why are you doing this in the first place? Founding or joining an early stage startup isn’t for everyone. If things go well it’s going to be HARD at times, and particularly if you are the Founder you are going to have to stick through those periods.

So you should know what you want to get out of it. I think the best reasons to do it are, to have a positive impact on the world by building something of value to others, and have a lot of learning and growth experiences doing it. These will come much faster than in a conventional role.

Even if you didn’t get into it for money and status, when things start going well it’s easy to get distracted by those things, and lose sight of why you were doing it. You need to consider when you’re no longer getting the right things out of the experience.

When I joined, I remember telling one of the founders Prolific, that I enjoyed extreme experiences. Little did I know what the years ahead would bring…

The risk/reward profile of joining as an early employee versus founding is different. Honestly, it’s probably not a good deal as an early employee, unless you are getting something other than money from the experience, or you’ve joined a guaranteed rocket ship.

So it’s best that you care about the mission of what you are building, or you are looking to learn and develop fast. Early stage startups need missionaries over mercenaries, later on as you scale and professionalise, you need mercenaries, but these require a different style of management and culture. If you are early in your career you are going to get a lot of chances to learn fast, and take on more responsibility quickly.

There'S no one way to succeed

Peter Thiel put a twist on Anna Karenina - "All happy companies are different: each one earns a monopoly by solving a unique problem. All failed companies are the same: they failed to escape competition.”

There are lots of ways to succeed, because successful startups by their nature have done something different from everyone else. There are common patterns in successful startups, but looking at these can be misleading, you don’t need to be in SF, have dropped out of Stanford, raise venture capital or appear in Tech Crunch to win.

These constraints can power your differentiation and success in important ways. Maybe you’ll learn to be capital efficient, get really good at hiring outside major centres, or solve hard regulated markets - “The obstacle is the way”.

Prolific was an unusual business, initially focused on academia as a market, bootstrapped for a long time (2014-2019 before going into YCombinator), based in Oxford, then 100% remote. Bootstrapping taught us capital efficiency that became desirable after the ZIRP era collapsed in 2022, but it took some time to adjust to being a venture backed business and learning to deploy capital well faster.

These more wandering paths to success seem common for successful non-US companies, where you can’t usually follow the VC funded hypergrowth path to winning.

The key is to stay alive long enough to get the opportunities (like staying in a poker tournament, until you eventually get dealt some good hands). As Paul Graham writes “Startups rarely die in mid keystroke. So keep typing!”

At Prolific we stuck around long enough, for the AI market opportunity to come into existence, fit the core product value we’d built for academia, and drive our next phase of growth.

On differentiation, it’s important to to decide what you believe different from other people, and when you should differentiate. Most of the time you should probably follow the market best practice, and not waste time re-inventing things, except for the few critical times you should do something unique.

The biggest mistake I saw from new hires coming into Prolific, was trying to apply the pattern of success from their previous startups, when it wasn’t appropriate to the context. And consistent hiring mistakes I made were overrating previous success in a different context. Now I think about Taleb’s surgeon paradox when hiring.

Network effects are powerful

What do I mean by network effects?

Network effects occur when a product or service becomes more valuable as more people use it. For startups, this creates a powerful growth engine where each new user increases the value for all existing users.

Network effects are so powerful that companies with strong network effects can do almost everything wrong and still win. Twitter is a great example, throughout most of its existence management has done almost everything wrong, and yet how many times have you seen users say they are leaving to Threads, Mastodon, Bluesky etc. none of these can begin to compete with Twitter’s network effects. As Mark Zuckerberg said "Twitter is such a mess – it's as if they drove a clown car into a gold mine and fell in”

2 types of network effects were critical at Prolific.

The network effects on the platform that drove growth

All kinds of positive network effects exist for Prolific as a marketplace platform; More participants, more data, more researchers, more studies, better data quality etc. all interact in positive feedback loops.

We spent a lot of time thinking about how to optimise these loops early on, creating very powerful self driving growth dynamics.

The network effects of ecosystems, we leveraged as a business

Joining YCombinator was the key acceleration moment for Prolific. YCombinator itself benefits from many network effects in its community of startups, leading to its position as dominant accelerator, in a power law effect.

It is much easier to build massive high growth startups as part of the biggest cluster, SF than it is elsewhere. I slightly regret not shifting more focus to the US in 2019, and building a US team then. I believe this would have allowed us to grow much faster in the US market, getting further ahead of competitors, being better positioned for the AI market opening up etc. I was very excited that we managed to open our NYC office before I left Prolific in 2024. There are many intangible benefits from being part of a bigger network that are hard to quantify, but compound towards success.

Some of the constraints we experienced building the company from the UK made us stronger, particularly working through challenges to succeed in European markets. While I’m an optimist about the UK and EU building more effective startup clusters, right now they are a long way behind the US, and for those that want to build huge, truly world changing companies, the US is still the place to be.

Teaching a horse to sing

Sometimes situations seem completely unfixable, but you’re still not dead. All startups experience these existential moments early on. During one such period at Prolific, I told the leadership team a story about teaching a horse to sign (which I’ve heard ascribed to Herodotus, although that’s probably not true).

”A condemned man who buys himself time through an impossible promise when sentenced to death by a king. To save himself, he promises to teach the king's favorite horse to sing within one year.

When his fellow prisoners mock him for making such an impossible promise, he responds - "I have a year now that I didn't have before. And a lot of things can happen in a year. The king might die. The horse might die. I might die."”

The point is that context changes. Your situation might truly be impossible now, but your market could change, your competitors could go bust, new technology could come along, just don’t die, and new solutions and opportunities may appear.

People really like stories, they are a very powerful communication and motivational tool. But we also broke the situation down with some first principles approaches.

We built a simple predictive model for the different possible outcomes for the company with a % assigned to each. I’m a believer in focusing on the optimal path first, even if least likely and trying to understand what needs to happen on the critical path to get there, and working to fix those blockers.

Situations like this can be allowed to drift on dangerously, until the situation worsens, so it’s also important to set checkpoints, to adjust your model and path analysis. Eventually things can change, and perhaps a horse can learn to sing.