We recently held a video panel discussion with experts in the field of education and educational technology, to further explore the topic we first raised in our report Investing in the Future of Work. The Cornerstone team was joined by:

The discussion centered on the need for ongoing, lifelong learning, and the different forms that may need to take in order to better enable the workforce of tomorrow (and today, in fact) to better adapt as technology changes and new skills become key to success.

Gender lens investment approaches have expanded in recent years. All asset classes have seen a tremendous increase in the number of funds and assets under management since 2014. Fund strategies range from empowering women and funding women-run businesses to reducing gender violence and poverty for women and children.

At the same time, investors have also been seeking ways in align their activities in support of the United Nations Sustainable Development Goals (SDGs). Cornerstone Capital Group has contributed to this effort by introducing the Access Impact FrameworkTM, which illustrates the alignment of investment strategies to each of the SDGs. We identified the concept of access — the ability of individuals and societies to achieve desired social, economic and environmental outcomes — as a key common denominator of the SDGs and identified 11 “access themes” that translate the SDGs into investable opportunities.

SDG 5 is “Achieve gender equality and empower all women and girls.” For investments to have an impact related to achieving gender equality and empowering women and girls, investors do not have to invest solely in gender lens funds. Our approach to gender lens investing incorporates traditional gender lens themes with an analysis of the access themes that align most closely to SDG 5.

In this report we discuss each of the access themes that underpin SDG 5 in some depth. We also offer examples of investment vehicles that bolster access to these themes for women, their families and communities. Download the full report here.

On October 17, 2018, ROBO Global, LLC (ROBO) hosted a panel on robotics, automation and artificial intelligence (AI) at the New York Stock Exchange prior to ringing the closing bell.  ROBO is an index, advisory and research company focused on helping investors invest in the fields of robotics, automation and AI globally. The firm is an index provider with an ETF, under the ticker ROBO, that tracks the index and is traded on the NYSE. Panelists included experts on Robotics and AI from academia, investment banking and industry. The panel discussion is timely given the relatively recent explosion of big data, which ROBO cites as the fuel for AI, the big leap in machine intelligence capability, and its impact on multiple sectors of the global economy. The following are some highlights of the panel discussion.

Disruptive technology – should it be feared or embraced?

Overall, the panel had a positive outlook for the future of AI, robotics and human productivity. A lot has been written in the American press about machines taking over human jobs and fear about the dangers and related disruptions attributable to AI and robotics. According to the panel, this is counter to the attitude in Asia and Europe, which seem to embrace the emerging technologies. U.S. fears, while understandable, may not be realized fully. One of the panel experts noted that machines make a lot of mistakes by themselves and human workers tend to be error-prone as well. But when humans partner with machines, mistakes plummet and productivity improves.

Big data fuels AI and robotics

Big data is the fuel of AI and it is growing by billions of gigabytes daily. Given the massive growth of data, improvements in AI, automation and robotics technology, the outlook for a new era of productivity can be realized through these disruptive technologies across multiple sectors of the economy. Everything from defense and manufacturing to the medical field will be impacted. For example, Wyatt Newman, a professor of computer science at Case Western Reserve, sees a day when physicians will be elevated to become mainly supervisors… of robots. Already, physicians direct robots built by companies such as Intuitive Surgical to more accurately perform surgery.  Long term, he believes there will be “home” robots far more sophisticated than Roomba, a vacuum cleaning robot, able to tackle many needed tasks.

What’s driving the evolution of this technology?

Dr. Newman noted that a fundamental change in the AI/robotics industry is happening due to a confluence of events.  The cloud, big data and automation are all benefiting from technological advances in the gaming industry, where advances in hardware have helped Google and Intel innovate. He notes that reusable software for robots is bringing costs down while “deep learning” helps make robots function more effectively.  Deep learning is part of a broader family of machine learning methods based on learning data representations. Specifically, deep-learning software attempts to mimic the activity of neurons in the brain where thinking occurs. The software learns to recognize patterns in digital representations of sounds, images, and other data as opposed to task-specific algorithms.

Asian and global juggernaut

Morton Paulson, head of research at CLSA Japan and an expert in the industrial sector, notes that while the U.S. fears AI and robotics, Europe, Asia and other countries broadly embrace the technology. China, India, Southeast Asia and Mexico are investing heavily in robot technology.

Panel members noted that China has been investing heavily in AI in recent years versus a relatively small investment just a few years ago. One panel member opined that the Chinese are investing five times as much in AI today compared to the U.S. China’s goal is to be on par with the U.S. by 2020 and to dominate the technology by 2030.  While most Chinese investments are in China, some are in Silicon Valley and other places outside of China. China has a huge amount of data and more users of the data versus the West, which adds up to enormous revenue potential.

China and Europe step up STEM Education Investment while the U.S. falls behind

According to Raffaello D’Andrea, Professor at ETH Zurich and Co-founder of Kiva Systems (Amazon Robotics), China is investing heavily in STEM education – emphasizing computational analysis. The takeaway is that the U.S. is likely missing a big opportunity, especially when it comes to investing in educating the next generation; in comparison, China and Europe are trying to give students a leg up regardless of their socioeconomic class. As measured by standardized test scores, it’s no secret that many public schools in the U.S. fail to educate students adequately in the STEM subjects. If the U.S. neglects to properly educate a broader swath of students, it may miss out on a huge technological opportunity in the decades to come.  The panel also noted that education must be life-long, instead of ending with four years of college, because technology is constantly changing and the pace of change is accelerating.

The catalyst for change

With regards to AI, Professor Newman noted that massive uses of data are still to come. Companies are collecting a huge amount of data but don’t know what to do with it yet. When data and AI connect, there will be a big explosion of innovation.

According to the panel, some potential catalysts for change in the AI industry include a China/U.S. trade war resolution and companies such as Apple getting on board with robotics and AI.  Apple and other companies will need to invest massively in AI/automation/robotics ahead of the innovation wave, as Netflix did five years ago to create content.

This is just the beginning of a massive technological wave

Most aptly, in the marketing piece for its ETF, ROBO quotes technologist Pete Trainer regarding the next wave of AI, big data and robotics: “We are at the precipice of one of the most significant discoveries since we learnt how to light a fire.”

I have a great investment opportunity for you. I have identified 60 corporations that have begun employing a novel method for sourcing products that leads to a 50% cut in overhead. There’s just one catch: The method these businesses employ has been linked to producing and maintaining racist procedures in the criminal justice system, gender pay inequity, and producing large but unforeseen consequences. So, how much would you like to invest?

The answer is clear if you are vetting investments by ESG criteria. If this is all the information you have, you simply cannot invest in those companies, at least not in a way that is commensurate with your ESG goals.

But this is the situation social impact investors are in when it comes to the introduction of artificial intelligence (AI) to business practices. By “artificial intelligence” I mean the set of tools computer scientists and engineers are developing that are meant to mimic or replace the kind of reasoning human beings instantiate when engaged in task-specific behaviors (e.g. judging the appropriate sentence for a person found guilty of a particular crime, recognizing where the street ends and the sidewalk begins, and determining whether a particular financial transaction is sufficiently anomalous to warrant further investigation).  The details are complicated but we need not concern ourselves with them here. All we need to know are two facts:

  1. Artificial intelligence has been linked to each of those ethically problematic results, and many more.
  2. Artificial intelligence is increasingly incorporated into businesses practices in virtually every industry.

ESG investors have three options. The first is to snub those businesses that employ AI. But that would ultimately result in leaving the investment game altogether. That is not going to happen. The second is to ignore the problem, or pretend it doesn’t exist, and go on investing as though it were not happening. But that amounts to giving up on ESG analysis. It is the path of despair and forfeit.

The third and only viable option is to insist that companies meet certain criteria with regards to their use of artificial intelligence. Just as there are criteria for how businesses source their raw materials, there ought to be criteria for how businesses use one of the most powerful tools any business has ever seen. But what should those criteria be?

ESG investors should insist that businesses incorporate ethical oversight of the creation and use of AI in their business practices, preferably by an independent party, in three ways.

First, businesses need a review board to vet AI that is either developed in-house or acquired from an AI company. That board would investigate, for instance, whether the data fed into the algorithm was checked for bias, whether there is a space for human deliberation and decision in between the output of an algorithm and an action, and whether the inputted data or the output violate people’s right to privacy.

Second, the business needs a review board – perhaps the same one that performs the initial review – to systematically monitor the impacts of the use of that technology. What are the unintended consequences? Are they ethically acceptable?  What do our stakeholders think of these impacts?

Third, the findings at both stages must be communicated to the developers of the AI so that appropriate alterations can be made.

ESG investors are in a crucial position. They have the responsibility – given their own identity-defining goals – to integrate AI ESG criteria into their investment strategies. And they have the power to push corporations to develop and incorporate new sustainable and ethical practices where they are desperately needed.

This article originally appeared in FA Magazine Online on August 24, 2018. 

The amount of data in the world has been rising exponentially for many years, and in the years to come people will rely more on it, interact with it more often and give more of it to the companies that provide us with products and services. Data collection will be more frequent, more pervasive and more likely to happen without the participation or even the awareness of the subject. And while this growing universe of data will enable new and useful services to improve our lives, the opportunities to use data for purposes we neither intend nor want will grow as well.

In particular, many people fear the loss of privacy as data analytics allow more accurate portraits of individuals’ lives and personal characteristics. While this kind of profiling can lead to improved services, it could also enable discrimination, undue political influence or deliberate efforts at social control and manipulation. Data-driven innovation will reach its full potential only if consumers trust companies with their personal data.

Investors should be concerned about two kinds of uncertainty as they consider the future of data privacy: first, how much privacy will consumers demand? And second, how much will government regulation impact the collection of data?

For many years, the tech industry has enjoyed greater public trust than other industries. But trust in tech companies is declining. In the wake of the Facebook/Cambridge Analytica scandal, many of the social media giant’s data-sharing practices have come under greater scrutiny. Most people now say that they do not trust social media sites to guard the privacy of their data.

To date there has been little consumers can do to prevent the collection of their data, at least in the United States. This isn’t because consumers are happy to share personal data: Polls show that consumers feel resentful yet powerless to prevent the unwanted digital intrusion. As data collection becomes more pervasive, either consumer ambivalence may evolve into outright opposition, or consumers may simply accept the lack of privacy as a fact of modern life.

The second uncertainty concerns the regulation of data. The European Union has enacted its General Data Privacy Regulation (GDPR), which requires companies that do business with European Union citizens to only collect data with the specific consent of users. The regulation, if fully implemented as intended, could hinder many forms of data collection, especially fast-growing passive data collection, which takes place without specific user participation (e.g. location data on your smartphone). While many countries (and the state of California) are adopting similar legislation, the U.S. government continues to take a narrower approach to data regulation that restricts data gathering only when there is evidence of specific harms. The future of data may be decided in part on which regulatory regime prevails globally.

How uncertainties regarding consumer demand for privacy and regulatory restrictions play out may be very significant for investors, since data-driven industries are increasingly important to portfolios. The most well-known tech names, Facebook, Amazon, Netflix and Alphabet (formerly Google)—The “FANG” stocks—by themselves make up 15 percent of the S&P 500. While data is central to these companies’ business models, companies in many other industries are also expecting improvements in data quality and quantity to improve their ability to compete—for example, the retail, telecommunications and financial services industries.

Which companies will thrive depends on the direction of data privacy demand and regulatory evolution. Heightened restrictions on data gathering could affect the ability of data-driven companies to execute on their long-term business plans. On the other hand, companies with less exposure to data and data-driven innovation may be relatively more competitive in a data-restricted environment. And If data flows more freely because privacy protection never becomes a priority, data competency could become a significant competitive advantage for companies in a variety of sectors.

Investors should be concerned that the governance of the companies in their portfolio is sufficiently prepared to manage the uncertain future of data privacy. Investors should ask about a company’s exposure to data privacy concerns, the level of stakeholder trust and how adaptable its governance structures to different future scenarios:

1. Exposure: What kind of data does the firm collect? How important is it to business strategy? Does the company share data with external business partners?

2. Trust: Do consumers have confidence that their data will be protected? Are employees engaged and satisfied with their work? Is trust stable or rising over time or is it declining?

3. Governance: Is data privacy part of strategic planning? Do boards have competency in this area? Are there proper incentives for balancing data privacy with growth objectives? Are companies monitoring stakeholder trust?

Who owns consumer data? Do consumers trust companies with their data? Will consumers embrace new technologies that reveal more information about them to companies? Are companies ready to respond to changing attitudes about consumer data?

These are core questions that investors need to consider about data privacy, the option to shield our personal data from public view or corporate use and sale. Currently, companies have largely unfettered access to the data they gather about consumers, even as technologies make it possible to know more about consumers’ online (and offline) activities. By some estimates, the number of devices connected to the internet will rise from 8 billion today to 100 billion by 2030. As devices proliferate and data-mining tools become more sophisticated, companies have increasing access to information such as our physiological traits, personal habits, location, political beliefs, lifestyle habits and purchasing behavior.

Such data may give rise to products and services that we can only imagine at present. But these potential advances come at the cost of diminished consumer privacy and the risk that our data will be used for purposes society may neither intend nor desire, such as discrimination, employee surveillance, social engineering, or unfair political influence.

Both consumer attitudes and the regulatory environment reflect deep ambivalence about the role of data in the modern economy. Studies show that many consumers do not support collection of their data, but feel powerless to prevent it. Although data flows globally, the regulations that govern it are regional and inconsistent: e.g., new EU regulations strengthen consumers’ control over the use of their personal data, while the US regulatory environment remains permissive.

Even as society struggles with the tradeoff between innovation and control, investors have demonstrated a keen interest in companies with strategies to monetize this growing pool of data. Companies have always sought competitive advantage through better information. The accelerating supply of personal data has raised the importance of data access and analytics to corporate performance, which in turns drives demand for even more data. At the center of this trend are the FANG stocks, for whom data does not merely support their business model but lies at the core of their strategy. Most of The FANG companies did not exist 20 years ago, but now make up nearly 15% of the S&P 500, having been favored by investors in the form of valuations far outstripping the rest of the index.

We believe that the ambiguity of the current circumstances is unsustainable. While the exact future of data privacy is not possible to predict with confidence, investors should be concerned that companies whose business models rely on increasing quantity and scope of consumer data are at risk if the public ambivalence turns to opposition.

To better understand that risk, we consider four potential operating environments that companies may face:

1. Low demand for privacy, low regulation: Consumer acceptance of data collection and use grows, and regulators prioritize the free flow of information over privacy. Technological innovation grows quickly, but the risk of unintended negative social consequences, such as discrimination, rises. In this scenario, companies that have high exposure to data would be best positioned to take advantage of the opportunities.

2. Low demand for privacy, high regulation: Overreaching regulations lead to dissatisfaction among companies and consumers, as market demands go unmet. Trust is difficult to obtain because the system lacks legitimacy. Companies with low exposure to data issues will avoid the regulatory risks associated with this scenario. New entrants will struggle to grow while managing compliance costs.

3. High demand for privacy, low regulation: Regulators fail to effectively respond to consumer concerns about data privacy. Technological innovation accelerates, as does the risk of unintended consequences. Lack of trust in the system creates challenges for new companies and products to gain acceptance, and consumers may take steps to restrict access to data on an individual basis. Companies that achieve high trust of employees and consumers are best positioned to navigate the instability of this scenario. In this scenario, new business models may emerge to help consumers protect their own privacy.

4. High demand for privacy, high regulation: Regulators restrict data gathering in response to consumer privacy concerns. Technological progress slows, but the system creates a high degree of trust that enables new companies and new technologies to achieve consumer acceptance with relative ease. Unintended consequences are kept to a minimum. Because no one positioning dominates this scenario, individual company management and governance to establish trust and engagement will take on particular importance.

This report:

Download the full report here.

This report was prepared by Cornerstone Capital Group for the Investor Responsibility Research Center Institute.


• assured reliance on the character, ability, strength, or truth of someone or something
• dependence on something future or contingent: hope
• reliance on future payment for property (such as merchandise) delivered

A ‘trustless’ technology

In the view of blockchain proponents, one of the technology’s major advantages is that it doesn’t require trust to conduct transactions. In today’s world, transactions require trust or, in the place of trust, intermediaries, which exist to facilitate the transaction, record the details, and serve as guarantors that each party has sufficient assets. However, intermediaries cannot always be trusted, as illustrated by the “Byzantine Generals problem,” an oft-cited analogy used to explain blockchain . Proponents argue that blockchain removes the need for trust and thus also removes the need for intermediaries.

Bitcoin, the first cryptocurrency application of blockchain technology, aimed to resolve concerns about the trustworthiness of financial intermediaries by creating a “trustless” system . Two inherent components of trust are vulnerability and expectation: there is a possibility for disappointment, but both parties accept the risk because they believe in a positive outcome . Bitcoin developers suggest that it can reduce reliance on trust by reducing the risk of disappointment.
This may be true if one takes a narrow view of the transformative quality of the blockchain. Our analysis suggests that blockchain technology may not reduce the need for trust so much as shift the burden of trust.

How does blockchain shift trust?

The focus on blockchain has been on ensuring that parties complete their transactions and that transactions are immutably recorded. However, we argue that trust is still a factor outside of the immediate transaction environment. To illustrate, we’ll use the trading of renewable energy certificates, which currently requires multiple steps.

Traditionally, a certificate-creating regulator must verify the validity of the renewable energy generator, while brokers aggregate certificates from the generators and link buyers and sellers. A simplified diagram of this process is shown below in Figure 1.

Figure 1: Current renewable energy trading (stylized)

Source: Cornerstone Capital Group

Proponents of blockchain renewable energy trading argue that no “trust” or intermediary is necessary because the technology allows parties to trade directly, and only when both parties have their assets ready. As shown in Figure 2, the renewable energy generator links sensors to a blockchain-enabled ledger. As each MWh of renewable energy is generated, the ledger updates its records with a new certificate, recorded as a digital token. Buyers can source certificates by buying tokens, and the transaction history is recorded on the blockchain ledger.

However, this argument looks at the renewable energy trading system through the narrow lens of immediate transaction mechanics. The broader “transaction ecosystem” still requires trust:

Figure 2: Blockchain-enabled trading system

Source: Rocky Mountain Institute, Cornerstone Capital Group

The ecosystem, therefore, looks more like Figure 3. In this diagram, the proposed blockchain renewable energy trading shifts the “burden of trust” from transaction intermediaries to parties outside the enclosed system. This shift might transfer responsibility to entities that are not prepared, which could be particularly dangerous as the blockchain purposefully does not allow for arbitration or the undoing of transactions. For example, a buyer who spends financial resources on a certificate from a generator who is improperly audited may have no means of recourse. Once the transaction is executed, it is recorded and unable to be undone or negotiated.

Figure 3: Cornerstone’s assessment of blockchain renewable energy tradingSource: Cornerstone Capital Group

Our assessment raises questions for investors in blockchain-enabled applications, as well as more mainstream investors assessing how to improve efficiency of trading. The ability of the technology to set the boundaries of the transaction environment may create as much complexity and risk as it resolves. At what point does it become prohibitively expensive to expand the scope of technological control to remove the need for trust? Is the need for trust such a significant drag on the economy or society?

These questions around how trust is shifted and at what cost apply beyond the current discussions of Bitcoin and blockchain bubbles. Investors across the market may benefit from assessing the relative cost versus efficiency of redistributing the responsibilities of trust outside the immediate transaction environment. A narrow view may position investors for unforeseen consequences, including negated efficiency gains or failure to deploy blockchain technology in the most valuable way.

Efficiency in shifting trust

Blockchain proponents speak of a future facilitated by trustless systems. Our assessment, currently, is that blockchain does not reduce the need for trust but rather shifts the burden of trust beyond the scope of the actual transaction. This may increase the efficiency of the immediate transaction, while moving intermediary roles to the edges of the transaction system. New responsibilities may be transferred to intermediaries who are unprepared or create bottlenecks. Investors purely focused on transaction-level trust may not be aware of the risks the might arise from such shifts.

Value in shifting trust

Investors interested in reducing the trust involved in transactions should determine whether the application is focused on the core vulnerability within the system. For renewable energy trading in particular, we view monitoring and auditing of the generators as a key vulnerability. At this point, blockchain-supported renewable energy does not address these concerns. The system still relies on trusting a third-party auditor or a set of sensors to ensure that the renewable energy is valid. Blockchain applications that address the critical issues in a trading system are likely to be more strongly positioned.

Click here to download the report and for important disclosures.

Emma Currier is a Research Associate at Cornerstone Capital Group. Emma graduated with a Bachelors of Arts degree in Economics from Brown University in May 2016. While at school, she worked with the Socially Responsible Investing Fund and as a teaching assistant for the Public Health and Economics departments. She spent her sophomore summer researching differences between American and Indian educational styles in Arunachal Pradesh, India, and completed a summer investment bank analyst position with Citi in the Media & Telecom group in 2015.

Sebastian Vanderzeil is Director, Global Thematic Research Analyst with Cornerstone Capital Group. He holds an MBA from New York University’s Stern School of Business. Previously, Sebastian was an economic consultant with global technical services group AECOM, where he advised on the development and finance of major infrastructure across Asia and Australia. Sebastian also worked with the Queensland State Government on water and climate issues prior to establishing Australia’s first government-owned carbon broker, Ecofund Queensland.

Executive Summary:

A blockchain taxonomy for investors. Blockchain technology is being used in a growing diversity of applications, offering a complex array of investment opportunities. While the technology is so new that any investment in it is speculative, patterns of use are emerging. In this report, we propose a taxonomy to enable investors to more quickly and effectively understand individual blockchain applications’ key attributes and to assess how blockchain technology will be used in the near and medium term. To illustrate use of the taxonomy, we apply the indicators to several blockchain applications that range in investment, purpose, and launch date, including Bitcoin, EOS, Tezos, Ethereum and Provenance.

Figure 1: Blockchain taxonomySource: Cornerstone Capital Group

What does the taxonomy tell us?

Our view. We remain skeptical about the ability for blockchain to replace existing non-digital transaction processes without clearer demonstrations of benefits to a wide range of users. However, blockchain is gaining traction within communities and marketplaces focused on the technology-enabled.

Download the full report here.


Sebastian Vanderzeil is a Global Thematic Research Analyst with Cornerstone Capital Group. He holds an MBA from New York University’s Stern School of Business. Previously, Sebastian was an economic consultant with global technical services group AECOM, where he advised on the development and finance of major infrastructure across Asia and Australia. Sebastian also worked with the Queensland State Government on water and climate issues prior to establishing Australia’s first government-owned carbon broker, Ecofund Queensland.

Emma Currier is a Research Associate at Cornerstone Capital Group. Emma graduated with a Bachelors of Arts degree in Economics from Brown University in May 2016. While at school, she worked with the Socially Responsible Investing Fund and as a teaching assistant for the Public Health and Economics departments. She spent her sophomore summer researching differences between American and Indian educational styles in Arunachal Pradesh, India, and completed a summer investment bank analyst position with Citi in the Media & Telecom group in 2015.

On June 23 Cornerstone hosted Jalak Jobanputra for a discussion on the blockchain’s transformative potential. Jalak is the Founding Partner of Future\Perfect Ventures, an early-stage venture capital fund focused on next-generation technology such as blockchain and machine learning. She was an early investor in the space, and is widely considered to be an expert.

Cornerstone’s Sebastian Vanderzeil, Director and Thematic Analyst at Cornerstone, led the session. In addition to recent developments in the space, Jalak and Sebastian discussed  a number of ways emergent applications for blockchain technology could address social challenges, particularly in less developed regions. The two also addressed issues of governance and accountability, following on from Sebastian’s recent report Governance and the Ungovernable: Implications of Blockchain Proliferation.



Theory and politics. The blockchain and related innovations represent a new and relatively complex set of potential investment opportunities. The technology has received significant attention from a range of individuals and institutions, from computer scientists to corporations to private equity groups. We outline the theory of the technology as well as the governance implications to guide investors.

Disruptive opportunity. Advocates of the blockchain believe it has tremendous potential to enable novel ways of creating, managing, and maintaining systems of fundamental rights. It is already being used to facilitate transparency and combat corruption — for example, Kenya is piloting its use to record land ownership and transfer, historically a poorly managed and easily manipulated process1. The blockchain operates, by design, independently of traditional arbitrators and regulators. Its widespread adoption could remove courts, central banks, and government policy makers from financial and sociopolitical transactions, which in turn has governance implications for the economic, legal, and institutional relationships as we know them today.

Governance implications. Innovation spurred by blockchain technology could result in more efficient and transparent business models. For this reason, investors should be actively assessing emerging blockchain-specific opportunities. However, in addition to understanding the basics of the technology, we believe it’s important to understand the governance implications of the blockchain ecosystem. This report undertakes an assessment of the current pillars of blockchain governance and compares these pillars to current norms of corporate governance as laid out in Cornerstone Capital’s proprietary framework. We see two scenarios: “A Blockchain World” and “Our World with Blockchain,” each offering distinct advantages and disadvantages.

Download the full report here.

Sebastian Vanderzeil is Director, Global Thematic Analyst with Cornerstone Capital Group. He holds an MBA from New York University’s Stern School of Business. Previously, Sebastian was an economic consultant with global technical services group AECOM, where he advised on the development and finance of major infrastructure across Asia and Australia. Sebastian also worked with the Queensland State Government on water and climate issues prior to establishing Australia’s first government-owned carbon broker, Ecofund Queensland.

Emma Currier is a Research Associate at Cornerstone Capital Group. Emma graduated with a Bachelors of Arts degree in Economics from Brown University in May 2016. While at school, she worked with the Socially Responsible Investing Fund and as a teaching assistant for the Public Health and Economics departments. She spent her sophomore summer researching differences between American and Indian educational styles in Arunachal Pradesh, India, and completed a summer investment bank analyst position with Citi in the Media & Telecom group in 2015.

The “skilling up” of the retail workforce has the potential to enable retail workers to improve their productivity and career prospects, while enabling retail companies to build their future workforces. Purposeful investors can identify these companies and take a long-term investor view to encourage these companies to train and deploy this workforce.

The widely reported decline of physical retail stores is alarming for a variety of reasons, but retail stores are likely to live on in one form or another for the foreseeable future (as evidenced by Amazon’s recent moves into the “bricks and mortar” space). The question for retail workers is “Which retailer should I work for?”

Our recent report Retail Automation: Stranded Workers? Opportunities and risks for labor and automation provides some insight into this question for people who are looking to join or currently work in the retail sector. The report highlighted structural changes under way in retail that have the potential to impact the size and wages of the retail labor force. More than six million of the 16 million retail workers in the US, especially women and  those located in smaller regional hubs and rural areas are at risk of losing their jobs to automation just in light of technology that is currently available.

Our research revealed two key automation-related trends likely to affect labor.

First is the “hollowing out” of middle-skilled workers who perform routine tasks, like cashiers and back office associates. These workers will either retrain for higher-skilled jobs or, without training, be pushed down into basic “innate ability” jobs (such as store greeters), with minimal career growth opportunities.

Second is the potential movement of retail stores to more clearly bifurcated strategies:

Companies that adopt an experience strategy are likely to invest in their workers and use technology to enhance the effectiveness of their workforce. In contrast, we see convenience strategies as reducing the absolute number of workers to save costs. Our report offers a framework for assessing a company’s movement towards a convenience or experience strategy—or its lack of clear direction.

What’s a retail worker to do?

Based on these two trends, retail workers looking to navigate the structural changes under way should favor companies that provide tuition reimbursement and/or technical and programming training. Workers who acquire the skills to advance beyond their current roles will be better positioned to benefit, or at least avoid harm, from these secular changes.

Examples of companies that provide such programs are shown in Figure 1.

Figure 1: Publicly disclosed tuition reimbursement and incentivized training programsSource:  Company reports, Cornerstone Capital Group

Amazon, Lowe’s, Gap, and Wal-Mart offer public disclosure around tuition reimbursement that suggests they are positioning their labor force for retail jobs of the future. Best Buy intends to increase its investment in employee development, while automotive retailers Advance Auto Parts and O’Reilly Automotive signal support of their labor force advancing within the automotive field.

Most retail companies are also actively hiring a range of programming, user experience, and merchandising workers. Retailers are competing with Silicon Valley for workers that are in high demand and have seen their wages grow significantly over the last decade, as shown in Figure 2. (Note: our original report did not explore this trend.)

Figure 2: Software developer hourly wage growth vs. total private hourly wage growth 

Source: BLS, Cornerstone Capital Group

Retailers already employ a large workforce that, with training, could provide these higher-skilled services. In addition, these workers have corporate knowledge that could allow them to be more useful to the organization than a Silicon Valley software developer.

The “skilling up” of the retail workforce has the potential to enable retail workers to improve their productivity and career prospects, while enabling retail companies to build their future workforces. We believe purposeful investors can identify these companies and take a long-term investor view to encourage these companies to train and deploy this workforce.

Sebastian Vanderzeil is a Director and Global Thematics Analyst at Cornerstone Capital Group.


Executive summary (download full report here)

The retail landscape is experiencing unprecedented change in the face of disruptive forces, one of the most recent and powerful being the rapid rise of automation in the sector. The World Economic Forum predicts that 30-50% of retail jobs are at risk once known automation technologies are fully incorporated. This would result in the loss of about 6 million retail jobs and represents a greater percentage reduction than the manufacturing industry experienced. Using Osborne and Frey study1 with the Bureau of Labor Statistics, the analysis suggests that more than 7.5 million jobs are at high risk of computerization. A large proportion of the human capital represented by the retail workforce is therefore at risk of becoming “stranded workers.”

As of 2002, retail employment exceeded total manufacturing employment, and now sits at about 16 million workers (Figure 1). Total manufacturing employment, which peaked in 1979 at approximately 19 million workers, has fallen to 12 million workers. The repercussions of manufacturing’s decline, which was driven by automation and globalization, have been felt at the local and national levels. For example, certain areas of the US that were once manufacturing hubs have experienced rising poverty, declining populations, and erosion of political trust.

Figure 1: Employment in manufacturing and retail trade 

Source: US FRED, Cornerstone Capital Group

The impact of significant reductions in retail workers may mirror the impact of manufacturing job losses. Retail sales at brick-and-mortar stores, as well as margins on those sales, are increasingly constrained as consumers shift to online shopping. At the same time, many parts of the country are experiencing upward structural wage pressure as concerns about income inequality are gaining political traction. Major retailers, including Macy’s, J.C. Penney, Kohl’s and Wal-Mart, have collectively closed hundreds of stores over the last few years in attempts to stem losses from unprofitable stores. These headwinds are pushing retailers to rethink the traditional retail business model.

Technology has the potential to automate part of the sales process and render a range of jobs redundant

Retailers are investing in technology to build out their omnichannel platforms. In some cases, technology is complementing labor by providing a better customer experience. Indeed, this report argues that companies which use technology to support their workers are likely to benefit from long-term productivity gains. However, technology also has the potential to automate part of the sales process and render a range of jobs redundant. Taken together, store closures and automation technology have the potential to accelerate job losses in retail, an industry that employs approximately 10% of the total US labor force[1].

An in-depth examination of retail automation was undertaken to enable investors to consider investment risks and opportunities by exploring how retail is addressing profit pressure and how employees are considered in the context of a broader shift in strategy. This report:

Key questions

Which factors are driving automation in retail?

Given that automation has been a central driving force for economic development for decades, it is important to understand why its application in the retail sector threatens to radically and rapidly reshape the retail labor force. The research identifies two key factors driving the automation conversation.

First, e-commerce has grown significantly over the last five years and now accounts for more than 8% of total US retail sales. Amazon has been a dominant force in e-commerce for years, and the company accounted for 43% of all online sales in 2016. While the consumer benefits from lower prices and greater price transparency, Amazon’s success is pressuring retailer profit margins as they fight to maintain market share and keep prices low to remain competitive.

Retail workers are disproportionately represented among recipients of public assistance

Second, a growing focus on income inequality and regulatory-driven minimum wage changes are a source of increasing wage pressure. Retail employs about 10% of the US labor force, and research finds that retail workers are disproportionately represented among recipients of public assistance.[3] Retailers have been increasing wages recently due to a tighter labor market, but retail faces a structural issue of increasing pressure for minimum wage hikes at the local and state level.

Taken together, retailers are facing structural price and cost issues that impact profitability and create meaningful long-term uncertainty. These headwinds will likely increase the industry’s propensity to automate, which would have significant impacts on existing labor. Companies are likely to respond through two consumer strategies:

While companies may pursue a mix of these two strategies, understanding which is the primary strategy will enable investors to understand how technology and labor are likely to be used, and how the overall labor profile of the company might change.

How is automation being adopted in retail?

The technology initiatives of 30 retail companies were assessed, and ten in-store technologies that will impact the retail industry were identified. The assessment provides an indication of the extent to which each technology is being deployed. These initiatives are focused on improving customer satisfaction, operational efficiency, or a combination thereof.

Research indicates companies are adopting mobile devices, self-checkout, digital kiosks, proximity beacons, and workforce and task management solutions

The review of company reports indicates that retail companies are implementing technologies such as mobile devices, self-checkout, digital kiosks, proximity beacons, and workforce and task management solutions.

What are the broader stakeholder implications?

An assessment of the gender composition of retail workers shows that the largest group, retail salespeople, has equal numbers of men and women. However, cashiers, the next largest group of retail workers, are predominantly women (73%). Cashiers are considered one of the most easily automatable jobs in the economy. Based on this analysis, large-scale automation of retail labor could disproportionately affect women, as noted previously in Cornerstone Capital Group’s September 2016 report, Women in an Automated World.

From a geographical standpoint, it appears that several major retail companies have store footprints that are concentrated in less densely populated metropolitan areas. For example, a UCLA study shows that Wal-Mart possesses an average market share of 25% in metropolitan areas with populations of fewer than 500,000 residents. This market share, if indicative of employment share (even if not directly proportionate), suggests significant potential impacts for local communities should Wal-Mart pursue an aggressive labor automation strategy.

How are companies managing labor issues associated with automation?

The retail sector provides little disclosure on labor issues. None of the 30 companies reviewed in this report provides key labor data such as employee turnover, labor costs as a percentage of SG&A, or employee satisfaction. Therefore, a series of proxy metrics were developed to evaluate the universe of companies:

No companies provide key labor data such as employee turnover, labor costs as a percentage of SG&A, or employee satisfaction

Based on the assessment, key takeaways include:

The analysis indicates that automation is set to alter the retail industry’s labor profile. If companies migrate towards a high-touch, experience-based strategy, then it is possible workers will receive improved training and higher wages, and there will be fewer layoffs. If companies adopt a heavily convenience-oriented strategy, more tasks will be automated and less labor required. To date, companies’ discussions around implementing technology suggest that technology is aimed at complementing labor. However, should structural price and cost issues persist, technology may be viewed as a potential substitute for labor.

A mix of experience and convenience strategies could still result in material lay-offs in the retail sector

The most likely outcome is a mix of experience and convenience strategies, though this could still result in material layoffs in the retail sector. Because retail represents approximately 10% of the total US labor force, any systematic deployment of automation is likely to reduce the number of retail jobs by a figure in the millions.

Download full report here.

[1] Calculated from retail trade employment, given by the Bureau of Labor Statistics Current Employment Statistics Survey

[2] Business Relationship Analytics for Value Enhancement.

[3] EPI analysis of Current Population Survey Annual Social and Economic Supplement microdata, pooled years 2012-2014


Michael Shavel is a Global Thematic Research Analyst at Cornerstone Capital Group. Prior to joining the firm, Michael was a Research Analyst on the Global Growth and Thematic team at Alliance Bernstein. He holds a B.S. in Finance from Rutgers University and is a CFA Charterholder.

Sebastian Vanderzeil is a Global Thematic Research Analyst with Cornerstone Capital Group. He holds an MBA from New York University’s Stern School of Business. Previously, Sebastian was an economic consultant with global technical services group AECOM, where he advised on the development and finance of major infrastructure across Asia and Australia. Sebastian also worked with the Queensland State Government on water and climate issues prior to establishing Australia’s first government-owned carbon broker, Ecofund Queensland.

Emma Currier is a Research Associate at Cornerstone Capital Group. Emma graduated with a Bachelors of Arts degree in Economics from Brown University in May 2016. While at school, she worked with the Socially Responsible Investing Fund and as a teaching assistant for the Public Health and Economics departments. She spent her sophomore summer researching differences between American and Indian educational styles in Arunachal Pradesh, India, and completed a summer investment bank analyst position with Citi in the Media & Telecom group in 2015.


According to Professor Boaz Golany of the Technion – Israel Institute of Technology, collaborations must be SMART: Sustainable, Mutually Attractive, with Reliable, Transparent partners. This is the guiding principle of the Technion’s venture with Cornell University to form the Jacobs Technion – Cornell Institute, a blossoming technology hub on New York City’s Roosevelt Island.

On January 6, Cornerstone  had the pleasure of hosting Professor Golany  for a provocative lunch conversation facilitated by Dr. Derek Yach, Chief Health Officer of the Vitality Group, also a member of the Board of Cornerstone Capital Group.  Professor Golany shared with us the remarkable story of the birth of the Technion – Cornell partnership, which is already becoming a vibrant and innovative addition to the applied research engine of New York.

The conversation touched on these questions and more:


If you are thinking about the relationship between technology innovation and global sustainability, what comes to mind most often are rows of solar panels in the Arizona desert or offshore wind turbines off the coast of Denmark. What you don’t typically think of are companies like Feetz (feetz.com), a Tennessee-based startup that uses 3-D printing technology to make custom-fit shoes.

The convergence of online commerce, mass customization, and 3-D printing technology (or what some people refer to as additive manufacturing) is underway, with customized shoes representing the latest model of what surely will be other customized consumer products hitting the marketplace.

While Uber and Airbnb get most of the media attention worldwide in terms of business model innovation, the importance of whether manufacturing in the US and worldwide takes a sustainable business trajectory cannot be overstated. Traditionally, manufacturing is most expensive part of the retail supply chain. Shoes, toys, and many consumer products are manufactured overseas, most notably in China, and shipped as finished products to the United States.

In the case of Feetz, the ordering is done online, where customers can download an app, take smartphone snapshots of their feet and create a 3-D model to be used as a model for their customized shoes.[1]  If companies like Feetz are “changing the ways goods are ordered, made and sold,”[2] what are the important sustainability consequences of such business models? Are they positive, negative or something else?

3-D printing or additive manufacturing technology can in theory dramatically reduce the amount of waste created in the manufacturing processes.  Like stacking bricks to build a house, additive manufacturing process creates objects in layers without the limiting constraints of molding requirements or human error in welding. The result maximizes material efficiency, ensuring that no material needlessly goes from welder’s torch to junkyard. For context, a typical car wastes about 10,000 kg of raw materials during the manufacturing process.[3]

Unlike traditional large-run manufacturing, the small scale of production typical of most 3-D printing efforts means that the cost of wasted material does not have to be ameliorated through economies of scale. Even in smaller 3-D printing projects, material use efficiency is an automatic consideration, not something to think about as an add-on consideration after the waste is produced or the environmental damage is baked into the product itself (think plastic bags).

Another example in terms of the potential sustainability benefits of 3-D technology can be seen in Shapeways (shapeways.com), a company that allows people to design custom products like furniture and household objects that might be hard to replace and encourages customers to save money by using less material. Companies like Patagonia already prompt their customers whether they truly need to ship their products overnight (since the mode of transportation has such a large impact on the overall sustainability of a product’s supply chain). But Shapeways takes this form of consumer engagement a step further by prompting its customers to actively think about the materials that go into the production of their products.

Bringing Scale to Hyperlocal

Since the business model of making as many products as cheaply as possible is still the dominant form (though this is rapidly changing), another innovative, sustainable feature of the additive manufacturing model is that it brings the possibility of scale to the emerging “hyperlocal” trend that can be seen from Northern California to Vermont. There are many emerging sustainable business enterprises that attempt to build on the growing consumer interest in all things local (e.g. food, energy, economic development, etc.) and additive manufacturing provides a market template, at least in theory, from which to scale a local business model to greater competitive advantage. [4]

Ultimately, the argument that the future of the US economy lies in sustainable business has been made before, and additive manufacturing cannot substitute for well-designed tax and other policy incentives for a wide assortment of clean energy and manufacturing research & development, including 3-D printing technology.  While the business case for sustainability is strong in the case of additive manufacturing, it remains to be seen whether companies like Feetz are going to transform the business and ultimately how consumers purchase, use, and dispose of shoes.

The potential is there but the story is still evolving and it may be too early to predict the outcome one way or the other. Case in point: Google announced in September 2016[5] that its Project Ara smartphone initiative, which began in 2013 with the concept of designing a phone platform that would incorporate a wide array of camera, audio and other modules as desired by users, has been suspended.

Product modularity, the flip side of consumer customization in many ways, is the key functionality that forces people to throw away perfectly sound electronic products because one small item is not working (for instance, one letter in a keyboard). The key lesson from the Google Project Ara might be that we need to better understand what consumers truly want in terms of product customization. Perhaps Feetz will be successful with 3-D printed shoes — but what about handbags?

Moreover, it is not yet clear the type and scope of market disruption “locavore production,” as Professor Gerald Davis, University of Michigan Business School, calls it, will have on existing firms and economic systems. While many industries will be unable to adapt to the changing 3-D technology-mediated business environment, some firms will find a way to adapt by creating and hosting the tools for locavore production, using their skills to create designs suited for locavore production, or hosting a marketplace for product recipes.[6]

As Cory Doctorow, author of Makers, suggested in a 2010 Wired magazine article: “The days of companies with names like ‘General Electric’ and ‘General Mills’ and ‘General Motors’ are over. The money on the table is like krill: a billion little entrepreneurial opportunities that can be discovered and exploited by smart, creative people.”[7]

[1] Gustke, C. “With Analytics and 3-D Printers, a Faster Fashion Just for You”, New York Times, September 15, 2016, p. B3.

[2] ibid

[3] “Waste and car production – Maps and Graphics at UNEP/GRID-Arendal,” Maps & Graphics, http://maps.grida.no/go/graphic/waste_and_car_production

[4]  Riley, D. and Park, J. “Manufacturing: The Key to Sustainable Business Innovation in the U.S.”, The Sustainability Review, 2012: Issue 2 http://www.thesustainabilityreview.org/manufacturing-the-key-to-sustainable-business-innovation-in-the-u-s

[5]  http://www.eweek.com/mobile/google-suspends-its-project-ara-modular-smartphone-efforts.html

[6]  Davis, G. “Buying Furniture on iTunes: Creative Destruction in a World of “Locavore” Production” Network for Business Sustainability, November 2012 http://nbs.net/buying-furniture-on-itunes-creative-destruction-in-a-world-of-locavore-production. A longer version of this analysis can be found in Davis, G. The Vanishing American Corporation: Navigating the Hazards of a New Economy, Berrett-Kohler Publishers, 2016.

[7]  Anderson, C. “In the Next Industrial Revolution, Atoms Are The New Bits”, Wired, January 25, 2010 http://www.wired.com/magazine/2010/01/ff_newrevolution/all/1


Jacob Park is Professor of Strategy, Innovation, and Entrepreneurship and Director, Sustainable MBA Program at Green Mountain College. He is also the Kevin Ruble Fellow in Conscious Capitalism, Rutgers University School of Management and Labor Relations. Professor Park specializes in the teaching and research of global environment & business strategy, corporate social responsibility, business ethics, and community-based entrepreneurship & innovation. He is a member of the Renewable Energy and Adaptation to Climate Technologies investment committee of the Nairobi, Kenya-based Africa Enterprise Challenge Fund and serves on the Board of Directors and Chair, Program Committee, of Vermont Businesses for Social Responsibility.


This article assumes a basic understanding of blockchain technology. For background information please see this overview from the founder of Bitcoin.

Blockchain-based smart contracts have generated ample buzz recently as they could eventually build entirely autonomous organizations, enable self-regulated peer-to-peer insurance models, and facilitate the renting of all internet-connected things. This article explores how smart contracts work. It provides a brief primer on smart contracts, then compares the coding, storage and execution of smart contracts on Bitcoin and Ethereum, the two permissionless blockchain protocols that have achieved scale thus far. While plenty of healthy skepticism still exists around smart contracts, ARK Invest believes Bitcoin and Ethereum will be instrumental in validating the potential of this budding innovation.

Primer on Smart Contracts

A smart contract refers to coded logic that moves digital assets when triggered by necessary events. It is akin to a series of “IF, THEN” statements, where the “ifs” are preconditions that must be met in order to trigger the “thens.” The idea fits well within blockchain technology because blockchains offer a guarantee of future execution, in a decentralized manner,[1] once the smart contract logic is stamped within a block.

The term “smart contracts” often puts mental imagery of complex documents in peoples’ minds, which ARK believes is misleading.  This misconception explains why Mike Hearn, an early pioneer within the Bitcoin space, called smart contracts a misnomer in a November 2013 conversation on the matter. While he preferred the term conditional payments, ARK prefers broadening the term to conditional transactions to capture the idea that this technology can facilitate more than the transfer of money for goods and services.

Conditional transactions executed via a blockchain are computationally expensive because every computer that is part of the network needs to execute the same logic and update the state of the blockchain. In other words, each time a smart contract is triggered every computer has to perform the same task, consuming considerable resources and making the process inefficient when compared to parallel processing architectures. Therefore, not every conditional transaction will be appropriate for execution via a blockchain, but rather only those use cases that demand the distributed and secure nature of a shared ledger. Once more sophisticated solutions are implemented — like sharding, which can help to better parallelize computing tasks and storage — conditional transactions may prove less computationally expensive to the network, further widening the scope of applications.

Coding of Smart Contracts

While Nick Szabo conceived the concept in the 1990s, Ethereum popularized the idea of smart contracts. It remains a common misconception that Bitcoin can’t facilitate the same range of functionality. Even though Ethereum currently has more flexibility in the programming languages that can be used — because it was designed with developers keenly in mind — the two blockchain platforms can accommodate the same smart contract functionality.

With its source code written in C++, coding for Bitcoin applications often occurs at a more granular layer, making it a less desirable sandbox for many new-age web and application developers. The high-level languages readily available within Ethereum, however, make smart contracts accessible to most developers.
For example, Geth is available to Ethereum developers; it is implemented in Go, the easy-to-learn open source programming language that Google released in 2009. Go was created in part because the founders hated the complexity of C++.  ARK believes people confuse “difficult” and “impossible” with regard to encoding complex smart contracts in Bitcoin. While it may currently be more difficult to encode complex smart contracts in Bitcoin, that does not mean it is impossible.

Storage and Execution of Smart Contracts

Bitcoin and Ethereum differ in the storage and execution of smart contracts, which became clear to us during conversations with relevant parties at Coala’s NYU Blockchain Workshops in April this year. We will start with Ethereum because it was designed specifically with smart contracts in mind and therefore easier to understand, whereas while smart contracts are cleverly layered into Bitcoin transactions.

Ethereum users load smart contracts into its blockchain via a transaction to the network that has a payload containing the logic of the contract. The transaction is not sent to a particular address.  Instead, the nodes processing the transaction on the network recognize the “smart contract payload,” and create a smart contract address. The underlying logic and interactions for smart contracts can be seen on blockchain explorer sites like Ether.Camp. Once uploaded to the blockchain, the logic underlying a smart contract can be activated by sending to its address a transaction with the preconditions necessary to trigger it. Triggering a smart contract can lead to the sending of another transaction, triggering another contract, theoretically ad infinitum.

With Bitcoin smart contracts, it’s important to understand that each transaction exists as a data structure composed of inputs and outputs, as can be seen in a Bitcoin block explorer. In order to send bitcoin, users must provide certain inputs meeting pre-determined requirements that prove they own, and therefore have the authority to send, the bitcoin they claim to own. Users can also create contract transactions that require a more complex set of inputs in order to trigger the release of bitcoin. A simple example of more complex inputs is a multi-signature transaction, which requires more than one entity to sign off on the release of bitcoin, proving useful in escrow situations where perhaps two of three parties must vouch for a transfer.

Often surprising is the complexity of operations that can be created from the combination of simple operators. With the introduction of Segregated Witness (SegWit), the flexibility of Bitcoin’s scripting system will increase. Since the scripting system is responsible for creating the logic embedded within Bitcoin transactions, making the scripting system more dynamic will allow transaction types to be more dynamic as well. Additionally, Rootstock is working on building an easy-to-use smart contracts platform atop Bitcoin. Given the many ways to construct and execute a smart contract using the Bitcoin network, we recommend those further interested to read BitFury’s white paper, “Smart Contracts on the Bitcoin Blockchain.”


The promise of smart contracts is that once the logic is live, the underlying blockchain guarantees future execution as long as the necessary conditions are met. For example, a company employing smart contracts within Ethereum is Slock.it, which ties the unlocking of real world devices with blockchain technology. Imagine a world where you rent a house via Airbnb and all of the internet-connected-devices within the house are updated with information about your digital wallet and date of arrival. Then, whenever you want to release a service within that house, whether unlocking the fridge to get a beer, the WiFi to sip megabytes of data, or the autonomous car for a trip, you send a transaction to the house with a request for that service. The funds are deducted automatically from your account and the device is unlocked with no credit card intermediaries and perfect record keeping via the decentralized ledger of the underlying blockchain. If the house were connected to the bank which services its loan, you can imagine homes that pay off their own mortgages with the funds they accrue from renters.

One of the more abstract implementations of smart contracts is The DAO, with “DAO” standing for decentralized autonomous organization. Such an organization has no concrete leader, but is instead composed of a series of logic and rules embedded in an interconnected web of smart contracts. While it may seem like science fiction, in May 2016 The DAO raised $160 million in the world’s largest crowdfunding to date. It was promptly hacked in June 2016 as an attacker drained $50+ million in funds, only for that breach to be reverted in July by a hard fork of the network. The space remains young, and now security is rightly top of mind for smart contracts and DAOs

Smart contracts could increase capacity utilization of houses and other assets, decrease legal disputes thanks to flawless ledgers, and cut intermediary costs across the board. Given the efficiency, transparency and economic value that smart contracts will provide to a multitude of sectors and services, they could enable a productivity boost from currently stranded assets. ARK Invest believes Bitcoin likely will be the environment of choice for mission critical contracts requiring utmost security, while Ethereum will continue to push consensus past what previously was thought possible.

Chris Burniske is an analyst with Ark Invest, specializing in big data, cloud computing, cybersecurity, bitcoin, and blockchain technology. He can be reached via his Twitter handle @ARKBlockchain if you have any questions, concerns, or would like to discuss further.

[1] If the blockchain is permissionless— providing open access to participants that would like to join it— ARK believes it will maintain greater decentralization than if it is a permissioned blockchain.





Health is becoming personal, predictive, and preventive through advanced technologies – wearable devices, embedded sensors, artificially intelligent robots, and virtual reality headsets. A deluge of data and feedback generated by these technologies nudge consumers to engage in healthier activities, or are aggregated and analyzed for insights about diverse populations across geographies. Major technology companies are investing in solutions powered by “big data” that promise to improve the health of populations worldwide. The opportunities appear boundless.

Despite this promise, ethical, legal, and social concerns associated with these technologies have emerged, which could very well hinder benefits to health. The US federal government has targeted several health technology companies that are unable to support their scientific claims with compelling evidence, and studies demonstrate that insufficient privacy and security features underlying such technologies can lead to harmful effects for users. If these challenges are not proactively mitigated, the potential improvements to health may not be realized at scale.

Overcoming these issues requires the collective views of disparate stakeholders and cross-sector collaboration. One voice is not as powerful as multiple in unison. As a start, colleagues from Vitality, Microsoft, and the Qualcomm Institute at the University of California, San Diego published an open-access, peer-reviewed commentary that called for a public consultation to identify best practices to eliminate ethical, legal, and social barriers to health technologies. For 90 days in 2015, a wide range of stakeholders offered input on a draft set of guidelines for the responsible innovation of health technology and the appropriate stewardship of data from these devices. Feedback came from organizations such as the EU Commission, the US Food and Drug Administration, the National Academy of Medicine, and the American Heart Association.

In March 2016, Vitality released the finalized guidelines for personalized health technology. They included five recommendations:

The guidelines provide the foundation for a working group to pilot the implementation of the guidelines. These will be measured independently using tangible metrics, and results will be shared. Collaborating across sectors, the proposed guidelines seek to shift the dialogue around health technologies to one that promotes shared values for all stakeholders. They are an attempt to convene leading industry players to consider bringing greater transparency and accountability to health technology and data—to avert the sorts of issues that recently emerged between the US Federal Bureau of Investigation and Apple. The guidelines are not an attempt to preempt government regulation, but aim to fill holes where needed in existing regulatory frameworks.

Can we learn from the past to know if we are on track? The Human Genome Project (HGP) is one example where proactive consideration of ethical, legal, and social concerns led to broader individual and societal benefits. Twenty-five years ago, the HGP was founded as an international research collaboration to sequence human genes. Leaders of the HGP set aside a portion of the budget to foster basic and applied research on these issues, and established the Ethical, Legal, and Social Implications Research Program. Today, the National Human Genome Research Institute (NHGRI) at the US National Institutes of Health has a legislative mandate to allocate no less than 5% of the NHGRI budget to these issues. As a consequence, established and accepted protocols facilitate the routine sharing of genetic data for research. The vision for our guidelines is informed by past achievements in proactive investigation of concerns with the possession of genetic information.

Technologies are created by people, for people. Technologies that improve the public’s health should be informed by science, affordable, safe and protect the user’s health data. We can collaborate to shape the future of this new frontier in health data, or we can wait in anticipation and uncertainty only to discover the unintended consequences.

Gillian Christie is a Health Innovation Analyst at The Vitality Group in New York City.

Kevin Patrick is a Professor of Family Medicine and Public Health, and a researcher at the Qualcomm Institute at the University of California, San Diego in La Jolla, California.

Chris Calitz is Director of the Center for Workplace Health Research and Evaluation at the American Heart Association in Dallas, Texas.