Re-Purposed Electric Car Batteries and Its Effects on Electric Car Adoption/Driverless Car Adoption

Last night I attended the Churchill Club event in Melbourne on the future of batteries. There was a great panel presenting and the discussions covered a range of battery technologies, including Ecoult which is commercialising the CSIRO ultracapacitor technology for lead acid batteries.

In particular I was interested in the presentation by Relectrify CEO and Co-Founder Valentin Muenzel who talked about Relectrify’s mission to use electric car batteries that were no longer useful in energy storage applications. This was interesting because as part of my research for a book that Chris Rice and I are writing on the future of driverless cars I had been looking at the adoption rates of electric cars as part of the rise of driverless cars. In that research I had come across an assessment by Ark Investments that had calculated the net present value of an electric car battery in a specific energy storage scenario as shown in the following table:

Ark Invest battery depreciation table

source: https://ark-invest.com/research/ev-batteries-value – accessed June 20th 2017

The basic principle is that while a battery in an electric car might have its performance degrade to a point where it is no longer useful for driving, that battery will still have significant storage capacity (think about your phone battery after 18 months – it still works but its capacity is reduced).  If you can buy that battery cheaply and adapt it to storage use then you have a cost effective solution.

Of course the Net Present Value calculation in the table is for a specified energy reserve use which has a higher price, and nobody buys an asset for its Net Present Value otherwise all you do is get your money back over time. In discussions with Valentin he told me that without giving away commercial secrets the model for them is about 50% of the value of a new battery. This is important because the cost of new car batteries is falling. An analysis of battery prices by Bloomberg New Energy Finance in January showed the pace of that change:

battery prices falling fast from Bloomberg

source: https://www.bloomberg.com/news/articles/2017-01-30/tesla-s-battery-revolution-just-reached-critical-mass  

Now this is the price of the battery itself which is not the same as an installed battery system but the progress has been amazing, and mirrors what we have seen in solar energy. No great basic technology breakthrough, but significant technology improvements driven by the cost learning curve. In a separate report Mckinsey has stated that electric vehicle batteries fell to $227/kWh in 2016 with Tesla claiming to be below $190 per kWh (Electric vehicle battery cost dropped 80% in 6 years down to $227/kWh – Tesla claims to be below $190/kWh) Rumours have also circulated that Tesla has got the battery costs down to $125 per kWh (Tesla is now claiming 35% battery cost reduction at ‘Gigafactory 1’ – hinting at breakthrough cost below $125/kWh) although the truth of that remains to be seen. There is no doubt about the rapid pace of changes occurring, just the quantum of that change.

Valentin and I also discussed the model for autonomous vehicles given that a fleet model or a car sharing model means that cars would travel far more kilometres in a year. For an electric car this means that the battery will reach its degradation limit more quickly as the battery would be charged and discharged more often. Interestingly for Valentin this meant that the battery would be worth more for repurposing, because outside of the energy capacity the battery still retained, its relative newness means the technology is likely to be more advanced, and safety and physical deterioration characteristics would be much better.

Given that storage is likely to become far more important in the future given changes in the energy generation mixes around the world it puts a slightly different complexion on the costs of electric cars. It is our view that the end game for driverless cars is mass fleets supplied as a service with hardly anybody owning a car. If electric car batteries only last 3 years in shared driverless vehicle but have significant re-sale value it lowers the lifetime cost of a kilometre travelled and therefore accelerates us to the point where the cost of running an electric car is lower than running a fossil fuel car. Lifetime cost factors less into individual car ownership decisions but if you own 50,000 cars in a mass fleet in a highly competitive market it becomes it becomes a much more important factor. This changes adoption rates and also the balance between fossil fuel and electric cars.

The effects of this will be lumpy as Valentin advised that different batteries have different degrees of difficulty for repurposing as stationary storage. This is related to the original design decisions made for the battery technology which were originally made with the purpose of electric cars in mind, not stationary storage. For example apparently Tesla has stated that their car batteries will not be repurposed and that is due to the design constraints in their battery technology.

A note of caution:

In a discussion with John Wood (CEO of Ecoult) he quite rightly warned me to be careful of the public statements of battery manufacturers and suppliers on their lifetime use. Given that we are talking about changes in technology and lifetimes of 10-15 years which are therefore untested in the real world, those are wise words.

 

Image credit: The featured image is from http://www.relectrify.com/

Strategy, Digital, and Governance

Estelle Metayer (@competia) who I greatly respect as a futurist and governance expert tweeted out today this article:

Digital directors in industrial boardrooms

The thrust of the article is that digital strategy is so important these days that having a “digital director” is crucial to board room governance and strategy.

There is no doubting the importance of digital technologies in the current environment and for a more comprehensive take on this I recommend you read:

Which Productivity Puzzle?

by Bill Janeway. It is a great discussion around an issue that is getting lots of airplay (is that a thing any more?) – the question of why we are not seeing greater productivity increases from the adoption of digital technologies. The last part of the post looks at some of the data that clearly shows that productivity is increasing much faster than the average in “digital leaders”. Given that productivity is a key driver of profitability and general economic growth it seems obvious that successful digital strategy is a key component of the future of nearly every business.

If we take that as a given then we come to the question of whether there should be a digital director. My view is that you cannot have every technical/strategic/financial/legal capacity on a board or board size becomes unmanageable. In my experience big strategic failures arise when strategy is driven by technology adoption rather than being customer driven.  Also on boards where I have been a director I have seen too many times a board abrogate its responsibilities by deferring to the expert on a particular issue. Rather than taking an open and questioning approach boards will turn to the legal director or the risk expert and follow their view. This reduces the collective intelligence that is brought to bear on the issue.

My concern is that if there is a digital director then strategy around digital technologies will be driven by the views of that person.  I want the following things to be uppermost in the mix of skills on a board:

  1. Enough industry experience – so that the board is not naive.
  2. Enough outside the industry experience – so that the board is not captured by the thinking in that industry.
  3. A mix of males and female (see my comment on the Uber board around this )
  4. A large focus on customers.
  5. Strong strategic minds with  the capacity to question strategy proposed by management.

If you are able to get all of those things then I do not see the room for a director with specific (and probably narrow) digital expertise.

I am particularly taken by the view expressed in:

What a digital organisation looks like

by Janet Hughes, who views a digital organisation as essentially an organisation wide attitude to become open, responsive, and efficient. A single person that is deferred too cannot achieve that as Janet eloquently represents in her image:

digital super hero from Janet Hughes on Medium what does a digital organisation look like

Paul Higgins

 

 

 

Augmentation of Human Capacity

On Friday I did the opening keynote for the Mindshop Australia conference. The title was “Bringing the Future into your Advisory Practice”. The focus was on ways of creating more value for the clients of advisors in the network. After the session there was much discussion from participants on the nature of work and the sorts of jobs that they should encourage their children to be aiming for.

My response to those questions was to use examples to highlight principles rather than recommend specific jobs because jobs will change. I used the example of the health sector and new AI developments in my presentation as well as in the discussions afterwards. For example:

Self-taught artificial intelligence beats doctors at predicting heart attacks

stylised heart image from sciencemag

On the weekend I was then reading Stowe Boyd’s  10 work skills for the postnormal era and I was struck by the statement on “Freestyling” from Tyler Cohen:

“When humans team up with computers to play chess, the humans who do best are not necessarily the strongest players. They’re the ones who are modest, and who know when to listen to the computer. Often, what the human adds is knowledge of when the computer needs to look more deeply”

This married up with the response I was giving to participants at the conference. The use of AI systems to augment the capacities of humans  does not augment everyone equally. In the world of medical specialists it is a commonly held view among patients that they will put up with specialists with poor social skills or high prices because of the knowledge they hold (putting aside the issues of the professions restricting supply to keep prices high).

If that knowledge moves largely to the realm of artificial intelligence then this re-weights the value of the medical specialist. If the machine can do things the individual or team cannot possibly do by being able to access more knowledge and make more connections in that knowledge than is humanly possible then it changes the system. Knowledge becomes less important and skills such as the capacity to work with the AI, patient empathy and general social skills become more important.

Augmentation  of human cognitive capacities will do that across sectors and industries.

 

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.

 

Tech Toys When Simple Will Do – $100,000 V $500

Give me $500 and I will fix the problem

 

On Monday there was a story here in Australia about traffic lights being installed in the pavement to reduce the risk of pedestrians crossing the road against the lights because they were looking down at their mobile phone screens: (Lights installed in Melbourne footpath to help distracted pedestrians cross safely)

pedstrians on phones abc traffic lights

source: ABC

Apart from the chorus of responses that people who were hit by cars when crossing against the light while looking at their phones is Darwinian evolution in action what struck me was the insane cost at $100,000 for one intersection. Now some of that cost is for the trial process but it would be enormously expensive to role out across the city. I am always reminded of the lyrics of 21st Century Digital Boy when I see this sort of stuff happening:

‘Cause I’m a twenty-first century digital boy
I don’t know how to live but I got a lot of toys

Not because of the phones but that we look at technology solutions when simple ones will do. Now some will argue that we should not be arguing about costs when lives are at risk but the hard truth is that if we spend money on this sort of thing then money is not available to spend on other things which may more effectively save lives.

Surely the simple solution here is to paint the approaches to the intersection a bright neon yellow so people who are looking down at know they are approaching an intersection and look up at the traffic lights, which are already there! This works for bikes on bike paths approaching risk areas. I have just come back from the World Science Fair in Brisbane where we walked in to South Bank from Milton each day and the bike paths/walking areas are great:

Bikeway_&_footpath_along_Brisbane_River_in_Milton,_Qld_07

Source: Wikimedia Commons

This issue is symptomatic of a wider problem across the community. As a futurist I talk to lots of people about technology, its impacts, and its capacity to change the way we live and work. However my constant refrain is if you lead a strategy with “shiny new toy syndrome” you will almost certainly fail.

Give me $100,000 and I will fix every intersection in the CBD with a paintbrush and a can of paint