It’s fashionable in certain quarters now to slate some of the billion dollar startups that have been created recently and the investors that helped them get there. Zebras Fix What Unicorns Break is a good example. The piece makes three criticisms of the status quo:
- Pursuit of extreme growth results in companies with unpleasant characteristics and a negative impact on society – e.g. Facebook (fake news) and Uber (where do I start…)
- Companies with pure for-profit motives aren’t well equipped to solve many of society’s most pressing problems – e.g. homelessness in San Francisco, education, healthcare
- Companies that aren’t chasing unicorn status find it hard to raise money
There’s some merit in these arguments, but they need to be put into context.
- There is clearly dysfunction in chasing growth at all costs – inherently unprofitable companies grow to employ thousands of people before going bust, resulting in much personal anguish and not a little wasted capital. However, that’s a cyclical dysfunction which hit notable peaks in 2000 and 2015 and which needs to be understood as an unfortunate part of a larger system which overall has been an incredibly positive force for good. Five of the six largest companies in the world today were venture backed startups and just about all net new job creation comes from young companies.
- It’s also true that many of society’s deepest problems aren’t likely to be solved by for-profit companies. That’s because there’s no money in solving them (otherwise the market would have been solved already). What we need here is government intervention.
- The startup community has taken the ‘go big or go home’ mantra so much to heart that good mid-level outcomes – including exits in the hundreds of millions – aren’t seen as sufficiently ambitious. There are structural reasons why we’ve ended up here. As Fred Destin explained in his recent post Why VC’s are obsessed with large outcomes, investors with large funds have to chase unicorns to make their numbers work. Those large funds are often the ones everyone wants on their cap table and so almost everyone in the food chain, from smaller funds to angel investors and entrepreneurs alike, orientates themselves around giving those larger investors what they want, with the result that companies without unicorn potential find it disproportionately harder to raise money. That’s not a good thing.
So what should we do?
- Recognise that the system is imperfect, but not broken. We need massively successful companies like Facebook, and even Uber to generate growth, employment and the profits needed in the venture industry to finance the next generation of companies. Some unicorns are bad, but lots are good. Some investors back unsustainable growth in pursuit of short term profit (often unknowingly) but most are sensible.
- Celebrate mid-level outcomes as much as massive outcomes. Or at least almost as much. For me companies that exit for $200m are as noteworthy as many of the companies that raise money with a $1bn valuation, and often the lessons they’ve learned are more widely applicable than lessons from companies in the unicorn club. Talking about their stories more would help shift some of the dialogue and mindset in the startup community away from the needs of larger funds, towards the middle of the bell curve where most founders exist.
It’s common for VCs to look at the market size for a potential investment from a top down and bottom up perspective. The top down perspective takes market research, often from an analyst firm or investment bank and the bottom up approach works by multiplying the number of customers by their likely spend – more detail in my old blog post here.
What I hadn’t thought of until recently is that it’s also helpful to take a top down and bottom up approach to assessing likely demand for a product.
The top down approach looks at how a startup fits with prevailing big picture trends. At the time of writing AI is the trend of the moment and it’s a good starting point to think that companies which intelligently apply AI techniques can create useful products. Moreover, it’s also true that raising money is easier for companies that are on trend (investors love a herd… or at least most of them do!).
However, the top down approach isn’t sufficient on it’s own. Even though it sometimes seems like companies doing AI for XYZ seem to be raising money almost as easily as companies doing Uber for ABC were a couple of years back, this strategy is unlikely to yield much success for either founders or investors.
To make good investments it’s important to combine the top down approach with a bottom up approach which looks at use cases. If it’s difficult to convincingly explain how someone will use a company’s product, it’s a fair bet that they will find it difficult to get customers. I’m consistently surprised how often entrepreneurs allow themselves to be satisfied with only a vague understanding of why they will make people excited.
When looking from the bottom up, a good first question to ask is ‘what behaviour potential customers are already exhibiting which shows that they will have demand?’ For young software companies a classic answer to this questions is that potential customers are building homegrown versions of the product they intend to build. If our young software company can build a software product that’s better and cheaper than the homegrown version then it’s a fair bet these companies will stop writing their own code and become paying customers.
A second technique is to employ Clayton Christensen’s ‘jobs to be done’ framework which starts from the insight that customers buy things because they have jobs they want to get done. Jobs can vary from the mundane (e.g. cutting the grass) to the exotic (e.g. become my better self) and companies that can articulate a good fit with a job that lots of us have to do or want to do are in with a good shout of selling lots of product. There’s more detail on the jobs to be done framework here.
For infrastructure companies the use cases are often not end user use cases. Rather the use cases are to help other companies build use case for the ultimate end user. For example a company that makes electric motors might sell to a lawnmower manufacturer who’s job to be done is to sell more lawnmowers. The electric motor opportunity can then be evaluated on the basis of whether it will allow the lawnmower manufacturer to help its customers (the end user) with their job of cutting the grass.
As with market size analysis the bottom up approach is harder to do well, but yields much richer insight.
We have been thinking about how to evolve our investment strategy recently. I will write about the full process when we’re done and I’ve got a better sense of which bits have worked and which haven’t, but for now I want to highlight a post by another VC which highlights a lot of the methods we like to use when thinking about the attractiveness of potential focus areas.
The post was written by Bradford Cross, partner at Data Collective. Superficially it’s a listicle with Five AI Startup Predictions for 2017, but you don’t have to read very long before finding some good structured analysis and original thinking.
It turns out that four or Bradford’s five predictions are about things that won’t work and one about something that will work. Each of his points has generalisable lessons that can be applied to analysis of any potential investment sector.
- Bots go bust – main reasons: bot interactions are utilitarian and don’t meet our emotional needs, and for most use cases they are less efficient than other UI paradigms (e.g. apps and menus – note that Facebook has just added menu features to Messenger).
- Deep learning goes commodity – main reason: the number of grad students with deep learning skills has mushroomed and the premium paid for deep learning acqui-hires will fall because companies now other options for bringing in talent.
- AI is cleantech 2.0 for VCs – main reason: cleantech failed as an investment category because it’s a cross-cutting societal concern with a self important save-the-world mentality and not a market. AI has similarities, albeit the self-important element is about forming ethics committees and saving the world from the fruits of it’s own labour – super intelligences that destroy humanity and robots that take all our jobs.
- Machine-learning as-a-service dies a death – main reason: machine learning APIs are two dumb for AI experts and too difficult for AI novices. They don’t have a market.
- Full stack vertical AI startups actually work – main reason: low level task based AI gets commoditised quickly whereas vertical AI plays solve full-stack industry problems with subject matter expertise and unique data which make them defensible.
The generalisable lessons here are:
- Use cases are paramount to good investing (ref points 1, 3 and 4). Bots are failing because they don’t solve any new use cases and are worse at their job than other options. Horizontally focused investment themes are tough because they don’t start with use cases. Machine learning APIs aren’t solving a problem for anyone. Good candidates for investment focus areas have easy to understand use cases – e.g. I buy from ecommerce companies because it’s more convenient and the range is better.
- Valuable businesses have strong barriers to entry (ref points 2, 3, 4 and 5). Deep learning, and AI more generally, got hot in part because talent was scarce. This reached the point where $m per PhD was talked about as an acquisition metric. However, talent is not a barrier to entry over the long term and neither is clever implementation of new algorithms. Proprietary data and uniquely trained models on the other hand, can provide a basis for high margins over the long term.
- Hype is dangerous (points 1, 2, and 3). Hyped sectors draw in lots of VC dollars which drive valuations up, creating an illusion of success which brings in more VC dollars (sometimes spurred on by M&A). It is possible to make quick money from investing in startups in hyped markets but it’s a lottery. Moreover, all the mania often causes founders and investors to lose their focus on use cases. Unsexy is harder work, but it wins in the end.
- Good focus areas allow for shared learning (point 3). One of the reasons that cleantech was a difficult place to make money is that there was little in common between different cleantech companies. Solar, wind, and biofuels, for example, all have very different technologies, different customers and different company building best practices. Mobile games, in contrast, has been a successful investment focus for many investors because key disciplines around game mechanics, monetisation and marketing are common across companies.
Many VCs are opportunity driven. Their primary strategy is to work on building their networks and then they invest in the best of what they see. Our belief is that focusing yields better results because deep understanding of a sector leads to better decision making and a greater ability to help entrepreneurs succeed. However, focusing is hard. It takes deep thought and hard work to find interesting areas and then it takes strong discipline to stick to your strategy. Focusing is also risky. If you choose a bad area to focus on at a minimum you will look stupid and if you don’t course correct in time you will have a bad fund. Still, if venture has taught me anything it’s that fortune favours the brave
I’ve come across Kahneman’s Peak-End concept before but only just grasped it’s significance. As with much of his work Kahneman is highlighting an area where our minds don’t work rationally. In this case it’s how we remember experiences.
If we were rational we would remember experiences as some kind of average of how they felt at the time, adjusted for their duration. However, it turns out we remember them as a function of two moments – the peak moment (best or worst) and the last moment. Duration and average are less important.
Kahneman makes his point by citing research into how patients undergoing conscious surgery rated their experiences. Their post-surgery rating of the overall experience correlated with the peak moment of pain and how the surgery ended rather than the average of their minute by minute scores for how much pain they were suffering. Indeed, changes in the final moments of their operation dramatically skewed their overall perception of how well it had gone, in both directions.
This difference between how patients experienced their operations and how they remembered them exists with all experiences, both pleasant and unpleasant.
That has big implications for how startups should build products. Customers come back or tell their friends because of how they remember the experience of a product or a service, not because of how they experienced it at the time. Accordingly products should be engineered to deliver moments of delight and happy endings rather than maximum overall utility.
At Forward Partners we always look for those moments of delight, which we call “eyes-light-up moments”. Going forward I will be pushing us to pay equal attention to the closing moments of a customer experience.
I’ve just read Atul Gawande’s Being Mortal, a book lots of people seem to be talking about all of a sudden. It’s a great book, and one I highly recommend. Mostly it is about making better choices for ourselves and our loved ones as we grow old, which is a far cry from startups.
One passage is different though. It’s about courage, which is required in spades if we are to make the most of our old age and if we want to make the most of our startups.
Gawande starts by turning to Plato who wrote “Courage is strength in the knowledge of what is to be feared or hoped” and “Wisdom is prudent strength”.
Which brings me to startups. It takes great courage to found and build a company, to maintain conviction in the face of naysayers, to get back up again when you get knocked down and to persevere when things look helpless. But it is also important to be wise. To know when to carry on or when to change course, or even give up. It was only after Ev and Biz Stone gave up on Odio that we got Twitter.
Gawande goes to on say that at least two kinds of courage are required in ageing and sickness. The courage to seek out the truth of what might happen both on the upside and the downside, and then the courage to act upon it. Again that’s super important at startups – the best founders tirelessly look for ways to expand the upside and ways their companies can go wrong. Other founders put their heads in the sand.
In a further parallel with startups, Gawande notes that ageing and sickness are highly complicated and uncertain, and that it is very hard to build an accurate picture of what’s going on or of the implications of any particular course of action.
It is in this uncertain environment that we must find the courage to act. Very often that choice means deciding which is more important, our hopes or our fears. Do we want the chance of a much better life, or do we simply want to stay alive?
That last question is as relevant in startup boardrooms as it is in the Emergency Room, but it is rare to see the difficult topic of potential permanent decline and failure addressed well enough that the the following course of action gives the best chance of happiness to all concerned. More common is to raise more money if it’s available, keep going with more or less the same plan and only make radical changes when the writing is well and truly on the wall. By this point there is much less cash left, the options for remedial action have become limited and the chances of achieving even a half decent outcome are much reduced.
The starting point for most founders is that they can overcome all the obstacles in their way and overcome what, to many, look like impossible odds. Possessing the self-confidence and resilience to maintain this perspective is an amazing gift that has enabled many entrepreneurs to achieve amazing things and it is something we look for in the founders we back. My closing point in this post though, is that in some situations the right thing to do is to stop trying to achieve the impossible, reduce our level of ambition and start building the best possible future within the constraints that face us.
Contemplating anything other than success is tough for most people involved with startups. Nobody likes a naysayer and fear of being thought of as unambitious or lacking tenacity made me think twice about writing this post. I decided to press ahead because the truth is that only a small fraction of startups achieve unfettered success (we will be happy if one in three of our portfolio are that lucky, and even those will face difficult periods). For the rest finding the courage to face the truth and make difficult decisions early will make them happier in the long run.