Last week, in a (virtual) entrepreneurial finance class at Harvard Business School, our professor pointed to a graph of IPO’s over the last several decades with various kinks in the graph and asked for explanations of what caused peaks and troughs at different points in time: “can anyone explain why there is a big upshoot in the 1960’s? What all contributed to a downward trend in the early 2000’s?”

As I raised my virtual Zoom hand to participate, I couldn’t help but reflect on the idea that 50 years from now, when students at Harvard Business School look back at time series data around the economy and the labor market, COVID-19 may have induced the single most impactful economic shift in modern history.

Even as I type that sentence, I am wary that it comes off with a tone that feels like it must be somewhat hyperbolic. But then you pause and stare at the below graph for 30 seconds.

At the peak of the 2008-2009 recession, there were 665,000 new unemployment claims in a week. Over the last 5 weeks, there were 3.3 Million, 6.9 Million, 6.6 Million, 5.2 Million, and 4.4 Million new unemployment claims. In the matter of only a few weeks, over 26 Million Americans lost their jobs, and the cumulative job gains over the last decade were wiped out. To say this is unprecedented is a gross understatement.

Even still, there is a lot of hysteria right now with the commotion of many speculating how exactly the future of work will be transformed. We need to separate the signal from the noise.

The Noise

A quick scan of Twitter quickly produces a plethora of worried sentiments about the labor market — internships are being revoked, full-time hiring is being frozen, the stock market is barrelling up and down, companies are moving to remote work.

Though these dynamics are certainly notable, and in some cases will cause significant pain, it’s important to realize that many of these challenges will pass and won’t be the longer-term implications of COVID-19 on the future of work.

Sure, remote work will continue to be increasingly popular, especially with droves of new enterprise software apps that make the experience more productive. But post-COVID-19 (including the potentially extended recovery), the same pros and cons of remote work will continue to exist, and though we will have seen a solid bump in employer acceptance, we will largely return to a similar balance.

The stock market is an important measure of the overall economy for a plethora of reasons, but we also need to understand that it is in many ways separate from the economic mobility of an American worker — the stock market represents (our own perception of) the value of corporations, which can lead to wide disparities between, in the words of Jim Kramer (pictured below), “a roaring stock market and an obliterated job market”.

Some might rationally ask, ‘well what does the data say” — and that’s part of the problem. We don’t have the data tracking infrastructure in place to understand our current economy and how it will be impacted by the pandemic. 

The unemployment rate and other BLS measures will not capture the full scope of economic harm that is done by COVID-19. Tens of millions of Americans have had their hours and income significantly reduced, have been ‘furloughed’ (temporarily laid off) or changed to part time, have been forced to take on lower-wage jobs in the gig economy with no stability, or have have been permanently laid off, and though they want to work, believe no job is available for them (‘discouraged workers’). Zero of these people will be counted in the unemployment rate, and the BLS continues not to track income or hours and not provide adequate measures of the true size of the gig economy.

So all in all, we are left struggling to understand what to pay attention to amidst all the economic noise (and lack of the right noise) in interpreting the health and structure of our workforce.

The Signals

If we sift through all of the intensity of the short-term noise, it starts to become clear that there are a few broader future of work trends occuring that are going to be massively accelerated with the economic collapse in the wake of COVID-19.

To be clear, none of these trends are new — but the important insight is that this recession will rapidly and dramatically accelerate these trends to levels that will have a significant and permanent impact on the structure of the labor market.

1. Employers will accelerate automation to fill the roles of laid off workers

In recent days, many inside and outside of our government have focused on how we can achieve the objective of ‘Getting Americans Back to Work’. The problem is, most of the plans released around this objective seem to have the implicit assumption that re-opening the economy will put the vast majority of Americans who have been laid off back to work. The reality is, many employers, even when they reach pre-crisis revenue numbers, will not employ pre-crisis levels of workers.

As a number of economists have proven, there is a strong correlation between economic recessions and a rise in employer adoption of increased automation. Their research suggests that during recessions, employers tend to lay off less skilled employees and replace them with a combination of technology and a fewer number of higher skilled workers.

This time will be no exception. In fact, it will likely be much worse. Since the 2008 recession, not only has the power of artificial intelligence, robotics, and other technologies risen at an exponential rate, they have also continued to become dramatically cheaper. This time, the tradeoff for employers will be a much easier decision.

2. Individuals will need to gain new skills through innovative retraining programs to re-enter the workforce

As employers increasingly look to automation to fill the roles of laid off workers, many individuals will need to re-evaluate their skill sets before re-entering the workforce.

In general, the counter-cyclicality of education is well documented — But given the rapid acceleration of automation mentioned above, the ‘skills gap’ – the gap between the skills the individuals have and the skills employers will demand – will be much higher than in previous recessions, significantly increasing friction in the labor market. That said, individuals will be hesitant to flock to traditional higher education institutions, with skepticism over their ROI at an all time high.

Instead, new retraining models must be built that are dramatically more connected to the workforce and shift financial risk off students (a concept I’ve written about before) through ISA’s, deferred tuition, employer-sponsored, apprenticeships and other innovative models — long gone are the days where students cough up thousands of dollars upfront for programs that may or may not generate a positive employment outcome.

Additionally, retraining programs must ensure student success by providing the flexibility and support that will maximize program completion — in the age of learning when many adult learners will need to pick up part time work to continue to support their families while they retrain, it’s crucial that the programs be flexible in the “when” and the “where” of learning, in stark contrast to many of the rigid programs in higher education today. NextStep, a CNA retraining program in Colorado, is a prime example of a program offering this flexibility, with asynchronous, bite-sized lessons on a mobile-first platform.

3. A sub-standard social safety net will force people into the gig economy, where they will lose the stability and benefits of full time employment

With people displaced from the workforce and unable to re-enter it without new skills, combined with basic living costs of housing, health care, education continuing to soar, inequality is likely to rise and the inadequacies of America’s social safety net are likely to be exposed. With this, we will see increasing calls for some form of universal basic income

As people who have been displaced from the labor market (and even those who haven’t) struggle to cover their expenses, they will increasingly look to the gig economy to gain quick access to income. Many (not all) gig economy platforms have low barriers to entry for workers given most of the tasks involve low-skill activities like driving, shopping, and delivering.

Even in the first few weeks of the pandemic, the flooding towards the gig economy has already begun — from individuals searching for primary as well as secondary sources of income. While it took Instacart 8 years to bring its first 200,000 shoppers on the platform, over 300,000 new shoppers joined the platform just in the past month. Though the gig economy can be attractive in the flexibility it offers, it also has meaningful disadvantages in comparison to full time employment (from income stability, to benefits, to upward mobility) that will cause substantial pain to workers in the long-term.

New social safety net models must be built, particularly those targeting gig economy workers, to provide them short-term stability and empower them to pursue long-term economic mobility. Encouraging models like Catch, Steady, and Wonolo are beginning to emerge, but are still not sufficient — the gig economy needs an entire infrastructure around it that recognizes and supports a unique class of workers. 

COVID-19 is accelerating these Future of Work trends that won’t just be a short-term kink in a graph, but rather, a transformational shift in the trajectory of the economy. While the first two signals identified have been observed, to some degree, in past recessions, the rapid degree of technological progress and massive widening of the skills gap compared to past recessions will be the differences that make the trends in this recession much more impactful and enduring.

As we weather that storm, it is essential that we are able to understand the evolving health and structure of the economy. When companies and their business models evolve, so do their key metrics, but as our economy continues to transform dramatically, the Bureau of Labor Statistics continues to focus on many of the same metrics that it did a century ago. We need innovative new ways to understand the lives of workers on the ground to successfully navigate this fast-moving labor market. 60Decibels is one company that is doing interesting work with potential relation to this need, amplifying voices as part of an impact measurement tool, but there is a need for more innovations, from both the private and public sector, that specifically allow us to understand the future of work.

With every crisis, there is opportunity. The COVID-19 crisis creates significant opportunity for new models of economic mobility that support displaced workers. In light of Marc Andreessen’s recent manifesto on why it’s time to build — to empower individuals in the future of work, we need to build the data infrastructure to understand our labor market, we need to build innovative retraining programs that shift financial risk off students, and we need to build new safety nets and support systems, particularly for a growing number of independent workers.

Let’s start building.

SOCAP is the world’s preeminent conference dedicated to increasing the flow of capital towards social good, and while in the 12th year of its history, 2019 was the first time the conference tapped the Future of Work as one its 10 focus areas.

Through the panels, coffee chats, breakfasts, happy hours, and more I had the chance to attend (90% of which I found myself running late to), there were a few central reflections that I left with when considering the flow of capital towards the workforce of the future:

1. Shifting Financial Risk Off Students

Despite investment in higher education soaring, with student loan debt at an astonishing $1.6T, the return of a higher education degree has never been more in question, with 40% of college graduates taking positions that do not require a college degree. Meanwhile, as progression in technology radically transforms the workforce, the average half life of a skill has shrunk to only 5 years, meaning that individuals will need to upskill or reskill much more frequently. And as such, society cannot afford for individuals to continue to take on the financial risk of whether or not investments in education will pay off.

To invest in the workforce of the future, we need to invest in models that shift the financial risk off students, and encouragingly, innovative models are emerging to accomplish this:

• Income Share Agreements (ISA’s) — by far the most talked about, of course, are ISA’s, whether being offered by education programs directly (Lambda School, Kenzie Academy), by companies partnering with institutions (Vemo), or by startups offering the financing structure directly to students (Stride, Blair, and MentorWorks)
• Job Guarantees — although they don’t have the sexy, controversial appeal of ISA’s, job guarantees effectively offer students much of the same downside protection, allowing students to only pay for an educational program if they land a job in their field of study (Springboard is a great example of a company employing this structure – they boast 1,730 students have gone through their data science program with zero requested refunds… imagine if a university had this same offer…)
• Student Loan Insurance — Ardeo Education Solutions partners with institutions to enable them to offer students loan repayment assistance programs, which assist them in paying off their student loans if they do not attain a job above a certain salary threshold
• Employer Paid Programs — when labor market demand is particularly high and education programs have close enough partnerships with employers to place students directly, some programs like SV Academy are employing models where students aren’t on the hook for any payment, as it is directly financed by the employer who hires them
• Employer Benefits — in contrast to the other models listed where training takes place prior to employment, employer benefits models like those provided by Guild support existing employees who need to continue to upskill to remain relevant as the skills needed for their careers evolve
• Innovative Staffing Models — staffing companies like Revature have pioneered an innovative model of hiring individuals who need additional skills, training them internally, before ultimately deploying them out to clients (or having those clients hire them full-time)

Though each model above has its own set of payoff curves and risk distributions, what can be said across all of them is that they largely increase access to education programs, align stakeholder incentives towards student success, and decrease the downside risk for the students.

My thesis around this area is that education programs that do not adopt at least some form of these models that shift financial risk off students will not survive in the long-run. That said, not all of these models are appropriate in all scenarios — my view is that many parts of the VC world have begun worshiping ISA’s much too early without thinking diligently enough about in which contexts they are most appropriate and in which another model would be best suited.

This is an area I am currently doing a deeper dive analysis on — I will likely share some of my insights on this blog, but please reach out if this is something you are interested in sharing and comparing views on.

2. In Alternative Pathways, the ‘Non-Traditional’ Student *is* the Traditional Student

Though in my view, shifting financial risk is the most critical way education programs need to evolve, close behind that is acknowledging the reality of support and flexibility needed by the students in these education to employment pathways. For too long, discussing ‘wrap-around support’ services within education pathways has been exclusively reserved for conversations focusing on the social mission of engaging a small population of ‘non-traditional’ students that are being left behind. While this is a worthy conversation to have, what I believe has failed to be seen is that, increasingly, a very *large* portion of learners will need some form of these support services in order to succeed in rapidly changing workforce, particularly as the need for lifelong learning implies the existence of a massive portion of adult learners with busy, complex lives.

While I have written about some of these support elements in the past (see: Reflections from ASU-GSV), I am repeating myself because I want to double down on (a) the size of the population that has these needs as discussed above, and (b) some of the ways in which I see it successfully materializing:

• Stipends — even in upskilling programs that are free or have deferred payment structures, organizations like Resilient Coders recognize that many students need stipends in order to take time away from working and invest time in education
• Emergency Loans — continuing on the financial track, some may be surprised to know that 40% of Americans would have difficulty covering a $400 emergency expense; companies like Edquity are supporting education institutions in ensuring their students have access to emergency funds such that such an expense is not the reason they don’t persist in an education program
• Professional Skills — while much of innovative programs like Kenzie Academy focus on technical skills like software engineering, the company also realizes the importance of extensive training on basic professional skills like communication that we incorrectly assume most students may already have exposure to
• 1:1 Mentoring — when students at Springboard enroll in data science, analytics, or design, they are also offered weekly 1:1 mentoring with an industry professional, which not only acts as a support mechanism, but also helps them start building the social capital often required to land a job
• Mental Health Support — Lambda School made a big announcement earlier this month with their partnership with Modern Health, providing all students access to resources that help support mental health and well-being; driven by their understanding that 1 in 3 people experience a mental health challenge at some point in their life
• Clothing — YearUp is one of the most successful workforce development non-profits out there, and one of their many wrap-around services offered is ‘career closets’ which make sure that access to professional clothing is not the obstacle that prevents students from getting a job

Important clarification — this list is far from exhaustive of the types of support services being provided and far from identifying all the companies that provide each of these, but my hope is that sharing just a short list of examples will start to illustrate the point of what is needed to ensure success for a large portion of students in these pathways.

My thesis on this area revolves around the idea that when we discuss investments in the workforce of the future, we must acknowledge that what has historically been known as the ‘non-traditional’ student is now the traditional student, and education programs will fail if they do not meet these students where they are with the necessary support and flexibility. Navigating which types of support are necessary for a particular student population and pathway, as well as which of those types are best served by which stakeholder group, will be critical to success going forward.

3. When is Venture Capital Not the Right Capital to Best Support Students?

Taking a step back from the financial arrangements and support services being offered by innovative companies to try to equip students for the workforce of the future, it’s worth reflecting on the underlying assumptions we may sometimes be making at events like SOCAP, which largely focus on how venture capital equity investments (whether aiming for market-rate or concessionary returns) can impact these large scale social issues.

It’s worth noting that some solutions in equipping individuals for the workforce of the future will need to be hyper-localized and with a high degree of wrap-around support services (as discussed above).

In thinking about the capital required to support these solutions, it’s important to acknowledge that not all types of capital are the same. Venture capital is a model that is best suited for fast-growing, scalable models that are poised for an eventual liquidity event. Some solutions required may be hyper-localized, have a high degree of wrap-around support services (as discussed above), need significant experimentation before scaling, or be based in emerging markets where a liquidity event is less likely.

So as we think about how to best invest in the workforce of the future, it’s worth considering the range of different capital flows in the market other than traditional venture capital equity investing:

• Grant Funding — perhaps as the largest step in the other direction, there are some challenges that may be better addressed by non-profit organizations and best sustained through grants
• Blended Financing — midway towards this first alternative is blended financing, which is a structure that allows public or philanthropic funders to catalyze private investment by impacting risk/return profiles
• Debt Financing — though debt has its own set of challenges, some models with sufficient cash flow may be better suited being largely financed through debt rather than equity
• Revenue-Based Financing — one of the more innovative models to emerge as an alternative to traditional equity financing (and being pioneered by investors like Village Capital) is revenue-based financing, which among other benefits, allows companies to pursue more sustainable growth targets
• Outcomes-Based Financing — thought not a direct alternative for organizational funding, a final unique capital flow model that I think is important to understand in the context of this conversation is outcomes-based financing, which provides governments an innovative way to fund service providers to achieve certain social outcomes; a relevant recent example is the Massachusetts Pathways to Economic Advancement project, enabling JVS to scale its workforce development model, brokered by Social Finance, and invested in by Maycomb Capital and others

Venture capital is a hugely powerful model for creating financial and social value. But traditional equity investing is a blunt instrument, and having a mismatch between capital and model can lead to imminent failure for both companies and investors.

My thesis around this area is that a diversity of different capital flows and financial arrangements are needed in order to best equip the workforce of the future, and venture capital firms will need to have more thoughtfulness around where their is capital is best suited, and where another financing structure is more appropriate.

In the last decade, many articles and reports have claimed that the acceleration in AI and other exponential technologies will lead to massive displacement in the workforce. The next wave of scholars then came to clarify that it is very possible that just as many jobs will created as jobs will be automated away, but that the true challenge in the Future of Work will be overcoming the ‘Skills Gap’ — in that even if the supply and demand for labor is equal, there will be a gap between the skills that displaced workers have and the skills that they need to succeed in the new jobs that have been created.

While this gap in the labor market has (rightfully) received a fair amount of attention recently, there has been less attention paid to another important gap that could throw a wrench in the labor market of the future, even if supply and demand for labor are roughly equal: the ‘Geography Gap’.

The Geography Gap is a term I’d like to introduce to refer to the existence of a labor market (such as the US) in which there is demand for labor (employers) and supply of labor (people), but that the inefficiency in the labor market is the location of where the employee is and where the employers want the job to be done.

So Firstly, why will the Geography Gap emerge?

Across the country, the economies within individual cities and communities are shaped by vastly different industry makeups and labor market skillsets. As such, given certain occupations have vastly different susceptibilities to automation, communities themselves will be impacted by automation in vastly different ways.

As an example, routine and repetitive occupations within manufacturing and office administration roles are some of the most likely to be automated, and thus communities in some Heartland states that have workforces that are more specialized in these sectors are more vulnerable to being hit the hardest by the Fourth Industrial Revolution. 

At the same time as some communities feeling a disproportionate amount of the job losses, others are feeling a disproportionate amount of job gains. For the last few decades, the benefits of increasing digitization and emerging technologies have tended to be realized most in specific geographic clusters — hubs such as Silicon Valley that have attracted both some of the most innovative employers and some of the highest skilled employees.

So when these trends are considered together, we may have an overall labor market where supply and demand of labor are equal (let’s say for simplicity sake), but where more excess supply exists in areas most impacted by automation, and where excess demand exists in high-growth hubs: A Geography Gap.

Secondly, what solutions will come into play to address this gap?

So if reskilling is the solution for the Skills Gap, what is the solution for the Geography Gap? There are multiple emerging trends that have the potential to chip away at this gap — all of them will likely comprise some portion of the end-state solution, but to what extent and in what way, remains substantially unclear:

1. The Rise of Remote

The first way to overcome the hurdle of geography is of course to not address the gap at all, but rather, to work through it. The number of remote workers continues to increase substantially, with remote working for non-self employed workers growing 159% since 2005, as well as the surge in freelancers, who are expected to make up the majority of the US workforce by 2027. Enabling remote work has allowed companies to access global talent, reduce office costs, and increase flexibility for employees.

Despite its benefits, working remotely continues to poise substantial challenges — with companies often times struggling with team cohesiveness, communication, and productivity. A number of companies have emerged in recent years to begin to tackle these challenges and make remote working a more viable option: from Andela facilitating the hiring of remote workers, to WeWork creating better remote workspace options, to Zoom offering better video communication technology, to Trello offering better digital collaboration tools.

I expect the number of startups in this space and the number of remote workers to continue to increase, pushing forward a virtuous cycle of one increasing the value of the other. But challenges will also continue to persist — and there will be many jobs that either simply cannot be achieved remotely or where the damage of remote to successful team collaboration is simply too great.

2. The Rise of Relocation

In situations where working remotely is not a viable option, and where individuals find themselves in communities with an excess supply of labor, there will be more interest in relocating to communities with higher demand for labor. While uprooting one’s life to move to another city can be a daunting and complex task, there are companies arising to decrease some of the challenges associated with relocation. One company, Placement, is actually proactively encouraging relocation (rather than waiting for inbound interest), advertising target salary increases of 30%, evaluating a person’s relocation options, preparing them for interviews, and finally facilitating the relocation itself. 

I expect relocation in the labor market to increase, particularly in communities that are the most impacted by automation and where a nearby high-growth hub exists. More companies will emerge to try to create value in various stages of the relocation process, both working with employers who are seeking new sources of talent, and working with individuals who are seeking better income prospects.

While relocation will be a viable option for some, it will be much more challenging for others, particularly for families who may have additional complex considerations, such as a spouse’s current job, children’s current schools, or an aging parent’s current support system.

3. The Rise of More Nomadic Living Arrangements

The option of relocation currently may come with a negative connotation of the difficulty of ‘uprooting one’s life’, but there exists a possible future scenario (based off trends that already exist in the present) of more flexible, temporary living arrangements, which would require significantly less ‘uprooting’.

Broadly speaking, millennials tend to want to change jobs more frequently, travel to new locations more frequently, and purchase housing and other material goods less. When considering these preferences alongside the rising need of remote work, companies have began to take advantage of these trends’ potential to results in more nomadic lifestyles. WeWork, under its broader We Company umbrella, now offers WeLive — a living arrangement that allows individuals to access flexible leasing options right next to their coworking space. A co-living company called Common allows you to easily “transfer” your residence between any of their 20 locations across the country. And a new startup, Selina, is building a global community of hostels with coworking spaces and other experiences, betting on an increasing number of global nomads and a future where someone can pay $1,000/month and live anywhere in the world on any given night.

If this trend were to escalate, a scenario could be imagined in which, as jobs pop up in particular locations, an individual who is part of a national network of temporary housing options could much more easily shift to another location where their skills align with the current job market (in addition to capitalizing on remote jobs) — dramatically increasing the fluidity of the labor market. The We Company seems even more prepared to tackle this scenario when considering their WeGrow branch, which could address skills gaps as well.

Granted this possibility may be longer-term, but it is possible future that should not be ignored as we consider scenarios of how an emerging Geography Gap might be addressed.

4. The Rise of New Geographic Hubs

While the previous three trends all relate to how an employee does or does not move in relation to their employer, the fourth possibility is that employers increasingly move to locations that are closer to untapped talent pools.

Currently, millions of Americans are flocking to high-growth hubs in the Bay Area, New York, Los Angeles, and Boston where jobs are increasing the most, but many more are unable or unwilling to move, and those who do are sometimes met with the grim realities of falling affordability and rising inequality. In order to continue to fill open roles, employers may look to create new regional hubs that could attract talent from the surrounding communities. This trend could be accelerated by the parallel effort by communities that are most impacted by automation (and perhaps had previously relied on a large degree of manufacturing roles) that will be looking to revitalize their economies and diversify across higher-growth industries. 

The combination of these value propositions is likely to lead to partnerships among employers, governments, and educational institutions to build new regional hubs that can create more jobs and then fill those jobs with local talent. There are companies like Bitwise who are already capitalizing on the need for these new partnerships to develop unique ecosystem-building solutions as communities grapple with their own labor market dynamics amidst vast changes in the workforce.

Looking Ahead

As AI and other exponential technologies continue to disrupt the workforce, we will have to grapple with the Geography Gap, and in many cases the combination of the Skills Gap and the Geography Gap, to maximize the efficiency of supply and demand in the labor market. Among the four possibilities outlined — remote work, relocation, nomadic living arrangements, and new geographic hubs — all will happen in some capacity, but to what extent, through what business models, on what timeline, and with what degree of success remains to be seen.

The ASU-GSV Summit in San Diego brings together thousands of individuals each year for one of the most notable education innovation conferences in the world. And multiple attendees who had been to the conference every year since its inception ten years ago commented that this year’s conference noticeably had more sessions specifically focused on education to employment pathways than ever before.

There are now enough jarring stats floating around the education world from places like McKinsey (up to 800m jobs may be lost to automation by 2030) and the World Economic Forum (54% of the workforce will require some level of reskilling by 2022) that we have reached a broad consensus in the education community that (a) the workforce is changing dramatically, (b) the skills needed to succeed in the workforce are changing dramatically, and (c) there is evolution needed across the education ecosystem in order to deliver those skills to students and workers. There is less consensus, however, in deciding how that evolution can be achieved.

Below are my 3 takeaway themes from the ASU-GSV Summit on evolving education-to-employment pathways to equip individuals for the future of work:

1. Beyond Credentials: The Importance of Real Experience in Demonstrating Skills

In cultivating closer relationships between education programs and local employers, an increasing number of business models are going beyond just facilitating collaboration around what skills are necessary and providing students actual opportunities to gain real world experience in demonstrating those skills. The most successful education programs are approaching skill acquisition not just by teaching concepts and tools, but by also providing real practice and assessments/feedback on that practice.

These lessons are applicable as early as K-12, where after providing exposure to some of the viable career pathways that exist (Nepris, Roadtrip Nation), schools have an opportunity to start providing opportunities to engage in the workforce directly through channels such as entrepreneurship (Real World Scholars) and workplace learning opportunities (Launchpath). The biggest hurdle to these opportunities at a K-12 level often remains the incentives facing schools and teachers, who face immense pressure to teach to the test and to prioritize college attainment (which is materially different than workforce readiness).

These lessons continue to be applicable all the way through workforce retraining models, where successful programs are now putting less focus on the credentials a student leaves with after passive activities like watching videos (for example) and more focus on project-based learning that allows students to demonstrate the skills in a job-specific context (Springboard, Oji Life Lab). These models are also in alignment with the way employers are increasingly hiring: by bringing skills to the center of the hiring process (Skillist, Degreed) and evaluating those skills with innovative assessments (Knac) rather than just relying on a candidate’s resume credentials.

2. Beyond Higher Education: The Bubble of the Accreditation System

If an outside observer totally ignorant and unfamiliar with the American education system happened to stumble upon a few ASU-GSV sessions, she might quickly assume that traditional four-year colleges were a model as antiquated as typewriters based on the remarkably consistent dialogue at the conference around their brokenness. But they are of course alive and well, and still the primary path society encourages students to take to enter the workforce, despite the remarkable rise in cost of higher education (and therefore student debt, which as of this year totals $1.5 Trillion in the US) and concurrent stagnation in mediocre outcomes, with many college graduates still unemployed or underemployed (a study last year found that 43% of recent college graduates are underemployed).

Despite higher education’s value being increasingly questioned, four year college degrees are still required to apply to the vast majority of jobs. Some education innovators such as Lambda School CEO Austen Allred have compared the nature of the industry and the accreditation system that it is regulated by it to the taxi medallion system, as the accreditation process makes it extremely difficult for new entrants to gain accreditation, thereby stymieing innovation. It is because of this lack of innovation that Harvard Business School Professor Clayton Christensen predicted in 2017 that over 50% of colleges would close down in the next 10 years. While it’s unclear if this level of closure will actually happen, it is clear that the current model isn’t working, likely leaving the potential for a few different directions the higher education sector might go:

• Incumbent universities rapidly scale up innovation to effectively equip students for the changing world of work in a cost-effective way
• New entrants drive innovation in sector by finding creative ways to comply with existing accreditation system (as Minerva Schools and Make School have done)
• The “Uber” of the higher education world emerges, driving innovation in cost and outcomes outside the confines of the accreditation system

Perhaps the most interesting emerging models that could pose a threat of fulfilling the “Uber” scenario above are workforce retraining models who offer income-share or deferred tuition arrangements (Lambda School, Springboard) such that their incentives are perfectly aligned with students. These education providers don’t get paid unless they equip graduates with the necessary skills to get a job, often above a certain salary threshold. For now though, the challenge in expanding these models down to four year college replacements is likely financial: they would either need to find a way to access federal funds or lower the cost to serve enough to put them in a position to be financially viable enough to scale.

3. Beyond (just) the ‘Skills’ Gap: Creating Economic Empowerment for Everyone

As introduced above, it is widely agreed upon that the ‘skills gap’ (that is, the divide between the skills people have / are being equipped with and the skills they need to succeed in the workforce) is a key obstacle that the education world needs to create solutions to overcome. The problem is — addressing the skills gap alone doesn’t empower everyone — there is still a large population of people (largely low income and other disadvantaged populations) who are left out of the workforce and need additional forms of support to be truly empowered in the future of work.

In addition to the technical skills needed to succeed in a role, individuals from low-income and disadvantaged groups need models that address other obstacles that stand in the way of them reaching their full potential:

• Soft Skills — individuals are less likely to have benefited from receiving basic soft skills training, which are skills that are becoming even more critical in the age of automation 
• Social Capital — 50% of jobs are obtained through a person’s network, putting those without a significant network at a tremendous disadvantage 
• Wraparound Services — individuals who were formerly incarcerated, who speak limited English, who are single parents, who have health issues, or who experience other challenges need the flexibility, support, and coaching to be able to succeed
• Visibility to People Like Them — Coursera found that women students completed their courses 26% more often when their professor was a women; having visibility to successful individuals who have navigated your pathway from a similar background is integral to success 

The challenge in effectively supporting all individuals to navigate the future of work at scale remains challenging. There are numerous high-touch models that do provide the social capital, wraparound services, and other support that empowers these individuals to have upward mobility in the workforce (Youthforce NOLA, Braven, Beyond12,  COOP), but often times the models that are able to scale are those that are lower-touch and more standardized. A truly sustainable solution in the future of work will have to find the delicate balance between the two.

Are robots and AI going to destroy more jobs or create more jobs?

This is so often the question that articles and reports debate relentlessly (see here, here, here, here), always eager to come up with the latest estimate of the exact quantification of what impact the Fourth Industrial Revolution will have on the employment landscape.

But there is a question that is arguably more important (that doesn’t come with as catchy of headlines on if the robots are coming for us or not): regardless of the net outcome, if there are going to be millions of jobs destroyed and millions of jobs created, how will we as a society facilitate the significant increase in skills that millions of displaced individuals must achieve in order to be able to successfully shift to these newly created jobs?

Among the numerous challenges that the prospect of successfully and efficiently “reskilling” millions of people poses, how we will finance such a massive amount of skills training efforts (for people who likely cannot afford to pay for that training themselves) is among the largest. At the World Economic Forum in Davos last month, the WEF “Towards a Reskilling Revolution” report was published, which analyzed the impending challenge of reskilling what it estimates to be 1.4 million Americans in the next decade, at an approximate cost of $34 billion. My thoughts the concept raised in that report are below:

1. The Challenge of Financing

As mentioned above, the first premise behind financing being a challenge is that the majority of displaced individuals who need to acquire new skills in order to gain employment will likely not be able to afford the training themselves. According to the WEF report, the average bill for such a task is $24,800 (while this may seem high, to make it more real, think of training someone who has been doing basic data entry to be able to be a successful data scientist — no small task).

While employers may be the most obvious candidate to invest in training the workforce, they may have capabilities and incentives that lead them towards investing in upskilling their existing employees moreso than the general population of potential candidates (except for in the case where a talent shortage for a set of roles becomes extreme).

Some existing workforce development organizations rely at least partially on philanthropic funds — while an excellent solution in the short-term that does contribute towards clear societal benefits, this source of funding has its obvious limits as the challenge becomes much larger.

Given the nature of the challenge is so intertwined with the economy and livelihood of individuals, it is logical to consider government funding as a reasonable way to cover the cost of reskilling the workforce. While this almost certainly will be the case to at least some degree in the future, training providers that wish to rely on this source at scale face the challenges of securing this funding from vastly different government bodies and sustaining it over time amidst changes in leadership of those bodies.

Perhaps the most intriguing funding model in its ability to overcome scale and sustainability challenges is income sharing agreements (ISAs), which typically allow individuals to enroll in reskilling programs at no cost unless the program results in their employment above a certain salary threshold, in which case they pay back a portion of their salary to the training program (or outside investor funding the program). Although organizations like General Assembly and Lambda School have began to successfully implement such models, questions remain on the inclusiveness of such an approach — it remains unclear if such a model could be applied successfully to accommodate more diverse careers, pathways, and people.

To dig slightly deeper into the challenge of inclusiveness in the reskilling space, as employers, governments, and investors consider who to reskill, there will likely be people that it does not make financial sense to invest in. If funders are weighing social outcomes in addition to financial ones, there is the possibility of investing in a “portfolio” of individuals, where the financial loss of some reskilling investments is subsidized by others, but there still remains the risk of a substantial portion of individuals being left behind. How we as a society would accommodate an increasing number of individuals who are permanently displaced is very much an open question.

2. Additional Barriers to Reskilling Success

While the economics of reskilling are a complex challenge to think through, it’s also important to remember that (finances aside) successfully reskilling individuals in a way that empowers them to shift industries is a daunting task that organizations continue to struggle with.

One reason for those struggles is the model of reskilling requires substantial behavior change from both job seekers and employers. For job seekers, the global education system for centuries has promised to equip one with skills to succeed in the workforce for a lifetime — now they may need to continuously re-assess what skills they have, what skills they need, and how to obtain those skills. For employers who are working with skills training providers, it can feel like a substantial (and risky) shift to invest in employees who are learning the required skills for a role in a just-in-time manner.

A second reason that successful reskilling is no small task is because it revolves around one extremely ambiguous term: skills. A job market centered around skills is much more complex than one centered around degrees because there is no common understanding of what skills are and what proficiency in skills looks like (as evidenced by what you may not hire someone to be a leader purely because their LinkedIn profile shows 10 endorsements for the skill of “leadership”).

A third obstacle for the reskilling revolution is that individuals who are being reskilled will likely need many other types of support besides just access to skills training in order to successfully gain new skills and ultimately gain employment. Many individuals who will be displaced in the coming decade may have little to no experience navigating the job market, may need coaching and other personal support to overcome life issues that stand in the way of reskilling successfully, and may not even have the financial resources to afford basic living expenses while going through the training. Our ability to address these challenges will be just as crucial as delivering the skills training itself to truly power a successful reskilling revolution.

“If we try to compete with machines on their turf, we lose.”

Joseph Aoun (President, Northeastern University)

MIT hosted the AI & the Future of Work conference last week to explore how exponential innovations driven by artificial intelligence will fundamentally disrupt the nature of the workforce and force society to grapple with important questions about both our labor and our livelihood.

Eric Schmidt, former CEO of Google, reflected in his opening “Fireside Chat” about the importance of Google’s innovative decision in 2010 to go completely “mobile-first” (designing all of their products around the mobile experience — an approach that swarms of technology companies now emulate) and articulated his strong belief that the next wave of companies will coalesce around a different approach: “AI-first”. That is, while AI may currently be seen by some to be a set of technologies that is applicable only for a narrow group of highly specialized products, the reality is that companies should and likely will start adopting some form of AI into every one of their products — and indeed start with that as a framework when designing new products. Such an approach would stem from not only the undeniable value that AI can bring upfront, but also over time as it is uniquely positioned to generate an incredible amount of training data to be able to continue to improve upon itself.

There was a noticeable consistency in the views conference speakers shared in arguing that AI and automation would not necessarily lead to mass unemployment in the workforce — with individuals suggesting some combination of (a) the future of work will offer more new jobs than the amount of jobs that will go away, (b) AI will act as a supplement to human productivity, not a replacement for the human, and (c) societal challenges will be much more of a barrier to implementation than the progress of technology itself (self-driving cars, for example).

Despite this fairly unanimous message, the overall tone of the day remained gloomy, highlighting challenges that a rapidly and continuously changing workforce will bring — particularly in finding ways to avoid accelerating income inequality and to empower people with the right set of skills they will need to succeed in the future of work. As one panelist summarized, “The robots aren’t going to take all of our jobs, but there’s plenty of other things that we need to worry about.”

The conference highlighted the overarching challenge that exists looking forward: we must ascertain how to harness the remarkable benefits of AI in a way that will minimize the potential negative side-effects of such technologies, as such side-effects certainly aren’t going to be minimized on their own. Below is my summary and perspective on three specific aspects of that challenge:

 

Income Inequality: Rising Tides, Sinking Boats

The U.S. economy is booming. Unemployment is hitting record lows. And productivity (in terms of output per hour) is hitting record highs. So what’s the problem? Rising tides lift all boats… right? Wrong — according to a plethora of measures.

Median real family income is stagnant. Total share of income in the overall economic output that is going to labor (as compared to capital) has decreased. The MIT Living Wage Lab conducted a study in 2017 that determined the living wage in America to be $16/hour — but 42% of Americans make less than that. It’s not an employment problem; it’s a wage problem.

Companies have long created economic value that is heavily driven by the number of employees they have (where adding more employees equals additional output), but this dynamic is changing with more value now being created in many companies by the customers themselves (where adding more customers equals more data or more advertising revenue, while keeping employees constant). One question that arises from this dynamic is — do customers deserve a portion of this additional economic value that their own data has generated?

Addressing this income inequality, which if not course corrected will only be amplified with the increasing adoption of AI and automation in the workforce, should be amongst the most important issues center stage in policy discussion. But it’s not. Until it is, we continue to have a tax system that encourages companies to use capital over labor, often times making it an even easier decision for companies on where to invest.

 

Education to Employment: A System Locked in Rigidity

There have never been more job openings that can’t be filled — it’s driven by a skills mismatch in the education to employment pipeline, and it’s only going to accelerate with further disruption in the workforce. The task at hand becomes how to modularize skills and make them widely accessible. Despite AI being at the center of this disruption (i.e. part of the issue), AI can also be part of the solution — with plenty of opportunity to leverage it to modularize skills, make those modules accessible, and make the learning more personalized.

There have been numerous studies about what skills will be most important in the age of automation (and their findings are typically consistent), but there is a chasm between the identification of those skills and any action on them. From K-12 systems, to higher education institutions, to government education policy, there is incredibly rigidity in the education system, that begs the question of if we can afford to wait for the entire system to be reformed or if we need innovation from the private sector to spur continuous agility and facilitate the necessary Just-In-Time processing of skills.

One education-specific conversation at the conference was a Fireside Chat with Joseph Aoun, the President of Northeastern University, who articulated the importance of making students ‘Robot-Proof’. He argued that in order to achieve this aim, education providers must focus on integrating technological literacy, data literacy, and human literacy (what we as humans do that machines are not able to replicate).

Aoun admitted that higher education providers can be stubborn to change, but remained confident that a “sense of competition and innovation” in the higher ed ecosystem would enable the sector to overcome the challenges that will be presented by the future of work. There is however an argument against this claim of innovation, given the bureaucratic higher ed accreditation system that currently exists (with an incredibly burdensome upfront process and a principle of self-oversight), acting as a massive barrier to new entrants and innovation.

No matter what the solution to the current rigidity in the education system, it’s clear that lifelong learning must be a central component. And as we develop a lifelong learning model, it’s important that it does not take on a problem that so many other parts of the education system have: inaccessibility. It’s unclear who will be funding the upskilling of employees as more and more people need to shift jobs, which is a question of heightened importance when considering employers are investing even less in workforce training as average employee tenure declines.

 

Uniquely Human Skills: For Now or Forever?

Throughout the conference, there was a large emphasis on human partnership with Artificial Intelligence — about it being humans “plus” machines, not humans “versus” machines. There was a consistent notion that we must simply identify what AI will be better than us at and what uniquely human skills exist that we can reign supreme at, and then organize future labor around those parameters. And the narrative around what skills are “uniquely human” was largely consistent with other existing research: creativity, empathy, judgment, leadership, teamwork, etc.

However, there seemed to be some degree of inconsistency between this narrative of uniquely human skills existing and some of the innovations in Artificial Intelligence the conference was simultaneously highlighting. In one session in particular that focused on “The AI-Enabled Organization”, panelists Sophie Vandebroek from IBM and Gabi Zijderveld from Affectiva illustrated AI’s incredible recent advances in judgment and empathy.

Earlier this year, IBM rolled out Project Debater, the first AI system that can debate humans on complex topics, which was able to win an argument against a champion debater. Vandebroek emphasized the huge range of future applications of such technology, including having the system in the boardroom to enhance a company’s executive judgment and decision-making.

While IBM did allude to its technology still not being able to master human emotion, Affectiva seemed to plug that exact gap with its emotion measurement technology — allowing it to gauge what emotions individuals are feeling based on facial and vocal patterns. Zijderveld emphasized Affectiva’s ability to humanize technology, with possible future applications such as call center bots that are able to detect the tone of a customer’s voice (possibly as well as or even better than humans) and adapt responses accordingly.

So are we sure that it’s really robots “plus” humans? Are skills like creativity, empathy, and judgment really “uniquely human”? Or are they just concepts that are more challenging to replicate, but will soon be conquered as the capabilities of artificial intelligence increase at a rapid pace? At the very least, we can take solace in the fact that there are still humans in the CEO seat of companies… for now.

Earlier this week, Chris Hughes (Co-Founder of Facebook and Co-Chair of the Economic Security Project) and Michael Tubbs (28-year old phenom Mayor of Stockton, California) spoke at the Harvard Kennedy School about their upcoming joint experiment with guaranteed income. The Stockton Economic Empowerment Demonstration (SEED) is the first-ever public-private initiative of its kind, where beginning in 2019, 100 Stocktonians will receive an unconditional guaranteed income of $500 per month for 18 months.

Why is this important?

To first establish why this topic is relevant to this blog (focused on workforce automation and the implications for the education sector) — it’s important to understand Hughes’ rationale for why we as society need to explore guaranteed income. And that is — there is a fundamental unfairness and brokenness that underlies the economy:

1. Corporate profits are increasing
2. But median wages for workers are stagnant
3. And basic costs of living (education + housing + health care) are all increasing

Clearly these are all massively generalized statements, but the reality is that income inequality is only accelerating, and millions of Americans find themselves unable to pay for their basic costs of living with income from their job alone. Therefore, we as a society must grapple with how we support individuals who are unable to support themselves and their families alone.

Thus, the connection to this blog is simple — the risk that this issue becomes extreme enough to require solutions such as guaranteed income is exponentially increased with the onset of workforce automation. As artificial intelligence and automation displace individuals in the workforce (even if temporarily), levers like guaranteed income will be an indisputable part of the conversation of how to best support those workers as they try to re-enter the workforce, likely directly accompanied by further education to up-skill them for the jobs of tomorrow. It also offers a potential opportunity to compensate individuals who are working, but not in ways that add enough financial value in the economy to be self-sustaining (e.g., caregiving, which is often cited as a “uniquely human” skill that cannot be displaced by automation).

Why Guaranteed Income?

One differentiation Hughes made clear at the event was the distinction between guaranteed income and universal basic income (UBI). While UBI is a popular term thrown around to imply that every citizen within a population (whether citywide or nationwide) receives some recurring monetary stipend, guaranteed income merely implies a recurring monetary stipend for a subset of the population, such that there is effectively an “income floor” that allows every individual to be able to afford basic living costs.

While there is some debate as to whether direct cash payments to be individuals would be the most effective instrument, the arguments for it include (a) from a moral perspective, Hughes argued that allowing people to decide how to allocate their cash themselves is the right thing to do, and (b) Hughes also cited that empirical evidence suggests that this allocation ends up being more efficient.

What happens next?

The Stockton Economic Empowerment Demonstration is likely the first of many experiments into possible arrangements involving direct cash that (among other situations) can help workers who are displaced by automation. By no means is the format that Hughes and Mayor Tubbs created necessarily the most optimal, but it is likely a solution which we will only find find the optimal through many iterations. Fundamentally, we must experiment to identify:

• What the best way of giving out money is (Guaranteed Income vs. UBI, quantity of income, conditions of income, etc.)
• What the best way of paying for money is (especially if this is going to be a large-scale, long-term aspect of our economy)
• How it relates to other support elements (i.e. upskilling) that we provide to people who cannot make enough income to meet their basic needs

Numerous other outstanding questions also remain that would have to be answered before implementing a large-scale guaranteed income program, including the ability for such a program to sustain itself at scale and whether or not the general public would rally behind such a program. Despite those hurdles, there is significant enough potential for us to consider it within the realm of levers at our disposal as society faces the onset of automation.

Among the most prominent companies and organizations in the world leading the thinking around the Future of Work, McKinsey may be at the very top. The firm has released numerous primary research-fueled reports in recent years that relate to issues covering automation, job displacement, the skill gap, and income equality — their most recent long-form report in 2017 titled Jobs Lost, Jobs Gained: What the future of work will mean for jobs, skills, and wages.

One of their more interesting additions to their Future of Work portfolio, though, started late last year and has continued into this year: an 8-episode (so far) podcast series titled The New World of Work that brings together McKinsey experts (notably James Manyika, who is one of the most well-known thought leaders in the world on the subject) and industry players to discuss various aspects of the changing workforce landscape and its implications on society. I had a chance to listen to the first 8 episodes and offer my takeaways below.

Overall: How McKinsey Views the Future of Work

The phrase “The Future of Work” gets thrown around an increasing amount as a hot topic to pay attention to, and there are actually a handful of sub-categories within it that people may be referring to when they use it. McKinsey breaks it down into four:

1. Jobs Lost & Gained — what the impact of the evolution of the workforce is on the number of jobs that exist
2. Type of Work — how the rise of the gig economy and other independent work are changing the nature and set-up of the work that people are performing
3. Organization of Work — how adoption of AI and other technology by companies will lead to a different distribution of work between man and machine within a company, and how the two interact with each other
4. Income Inequality — how the evolution of the workforce will risk accelerating income inequality and the implications for society

The first category “Jobs Lost & Gained” is often the most discussed — with speculation into which jobs will most certainly be automated, and which jobs may be “safe”. The unique lens that McKinsey brings to the discussion is less of a focus on the jobs, and more of a focus on the activities within them — that is, you must first analyze which activities within different jobs are susceptible to automation before speculating if the job itself may be at risk of phasing out of the economy.

Takeaway #1: There may be more jobs that change than are displaced 

McKinsey’s above premise that activities within jobs should be the initial focus has led them to extensive primary research on what the specific activities are that make up each job in the economy and how “at risk” each of those activities is to automation.

Their most notable finding is that there are only about 5% of jobs in the economy that are made up of 100% activities that are highly automatable, but a whopping 60% of jobs in the economy are made up of at least 30% activities that are highly automatable. These two stats drive one of their primary conclusions that there may be more jobs that change than jobs that are displaced entirely.

With this conclusion in mind, they argue that moreso than focusing on the risks around mass unemployment, we should worry and look for solutions to the risk of mass transitions in the workforce that may need to occur from these “changes” in jobs. Amidst every transition will be underlying questions such as:

• Can an individual find a new job?
• If so, can they move to where that job is?
• If so, do they have the necessary skills for that job?
• If so, is the income sufficient for them?

These and other questions will drive a challenge (but also a market opportunity) of how to best offer job dislocation support to employees as more and more find themselves in the position of making previously unanticipated workforce transitions.

While I appreciate and agree with McKinsey’s push to go deeper on jobs to examine the true nature of activities that might lead to automation, I think there is substantial risk of job loss that could be interpreted from their statistics that they aren’t taking into account. That is, inherent in McKinsey’s conclusions is an assumption that when a job has a set of activities that is only partially automatable, the most likely outcome is for the nature of the job to change, but for the number of jobs available to stay the same. I would argue that there are two other outcomes that are at least equally likely: (1) that the company needs to retain employees to perform the activities that are not automatable, but that they are able to significantly reduce the amount of employees necessary to do so, and (2) that the companies simply abandon the activities that are not automatable, in favor of simplifying their workforce and leveraging technology to increase the cost effectiveness of the set of activities that were automatable.

Thus, I agree with McKinsey’s conclusion that there will be a significant amount of job “change” and that a focus on transitions in the labor market is absolutely crucial, but I also believe that they may be understating the amount of jobs that could be displaced.

Takeaway #2: Regardless of displacement, there are obstacles ahead for the middle class employee

Regardless of where you come down on how many jobs will be displaced in the economy, McKinsey points out multiple fundamental shifts that put a middle class employee in the workforce in a more challenging position.

While the rise of the “gig economy” (and other independent work) is often described with a certain degree of excitement, as indeed many individuals are driven into working in the gig economy because of their own interest in it, it’s important to observe that there are also many who are driven to it because they either can’t find work elsewhere or can find work elsewhere, but aren’t making enough money and need to use it to supplement their income.

It’s helpful to start with this understanding because upon analysis of the standing of workers in the gig economy, it becomes evident that there are drastically fewer rights for workers (and significantly less power for labor unions). Often times workers have less access to benefits, less guarantee of stability, and less legal recourse if something goes wrong. As a prime example, Uber is entrenched in legal debates in multiple geographies arguing that their drivers are not employees of the company because acknowledging so would require the company to provide them with more rights, benefits, and stability.

In addition to the decrease of the leverage that some workers may experience, there is also already a challenge of income inequality that many in the economy are facing. McKinsey cites a variety of statistics that all lead to this same conclusion, including declining labor share of GDP, stagnant wages for the middle class, and the increasing gap between rich and poor. And the important layer on top of these statistics, of course, is that they are all at risk of getting significantly worse as workforce automation continues to rise.

Many of these observations lead to the natural question of what the response is from a policy perspective. In the age of automation and an evolving workforce, there is undoubtedly an opportunity to examine policy levers related to upskilling, labor market mobility, labor regulations, and income support. But with all that said, for me it remains unclear how governments will develop the agility by which to not only solve the current challenges, but to continue to rapidly solve challenges on a continuous basis in the ever-changing workforce of the future — an agility that has not often been demonstrated thus far.

Takeaway #3: There is a market opportunity to impact the new economic landscape (and LinkedIn is in a leading position), but open questions remain

As the future of work gets closer to becoming the present, there emerges a set of new market opportunities as dynamics and needs change in the overall economic landscape. In particular, there is an opportunity for information flow between job seekers, employers, and learning programs in an effort to connect people to skills and then to jobs that match those skills.

One especially interesting conversation in the podcast series was between James Manyika and LinkedIn CEO Jeff Weiner, in which the two discussed the remarkable position that LinkedIn is in to add value in that new landscape. One initiative LinkedIn is working on is the Economic Graph, in which they are attempting to create a digital representation of the economy that includes: 560 million individuals in the global economy, 20 million companies, 15 million open jobs, 60 thousand schools, and thousands of underlying skills that connect all of these groups. In addition to the innate value of having a large set of digitally organized information and connecting some of these different stakeholder groups, LinkedIn (or whichever company of set of companies ends up succeeding) is also in a position to proactively identify where skills gaps exist in specific industries or geographies.

All that said, my perspective is that still remains a difference between universal knowledge of this information (which is no doubt a major step in progress) and actual action on it in a way that makes the economy more efficient. While it is reasonable to understand how an individual job seeker, or individual company, or individual learning program would take such information on skills gaps in the economy and use it to better their own individual position, I think the larger question is how such a data set could be used on a systematic and collaborative level for the betterment of the economy overall (included, but not limited to from a policy level).

On top of all of these outstanding questions, I believe the most crucial one to answer is around financing. The most important activity in the connection between the above stakeholder groups is the upskilling of an individual in a specific set of skills that will prepare them for a specific job — but who is paying for that upskilling? The job seeker? The employer? The taxpayer? An investor who shares future income? There are plenty of innovative possibilities that can be considered, but it’s imperative that we begin to lay the foundations for those possibilities now, as they will be essential to quickly deploy as the pace of change in the workforce continues to accelerate.

The IFC 8th Global Private Education Conference was held last week in Cape Town, South Africa which had the theme of ‘Learning for the Jobs of Tomorrow’, bringing together leaders impacting education from across the globe — a total of 370 participants, from 238 organizations, in 50 countries. I had the privilege of attending as the Chief of Staff of Nova Pioneer, an organization aiming to empower African youth to be the innovators and leaders of the 21st century.

Keynote speaker Gary Bolles (Chair for the Future of Work at Singularity University) highlighted trends in exponential technologies like AI and the dramatic impact they will have on both what future will look like and what future learning models must necessarily put more focus on to prepare students to succeed. Other sessions covered a range of related topics, including how foundational education models can evolve related to both the “what” and the “how” of learning, how a range of alternative learning providers and associated credentials can make the employment market more efficient, and how all stakeholder groups can remain agile in optimizing the education to employment pipeline.

Among the plethora of intriguing ideas and insights shared across the different conference sessions, I walked away with a few main reflections on the future of learning:

1. Evolution & Interdependence Among Stakeholder Groups

When considering the challenges that relate to an individual student/job seeker successfully progressing through the education to employment pipeline, there are numerous stakeholder groups that may impact the success of that student: K-12 education providers, higher education institutions, other education to employment initiatives, other nano-degree learning programs, the government, and employers themselves.

When we say the education “system” must evolve to adapt to the world of workforce automation, what we are really saying is that each of these individual stakeholder groups must evolve — and for each of them, that evolution may look different. For example:

• K-12 Education Providers — can focus more on empowering students to succeed in an ever-evolving and ever-more-automated workforce by focusing on skills that are more “uniquely human” such as problem-solving, adaptability, creativity and empathy
• Higher Education Institutions — can adapt more quickly to how to best equip students with the skills they need by creating tighter feedback loops with employers
• Employers — can re-design their organizational structures to have humans working alongside technology to work in the most efficient way, but can also look beyond college degrees to focus on the skills that really matter and how to best test for them

One challenge in each of these stakeholder groups successfully evolving though is that many of them are interdependent on each other in a way that creates sub-optimal outcomes. For example, the vast majority of employers continue to focus on college degrees as a primary requirement for job readiness, despite their track record of an inability to consistently deliver optimal skill sets (not to mention their relative lack of accessibility). Continuing further back down the education lifecycle, the vast majority of universities continue to focus on test scores that incentivize K-12 education providers to put a disproportionate focus on test scores in a way that gives them less time to spend on innovative new models building the uniquely-human skills described above.

These interdependencies between education to employment stakeholders have created an inefficient gridlock, where none of the groups is operating in a way that allows the system as a whole to reach its optimal outcome — which is each student being most effectively prepared to succeed in the workforce and add maximum value to the economy. In order for the education system to successfully evolve, we must figure out how to breakthrough this gridlock.

2. The Valuable Role of a Central Facilitator

One dynamic that makes the challenge of designing the future of learning even more complex is that we don’t just have to solve the skills gap once. We have to be able to solve it over and over again. The exponential pace by which the workforce will evolve will mean that the education system itself will not only have to continue to evolve as well, but also do so at an increasingly faster pace. Therefore, agility of the system is crucial.

A second dynamic that is important to consider in this future state is the ability for the different stakeholder groups (mentioned above) to have some degree of a common language, including not only what skills are, but what proficiency in those skills looks like. This alignment would allow different stakeholder groups to be able to quickly and effectively communicate around the skills that are necessary in the workforce and how those skills can be developed through a combination of traditional and alternative learning providers. One company present at the conference, Degreed, was founded partially out of the dissatisfaction that college degrees are the only credential in the world that are universal; their aim is to change that by creating a lifelong learning platform that recognizes the diverse set of learning outcomes each job seeker builds over their lifetime that should be considered by employers.

When considering these dynamics together, it begs the question of whether there should be a “central facilitator” of sorts that acts as the connective tissue between job seekers, employers, and learning providers, and if so, what this player should look like (e.g., public vs. private? An aggregation of organizations or one large company?) A central facilitator could not only establish a common language among stakeholders, but also communicate (even proactively) where skills gap exist (or may exist) based on the skills that employers are demanding and the skills that job seekers are exhibiting and/or learning programs are providing.

One existing company that seems in a particularly good position to play this role of a central facilitator is LinkedIn — given their work connecting job seekers, employers, and learning programs (particularly after their acquisition of Lynda.com). At the IFC Conference, I had the opportunity to ask the Head of R&D for LinkedIn Africa about LinkedIn’s role as a central facilitator during a panel Q&A, which you view at the 55:30 mark of this video.

3. Automation’s Impact on the Global Talent Marketplace

Though I thought speakers and panelists at the IFC Conference did an excellent job speaking to the different ways different education stakeholder groups can evolve to better prepare for the onset of workforce automation, one critique I had was not focusing enough on the reality that despite efforts from all those stakeholder groups, there remains the significant possibility that a significant portion of the global workforce is displaced from their current roles in the economy (even if just temporarily).

One dynamic that was pointed out during the conference that relates to this assertion is the fact that different geographies (countries, states, etc.) will be impacted by automation in different ways, largely influenced by the breakdown of activities in within a geography’s economy and how that breakdown relates to the type of activities that are more or less susceptible to automation. In Mexico for example, there are millions of people still working in manufacturing roles that are extremely susceptible to automation, making their economy (and the individuals in it) even more at risk.

One trend also covered at the conference that does offer some hope in response to this risk is the decreasing amount of friction between global talent markets — that is, if demand (from employers) for a particular set of skills is present in one region, and supply (from job seekers) of that skill set is present in another region, the global economy is getting better at matching that supply and demand, which in theory may slightly lessen the blow of automation to particular regions.

Andela was the most notable company present at the conference that is actively contributing to breaking down barriers in the global talent marketplace. The company’s model involves training software developers across Africa and providing global technology companies access to this talent, while the individuals remain in their home countries. This is just one example of how barriers can be reduced in the global 21st economy, and it is a huge opportunity for players in the global economy to explore how the success of Andela can be replicated in other industries and geographies.

1. The Scenario

As you pull up to LaGuardia airport, you hop out of the Uber that transported you, along with two other Manhattanites who set across the table from you in the car, which was quite spacious in the absence of a driver.

You head to the Delta check-in desk, where a computer prompts you to look directly into the camera above the screen. After the facial recognition technology kicks in, a check mark pops up on the screen along with your SkyMiles rewards number and prompts you to put your checked bag on the conveyor belt. At the security checkpoint, you make your way to the TSA Pre-Check line, and after the 10-second full body and baggage scan, a green light appears to signal you are free to proceed.

Finally on the plane, you order an orange juice and a decaf coffee from the television on the seatback in front of you, and about thirty seconds later, the two drinks emerge on the armrest next to you. Just under an hour later, a robotic voice comes on over the intercom to update you on the local temperature in Boston and the updated arrival time of the autonomous Boeing 717.

As you are getting into the aisle to depart the plane, you motion to the family in the row behind you that they can go in front of you. On a Monday morning in a previous life, you may not have been as generous with your time, but hey, you don’t have anywhere to be today. You don’t have a job.

But then again, neither does anyone else.

2. The Issue

The accelerating rise of artificial intelligence, robotics, and other exponential technologies threatens to disrupt the workforce as we know it, with millions of jobs at risk of either partial or complete automation. The potential disruption could result in anything from, taking a conservative view, a drastic shift in the the makeup of the type of tasks humans perform in an economy and the associated skill sets that will be required from them, to at worst, a significant proportion of the global population being displaced by automation to the point of long-term mass unemployment across all industries and regions.

Some skeptics may rightfully point out that ill-founded stories of robot-induced mass unemployment have been threatened to be on the horizon for decades, from former US President Lyndon Johnson, who in the 1960’s warned of an impending “cybernation revolution” that would result in widespread unemployment, all the way back to John Maynard Keynes in the 1930’s who predicted an imminent “disease” of “technological unemployment” that would have a dramatic impact on the economy.

However, the current wave of automation is radically distinct from these previous eras, with exponential technologies having accelerated more in the last 5 years than they have in the last 50, leading to more and more companies starting to automate not only routinely programmable tasks, but also tasks that require complex cognitive capabilities that have until recently been assumed to require “uniquely human” skills.

A detailed study by Oxford University in 2013 suggested that 47% of jobs in the economy were at risk of being automated in the next 20 years, which was one of the first smoke alarms in recent years that seemed to convince some of the general public to start paying more attention to the issue. Since then, other leading research centers have conducted a number of additional analyses with varying methodologies that have predicted similarly worrisome results. In 2016, the World Economic Forum released findings that even when accounting for the jobs that would be newly created from exponential technologies, the net impact of automation in the 15 countries studied would be 5 million jobs lost by 2020. And in 2017, the McKinsey Global Institute conducted extensive research on the potential for automation and concluded that 50% of all activities in the global economy could be automated using technologies that are already in existence today.

With more and more data-driven studies supporting the validity of this impending shift in the workforce, and more and more companies providing real word case studies by beginning to automate certain parts of their labor force, there is an emerging consensus of the large impact that automation will have on the workforce. There are significantly divergent views, however, on what exactly that impact will be, what the implications of that impact are, and what society should do about it.

While influential figures such as Bill Gates, Elon Musk, and Stephen Hawkings (prior to his passing) have infamously leaned towards the more negative side of the spectrum of the doomsday-like risk that Artificial Intelligence poses (both in the workforce and more broadly in society), others have predicted a mere shift in the workforce that results in less unemployment and more “redeployment” (that is, many jobs will be lost, but many jobs will also be created, so a large number of people will have to pivot to new occupations).

Suggestions on what society should do about this impending impact have diverged even further from a consensus, ranging everywhere from implementing a robot tax, to numerous deviations of some form of universal basic income (UBI), to ultimately fusing human brains with artificial intelligence (if you can’t beat ‘em, join ‘em?)

Whatever the result of accelerated artificial intelligence, robots, and automation, large scale impact to the workforce is imminent. And among the numerous areas in society that will likely experience domino effects of dramatic changes when such a large scale impact to the workforce occurs, the education sector may be one of the most significant. The inherent objective of education in society, in its most simplified form, is to prepare students for the workforce. Thus, if the workforce itself transforms dramatically, the education sector may have to undertake a proportionally dramatic transformation to continue to deliver on its aforementioned objective.

This imminent challenge for the education sector may prove to be incredibly difficult, especially when considering the “Skills Gap” (that is, the disconnect between the skills required in the workforce and the the skills the education sector is equipping students with) in the United States and around the world has already increased dramatically in recent years, with substantially larger changes to the workforce still on the horizon.

3. The Blog

I’m starting this blog to follow an issue that I believe will be hugely important to the future of our economy, our livelihood, and society as we know it. I’ll aim for my analysis to be evidence-based when possible, but rather than dive into technical nuances of artificial intelligence, I’ll focus more on theorizing about the broader societal implications with a healthy dose of everyday layman speculation and conjecture.

While the blog’s title and opening scenario above are intentionally provocative, painting the picture of a Hollywood-ready storyline about the complete takeover of robots in the economy, the reality is that even if we were headed towards significant unemployment, there would likely be a substantial runway time period of adoption, which is why questions like educating for the workforce of the future would still remain relevant.

Fundamentally, the guiding questions of my blog will be:

• What impact will automation have on the workforce?
• How will the education sector need to adjust in response to that new workforce?
• What other implications will workforce automation have for society as a whole?

I’ll kick-off the blog by summarizing some past material that I believe is most important to a foundational understanding of the issues. From that point on, I’ll use the blog as a way to aggregate the latest research, analysis, and opinion on workforce automation and related issues, with a small dose of unsolicited commentary over the top — connecting dots, playing devil’s advocate, and posing further questions.

Though the original impetus to create a blog came from a personal desire to follow the issues around workforce automation and organize my own thoughts around its implications, my hope is that this can also be helpful to those who share similar interests and who might offer their own feedback, comments, and suggestions, as we as humanity face what I think will be the largest societal shift of our lifetime.