A New Company Lifecycle

In the past, the fundamental lifecycle of a company has remained broadly the same. Underlined by models like Steve Blank’s customer development process, young companies are responsible for taking risks and discovering customer needs. This means they go through a fast process of testing and learning, establishing whether anyone needs the solution they’re providing, and succeeding or dying based on the result. As a consequence, the most common reason start up founders give for the failure of their companies is that they were producing a solution for a problem that didn’t exist. This phase of a company lifecycle is often called ‘customer discovery/validation,’ and it always happens in the early stages.

Once a consumer need has been discovered, it is then up to the company to scale up and deliver the solution in as efficient a way as possible. This phase - which is often called the “company building” phase - involves hiring the right people, outsourcing when required and working out efficiency gains to optimise the core value proposition. Experimentation can still be a big part of this phase, but typically the efficiency gains will be incremental, and will rely heavily on growing the customer base through activities like marketing. Most companies, and all the large ones that you care to think of, are brilliant at optimising their operations towards this goal. Why do large companies tend to be more risk averse than smaller ones? Because rather than making dramatic pivots, the goal is to make constant tiny adjustments to bring operating costs down whilst keeping customer satisfaction the same.

This model has held for the past hundred or so years, because even when new technology is introduced that allows for a dramatic efficiency gain the time taken to introduce it to the market is usually measured in months and years, giving incumbents the opportunity to adopt or die out.

The Technology Enabled Model

As a result of technology trends, in today’s world once a company gets to a certain size or maturity the model of linear efficiency gain ceases to explain what is actually happening. Instead of the incremental efficiency gains seen over time, the reality is something that looks like this:

Radical drops in cost and increases in need satisfaction can be achieved by companies that use the right technology, and this change can come into effect overnight. It is for this reason that a company like WhatsApp could have 55 employees and a multi billon dollar valuation - in the recent past something like this would simply not be possible.

The reason why these accelerated jumps in customer satisfaction and reduction in cost are possible today are because of four key trends:

Speed of software delivery

Smartphone proliferation and increased connectivity has meant that a company’s customers can have access to improved technology overnight. One of the best examples is Tesla, constantly shipping updates to cars that contain not just efficiency tweaks, but features like autonomous driving. These features fundamentally change the workings of the product — either in a positive way like autonomous driving, or averting a negative like a security update. In the past competitors had time to adjust to these jumps in customer satisfaction, but in the new company operating model you have minutes to respond.

Low barriers to entry

As a result of low technology costs companies can experiment at much lower cost than before. Research and development labs used to be restricted to only the largest firms, but today a company of twenty can build physical devices, push software updates and try new things. New discoveries that dramatically reduce cost can be found and released to the public in moments — open source libraries that replicate the functionality of already established products (or currencies, as in the case of Bitcoin).

Low switch friction

Consumers used to be disincentivised in using radically new products because of the high switch friction of a piece of hardware, or high replication costs. Today, the switch cost to a consumer is searching for an app and adding it to their phone. A culture of experimentation and trying new pieces of software only continue to rise, making it difficult for incumbent companies without significant competitive advantages to defend themselves.

Lack of regulatory understanding

The speed of technology’s entrance to market is often aided by a lack of regulatory understanding. In the past, regulators had a firm grasp of technology that entered a specific market. Technology today is by its nature cross industry, and it takes time for regulators within industry to recognise what the impact of a new technology might be. In the past, radically new technologies were sheltered from the market because of the time that it took to approve them (healthcare being the best example of this). Today, there is a large grey area that technology exploits, hitting the market instantly and forcing regulators to enforce the past rather than the present.

Taking advantage of this new model

This new operating model presents significant challenges for big business. The question that will differentiate those that flourish and those that fail will be who recognises not only what’s possible with new technology, but also what’s right for their business and customer base.

For most companies, the key problem is that to get this synthesis of what’s possible and what’s right, you need to involve completely disparate groups of people. Those who understand what’s possible will typically be the developers, engineers and technologists. Those who understand what’s right for the business will often be those in client development and senior management.

Companies like Tesla, Facebook and Google who are able to relentlessly evolve their offering and radically reduce cost and increase satisfaction are only able to do this precisely because the people that know about the technology (the engineers) also know about what’s right for their business and customers, often because they are founded by or run by engineers.

For non-tech companies, bridging this gap is essential to enable anyone in an organisation to develop company sustaining innovations that will allow a company to not only survive but thrive in the new world.

The modes of production have been democratised: so where's the revolution?

Today, technology is smashing down barriers to entry. Want to start a magazine? An insurance marketplace? A travel agency? In decades past you'd have to have a lot of investment up front in order to make your dream come true. One of the core tenants of capitalism - that an investment in capital brings return - meant that class systems were ingrained and the owners of capital were pretty much untouchable.

With technology, the 'untouchables' of business are being severely disrupted. Owning the modes of production today helps, but it isn't enough. Those with an entrepreneurial spirit can spin up an app and take away your business overnight. In the past decade this has happened to countless unfortunate industries; some centuries old. This disruption is powered by a profoundly open and democratic force: the web. Unlike modes of production before it, the web is at its core open (anyone can view the source for any page), and accessible (anyone can write code, put up a website and have access to a market of billions.) Hardware used to be the biggest barrier to entry for technology, but computing hardware is now so cheap that soon every global citizen will have access.

This would lead you to predict, surely, that forces like technology and the web would allow capitalism to become much more meritocratic, and for Marx's demands to look a little, well, 1800s.

The new mode of production: The Mind

In fact, the state of the world in 2016 is exactly the opposite. Technology has helped create the most unequal global economy in history. In the UK the top 1% own 25% of the UK's wealth. Globally, the picture is even bleaker. And yet the same thought keeps coming back: if anyone can start a company and disrupt the status quo, what is going wrong? Why is tech one of the least diverse industries?

The answer, I think, lies in the skills required to participate in the disruption of technology. Whilst some are taught to code at a young age, other's don't have enough access to technology or mentorship. How can you disrupt industries if you don't have the skills to be able to do so? There is the illusion of a meritocracy but in reality something very different. Initiatives like Code Club and Coding in schools are good but they're not enough.

Everyone needs to believe that they can disrupt and change the world, and be given the tools in order to follow through. Technology is not enough. Without the training and education required to allow everyone to participate we'll create a world more unequal than even Marx could have imagined.

Technology creates the wrong sort of jobs

We live in the era of the data scientist and the delivery driver. Semi-skilled labour has been hollowed out by technology, and in the past decade the jobs that have boomed have been hyper-skilled and under-skilled jobs. Globalisation has accelerated this change, creating pockets of extreme wealth and great swathes of non-skilled and insecure workers.

This isn’t like the industrial revolution

Many argue that we’ve seen a restructuring of our economic skill base before during the industrial revolution. This is true, but it doesn’t appreciate the fact that whereas the industrial revolution was a restructuring, the information revolution is a repurposing. Information technology, as opposed to industrial technology, changes not just the procedure related to labour but the value system associated with it.

Imagine a farmer confronted by the industrial revolution. Before the introduction of the wheel-less plough, the ratio of human work to agricultural output may have been 1:5. For every one day of a single human worker you may harvest five measures of wheat. After the wheel-less plough was introduced, this ratio may double to 1:10. This would mean that to achieve the same economic output, half the human effort would be required and therefore the farmer could have the same economic output with half the human input. The skills related to this new sort of farming are different, and so workers may need to retrain to use the new machinery. However, the structure is still the same: a farmer owns a field and uses technology to yield crops.

Imagine the same farmer confronted by the information revolution. To improve productivity, a driverless combine harvester could be introduced to the same field. The goal of the combine harvester is to optimise yields based on various data points, and therefore two categories of job are created. First, the data analyst whose responsibility it is to optimise the automation of the combine harvester. And second, the unskilled worker who is required to make sure the combine harvester carries out its task. The task of combine harvester optimisation is global and macro — therefore it requires a small, centralised team of hyper-skilled data scientists. The task of combine harvester supervision is global and micro — therefore it requires a large, distributed team of under-skilled and under-utilised workers.

The bifurcation problem

In all industries, all over the world, this bifurcation is taking place. Economic and skills based disparity is fuelled by technology, allowing fewer, higher skilled individuals to create business mechanics that disempower the majority of the global population. This isn’t the fault of technology entrepreneurs, it is caused by the data created by the mechanics of technology. Information technology always bifurcates.

Take the information product Google Maps. Twenty years ago having “the knowledge” was a skill that allowed a taxi driver to navigate around London. This information drivers invested in acquiring allowed them to offer value to a potential customer. Google Maps is essentially an aggregation of this data, allowing anyone in the world to navigate through London as an expert. This commoditization of data creates two sort of jobs. The hyper-skilled map maker who collects and distributes data, and the under-skilled driver who follows the map.

The regulators are the problem

Technology and information in themselves are not the problem, they are like any new force unleashed on a society that can have a positive or negative impact. However, close regulation is needed to ensure that the economic repurposing that’s occurring across the world is supervised and not left to an unsympathetic and profit centred system. If left unchecked the consequences are dramatic: the complete deskilling of a generation and the creation of an untouchable hyper-skilled elite. So far technological regulation has been minimal and late; something that needs to change.

Interfacing with the world

Technological advances have always changed the way that humans interface with the world around them. Our brain is a black box that uses senses to gather data, and so when additional data sources are available then it naturally expands our mind. The advent of the printing press allowed adventures in far off lands to be relayed back to those at home, creating whole worlds in people’s imaginations. Television allowed these worlds to be given shape and colour – framing our ideas of unknown places and people. Today, before you visit somewhere on holiday, you will almost certainly have seen a picture or video of it.

This additional data – pre-experience-data – completely changes the way that we see the world. Rather than relying on our immediate senses: the smell of the air in Rome, the glint of the sun on a puddle in Paris, we conceive a detailed and data rich model of the place in our mind. We know what Rome looks like from friend’s instagram photos; you can get live streams of Eifel Tower to see exactly how many puddles there are in May.

For the purposes of travel the impact of experiencing a place through pre-data is relatively limited and has been going on for centuries. Whilst it may reduce the magic and surprise of exploring a place for the first time it may also remove the chance of getting lost on the way to the hotel. However, since the introduction of the smart phone we are not just experiencing places before we see them. We’re experiencing people before we meet them, jobs before we do them and events before they happen.

As you have probably experienced, the weather when visiting a place is not always as expected; the photos may have not captured the essence of the mist in the mountains (or the lack of a window in the bedroom). And in the same way the flat two dimensional data points through which people meet the person they’ll marry profoundly shapes their first real interactions. You can often tell a first Tinder date because the couple spend a few moments trying to connect the text and photo data points to the real thing. Often, if they don’t match up, it can spell danger for the next date.

The more time one spends interfacing with technology, downloading data to inform the models in one’s mind, the less one actually experiences the world. Even a tourist’s journey can be almost completely interfaced through technology. Book online, look at pictures online, arrive and instagram, visit famous sites and live stream. The danger of a complete interface with technology is that whilst real and unpredictable data streams can create the true magic in life – that unexpected moment of sheer joy – these moments can never come through technology. Even with virtual reality, the canned thrill rides are calculated and algorithmic. Magical experiences aren’t random, they’re chance interacting with an individual; a dialog rather than a monologue with a VR headset.

The best instances of technology are when we are pushed to interface more with the world, rather than less. It gets out of our way, and let’s us experience the thrill of the world directly, rather than through a series of pixels. So when. tempted to research fully that next place you go to, or resteraunt you want to try – why not do something different and just go there.