Category Archives: Capitalism

… Creative Economy

This is NOT an article about how AI, automation, and robotics are coming for non-knowledge jobs. That is happening, but this is an article about how AI is coming for traditional knowledge economy jobs too, and how it will change our economy and society, and I think for the better!

A few days ago, I was watching NCIS with my mom (it’s always on some channel). As per usual, a clue came in, and with a few tips and taps on a computer they had traced it back to its source, cross-referenced it with a database, and sent the results to the field agents’ phones. In all, the scene lasted about 30 seconds. My mom said, “How can they do they so fast and by only typing? It takes me 20 minutes just to remember my password.”

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NCIS is dramatized television. There are very few, if any, people or organizations with that level of computing sophistication and coding skill. However, it’s close enough to how we think computers work to be believable. More remarkably, we’ve at least thought we’ve been at the cusp of this level of computational sophistication for nearly 20 years. I remember watching 24 with my dad in the early 2000’s and very similar tip-tap-success was going on back then in that show. Yet we all know, by sheer fact of our daily lives, that working with digital information is cumbersome, time consuming, and does not always end in success.

Societally, we’ve convinced ourselves that we are living at the leading edge, if not the pinnacle, of the Digital Revolution. The advent of AI is just around the corner, and our 40+ years of digitization are poised to pay off into more leisure and more accurate and easy computing for all of us. On the contrary, I contend that we are merely at the beginning of the Digital Revolution, and there are still many years of work ahead of us before we can enjoy the tip-tap-success that we see on television.

Data remain very compartmentalized. Throughout the digital age, companies, governments, and other entities created databases, data protocols, and computing and data languages ad hoc. Even within large organizations different databases exist to house purportedly the same data, and sometimes these databases contradict each other. Furthermore, data are often user generated, so discrepancies propagate over time. Remember when they rolled out the electronic medical record (EMR) at your office and you could not find the field for pulse until someone told you to look for “heart rate”? And is the accounting system in dollars or thousand dollars? These are the discrepancies that real life NCIS confronts when they perform data analysis, and it takes far more time than we seem to think to sometimes get less than clear results.

A few weeks ago, I met my friend at a food hall in Midtown. I couldn’t help but look around and try to imagine what everyone did for work. Most patrons were young workers in business casual. Being Midtown, I imagined a lot of bank and finance workers, with a smattering of consultants, business people, and people in media and publishing. I know how they spend their days. I used to be a finance consultant. They spend all day pulling together data from disparate sources and collating them into something that their superiors can use to make decisions. “Where’s the data?” “Who has it?” “Is it any good?” These were my daily lines. Most work time for these “Midtowners” is spent replicating data, models, and results. Much less time is spent deciding what they make of results and numbers. I had entire projects where I figured out how the data came together and simply documented the process. Despite all of our advanced statistics and calculus classes, most people in these “Midtown” jobs are just performing basic arithmetic, if that.

Food Hall

 

However, there is reason to believe that the Digital Revolution will soon be accelerating. Emerging innovations like blockchain and internet of things (IoT) are streamlining the collection, storage, and sharing of data. The rate at which we generate data is accelerating, so having clear protocols for the sharing of data is key if we want to continue to move up the digital curve. If we continue to generate astonishing amounts of data but do nothing about their balkanization then making connections between data – the tip-tap-success we see on NCIS and 24 – will be more and more difficult, not easier, as we often assume as default for digital processes.

24Over time, as fiefdoms of data come crashing down and the Digital Revolution truly does bring us closer to tip-tap-success, all of these Midtowners in clerical and finance roles will find themselves with a lot of free time on their hands (so will the consulting firms). Banks will finally be able to cut lose the throngs of high paid workers that spend their days knee deep in Excel, jockeying numbers for the few actual managers in firms whom make decisions. Managers will be able to easily retrieve <tip> the data they need, perform some manipulations as they see fit <tap>, and then make decisions based on their results <success>.

This Digital Revolution is a necessary prerequisite for the full advent of AI. Data are the fuel for AI. Machine learning algorithms require vast quantities of data, and preferably data that update in real time, so the algorithms can truly learn and improve upon themselves. As it stands now, all of the world’s data are too balkanized for machine learning algorithms to pull them in and turn them into the true putty that will lead to cognition-level algorithms. However, when it does, it’s not only the Midtowners that need to worry about their jobs. Managers – true actually make decisions of import managers, will begin to see their judgement challenged by algorithms. When there is little uncertainty in what has transpired in the past and what the forecast prognosticates for the future, there is little room for what we now think of as managerial judgement in decision making.

When I discuss this future with business people they see it as a hard pill to swallow. This is a natural response, but I’m apt to point out that there are excellent companies that are already working on AI for managerial decision making. As consumers, we are most familiar with Alexa or Google Home as voice-enabled personal digital assistants. However, Salesforce has Einstein, which helps sales and marketing teams with routine tasks. They’re already working on more advanced business applications for Einstein, and before long you’ll be able to ask Einstein, “Should we acquire a company or build a new capability in-house?” We are taught analytical frameworks to solve these questions in business school, so once we have the requisite data packaged into something that a machine can consume, why couldn’t, and why shouldn’t the machine answer the question for us (or even alert us to what questions we ought to be asking)?

[Salesforce CEO] Benioff even told analysts on a quarterly earnings call that he uses Einstein at weekly executive meetings to forecast results and settle arguments: “I will literally turn to Einstein in the meeting and say, ‘OK, Einstein, you’ve heard all of this, now what do you think?’ And Einstein will give me the over and under on the quarter and show me where we’re strong and where we’re weak, and sometimes it will point out a specific executive, which it has done in the last three quarters, and say that this executive is somebody who needs specific attention.”

 – Wired

I am not saying that the clerical workers of today are overpaid data jockeys not worth their weight in avocado toast. Nor am I criticizing their managers for hiring them and needing their assistance. I recall a particularly large project from my old consulting firm. It required an army of fresh-out-of-college consultants to comb through loan files and flag missing documents and other discrepancies. The work was tedious, but it required attention, occasional analytics, and downright intelligence. The young consultants did not find it particularly rewarding, only repetitive, and the bank certainly did not want to be paying the millions of dollars for error identification. Nevertheless, we still live at the dawn of the Digital Revolution, and this work was a necessary evil for everyone involved in the project. With AI far more popularized now than it was only ten years ago, nearly everyone can see the promise of AI in automating audit work like that. Nevertheless, it is still not a reality.

Salesforce EinsteinWhen the Digital Revolution does usher in true machine-powered cognition, I foresee banks, investment houses, insurance companies, and trading businesses, just to start, operating drastically differently from what we are used to today. Midtown will be cleared out – both the food halls and the corner suites. A few managers will rely on AI for most decision making, and the remaining workers will be more creative in nature, delving into new and emerging business models, or possibly still toiling in the age-old task of sales (with Einstein’s help, of course).

I hardly see this as apocalyptic for our knowledge economy. Yes, Midtown will be desolate, but Brooklyn will be bustling. Suit peddlers will be out of business, but hipster boutiques will be teeming. The advent of AI will be the advent of what I call the Creative Economy. Today, the creative economy is the corner of our economy focused on arts and leisure, design, media and entertainment, performing arts, fashion, and a smattering of other cottage industries.

Although people will lose jobs (or fewer new jobs will be created in the knowledge sector), our economy will be operating more efficiently. This relieves pressure on prices and leaves employed people with more disposable income. As an economy we can then deploy this disposable income into new interests, hobbies, passions, and arts. With more of the world’s most intelligent people free to devote themselves to their passions and leisure, there will be an explosion in creativity and creativity-as-commerce. Rather than focusing on high paying jobs devoid of meaning (if anyone who spends their whole day collating data says they “love their job,” they are lying), far more of our collective intellect can be dedicated to creative pursuits. We can create more content of an intellectual nature and consume more downright leisure.

I see four creative sectors staking claims for themselves and growing rapidly alongside AI:

  • Pure Leisure & Arts
  • Digital Arts
  • Creative Enablement
  • Physical-Digital Interaction

Pure Leisure & Arts

We already consider leisure & arts as very virtuous pursuits, although one that relegates all but the luckiest artists among us to being the perennial starving artist. These arts include writing, painting and drawing, film-making and acting, music, dance, fashion, gastronomy, architecture, other forms of literature, performing arts, and visual arts. With more time left to pursue the consumption of leisure or the practice of these arts, the traditional arts will proliferate.

Digital Arts

Digital arts will be one of the fastest growing new creative pursuits. With more immersion in the form of augmented reality and virtual reality (AR/VR), there will be immense demand for graphic design, 3D design, animation, and VR environment design.

Creative Enablement

 

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All of this art begs for software in which it can be designed, rendered, mixed, shared, and experienced. Today we have a knowledge economy, and Microsoft, along with the likes of SAP and Oracle, dominate knowledge software, so they are some of the largest companies in the world. In the future, the creative economy software manufacturers will be among the largest companies in the world. Design software by companies like Autodesk and Adobe will dominate our daily lives, and those companies will be vaulted into the Dow 30. There are even companies that are merging many technologies, from teleconferencing and virtual reality to design and architecture. They are creating software that will allow remote teams to interact in virtual reality environment and collaborate on design and creation real-time. Imagine a team of architects, all over the world, being able to virtually fly around the buildings they are designing and make changes together based on each other’s comments.

Physical-Digital Interaction

We will continue to live in a physical world (I am not predicting The Matrix), and manufacturing, engineering, medicine, and other physical sciences and fields will continue to be of the utmost importance. While less creative in nature, companies that bridge the physical-digital divide and allow AI and automation to assist in these fields will be extremely valuable. Importantly, they will continue to fuel the creative economy by freeing workers from tasks that can be performed by computer and machine, allowing them to more freely contribute to the creative economy.

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3D Printer in action

The shift from a knowledge economy to a creative economy will have to be supported by the educational system. Training for trade and business will diminish. Instead, there will be more learning how to learn. Liberal arts will flourish, alongside an emphasis on mathematics and statistics, engineering, biology and medicine, and hard sciences. Coding, which is already moving to the mainstream of education, will gain even more importance, and humanities and the arts will once again be respected and valued fields of study. Education will also be prolongated and emphasized throughout one’s life, not just at its beginning, and there will be more economic emphasis on education. The creative economy will also self-reinforce the education sector by more effectively immersing learners in their education and create new and innovative ways to learn. If we can align our education system with the promises of the future and coordinate our data protocols for our collective well-being, the future will be bright, colorful, and fun and filled with enjoyable work and pleasurable leisurely pursuits.

 

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… Local

A few miles west of Chapel Hill, North Carolina, there is a raised wooden pavilion with tables and chairs, surrounded by acres of farmland. On these fields they grow berries, herbs, and even tea leaves, which are steeped and served at Honeysuckle Tea House, an establishment inside the breezy open-air structure.

Honeysuckle

The farm and Honeysuckle Tea House are part of a large ecosystem of corporate entities that work together to reinforce each other and enrich the local community.

EastWestHoneysuckle and the farm are owned by EastWest Organics, a farm management company that owns or leases 227 acres of farmland and food producing forest land in Orange County, NC. They produce agricultural products that are used in beverages, such as berries, nuts, tea leaves, and honey.

In addition to the farms, EastWest owns the aforementioned tea house, as well as the Looking Glass Café in Carrboro, NC and the Honeysuckle Meadery and Wine Bar, also in Carrboro. Looking Glass is a traditional coffee shop which is attached to the meadery’s tasting room. Here you can sample Honeysuckle’s meads as well as other local beverages, such as wines, ciders, beers, and fermented beverages like kombucha. The meadery makes Honeysuckle’s meads, with ingredients sourced from EastWest’s farms.

EastWest is 20% owned by Unique Places to Save, a local non-profit. Unique Places (“UP2Save”) conserves unique social, cultural, natural, and agricultural places around the United States, and has a lot of assets in North Carolina. One such is the Keith Arboretum and Forest, in Chapel Hill. The arboretum was founded by a University of North Carolina professor over 40 years ago, and now has more than 4,000 species from temperate forests around the world. The core arboretum is buffered by additional protected woodlands and the Keith family farmstead which is available for private rental.

Unique-Places-to-Save-1

UP2Save has allowed Bee Downtown, a local startup, to house their beehives at the arboretum. Bee Downtown places beehives on the premises of corporations and other institutions who pay to sponsor hives. In exchange, the corporations receive educational content, have a new method of engaging with their employees, and get the honey from the hives. Bee Downtown is a successful, growing company that is now expanding to Atlanta from the Research Triangle region of North Carolina. The arboretum is also one of the farms for EastWest Organics. The company cultivates botanics from the forests and fields and also uses the bees’ honey for its meads (Bee Downtown also has hives at the tea house location farm. The bees pollinate the plants at the farm which produce the crops, such as berries). Given the extreme biodiversity of the arboretum, the honey from these hives is arguably some of the most unique in the world!

bee-logo-orange-01The number of large corporations that Bee Downtown works with is remarkable. Just in the Research Triangle area they have hives at IBM, SAS, Murphy’s Naturals, North Carolina State University, and even Burt’s Bees. Although many of its customers are large companies, it partners with other local companies as well. They have designed t-shirts with Durham, NC-based apparel company Runaway. Local companies that sponsor hives can also take the honey they collect and sell it under their own brand name (as Runaway has done).

One of the interesting aspects of these strong ties between entities is that UP2Save is a non-profit. Nevertheless, it sits as an anchor and facilitates a lot of commercial activity. Another revenue generating activity for UP2Save (beyond the proceeds it receives from EastWest Organics) is mitigation banking. The US Army Corps of Engineers requires new developments to offset their impact on wetlands by purchasing mitigation credits. These credits are created by mitigation banks. The “banks” are conserved wetlands which conform to Army Corps of Engineers environmental standards. UP2Save is able to meet its conservation mission, generate revenue, and help facilitate the development of new properties in local communities.

Keith ArboAll of these interconnected activities also create educational opportunities. The various entities offer educational programs as well as have spaces for formal or informal educational events. Bee Downtown educates its corporate clients about the importance of bees to the world, and they also host school groups at the arboretum. The arboretum doubles as an educational and research laboratory, given the array of species that it houses. Honeysuckle’s beverages educate consumers about agricultural practices and beverages and wellness. Furthermore, the agritourism promoted by Honeysuckle Tea House, the arboretum, and Bee Downtown grow interest in conservation and agribusiness, and all of these entities offer internships and other practical experiences for students from all of the local universities.

Local business is not an ideal in and of itself. The economics of local and small business is more nuanced than money staying in communities. However, local businesses that offer unique and superior value are worthy of our praise and patronage and can certainly strengthen communities all around the United States. Ecosystems of local businesses, such as the one highlighted here centered around UP2Save, promote culture, provide sustainable products for consumption, offer employment for individuals of varying levels of skill, and integrate with the society and education system. Importantly, they also offer partnerships and platforms for new local businesses to start up and grow. Perhaps the best argument for supporting local is that strong local business promotes income equality, whereas big box retail continues to consolidate wealth in the hands of a few wealthy individuals.

ROMRStrong local economic ecosystems also enhance service companies, and tech and platform companies. Many of the logos above were designed by a local creative agency called The Splinter Group. ROMR, a local tech company that allows individuals to book recreational access to private land (kind of like the AirBnB for conservation), has the Honeysuckle farm and the Keith Arboretum on its platform. However, these local economic ecosystems also rely on one-to-many companies for a variety of critical business services, and I continue to believe that one-to-many companies have an important role to play in the national economy and our local economies.

Splinter Group

Splinter Group’s work

For instance, all of these local companies and non-profits have websites, so they hosting and domain name registration services from companies like GoDaddy and Digital Ocean (I use these providers for my company). Splinter Group probably relies on Adobe for its design software. Nearly all of the companies promote themselves on a combination of Facebook, Twitter, Instagram, and Pinterest. Some also use LinkedIn for certain services. In addition, the local companies use payment services like Square or Clover to take payments and keep track of their finances. Companies that cheaply provide important business services to many companies will continue to play an outsize role in our economy. However, they need to proactively support the country’s economic values, or they run the risk of becoming vilified by the country, as we have recently seen with Facebook and other large tech companies.

… One-to-Many

Think back forty years to how businesses used to operate and grow. Someone would cook up a good idea for a product. A company would be formed around that idea to manufacturer it, warehouse it, and then distribute it. Supporting these functions were a sales, marketing, and finance workforce. If you invented something, you then had to get machinery to manufacture it and hire a sales force to get it into the hands of customers. There were few other options.

Since then, businesses have evolved considerably. If someone has a good idea for a product they design it and market it. That’s it. Manufacturing is outsourced, often offshore. Sales are facilitated through e-commerce marketplaces. Fewer and fewer companies are bothering to have their very own websites, let alone a brick-and-mortar store. Logistics are handled by third-party logistics providers. Going asset-lite is the way to go. That lets you rapidly scale scale scale if you want to.

The other side of the coin is the companies that are providing all of these support functions. Since they have to own the assets – whether they be warehouses, vehicles, servers, machines, or financial capital, they have an incentive to be very large companies. Scale is their friend. In fact, it is imperative for success.

What has emerged is a dumbbell economy: On one side, you have many small weights that all add up to a hefty load of economic activity. There is a bar connecting these weights to the other side, which is comprised of a few heavy weights. Summed together, the few heavy weights equal the weight of the smaller weights on the other side, but there are far fewer heavy weights than there are small ones.

The dumbbell economy can be best illustrated through these two graphs. While companies with 1,000 or more employees make up just 0.21% of all companies in the United States, nearly 40% of all privately employed workers in the United States work for one of these companies.

Source: https://www.bls.gov/bdm/bdmfirmsize.htm

I’m not the only person to have made this observation about the shape of the economy. The Economist reported on it in 2016. Deutsche Bank also wrote a report on the matter.

In this dumbbell economy, the few heavy weights represent large service corporations that provide services and other knowledge-economy goods to other companies. These include Microsoft, Dell, IBM, Amazon, the Big 4 accounting firms, UPS and FedEx, Google, Maersk, Delta, Caterpillar, John Deere, and Salesforce.com. The many smaller weights represent small and medium businesses. These include mom and pops and specialty stores, but they are not only brick and mortar. Growing online brands, artists, and many other private companies are on the lightweight side of the economic dumbbell. To complete the metaphor, the bar connecting the two sides of weights represents infrastructure. Roads, airports, cables, and public utilities, which are essential for commerce to be able to function. Government is the arm that curls the dumbbell and makes it move. If the government is not exercising then iron won’t get pumped – the economy will not function properly and grow.

The heavyweight companies invest in assets and then sell them as a service to the lightweights. Without the heavyweights, the lightweights would not be able to grow as fast and expand their reach as wide. The most successful heavyweights are those that are most valuable to the lightweights. Those that can help the smaller, growing companies the most while pinching the lightweights’ bank accounts the least will find themselves with the most demand, as well as investors willing to provide cash to allow them to invest and grow.

While the dumbbell is a useful analogy to understand this phenomenon, I think that it is most appropriate to call this one-to-many. One company can support activity for many others.

It may be best to provide a hypothetical example. For the sake of illustrating my point, I will bold all of the heavyweight companies that I mention in my narrative:

Imagine that I am an engineer, and with the assistance of Autodesk design software I invent a great new device that I want to start selling. I’ve drummed up a lot of support for my nascent product by creating an Instagram account for the device, but I do not have any capital to start production, so I start a crowdfunding campaign on Indiegogo. I raise enough money to begin production, and I hire a contract manufacturer in Asia to begin batch producing the device. I pay FedEx as my freight forwarder to get the batches from Asia to the United States, where I will offer the device for sale. My primary sales channel is Amazon.com, although I store the units in my home and ship them upon receiving an order using UPS and the US Postal Service.

I do have a website which is hosted by GoDaddy.com. It’s basically just a landing page for people who find the device through Indiegogo. It forwards them to the Amazon product page so they can make purchases. However, as sales continue to grow, I continue to design, and support grows for my ideas, I raise some additional money from a venture capital firm. With this money, I hire some employees, and we move into an old industrial area in my city that has been repurposed as a business campus. It’s perfect for my small business that needs a lot of space for industrial design work.

As we launch new products and have a steadier flow of inventory crossing the ocean from Asia, we make a concerted effort to strengthen our brand and diversify our sales channels away from Amazon.com. We build out our website and hire a third-party logistics provider to warehouse our inventory. We want to be able to take orders online and receive payments so we pay for some third-party software plugins and also work with Paypal. We also use some third-party digital marketing software services and pay for adds on websites like Google and Facebook.

The beauty of the one-to-many economy is that it allows new ideas for products and services to be born, grow, and be distributed to consumers who find them appealing and valuable. The problem, some may say, is that we continue to feed the “heavyweights” and cede more and more economic power to them as a result.

One-to-many supports a vast number of growing small and medium businesses, but it also necessitates the existence of a few large third-party providers – the heavyweights. Many people dislike the control of these large companies. In the wake of Amazon’s acquisition of Whole Foods some people said that there were anti-trust concerns. Goldman Sachs has been described as rather tentacular. The EU just slammed Google with a multi-billion Euro fine.

“[Goldman Sachs is a] great vampire squid wrapped around the face of humanity, relentlessly jamming its blood funnel into anything that smells like money.”Matt Taibbi, “The Great American Bubble Machine,” Rolling Stone, July 9-23, 2009

To ease some of the discomfort that we have with the heavyweights having more and more control over the economy, we have to think about ways that we can leverage the one-to-many economy to nurture the economic activity that we most value. Use the heavyweights to make the lightweights as attractive as possible.

What exactly are the elements of capitalism that we value, particularly in the United States?

  • Creativity and art
  • Entrepreneurship (and equality of opportunity)
  • Diversity and inclusion
  • Equality
  • Innovation
  • Distribution of income and wealth
  • Family business
  • Sustainability
  • Poverty alleviation

The heavyweights need to proactively ensure that they are not hindering the attainment of any of these values. They may also go a long way in earning widespread public support if they are proactively supporting these values. Aligning themselves with these values may also strengthen their businesses. However, just creating a foundation and donating to community causes is not enough when it comes to the heavyweights. Their core business functions need to keep these values in mind.

My hypothesis here is far from original. Michael Porter, Harvard professor of Porter’s five forces fame, called this concept, “creating shared value,” in his 2011 HBR article with Mark Kramer. I’ve simply contextualized it here using some new data and appended my proposals to the “shared value” idea.

Here are some examples of companies that I think are going a good job:

3dp_fusion360_autodesk_logo

Autodesk, which creates and sells software for design (AutoCAD is one of their hallmark products, but they have countless other programs which are used in 3D design and printing, animation, architecture, and more fields), has heavily invested in education (primary through university), entrepreneurship, sustainability, and medical innovation. Students, entrepreneurs, and teachers actually receive free access to Autodesk software. They figure that by advancing these fields they will build goodwill, train practitioners that will in the future become loyal customers, and the company may even position itself to capture a lot of business in emerging fields, such as Internet of Things and augmented reality/virtual reality.

UPS has gone one step further and has come up with a way to help the customers of their customers. Rio Grande (whose parent company is Berkshire Hathaway) is a manufacturer of speciality jewelry equipment. They ship with UPS. This year, Rio Grande created a membership program for their customers called Rio Pro. UPS became a partner with Rio Pro and offers discounted shipping services to Rio Pro members. In this relationship structure, everyone wins. Rio Grande’s customers are pleased because they get access to discounted shipping. Rio Grande wins because their customers are more satisfied and more successful. UPS wins by bolstering its relationship with Rio Grande as well as building a trusted relationship with a number of growing small businesses.

RioPro

DHL, one of UPS’s main competitors, particularly outside of the United States, is creating an online marketplace for brick and mortar retailers in its headquarters city of Bonn, Germany. The marketplace is called Allyouneedcity, and I highly doubt that DHL is doing this just altruistically. They certainly hope that it will help support the retailers, but it also opens up opportunities for them. For example, they could use this marketplace to launch a pilot of same-day delivery, or they could expand the marketplace to compete with increasingly powerful e-retailers and expand DHL’s own revenue channels.

Amazon launched an interesting program this year, in which families on government assistance (SNAP and TANF) can receive a discounted membership to Amazon Prime. Prime is a large sales channel for retailers selling on Amazon’s marketplace (third-party e-commerce sales are actually growing faster than traditional e-commerce), so this will give those retailers more access to lower-income earners. On the face of things it might just seem like Amazon is shooting across the bow of Walmart, or even that they are trying to exploit lower-income individuals. With Amazon forging more and more into e-groceries, different kinds of merchandise, and value-added services, this discounted Prime service may actually improve access of lower-income individuals to products that they need for their health, nutrition, well-being, and income generating activities. Access, particularly in areas known as “food deserts” where there are no nearby grocery stores, is a serious problem for lower-income individuals. Amazon’s new service may be beneficial to its bottom line and help its customers as well.

In my opinion, Facebook may be the heavyweight that is most struggling with its vaunted position. It grew to prominence by facilitating a kind of social interaction that had never been conducted before. However, these sharings are increasingly being pushed to other platforms in the Facebook ecosystem, such as Messenger, Instagram, and WhatsApp. That’s left Facebook with a free speech billboard. At its most benign, people would say that Facebook is just a dumping ground for useless videos, blurry photos, and pitiful updates on people’s lives. At its worst, it’s a hate-littered echo chamber that is being used to manipulate people into believing things that are patently false.

Facebook is far from doomed. It remains a very successful company, and I believe that it offers a lot of value to society, despites the criticisms. However, to remain relevant, it needs to start focusing more on economic values. For instance, I could imagine Facebook launching an AI assistant (a la Siri or Alexa – it’s so in vogue these days) that focuses on social connections (I’m partial to the name Joffrey, but that’s up to Mark).

“Joffrey … when was the last time I talked to George?’

“It’s been six years since you last heard from George. Would you like to contact him now?”

“No, that’s alright, he’s probably busy.”

Meanwhile, in George’s house:

“George, I see it’s been awhile since you spoke with your friend. Would you like to contact her?”

“Yes, I would.”

“How would you like to contact her?”

“By e-mail.”

“Would you like a summary of your friend’s activity since the last time you spoke to her before we write an e-mail?”

“Yes I would Joffrey.”

There are various permutations of how Joffrey could facilitate meaningful interpersonal interactions around the world, and help with networking, friendships, tourism, mental health, and education. Facebook would benefit as well, because the more activity that takes place within their network, the more opportunities it has to monetize the activity.

There are countless more examples of how heavyweights can focus on economic values. Imagine if AT&T offered a comprehensive telecommunication plan for refugees arriving to the United States. Even more so, they could offer a special plan to refugees starting new businesses. AT&T would make great strides in facilitating communal integration for some of the neediest people in the world and helping them become successful members of society. If AT&T could expand this plan to include additional social support services, educational and NGO partners, as well as language assistance (Rosetta Stone or DuoLingo) then even more value could be created, and at little additional cost to AT&T (I only choose AT&T because it is one of the largest telecommunication companies in the country).

In coming years, one-to-one many businesses will continue to not only shape, but shake markets. Much has been said about the impending doom of brick-and-mortar retail. However, emergent one-to-many type companies are already nipping at the heels of e-retail juggernauts like Amazon. Instagram, owned by Facebook, has a retail team. Their goal is to let users make purchases without ever leaving the app. Amazon has already taken steps to respond to this emerging threat by introducing Spark, its own social media platform for product discovery.
Even being an established one-to-many titan for many years no longer guarantees the continuation of a business model.

For instance, banks

, credit card companies, and other financial one-to-many companies need to make plans to integrate with these the new heavyweights of commerce or they may find themselves cut off from what were once reliable and steady revenue streams. Other one-to-many companies can partner up behind your back and uproot your market position. UPS and FedEx should heed this warning and consider new one-to-many retail patterns. Transportation and logistics upstarts, such as Uber RUSH and Deliv (which, granted, UPS has invested in) are already directly integrating with large retailers as well as payment processing companies such as Clover.

As one-to-many companies begin to support more and more smaller businesses, and the connections among firms become more and more complex, I begin to see the economy less traditionally, with individual companies making one-off transactions of goods or services. Instead, I see an economic mesh. Every transaction is distributed among many different firms in the economy, and these transaction could not be successfully completed without all of the firms working together. The heavyweights may continue to grow, and some people may get very rich as a result, but so long as they are supporting opportunity for millions of other people and in-line with our economic values then it is a win-win outcome.

Joffrey: “Catelyn, I noticed that your sister Lisa just posted a photo on Instagram from near you. Would you like to send her a message?”

Catelyn: “No Joffrey, that’s ok, thank you.”

… Shipping Packages

… Artificial Intelligence

Over the last year artificial intelligence (AI) has become nearly ubiquitous in the news. Just recently, Elon Musk called it a threat to human civilization. His warnings have been the direst, but many other people think that AI has the potential to replace billions of human jobs, and we need to adapt now to prevent mass-unemployment.

This represents a naïve view of capitalism, but one that is increasingly popular with politicians, pundits, and people who listen to them. Jobs may certainly be cut, but it is more likely that new jobs, in the traditional sense, simply will not be created. Companies will reduce labor costs across the board, leaving more profits for business owners and their remaining knowledge workers. Prices for goods and services that involve automated labor will also come down, relative to all other prices. The result will be more discretionary income, and where we choose to spend it will determine in what sectors new jobs will be created. Certainly, there will be people left behind, at least temporarily. Society may need to step in and assist those people. However, in the long-term, so long as workers have the necessary knowledge skills to manage AI, automation, and other technologies, the economy will benefit, and not be harmed, by the AI-age.

As more and more work becomes automated, there will certainly be less work to be done, in the aggregate, by humans. There is always the opportunity for new work to emerge – work that does not exist today and work that we have not conceived of yet as being possible, necessary, or important. However, this work may also be able to be automated. Some may say that there will always be work for humans to manage the automation – repairs on robots and writing code for the automation software, to begin with. I see no reason to think that this cannot be automated either.

As a result of this ubiquitous automation, there may be no jobs left for humans at all, sometime in the future. People fear that we would be left with artificial/robotic economic overlords. I also think that this is a naïve understanding of the economy. In fact, I think that the AI-age could also be a post-capitalism age. People would work less and the work left to us would be judgement based. How do we apportion the food that the robots are cultivating? Who should have the rights to exploit minerals that we can mine from the Earth (and asteroids!), since nearly everyone would have limitless abilities to produce with those metals and minerals? I doubt that we would want to automate the answers to these types of important questions. Even if we did want to, it would not be wise, because the ability to think critically would then be diminished worldwide and not be passed down to future generations. In some respects, everyone in the post-capitalism age would be one of Plato’s philosopher kings. We could also dedicate more work-time to art and creation, as well as its consumption.

AI isn’t just ubiquitous in the news anymore. It is become increasingly common in our homes and in our pockets. Chat bots and digital personal assistants and home devices like Amazon Echo’s Alexa, Siri, and Google Now are all examples of artificial intelligence. My phone is always trying to guess where I am and when I should leave for events. That’s AI in my pocket (I’ve actually been meaning to turn that off, since I don’t have a car).

As AI proliferates, so too does how we are talking about it. Along with AI, people mention machine learning, deep learning, and cognitive computing. In general, it seems to me that AI is an umbrella term that encompasses all of these techniques. In popular terms, AI refers to consumer applications where a computer is emulating activities that we would typically conduct with another person. Think of talking with Alexa as a prime example. Getting down-to-the-minute weather predictions from an app, rather than a meteorologist, is another good example. In more technical terms, AI refers to all applications in which a computer is doing what used to be restricted to the domain of a biological brain: sensing and cataloguing information, processing and analyzing it, and using that synthesized information to recognize patterns, to make predictions, and to take decisions.

Ex Machina

Machine Learning

Consider a smart watch or wrist band that records the time its user wakes up every morning for an entire month. After collecting that data for a month, it calculates an average weekday wake up time and sets an alarm automatically. For three out of five proceeding weekday mornings the user snoozed the alarm ten minutes, and on two of the mornings the user got up as soon as the alarm went off. Using this new information, the wearable revises the wakeup time to be slightly later, and thereafter continues to monitor and revise the wakeup time according to the user’s actual behavior.

This is an example of machine learning. Without any user input the machine makes inferences, assesses their veracity, and iterates accordingly. However, it’s quite rudimentary. The techniques used are fairly basic, and the result was not something that the human mind could not have arrived to on its own.

A more complex application would involve inferring where the user works based on normal daily travel patterns (unless you have turned it off, your smartphone is probably already transmitting this information), and then analyzing traffic on the roadways and the activities of other users to automatically set the user’s alarm so that they arrive at work (or school, or the gym, etc.) at their preferred time. By relying on more information for decision making the analysis techniques become more complex and begin to resemble artificial intelligence.

More important to understand than the capabilities of machine learning, is that its approach to information analysis vastly different from traditional analytical decision making. For instance, a financial institution can feed a computer vast quantities of information on borrowers and their loan performance history. A machine learning program could then process all of this information and determine what variables are best correlated with loan performance. Traditionally, a bank would apply financial and economic theory to create credit models and then test the model, altering it to find the best fit. The machine learning approach relies on a completely different paradigm. Rather than approaching the problem with a basis of assumptions, using machine learning implies ignorance, or at the very least an openness to unanticipated patterns and relationships. Machine learning tests all possible relationships and patterns and makes the best predictions, even if they go against our intuitions. Industries and practitioners that are not accustomed to this approach or unwilling to appreciate its merits may soon find themselves outpaced and outperformed by more machine-savvy competitors.

Deep Learning

Deep learning is an even more sophisticated form of machine learning. Deep learning employs a non-parametric data analysis technique called neural networks (or neural nets) to identify relationships between data. The technique is referred to as a neural net because it resembles the structure of neurons in the brain.

Here is a YouTube video that does a fairly good job of explaining the technique in a short amount of time:

https://www.youtube.com/watch?v=i6ECFrV_BVA

Simple!

Deep learning is powerful enough to accomplish advanced pattern recognition – pattern recognition which can be deployed in situations as diverse as understanding what is happening on city streets and high-speed highways (self-driving vehicles) to learning what different types of animals look like and then making drawings of them. I can imagine a deep learning application that is fed many thousands of oncological images and trains itself to identify cancer. As doctors confirm or reject the conclusion of the program it would store this information and refine its own predictions. Eventually, the program would become more accurate than doctors and radiologists.

This extreme accuracy is what has people such as Elon Musk concerned about artificial intelligence. Will we need doctors if algorithms are better at their work than the doctors themselves?

Cognitive Computing

Cognitive computing is a term I have been hearing less and less. Artificial intelligence seems to have become the preferred buzzword. However, I think that cognitive computing retains a unique definition and is useful to understand many technologies. Generally, cognitive computing are computing processes that are designed to emulate how humans process information and think. Watson is the most famous cognitive computer, and its name and its promoted abilities all seem to allude to a human mind.

One of Watson’s abilities is natural language processing. Rather than having to be fed data in a neat spreadsheet or form, Watson can consume unstructured data, make heads or tails with them, and then process the data. In business school a common assignment is creating a pro forma financial statement from a professor’s explanation of the financial conditions of a company. It’s fairly rote and mechanical. Students have to translate the explanation of the finances into a familiar form which then does the mathematical processing. Cognitive computing skips that translation step. It can understand the natural language explanation of the company’s finances and directly make the necessary computations for the pro forma.

In fact, it seems that Goldman Sachs and other investment banks are doing just that. They’ve been announcing more and more investments in AI along with reductions in the sizes of their M&A teams over the last few years. Goldman Sachs may have gone the furthest. Their CEO has declared that Goldman is really just a “tech” company, and the former Chief Information Officer is now the CFO of the company.

Machine-powered gaming is also a direct application of cognitive computing, because it pits computer cognition directly against human cognition. AI watchers were stunned early this year when a Google designed machine was able to defeat a Go master. Go is an ancient Chinese game that is strategically very complex. For those of us of the Indo-European persuasion, Go is more difficult and complex than chess.

Quantum Computing

One of the challenges with artificial intelligence is that conventional super-computers do not possess enough processing power to crunch through all the nodes in deep neural networks fast enough. Physicists and computer engineers are working on a solution known as quantum computing. Traditional computers store information in bits, which can either represent a 1 or a 0. However, using the quantum physics concept of superposition, a quantum bit, or qubit, can exist in both states at once. If engineers can create stable computers that harness qubits, computing power will exponentially increase.

Here is a good explanation of the concept and recent developments in the field.

Quantum computing would rapidly improve our abilities to create deep neural networks and accelerate the development of artificial intelligence. However, quantum computers will be so powerful, they may be able to easily crack the codes of even the most powerful computer encryption and security systems. Parallel to the development of quantum computing, society needs to invest in new cyber-security techniques that are complementary to quantum computing, not made obsolete by it.

 

I encourage everyone, no matter what job they have, what they enjoy doing, or how they interact with other people in the world, to consider, ‘how can some of the tasks that I do be automated?’ Try to imagine what it would take to automate the task and what the analytical system would be structured like. Then think about what value you can add as an individual, so that you remain necessary, despite the elimination of a human performing the task. Also consider how society needs to prepare and train its members so that the most people benefit from the advantages of AI, and the fewest people are left behind. That is likely the true message the Elon Musk is urging our policy makers to hear, and I hope that they hear it.

… Uber

Uber_logoTo me, Uber is the pinnacle of economic innovation. It harnessed technology, the app revolution, and used it to improve efficiency and provide a new service. The service is designed to deliberately overcome a classic economic problem, asymmetric information. And the pricing model is based on sound economic principles. It is at the forefront of a technology driven revolution that economists are calling “the sharing economy.” Similar services include Lyft, AirBnB, and Craigslist.

Uber, at its core, is simply an app. You download it to your smartphone from an app store, and then you give it some basic information, including a credit card number. Then, whenever you need a ride you use a map to tell the app where you want to be picked up and you specify the level of service you want – a private car, a taxi, a black car, or an SUV. The app takes care of the rest. It dispatches a driver and gives you his or her name, user rating, contact information, and estimated time of arrival.

When the driver arrives the app alerts you and you can head outside and hop in. All you need to do is tell the driver where you are headed and he or she will take you. Then when you arrive at your destination you just get out. No fumbling for change in your pocket or listening to the taxi driver grumble about you wanting to use a credit card. The app, which already has your credit card information, automatically bills you based on level of service, distance traveled, and time elapsed. All of this information, including a map, is e-mailed to you. I’ve used these e-mails in the past to compile invoices for my business trip expenses at my old job. Gratuity and any taxes are automatically subsumed into the rate. The app simply asks you to rate the driver, out of five stars, and provide any comments or feedback.

Black car

Asymmetric information is a classic economic challenge. It is when the two sides of a transaction – the buyers and sellers, demanders and suppliers, consumers and providers, whatever you want to call them – do not have the same information about the product or service. For example, the used car market suffers considerably from asymmetric information. The dealers know a car’s history and if it’s a lemon, but the buyers don’t have this information, which saps demand and can lead to less than optimal prices on vehicle sales.

Uber has solved this problem for the chauffeured rides-on-demand market. Not only does the rider have to rate the driver, but the driver has to rate the rider. Riders and drivers both have average ratings out of five stars, which are revealed to both parties when the service is called for. Riders can decline a driver with a poor rating, and drivers can decline service to rude riders with a poor rating. This symmetrical access to information improves the efficiency of the market and helps secure the fairest price for service possible. It also ensures quality. I’ve taken many uber-clean Ubers in which I’ve been offered a small bottle of water or a nice sucking candy, at no additional cost.

Pricing is another strong feature of Uber. Accurate prices take into account both real-time supply and demand for a service. But sometimes prices are fixed and only take into account one side of the curve. For example, one problem with certain popular “hot lanes” on interstates is that they only take into account demand. The price goes up when the normal lanes are congested, but it doesn’t take into account how congested the express lanes are. When the express lanes are also jammed this has the effect of unnecessarily attracting more vehicles, worsening the congestion. And when the fast lanes are empty the prices are not optimized to attract more vehicles, improving traffic flow for the normal lanes.

Uber, on the other hand, has base rates for distance and time traveled. However, when demand is high or there are not a lot of cars available, they implement a surge pricing scheme which multiplies the price of the ride based on the real-time state of the market. At first this pricing scheme got Uber some bad press due to lack of transparency, but now that they have improved the app to make the user aware of the price it is a great system. The people who are most willing to pay for the service, as expressed by price, are most efficiently matched with the drivers most willing to provide the ride.

I've never seen surge pricing this high, but it only reflects real-time market conditions. It could have been during a bad storm when no one wanted to be out driving, or during a high-demand period, like New Year's Eve

I’ve never seen surge pricing this high, but it only reflects real-time market conditions. It could have been during a bad storm when no one wanted to be out driving, or during a high-demand period, like New Year’s Eve

The app is sleek. The pricing system is fair, and the its rating system overcomes classic economic challenges to ensure as free of a market as possible. So what’s the problem? What’s all the fuss with Uber about? The problem with Uber and the sharing economy is that everyone who has their hands in the traditional economic structure is throwing a fit. Uber’s success is partially at the expense of traditional taxi drivers. And taxi services are heavily regulated in most cities around the world. This is leading to taxi commissions throwing up roadblocks (some literal, most legal) all across the world. However, regulated taxi markets are a relic of an over-regulated past. Like most markets, regulation and government intervention benefits few and passes on unnecessary costs and under-service to most. I can certainly understand why taxi commissions are protesting. The livelihood of their beneficiaries is being completely upended. However, innovation, invention, and new technology, are the engine of economic growth. When government policies suppress the urge for unnecessary regulation and allow innovation to flourish economic prosperity reaches the most people – even the disrupted taxi drivers, once they adjust their service or find another job.

SAN FRANCISCO TAXI DRIVERS PROTEST AGAINST LYFT AND UBER WHICH TAXI DRIVERS SAY ARE OPERATING ILLEGALLY IN SAN FRANCISCO

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