Want to see the confirmation bias at work?

Then read on.

I just read Spending 10 Minutes a Day on Mindfulness Subtly Changes the Way You React to Everything and loved it. I meditate for 15 minutes first thing every morning and feel that it makes a big difference to me. As I meditate I can feel stress dissipate and my mind feels somehow less taut. In the same way a flexible muscle is able to absorb more shock than a taut one I feel I’m more able to control my response to things that go wrong or might otherwise instigate a knee jerk reaction. The article tells me that my experience is common and this excerpt goes a little way to explaining why.

Leaders across the globe feel that the unprecedented busyness of modern-day leadership makes them more reactive and less proactive. There is a solution to this hardwired, reactionary leadership approach: mindfulness.

Having trained thousands of leaders in the techniques of this ancient practice, we’ve seen over and over again that a diligent approach to mindfulness can help people create a one-second mental space between an event or stimulus and their response to it. One second may not sound like a lot, but it can be the difference between making a rushed decision that leads to failure and reaching a thoughtful conclusion that leads to increased performance. It’s the difference between acting out of anger and applying due patience. It’s a one-second lead over your mind, your emotions, your world.

Mindfulness is a powerful practice.

This growth equation is brilliant in it’s simplicity. I love that it forces the focus on a magic moment and core product value. Both are essential. Read more about this in First Round Capital’s latest article.

I just read Andrew Chen’s Growth is getting hard from intensive competition, consolidation, and saturation. His argument is that we are at a point in the cycle where distribution is controlled by a small number of companies who limit the opportunities for differentiation via marketing. He mentions Google, Apple, and Facebook, and I agree wholeheartedly. Facebook’s growth in revenue per use shows just how successful they’ve been in extracting value from the system and I’m sure we could find similar charts for the other two.

Amazon is the other company that is dominating distribution, and for me most impressive and therefore ultimately the most scary of the lot.

All four of these businesses have monopolistic tendencies that make it hard for startups to compete and get noticed. Chen identifies six trends which continue to make life more difficult:

• Mobile platform consolidation – The App Store and Google Play dominate, and they are in turn dominated by Facebook and Google apps
• Competition on paid channels
• Banner blindness = shitty clickthroughs (now extending to blindness for referral programmes)
• Superior tooling – makes it easier for companies everywhere to be data driven
• Smarter, faster competitors – copying successful new product ideas more and more quickly
• Competing with boredom is easier than competing with Google/Facebook – the bar for new products to gain traction gets ever higher

Against this background, a great product is a startup’s only weapon. Great product gets companies heard above the noise and gives them good conversion rates which in turn allow them to out spend competitors on Facebook and Google. Being data driven and first class at exploiting paid marketing channels is now table stakes.

And the only way to reliably build great product?

To understand your customers better than anybody else.

In our experience the three best ways to get that experience are:

• Work with customers in your target market for years before starting your company (the founders of all three of our last investments have done this)
• Do “Mom test” style interviews with target customers before building your product (not customer surveys or focus groups)
• After you’ve launched build processes that keep you in constant communication with customers and pump them for insights

I’ve been thinking a lot about authenticity recently.

The first thing to say is that it’s a woolly concept. As an individual I am clearly me, and therefore of undisputed origin and not a copy. In common parlance then to be authentic is to be genuine, to be truly what we say we are. The problem with this is that most of us are, in fact, many different people. The person we are with our kids might be different from the person we are with our partner, which might again be different to who we are at work, and we might even be a different person depending on which group of friends we are with.

There’s a tempting notion that the true person sits somehow at the middle of these different external facing people, but if you subscribe to the view, as I do, that we are no more than the sum of our actions, then it follows that we are really just a collection of different people. That’s born out for me by the tension we sometimes feel between our different personas. I often suffer from inner conflict because I genuinely want to do more at home and at Forward Partners, but there’s no time for both. If there was one inner person governing everything then it should be possible to resolve the issue, but I find that when I’m in family mode my desires are different from when I’m in work mode.

All that said I am a firm believer that, generally speaking, if we can be more authentic we will be happier and more effective in our lives. Firstly, from a selfish perspective, maintaining multiple personas is tiring. We constantly have to remember where we are in order to remember how to behave and there’s cross over between the different areas of our lives that threatens to expose the differences. For most people this is a low level stress that’s eminently manageable, but it’s there and it impacts performance. I was discussing authenticity with a friend recently who partly thought of it as being able to say what he genuinely thinks. Many of us censor what we say a lot of the time, and that becomes exhausting after a while.

Secondly, the more authentic we are the easier it is for other people to trust us, making us more effective as friends, partners and leaders. The more knowable we are, the easier it is for people to rely on us, which means they can spend less energy worrying about whether we will do what we say and whether we will look after them.

Bringing this all together, it follows for me that the first key to being authentic is achieving an alignment between our different personas. The more aligned we are the more we have one true self, which makes us more genuine by definition.

However, there is a caveat. If we are successful in achieving an inner alignment, but there’s is a lack of alignment between what I want and what my friends, family or colleagues want, then being true to what I think all the time might make me feel better, but can put a burden on others. In most of our relationships we find a shared space that works for both parties. That space defines what we talk about, the topics we avoid, what we expect of each other, and a whole host of other things. The process of getting to know somebody is in large part a process of defining that shared space. If we unthinkingly change our behaviour to be more authentic then we unthinkingly change that shared space with each of our friends, colleagues and partners. That can be a jarring experience for them and could well be a selfish thing to do. You might be able to think of someone you know who has achieved a good degree of internal alignment but comes across as selfish. I know I can.

Which brings us back to alignment. The journey to authenticity is ultimately a shared journey towards alignment with everyone we share our lives with. Writing that sentence really made the penny drop for me, so I’m going to repeat it. The journey to authenticity is a shared journey towards alignment with everyone who shares our lives. Full alignment with everybody will be out of reach for most people, but the more aligned we can get the more authentic we can be and the better everything will work.

That’s one of the reasons why great leaders place huge stress on aligning their people around a unifying company mission and why many successful couples are aligned that their relationship is the most important thing in their lives. As I think this through we have many tools for building alignment in the work place (vision, mission, company values and OKRs spring to mind) but we don’t have anything comparable for our personal lives. That feels like a gap to me.

Growing from a founder to a scale-up CEO is challenging. Thinking up game changing ideas couldn’t be more different to running a large business. Many transitions are required along that journey but the one that I’ve been thinking about recently comes when the first product starts to take off. To simplify, before then success comes from trying lots of things, but after that success comes from making one thing work.

Creativity is the main skill required in the first phase. It’s all about coming up with lots of ideas and seeing which ones have merit. It’s a time when the options seem limitless and new ones are opening up all the time. Conversations with customers and other industry players frequently go off on tangents revealing new opportunities and adding to the sense of upside. It’s all about getting a few irons in the fire and founders often have a growing belief that at least one of the ideas will work out, even if they aren’t sure which one.

Then one of the ideas starts to work. Customers are buying and levels of excitement and optimism grow still further.

We are now into the second phase. The onus has moved from trying lots of things to making one thing work, and that requires a very different mindset.

The first thing that’s required is focus. It’s difficult moving from adding irons to the fire (and feeling good about the security that brings) to taking irons out of the fire to focus on something that is promising but still unproven. For many founders giving up on the optionality of having lots of horses in the race is hard, and that’s despite the fact that ideas put on the backburner at this point can be re-ignited later. The difficulty isn’t rational. Everyone understands the benefits of focus from an intellectual perspective, but in practice many find it very challenging emotionally. Buckets of self belief play a part here too – most great founders believe they are snowflake special, which is great, but that confidence can give them the excuse to think that whilst everyone else should focus, they are good enough to keep all the options alive without compromising on delivery. I’m here to say that’s rarely the right strategy.

The second thing that’s required is discipline. The fun creative process of dreaming up new user flows and product features gives way to disciplined experimentation. For an ecommerce company or marketplace that means analysing the whole funnel from marketing spend through to checkout, looking where people fall out, and experimenting with fixes. Many good companies run regimented experiment programmes with a weekly cadence. Every seven days they identify a metric they want to move, develop a hypothesis on how to move it, implement an experiment to test the hypothesis, and then kill or roll out depending on the result.

Moving from adding irons to the fire to taking them out and from creativity to discipline is quite a shift. Writing this post has got the transition clearer in my mind. I hope it’s helped you too.

A common mistake founders make at the early stages of a company is to put too much detail into their business plan. Sometimes we see a level of detail which amounts to spurious accuracy given the stage the company is at and the attendant uncertainty. Two concerns follow:

1. The founder doesn’t understand how much things change in startups (or, worse, are trying to project a greater level of certainty than they feel)
2. They may not be flexible enough to ride with the punches

This happens most often with projections about how products will work and with financial models. I won’t name companies but one we spoke with recently was building a three sided marketplace. They were pre-launch but had developed a complicated six step transaction flow they thought their customers would go through which included commission splits and transaction timelines. They had taken users through the potential flow and got positive feedback but I was left thinking that the questions they asked those users wouldn’t have passed the Mom Test and that there was a high chance that when they launched the process would bamboozle even their early adopters. For me, it would have been much better if they had focused on describing the value participants would garner from using the service and either planned to manage transactions manually in the early days or documented a very simple transaction flow. That would have shown me that they understood the inherent uncertainty in building products and would have had the additional benefit of really hammering home the value proposition.

When it comes to financial models people sometimes take false comfort from the spurious detail they’ve built in, which results in relying on the model rather than on common sense. I’m thinking now of an ecommerce company that was in its first six months post launch. Pretty much all their traffic came from Google and in their plan they had projections for growth in organic traffic and for traffic from Facebook, referrals, and other new channels. That showed they were planning to diversify their sources of traffic and understand the different options available to them which isn’t a bad thing in and of itself. However, when we asked them to explain why they believed their customer acquisition costs would reach the levels they were projecting their answer was pretty much “because the model says so”. Models can, of course, be made to say anything and their answer left me feeling that they didn’t really understand the drivers of their unit economics. It would have been better to say “We believe the major levers for reducing customer acquisition costs will be increasing organic traffic and reducing our CPAs on Google. Based on [insert justification here] we believe that X and Y are achievable.” Modelling at that level would have been sufficient too, with commentary about plans to expand to other channels in the pitch deck.

Don’t let over detailed plans distract you from the bigger picture and the flexible thinking required to navigate the startup ecosystem successfully.

Consider the three charts above. They are all representations of the same exponential function where the Y value is equal to two times it’s previous value. The first chart shows the data series for the first twenty values, the second chart shows the data series for values on through ten, and the third chart shows the data series for values eleven through twenty. Notice that all the charts look similar and that the second and third charts are virtually identical.

The takeaway: when you are on an exponential curve the trajectory looking forward is the same at any point on the curve.

For me, at least, this is highly counter-intuitive. I think that’s because the mind sees change in absolute rather than relative terms. I know that things like processing power, storage, bandwidth (fixed and wireless), solar, and genome sequencing have been improving exponentially for some time so I expect to feel the change to a much greater extent today than I used to, and by extension my natural inclination is to expect that change will get bewilderingly fast in the next decade or two.

However, when you think it through properly our experience of change will remain the same. There will be a doubling each year (or halving, or whatever the exponential function is).

This is all very abstract. Let me try and make it real. When I think about Moore’s law and the acceleration in computer power, it feels that the change should be faster than it was when I was a kid. I remember when I was 8 the Sinclair ZX81 was released and then the big news a year later was when the Sinclair ZX Spectrum came out. Memory went from 1k to 16k and there was colour! More importantly for me at the time, the games were much better :). That was a notable advance, however, for the next few years after that there were no really major steps forward. When I compare that to progress in computing over the last few years or so it seems to me we have seen a similar rate of change, although we have to look to the cloud services we use rather than our personal devices to see the change. I will call out Uber and the Amazon Echo as two new things that are changing the way we go about our lives in a way of similar significance to what those Sinclair computers did in the 1980s.

I should say at this point that in the real world exponential curves don’t continue for ever. We get S-curves which closely mimic exponential curves in the beginning, but then tail off after a while often as new technologies hit physical limits which prevent further progress. What seems to happen in practice is that some new technology emerges on its own S-curve which allows overall progress to stay on an something approximating an exponential curve.

The chart above shows interlocking S-curves for change in society over the last 6,000 years. That’s as macro as it gets, but if you break down each of those S-curves they will in turn be comprised of their own interlocking S-curves. The industrial age, for example, was kicked off by the spinning jenny and other simple machines to automate elements of the textile industry, but was then kicked on by canals, steam power, trains, the internal combustion engine, and electricity. Each of these had it’s own S-curve, starting slowly, accelerating fast and then slowing down again. And to the people at the time the change would have seemed as rapid as change seems to us now. It’s only from our perspective looking back that change seems to have been slower in the past. Once again, that’s only because we make the mistake of thinking in absolute rather than relative terms.

I’m writing this now because I only just created the charts at the top of the page. The mathematical side of my brain has known for some time now that when you are on an exponential curve the trajectory going forward is always the same, but there was some other part of my mind that didn’t quite believe it. If you’ve reached this far in the post you have seen my mind in action getting to the bottom of this piece of inner conflict. I think I see the world a little more clearly now. I hope you do too!

As a keen observer of startups over the last 17 years, one of the most remarkable and welcome developments has been the application of scientific method to building startups. In 1999 when I started in venture capital there were no blogs and very few business books that were useful for entrepreneurs. All founders could do was accumulate wise advisors and rely on their wits and instinct.

If I was to pick a watershed moment in the emergence of ‘entrepreneurship as a science’ it would be the publication of Steve Blank’s Four Steps to the Epiphany in 2005. It’s not the easiest read, but for the first time founders had a playbook they could follow. However, it was also around that time that Brad Feld, Fred Wilson and a number of other wise souls started blogging and startup best practices started to be widely shared.

There were two great things about that. Firstly sharing leads to discussion and discussion leads to iteration, making everybody involved smarter. Thus it was that Eric Ries both extended Blank’s work and made it more accessible with the publication of The Lean Startup in 2011. Secondly, people outside of Silicon Valley were able to join in the conversation and get smarter to a much greater extent than they ever had been before which was a massive boon to other startup ecosystems around the world, including London.

Here at Forward Partners we have worked hard to contribute to this development by publishing The Path Forward – a playbook and set of practical guides for founders in their first year or two.

All this work has, I think, made it easier for founders to climb the learning curve and become masters at running their companies. It’s easier to know about and avoid common pitfalls (e.g. assuming you know what customers think) and to pick up tactics and best practices (e.g. OKRs for managing objectives). Of course, that doesn’t mean it’s now easy to be founder, far from it, but it is easier than it was.

However, building a startup can never be reduced to pure science. Some magic, art and wit is always required. I was talking to the chairman of one of our companies a year or so back (I won’t name him for reasons that are about to become obvious) as he was helping them through a rebuild of their product. The founder is a disciplined practitioner of lean startup principles who had achieved good growth through lots of experimentation and optimisation, but they had got stuck. They had hit a local maxima. The chairman explained how they had over indexed on startup science and ended up with a product that was boring. They needed more soul.

This story has a happy ending; they rebuilt the product and are now growing fast once again, but it is a reminder that there needs to be a balance between the disciplined application of startup best practice and inspiration.

I’m writing this today because whilst reading Are Liberals on the Wrong Side of History in The New Yorker I was struck by the similarity between the recent evolution in startup thinking and the way The Enlightenment impacted western thought in the eighteenth century. I don’t have the deepest grasp of the history of philosophy, but it was during The Enlightenment that thinkers like Descartes, David Hume, Adam Smith, and Immanuel Kant had the great rationalist vs empiricist debate which developed the concept of the scientific method, introduced the idea that everything might be explainable through thought and rules, and then hotly debated the limitations of that approach to understanding the world.

As The New Yorker points out, you can, in fact,  trace this debate back to the ancient Greeks with Plato on one side and Aristotle on the other, so the rationalist vs empiricist debate has actually been running for millennia.

When I was an under graduate studying social science in the 1990s I had a good run synthesising the work of the leading thinkers of the time across sociology, political science, social psychology and social anthropology. It worked for me then and I find myself repeating the pattern here.  When there is a significant change in society then the pendulum almost always swings too far, whilst what we really need is to find the right balance. During the great debates of The Enlightenment in a sense both sides were right. It is beyond doubt that rationalist thought and the scientific method brought great advances to our understanding of the world and many great things flowed from that, including the liberal-capitalist system which has given us unprecedented individual freedom and prosperity. However, there are still many things that we don’t understand from first principles where all we can do is treat them like a black box developing predictions for what will happen next based on what we’ve seen in the past without understanding the underlying workings – the human brain is one example, and the workings of the economy being another (hence our difficulty understanding the impact of Brexit).

Returning to startups (and this is a bit of a stretch, but bear with me) – Steve Blank and Eric Ries can be likened to Descartes and other early enlightenment thinkers from the rationalist camp who achieved great advances by using scientific method to shine light into areas that had previously relied upon intuition and rules of thumb. The next step is to balance that thinking with the an approach that can be likened to the work of David Hume who pushed back on the rationalists noting that great insights can also be had by drawing on our experiences.

Throughout his career Steve Jobs famously eschewed market research and relied on his intuition to build amazing products. That’s an extreme position which worked for him, but doesn’t work for most of. The balance I’m talking about cultivates that sense of intuition but then finds ways to quickly and cheaply test the resulting ideas with customers. Now that we are in an era where our basic needs are sated MVPs need to be increasingly sophisticated before customers will engage. That means more investment in development before ideas can be tested than was the case ten years ago, increasing the cost of failure (hopefully not too much) and thus making it more important that only good ideas are tested (again, hopefully not too much). Hence the point of balance is shifting. At the margin the value of good intuition is increasing and the value of disciplined application of lean startup principles is decreasing.

The pendulum is starting to swing back the other way.

Unless you’ve been hiding under a rock, you will have noticed there’s a lot of heat around AI as an investment theme right now. Octopus’s recent announcement of a £120m dedicated AI fund is one of many recent events I could cite as evidence.

In that same announcement Octopus mention that they have had three AI exits (Swiftkey, Magic Pony and Evi) so this is not a new investment trend.

It is, however, a trend that is changing. Up until this point AI exits have largely been driven by a desire to acquire talent. Even Deep Mind’s $400m sale to Google in 2014 is, I think, best understood as an acqui-hire.

Going forward two things will be different. Firstly, universities have responded to the demand for AI PhDs. Hence talent will be less scarce going forward and acqui-hires will be less necessary.

Second, and perhaps more interesting, is that it’s becoming much easier and much cheaper to build AI driven products and we are seeing an explosion in the number of AI startups with a clear path to delivering value to their customers and making profits. There were, of course, numerous companies in the previous generation of AI startups that were on this path, just nothing like as many as we are seeing now and expect to see in the years ahead.

AI startups are becoming cheaper and easier to build, because many of the underlying technologies are now mature enough to apply predictably, and because of the declining cost of cloud computing – including many AI as a service products on AWS and Google Cloud.

I liken this development to the time when cloud computing first emerged around ten years ago. Resources that were previously the preserve of cash rich companies became available to anyone who could pull together a few grand and a thousand flowers bloomed. I think we will see something similar again now.