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#432271 Your Shopping Experience Is on the Verge ...

Exponential technologies (AI, VR, 3D printing, and networks) are radically reshaping traditional retail.

E-commerce giants (Amazon, Walmart, Alibaba) are digitizing the retail industry, riding the exponential growth of computation.

Many brick-and-mortar stores have already gone bankrupt, or migrated their operations online.

Massive change is occurring in this arena.

For those “real-life stores” that survive, an evolution is taking place from a product-centric mentality to an experience-based business model by leveraging AI, VR/AR, and 3D printing.

Let’s dive in.

E-Commerce Trends
Last year, 3.8 billion people were connected online. By 2024, thanks to 5G, stratospheric and space-based satellites, we will grow to 8 billion people online, each with megabit to gigabit connection speeds.

These 4.2 billion new digital consumers will begin buying things online, a potential bonanza for the e-commerce world.

At the same time, entrepreneurs seeking to service these four-billion-plus new consumers can now skip the costly steps of procuring retail space and hiring sales clerks.

Today, thanks to global connectivity, contract production, and turnkey pack-and-ship logistics, an entrepreneur can go from an idea to building and scaling a multimillion-dollar business from anywhere in the world in record time.

And while e-commerce sales have been exploding (growing from $34 billion in Q1 2009 to $115 billion in Q3 2017), e-commerce only accounted for about 10 percent of total retail sales in 2017.

In 2016, global online sales totaled $1.8 trillion. Remarkably, this $1.8 trillion was spent by only 1.5 billion people — a mere 20 percent of Earth’s global population that year.

There’s plenty more room for digital disruption.

AI and the Retail Experience
For the business owner, AI will demonetize e-commerce operations with automated customer service, ultra-accurate supply chain modeling, marketing content generation, and advertising.

In the case of customer service, imagine an AI that is trained by every customer interaction, learns how to answer any consumer question perfectly, and offers feedback to product designers and company owners as a result.

Facebook’s handover protocol allows live customer service representatives and language-learning bots to work within the same Facebook Messenger conversation.

Taking it one step further, imagine an AI that is empathic to a consumer’s frustration, that can take any amount of abuse and come back with a smile every time. As one example, meet Ava. “Ava is a virtual customer service agent, to bring a whole new level of personalization and brand experience to that customer experience on a day-to-day basis,” says Greg Cross, CEO of Ava’s creator, an Austrian company called Soul Machines.

Predictive modeling and machine learning are also optimizing product ordering and the supply chain process. For example, Skubana, a platform for online sellers, leverages data analytics to provide entrepreneurs constant product performance feedback and maintain optimal warehouse stock levels.

Blockchain is set to follow suit in the retail space. ShipChain and Ambrosus plan to introduce transparency and trust into shipping and production, further reducing costs for entrepreneurs and consumers.

Meanwhile, for consumers, personal shopping assistants are shifting the psychology of the standard shopping experience.

Amazon’s Alexa marks an important user interface moment in this regard.

Alexa is in her infancy with voice search and vocal controls for smart homes. Already, Amazon’s Alexa users, on average, spent more on Amazon.com when purchasing than standard Amazon Prime customers — $1,700 versus $1,400.

As I’ve discussed in previous posts, the future combination of virtual reality shopping, coupled with a personalized, AI-enabled fashion advisor will make finding, selecting, and ordering products fast and painless for consumers.

But let’s take it one step further.

Imagine a future in which your personal AI shopper knows your desires better than you do. Possible? I think so. After all, our future AIs will follow us, watch us, and observe our interactions — including how long we glance at objects, our facial expressions, and much more.

In this future, shopping might be as easy as saying, “Buy me a new outfit for Saturday night’s dinner party,” followed by a surprise-and-delight moment in which the outfit that arrives is perfect.

In this future world of AI-enabled shopping, one of the most disruptive implications is that advertising is now dead.

In a world where an AI is buying my stuff, and I’m no longer in the decision loop, why would a big brand ever waste money on a Super Bowl advertisement?

The dematerialization, demonetization, and democratization of personalized shopping has only just begun.

The In-Store Experience: Experiential Retailing
In 2017, over 6,700 brick-and-mortar retail stores closed their doors, surpassing the former record year for store closures set in 2008 during the financial crisis. Regardless, business is still booming.

As shoppers seek the convenience of online shopping, brick-and-mortar stores are tapping into the power of the experience economy.

Rather than focusing on the practicality of the products they buy, consumers are instead seeking out the experience of going shopping.

The Internet of Things, artificial intelligence, and computation are exponentially improving the in-person consumer experience.

As AI dominates curated online shopping, AI and data analytics tools are also empowering real-life store owners to optimize staffing, marketing strategies, customer relationship management, and inventory logistics.

In the short term,retail store locations will serve as the next big user interface for production 3D printing (custom 3D printed clothes at the Ministry of Supply), virtual and augmented reality (DIY skills clinics), and the Internet of Things (checkout-less shopping).

In the long term,we’ll see how our desire for enhanced productivity and seamless consumption balances with our preference for enjoyable real-life consumer experiences — all of which will be driven by exponential technologies.

One thing is certain: the nominal shopping experience is on the verge of a major transformation.

The convergence of exponential technologies has already revamped how and where we shop, how we use our time, and how much we pay.

Twenty years ago, Amazon showed us how the web could offer each of us the long tail of available reading material, and since then, the world of e-commerce has exploded.

And yet we still haven’t experienced the cost savings coming our way from drone delivery, the Internet of Things, tokenized ecosystems, the impact of truly powerful AI, or even the other major applications for 3D printing and AR/VR.

Perhaps nothing will be more transformed than today’s $20 trillion retail sector.

Hold on, stay tuned, and get your AI-enabled cryptocurrency ready.

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Posted in Human Robots

#432249 New Malicious AI Report Outlines Biggest ...

Everyone’s talking about deep fakes: audio-visual imitations of people, generated by increasingly powerful neural networks, that will soon be indistinguishable from the real thing. Politicians are regularly laid low by scandals that arise from audio-visual recordings. Try watching the footage that could be created of Barack Obama from his speeches, and the Lyrebird impersonations. You could easily, today or in the very near future, create a forgery that might be indistinguishable from the real thing. What would that do to politics?

Once the internet is flooded with plausible-seeming tapes and recordings of this sort, how are we going to decide what’s real and what isn’t? Democracy, and our ability to counteract threats, is already threatened by a lack of agreement on the facts. Once you can’t believe the evidence of your senses anymore, we’re in serious trouble. Ultimately, you can dream up all kinds of utterly terrifying possibilities for these deep fakes, from fake news to blackmail.

How to solve the problem? Some have suggested that media websites like Facebook or Twitter should carry software that probes every video to see if it’s a deep fake or not and labels the fakes. But this will prove computationally intensive. Plus, imagine a case where we have such a system, and a fake is “verified as real” by news media algorithms that have been fooled by clever hackers.

The other alternative is even more dystopian: you can prove something isn’t true simply by always having an alibi. Lawfare describes a “solution” where those concerned about deep fakes have all of their movements and interactions recorded. So to avoid being blackmailed or having your reputation ruined, you just consent to some company engaging in 24/7 surveillance of everything you say or do and having total power over that information. What could possibly go wrong?

The point is, in the same way that you don’t need human-level, general AI or humanoid robotics to create systems that can cause disruption in the world of work, you also don’t need a general intelligence to threaten security and wreak havoc on society. Andrew Ng, AI researcher, says that worrying about the risks from superintelligent AI is like “worrying about overpopulation on Mars.” There are clearly risks that arise even from the simple algorithms we have today.

The looming issue of deep fakes is just one of the threats considered by the new malicious AI report, which has co-authors from the Future of Humanity Institute and the Centre for the Study of Existential Risk (among other organizations.) They limit their focus to the technologies of the next five years.

Some of the concerns the report explores are enhancements to familiar threats.

Automated hacking can get better, smarter, and algorithms can adapt to changing security protocols. “Phishing emails,” where people are scammed by impersonating someone they trust or an official organization, could be generated en masse and made more realistic by scraping data from social media. Standard phishing works by sending such a great volume of emails that even a very low success rate can be profitable. Spear phishing aims at specific targets by impersonating family members, but can be labor intensive. If AI algorithms enable every phishing scam to become sharper in this way, more people are going to get gouged.

Then there are novel threats that come from our own increasing use of and dependence on artificial intelligence to make decisions.

These algorithms may be smart in some ways, but as any human knows, computers are utterly lacking in common sense; they can be fooled. A rather scary application is adversarial examples. Machine learning algorithms are often used for image recognition. But it’s possible, if you know a little about how the algorithm is structured, to construct the perfect level of noise to add to an image, and fool the machine. Two images can be almost completely indistinguishable to the human eye. But by adding some cleverly-calculated noise, the hackers can fool the algorithm into thinking an image of a panda is really an image of a gibbon (in the OpenAI example). Research conducted by OpenAI demonstrates that you can fool algorithms even by printing out examples on stickers.

Now imagine that instead of tricking a computer into thinking that a panda is actually a gibbon, you fool it into thinking that a stop sign isn’t there, or that the back of someone’s car is really a nice open stretch of road. In the adversarial example case, the images are almost indistinguishable to humans. By the time anyone notices the road sign has been “hacked,” it could already be too late.

As the OpenAI foundation freely admits, worrying about whether we’d be able to tame a superintelligent AI is a hard problem. It looks all the more difficult when you realize some of our best algorithms can be fooled by stickers; even “modern simple algorithms can behave in ways we do not intend.”

There are ways around this approach.

Adversarial training can generate lots of adversarial examples and explicitly train the algorithm not to be fooled by them—but it’s costly in terms of time and computation, and puts you in an arms race with hackers. Many strategies for defending against adversarial examples haven’t proved adaptive enough; correcting against vulnerabilities one at a time is too slow. Moreover, it demonstrates a point that can be lost in the AI hype: algorithms can be fooled in ways we didn’t anticipate. If we don’t learn about these vulnerabilities until the algorithms are everywhere, serious disruption can occur. And no matter how careful you are, some vulnerabilities are likely to remain to be exploited, even if it takes years to find them.

Just look at the Meltdown and Spectre vulnerabilities, which weren’t widely known about for more than 20 years but could enable hackers to steal personal information. Ultimately, the more blind faith we put into algorithms and computers—without understanding the opaque inner mechanics of how they work—the more vulnerable we will be to these forms of attack. And, as China dreams of using AI to predict crimes and enhance the police force, the potential for unjust arrests can only increase.

This is before you get into the truly nightmarish territory of “killer robots”—not the Terminator, but instead autonomous or consumer drones which could potentially be weaponized by bad actors and used to conduct attacks remotely. Some reports have indicated that terrorist organizations are already trying to do this.

As with any form of technology, new powers for humanity come with new risks. And, as with any form of technology, closing Pandora’s box will prove very difficult.

Somewhere between the excessively hyped prospects of AI that will do everything for us and AI that will destroy the world lies reality: a complex, ever-changing set of risks and rewards. The writers of the malicious AI report note that one of their key motivations is ensuring that the benefits of new technology can be delivered to people as quickly, but as safely, as possible. In the rush to exploit the potential for algorithms and create 21st-century infrastructure, we must ensure we’re not building in new dangers.

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Posted in Human Robots

#432031 Why the Rise of Self-Driving Vehicles ...

It’s been a long time coming. For years Waymo (formerly known as Google Chauffeur) has been diligently developing, driving, testing and refining its fleets of various models of self-driving cars. Now Waymo is going big. The company recently placed an order for several thousand new Chrysler Pacifica minivans and next year plans to launch driverless taxis in a number of US cities.

This deal raises one of the biggest unanswered questions about autonomous vehicles: if fleets of driverless taxis make it cheap and easy for regular people to get around, what’s going to happen to car ownership?

One popular line of thought goes as follows: as autonomous ride-hailing services become ubiquitous, people will no longer need to buy their own cars. This notion has a certain logical appeal. It makes sense to assume that as driverless taxis become widely available, most of us will eagerly sell the family car and use on-demand taxis to get to work, run errands, or pick up the kids. After all, vehicle ownership is pricey and most cars spend the vast majority of their lives parked.

Even experts believe commercial availability of autonomous vehicles will cause car sales to drop.

Market research firm KPMG estimates that by 2030, midsize car sales in the US will decline from today’s 5.4 million units sold each year to nearly half that number, a measly 2.1 million units. Another market research firm, ReThinkX, offers an even more pessimistic estimate (or optimistic, depending on your opinion of cars), predicting that autonomous vehicles will reduce consumer demand for new vehicles by a whopping 70 percent.

The reality is that the impending death of private vehicle sales is greatly exaggerated. Despite the fact that autonomous taxis will be a beneficial and widely-embraced form of urban transportation, we will witness the opposite. Most people will still prefer to own their own autonomous vehicle. In fact, the total number of units of autonomous vehicles sold each year is going to increase rather than decrease.

When people predict the demise of car ownership, they are overlooking the reality that the new autonomous automotive industry is not going to be just a re-hash of today’s car industry with driverless vehicles. Instead, the automotive industry of the future will be selling what could be considered an entirely new product: a wide variety of intelligent, self-guiding transportation robots. When cars become a widely used type of transportation robot, they will be cheap, ubiquitous, and versatile.

Several unique characteristics of autonomous vehicles will ensure that people will continue to buy their own cars.

1. Cost: Thanks to simpler electric engines and lighter auto bodies, autonomous vehicles will be cheaper to buy and maintain than today’s human-driven vehicles. Some estimates bring the price to $10K per vehicle, a stark contrast with today’s average of $30K per vehicle.

2. Personal belongings: Consumers will be able to do much more in their driverless vehicles, including work, play, and rest. This means they will want to keep more personal items in their cars.

3. Frequent upgrades: The average (human-driven) car today is owned for 10 years. As driverless cars become software-driven devices, their price/performance ratio will track to Moore’s law. Their rapid improvement will increase the appeal and frequency of new vehicle purchases.

4. Instant accessibility: In a dense urban setting, a driverless taxi is able to show up within minutes of being summoned. But not so in rural areas, where people live miles apart. For many, delay and “loss of control” over their own mobility will increase the appeal of owning their own vehicle.

5. Diversity of form and function: Autonomous vehicles will be available in a wide variety of sizes and shapes. Consumers will drive demand for custom-made, purpose-built autonomous vehicles whose form is adapted for a particular function.

Let’s explore each of these characteristics in more detail.

Autonomous vehicles will cost less for several reasons. For one, they will be powered by electric engines, which are cheaper to construct and maintain than gasoline-powered engines. Removing human drivers will also save consumers money. Autonomous vehicles will be much less likely to have accidents, hence they can be built out of lightweight, lower-cost materials and will be cheaper to insure. With the human interface no longer needed, autonomous vehicles won’t be burdened by the manufacturing costs of a complex dashboard, steering wheel, and foot pedals.

While hop-on, hop-off autonomous taxi-based mobility services may be ideal for some of the urban population, several sizeable customer segments will still want to own their own cars.

These include people who live in sparsely-populated rural areas who can’t afford to wait extended periods of time for a taxi to appear. Families with children will prefer to own their own driverless cars to house their childrens’ car seats and favorite toys and sippy cups. Another loyal car-buying segment will be die-hard gadget-hounds who will eagerly buy a sexy upgraded model every year or so, unable to resist the siren song of AI that is three times as safe, or a ride that is twice as smooth.

Finally, consider the allure of robotic diversity.

Commuters will invest in a home office on wheels, a sleek, traveling workspace resembling the first-class suite on an airplane. On the high end of the market, city-dwellers and country-dwellers alike will special-order custom-made autonomous vehicles whose shape and on-board gadgetry is adapted for a particular function or hobby. Privately-owned small businesses will buy their own autonomous delivery robot that could range in size from a knee-high, last-mile delivery pod, to a giant, long-haul shipping device.

As autonomous vehicles near commercial viability, Waymo’s procurement deal with Fiat Chrysler is just the beginning.

The exact value of this future automotive industry has yet to be defined, but research from Intel’s internal autonomous vehicle division estimates this new so-called “passenger economy” could be worth nearly $7 trillion a year. To position themselves to capture a chunk of this potential revenue, companies whose businesses used to lie in previously disparate fields such as robotics, software, ships, and entertainment (to name but a few) have begun to form a bewildering web of what they hope will be symbiotic partnerships. Car hailing and chip companies are collaborating with car rental companies, who in turn are befriending giant software firms, who are launching joint projects with all sizes of hardware companies, and so on.

Last year, car companies sold an estimated 80 million new cars worldwide. Over the course of nearly a century, car companies and their partners, global chains of suppliers and service providers, have become masters at mass-producing and maintaining sturdy and cost-effective human-driven vehicles. As autonomous vehicle technology becomes ready for mainstream use, traditional automotive companies are being forced to grapple with the painful realization that they must compete in a new playing field.

The challenge for traditional car-makers won’t be that people no longer want to own cars. Instead, the challenge will be learning to compete in a new and larger transportation industry where consumers will choose their product according to the appeal of its customized body and the quality of its intelligent software.

Melba Kurman and Hod Lipson are the authors of Driverless: Intelligent Cars and the Road Ahead and Fabricated: the New World of 3D Printing.

Image Credit: hfzimages / Shutterstock.com

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Posted in Human Robots

#432027 We Read This 800-Page Report on the ...

The longevity field is bustling but still fragmented, and the “silver tsunami” is coming.

That is the takeaway of The Science of Longevity, the behemoth first volume of a four-part series offering a bird’s-eye view of the longevity industry in 2017. The report, a joint production of the Biogerontology Research Foundation, Deep Knowledge Life Science, Aging Analytics Agency, and Longevity.International, synthesizes the growing array of academic and industry ventures related to aging, healthspan, and everything in between.

This is huge, not only in scale but also in ambition. The report, totally worth a read here, will be followed by four additional volumes in 2018, covering topics ranging from the business side of longevity ventures to financial systems to potential tensions between life extension and religion.

And that’s just the first step. The team hopes to publish updated versions of the report annually, giving scientists, investors, and regulatory agencies an easy way to keep their finger on the longevity pulse.

“In 2018, ‘aging’ remains an unnamed adversary in an undeclared war. For all intents and purposes it is mere abstraction in the eyes of regulatory authorities worldwide,” the authors write.

That needs to change.

People often arrive at the field of aging from disparate areas with wildly diverse opinions and strengths. The report compiles these individual efforts at cracking aging into a systematic resource—a “periodic table” for longevity that clearly lays out emerging trends and promising interventions.

The ultimate goal? A global framework serving as a road map to guide the burgeoning industry. With such a framework in hand, academics and industry alike are finally poised to petition the kind of large-scale investments and regulatory changes needed to tackle aging with a unified front.

Infographic depicting many of the key research hubs and non-profits within the field of geroscience.
Image Credit: Longevity.International
The Aging Globe
The global population is rapidly aging. And our medical and social systems aren’t ready to handle this oncoming “silver tsunami.”

Take the medical field. Many age-related diseases such as Alzheimer’s lack effective treatment options. Others, including high blood pressure, stroke, lung or heart problems, require continuous medication and monitoring, placing enormous strain on medical resources.

What’s more, because disease risk rises exponentially with age, medical care for the elderly becomes a game of whack-a-mole: curing any individual disease such as cancer only increases healthy lifespan by two to three years before another one hits.

That’s why in recent years there’s been increasing support for turning the focus to the root of the problem: aging. Rather than tackling individual diseases, geroscience aims to add healthy years to our lifespan—extending “healthspan,” so to speak.

Despite this relative consensus, the field still faces a roadblock. The US FDA does not yet recognize aging as a bona fide disease. Without such a designation, scientists are banned from testing potential interventions for aging in clinical trials (that said, many have used alternate measures such as age-related biomarkers or Alzheimer’s symptoms as a proxy).

Luckily, the FDA’s stance is set to change. The promising anti-aging drug metformin, for example, is already in clinical trials, examining its effect on a variety of age-related symptoms and diseases. This report, and others to follow, may help push progress along.

“It is critical for investors, policymakers, scientists, NGOs, and influential entities to prioritize the amelioration of the geriatric world scenario and recognize aging as a critical matter of global economic security,” the authors say.

Biomedical Gerontology
The causes of aging are complex, stubborn, and not all clear.

But the report lays out two main streams of intervention with already promising results.

The first is to understand the root causes of aging and stop them before damage accumulates. It’s like meddling with cogs and other inner workings of a clock to slow it down, the authors say.

The report lays out several treatments to keep an eye on.

Geroprotective drugs is a big one. Often repurposed from drugs already on the market, these traditional small molecule drugs target a wide variety of metabolic pathways that play a role in aging. Think anti-oxidants, anti-inflammatory, and drugs that mimic caloric restriction, a proven way to extend healthspan in animal models.

More exciting are the emerging technologies. One is nanotechnology. Nanoparticles of carbon, “bucky-balls,” for example, have already been shown to fight viral infections and dangerous ion particles, as well as stimulate the immune system and extend lifespan in mice (though others question the validity of the results).

Blood is another promising, if surprising, fountain of youth: recent studies found that molecules in the blood of the young rejuvenate the heart, brain, and muscles of aged rodents, though many of these findings have yet to be replicated.

Rejuvenation Biotechnology
The second approach is repair and maintenance.

Rather than meddling with inner clockwork, here we force back the hands of a clock to set it back. The main example? Stem cell therapy.

This type of approach would especially benefit the brain, which harbors small, scattered numbers of stem cells that deplete with age. For neurodegenerative diseases like Alzheimer’s, in which neurons progressively die off, stem cell therapy could in theory replace those lost cells and mend those broken circuits.

Once a blue-sky idea, the discovery of induced pluripotent stem cells (iPSCs), where scientists can turn skin and other mature cells back into a stem-like state, hugely propelled the field into near reality. But to date, stem cells haven’t been widely adopted in clinics.

It’s “a toolkit of highly innovative, highly invasive technologies with clinical trials still a great many years off,” the authors say.

But there is a silver lining. The boom in 3D tissue printing offers an alternative approach to stem cells in replacing aging organs. Recent investment from the Methuselah Foundation and other institutions suggests interest remains high despite still being a ways from mainstream use.

A Disruptive Future
“We are finally beginning to see an industry emerge from mankind’s attempts to make sense of the biological chaos,” the authors conclude.

Looking through the trends, they identified several technologies rapidly gaining steam.

One is artificial intelligence, which is already used to bolster drug discovery. Machine learning may also help identify new longevity genes or bring personalized medicine to the clinic based on a patient’s records or biomarkers.

Another is senolytics, a class of drugs that kill off “zombie cells.” Over 10 prospective candidates are already in the pipeline, with some expected to enter the market in less than a decade, the authors say.

Finally, there’s the big gun—gene therapy. The treatment, unlike others mentioned, can directly target the root of any pathology. With a snip (or a swap), genetic tools can turn off damaging genes or switch on ones that promote a youthful profile. It is the most preventative technology at our disposal.

There have already been some success stories in animal models. Using gene therapy, rodents given a boost in telomerase activity, which lengthens the protective caps of DNA strands, live healthier for longer.

“Although it is the prospect farthest from widespread implementation, it may ultimately prove the most influential,” the authors say.

Ultimately, can we stop the silver tsunami before it strikes?

Perhaps not, the authors say. But we do have defenses: the technologies outlined in the report, though still immature, could one day stop the oncoming tidal wave in its tracks.

Now we just have to bring them out of the lab and into the real world. To push the transition along, the team launched Longevity.International, an online meeting ground that unites various stakeholders in the industry.

By providing scientists, entrepreneurs, investors, and policy-makers a platform for learning and discussion, the authors say, we may finally generate enough drive to implement our defenses against aging. The war has begun.

Read the report in full here, and watch out for others coming soon here. The second part of the report profiles 650 (!!!) longevity-focused research hubs, non-profits, scientists, conferences, and literature. It’s an enormously helpful resource—totally worth keeping it in your back pocket for future reference.

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Posted in Human Robots

#431343 How Technology Is Driving Us Toward Peak ...

At some point in the future—and in some ways we are already seeing this—the amount of physical stuff moving around the world will peak and begin to decline. By “stuff,” I am referring to liquid fuels, coal, containers on ships, food, raw materials, products, etc.
New technologies are moving us toward “production-at-the-point-of-consumption” of energy, food, and products with reduced reliance on a global supply chain.
The trade of physical stuff has been central to globalization as we’ve known it. So, this declining movement of stuff may signal we are approaching “peak globalization.”
To be clear, even as the movement of stuff may slow, if not decline, the movement of people, information, data, and ideas around the world is growing exponentially and is likely to continue doing so for the foreseeable future.
Peak globalization may provide a pathway to preserving the best of globalization and global interconnectedness, enhancing economic and environmental sustainability, and empowering individuals and communities to strengthen democracy.
At the same time, some of the most troublesome aspects of globalization may be eased, including massive financial transfers to energy producers and loss of jobs to manufacturing platforms like China. This shift could bring relief to the “losers” of globalization and ease populist, nationalist political pressures that are roiling the developed countries.
That is quite a claim, I realize. But let me explain the vision.
New Technologies and Businesses: Digital, Democratized, Decentralized
The key factors moving us toward peak globalization and making it economically viable are new technologies and innovative businesses and business models allowing for “production-at-the-point-of-consumption” of energy, food, and products.
Exponential technologies are enabling these trends by sharply reducing the “cost of entry” for creating businesses. Driven by Moore’s Law, powerful technologies have become available to almost anyone, anywhere.
Beginning with the microchip, which has had a 100-billion-fold improvement in 40 years—10,000 times faster and 10 million times cheaper—the marginal cost of producing almost everything that can be digitized has fallen toward zero.
A hard copy of a book, for example, will always entail the cost of materials, printing, shipping, etc., even if the marginal cost falls as more copies are produced. But the marginal cost of a second digital copy, such as an e-book, streaming video, or song, is nearly zero as it is simply a digital file sent over the Internet, the world’s largest copy machine.* Books are one product, but there are literally hundreds of thousands of dollars in once-physical, separate products jammed into our devices at little to no cost.
A smartphone alone provides half the human population access to artificial intelligence—from SIRI, search, and translation to cloud computing—geolocation, free global video calls, digital photography and free uploads to social network sites, free access to global knowledge, a million apps for a huge variety of purposes, and many other capabilities that were unavailable to most people only a few years ago.
As powerful as dematerialization and demonetization are for private individuals, they’re having a stronger effect on businesses. A small team can access expensive, advanced tools that before were only available to the biggest organizations. Foundational digital platforms, such as the internet and GPS, and the platforms built on top of them by the likes of Google, Apple, Amazon, and others provide the connectivity and services democratizing business tools and driving the next generation of new startups.

“As these trends gain steam in coming decades, they’ll bleed into and fundamentally transform global supply chains.”

An AI startup, for example, doesn’t need its own server farm to train its software and provide service to customers. The team can rent computing power from Amazon Web Services. This platform model enables small teams to do big things on the cheap. And it isn’t just in software. Similar trends are happening in hardware too. Makers can 3D print or mill industrial grade prototypes of physical stuff in a garage or local maker space and send or sell designs to anyone with a laptop and 3D printer via online platforms.
These are early examples of trends that are likely to gain steam in coming decades, and as they do, they’ll bleed into and fundamentally transform global supply chains.
The old model is a series of large, connected bits of centralized infrastructure. It makes sense to mine, farm, or manufacture in bulk when the conditions, resources, machines, and expertise to do so exist in particular places and are specialized and expensive. The new model, however, enables smaller-scale production that is local and decentralized.
To see this more clearly, let’s take a look at the technological trends at work in the three biggest contributors to the global trade of physical stuff—products, energy, and food.
3D printing (additive manufacturing) allows for distributed manufacturing near the point of consumption, eliminating or reducing supply chains and factory production lines.
This is possible because product designs are no longer made manifest in assembly line parts like molds or specialized mechanical tools. Rather, designs are digital and can be called up at will to guide printers. Every time a 3D printer prints, it can print a different item, so no assembly line needs to be set up for every different product. 3D printers can also print an entire finished product in one piece or reduce the number of parts of larger products, such as engines. This further lessens the need for assembly.
Because each item can be customized and printed on demand, there is no cost benefit from scaling production. No inventories. No shipping items across oceans. No carbon emissions transporting not only the final product but also all the parts in that product shipped from suppliers to manufacturer. Moreover, 3D printing builds items layer by layer with almost no waste, unlike “subtractive manufacturing” in which an item is carved out of a piece of metal, and much or even most of the material can be waste.
Finally, 3D printing is also highly scalable, from inexpensive 3D printers (several hundred dollars) for home and school use to increasingly capable and expensive printers for industrial production. There are also 3D printers being developed for printing buildings, including houses and office buildings, and other infrastructure.
The technology for finished products is only now getting underway, and there are still challenges to overcome, such as speed, quality, and range of materials. But as methods and materials advance, it will likely creep into more manufactured goods.
Ultimately, 3D printing will be a general purpose technology that involves many different types of printers and materials—such as plastics, metals, and even human cells—to produce a huge range of items, from human tissue and potentially human organs to household items and a range of industrial items for planes, trains, and automobiles.
Renewable energy production is located at or relatively near the source of consumption.
Although electricity generated by solar, wind, geothermal, and other renewable sources can of course be transmitted over longer distances, it is mostly generated and consumed locally or regionally. It is not transported around the world in tankers, ships, and pipelines like petroleum, coal, and natural gas.
Moreover, the fuel itself is free—forever. There is no global price on sun or wind. The people relying on solar and wind power need not worry about price volatility and potential disruption of fuel supplies as a result of political, market, or natural causes.
Renewables have their problems, of course, including intermittency and storage, and currently they work best if complementary to other sources, especially natural gas power plants that, unlike coal plants, can be turned on or off and modulated like a gas stove, and are half the carbon emissions of coal.
Within the next decades or so, it is likely the intermittency and storage problems will be solved or greatly mitigated. In addition, unlike coal and natural gas power plants, solar is scalable, from solar panels on individual homes or even cars and other devices, to large-scale solar farms. Solar can be connected with microgrids and even allow for autonomous electricity generation by homes, commercial buildings, and communities.
It may be several decades before fossil fuel power plants can be phased out, but the development cost of renewables has been falling exponentially and, in places, is beginning to compete with coal and gas. Solar especially is expected to continue to increase in efficiency and decline in cost.
Given these trends in cost and efficiency, renewables should become obviously cheaper over time—if the fuel is free for solar and has to be continually purchased for coal and gas, at some point the former is cheaper than the latter. Renewables are already cheaper if externalities such as carbon emissions and environmental degradation involved in obtaining and transporting the fuel are included.
Food can be increasingly produced near the point of consumption with vertical farms and eventually with printed food and even printed or cultured meat.
These sources bring production of food very near the consumer, so transportation costs, which can be a significant portion of the cost of food to consumers, are greatly reduced. The use of land and water are reduced by 95% or more, and energy use is cut by nearly 50%. In addition, fertilizers and pesticides are not required and crops can be grown 365 days a year whatever the weather and in more climates and latitudes than is possible today.
While it may not be practical to grow grains, corn, and other such crops in vertical farms, many vegetables and fruits can flourish in such facilities. In addition, cultured or printed meat is being developed—the big challenge is scaling up and reducing cost—that is based on cells from real animals without slaughtering the animals themselves.
There are currently some 70 billion animals being raised for food around the world [PDF] and livestock alone counts for about 15% of global emissions. Moreover, livestock places huge demands on land, water, and energy. Like vertical farms, cultured or printed meat could be produced with no more land use than a brewery and with far less water and energy.
A More Democratic Economy Goes Bottom Up
This is a very brief introduction to the technologies that can bring “production-at-the-point-of-consumption” of products, energy, and food to cities and regions.
What does this future look like? Here’s a simplified example.
Imagine a universal manufacturing facility with hundreds of 3D printers printing tens of thousands of different products on demand for the local community—rather than assembly lines in China making tens of thousands of the same product that have to be shipped all over the world since no local market can absorb all of the same product.
Nearby, a vertical farm and cultured meat facility produce much of tomorrow night’s dinner. These facilities would be powered by local or regional wind and solar. Depending on need and quality, some infrastructure and machinery, like solar panels and 3D printers, would live in these facilities and some in homes and businesses.
The facilities could be owned by a large global corporation—but still locally produce goods—or they could be franchised or even owned and operated independently by the local population. Upkeep and management at each would provide jobs for communities nearby. Eventually, not only would global trade of parts and products diminish, but even required supplies of raw materials and feed stock would decline since there would be less waste in production, and many materials would be recycled once acquired.

“Peak globalization could be a viable pathway to an economic foundation that puts people first while building a more economically and environmentally sustainable future.”

This model suggests a shift toward a “bottom up” economy that is more democratic, locally controlled, and likely to generate more local jobs.
The global trends in democratization of technology make the vision technologically plausible. Much of this technology already exists and is improving and scaling while exponentially decreasing in cost to become available to almost anyone, anywhere.
This includes not only access to key technologies, but also to education through digital platforms available globally. Online courses are available for free, ranging from advanced physics, math, and engineering to skills training in 3D printing, solar installations, and building vertical farms. Social media platforms can enable local and global collaboration and sharing of knowledge and best practices.
These new communities of producers can be the foundation for new forms of democratic governance as they recognize and “capitalize” on the reality that control of the means of production can translate to political power. More jobs and local control could weaken populist, anti-globalization political forces as people recognize they could benefit from the positive aspects of globalization and international cooperation and connectedness while diminishing the impact of globalization’s downsides.
There are powerful vested interests that stand to lose in such a global structural shift. But this vision builds on trends that are already underway and are gaining momentum. Peak globalization could be a viable pathway to an economic foundation that puts people first while building a more economically and environmentally sustainable future.
This article was originally posted on Open Democracy (CC BY-NC 4.0). The version above was edited with the author for length and includes additions. Read the original article on Open Democracy.
* See Jeremy Rifkin, The Zero Marginal Cost Society, (New York: Palgrave Macmillan, 2014), Part II, pp. 69-154.
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