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.

Why thinking in absolutes can paralyse innovation in large companies

The challenges associated with innovating in large companies have been well documented. Typically explanations of these challenges reference the need for established organisations to focus on process efficiencies rather than disruptive innovations. Startups are much more focussed on finding product market fit, and then once you have it you are driven to maximise profit and revenue. However, this catch all explanation doesn’t explain why some large companies are more successful than others at innovating. It is simply not true that companies of over 500 people have been unable to bring successful innovations to market.

When large organisations get it right, they are able to harness the values and mentalities that are ingrained so deeply in early stage companies. The genius of these larger companies is to recognise that these mindsets are not absolutes in themselves, they are variable and can be adopted when it is strategically advantageous to do so.

Autonomy vs control

Whether it’s Clayton Christensen arguing that one needs completely autonomous business units in order to explore disruptive innovations, or Elon Musk’s mantra: “Don’t worry about the methods or if they’re unsound. Just get the job done,” autonomy has long been cited as a necessity for innovation. However, in most cases the need for autonomy is seen as a binary decision. If you start your own company, you decide exactly what to do, where to work, when to work and whose advice to listen to. When you work for a large company all of the above is dictated to you. This is insane. It shouldn’t be one or the other, but a combination of both.

When companies get it right, there is enough control to set frameworks and objectives, but enough autonomy for individuals to navigate difficulties that may come up. The solution could be even more simple than this, for a percentage of an employee’s time there could be complete autonomy, and for the rest it could be much more guided. Autonomy isn’t just about strategic direction either, it could be about letting people choose what devices to use — or what tools they download to execute the job.

The question for any large organisation is where and when employees are afforded autonomy. Sadly in many cases the default is nowhere — a sure fire way to squash innovation across the business.

External Accountability vs Internal Accountability

Another area where startups are often celebrated is in the relentless accountability that follows them wherever they go. Coupled with complete autonomy is the inevitability that you will be personally accountable for whatever decisions you make. Decide to create a product that no one wants? Well, you’ll know it as soon as your money or enthusiasm runs out.
Within large corporations it may be easier to hide — again this is well documented with the sort of vanity metrics often used to judge the performance of new products or innovations. Whether it’s number of likes or simply your boss’s approval, it is much easier to achieve internal approval than it is to venture outside.

This, too, need not be an absolute. In the most successfully inventive enterprises the success of an innovation is measured by external data rather than internal rubber stamps.

Necessity vs Process

When something needs to get done, startups are often stereotyped as doing it “by any means necessary.” Whether that is staying up into the early hours coding, or by pulling in whatever favours they can, necessity almost always trumps process. This approach is naturally disconcerting to large companies that develop process to mitigate the risk of something going wrong due to someone doing something our of urgency.

Yet this sense of urgency is something that, when harnessed, can be exceptionally powerful. Often it’s the inefficiency of process within companies that startups on the outside recognise and capitalise on. Think of this in the record business or taxi market. Process is undoubtedly essential for big business, but there is no reason there can’t be competing ways to do things — letting the old way die out to be replaced by the new. Whether one likes it or not employees will always have workarounds where the process is too slow, and it’s precisely in these instances where people should be able to celebrate and publicise these. Within a workaround is often the beginnings of a much more efficient process.

Salary vs Equity

Finally, one of the most fundamental absolutes is when it comes to renumeration. In a large organisation this will likely be done with a salary, and maybe a bonus. This means that the metric for success is inevitably internal. Unless you work on commission it will be your boss deciding on your bonus for a given year, based on their perception on how well everything has gone. In a startup, you will very likely own equity in the business which will have precisely zero value if you create something that no one wants, and potentially limitless value if you create something everyone wants. On top of this, if you’re lucky, you will probably also have a salary.

For a company to be truly innovative people within the organisation must feel that it is in their interests to suggest and follow through with new ideas. Just because you have a better way of doing things doesn’t mean you’ll give it to the company, yet in a startup there is a sense of shared ownership — you will do whatever you can to ensure that the venture succeeds. This is not, again, an absolute. Whether it’s a partnership model — or something more innovative — experimenting with new ways of rewarding people who bring innovation to the company is essential if you want to work somewhere that can harness the ingenuity of its people.

Where do you want to be?

These absolutes outlined above seem intrinsically linked with the type of organisation you work for — small and nimble or slow and lumbering. But this is merely the default, through demonstrated action this can be changed. You just have to move the slider an inch in the right direction.


I'm the co-founder and CEO of Hook - a company that is using psychology and technology to stop email fraud. Use Hook to defend yourself against hackers at