Driverless Electric Cars as a Service – One Adoption Scenario

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Self-driving Uber vehicles are lined up to take journalists on rides during a media preview at the company’s Advanced Technologies Center in Pittsburgh earlier this month.

As I ponder the changes that driverless cars will cause across our societies, one of the difficulties is trying to understand what the speed of adoption might be. That is because speed of adoption has huge consequences on the levels of disruption that will occur. The speed of adoption linked into all sorts of factors including:

  • Production bottlenecks in the supply chain such as raw materials, and battery supply.
  • Production capacity for manufacturing of electric driverless cars.
  • Technical issues in achieving true Level 5 automation (think no steering wheel).
  • Political and legal issues around liability and insurance.
  • The balance between the numbers of cars in personal ownership, and the number owned by big companies providing transport services (think Uber/Lyft/Didi and new entrants like Waymo).

 

In turn these factors will be affected by the business models and strategic thinking of the major players. This includes car manufacturers, car ride companies, and governments.
The competition is going to be brutal. Existing car companies will be going up against each other, and new competitors. The new competitors include car ride sharing companies, and new car manufacturers.

Some of the major problems in implementing driverless electric cars as a transport service can be largely solved by car ride companies. The problems include the following:

The Initial Scale/Network Problem

The problem if you want to offer transport as service is you have to get to scale before customers will even contemplate using your service. You cannot put 100 cars on the road and say here I am. This is like the network problem of the first fax machine but on a much larger scale. Who buys the first fax machine? You might buy half a dozen of them so you can communicate between distant offices of the same company. The real value comes when it is a standard system adopted by many people. The problem with transport as a service is that it is much larger. The reality is that people will not use your service until they can consistently get a car in a reasonable time frame to take them wherever they want to go. That needs massive scale and is the reason why Uber was so aggressive in recruiting drivers in target cities. They needed a critical mass to drive customer demand.
A new entrant with electric driverless cars can provide this service but they will need a lot of cars. If you need 100,000 vehicles at peak time in Melbourne (where I live) to supply that service it requires a lot of capital. If the cars cost $50,000 each it is going to cost you $5 billion just for the cars.  That is apart from the costs of the platform to run the system, plus the initial trading losses that will be incurred before breaking even. Not many organisations will have that sort of money, and that might just be for one city. Waymo or Apple might be an exception given the masses of cash that they have. The existing car ride companies got around this problem by using other people’s cars. Difficult to do that for driverless cars although we will explore a model for that in a later post.
The existing car ride companies (Didi, Lyft, Uber, etc) are already at the scale needed to supply the services for their existing customer base. Adding driverless electric cars into that service is somewhat analogous to the electricity grid. Once the standard utility service is in place (think poles, wires, plugs and standards for electricity, and roads and traffic systems for cars) you can make additions as long as they fit the system. You can add a new coal generating plant, or a new gas plant, or new solar capacity and power comes out at the socket. My desk lamp does not care how the power was generated. In the case of a car ride service as long as the customers accept a driverless car you can put that into your system alongside your existing fleet. You may need to drive (sic) demand by offering discounts for the driverless vehicle to get people past their first stages of discomfort. In my case the safety factor is likely to be the key initial driver for change but I am an outlier.

The Capital Problem

If you are a company that wants to supply transport as a service you will want to scale as fast as you can. Ideally you will offer a service in 50 cities in the first 3 – 5 years. If we assume that takes 100,000 vehicles in each city and each car costs $50,000 you now have a cost of $250 billion. If you add in technology platform costs , and initial losses you might have to find $350 billion. That is a lot of coin in anyone’s language. Even if the required vehicle numbers are much lower it is still going to be a massive capital investment.
Even if you don’t move that fast you will have to make large bets in the target cities where you first invest.
If you are a car ride company you can scale by steadily adding cars to your existing services in all those cities. That should have the effect of reducing your costs, and improving your bottom line at a much slower capital burn rate. You can also play a much more agile strategic game. If you perceive a threat in a particular market you can scale faster in that market and slower in other markets. If adoption rates are faster in one city you can rapidly scale up volume in that city by slowing each of your other markets just a little.

The Technical Problem

The technical problem is getting to level 5 automation as soon as possible. Level 5 automation is when there is no driver required in any location or conditions. Any driverless car company will have to convince the regulatory authorities of safety at level 5.  The existing ride companies have an advantage here. They already have masses of data on the travel their existing cars undertake. They can also start with say 100 driverless vehicles within their existing service. That will consist of testing them in real conditions with paid drivers in the vehicles. While a lot of advances in driverless car systems are being made using computer simulations nothing fully substitutes for real world data. Especially for politicians and for regulatory authorities.  That real world data can then be fed back into simulation systems to gain an advantage in simulation programs

Existing car ride companies are the most likely path to adoption of driverless electric cars. These types of cars provide significant reductions in the cost structures for car ride services. This means that if they sit on their hands someone will come along and blow their existing services out of the water. The car ride companies have the competitive imperative to go down this path. They also have some significant competitive advantages in executing the strategy. That does not mean they will be successful, just that they have a head start.

In our next post we will take a closer look at some of the players, and the tactics that might be involved.

I am writing a book on autonomous vehicles with Dr Chris Rice from Texas. It is called Rise of the Autobots: How Driverless Vehicles will Transform our Economies and our Communities. Stay tuned for more excerpts as we finalise the book.

Note: Featured image is from NPR

 

 

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Sell Your Crash Repair Business Now*

*this should not be taken as financial or business advice. If you own a crash repair business please take professional advice before making any decisions.

I am just going through the process of getting some minor damage repaired on our car and have been ruminating on the future of the insurance and repair model when we have driverless (autonomous) cars. This was also prompted by a couple of stories in The Age here in Melbourne:

Crash repair: How Ray Malone became head of ASX-listed company AMA Group

and

Driverless vehicles technology to roll out on the Tulla under trial

 

The first story describes how Ray Malone has built a Australia’s largest crash repair business, and is aiming to grow it even further. That would seem to go against the title of this post but it actually feeds into my thinking because Ray’s company provides wholesale service aftercare which will be vital in the scenario I am describing.

The second story is about how trials of driverless cars are starting here in Melbourne. This follows a large number of trials that are being conducted in various countries around the world.

Once we move to a reasonably widespread adoption of autonomous/driverless cars the local crash repair business will basically disappear except for a few large operators like Ray Malone but even his business could be under threat . The key reasons for this are:

1/ It has been forecast that autonomous cars will significantly reduce the number of car accidents that occur. This is based largely on the statistics that human error causes more than 90% of traffic crashes. So if we can eliminate the crashes caused by idiots, people under the influence of drugs and alcohol, and people driving tired or angry (Police looked into the deaths of 86 people on Victorian roads last year and found that in more than 10 per cent of cases the driver had experienced a traumatic or upsetting event.) we can significantly reduce the number of accidents.

Against this argument is that autonomous cars supplying a transport service may result in people travelling further and perhaps take more risks. Certainly it will allow elderly people who cannot drive, and young people who do not have a licence to travel in cars more than they otherwise would. There have also been arguments that because we feel safer we may take more risks as pedestrians or cyclists.  If we are conservative and say that only 50% of accidents caused by human behaviour will be eliminated we still have a significant fall in accidents.

2/ It is highly likely that we will see large fleet models emerge where large numbers of people choose not to own a vehicle. If the overall travel costs are lower than owning your own vehicle, and you can get a vehicle anytime you need one then the convenience of transport as a service outweighs the personal ownership model.  The economics for fleet owners are different than for individual owners when it comes to crash repair services. Fleet owners will want large scale service operations to reduce costs or will pay far less for the services of smaller scale operators. This feeds into a large supplier (such as Ray Malone’s company) snapping up more business. Larger scale crash repair businesses will benefit from the economies of scale that allow them to use new technologies such as robotics to increase throughput and reduce costs.

3/ The model for crash repair business location will change. Currently crash repair businesses are located in scattered locations throughout the suburbs and inner city. This is because if I want to take my car in for crash repairs there is a significant time cost for me to take my car to a location that is not near to my house or business. I have to travel to the crash repair business, and then get back to my home or place of work. So I want the crash repair business to be reasonably close. The location is mainly driven by the customer. If my personal driverless car needs crash repairs it can drive itself to the crash repair site, and a fleet service or a shared personal car service can replace my transport needs in the meantime.

If I was asked to drive my car (actual damage pictured below) to a service centre 40 km away I would not be very happy, but if my car can take itself then location becomes much less important and the costs of the business become far more important. Locating the crash repair business in areas of lower property costs with good transport links makes far more sense. It also means that the employees of the business will have lower property costs if they live locally. We already see this model in light manufacturing and food processing/handling facilities locating around hubs on ring roads, away from  inner suburbs with high property prices.

If a fleet ownership model predominates over personal ownership this effect will be even higher as large scale fleets look for cost reductions through economies of scale.

corolla damage 1

 

So if we summarise all the factors together if we assume a 45% reduction in total accidents (50% of human error crashes) and a tripling of scale that comes from the changes described above we get an 82% reduction in the number of crash repair businesses in any city.  I believe that the changes in scale may be even higher and we may end up with only 5-10% of the number of current crash repair businesses being economically viable.

If I own a crash repair business in any suburb in any of our major cities I will come under pressure from a high scale panel beater business set up on the fringes of the city with lower property costs.

So, if you are a crash repair business:

  1. Assess whether now is a good time to sell to someone else who does not understand these changes.
  2. If you think I am wrong then you should suspend that thought for just a few minutes and  think about what it means to your business and your assets if I am right. Even if you think that chance is only 5% you should set up a series of questions for yourself to monitor in coming years so that you can change your mind if the changes start to happen. Those questions include:
  • Is the practical outcome of accident reduction matching the rhetoric of the technology experts and the modellers? Look for signs of early change, cities where adoption is at the forefront of the change and make an assessment as to whether the predictions on accident reduction are true (or even going to be exceeded) and then think about the timing of the implications.
  • Look for areas or cities where the first full scale mass adoption of driverless cars might take place. For example Singapore, with a small land mass, and a relatively authoritarian government might be one. This will give you early signs of what larger scale adoption might look like.
  • Is the adoption model going to be a personal one or a mass fleet one? If the model is primarily a personal one then you should be thinking about whether you can become one of the new mega panel beaters on the fringes of the city that will survive the change. If the model looks to be a primarily mass fleet adoption one then there are less possibilities. Those fleet operators will either run their own operations which are standardised and mechanised or they will use their economies of scale to drive down margins in the businesses that supply them. You can still run a good business that way but the opportunities will be limited and will require lots of capital to create the volume throughput and economies of scale required. You will have to compete with the Ray Malone’s of this world.
  • Are any early models of very large scale, city fringe located crash repair businesses starting to emerge anywhere around the world? Are they successful?
  • Are car companies changing their business models for car repairs. For instance electric cars have far less moving parts than internal combustion cars. Does that make a difference to your business model? Are modularised car construction and repair systems emerging that will increase the capacity to adopt robotic repair and maintenance systems that will advantage large throughput car repair and maintenance systems?

While these changes may take 15 years to start to significantly impact on the crash repair business, once they become obvious the window to realise the business value by sale will quickly snap shut.

This is just one of the many implications of change from the widescale adoption of driverless cars.I am writing a book on driverless vehicles with Chris Rice (@ricetopher). It is called “Rise of the Autobots: How driverless vehicles will transform our economies and our communities. Stay tuned for more writing as we develop our thinking further.

 

Paul Higgins

The Supermarkets Demise – A Scenario

Back in November I wrote a post entitled: Are The Two Major Supermarkets in Australia Doomed?

If you are at all involved in the retail food chain I suggest you go and read it in full. The short answer is yes, but it will be a slow train crash.

A story in MIT Technology Review last week illustrates one of the possible models that can replace the supermarket model of today:

Autonomous Grocery Vans Are Making Deliveries in London

 

Of course supermarkets will be trying to incorporate such systems into their business model as well but my view is that because of their underlying legacy systems they will find the transition close to impossible.

The story is about a quite limited trial but it points towards a possible future:

“On the back of the vehicle are eight pods, each with a crate that can hold three bags of groceries. The van is filled by human hands from a small distribution center—in this case, a larger Ocado van, which stores 80 of those crates—and sets off following a route to its drop-offs, which is broadly planned in the cloud but ultimately executed by the vehicle. When it arrives at an address, the customer is alerted via smartphone and must press a button on the vehicle to open a pod door and grab the groceries.”

In terms of the final use case:

“Clarke imagines vehicles like these being used to provide on-demand delivery of groceries from a small nearby distribution hub, so that instead of booking a delivery slot customers hail their groceries—when they arrive home from work, say, even if it’s late at night.”

This ties in with an interesting analysis of the IPO for Blue Apron, the food company which delivers meal recipes and the main ingredients for those meals to your door. In that analysis in the New York Times, chef Amanda Cohen theorised that the Blue Apron model may destroy itself. She describes the fact (which went against her initial view) that many people she has spoken to said that the Blue Apron process had given them the confidence to cook more. If she is correct then this means that Blue Apron is training its customers not to need it any more, not a great business model as it means lifetime value of a customer may be severely limited.

The combination of these stories may point to a completely different future. As Amanda Cohen says:

‘” In Hong Kong, many people swing by a “wet market” on their way home from work and pick up the vegetables, fish or beef they’re going to eat that night. Same thing in France, Latin America, South Korea or pretty much everywhere people don’t load up their giant S.U.V.s with giant quantities of groceries to store in their giant fridges once a week. The meal kit model of keeping some staples in the cupboard and getting the fresh stuff as you need it is the market way of doing things”

One of the major problems with food delivery systems and in particular with automated delivery systems is what do you do with the fresh stuff because timeliness and the refrigeration process really matters. This is exacerbated by the fact that people are home at different times of the day or night and cannot necessarily take delivery when the delivery system wants to deliver . Various ways of solving this have been proposed including smart delivery lockers in apartment buildings or the local post office, etc. I can see that models emerging where all of the non-fresh goods can be delivered by an automated delivery system from a small local storage facility where you request delivery when you are home, just like you do when requesting an Uber right now. There may even be discounts for people who take quick delivery so storage space is always available, or people who will take a shared delivery and therefore will wait longer.

If this is part of a wider adoption of driverless cars then it can be part of a larger change. Driverless cars do not need to park, or at least do not need to park in busy or congested areas. I am an advocate for a driverless car adoption model where government or privately owned fleets provide transport as a service and surpasses the personal vehicle ownership model that has dominated the last hundred years. Even if that does not come true individual owners can hire out their driverless car when they are not using it so it does not have to be parked in front of the house or the office, or at the train station.

I I imagine a changed urban environment where mass adoption of autonomous vehicles changes the urban landscape by freeing up parking areas on streets and parking facilities . The freed up space on streets creates the capacity for more foot traffic, and increases in safe bike lanes while, driverless vehicles increase the capacity for people to travel for short trips locally. The parking facilities can be repurposed for storage and/or specialty markets for fresh products.
In that changed local environment we could see a model where large scale supermarkets are no longer the norm, where specialty fresh food stores spring up everywhere within easy travel distance of people’s homes. These specialty stores would be powered by the back end logistics that Amazon creates for Whole Foods, or their competitors (go read Ben Thompson’s excellent post: AMAZON’S NEW CUSTOMER for more details on their strategy) You would pick up your fresh product and speciality items on your way home from work or by a short walk or bike ride, or driverless car ride to the local store. Automated vehicles would deliver the staples to your door on request using pre planned orders or automated ordering systems like the Amazon Dash Wand.

In many areas this could revive the concept of neighbourhoods that really work in urban environments.

There are many ways the supermarket model will be attacked in the future. This is just one possible scenario. Given the pace of driverless car adoption and capacity for the car industry to deliver the full model is still a fair way off. The automated delivery system is not so far off. It fits the four level of automated driving systems by being in a geofenced area (local delivery only from a small storage/transfer facility), and carried out at low speed to reduce the risk of accidents. Full level 5 driving automation where vehicles can go anywhere in all conditions and no driver actions required are a lot further off. That does not mean there will not be continuing experiments with automated food delivery systems.

As Ben Thompson states in his article groceries are about 20% of consumer spending (USA). That is a big prize and lots of people are going to be going after it. Long term an automated vehicle delivery system will be a part of that. How big a part, and in what form remains to be seen.

 

I am writing a book on the adoption of driverless cars with Chris Rice entitled Rise of the Autobots: How driverless vehicles will transform our societies and our economies. Follow me here or on Twitter for more updates as we write and publish.

Paul Higgins

 

 

 

The Future Competitiveness of Corporate v Individual Medical and Veterinary Practice Models. Is AI the key?

Before Christmas I did some work on the future of veterinary surgeons and the education and regulatory changes that might have to occur to move with those changes. One of the things that occupied my mind with that work was the issue of artificial intelligence systems on the competitive position of veterinary practices. My belief is that artificial intelligence augmentation of medical/veterinary capabilities is coming quite quickly and a large scale corporate practice model has significant advantages in this space over the individual practice.

Increasingly we have seen a more large scale corporate model in the veterinary practice market in Australia and around the world. Examples in Australia include Green Cross for major city practices and Apiam Animal Health in rural practice.  This has followed similar trends in medical practices where you have organisations like Medical One and Tristar which started out in rural Australia but has expanded into the cities as well.

The basic business model and value proposition of the corporate model is:

  • Presenting a single branded product almost like McDonalds so a patient or client can be confident of going to the practice no  matter where they are.
  • Increasing purchasing power of all of the back end parts of the business from pathology services down to supplies of bandages, etc. This is more important in the veterinary model where practitioners are able to prescribe and supply vaccines and S4 drugs and make a profit from them.
  • Taking over the administrative and compliance parts of the business to allow practitioners to focus on patients. This can include the standardisation and delivery of training requirements, building management, OH&S requirements etc. It can also extend to the supply of consulting hours as a service package.
  • Networking and support for practitioners in smaller practices.
  • The spread of investment and risk over a larger geographic range and customer base.
  • A much more secure retirement/part time/business sale option for owners and practitioners. On a personal note my previous long time GP semi-retired by moving into a Medical One practice and then progressively handing over his clients to the other doctors in a very caring and professional manner.

These models have expanded quite significantly over the last decade which speaks to the financial viability of the model and while there has been the odd flare up and accusation of over-servicing in general the models seem to have worked. I originally started my working life as a general practice veterinarian and the corporatisation of veterinary practices is a hot topic at the reunions I have attended.

The competitive position of the individual practice has been built around personalised service and attempting to be portrayed as caring more for the animals they serve than the big corporate competitor. many of these practices are still doing quite well but the trend is towards the corporate practice.

Which brings us to future competitive positions. I believe that we are rapidly heading towards a future where augmentation of medical and veterinary skills via artificial intelligence is going to be  a ticket to play in the game. This is going to be a narrow focused intelligence rather than a general intelligence. In the medical space we are seeing story after story emerge of new models where AI systems are getting as good as human doctors or have an edge over human doctors:

This AI Can Diagnose a Rare Eye Condition as Well as a Human Doctor

eye image from motherboard vice artificial intelligence

Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

AI is nearly as good as humans in detecting breast cancer

Self-taught artificial intelligence beats doctors at predicting heart attacks

All of the examples above are based on variants of machine learning and one of the defining characteristic of machine learning artificial intelligence systems is they need large data sets. In the heart attack example above the artificial intelligence trained itself on almost 300,000 case records. As we currently understand artificial intelligence, that system is not transferable to cancer diagnosis, it remains a specilaised cardiac application. The non-small cancer system above was trained  on almost 3,000 images and subsequent patient follow up records.

There are two ways to get a large data set to train on in the medical/veterinary field. The first is to work on aggregated image and case records as in the examples above. That will certainly be a major part of the market. Large capital expenditures will be required to assemble the required images/case files and process them in a way that improves patient diagnosis and outcomes when used in conjunction with human doctors. So we will see services offered for specialised areas such as heart attack prediction, organ by organ cancer diagnosis, etc. As always the areas that have the largest and most affluent markets will be the services that are first offered.

However we are also moving in to a world where artificial intelligence systems can be harnessed by smaller players at much lower cost. Take this example of How a Japanese cucumber farmer is using deep learning and TensorFlow:

cucumber-farmer-14 tensor flow artificial intelligence

The son of a Japanese cucumber farmer (who admittedly had some very good tech skills) built an artificial intelligence based cucumber sorting system for his parents. The problem was training the system required a lot of images and the process took a lot of computing power. More and more in the future you will be able to plug into a cloud based machine learning platform that will enable you to harness much more computing power that is specialised to do this sort of a job for you.

So a large network of medical or veterinary practices could offer services that are not offered in the general market by collecting all the data on their patients across all their practices and using machine learning platforms to train a system that is specific to their patient database.

This will be impossible for the individual practice to match. Is this the killer application for the corporate model to almost completely squeeze out the individual practice?

If so I will be watching for models emerging in the veterinary space because the regulatory hurdles and insurance requirements are much lower. If they are successful then I see those sorts of models flowing into medical practices.

 

 

The Future for Accountants

The story for accountants the last few years has been increasing levels of outsourcing tasks to low wage environments such as India, and increasing levels of automation for their tasks and their clients. The early stage of that process has been the automation in accounting software systems such as QuickBooks and Mint. Increasingly this automation will move into more and more of the accounting space including real time artificial intelligence auditing systems, automatic preparation of increasing complex tax returns, and structuring credit arrangements.

These things generally start out small and at the less complex end of things and accelerate into more complex areas before people realise it has happened.

So where is the new value for accountants. Primarily this has to be in the process of value creation for clients. Therefore accountants need to move up the value chain and Examples include:

1/ Transformation of business processes around technology changes and the re-training of staff for their SME clients.

As I wrote in Questions on the Future of Work a recent McKinsey report has stated that

According to our analysis, fewer than 5 percent of occupations can be entirely automated using current technology. However, about 60 percent of occupations could have 30 percent or more of their constituent activities automated”

This supports the notion that apart from a few isolated cases (e.g. truck drivers with driverless trucks) technology does not replace jobs but replaces particular skills or tasks. More importantly business processes and the ways in which we serve customers are changed by the introduction of various forms of artificial intelligence into technologies. This can be a customised approach for vendors like Salesforce Einstein which is adding AI services to its sales, and customer service offerings at around US$50 to US$75 per user per month. Or it can be more fundamental changes to value propositions and business models and the underlying capabilities required to deliver them.

Either way we appear to be entering an era where the jobs people will do will change even more rapidly than they have over the last 10 years and will constantly change rather than be part of a single change management process. In my experience most organisations with under 1,000 employees have little idea on how to approach this problem. This is a huge opportunity for accountants who already have close contact with their clients.

2/ Assisting clients with understanding their strategic landscape

In a world that is moving faster and changing more rapidly than ever before operators of SME businesses are facing greater uncertainty than ever before. They are also facing a paradox. The pressure on them means that they must spend more time focusing on the operational matters in their business but they are doing so right at the time that looking around to see what is happening becomes more important. Just last week I was working with an SME business that is very well run and focused on all the right things that need to be done for the next 12 months. At the same time they were not thinking very deeply about the future and that their decisions (that were absolutely correct in a short term sense) might mean for their long term future.

This means that there is great value for an independent adviser that sees a wide range of other businesses and can:

  • Provide a better strategic understanding of the industry in which the client business operates. Examples include looking at possible industry scenarios for 5 years time and trying to understand what the interim competitive position might be.
  • Cross pollinate ideas and ways of doing things from other businesses in other business sectors. Sometimes very simple tools and approaches from somewhere else can significantly improve a businesses bottom line.
  • Look at the business from a dispassionate but involved perspective and ask questions the business is not asking itself. Examples might include – does your logical short term investment in cost improvements weaken your balance sheet and capacity to respond to x/y or z which are significant risks?  OR What custom built systems are you using which can be supplied via industry standard products or new utility services.
  • Run a structured red team/blue team process to attacking and defending the business from an outside perspective.

 

3/ In the future: utilisation of AI to augment their own capabilities

The reality of artificial intelligence is narrow expertise systems rather than a general intelligence. So we will see artificial intelligence systems that can aid sales people and customer service people but cannot do other things (see Einstein above). We will see narrow artificial intelligence systems that can assist doctors but not do much else. The list goes on.

The modern approach to artificial intelligence systems is basically on of machine learning which requires large training data sets and a large market to justify to expenditure on development and training. Therefore we will see AI systems developing in markets where there are either a lot of customers, or high margin customers, or both. Given how many accounting practices there are around the world the accountancy business is one that is ripe for such a development.

Veterinary Schools as a Platform (VSaaP)

Late last year we worked with the Australian Veterinary Board Council and the Deans of all the Veterinary Schools in Australia and New Zealand looking at what the future of veterinary education and regulation might look like in 2031.  The date was chosen to be the time when a 12 year old just starting secondary school now would be a graduate of 2-3 years standing. We looked at a whole range of issues including availability of smart phone based diagnostic kits for pet owners, artificial intelligence systems for diagnosis, urban densification and its effects on pet ownership, veterinary practice corporatisation, international trade requirements, and the need for wide or narrow scope veterinary degrees.

One of the ideas that emerged from the process has stuck in my head and I think has great scope to revolutionise how we provide a much wider range of education at universities and for education post graduation.

Essentially the is one where  the veterinary school acts as the primary site of education for those subject areas that need face to face contact and technical expertise that cannot be achieved in an online, video, or virtual reality environment.

All other subjects/modules are accessed by the students via the school platform and the teaching material and processes that form the basis of those modules can be supplied by any accredited service across the globe. The model looks like the following diagram if we just look at one module, in this case cat medicine at the vet school at the University of Melbourne:

Vet school as a platform image

On the supply side of the platform (above the line in the diagram) cat medicine courses are supplied by all the possible services globally that wish to provide that service and who are able to meet the curriculum needs.  The platform would be agnostic on delivery systems as long as outcomes where met.

On the demand side (below the line in this diagram) each student in this model can choose who their supplier of education in cat medicine is. In the picture above Isabelle has chosen Sydney University because they have a great reputation but also provide face to face services at the school in Melbourne. Ivy and Anne have chosen Seoul National University because they have a great reputation and their virtual reality applications suit their learning style and they have been offered lifetime professional development at a low cost as part of  the deal.

The school accredits multiple providers from interstate and/or overseas for each module (the school itself can provide modules in competition with these modules if it desires). Students can choose the best provider for the module or subject they wish to complete.

Competition on the platform fosters innovation in teaching content, support and methodologies that best meet the student’s needs.

Collaboration may occur between schools with centres of excellence formed to compete with international providers. E.g. Sydney University could be the cat medicine centre of excellence that allows economies of scale to be achieve on content creation and methodologies (for example virtual reality technology is still quite expensive but spread over 1000 students the costs come down).

Uncertainty about the future education and information needs is dealt with by the system working as “plug and play” with new subject matter being able to be added as flexibly as possible, and many providers producing a much larger resource base. This should allow more rapid adoption of new content as the world changes (e.g. big data systems/network facilitation for clients with home diagnostic tests).

On top of the pre-registration process it could also be used for post registration professional development with or without limited degrees. Currently vets have to learn and qualify across a massive range of animal species but many go into small animal practice and never see a cow, sheep, or pig again. Shorter narrow species based degrees could be supplemented by post graduation systems that allow vets to qualify in other areas if they wish to change careers or specialties.

By taking advantage of education technologies to improve the efficiency and quality of education a school as a platform system. There are multiple advantages to this beyond what has been discussed above:

  • The time and costs of delivering some content can be reduced.
  • Greater value can be created in other areas by increasing the time and resources applied to the teaching of those areas.
  • Self paced degree systems could be put in place where the pace of learning is determined by the student rather than the needs of the lecturers or the school.

Regulatory/Accreditation issues could be relatively straightforward if the existing schools are accredited and the content partners are required to meet content and/or competency based assessments. Combined with limited degrees, intern models, etc. the issues can become quite complex. The accreditation process itself may need to become more flexible and capable of responding faster to changes in technology.

Technology in delivery of course and maximising flexibility in systems is rapidly advancing. For example the University of Texas has a major collaborative project going on with Salesforce:

UT System partners with tech industry leader to develop next-generation learning platform

The future is coming faster than we think and it has the potential to radically change education models.

 

Are we entering a world of the new portals?

tim_tams

Yes we are, and it has serious possible consequences for a whole range of businesses.

Gather around children for a story when grandpa was young (in internet years anyway). Once in the deep dark history of the internet portals were all the rage. They were the place that companies hoped you would use as your stepping off point into the internet. If you were part of their portal and had it set up as your home page then they could offer you services, and make money from you being there rather than you wandering around on your own. Truly capable search killed off a lot of these efforts by allowing people to more easily find what they were looking for although portals still make sense in a number of areas. For example here in Australia the Our Community organisation which supports about 80,000 community organisations has a grants portal (Our Community) which aggregates all of the grant information across the country. Essentially it provides a specialised aggregation and search service. The government also offers a portal called myGov which aggregates a range of government services, offering both specialised search services but also common identification and log in systems.

Besides these sorts of services we seem to be moving into a new era of portals based primarily on mobile systems. On March 3rd TechCrunch stated that “Uber plans to turn its app into a ‘content marketplace’ during rides“. I have long believed that Uber is a data and services company with the transport model being an interim stage, and this fits perfectly with that theory (confirmation bias anyone). As a transport company they are in a brutally competitive world. They have already signaled their intention to be in the autonomous car business, but I think that business is going to be an even more brutal fight that will require huge reserves of cash. That fight will include the existing car companies and a host of new competitors and some will inevitably lose, and have their whole business model destroyed. In those sorts of fights technology plays a role but quite often it is a last player(s) standing sort of a fight where people bleed cash until they can no longer operate. In that sort of fight it makes sense to have multiple possible strategies rather than a single win or lose one. If, in the interim period before autonomous cars are widespread Uber can build a huge trove of data and insight for autonomous cars, but more importantly insight into the movement of people, and what they do during and after their trip then they have a separate strategy. If they are not able to slug it out in the transport space then they can supply data and services across the whole sector instead.

A content market place for Uber makes sense. According to the TechCrunch article they now are providing 10 million rides a day and to a large extent those riders are a captured audience for them. They also know in advance where you or I are going and when we will arrive which is enormously useful information. If they know you are going to a shopping centre then businesses at that shopping centre would really like to know that so they can send you offers in advance. If you are going to restaurant area at lunchtime, or an airport the same applies. If they can tie your trip data to what you do after the trip by tracking you through their application and partner applications then the value of the data increases astronomically.

In this new portal era they are the aggregators of customers for other businesses but they are also aggregators of data that increases in value as time goes on. In some ways it is the perfect business – profitable while it is building a new capital asset that is much more valuable.

There are lots of other companies that are pursuing this strategy. WeChat in China now has enormous capacity inside its app that is intended to keep you inside their ecosystem so they can aggregate demand and sell it to other companies ( WeChat is morphing so Chinese smartphone owners will never have to download an app again). Facebook is attempting to copy them while also trying to copy Snapchat and is a huge percentage of people’s mobile we traffic (Benedict Evans).

All of this raises a very serious question. Where will the profit accrue to in this new world? Here in Australia we have had retail dominated by two major supermarkets although this is slowly changing. As a result they  have had the highest retail supermarket margins in the world, which have now crashed back to earth due to competition (see my post: Are The Two Major Supermarkets in Australia Doomed?). Through that time the suppliers of food into the Australian markets have been mostly constrained by having to supply to those two companies in large volumes. If you are Coca Cola or Mars this has not mattered too much but it has mattered to most suppliers (hence the high margins).

The advent of the internet and the capacity to connect to anyone around the world even if you are a one person business was supposed to break some of this  down, to usher in a new age of commerce. To an extent that has been true. However I have been working with a couple of businesses that want to use some of the new technologies to connect directly to the customer rather than going through the major supermarkets as “the agent of the consumer”. One of the key concerns is whether they are swapping one master for another. If they end up with channels going through systems and applications like WeChat or Facebook does the profit accrue to them or the platform, and how much can they rely on the platform continuing to deal with them in a fair and consistent manner. In part that question is answered by platform economics – if the deal does not work for the customers on both sides of the platform then the platform disintegrates. On the other hand if you are not key to the relationship between the consumer and the platform itself it leaves you in a very weak position.

If we use that thinking and go back to Uber then I do not believe that a coffee shop at my local shopping centre is going to have the clout or expertise to efficiently partner with Uber to market specials through their App. It seems far more likely to me that another aggregation platform for coffee shops (and others) will partner with Uber and connect up the systems required to send me a notification of a special as I am on my way, or take my order via the Uber app as I travel . That means if there are three coffee shops at my local shopping centre, and Uber has a significant transport footprint, as soon as one of them has joined up the others are forced to do so or lose customer traffic. In that case none of the coffee shops actually wins because the actual customer levels between all of them are likely to be the same (ignoring any marginal traffic that may come from signalling). However the two aggregators (coffee shops and Uber) are going to want a cut of the action so it is likely that the profit margins of the coffee shops will fall.

So in a world that promised better contact and relationships with customers for small businesses the result may actually be less contact, more distant relationships, and less profits as a result. The advantages go back to the portal holders.

Of course in a modern world there is always the opportunity for the coffee shop to make a direct contact with me when I come to their shop. If they can establish a contact with me via my messaging App of choice (WhatsApp in my case) then they can form a direct relationship. However that relationship lacks a geo-location and proximity/arrival time context so unless my messaging App can supply that then the direct relationship is at a significant disadvantage. It also lacks the sophistication to be able to take my order, so unless another aggregator can link to my messaging app and provide those services the friction of the relationship will be too high. And so the dance continues.

The whole process is complicated by the fact that people actually use very few apps. In theory the coffee shop could have an App that does all these things and connects with me but given the limited real estate on my device and the fact that most people only really regularly use 5 apps that is not going to happen.

It is going to be very interesting to see where all this goes in the not too distant future.

p.s. – for those of you not from Australia the picture is one of Arnotts Tim Tams which one of the major supermarkets tried to force a price reduction on but was unable to do so because of their popularity. Therein lies a lesson. You should try them, they are awesome.

Addendum

An additional point is that this means that the brand reputation issues that are afflicting Uber today are far more important than might be thought. If one of your key strategy pillars is that people need to trust you to share their data with you then your culture, behaviour and leadership become critically important. Because of the network effects of the data collection and utilisation if volume falls in broad terms but data sharing falls more then capital value of the company falls far more rapidly. I am sure that the board is having that discussion with the CEO right now