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This is a long post (1,900 words). For those of you who are time poor here’s the tltr:

  • Forward Partners operates a focused investment strategy because it helps us make better investment decisions and provide better support to our companies
  • A good focus area for us is one that can generate 50+ deals and where we can build some generalised expertise that helps with our decision making and value add
  • Until now we have focused on marketplaces and next generation ecommerce
  • Recently we evaluated lots of options and did a deep dive on Applied AI before selecting it as our next area of focus

For the three and a half years that we’ve been going, Forward Partners has operated a focused investment strategy. We observed that small transactions of all types are increasingly moving online and backed the companies that were helping to accelerate that trend. That meant lots of consumer and small business focused marketplaces and next generation ecommerce companies. Lost My Name, Appear Here and Thread are three of the better known examples, but overall there are 37 companies in that portfolio.

We chose to be focused for three reasons. First, and perhaps most important, being focused enabled us to build up expertise that resulted in better investment decisions. Specifically, we feel we have strong capabilities in working out whether customers will value products highly and whether it will be possible to market them cost-effectively online. Secondly, we have seen so many similar companies now that we have a good sense of what they should be achieving by when. We are better able to see problems coming and advise on strategies to work around them. Being expert in an area makes us better board members and hence better able to win deals with the best entrepreneurs. Finally, focusing allows us to add more value operationally so our companies can execute faster and with higher quality. The companies we back often share the same challenges as each other and because we focus our team has solved those problems many times over.

However, venture capital is a dynamic business and good focus areas don’t last forever. We are still seeing lots of marketplace and next generation ecommerce opportunities, but as we move into our second fund we decided to add another focus area to make sure we will continue to have enough high quality opportunities to invest in over the next four years.

Our first step was to define the what we mean by a “good focus area”. For us the following characteristics are important:

  • Will generate 50+ deals
  • We can build knowledge that’s broadly applicable across the focus area and gives us an advantage versus other investors
  • We can articulate a few underlying investment theses
  • We can articulate use cases
  • Suitable for early stage investment
  • The UK has some kind of advantage

Then we had a high level discussion about what areas we might focus on next. A couple of interesting things came out of that. Firstly we like to invest in sectors that are rising from the low point of the Gartner Hype Cycle. Investing at this point leverages our key capabilities of assessing whether customers will love products and whether companies will be able to market them cost-effectively. If we get the timing right then mass adoption should be achievable. Investing with this strategy means we don’t chase the very rapid value appreciation that sometimes occurs at the beginning of the Hype Cycle, but we think the benefits of focus outweigh the cost of the lost opportunity.

The other interesting point to come out is that investing in deep tech at the very earliest stages is difficult. One of the key drivers of success for us as a fund is backing companies that make rapid progress and are able to raise up rounds a year or so after we invest. To do that they must pass valuation milestones. With ecommerce and marketplace companies those milestones relate to sales and unit economics and are easily demonstrable. Progress at deep tech companies, on the other hand, is based on internal development milestones and it’s difficult to predict how next round investors will respond. Until a product is released and is in the hands of customers, which can take years, the only evidence of success is internally reported improvements in algorithms and the production of code. I’m sure there’s a way to solve this for deep tech investments, but we haven’t figured it out yet.

The next stage for us was to brainstorm potential areas of focus. Each member of the investment team went away and over a couple of weeks contributed ideas to a shared Google Doc. Then we reconvened with the objective of choosing a single area on which to focus. Via a process of discussion, voting and then amalgamation of ideas we decided to look seriously at making “Applied AI” our next focus area. That would mean investing in companies that were using well understood artificial intelligence techniques to build new and superior products.

We felt that Applied AI is attractive because:

  • It’s a broad enough area to generate 50+ deals
  • Is one where we already have knowledge and could could go on to develop a deep expertise in the different techniques and their application
  • Is at the right point in the Hype Cycle and plays to our strengths in evaluating demand

The major concern we had is that AI more generally has been a popular investment theme with other investors for some time and we wanted to make sure that Applied AI is sufficiently differentiated to be a viable investment focus for Forward Partners.

We decided to go away and do some work to improve our understanding of the area with the aim of answering the differentiation question and convincing ourselves more generally that Applied AI has the potential to yield a flow of high quality investment opportunities over the next 3-5 years.

To that end we sought to answer the following questions:

  • What are the AI techniques that can be applied cheaply and predictably by startups?
  • What capabilities do those techniques enable? (e.g. natural language processing enables conversational interfaces)
  • What use cases can these techniques be put to? (e.g. conversational interfaces to FAQ databases can improve customer service)
  • Are there enough use cases where the addition of ‘intelligence’ makes the product meaningfully better?
  • How can Applied AI startups meaningfully show progress in their first year of operations?
  • How much AI talent is required at pre-seed and seed stage Applied AI startups and can we find enough companies with that talent?
  • How can we add value to Applied AI companies?
  • What are some hypothetical strategies for Applied AI startups to obtain the data they need to train their algorithms? (Addressing the “cold start” problem.)

The first three of these questions relate to the size of the opportunity set. To choose Applied AI as a focus area we had to believe there is the potential for 50+ deals that would make sense for us. To get an answer we mapped an extensive list of Applied AI techniques against the Gartner Hype Cycle, and put them into a spreadsheet linking them to the capabilities they enable, then linked those use cases to capabilities, and finally the use cases to ideas for companies. After that we scored the company concepts based on their attractiveness as Forward Partners investments and looked to see how many high scoring opportunities there were. Fortunately there were many.

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Once we had comfort on the size of the opportunity we turned to the final three questions which relate to whether the opportunities will work as early stage investments. Our approach this time was to hold workshops and meetings with people who had experience of building applied AI businesses. Thank you in particular to Matt Scheybeler, Steve Crossan, and Martin Goodson for helping us with this part of the journey.

One important learning at this point was that in the early stages of Applied AI startups the artificial intelligence component isn’t that complicated. We heard multiple times that you can get 80% of the way there with statistics, that almost any AI technique will get you the next 10% and that it’s only when you get to the last 10% that you need to get clever. That was great to hear for two reasons:

Most startups with true potential don’t get to the last 10% in their first couple of years so hard to find AI talent isn’t a prerequisite to get started.

Our existing strengths in building products that resonate with customers and driving growth aren’t eclipsed by a requirement for deep tech knowledge – i.e. we can help.

The other important point we learned is that Applied AI startups can get product to market quickly and drive predictable value appreciation in the timeframe of a pre-seed or seed investment. We talked through numerous real and hypothetical examples and got confident that when we make Applied AI investments they will be able to raise their next rounds at a good step up in valuation. That’s one of the most important questions any VC has to answer and we were pleased to find that because they can get started with simple algorithms, Applied AI startups aren’t different from other software startups in this regard.

The final piece of our investigation was to think about the “Cold start” problem. We talked about three different data strategies for Applied AI startups and what that would mean for us:

  • Founders have access to some proprietary data
  • Founders have an innovative idea for using publicly available data
  • Founders will generate data from their business and develop algorithms later

In the first two of these cases Forward Partners needs to evaluate whether there is value in the data pre-investment and to help the founder extract value from the data post investment. In the third case we need to be able to evaluate whether the business will be able to generate data, and then if they can the evaluation is the same as in the first two cases. All of this points to us enhancing our data science capability at Forward Partners.

Our conclusion therefore, is that Applied AI is an attractive focus area for Forward Partners. It looks promising that there will be the required volume of dealflow, we can see how an early stage investment strategy will work, and we can leverage our existing strengths to help businesses in this new area. The only new requirement is that we enhance our data science capability.

Hence for the last couple of months we have been targeting Applied AI deals alongside our traditional focus area of marketplaces and next gen ecommerce. Wherever possible we like to take an experimental approach so we have decided that we will run with it until the end of the year and then evaluate. In parallel we are investigating what sort of data science capability we need. That will in large part be determined by the sort of opportunities we see and end up investing in, so for now we are relying on relationships with people who help us on an ad hoc basis with a plan to bring the capability in house when the picture gets clearer.

And I’m pleased to report that we have already made our first two Applied AI investments. Neither is announced yet, but watch this space ”</p

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As you may know Google has done an almost obsessive amount of research into what makes a high performing team. This post from Michael Schneider summarises what they found, into five traits that are easy to understand and easy to identify. Very powerful.

1. Dependability.
Team members get things done on time and meet expectations.

2. Structure and clarity.
High-performing teams have clear goals, and have well-defined roles within the group.

3. Meaning.
The work has personal significance to each member.

4. Impact.
The group believes their work is purposeful and positively impacts the greater good.

5. Psychological Safety.
When everyone is safe to take risks, voice their opinions, and ask judgment-free questions.

I love this for it’s simplicity and completeness. Unfortunately implementation sometimes remains challenging.

 



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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.

 



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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.



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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

 

 

 



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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.



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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.

 



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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.

 



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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!

 

 



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I am in the Elon Musk fan club. It’s hard not to be in awe of what he’s achieved – four multi-billion dollar companies and he’s only in his forties. I’ve even read his biography, not something I’ve done for many people.

Lots has been written about why he is successful, mostly focused on his drive, vision, tenacity, resilience and intelligence, but I happened on a post morning which highlighted something that was new for me. Forbes columnist Michael Sims was seeking to understand how he has been successful across a wide range of very different industries – auto, space travel, energy and software.

The answer, he believes, is that Elon Musk is an expert-generalist:

Expert-generalists study widely in many different fields, understand deeper principles that connect those fields, and then apply the principles to their core specialty.

That struck a chord with me because that is what good venture capitalists do. In his book The Second Bounce of the Ball, Ronald Cohen, who has a good claim to being the first true VC here in the UK, wrote:

[investors] have to be financially trained and to have an understanding of management, but you also have to have a strategic brain while being sensitive to tactical and people issues

To that I would add empathy, patience, grounding, creativity and hustle. So we have to be generalists in that sense. Then on top of that we need to master multiple areas of investment – at least if you are to have a long career. In my seventeen years in this industry, I have invested in enterprise software, semiconductors, SaaS, social media, adtech, and ecommerce across multiple sectors. That has required a lot of reading! Then right now I am getting to grips with Bayesian Networks, Hidden Markov Models, Convolutional Neural Networks and back propagation as Forward Partners investigates whether to have a big push in what we are currently calling “Applied AI”. Further, all of this applies across multiple industries, from fintech to fashion to healthcare (one of my colleagues is up to his neck in microbiome research as we speak).

You can see the need to be an expert-generalist.

All this begs the question of how one becomes an expert-generalist, or if you are already an expert-generalist, how you become a better one.

The answer is to get good at learning. Fortunately Sims spells it out for us. Here is what he describes as Musk’s two stage process for learning:

  1. Grasp the fundamental principles
  2. Reconstruct those fundamental principles in new fields

There are no short cuts here. Musk used to read 60 books per month. But when, and only when, you understand the fundamentals you can more quickly learn and apply things in new areas. Returning to AI – Bayesian Networks are much easier to understand if you grasp the fundamentals of statistics, and once you grasp the fundamentals of Bayesian Networks (and all the other components of AI) it is much easier to understand where they can be successfully employed and where they can’t. Similarly with regard to human behaviour, a solid grasp of behavioural psychology makes it easier to predict how people will react to new products and services.

And getting good at learning isn’t just important for VCs. It’s important for everybody. The world is changing so fast now that one area of knowledge is most unlikely to be enough to build a career. A quick look at this Wikipedia article on the history of programming languages shows what developers have to deal with, but something similar is true for just about everyone else.

 



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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 DescartesDavid 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.



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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.



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A couple of times recently I’ve found myself coaching people to stay positive. In both cases they very reasonably pushed back, saying great idea, but they didn’t want to be false and pretend to feel positive when inside they felt anything but. Two conversations about the art of being authentically positive ensued and I’ve been collecting my thoughts on the subject since then.

Let me start by taking a step back. This may be obvious to many of you, but we all like being around positive people. It’s more fun and it helps us keep our own energy up.

Positivity is doubly important in startups where the ups and downs will inevitably lead to periods where we question whether the whole endeavour is worth our time. Happiness is contagious and companies full of positive people climb out of the dark patches more quickly.

However, to really work, the positivity must be authentic. Saying or implying you feel good when you’re really not sure is better than giving into cynicism, but people can tell, and after a while it will chew you up inside.

One trick for staying authentically positive is to avoid dwelling on the big problems and focus on the little wins. When someone asks how you are doing, reflect on something that has gone well recently. If you made minor progress with a major client in the last 24 hours, say so. It’s genuine, and will make you and the person you are talking to feel better than a negative or neutral statement.

Underlying this is a really important point, which is that effective operators respond to feeling down by finding something positive to do. When we were still working out the details of our model here at Forward Partners we had a chap who started to get cynical about key aspects of his role. To his credit he responded by taking ownership of one of our content initiatives. It was a side project for him, but he had success there which kept him positive whilst we sorted out his bigger issues.

Other helpful tricks are getting enough sleep, exercising, eating well, meditating, and – simplest of all – remembering to smile. If you feel good in your body you will have more energy and find it easier to stay positive.

Like happiness, positivity is a function of mindset and behaviour. It can and should be cultivated.



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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?

  1. 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.
  2. 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.

 



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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.

 



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