I have been closely following Tesla and Elon Musk and I have made some observations which make me believe that Tesla will be the Amazon as well as the Apple of Self Driving Cars. I have done a fair bit of research on self-driving, autonomous cars and AI in the past year and I am pretty excited about this space.
Tesla has Elon Musk at the helm who I feel is the Edison of our generation. Over the years he has been building an arsenal of artificial intelligence and self-driving technology coupled with strategic execution which will propel the company to become a monopoly for self-driving cars of the future.
Self Driving Cars is a game of long tail execution. Getting to doing parts of Self-driving isn’t enough and Elon has proved time and again that he is in the long game. All Tesla cars help gather data for self-driving which is one of the most crucial areas in which other self-driving companies struggle with.
Self Driving Cars require tremendous computational power to execute AI algorithms. For the long term it is economically more feasible for the vehicle to be electric than to be operated by gasoline. This is one of the reasons why GM's cruise automation uses GMs electric cars for development. This is the first distinction where Tesla has already started with electric vehicles to begin with. Even though, this sounds trivial it can be the game changer for Tesla.
The Apple Model
Apple sells user-centric software on top of differentiated hardware products. It owns the entire process from procuring the best and building the best product. It sells the product via its own designed stores. It owns the relationship with the customer end to end. Tesla follows a similar model. It has the best electric cars running the best software on top of it. Its OTA which improved the braking in its cars was the first of its kind in consumer car world. Tesla also acquired some of the best automation companies to manufacture their cars. Moreover, Tesla has no dealerships. It sells its cars directly to the customer no middleman involved. This helps Tesla to own the relationship with the customer. Tesla’s car design, attention to detail and safety is considered the best in the consumer car world. With Tesla, once you drive a Tesla you will know there is nothing like it. Tesla autopilot is shown on this youtube video which is pretty amazing. All these points tend to Tesla becoming more and more like Apple where it sells the best self-driving/artificial intelligence software on differentiated electric cars.
The Amazon Model
When Amazon bought the company Kiva Robots for its warehouses, the first thing they did was to stop selling those robots to other customers. Amazon knew that those were the best warehouse robots and they wanted to keep those exclusively for their own warehouses. Similarly when Tesla bought the German automaker as part of their ‘Machine that builds the machine strategy’; it did something similar so that the best automation company exclusively builds only for Tesla. Since Tesla has also acquired more companies for this strategy.
Amazon fuses the horizontal and vertical business models. Horizontally, Amazon aims to fulfill all the product needs for consumers. For an electric car the most important need is the battery. Tesla has open sourced it technology and aims to scale its battery producing technology for the masses. This is the start to Tesla’s horizontal strategy. Vertically, it means providing the best experience out there. Tesla Cars are a customer of the Tesla Gigafactory in the same way Amazon is a customer for its own AWS infrastructure.
This puts Tesla on a path where it will be head to head with Google with its successful self driving car program so far. Google like it has always done will outsource hardware and will fit its software on top like it has done for Android. This would still leave Tesla completely differentiated in many aspects. Only time will tell if Tesla will become the Amazon and Apple for Self Driving.
Needless to say, the next few years will be something to watch out for. The self-driving car space and the competition between Google and Tesla will be exciting.
Developments in algorithms, data, and hardware have increased the opportunities in AI tremendously. To capture value in this market, suppliers need to clearly understand the key trends in the industry and devise a go-to-market strategy accordingly.
With better algorithms, increased stores of data and improved hardware performance, AI is finally beginning to achieve its potential. Sophisticated technologies like Deep Learning further increase the need for investments in leading-edge hardware and software. While several companies have taken early steps to win in this market, the industry is still nascent, leaving the field open for supply-side companies to capture value and see a return on their huge AI investments.
The supply-side landscape
Before moving on to a strategy, it is important to clearly understand the chaotic supply landscape in AI. The offerings in AI can be seen as a nine-layered technology stack:
Positioning to win in AI
To capture value in the growing AI market, companies on the supply side need to heed the following six points:
Value capture in the consumer sector will be limited initially – Early AI offerings are product and service enhancements that appeal to consumers and increase their engagement level, but do not contribute to the bottom line, e.g. online translation, digital voice assistants. These are typically offered by large tech companies, who have bigger pockets and access to more data, leaving little opportunities for smaller companies initially.
But fee-based offerings, like home assistants and self-driving cars, will create more opportunities in the next wave of innovations.
Winners will focus on micro verticals in promising industries – Instead of getting confused by the hundreds of opportunities available, suppliers should focus on specific industries.
To select the right industries, following criteria can be helpful:
Size of the industry
Potential for disruption within the industry
Maturity (or readiness to embrace new solutions)
Based on these criteria, the strongest opportunities appear to be available in the public sector, banking, retail and automotive industry.
Once an industry is identified, suppliers should identify specific verticals where solutions can result in high ROI. The value proposition is not compelling for broad, horizontal, industry-wide solutions.
Suppliers will need to provide end-to-end solutions – Customers look for solutions across all nine layers of the technology stack from the same supplier. This saves effort spent in getting different components to work together, and also provides suppliers with strategic foothold with customers. Such wide-ranging capabilities could be completely in-house, through acquisitions or through collaborations. Nvidia, for example, offers its Drive PX platform for cars as a module, not just a chip, combining processors, software, cameras, sensors and other components.
Most value will come from services and hardware – Bucking the general trend of commoditization of hardware, about 40-50% of value to AI vendors will come from this part of the technology stack. This is because, in AI, every use case has slightly different requirements, needing partially customized hardware (head nodes, inference accelerators, training accelerators).
Another 40-50% value will come from services (solutions and use cases). System integrators, who often have direct access to customers, will capture most of these gains by bringing solutions together across all layers of the stack.
Software, surprisingly, is unlikely to be a differentiator. Since data – another important component of AI – comes from the customer itself in most cases, it will not provide much value either. Though a market for third-party data might emerge in the future.
Specific hardware architectures will be critical differentiators – Cloud will continue to be the favored option for many applications, given its scale advantage. Within cloud hardware, customers and suppliers vary in their preference for application-specific integrated circuit (ASIC) technology over graphics processing units (GPUs), and the market is likely to remain fragmented.
There will be a growing role for inference at the edge, where low latency or privacy concerns are critical, or when connectivity is problematic. At the edge, ASICs will win in the consumer space because they provide a more optimized user experience, including lower power consumption and higher processing, for many applications. Enterprise edge will see healthy competition among field programmable gate arrays, GPUs, and ASIC technology. However, ASICs may have an advantage because of their superior performance per watt, which is critical on the edge.
The need to act is now – To win in AI, reliance on status quo won’t work. Strategy revision and big bets to develop solid offerings right now is the key, instead of waiting two to three years. Unconventional strategies are already seeing high returns. For instance, Nvidia is increasing its AI R&D expenditure and creating an end-to-end product ecosystem, gaining market share and seeing exceptional returns from AI offerings.
Keeping in mind the above points, these three questions could be useful in devising a strategy for the AI market:
Where to compete – Look at industries and micro-verticals, select a use case that suits your capabilities and address the customers’ most pressing needs
How to compete – Look for partners to provide end-to-end solutions; Be open to creative pricing options
When to compete – Instead of striving for perfection, focus on solutions that help you establish a presence now
It is impossible for anyone to accurately predict how the next several years will unfold in relation to AI, given its complex nature and rapid rise. But, it is possible to make specific predictions about AI trends for 2018 and analyze its key implications for business, government, and society. Based on insights from AI visionaries and PwC’s own advisory experience, the firm has come up with 8 such predictions.
AI will impact employers before it impacts employment
The job market will not be hit, but work’s nature will change. Loss of manual and repetitive jobs will be offset by new jobs performed by centaurs (human-machine hybrids). To prepare, business will need to bring together teams and data from different disciplines. Many organizations will need to start retooling.
AI will ramp up its presence in the workplace
Automation of complex processes, identification of trends and forward-looking intelligence will mean less busywork for humans and better strategic decisions. Organizations will want to figure out specific problems AI can help solve; new measures to assess business value will be needed.
AI will help answer the big ROI question about data
With data now being used to solve specific business problems, development and funding of AI solutions that draw heavily on data will get easier. Firms’ data infrastructure will have to be put into order.
AI talent race will not be decided by technologists alone
An increasing need will be felt for domain experts: retail analysts, engineers, accountants, etc., who can prepare and contextualize data, and work with AI experts. These employees will need to be trained for the specific data skills needed.
AI will make cyberattacks more damaging, and cyberdefense more effective
Pattern interpretation techniques like machine learning, deep learning, and neural networks also make it easier for hackers to find and exploit vulnerabilities, forcing businesses to start thinking about AI’s security applications. Cyberdefense may be the portal through which many enterprises get their first taste of AI.
AI’s Black Box will become a priority
Growing pressure from end users, clients, and regulators, counterbalanced with the costs involved, will mean companies will need an assessment framework to determine how much information each AI application should produce about its decisions.
AI will see nations sparring over it
China may take the lead; Canada, Japan, the UK, Germany and the UAE will do well too. International collaboration is on the horizon in some areas. Governments will need to increase funding; companies will need to keep an eye on the international competition.
AI’s responsible use will not be tech companies’ responsibility alone
With an emerging global consensus around responsible AI, public and private sector institutions are likely to collaborate on AI’s societal impact. Businesses might collaborate to form self-regulatory organizations.
Competing in the Age of Artificial Intelligence
As AI finally begins to realize its long-heralded potential and change the business environment drastically in the process, companies need to develop an understanding of these fundamental changes and develop well thought out, but flexible, strategies to harness the strongest capabilities of both men and machines.
After decades of unfulfilled promise, artificial intelligence has finally begun to realize its potential, driven by massive gains in processing power and better data collection methods. Developments in natural language processing and computer vision have played particularly important roles in helping machines perform tasks traditionally reserved for men. AI has also caught the public imagination over the years thanks to well-publicized events like Deep Blue’s chess victory over Kasparov, AlphaGo’s recent victory over Lee Sedol, IBM Watson’s victory in Jeopardy or even Google’s demonstration of self-driving cars. All this has prompted massive investments in AI-related areas in industries like finance, retail, and healthcare.
Differences between human and machine thinking
Because AI systems ‘think’ and interact, they are often compared with people. Humans are fast at parallel processing (pattern recognition) and slow at sequential processing (logical reasoning); exactly opposed to this, while computers have mastered parallel processing in some narrow fields, their strength lies in superfast logical reasoning.
Human intelligence allows for different types of problem-solving capabilities. Compared to this, at current processing power growth rate, artificial general intelligence is a long-term possibility at best. AI excels at performing specific tasks, fast and thoroughly.
So while investment in AI is critical, it is useful to ask this question: How can business leaders harness AI to take advantage of the specific strengths of man and machine?
AI’s effects on traditional ways of doing business
On notions of competitive advantage
In the 1980s, a technology tool in itself (such as Wal-Mart’s logistics tracking system) could serve as a source of advantage. But, now, because of open source software, algorithms in and of themselves will not provide an edge. Other traditional sources of advantage, like positional advantage and capability, are also being reframed by AI. Companies need a more fluid and dynamic view of their strengths, instead of a focus on static aspects. These three examples show how traditional notions of competitive advantage are changing:
Data – This is the raw material for AI systems, where large and early-moving companies like Google, Facebook, and Uber have an apparent advantage. They have created massive data repositories, which helps them collect more data, and also leverage it for better ad-targeting or in self-driving cars. But other companies without these resources can still do well through collaborations, even with competitors, to create their own privileged zones. They need to figure out areas where sharing works, and where it may not.
Customer Access – Traditional notions of customer access, like well-placed physical stores, are being replaced by AI-driven customer insights that help with personalized marketing and appetite prediction, generating higher revenues at negligible extra cost.
Capabilities – AI-driven automation is encouraging the replacement of traditional segmented and discrete areas of knowledge by cross-functional capabilities and agile ways of working.
On decision making
The speed of decision making is changing rapidly.
Predictive analytics and objective data are replacing decisions based on gut feel and experience (as can be seen in stock trading, online ads, supply chain management and retail pricing).
On the role of human employees
Humans will not become obsolete but will see rapid and major dislocations into new areas of work.
They will be needed in large numbers to build the AI systems.
They will be needed to help machines with common sense, social skills, and intuition as well as for quality checks.
In such an AI-inspired world, strategic issues are enmeshed with organizational, technological and knowledge issues. Agility, flexibility and continuous education are important for winning strategies.
Getting started towards winning strategies
Companies need to identify the jobs that machines or men are better at, develop complementary roles and redesign processes accordingly. Plus, they should be willing to change and adapt strategies at short notice. This is true in general for all areas in today’s business world, but especially so for AI-enabled processes.
Instead of a brute force implementation of AI everywhere, companies could evaluate through four lenses whether AI can create a significant and durable advantage:
Customer needs – Define the fundamental needs of your customers and see if AI can better address them
Technological advances – Study if the right technology exists to address your requirements and if you can make use of work already done instead of building a system from scratch
Data sources – Create a holistic architecture that combines existing data with novel sources, even if they come from outside
Decomposition of processes – Break down processes into relatively routinized and isolated elements that can be automated, taking advantage of tech advances and data sources identified above
These steps can be challenging for most companies. Setting up a center of excellence can help keep track of current and emerging capabilities, incubate technical and business acumen and disseminate expertise through the organization. It can then collaborate with the functions that would eventually put AI to use.
Thus, the full potential of AI can be achieved only if humans and machines solve problems together and learn from each other.
The intangible nature of AI technology can make it difficult for executives to build a business case for investing in new projects. Two examples show how well-thought out, high-impact projects can help with this.
The difficulty of making a business case with AI
The excitement around artificial intelligence has reached a tipping point, with its presence across sectors making it the primary battleground for technology vendors. But, despite the desperation on the part of companies, accelerators, and VCs to find a foothold, this vibrant market remains more conceptual than one of tangible substance. This makes an investment in AI technology a step into the unknown. A business case for such projects is not easy, needing reliance on intuition rather than ROI figures. Taking risks by investing in one or two high-impact scenarios can be very rewarding. The article covers two instances of how major organizations are doing this.
Sizing up the opportunity
AI requires using large amounts of data smartly, which the global law firm Linklaters is doing by turning its 175-year-old knowledge base into a competitive advantage. AI can create more sophisticated approaches for searching through this knowledge base, helping lawyers with quick information regarding legal precedents and previous projects. Linklaters’s CIO expects the ability to digitize and search contracts for key legal themes to become commonplace very quickly. The firm has already created an AI working group to analyze services in the marketplace and to work out how these technologies might impact the business.
But this change in how lawyers work also involves a cultural challenge. Senior partners will have to start trusting computers to do the same kind of work in seconds they have traditionally relied on associates to get done after spending hours with legal documents. Among other things, it’s the reputation of the lawyer and the firm on the line.
Using data to save lives
Moorfields Eye Hospital NHS Foundation Trust is involved with DeepMind Research, a project that involves the Trust sharing a set of one million anonymized eye scans. The hope is that these historic scans will improve future care, and lead to discoveries that make early detection and reduction in preventable eye disease possible. Challenges related to data security and confidentiality make it difficult to use non-anonymised data, which is actually more useful if demographic information is to be used to inform patient care. But stakeholders trust that similar projects will eventually lead to significant change in terms of how humans look at AI.
The future with AI
These experiments show that the potential of this self-learning technology is exceptionally exciting, and should encourage everyone involved in IT to investigate its uses. What also emerges is that, contrary to reports, automation does not simply lead to job cuts, but can create a new range of data science roles.