Archive by Author | thommenju

Overview and Future Development of the Ride-Sharing Market: BlaBlaCar and Uber

BlaBlaCar and Uber are two comparable examples of platform-mediated networks that have revolutionized parts of the transportation industry by connecting drivers with passengers for travel purposes without owning a car fleet. In essence, the platforms offer means of communication between the drivers and co-drivers to agree on transportation activities. Thus, positive cross-side network effects are predominant, when considering that passengers benefit from a higher participation of drivers, since it increases the number of possible rides. Hence, an increased user base helps the companies to set themselves apart from competitors. Nevertheless, switching costs are considered to be very low if rivals enter the market. Overall, the platform’s value is exchanged in a triangular set of relationships between demand-side users, i.e. passengers; suppliers of the service, i.e. drivers; and the platform itself. The platform hence takes a role of network orchestrator bringing together supply and demand for car rides.


BlaBlaCar Logo

To the present day both companies have mainly build on word-of-mouth and social media advertising.

They focus on offering low transportation costs when compared to other travel services and have gained high media attention in the relative recent past. This was mainly brought about by vast financing rounds as well as their current company valuations.

Nevertheless, the companies’ offerings differ with regard to their users’ transactions. On the one hand, BlaBlaCar connects drivers to share their pre-planned long-distance rides with fellow passengers in order to share their costs. In general, BlaBlaCar benefits from the growing trend of sharing between private individuals. On the other hand, Uber offers a platform connecting drivers with passengers who wish to be transported from A to B.

These differing models entail divergent legislative and competitive environments. Since BlaBlaCar circumvents legal issues by not allowing drivers to make a profit, competition is increasing in this field. This will in turn lower the firm’s margins in markets, where it charges service fees, if it cannot outperform its rivals by an unbeatable user base or other valuable features. In contrast, Uber has faced various legislative issues up to the present. In particular, it has faced ongoing protests and legal actions against it operations. Among the main arguments against its service offering are: unsafe conditions for customers, unfair competition, uninsured drivers and non-transparent operations. Consequently, the future of the market highly depends on how the legal framework is shaped by governmental institutions. Moreover, competition arises in those countries and cities, where no legal constrains exist. Hence, the future development of Uber depends on the company’s ability to maintain a high-quality service offering that provides value added in comparison to cheaper copycats. Once competitors strengthen their position in Uber’s markets, the company will have to adjust its margins in order to stay price-competitive in the long run.

Technology of the week, Team 44


Pommereau, I. d. (2014, August 13). Move over Uber: BlaBlaCar brings a different kind of ride-sharing to Europe. Retrieved October 9, 2015, from The Christian Science Monitor:

Kasanmascheff, M. (2015, January). Uber, Blablacar und Co: Apps für Mitfahrgelegenheiten, Social-Taxis und Carsharing. Retrieved October 6, 2015, from Softonic Technology: Top-Apps:

Ahmed, M. (2015, September 16). BlaBlaCar zooms ahead with $200m investment valuing it at €1.4bn. Retrieved October 6, 2015, from Financial Times:

Schechner, S. (2015, September 16). BlaBlaCar Valued at $1.5 Billion After New Funding Round. Retrieved October 7, 2015, from The Wall Street Journal:

Gandel, S. (2015, July 31). Uber just beat Facebook’s $50 billion record. Retrieved October 7, 2015, from Fortune:


Deep Learning: Teaching Machines To Act Human

Deep Learning -Teaching Machines To Act Human

Recently there were increased news articles about AI: Artificial Intelligence. Some very smart people were concerned about the progress made in the field of advanced machine learning. Among them were serial Entrepreneur Elon Musk, the famous researcher Steven Hawkins and legendary philanthropist Bill Gates. All of them signed an open letter expressing their concern about the future of AI. Cause for the signage was a video showing Google owned company Boston Dynamics recording a trial run of their human robot ‘Atlas’ running through the woods among other recent advances in advanced machine learning.

What is advanced machine learning?

The field of machine learning in computer science has been there for a while. Starting during the second world war, the first attempts to teach computer to learn and being human were made. The recent movie around pioneer Alan Turing shows the origins of this scientific research field. Until today the Turing Test is still applied to evaluate if a computer is categorized as intelligent.

During the 80ies and early 90ies further attempts were made to teach computers to behave human. Early solutions weren’t practical caused by the limited processing power during that time. A lot of time passed since then.

So what exactly is machine learning? It’s basically to teach a computer to make sense of data. To teach him to recognize patterns in input values and gain insights from the process. Simple machine learning can be a regression analysis or simple classification of data depending on a single value pair into different categories. Advanced machine learning, of which deep learning is a part of, applies multiple analysis layers in analyzing big data sets. The first layer of an algorithm look only at certain parts of the data and then deliver the output value to an analysis layer further up the hierarchy conducting more abstract calculations with the input from the lower layer and itself delivering values to an even more abstract layer of algorithms. This structure allows the modeling of the human brain, imitating the network of neurons in the brain with many (trillions) of synapses.

Tapping into the huge potential

Today many layers are applied to solve difficult data analysis problems, therefore the name deep learning. With this methodology it is possible to teach a computer to analyze pictures, handwriting, speech, maps or even videos. In the future all applications that seem to be ‘magical’ will be the result of some kind of deep learning. The application are many: Categorization of images, indexation of unlabelled data, analysis of maps, using big data of many sources to refine and improve prediction models and so forth.

Facebook, Google, IBM and many start-ups today already apply deep learning technologies to gain an edge solve difficult problems. Until today there is no computer who can itself program something that can program. But that day will come, its just a matter of time.

Is it dangerous? Maybe. But it can also do much good if applied correctly.

If you’re interested in deep learning, here are some very interesting companies applying this cutting edge technology:

Have you heard about deep learning before? What do you think: Is it the future? Are you afraid of AI? I’m interested what you think so please leave a comment!


Why API-Centric Software Will Dominate the Future

Why API-Centric Software Will Dominate the Future

There are thousands of apps around. For multiple platforms (iOS or Android) or in multiple browser. You probably use them on many devices: Your phone, tablet or laptop. But all those applications have very limited functionality on their own. Only by communicating to their user, connecting them between each other and swapping all kinds of information they become powerful.

And that’s where APIs come in. API stands for Application Programming Interface and describes the information and rules software programs interact with each other.

The traditional way of development focusing on web frameworks (e.g. Microsoft .NET, Ruby on Rails, PHP) can require costly integration into other software when not set up properly. Adaption to special needs can easily amount to a project in middle five figures.

An API centric piece of software executes most or all functionality through API calls. So why is this important?

API Centric Design

Source: Nikko Bautista

API-Centric Design

With API-Centric Design the core function of a software (for example the Twitter Stream of new Tweets) is build separately from the way a user accesses it (in our example Twitter can be accessed through a browser, an iOS app from an iPhone, iPad, Android devices, aso.). There is only one core product running in the background and then many different customized front-end ways of accessing the core product running in the back-end. All the communication between those parts happens over? You guessed it: APIs!

No more changing and tweaking the core product because on a windows phone was a display error. You just handle that over the windows phone front-end client.

Bah…. that was a lot of techie talk. So what?! Well that brings us to our next big thing:

The Internet of Things

There are estimates that until 2020 there will be more than 50billion connected devices. That’s a lot! And it will shift who and what communicates over the internet. Today people communicate with people or people communicate with machines and systems. But in the age of the internet of things systems mostly communicate directly with systems. And they don’t care about pretty graphical interfaces on some gadget with touch screen. For those systems to work you need solid APIs connecting many back-ends fast and in a reliable way. And what would be more suitable for this task than software created through API Centered Design?

Oracle recently released an API Management Tool. So did IBM and Intel. These big corporations undertake those steps to be well prepared for what is about to come: The internet of things. It’s gonna be a paradigm shift.

But Where is the Money?

APIs aren’t new. And there are a lots of them. In the Programmable Web Database are more than 14’000 APIs registered. But with the emergence of mobile and the internet of things, they’re in the spotlight again. API centered software enables micro services that fit a specific need an solve a well detailed problem. Other programs can build upon existing APIs using their functionality to expand and build their own. This layer structure can help to automate tedious tasks by integrating and arranging the right APIs. There are many offerings already that allow fast creation of API-based back-ends (e.g. Treeline or Stamplay). APIs therefore build a solid foundation others can build upon. Google does that for a while already and offers a ton of APIs for others to use (e.g. Google Maps). But if you and especially your users call them regularly you have to pay for them. And they’re not cheap:

Google Maps API Prices

Google Maps API Prices

This example brings us to our first business model with APIs: If you’re providing some service that is of value to others, you can charge for every time a user or program is calling your API and uses its functionality. Even if it’s just a couple cents per call, if your API gets used thousand times a day, that’s steady income.

Another business case is to offer your API for free and animate other developers to build upon your existing API. Through  referrals from that software you then generate additional sales. Uber does this with success: By offering their API for free they animate developers to build upon their core product. If someone signs up for Uber through another program that uses the Uber API, they pay the developer who build the new product a commission of $5-10.

There will be many more business models emerging around API. Especially connected to the Internet of Things. The paradigm shift opens up new business opportunity ready to exploit.

What  business models including APIs do you see? I’m very interested in reading about them, so please leave a comment!