Uber is trying to be like Amazon. It’ll fail

Uber in inaction. Image: Getty.

Following TfL’s decision to withdraw Uber’s license to operate in London, there has been a widespread picking over of the ride-hailing app’s recent history – and speculation about its future. A fairly common conclusion is that Uber needs to become more ethical if it is to survive.

I want to suggest that this may not be possible. After the calamitous year Uber has had, it should not be difficult for the company to improve its reputation – simply by avoiding many of the unnecessary embarrassments heaped upon itself in 2017. However, merely improving its PR will not get Uber out of the hole it has now dug for itself. It is looking as though, in many territories such as London, Uber’s survival will rely on concrete measures to better care for both its drivers and customers.

Herein lies the problem. It is not that Uber is incapable of such ethical measures. But for this company specifically, the additional cost that is required to look after drivers and customers is likely to be too great. It all comes down to the economic model on which Uber is built.

There is a great tendency among commentators to focus on the capabilities of Uber’s app, when making sense of its explosive growth across the world. This is a mistake. Figuring that Uber’s app explains its growth is like putting the birthday cake’s appeal down to the candle on top. The engine of Uber’s growth to date has been the $11.5bn it has raised from banks and investors. The company has never made a profit, and in 2016 alone lost nearly $3bn.

These are staggering amounts, and to make sense of them we need to understand that Uber’s business model is the same as Amazon’s. Amazon became the largest online retailer on the planet by burning through huge sums of investment on the way to becoming dominant in an ever-increasing number of sectors, and a de facto monopoly in some such as books.

Now Amazon is able to use its position to generate the vast profits expected by those that funded its expansion. Effectively, what both companies surely rely on is investors subsidising the prices customers pay in the short term, in return for a long-term monopoly with higher prices.


Trump card

In reaching this point, Amazon has itself received plenty of criticism, particularly around its tax arrangements and working conditions in its Orwellian “fulfilment centres” (warehouse to you and me). But Amazon has benefited, throughout its growth, from a trump card: its use of a virtual shopfront makes its overheads significantly lower than bricks-and-mortar rivals.

Uber’s fundamental problem is that it does not have this advantage. In his comprehensive critique of Uber, transport expert Hubert Horan made a key observation about the taxi business, which separates it from retail. While shops have used economies of scale to operate first nationally, then internationally, for over a century, taxi companies have remained highly localised. The reason for this, argued Horan, is that the economies of scale are not there for the taking in this market. Some 85 per cent of taxi company costs are drivers, cars and fuel, and this applies whether you cover one city or a dozen.

Not only does Uber not avoid these costs, its model actually introduces new ones. Most dramatically, the costs of becoming established in new markets is vast. This, particularly the artificial subsidising of passenger fees/driver wages to drive growth, is the source of the $3bn net loss last year. Ultimately – whether in the form of debt or equity – these sums will have to be paid back, and then some.

Eventually, this additional cost will be felt. Either the driver has to bear it, and so is motivated to look to rival employers, or the customer does, with the same outcome. Uber’s hope must be that when it gets to this stage there will be no alternatives left to chose from.

Elusive goal

So can Uber afford to become ethical? Its growth to date has been so costly that even after the raft of regulations it has managed to sidestep, and measures forcing down the income of its drivers, it is losing billions every year. In a properly regulated market, in which Uber has to give its drivers appropriate employment protections, and passengers the safeguards they need, its goal of apparently aping Amazon becomes even harder.

If Uber can achieve market dominance before it runs out of funding, the inefficiencies in its model cease to matter. Society will simply have to carry the cost of higher fares and lower driver wages.

If it fails to achieve near monopoly status and has to continue to compete against local firms, in my view it has little hope of ever repaying its investors. For customers that travel to different cities frequently, Uber’s scale gives them a clear edge. For everyone else, is an app slightly shinier than its competitors’ clones enough to outweigh the higher fares that should come with Uber’s model?

Should Uber ultimately fail, it would open up the possibility of a taxi company fit for the 21st century: one that harnesses the possibilities of digital technologies not to enrich venture capital, but drivers themselves, in the form of cooperatives like the one currently developing in the absence of Uber in Austin, Texas.

Murray Goulden is senior research fellow at the University of Nottingham.

This article was originally published on The Conversation. Read the original article.

 
 
 
 

Smart cities need to be more human, so we’re creating Sims-style virtual worlds

The Sims 2 on show in 2005. Image: Getty.

Huge quantities of networked sensors have appeared in cities across the world in recent years. These include cameras and sensors that count the number of passers by, devices to sense air quality, traffic flow detectors, and even bee hive monitors. There are also large amounts of information about how people use cities on social media services such as Twitter and foursquare.

Citizens are even making their own sensors – often using smart phones – to monitor their environment and share the information with others; for example, crowd-sourced noise pollution maps are becoming popular. All this information can be used by city leaders to create policies, with the aim of making cities “smarter” and more sustainable.

But these data only tell half the story. While sensors can provide a rich picture of the physical city, they don’t tell us much about the social city: how people move around and use the spaces, what they think about their cities, why they prefer some areas over others, and so on. For instance, while sensors can collect data from travel cards to measure how many people travel into a city every day, they cannot reveal the purpose of their trip, or their experience of the city.

With a better understanding of both social and physical data, researchers could begin to answer tough questions about why some communities end up segregated, how areas become deprived, and where traffic congestion is likely to occur.

Difficult questions

Determining how and why such patterns will emerge is extremely difficult. Traffic congestion happens as a result of personal decisions about how to get from A to B, based on factors such as your stage of life, your distance from the workplace, school or shops, your level of income, your knowledge of the roads and so on.

Congestion can build locally at pinch points, placing certain sections of the city’s transport networks under severe strain. This can lead to high levels of air pollution, which in turn has a severe impact on the health of the population. For city leaders, the big question is, which actions – imposing congestion charges, pedestrianising areas or improving local infrastructure – would lead to the biggest improvements in both congestion, and public health.

We know where – but why? Image: Worldoflard/Flickr/creative commons.

The irony is, although modern technology has the power to collect vast amounts of data, it doesn’t always provide the means to analyse it. This means that scientists don’t have the tools they need to understand how different factors influence the way cities function and grow. Here, the technique of agent-based modelling could come to the rescue.

The simulated city

Agent-based modelling is a type of computer simulation, which models the behaviour of individual people as they move around and interact inside a virtual world. An agent-based model of a city could include virtual commuters, pedestrians, taxi drivers, shoppers and so on. Each of these individuals has their own characteristics and “rules”, programmed by researchers, based on theories and data about how people behave.

After combining vast urban datasets with an agent-based model of people, scientists will have the capacity to tweak and re-run the model, until they detect the phenomena they’re wanting to study – whether it’s traffic jams or social segregation. When they eventually get the model right, they’ll be able to look back on the characteristics and rules of their virtual citizens, to better understand why some of these problems emerge, and hopefully begin to find ways to resolve them.

For example, scientists might use urban data in an agent-based model to better understand the characteristics of the people who contribute to traffic jams – where they have come from, why they are travelling, what other modes of transport they might be willing to take. From there, they might be able to identify some effective ways of encouraging people to take different routes or modes of transport.


Seeing the future

Also, if the model works well in the present time, then it might be able to produce short-term forecasts. This would allow scientists to develop ways of reacting to changes in cities, in real time. Using live urban data to simulate the city in real-time could help to inform the managers of key services during periods of major disruption, such as severe weather, infrastructure failure or evacuation.

Using real-time data adds another layer of complexity. But fortunately, other scientific disciplines have also been making advances in this area. Over decades, the field of meteorology has developed cutting-edge mathematical methods, which allow their weather and climate models to respond to new weather data, as they arise in real time.

The ConversationThere’s a lot more work to be done before these methods from meteorology can be adapted to work for agent-based models of cities. But if they’re successful, these advancements will allow scientists to build city simulations which are driven by people - and not just the data they produce.

Nick Malleson, Associate Professor of Geographical Information Systems, University of Leeds and Alison Heppenstall, Professor in Geocomputation, University of Leeds.

This article was originally published on The Conversation. Read the original article.