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The Skills Nobody Has Yet — And How We’ll Find Them

Thomas Frey — Sat, 21 Mar 2026 00:13:11 +0000

By Futurist Thomas Frey

How employers will identify, define, and develop the capabilities the future demands — before those skills even have names

A Job Description Written for Someone Who Doesn’t Exist Yet

It’s 2031. A mid-sized logistics company in Columbus, Ohio is trying to hire for a role it’s calling an “AI Operations Interpreter.” The job isn’t about programming. It isn’t about driving. It’s about sitting at the intersection of human judgment and autonomous systems — reading what the machines are doing, translating their outputs for a team of human workers, and flagging the edge cases that no algorithm has been taught to handle.

Six months earlier, this job title didn’t exist. There was no degree program for it. No certification. No LinkedIn skill tag. But the company needed it desperately, so they wrote the description themselves — drawing on a data analyst, a former warehouse supervisor, and a machine learning consultant to figure out what the role actually required.

This is the new normal. And it’s already happening today.

The challenge facing every employer, every educator, and every ambitious professional over the next decade isn’t finding people with the right skills. It’s figuring out what the right skills even are — before the job that requires them becomes urgent.

Why This Problem Is Different From Any We’ve Faced Before

Workforce transitions aren’t new. The industrial revolution wiped out cottage industries and created factory jobs. The computing era eliminated typing pools and created software developers. Every major technological shift scrambles the labor market, and we eventually adapt.

But those transitions played out over decades. A child born into a farming community in 1890 had forty years before the mechanization of agriculture fully restructured rural employment. A typist in 1975 had fifteen years before word processing made her skill obsolete — long enough to reskill.

The AI transition is different because the window is collapsing. Skills that were highly valuable three years ago are already being automated. Skills that will be critically needed in five years haven’t been codified yet. The gap between “this skill matters” and “this skill is obsolete” is shrinking from decades to years — in some fields, to months.

The World Economic Forum’s Future of Jobs Report 2025 surveyed over 1,000 major employers representing 14 million workers and found that 39% of key job skills will change by 2030. That’s nearly four in ten skills that today’s workers rely on, transformed or replaced within five years. The same report identifies analytical thinking, AI literacy, and creative problem-solving as the fastest-rising capabilities — but what’s notable is how few people are being trained in any of them in a systematic way.

So how do we get ahead of this? How do employers identify the skills they’ll need before the need becomes a crisis? And how do workers know what to develop when the target is moving so fast?

Leading organizations don’t wait for talent markets—they read weak signals early and build the skills for roles that don’t exist yet.

Signal Reading: How Forward-Looking Organizations Spot Tomorrow’s Skills Today

The companies doing this well aren’t waiting for the labor market to tell them what they need. They’re reading signals — from technology adoption curves, from emerging competitor behavior, from the friction points in their own operations — and working backwards to define the human capabilities those signals imply.

Consider what happened at Amazon. Before drone delivery was operational, Amazon’s workforce planning teams were already modeling what roles would be needed to manage autonomous aerial logistics — not pilots, not warehouse workers in the traditional sense, but people capable of monitoring fleets of autonomous systems, interpreting anomaly reports, and making rapid judgment calls on edge cases. They built internal training programs for roles that had no external hiring market yet, because they knew the external market would take years to catch up.

The same logic applies in healthcare. Radiologists have known for years that AI would handle routine image reading. The forward-thinking hospitals didn’t respond by cutting radiology programs. They asked a different question: what does a radiologist do when the AI flags something unusual and needs a human to make the final call? That question led to a completely new skill profile — less about reading images from scratch, more about supervising and interrogating AI outputs, communicating uncertainty to clinical teams, and making high-stakes decisions under time pressure with incomplete information. Some medical schools are already building this into their curriculum. Most are not.

The Three Lenses Organizations Use to Define Future Skills

From what I’ve observed working with organizations across dozens of industries, the most sophisticated approaches to future skills identification tend to use three distinct lenses — and the organizations that use all three simultaneously are the ones that rarely get caught flat-footed.

The first lens is technology forecasting. You map where the technology in your industry is heading over a three-to-seven year horizon, then ask: what human tasks will this technology automate, what new tasks will it create, and what hybrid roles will emerge at the intersection? This is analytical work, and it requires genuine technical literacy — not deep coding skills, but enough fluency to have an honest conversation about what AI and automation can and cannot do.

The second lens is friction mapping. Every organization has places where work breaks down — where handoffs fail, where decisions stall, where the output of one system doesn’t translate cleanly into the input of the next. These friction points are usually where new skills will be most urgently needed. When a hospital’s AI diagnostic tool flags a result that falls outside its training data, someone has to handle that. When a financial services firm’s algorithmic trading system encounters a market condition it wasn’t built for, a human needs to make a fast call. The friction is the signal.

The third lens is competitive intelligence. If your most innovative competitors are hiring for job titles you’ve never seen before, that’s one of the most reliable leading indicators available. LinkedIn’s labor market data has become one of the most watched signals in workforce planning precisely because emerging job titles cluster in waves — first appearing at a handful of pioneering companies, then spreading across an industry within two to three years. By the time a skill appears in a majority of job postings, you’re already late.

The Skills Taking Shape Right Now

So what does this actually look like in practice? What are the specific skills that are currently moving from “barely mentioned” to “urgently needed” in the labor market?

AI output auditing is one. As organizations deploy large language models in customer service, legal review, medical documentation, and financial reporting, the ability to systematically evaluate AI outputs for accuracy, bias, and appropriateness is becoming a distinct professional skill. It’s not the same as prompt engineering. It’s closer to quality assurance with a domain-specific layer on top — and companies are struggling to find people who can do it well.

Human-machine teaming is another. This is the capacity to work fluidly alongside autonomous systems — knowing when to defer to the machine, when to override it, and how to communicate those decisions to people who don’t share your technical context. It’s part operational skill, part communication skill, and part psychological comfort with ceding control. McKinsey’s research on defining future workforce skills identifies adaptability and comfort with uncertainty as among the fastest-rising needs — and this is precisely why. The people who will thrive are the ones who can hold their judgment loosely enough to update it when the machine sees something they don’t.

Narrative translation is a third emerging capability — and it’s one I find particularly interesting. As AI generates more of the raw data, analysis, and initial drafts across industries, the distinctly human contribution shifts toward interpretation and meaning-making. What does this data actually mean for this specific audience? How do we communicate this risk to people who don’t share our technical vocabulary? How do we make this decision legible to stakeholders with very different frames of reference? These are storytelling skills with professional stakes, and they’re becoming more valuable, not less, in an era of AI-generated content.

The best companies don’t wait for skills to be defined—they spot them early, shape them, and turn raw behaviors into the future’s most valuable capabilities.

Refining the Skills: How They Move From Emerging to Essential

Identifying a future skill is only the first step. The harder work is refining it — turning a vague capability into something teachable, assessable, and hireable against.

This refinement process tends to follow a predictable arc. A skill starts as a job task — something specific people are observed doing in high-performing teams. It gets named, usually informally at first, by practitioners inside a company or industry. Early-adopter organizations build internal training for it. Then credentialing bodies, universities, and certification programs formalize it into curriculum. By the time it appears as a standard qualification in job postings, it’s already been through years of informal development.

The organizations winning the talent competition are the ones who enter this arc as early as possible — ideally at the “observed task” stage, before the skill has even been named. Google’s Project Oxygen, which famously studied what made its best managers effective and built training around those behaviors, is a clean example. The skills they identified — clear communication, psychological safety, technical coaching — weren’t invented. They were observed, named, and then systematically developed. The same methodology applies to emerging AI-era skills, just on a faster timeline.

What This Means for the Individual

For anyone navigating their own career through this period, the practical implication is clear: the most valuable thing you can develop isn’t a specific skill. It’s the ability to identify which skills are worth developing, earlier than the people around you.

That means paying attention to where friction exists in your industry. It means reading the job postings at companies two years ahead of yours on the technology adoption curve. It means noticing which conversations in your organization keep hitting the same wall — where the AI output goes, but nobody quite knows what to do with it next. Those walls are where the next round of valuable skills live.

The workers who come out of this transition ahead won’t necessarily be the ones who were best at the old jobs. They’ll be the ones who saw the new jobs coming and started practicing for them before those jobs had titles.

The Bottom Line

The future of skills isn’t a mystery we’re waiting for someone to solve. It’s a signal we can read, if we know where to look. The companies doing this work seriously — mapping technology trajectories, locating friction points, watching competitive hiring behavior — are building talent pipelines for roles that don’t yet exist at scale. The workers paying the same kind of attention are positioning themselves for opportunities that most of their peers haven’t even noticed yet.

The skill that matters most in the years ahead might be the one you’re exercising right now, reading this: the willingness to think seriously about where the world is going, and to start preparing before everyone else catches up.

The Future of Jobs Report 2025
World Economic Forum — Survey of 1,000+ employers across 55 economies on skills and workforce transformation through 2030

Defining the Skills Citizens Will Need in the Future World of Work
McKinsey Global Institute — Deep research into 56 distinct workforce capabilities and which will matter most

The Jobs of the Future — and the Skills You Need to Get Them
World Economic Forum — A practical breakdown of the fastest-rising skills and roles through 2030

The post The Skills Nobody Has Yet — And How We’ll Find Them appeared first on Futurist Speaker.

You Can’t Automate Purpose

Thomas Frey — Tue, 10 Mar 2026 01:47:26 +0000

The real crisis isn’t automation—it’s that society can’t even agree on the problem, let alone the solution.

When the economy moves faster than the social contract, someone has to ask the hard questions

By Futurist Thomas Frey

Nobody Agrees on the Problem, Let Alone the Fix

Here’s where we actually are. Millions of people are losing not just jobs but the specific kind of job that gave their life structure — the middle-skill, middle-income work that built the middle class. The automation wave didn’t start with AI. It started forty years ago with assembly lines and spreadsheets and ATMs. What’s different now is the pace and the altitude. The disruption has climbed up the organizational chart and is now touching work that we genuinely believed required human judgment, creativity, and expertise. Harold Jensen at Meridian Analytics believed that too. Right up until a Tuesday afternoon in 2031.

So the conversation about what we do next — economically, as a society — is not a fringe conversation anymore. It’s the conversation. And it’s happening in the worst possible way: loudly, in silos, with people talking past each other using terms they haven’t bothered to define.

Let’s try to fix that.

What These Terms Actually Mean

Universal Basic Income (UBI) is the simplest concept to explain and the most politically explosive to propose. Every adult citizen receives a fixed cash payment from the government, unconditionally and regularly — regardless of whether they work, how much they earn, or what they spend it on. No means test. No application. No caseworker. Just money. The amounts discussed vary wildly, from $500 a month in modest pilot programs to $2,000 or more in bolder proposals. The core idea is that cash is the most efficient and dignified form of support, because people know their own needs better than any bureaucracy does.

Universal Basic Services (UBS) takes a different angle. Instead of giving people money to buy what they need, the state provides those things directly: healthcare, housing, education, transportation, digital access, legal aid, childcare. The argument is that cash benefits get clawed back by markets — if you give everyone $1,000 a month for rent, landlords raise rents by $1,000 a month. But if you provide the housing itself, you actually solve the housing problem. UBS is less about the freedom to choose and more about guaranteeing the floor is real and not gameable.

Universal High Income (UHI) is a newer framing, less a formal policy proposal than a challenge to the imagination. The question it asks is: what if this moment — AI generating extraordinary productivity and wealth — is actually an opportunity to lift the floor dramatically rather than just maintain it? Not $1,000 a month. Not subsistence. Enough that people could genuinely choose meaningful work, start businesses, care for family members, make art, invest in communities. Enough that the concept of “taking a job you hate because you have no other choice” becomes historical rather than universal.

These aren’t the only models being discussed. There’s negative income tax, championed decades ago by Milton Friedman and recently by economists across the political spectrum, where people below a certain income threshold receive government payments that taper off as income rises, replacing the patchwork of existing benefits with a single, cleaner mechanism. There’s stakeholder grants — a one-time lump sum given to every citizen at adulthood to invest in education, a business, or housing. There’s sovereign wealth redistribution, where returns from a national investment fund flow directly to citizens, the way Alaska’s Permanent Fund already sends annual dividends to every Alaskan resident.

The proposals are not identical. Their implications are radically different. But they share a common origin: the recognition that when an economy generates unprecedented wealth and simultaneously eliminates the traditional mechanisms by which ordinary people accessed that wealth, something has to give.

UBI sounds simple—until you confront the cost, the incentives, and the deeper question: what replaces the meaning work once provided?

Why This Is Harder Than It Sounds

The objections are real, and dismissing them doesn’t help anyone.

The first is cost. A genuine UBI at meaningful levels — let’s say $1,500 a month for every American adult — would cost somewhere in the range of $4 trillion annually. The US federal budget is roughly $7 trillion. So we’re talking about restructuring the entire fiscal architecture of the country. Proponents point out that much of that cost is offset by eliminating existing benefits programs, and that the productivity gains from AI will generate taxable wealth at a scale we haven’t yet accounted for. Skeptics note that those gains are currently flowing to a remarkably narrow band of people and companies, and that taxing them requires political will that has historically been in short supply.

The second objection is behavioral. If you give people money without conditions, will they stop working? The evidence from pilot programs — in Finland, Kenya, Stockton, Manitoba — is actually surprisingly consistent: most people don’t stop working. Many work more purposefully, because they’re no longer trapped in survival mode. Entrepreneurship goes up. Health outcomes improve. Educational enrollment rises, particularly among young people who can now afford to think beyond immediate income. But pilots are small. Pilots are temporary. And the psychology of a society where nobody is compelled to work by economic necessity is something no pilot has fully tested.

The third objection is meaning, and this one is the least discussed and probably the most important.

The Problem That Money Doesn’t Solve

Work is not just income. For most people, work is identity, structure, social connection, a reason to get out of bed, a way of feeling useful in the world. When economists model the effects of job displacement, they typically measure income loss. But the research on what happens to people when work disappears — from factory closures, from disability, from early retirement, from long-term unemployment — tells a more disturbing story. Depression. Substance use. Relationship breakdown. A kind of purposelessness that no check in the mail addresses.

The places that have been hit hardest by deindustrialization over the past forty years didn’t just lose wages. They lost the organizing principle of daily life. The shift, the routine, the team, the skill, the sense of being someone who makes something or does something that matters. That loss is not fixed by a floor income. It requires something else entirely, something we don’t have a clean policy name for.

This is what makes the coming disruption genuinely different from previous ones. When textile workers were displaced by mechanization, there were factories to go to. When factory workers were displaced by automation, there was a services economy to absorb them. When services workers are displaced by AI, the question of what comes next is one we have not answered — not economically, and certainly not existentially.

Some people will do what Harold Jensen did: take what they know, find who needs it, and build something new. Entrepreneurship will absorb a portion of the displaced workforce. So will care work — teaching, nursing, therapy, mentorship — work that is technically automatable but that humans persistently prefer to receive from other humans. So will the creative economy, the trades, the local and the handmade and the bespoke. There is enormous amounts of meaningful work to do in the world. The challenge is that we have not yet built the bridges between the work that is disappearing and the work that remains to be done.

Work isn’t just income—it’s identity, purpose, and belonging. When jobs disappear, something deeper than paychecks disappears too.

What a Sane Path Forward Might Look Like

Here’s the honest answer: there is no single solution. Anyone selling one is either naive or running for office.

What a thoughtful approach might include is a combination of things. A genuine income floor — not at subsistence level, but at dignity level — that removes the desperation from the equation and gives people real choices. Universal services that guarantee healthcare, housing security, education, and digital access are not charity but infrastructure, the same way highways and power grids were infrastructure in the last century. Massive reinvestment in the institutions that create meaning: libraries, community centers, apprenticeship programs, public universities, mental health resources. Tax structures that capture a portion of the wealth being generated by AI and return it to the public that, through decades of government-funded research, largely made that AI possible in the first place.

And alongside all of it, a cultural reckoning with the story we tell about work. We have organized human worth around employment for so long that we’ve forgotten it was a choice, not a law of nature. Many of the most valuable things people do — raising children, caring for elderly parents, volunteering, creating art, building communities — have never been paid. We’ve decided, as a society, that if it doesn’t have a wage attached to it, it doesn’t fully count. That decision is going to become increasingly untenable as the economy continues to automate the things that do have wages attached.

The rocky road ahead is genuinely rocky. There are no clean solutions, no painless transitions, no policy levers that fix this without trade-offs. What there is, if we choose it, is the possibility of a society that uses this moment of extraordinary productivity to build a floor solid enough that nobody falls through it — and then asks what people want to do with their one life once survival is no longer the only thing on the agenda.

That’s not a utopia. It’s a design problem. And design problems, at least, have solutions.

How Disruption, Displacement, and Disappearing Entry-Level Roles Are Reshaping Entrepreneurship in the US — The structural data behind why necessity entrepreneurship is surging as AI displaces white-collar work, with US business formation applications at historic highs.

How Will AI Affect the Global Workforce? — Goldman Sachs Research on which jobs face the most disruption, the timeline, and why the overall impact may be more transitory than the headlines suggest.

AI Labor Market Impact: Jobs, Skills & Workforce Changes — A comprehensive breakdown of what the real displacement numbers look like, which industries are transforming fastest, and why skills — not degrees — are becoming the new currency of employment.

Word count: 1,487

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The Neumann Engine: How Cities Can Thrive and Grow in the Age of AI

Thomas Frey — Thu, 10 Jul 2025 08:00:49 +0000

The Neumann Engine: How Cities Can Thrive and Grow in the Age of AI

A revolutionary approach to urban revitalization that creates prosperity from within.

Technology is dismantling outdated urban economies—yet for communities that stop chasing outsiders and start building locally, it’s the key to homegrown prosperity.

American cities face an unprecedented challenge. While artificial intelligence eliminates traditional jobs across all skill levels, most communities still rely on economic development strategies designed for the industrial age—chasing corporate relocations, offering tax incentives to large employers, and hoping external companies will solve local economic problems. This approach isn’t just failing; it’s becoming counterproductive as AI enables companies to operate with fewer workers from anywhere with decent internet access.

But what if cities could systematically create their own economic prosperity instead of waiting for outside salvation? What if communities could leverage AI tools to build thriving entrepreneurial ecosystems that generate wealth rather than merely redistributing existing resources?

This is the vision behind the Neumann Engine—a revolutionary approach to urban revitalization that transforms declining cities into entrepreneurial powerhouses through systematic business creation, AI-enhanced opportunity identification, and comprehensive startup support.

Beyond Industrial-Era Economic Development

Named after John von Neumann, whose pioneering work on self-replicating systems provides the theoretical foundation, the Neumann Engine operates on a fundamentally different principle than traditional economic development. Instead of recruiting existing businesses from other places—a zero-sum game that merely shifts economic activity—it creates new businesses from scratch that serve external markets and bring fresh capital into communities.

The distinction is crucial. Primary businesses export products or services beyond local markets, generating the “new money” that enables communities to import goods and services they can’t produce efficiently while creating high-skill employment. Secondary businesses—restaurants, retail stores, local services—are essential for quality of life but primarily redistribute existing local wealth rather than creating new wealth.

The Neumann Engine focuses laser-like on primary business creation, recognizing that secondary businesses emerge organically once primary businesses establish the economic foundation. A software company that serves national clients brings new money into the community; the coffee shops and restaurants that spring up to serve that company’s employees represent healthy secondary development supported by primary business success.

The AI Opportunity Window

While AI threatens many traditional jobs, it also creates an unprecedented opportunity for entrepreneurial communities. AI tools now enable individual entrepreneurs to access capabilities previously requiring teams of specialists and substantial capital investment. A single person can use AI for market research, product design, customer service, and business management while focusing their human creativity on innovation, relationship building, and cultural understanding that AI cannot replicate.

This democratization creates a critical window of opportunity for communities to build AI-enhanced entrepreneurial capacity before these tools become commoditized or concentrated in large corporations. The key is understanding that successful AI-age businesses combine AI efficiency with distinctly human value—creativity, empathy, cultural authenticity, and trust-building capabilities that become more valuable as routine tasks are automated.

John von Neumann was a pioneering polymath who laid the foundations for modern computing, game theory, and nuclear strategy.

How the Neumann Engine Works

The Neumann Engine operates as a specialized venture studio—a business creation entity that systematically builds companies from scratch rather than investing in existing startups. Unlike traditional venture capital that provides funding to entrepreneurs who already have ideas and teams, venture studios actively create businesses by identifying opportunities, assembling teams, and providing comprehensive operational support throughout the development process.

The model proves particularly powerful for community revitalization because it ensures that created businesses align with local assets and market opportunities while maintaining community ownership and cultural authenticity. The venture studio team becomes integral to the community fabric, understanding local dynamics while having access to sophisticated business creation methodologies and capital resources.

The Five-Year Implementation Strategy

Successful Neumann Engine implementation requires a five-year operational runway encompassing both team development and startup capital deployment. Year 1-2 focuses on foundation building—facility development, team recruitment, and system implementation. The venture studio establishes community relationships while developing urban intelligence systems and educational programming that support business creation.

Years 2-3 emphasize business creation and market validation. With infrastructure operational and community relationships established, the venture studio begins systematic business formation. Initial businesses focus on market validation and customer development, with the studio providing comprehensive operational support including marketing, legal, accounting, and business development services.

Years 3-5 concentrate on scaling and portfolio development. Successful initial businesses begin generating returns that can be reinvested in additional business creation, while the community develops local investment capacity and entrepreneurial expertise. The venture studio transitions from external dependency to local sustainability as successful entrepreneurs become mentors and investors for subsequent business development.

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Neumann Engine’s venture studio funds and supports startups from day one—providing capital,
shared services, and market access so founders can focus on growth, not fundraising.”

Infrastructure Foundation

Each Neumann Engine community builds comprehensive entrepreneurial infrastructure centered around three core facilities:

The Entrepreneurial Hub provides flexible coworking space, private offices, meeting rooms, and presentation areas where emerging businesses can develop professional capabilities while building networking relationships essential for entrepreneurial success.

The Fabrication Studio enables rapid prototyping of physical products through desktop-scale 3D printing, CNC machining, laser cutting, commercial kitchen facilities, and basic electronics prototyping. Rather than attempting full manufacturing capability, the studio focuses on transforming ideas into testable prototypes that can be validated with real customers.

The How-To School delivers hyper-practical education in AI-enhanced entrepreneurship—teaching residents how to sell on Amazon using AI market research, create AI-powered customer service systems, build websites with no-code tools, and develop products using AI-assisted design. Classes emphasize immediate implementation rather than theoretical knowledge, building both skills and the social networks that support business formation.

Cities Ready for Transformation

Twenty American cities possess the assets necessary for Neumann Engine implementation—educational institutions, cultural heritage, strategic locations, and community character that cannot be replicated through technology alone:

Youngstown, Ohio – Former steel center with strong university presence and affordable real estate
Flint, Michigan – Rebuilding community with engineering expertise and urban agriculture potential
Camden, New Jersey – Proximity to Philadelphia markets with significant redevelopment momentum
Gary, Indiana – Chicago access with manufacturing heritage and lakefront assets
Albuquerque, New Mexico – Research institutions and Hispanic cultural heritage
Shreveport, Louisiana – Regional hub with cultural authenticity and logistics advantages
Stockton, California – Central Valley location with agricultural and transportation assets
Dayton, Ohio – Aerospace heritage and Wright-Patterson Air Force Base connections
Rockford, Illinois – Manufacturing expertise and Chicago market access
Bakersfield, California – Energy sector knowledge and agricultural processing capabilities
Trenton, New Jersey – Government connections and New York-Philadelphia corridor location
Wilmington, Delaware – Financial services heritage and East Coast access
Springfield, Massachusetts – Education cluster and Boston market proximity
Peoria, Illinois – Manufacturing center with Caterpillar heritage and river transportation
Lansing, Michigan – State capital with university presence and automotive expertise
Akron, Ohio – Polymer science leadership and Cleveland market access
Waco, Texas – Central Texas location with university and agricultural assets
Evansville, Indiana – River city with manufacturing heritage and regional access
Fayetteville, North Carolina – Military connections and research triangle proximity
Beaumont, Texas – Energy sector expertise and Gulf Coast logistics advantages

The How-To School will be tasked with transforming residents into AI-capable entrepreneurs.

Prime Business Opportunities

The Neumann Engine adapts proven business models to local assets and market conditions. Twenty prime business opportunities can be implemented in most communities:

AI-Enhanced Specialty Food Products – Regional agricultural assets and cultural traditions creating premium products for national markets>
Craft Beverage Networks – Local breweries or distilleries connecting with similar operations in other Neumann Engine communities
Global Health & Wellness Retreats – Destination wellness centers serving international clients while building permanent community health infrastructure
Compounding Pharmacy Operations – Specialized pharmaceutical capabilities serving both local facilities and external markets
Remote Technical Services – AI-powered customer support, technical writing, and digital marketing serving national clients
Subscription Box Manufacturing – Leveraging fabrication studios to create customized products for niche markets
Agricultural Technology Services – Precision farming, drone services, and AI-enhanced crop management for regional farmers
Healthcare Data Analytics – HIPAA-compliant medical data processing and analysis for healthcare systems
Educational Technology Development – AI-enhanced learning platforms and educational content creation
Renewable Energy Maintenance – Solar panel cleaning, wind turbine maintenance, and energy efficiency consulting
Artisan Manufacturing Networks – High-quality craft goods leveraging local cultural heritage for premium markets
Logistics and Fulfillment Services – E-commerce support, inventory management, and last-mile delivery optimization
Digital Marketing Agencies – AI-enhanced social media management, content creation, and SEO services
Cybersecurity Consulting – Small business security services, vulnerability assessments, and compliance support
Vertical Farming Operations – Indoor agriculture producing high-value crops for regional and national markets
Medical Device Manufacturing – Specialized equipment production leveraging local manufacturing expertise
Software Development Studios – Custom applications, mobile apps, and web platforms for niche markets
Environmental Consulting – Pollution monitoring, remediation planning, and sustainability assessments
Elder Care Technology – AI-enhanced monitoring systems, medication management, and health tracking services
Cultural Tourism Development – Authentic heritage experiences, culinary tourism, and destination marketing

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The Neumann Engine is designed to help cities harness AI today and build self-sufficient local economies.

Investment Requirements and Returns

The Neumann Engine requires $7.5-10.5 million over five years, with returns beginning in year three as businesses mature and achieve market success. The venture studio model generates revenue through equity participation, service fees, educational programs, facility rentals, and consulting services.

Unlike traditional economic development that depends on external funding, the Neumann Engine creates permanent economic assets that generate increasing returns over time. Communities transition from economic supplicants to wealth-creating engines through systematic business formation that strengthens rather than depletes over time.

The Path Forward

The Neumann Engine represents more than an economic development program—it’s a new model for how communities can adapt and thrive in an AI-driven economy while maintaining human agency and authentic cultural character. Rather than being passive recipients of technological change, these communities actively shape how AI technologies integrate into economic and social systems in ways that benefit residents and strengthen community bonds.

The time for implementation is now, while AI tools remain accessible to community-scale entrepreneurship rather than being concentrated in large corporations, and while communities still possess the human capital and cultural assets necessary for transformation. The Neumann Engine provides the roadmap for cities ready to take control of their economic destiny by creating prosperity from within rather than depending on promises from outside.

For communities ready to transform from economic development supplicants into wealth-creating engines, the Neumann Engine offers both the methodology and the hope that local action can generate lasting prosperity in the age of artificial intelligence.

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25 Questions to Help You Understand How AI will Affect Your Job in the Future

Thomas Frey — Thu, 08 Aug 2024 11:45:00 +0000

25 Questions to Help You Understand How AI will Affect Your Job in the Future

How can AI become your friend, your buddy, and coworker in the future?

As artificial intelligence continues to advance at a rapid pace, its impact on the job market is becoming an increasingly important topic of discussion. To gain a clearer understanding of how AI might shape the future of work, it’s crucial to engage in self-reflection and critical thinking. The following set of 25 questions is designed to encourage individuals to explore various aspects of AI’s potential influence on their careers, industries, and the broader job market.

These questions cover a wide range of topics, from the automation of specific job tasks to the emergence of new roles and the ethical considerations surrounding AI integration in the workplace. By contemplating both positive and negative potential outcomes for each question, individuals can develop a more nuanced and comprehensive view of the challenges and opportunities that lie ahead.

As you consider these questions, remember that the future impact of AI on the job market is not predetermined. By actively engaging with these ideas, you can better prepare yourself for the changes to come and potentially influence the direction of AI integration in your field.

25 Questions

1. Which aspects of my current job could potentially be automated by AI?

Positive: AI automation could free me from repetitive tasks, allowing me to focus on more creative and strategic aspects of my job.
Negative: A significant portion of my job could be automated, potentially making my role redundant and threatening my job security.

2. What unique human skills do I possess that AI may struggle to replicate?

Positive: My emotional intelligence, creativity, and ability to handle complex social situations are uniquely human traits that AI can’t easily replicate.
Negative: As AI continues to advance, even skills I consider uniquely human might eventually be replicated, leaving me with fewer competitive advantages.

3. How might AI augment or enhance my current role rather than replace it?

Positive: AI could handle data analysis and routine tasks, allowing me to make more informed decisions and focus on high-level strategy and innovation.
Negative: While AI might enhance some aspects of my role, it could also reduce the need for human involvement, potentially diminishing my importance in the workplace.

4. What new job opportunities might emerge as AI becomes more prevalent in my industry?

Positive: Exciting new roles could emerge, such as AI trainers, ethics officers, or human-AI collaboration specialists, offering fresh career paths.
Negative: New jobs might require extensive retraining or education, and competition for these roles could be fierce as traditional jobs disappear.

5. How can I adapt my skill set to remain relevant in an AI-driven job market?

Positive: This challenge presents an opportunity for continuous learning and personal growth, helping me develop a more diverse and valuable skill set.
Negative: Constantly adapting to keep up with AI advancements could be stressful and time-consuming, with no guarantee of long-term job security.

6. Which industries are likely to be most disrupted by AI, and which might be more resilient?

Positive: Understanding this could help me make informed decisions about my career path, potentially leading to more stable and rewarding opportunities.
Negative: My industry might be among those most disrupted, forcing me to consider a potentially difficult career change.

7. How might AI impact the demand for emotional intelligence and interpersonal skills in the workplace?

Positive: As AI handles more analytical tasks, my emotional intelligence and interpersonal skills could become increasingly valuable, enhancing my career prospects.
Negative: If AI develops sophisticated emotional intelligence capabilities, even these traditionally human skills might become less in demand.

8. What ethical considerations might arise in my field as AI becomes more integrated?

Positive: This could lead to meaningful discussions and the development of important new roles focused on ensuring ethical AI use in my industry.
Negative: Ethical dilemmas could create conflicts in the workplace and potentially limit the benefits of AI integration.

9. How could AI affect wage structures and income inequality in my profession?

Positive: AI could lead to increased productivity and profitability, potentially resulting in higher wages for skilled workers who can effectively work with AI.
Negative: AI might exacerbate income inequality, with benefits concentrated among a small group of highly skilled workers or company owners.

10. What new collaborations between humans and AI systems can I envision in my line of work?

Positive: Exciting new forms of human-AI collaboration could emerge, leading to more efficient and innovative work processes.
Negative: The need to constantly adapt to new AI collaborations could be stressful and might blur the lines between human and AI contributions.

11. How might AI influence the gig economy and freelance work opportunities?

Positive: AI could create new opportunities for freelancers, such as AI training or specialized data analysis, and make it easier to find and manage gig work.
Negative: AI might automate many tasks currently performed by gig workers, reducing available opportunities and increasing competition for remaining jobs.

12. What potential biases in AI systems should I be aware of in my industry?

Positive: Awareness of AI biases could lead to improved systems and create opportunities for specialists in AI fairness and bias mitigation.
Negative: Persistent AI biases could perpetuate or exacerbate existing inequalities in my industry, potentially harming certain groups or individuals.

13. How might AI change the geographic distribution of jobs in my field?

Positive: AI could enable more remote work opportunities, allowing me to work from anywhere and access a global job market.
Negative: AI might concentrate high-level jobs in tech hubs, forcing me to relocate or face limited local opportunities.

What new forms of human-AI teams will be formed in the future?

14. What new regulations or policies might be necessary as AI becomes more prevalent in the job market?

Positive: New regulations could protect workers’ rights and ensure responsible AI use, creating a more stable and fair work environment.
Negative: Excessive or poorly designed regulations might stifle innovation and limit the potential benefits of AI in the workplace.

15. How could AI impact work-life balance and job satisfaction in my profession?

Positive: AI could automate tedious tasks, allowing for more flexible working hours and increased focus on fulfilling aspects of my job.
Negative: The integration of AI might lead to higher productivity expectations, resulting in increased pressure and potential burnout.

16. What new safety considerations might arise with increased AI integration in my workplace?

Positive: Increased awareness of AI-related safety issues could lead to improved workplace safety measures and new roles in AI safety management.
Negative: New AI-related safety risks could emerge, potentially making my workplace more dangerous or increasing job-related stress.

17. How might AI affect the power dynamics between employers and employees?

Positive: AI could provide more objective performance metrics, potentially leading to fairer evaluations and promotions.
Negative: Employers might use AI to more closely monitor employees, leading to decreased autonomy and increased pressure.

18. What new training or educational programs might emerge to prepare workers for an AI-driven job market?

Positive: New, cutting-edge training programs could offer exciting opportunities for skill development and career advancement.
Negative: The need for constant retraining could be time-consuming and expensive, potentially creating barriers for some workers.

19. How could AI impact job security and employee turnover rates in my industry?

Positive: By handling routine tasks, AI could allow companies to focus on employee development, potentially increasing job satisfaction and reducing turnover.
Negative: AI could make it easier for companies to replace workers, leading to decreased job security and higher turnover rates.

20. What new metrics for job performance might emerge as AI becomes more integrated into work processes?

Positive: AI could enable more comprehensive and fair performance evaluations, considering a wider range of factors beyond traditional metrics.
Negative: New AI-driven metrics might not accurately capture the full value of human contributions, potentially undervaluing certain skills or traits.

21. How might AI affect the pace of work and productivity expectations in my field?

Positive: AI could streamline workflows, reducing stress and allowing for a more balanced work pace while maintaining high productivity.
Negative: AI might lead to unrealistic productivity expectations, increasing pressure and potentially leading to burnout.

22. What new forms of human-AI teamwork might develop in my profession?

Positive: Novel human-AI collaborations could lead to unprecedented levels of creativity and problem-solving, making work more engaging and rewarding.
Negative: Adapting to AI teamwork might be challenging and could reduce human-to-human interactions, potentially affecting workplace relationships.

23. How could AI impact the hiring and recruitment processes in my industry?

Positive: AI could make hiring processes more efficient and objective, potentially reducing bias and helping me find better-fitting job opportunities.
Negative: AI-driven hiring might overlook unique human qualities or unconventional career paths, making it harder for some candidates to get noticed.

24. What new job titles or roles might emerge in my field as AI becomes more prevalent?

Positive: Exciting new roles could offer fresh career paths and opportunities to work at the cutting edge of AI integration in my field.
Negative: Traditional roles might disappear, forcing me to compete for limited new positions that may require significantly different skills.

25. How might AI affect the retirement age and career longevity in my profession?

Positive: AI assistance could make it easier to work longer if desired, potentially leading to extended careers and greater financial security in retirement.
Negative: Rapid AI advancements might make it difficult for older workers to keep up, potentially forcing earlier retirement or career changes.

How will we learn to compete in our AI-driven future?

Final Thoughts

The questions and responses presented here illustrate the complex and multifaceted nature of AI’s potential impact on the job market. While AI presents numerous opportunities for innovation, efficiency, and new career paths, it also poses challenges related to job displacement, skill adaptation, and ethical considerations. It’s important to recognize that the future is not set in stone. The way AI will shape the job market depends on various factors, including technological advancements, policy decisions, and how individuals and organizations choose to implement and interact with AI systems. By regularly reflecting on these types of questions and staying informed about AI developments in your field, you can better position yourself to adapt to changes and seize new opportunities as they arise. This proactive approach can help you navigate the evolving job market with greater confidence and resilience. Remember that while AI may automate certain tasks, uniquely human skills such as creativity, emotional intelligence, and complex problem-solving are likely to remain valuable. Focus on developing and honing these skills while also staying open to learning about and working alongside AI technologies. Ultimately, the key to thriving in an AI-influenced job market lies in maintaining a growth mindset, embracing lifelong learning, and being adaptable to change. By doing so, you can help shape a future where humans and AI work together to create more value and opportunities than either could alone.

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In Case You Didn’t Get the Memo, the Nature of Work Has Just Changed

Thomas Frey — Thu, 22 Dec 2022 12:45:00 +0000

In Case You Didn’t Get the Memo, the Nature of Work Has Just Changed

COVID changed many things in our lives; not the least was the nature of work. While we’ve been busy figuring out if remote and hybrid work is here to stay, another more fundamental change has been picking up steam – the four-day workweek. Curmudgeons and traditionalists may be too quick to suggest this is just another misstep down the slippery slope of accommodating non-motivated workers and coddling the workforce.

Whether they’re in an office or working at the kitchen table, you will always have employees who will try to skimp on deliverables and minimize their workplace footprint no matter what kind of time clock is in place or the calendar is followed. A shortened workweek is really no riper for abuse than the traditional five-day workweek.

A new study on the future of work released by Ernst and Young found that both businesses and workers are better off and prefer a four-day workweek. In spite of recent hiring slowdowns and layoffs in the tech and financial industries, 40% of the companies in the survey have implemented a four-day workweek or plan to do so.

The story is the same overseas, from Iceland and the UK to Japan and New Zealand.

Not as Big a Change as You’d Think

What’s the best arrangement? Four 10-hour days? Five 8-hour days? Four 8-hour days? That debate almost seems as irrelevant as the idea of a 40-hour work week itself. Work has become much more fluid for many so-called “knowledge workers,” the new euphemism for white-collar workers.

Many people can’t even quantify how many hours they work NOW, as they check company inboxes on personal phones at all hours and accept videoconference invites from overseas for meetings early in the morning or late in the evening.

And at the opposite end, employers have seemed happy to accommodate employees who must leave early to pick up a child from school or step out for a doctor’s appointment. How many object when an employee asks to leave at 2:30 on Friday to get a head start on their weekend trip? Very few.

Work from home accelerated both of those trends – stretched-out workdays and day-to-day flexibility. But we were well on our way in these areas prior to the pandemic, too, with flex work schedules and summer Friday free days … combined with our tendencies toward workaholism.

What About Employee Productivity?

Whether employees are equally productive with a four-day, compressed workweek is less easy to measure than their satisfaction with the arrangement. The evidence in favor seems pretty significant, even though it’s mostly anecdotal.

But the fact that so many companies are moving in this direction, even though the job market is loosening up a bit and the economy is heading toward a recession, is probably an indication that employers aren’t concerned with a falloff in the quantity or quality of work getting done.

Many leaders were pleasantly surprised when remote workers, for the most part-maintained productivity levels during the pandemic. They seem willing to swallow hard and give this a go, too.

They’re probably considering this factor too — with an extra weekday at their disposal, employees can schedule things like car repairs and doctor appointments for that day. And there will be no more slipping out at 2:30 on Friday. Four-day workdays may mean they can draw a darker line between work days and off days.

The chief strategy officer for the crowdfunding site Kickstarter will vouch for the four-day schedule, stating, “The 4-day week has been transformative for our business and our people. Staff are more focused, more engaged and more dedicated, helping us hit our goals better than before.”

The Psychological Effect

I interact with many people who are in a four-day work environment.

They seem more focused and deliberate with their work. They’re less likely to waste time or put things off, knowing they’re always under a bit of a time crunch. They don’t want to lose the privilege of this work arrangement, so they seem more committed to getting things done in that compressed timeframe. Maybe that tendency will ease over time.

For now, at least, four-day workweeks are a privilege, not a “right,” and those who enjoy this workplace benefit will be on their best productive behavior to keep it.

A Recruitment Tool

There’s no question that employees feel more emboldened about their status vis-à-vis their employer than in many years. The Great Resignation may have fizzled just a bit, but the labor market still places a premium on good workers. Labor unions seem to be on the ascent.

Now that employees have experienced the flexibility of a pandemic, work-from-home environment, they’ve set their sights higher – on expanded work-life benefits like shorter workweeks. “Zoom-free Fridays” are good, but they want more.

And Twitter aside, for the foreseeable future, employers will be eager to offer the perks and niceties needed to keep and retain their people. The perceived value of in-house baristas and catered lunches will pale compared to the recruiting power of a four-day week for the new generation of workers.

What’s the Big Deal?

Many employers are wising up and realizing that enforcing productivity through mandatory hours is a hopeless proposition. Knowledge workers will produce as much as they feel like producing. Period.

In fact, ideal productivity in the knowledge workplace can’t be enforced, and it can only be motivated – primarily from within. Today’s office workers will produce if they feel valued and wanted. The more creative the work, the less meaningful mandatory hours and days become.

The Four-Day Workweek will Change Workplaces

Much of what we hear about shortened workweeks is from the employee’s perspective. But this trend will create challenges for many companies. Customers and clients won’t always match their schedules to the company’s four-day workweek.

As the movement progresses, we’ll have similar kinds of challenges and dichotomies as we had during the pandemic. COVID pushed many jobs to remote status but not all. Condensed workweeks will work for many industries, companies, and departments, but not all.

For example, help desks and customer service departments must operate at least five days a week, even if employees are remote. Members of customer service teams will need to stagger schedules so that one or two are available on each of five or six days. While the employees might have four-day workweeks, supervisors and managers might not.

Conversely, though, while factory workers missed out on the opportunity to work remotely during the pandemic, there’s no reason these companies can’t adopt condensed workweeks for many of their employees. After all, almost a century ago, Ford Motor Company led the way in transitioning workweeks from six days to five.

Our current five-day workweek will soon seem as backward as working Monday through Saturday. I have no doubt that 100 years from now, the average employee will have nearly as many days off as on. Thanks to IT advances, the productivity levels of workers in 2123 will be off the charts. Demographic trends will slowly shrink the skilled workforce between now and then, meaning workers will have even more clout.

Take those factors together, and “work-life” will assuredly flip to “life-work.”

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57 Jobs of the Future

Thomas Frey — Thu, 23 Dec 2021 05:45:42 +0000

57 Jobs of the Future

In hindsight, it’s easy to see that in nearly every generation, innovation impacted job markets and employment trends. It’s not news that human workers are replaced by new technology and that, fortunately, new technology spurs the creation of new jobs and job categories. The list of outdated or diminished jobs is almost nostalgic: switchboard operators, travel agents, librarians.

And twenty years from now we might be fondly recalling our former interactions with the likes of postal carriers, taxi drivers, flight attendants, and bank tellers.

In fact, the job market is an excellent barometer of society’s priorities and technology trends. Pick nearly any futurist topic we’ve explored this year and it’s not too hard to imagine the new jobs that will surround it.

In many cases, the new jobs and categories of jobs will be quite technical as people work to bring blockchain, cryptocurrency, the metaverse, self-driving transportation, and other IT innovations to fruition and then into our daily lives.

Further below, I’ve identified 57 technology-related new jobs in 15 industries that will directly support or respond to these kinds of technology breakthroughs.

It’s Not Just IT

But before we get there, we should recognize that in some cases, technology breakthroughs will lead to more than just technical jobs. Take geriatric services, for example.

Thinking beyond COVID, medical technology is extending life expectancy for seniors. That trend will continue to drive a critical demand for a variety of personal care services for retirees, who are likely to enjoy over 20 years of time after retirement at 65.

And not to overgeneralize or typecast seniors, but as technology increasingly supports our daily lives, many of them will need personalized support just to keep up. Their challenge today may be programming the TV remote or the thermostat, but it’s only going to get more complicated from there. The solution? Personal IT assistants and generalized IT help desks. Sounds like a mission for AMAC and AARP!

IT Redefining Jobs

I should also note that technology is also changing and even upgrading certain jobs, but not necessarily doing away with them. Construction workers and auto mechanics, for example, used to be generalists. But with new technology-based tools, and the need for training on those tools, specialization in these kinds of blue-collar jobs will increase. This specialization increases these workers’ labor clout, market value, and compensation.

There’s another category of jobs enhanced by technology and other tools where the job is the same but the work is not. Recently I’ve watched laborers build a retaining wall and a brick wall on the property across the street. Back-breaking work, right? Not so much as you’d think anymore. These laborers now have access to specialized backhoes, caterpillars, and other equipment downsized for residential work compared to what you’d find on a typical commercial construction site. Thanks to this equipment, you don’t see anyone carrying heavy bricks from the truck to the wall or using a shovel.

What About Retail?

The future isn’t as bright for the approximately 32 million Americans who work in retail jobs. Consumer demand isn’t going away, but how retailers choose to meet that demand will continue to evolve with technology innovations that drive efficiency.

Current trends in e-commerce and in-store automation mean there will be fewer traditional customer-facing jobs. But some of these will be replaced by new jobs managing these platforms and servicing these systems. As with the next generation of blue-collar jobs, these specialty positions may require specialized skills with a level of IT training.

Basically we will see technology taking away much of the mind-numbing tedium of retail just as it did for so many factory workers.

The Cool New Jobs

So far we’ve only talked about incremental changes and the evolution of current employment situations. On another level, we’ll see entire new professions within entire new industries built around new technology. We’ll see job descriptions that weren’t even imaginable 20 years ago.

Yes, there will be high-level computer science jobs with smart people creating new breakthroughs and new versions of breakthroughs.

But at the same time, there will be the need to define and fill some less-obvious positions along with spin-off jobs made possible and necessary by those breakthroughs.

57 Jobs of the Future

Here’s a sampling of future jobs. Some may already sound familiar and plausible. Others are just a few years away. But it would be relatively easy to concoct others and maybe add over a hundred more to this list:

Metaverse Jobs

Metaverse World Designers
Avatar Designers
Metaverse Storefront Creators, Developers, and Operators
Metaverse Law Enforcement
DAO Attorneys

Cryptocurrency

Crypto Coaches and Advisors
Crypto Mortgage Specialists
Decentralization Managers

Future Entertainment

Robot Personality Engineers
Macro VR Projection Artists
Entertainment Trends Analyzer

Commercial Drone Industry

Swarmbot Engineers
Swarmbot Entertainers
Drone Command Center Operators

Smart Cities

Insect, Rodent, and Snake Monitors
Noise Analyzers
Police Drone Operators

Healthcare

Amnesia Surgeons – Doctors who are skilled in removing bad memories or destructive behavior.
Memory Augmentation Therapists – Entertainment is all about the great memories it creates. Creating a better grade of memories can dramatically change who we are and pave the way for an entirely new class of humans.
Digital Implant Architects
Genetic Troubleshooters
Body Part Fabricators
AI Health Managers

Digital Twins

Sensor Engineers
Remote Operation Designers
Digital Twin Command Centers

Big Data

Privacy Strategists
Personal Data Managers, Archivists, and Protectors
Blockchain Designers
Vulnerabilities Analyst

3D Printed Houses

3D Print Architects, Designers, and Engineers
Fabrication Engineers
Site Prep and Planning Crew

Autonomous Transportation

Future Highway Architects and Designers
Traffic Analyzers
Drone Taxi Ground Crew

Cultured Meat Industry

Growth Process Technician
Vat Managers
Demand Tracking Planners

Future Education

AI Memory Assessment Engineers
AI Coach-Bot Designers
AI Teacher-Bot Developers

Future Agriculture

Soil Analyzers, Preppers, and Managers
Crop Techs
Harvester Team Planners and Supervisors
Irrigation Managers

Commercial Space Industries

Space-based Power Generators
Asteroid Trackers
Space Pilots
Space Journey Attendants

Robots

AI Capabilities Engineer
Robot Classification Gurus – Different laws will apply to different classifications of robots
Operator Certification Specialists

Other

De-extinction Engineers
Can We / Should We Ethicists
Ransomware Hostage Negotiators
Ransomware Police

As we’ve discussed in earlier columns, our primary and secondary school systems need to be thinking outside the box. That includes K-12, trade schools, and colleges. Each one should continually evaluate and revise their curricula and major courses of study so that we’re well-prepared to fill future jobs like these – and the hundreds of others that come to mind – so we can fully employ our future workforce and meet the needs of future societies.

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The Critical Competency Needed for the Future – The Creative Edge

Thomas Frey — Thu, 24 Jun 2021 04:00:00 +0000

The Critical Competency Needed for the Future – The Creative Edge

As a Futurist, it’s easy to get caught up in the technology trends and breakthroughs that will mark our future lives – the amazing advances in devices and concepts, from flying taxis to cryptocurrency.

But life is more than code, supercomputers, and the sciences. Healthy and advancing societies must integrate creativity into the mainstream in two fundamental areas: the creative arts and in support of the digital working world.

Keep in mind, the same technology that is automating jobs out of existence is the same technology that will be creating entirely new business and industry.

Since AI doesn’t have the ability to invent something that currently doesn’t exist, we need to focus on our uniquely human strengths, and that’s where creativity comes into play.

Creative Arts

Creative arts can define and unify a culture. Throughout history, the creative arts have brought people together both physically and emotionally. Art, and the creativity behind it, is a unifying force. That’s why oppressive regimes over the course of history often seek to destroy a community’s revered artistic objects when they try to impose their will and authority.

Creative arts can inform and inspire technology. Before most technological breakthroughs occur, there’s usually a necessary phase of imagination and artistic creativity. For example, I have to think that today’s engineers who are deeply immersed in flying car technology were inspired earlier in their lives by artistic renderings of these vehicles.

And on an even more pragmatic front, the creative arts contribute to the economy. The “cultural and creative industries” (CCI) portion of the world economy is poised to rebound after being decimated by pandemic-induced closures. That’s a good thing for a lot of reasons, including economic. The CCI sector is responsible for nearly 30 million jobs worldwide and it’s the most important employer for young people between the ages of 15 and 29 compared to any other sector.

Creativity in the Larger Work Environment

Not all of us are artists, but outside of the CCI, there will also be a need for creativity and a creative mindset in most industries and business sectors.

Our focus on technology and our dependence on it may be making our workforce and many jobs less creative. Fortunately, employers seem to be trying to resist this trend, often defining relatively straightforward jobs as “creative.” They understand that employees, probably for reasons of job security and self-esteem, don’t want to invest their time and effort in jobs in which it seems they can easily be replaced by technology.

Additionally, employers value the differentiating edge that human creative skills can contribute to design, service levels, and branding, even in the process of conducting digital tasks and the production of basic products.

As the former Chairman of Sony pointed out recently, “We assume that all products of our competitors have basically the same technology, price, performance, and features. Design is the only thing that differentiates one product from another …” Creativity.

The Future is Bright for Creative Skills

With that in mind, how will the definition and nature of creativity evolve as we move into the future? How will it be different even 5-10 years from now than it is today?

As noted above, employers will continue to emphasize the creative aspects of the digital-empowered jobs they post. They will encourage employees to bring creative new approaches to their jobs in order to maintain an engaged workforce and in the hopes of delivering differentiation to their competitive products and services.

Additionally, the more digitized the working environment becomes, the more important it will be to combine – some would say temper – objective calculation and machine-informed projections with human intuition and insight. Computers aren’t visionaries. They lack imagination and emotion.

As long as we have problems, we will always have jobs! And there is no shortage of problems! And as long as work teams and our world itself are made up of human beings, this type of managing, planning, and problem-solving will require the creative human touch.

Implications for our Educational System

In many ways, technology has made us less dependent on gaining knowledge. For example, we don’t need to learn geography, rules of grammar, or math skills, thanks to apps and features in apps. These advances have been a source of equalization among mankind – a very positive trend.

Since to a large extent, knowledge is no longer just for the privileged but for everyone, and can be stored in devices and not our brains, our education system will need to shift from knowledge-based training (how to do something), to the more creative side of work (when to do it or not, why to do it, and ways to do it even better).

Unfortunately, that necessity is at odds with our current education system. In fact, some experts claim the education system is actually “killing creativity” … at a time we need it the most.

Knowledge is merely the starting point of creativity; but unfortunately, it seems to be the endpoint of our education system. Instead, the future of education instruction should encompass creative explorations of “How can we apply this knowledge?” and “What areas of technology can we apply in order to create solutions to this challenge?”

The Need to Teach Creativity

From the earliest grades through graduate school, creativity should not simply be tolerated or encouraged, it should be taught along with related key skills like critical thinking, problem-solving, and collaboration.

Creativity doesn’t always come naturally. Some students inherently have more of a knack for it than others, just as some students are math whizzes beginning in first grade.

There’s no arguing that technology like AI, robotics, drones, and ever-faster computers will continue to displace tasks, lessen the workload, and force businesses to rework virtually every conceivable job. But rather than merely responding piece-meal with specialized training programs when this happens, it’s time to go back to the fundamentals and add a fourth “R” to our basic education curriculum. In addition to “readin’, writin’, and rithmetic,” we’ll need to teach and promote “resourcefulness.”

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With the Coming AI-Robot Jobs Armageddon, Why is this Time Different?

Thomas Frey — Thu, 10 Jun 2021 06:05:41 +0000

With the Coming AI-Robot Jobs Armageddon, Why is this Time Different?

Are computers, robots, and machines automating too many jobs out of existence. Is it accurate to describe the short-term jobs market an “AI-robot jobs Armageddon,” as some have?

The future is rarely as comfortable as the status quo. For certain demographic groups, technological innovation and the evolution of ideas can be disruptive, messy, and worse. This is reflected to some extent in the labor market, where currently the average 50-year-old person has held 12 different jobs.

The only thing certain is change, and to a large extent, change is driven by technology advances.

A Historical Perspective on the Future

In 1964, an open letter was drafted and sent to President Johnson, warning him of the coming Triple Revolution. The letter was composed and signed by 35 members of the “Ad Hoc Committee on the Triple Revolution,” which included luminaries like Nobel Chemist, Linus Pauling; civil rights activist, Tom Hayden; and Swedish Nobel Economist, Gunnar Myrdal.

The letter focused on three revolutions that seemingly were taking place at the time:

Cybernation Revolution – increasing automation
Weaponry Revolution – mutually assured destruction
Human Rights Revolution – growing civil unrest

While the letter addressed all three issues, it focused primarily on the Cybernation Revolution. The authors predicted that machines and automation would cause massive new unemployment as jobs shifted significantly. They stated:

“A new era of production has begun. Its principles of organization are as different from those of the industrial era as those of the industrial era were different from the agricultural. The cybernation revolution has been brought about by the combination of the computer and the automated self-regulating machine. This results in a system of almost unlimited productive capacity which requires progressively less human labor. Cybernation is already reorganizing the economic and social system to meet its own needs.”

Of particular interest to me was the work of one of the signers, Robert Theobald, a futurist who had written extensively on the economics of abundance and his advocacy of a Basic Income Guarantee.

These are the same topics being discussed by those today who fear massive technological unemployment in the years ahead.

Were They Right?

This warning from more than 50 years ago still seems to haunt us. Computers obviously have dramatically changed the jobs landscape, as well as the skills required to perform those jobs. And the degree to which this is happening is speeding up, along with the concern about technology-driven unemployment.

Additional factors are at play as well. The pandemic accelerated change trends in many areas of our lives – from healthcare to work environments to shopping … to the jobs market.

Post-COVID hiring has been slower than many expected. There are many possible reasons for this, but I would suggest that during the past 12 months, our economy has undergone decades-worth of automation innovation.

During that same time, we’ve also experienced a decade’s worth of people reconsidering their job futures – for example, getting serious about long-term careers and, in many cases, shifting away from retail and other front-line jobs. Those are the jobs most amenable to automation, according to economists.

And even manufacturing, the epitome of automation, seems to have taken another major leap recently in that direction. The sector has recovered strongly from the pandemic while only bringing back 60% of the workers that were lost.

What the Numbers Show

In the past, researchers typically suggested that major steps in automation within an industry rarely led to mass lay-offs. Instead, they would suggest automation was spurring job retraining, retirements, and shifts to service-related industries.

And when you get past the anecdotal information and dig into the numbers, a similar situation is evident today. A recent MIT study found a fairly significant relationship between the deployment of robots and the jobs that they displaced.

According to their analysis, in any given metropolitan location, each robot that was added to the workplace replaced 6.6 jobs within that region. The impact is ameliorated a bit in that the resulting efficiencies and reduced cost of goods benefitted employment in other industries and locations. Thus, the net national effect of the deployment of one robot was the loss of 3.3 jobs.

It’s not surprising that the researchers found that this impact was borne disproportionately by blue collar workers – low-skilled and especially middle-skill workers, suggesting that technology breakthroughs were contributing to income inequality.

It all adds up to a lot of jobs

On a global level, the jobs impact of technology automation is pretty staggering.

Oxford Economics predicts that, thanks to improved robotic technology, better AI, and lower costs for both, robotic automation will cause the loss of 20 million jobs globally by 2030 – including 12.5 million in China, two million in the EU, and 1.5 million in the U.S. In contrast to the MIT study, though, they’re basing this on an estimate that the displacement effect to date has only been around 1.6 workers per robot.

They point out that at this macro level, the impact will be disproportionately more severe not just on blue collar workers but less advanced countries overall.

But here’s where they were wrong

If you paid close attention to the doom and gloom of the economist’s predictions above, the thing most noticeably absent was predictions about new job creation.

The same technologies that are used to automate jobs out of existence are the same technologies that will be creating the jobs and industries of the future.

Let me say that again. The same technologies that are used to automate jobs out of existence are the same technologies that will be creating the jobs and industries of the future.

The job market is constantly changing. In fact many of the jobs people perform today did not even exist 10 years ago, and most of the jobs that did exist, require a new vocabulary, additional skills, and new forms of understanding.

So if we ask the question – “Is this time truly different?” – the answer is always “yes.”

Every wave of innovation brings with it new demands, new standards, new policies, and a need for new skills.

But perhaps the more salient question we should be asking – “At what point do we start to exceed our human ability to adapt?”

Countless times in the past, people have predicted jobs disappearing, but virtually none have registered more than a ripple effect on the job creation engine, a job creation engine that is being driven by a series of complex self-organizing systems.

Self-organizing systems tend to develop around a series of human management processes where complex systems with many moving parts orient around human need as a core driver to both self-correct and reorganize to build a more stable, pulsing state of operation.

Today’s teachers, coaches and vocational guides should be steering young people towards jobs and careers both in and with these technologies, and that’s where new learning technologies come into play.

In 1984, educational psychologist Benjamin Bloom found that he could take an average student scoring in the 70th percentile range in school and move them into a setting with one-on-one tutoring coupled with a mastery learning process and they would suddenly score in the 98th percentile range.

Bloom’s process led to a huge improvement in student performance. And his results were replicated numerous times by several different researchers.

Going from a 70th percentile to 98th percentile student was a two sigma improvement. This meant that his students performed two standard deviations better than students who learned through conventional instructional methods.

In his papers, Bloom asked the very pragmatic question, “How do we achieve these results in a more practical, more scalable way than one-on-one tutoring?”

Until now, there has really been no practical way of offering one-on-one tutoring for the masses. However, AI and an assortment of other emerging technologies is about to change all that.

The unwritten human mandate

The human race has an unwritten mandate to pass our rapidly growing base of knowledge and information on from one generation to the next.

Our base of knowledge is growing rapidly. Before 1900, it’s estimated that human knowledge doubled every century. However, the doubling of information has speeded up considerably, and the latest research now shows our base of human knowledge doubling every 12 hours.

Think of all the blog posts, podcasts, video recordings, papers being written, social media posts, and surveillance camera records being added to the information universe. It’s truly a staggering amount of information being added every minute of every day.

That said, the tools we have today for passing knowledge from one generation to the next are simply not up to the task. In their present form, libraries are not good enough, schools are not fast enough, and technology still has a poor interface for the human mind. But changes are happening very quickly!

With the base of human knowledge doubling every 12 hours, no it’s not possible to increase our mental sphere to a point where we’re actually absorbing all this information. But we can improve our interface with it, to the point where it is as seamless and as invisible as possible.

Even though it’s outside our present cognitive abilities, we’ll be able to interact with it as though it is. People will be able to ask us a question and we’ll be able to quickly think our way through to an answer.

Our minds will be able to surf the compendiums of the Internet to find the answers.

We will no longer need to have all of our most important data committed to memory. Instead, our minds will have a seamless interface with the knowledge universe, we’ll train our minds to know where to look, and the answers to all our questions will only be a few seconds away.

Final Thoughts

Young people today will be our future designers, craftsmen, engineers, architects, doctors, dentists, scientists, researchers, politicians, lawyers, retailers, and future business leaders. They will be inheriting a world filled with problems – aging infrastructure, crumbling systems, corrupt governments, radically disruptive technology, ethical dilemmas, and a host of seemingly impossible situations.

At the same time, they will be using tools, systems, and techniques that will enable them to be exponentially more capable, productive, and accomplished.

We are on the verge of making the transition from the here-and-now to what-comes-next, and that’s where it gets interesting.

Robots are coming. They’re coming with or without our blessing, and in shapes and forms we can’t even imagine.

But they also come with limits, limits that we will soon discover along the way.

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Podcast College

Thomas Frey — Thu, 20 Feb 2020 17:40:53 +0000

Podcast College

Podcast College has the potential to short circuit the path to a newer you!

What if getting a college degree involved little more than listening to podcasts and taking tests?

Podcasts can be squeezed into every crack in every day, like riding in a car, working out at the gym, going on a bike ride, or waiting at the doctors office. Most college classes cannot!

Colleges and higher ed are about to be transformed and it is in this transformation where amazing new opportunities will spring to life.

At the moment, we don’t know if the disruption will come from AI, videos, podcasts, mentor networks, or something else. But ten years from now, the process for getting a college degree, learning new skills, or simply brushing up on your latest hobby techniques will look vastly different than it does today.

We all have our own idea of what success should look like. But our notion that college degrees are a must-have component of success is quickly eroding.

For teenagers, their heroes are people who have launched their own video games, started a band, filmed a rockumentary, created a mobile app, written a graphic novel, or won a major video game tournament. To them, the accolades and notoriety that come with this kind of experience far outweighs the tedium involved in credentialing new skills.

For others, nothing resonates quite like being involved in an authentic accomplishment-based learning experience where meaningful work is making a meaningful impact.

Experience trumps diplomas every day of the week.

That said, we still have a need for credentialing.

Our Mandate for Humanity

One of our underlying mandates has been the transfer of knowledge from one generation to the next. And no, we’ve never been very good at it.

History is filled with stories of invading armies determined to wipe out every possible remnant of ancestral tribes, races, cultures, and ethnicities, and in doing so, many of the inventions, discoveries, and research projects that have brought us to this wobbly point in history have been lost along the way.

Every time there has been a burning of books, destruction of libraries and museums, or dismantling of schools, our ancestors have been forced to relearn, rediscover, and reinvent each of these pillars of accomplishment one sand pebble at a time.

Simply put, it is our ability to look back at everything we’ve learned, discovered, invented, and imagined in the past that helps jumpstart every new generation of young people by placing the starting blocks on a much higher plain of understanding. At least that’s the way it’s supposed to work.

Over the years we’ve developed a number of tools for doing this including libraries, books, museums, colleges, and more recently countless forms of digital archive sites including YouTube, Facebook, LinkedIn, and Instagram.

Until recently, the greatest responsibility for this generational transfer of knowledge has fallen on colleges.

In much the same way books have evolved into a convenient way of packaging information, college courses have become the default form of packaging learning units.

Yet, anyone who’s written a book knows how onerous and demanding the process can be. And creating a curriculum for a college course is equally demanding.

Of course we’ve seen a number of shortcut processes come out of the woodwork, but for truly meaningful books and courses, it’s still a painful process.

More recently, however, the online digital world has given us a completely new set of tools to work with, and the reinvention of learning processes will send shockwaves of disruption through our existing school systems.

Listening to a podcast becomes an intensely personal experience!

Enter the Podcast Universe

At the DaVinci Institute, we hosted our first Podcast Bootcamp in 2005, shortly after podcasting itself was invented.

Podcasting was first created in 2004 by former MTV video jockey Adam Curry and software developer Dave Winer. Curry wrote a program called iPodder that enabled him to automatically download Internet radio broadcasts to his iPod. Several developers improved upon his idea, and podcasting was officially born. Curry now hosts a show called The Daily Source Code, which is still one of the most popular podcasts on the Internet.

The name itself came from the idea of loading audio files onto an Apple iPod.

I remember how our Podcast Bootcamp turned into a bit of a geek-fest because the whole process of recording and loading audio files online was still in the prehistoric, cutting-edge early years.

Today, most of the rough edges are gone and podcasting itself has become a massively fast growing and hugely popular medium.

Currently there are over 850,000 podcasts featuring over 30 million episodes. And these numbers are moving up a steep growth curve with projections that we’ll break the million podcast barrier later this year.

51% of people in the US have listened to podcasts, and the audiences are split into 56% men and 44% women.

Most of the growth in podcasting started in 2014, with 157% growth since then, when smartphones started to become more podcast-friendly.

Right now, podcasting is free from government regulation. Podcasters don’t need to buy a license to broadcast their programming, as radio stations do, and they don’t need to conform to the Federal Communication Commission’s broadcast decency regulations. That means anything goes — from four-letter words to sexually explicit content. Copyright law does apply to podcasting, though. Podcasters can copyright or license their work, and Creative Commons is just one online resource for copyrights and licenses.

Testing will be a key component of every future learning system!

Inventing Podcast College

What if college lectures, with a single professor discussing a topic, were replaced with 2, 3, or 4 very bright people discussing the same subject matter on a podcast?

Podcast courses already exist, and many colleges already have their own podcast course offerings, but so far no one has managed to pull together an organically scalable YouTube-for-podcast-courses site with testing and revenue sharing that offers legitimate credentialing for next generation learners.

Here are some of the components that could make it a rapidly scalable online service:

Audio and Video

Since there are no one-size-fits-all forms of learning, podcasts will include both audio and video.
AI-Driven Recommendation Engine

Finding good podcasts is easy. But finding great ones is hard. Over time an AI recommendation engine will learn your interests, quirkiness, idiosyncrasies, and personal preferences.
Industry-Driven Certification Standards

Every profession, personal skill, or area of learning has logical points where experts in that field would consider the necessary learning to be sufficiently complete. But every profession or skill is different.
Instant Language Translatio

Switching from French to English to Mandarin should be automatic based on your profile.
Official Record Keeping System

Building a system with impeccable integrity means that the system for archiving the accomplishments of every participant must be secure, private, and managed by an organization with impeccable credentials. While many people will think that a government-run archive is the best solution, the best possible record-keeping system will be one that transcends governmental boundary lines.
Participative Wealth Pricing

The revenue stream generated by each courseware unit will be divided between the courseware producer, distribution company, transaction company, system operations company, and the official record keeping system. Maybe more. Courseware prices need to be kept low to make courseware accessible to anyone interested in learning.
Multi-Dimensional Tagging Engine

Tagging will help train the AI. Much of the system usability will be driven by multi-dimensional tagging engines that will include:
- Personal Rating Tags
  
  Upon completion, each student will be asked to rate the pod-course. Courses will be graded on accuracy, quality of the learning experience, ease of use, and overall effectiveness.
- Quality Assessment
  
  Listeners will up-vote and down-vote podcasts so the best-of-the-best will rise to the top.
- Truth Assessment
  
  Virtually every aspect of society has their own version of the ‘truth’ – religious truths, scientific truths, legal truths, etc. For this reason, individual groups will place their tags of approval or disapproval on courses. For example, organizations like the American Chemical Society, People for the Ethical Treatment of Animals, Focus on the Family, American Civil Liberties Union, National Rifle Association, National Education Association, or the Catholic Church can all review the new courseware that is being introduced and make a determination as to whether or not it meets their criteria.
- Taxonomy Tags
  
  Invariably, courseware topics will be understood differently by every student. For this reason, allowing students to place descriptor tags on all completed courses will create the necessary taxonomy markers so an AI recommendation engine can find it.
- Prerequisite & Post-Requisite Tags
  
  Knowledge builds on knowledge. As an example, students cannot study literature until they know how to read, and they cannot study computer programming until they know math and algebra. So courses have to happen sequentially, and it becomes imperative to develop a system for sequencing courses based on the order of which learning must take place.
- Comment Tags
  
  Comment sections will allow students to voice their own thoughts on each course.

Podcast college has the potential to become the most liberating shift in learning in all history!

Launching the Business of “You”

How many traditional schools are currently prepping students to be “freelancer-ready?” In a word – none. It’s simply not happening.

Instead, the hard transition from student grunt to skilled worker is occurring in radically different ways – through friends, through trial and error, and through existing project teams.

Mentorship is quickly becoming the new classroom.

When LinkedIn founder Reid Hoffman said, “You need to think and act like you’re running a start-up of ‘you’,” he’s referring to your own career.

Every new free agent that enters the project-to-project job world quickly realizes that their growing lists of questions simply don’t have textbook answers. They have to find their own answers, and the quickest way is through peer groups and mentors.

Living in the U.S., the country with the highest educated waitresses and bartenders in the world, an increasingly vocal underground feels they’ve been lied to. Academic credentials no longer live up to the promise implied with every new student loan that’s being applied for.

That’s one of the reasons coworking is becoming so trendy; they’re looking for a better network.

Your next career move may only be a few podcasts away!

Final Thoughts

The best schooling occurs when you have a project where you can instantly apply the things you learned.

Writing a book, receiving a patent, or starting a business are all noteworthy symbols of achievement in today’s world. But being the author of a book that sells 50,000 copies, or inventing a product that a million people buy, or building a business that grosses over $10 million in annual sales are all significant accomplishments far more meaningful than their academic equivalents.

Most of what happens in today’s universities is based on “symbols of achievement,” not actual accomplishments.

Academic competitions pit students against each other to produce results that best match their teacher’s expectations. Only rarely will they produce anything noteworthy.

Completing a class is nothing more than a symbol of achievement. Similarly, completing many classes and receiving a diploma is noteworthy, but still only a distant cousin to a real accomplishment.

No, this doesn’t mean that classroom training has no value. But, what we achieve in a classroom is at least one level of abstraction removed from a real-world accomplishment.

In the business world, it’s only an accomplishment if someone is willing to pay for it.

The global marketplace is not looking for people who have learned how to be great students. It wants results.

Podcast College has the potential to short circuit the path to a newer you!

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Visiting the Future with Thomas Frey of the DaVinci Institute

Charles Warnock — Wed, 10 Jul 2019 18:12:06 +0000

Visiting the Future with Thomas Frey of the DaVinci Institute

Meet Thomas Frey: futurist, speaker, and founder of the DaVinci Institute in Westminster, Colorado—and SU community member. He has spoken around the world to such audiences as NASA executives and other high-level government officials and such Fortune 500 companies as IBM, AT&T, Lucent Technologies, First Data, Boeing, Capital One, Bell Canada, Visa, Ford Motor Company, and Qwest. Formerly an engineer at IBM for 15 years, Frey now works to develop original research at the DaVinci Institute in areas not normally addressed by futurists.

Some of Frey’s predictions include:

The reunification of North and South Korea will happen by 2020
By 2030, the largest company on the Internet will be an education-based company that we haven’t heard of yet
Cryptocurrency is very much here to stay and is going to displace 25% of national currencies by 2030
By 2030, over 2 billion jobs will disappear
We will reach the first billion drones in the world by 2030

Thomas, we’re big fans of visiting the future frequently to inform our thinking and planning for today. It seems you visit the future often, and come back with predictions that are sometimes controversial.

Well, the point is not just to be controversial, but definitely to be provocative in terms of expanding our thinking and understanding about the future. Sometimes, the best way to shake us out of our typical thinking patterns is to throw out a very controversial starting point, or premise, or approach existing arguments in a different way. For example, everyone agrees that understanding the future is important.

But what if I said that ignorance is a valuable part of the future—that our greatest motivations in life come from NOT knowing the future. Once a future is known, we quickly lose interest in trying to influence it. If we knew the future, we would have little reason to vote in an election, host a surprise party, or begin a new project. For this reason, our greatest motivations in life come from NOT knowing the future.

Because no one has a totally clear vision of what lies ahead, we are all left with degrees of accuracy. Anyone, any organization, that achieves a higher degree of accuracy, even by only a few percentage points, can achieve a significant competitive advantage. I have said that humanity will change more in the next 20 years than in all of human history. At that rate of change, developing a clear vision of the future becomes not just important, but critical to an organization’s survival.

So is it true that we must be proactive and disrupt ourselves or be disrupted by someone who’s moving faster? Are there only two choices?

You can do the quantum leap thing where you leapfrog forward with a brand new solution that’s better than any current solution—and I would encourage people to stretch their minds and think in those terms. There’s a huge effort in the world today to solve all of our problems, and really these are all the historic problems that have built up to this point. And we haven’t really spent much time looking at the innovations in the future that will actually change the problem sets.

I mean, if we colonize Mars, that’s a whole different set of problems. It gives us a different perspective. Instead of the idea of having an overpopulated Earth, suddenly we’re thinking in terms of an underpopulated universe. It’s the same issue, but it’s looking at it from radically different perspectives.

All of these things change as our technologies change, as our reach changes. Our capabilities change. This technology that we’re creating, it’s giving us additional capabilities. It’s giving us the ability to do things faster, better, cheaper than ever before. And that’s what’s fascinating to me: as we step further, as we step into the next realm of technology, we acquire a different vantage point and things just look different from there. Almost anyone who has ever started a business will tell you that once they get into the business, they start spotting relevant opportunities that they never realized were there.
You’ve also talked about existing technologies that will keep morphing and developing, as opposed to being replaced. What’s an example?

I was thinking about the idea of what technology will replace the photograph, but the photograph itself seems pretty durable. We’re adding more capabilities to that one photograph and how we store it, how we display it, and how we interact with it. The photograph might start out as just a still image from the past, and then we add a timestamp, geolocation, and machine learning is getting a lot better at recognizing people and objects. We can actually go back and sort through the time frame, the people, and possibly where the photo was taken because of objects in the background.

Then we start adding human relationships, like “This is my great uncle, and he was related to this person and that person.” Suddenly, we have a genealogy map that we didn’t know existed. It’s fascinating what might happen as people start digging away into all the attributes every photograph has. 2D photos can become 3D photos. Still images can become animated. With AI we’ll soon be able to “fill in the blanks” between photos. Photography has been around since 1839, and I think that we’re going to be reinventing the photograph 50 years from now, still. It has that much potential.

You also effectively step back into the past to imagine the future of transportation.

It’s a good example of the cascading effect. The transportation industry affects so many other industries. The cars that we drive today have been in development for over 120 years, primarily around the assumption of a human driver. Through most of the incremental advances in auto technology, we’ve haven’t planned for autonomous vehicles. Now designers are more focused on electric vehicles, and not focused on the human’s relationship with the steering wheel, the dashboard, gas pedals, and so on.

So then there’s this growing idea that we’ll no longer have to own vehicles. We have 273 million registered vehicles in the United States—a lot of hardware that’s just sitting idle 96% of the day, on average. We have built garages for our houses in which to store our cars, we have parking garages in town, parking spaces all over. And you start asking the question, “Well, what if we didn’t need all that? What if one car could actually take the place of 30 cars?”

The implications are huge when you start looking at the cascading effect that this shift could have. It’s beyond the future of transportation to the future of work, finance, real estate, travel, and all these aspects of our lives. If I don’t have to own my own car, the people making the car loans are out of a job. The car insurance people, suddenly they’re selling fewer and fewer policies and a lot of them are out of jobs.

And we can start using that real estate for other purposes. Over 10% of retail space in cities is car-related. That’s not only car dealerships, but car washes, the auto parts stores, the brake shops, tire shops. All of these things start to dwindle over time, and how does that change our lifestyle?

Could I actually have a mobile platform where I go to work and my office is mobile and it drives around town all day and picks up people that I’m meeting with? That’s a different type of lifestyle for some business executives. If I want to go to Chicago, Miami, or Dallas, rather than go through all the security hassle at the airport I can just jump in a vehicle and take off, and I’m watching movies and playing video games for the next 12, 15, or 18 hours. Imagine if you don’t have to deal with feeling like you’re packed in like cattle on an airplane. Soon, you may be able to forget about airport security or crowds, and watch the sights roll by as you cruise cross-country in an autonomous vehicle.

Technology will have a massive impact on the travel and transportation industries. What will happen to airlines and airports? What about those massive parking garages that surround our airports? About 41% of income for the average airport in the United States comes from parking and transportation fees. All the limo drivers, the shuttle bus drivers, tow-truck drivers, all of those jobs start to go away. So the cascading effect of all of this is just staggering. And all of the income from traffic violations, traffic courts, the highway patrol—all of that starts to go away, too.

It sounds like the future of transportation is closely tied to the future of work.

There are connections wherever you look. In 2012, I was giving a TEDx talk in Istanbul, and I made the prediction that by 2030 over two billion jobs will disappear. Now I am quoted about this in newspapers, magazines, and TV stations all over the world. That was never intended to be a doom-and-gloom statement. I never intended it to mean we’re going to have two billion people unemployed in the world. Rather, it was intended to be a wakeup call that we must create new jobs at a faster rate than ever before.

We’re going to baby-step our way into the future and we will have a lot of failures along the way. And as long as we have these failures, and all the problems that come with failure, we’re always going to have jobs—certainly not the same jobs we have today, but we’re always going to have a lot of work to do to fix all these things that we’ve created.

So, you must ask the question, “Is it the same job?” That’s very important because 20 years from now, we’re still going to have teachers, accountants, lawyers, and nurses, but the tools, techniques, and strategies they use are going to be radically different. Teachers are in the education profession, and they may be in the same profession in 20 years—but their roles will look very different than they do today. I think the idea of a profession evolving with new tools and technology is more realistic than a scenario in which a new job comes along and it eliminates that old job. It’s not that simple. It’s much messier than that; it’s much blurrier. So yes, we should just assume that our jobs will get redefined again, and again, and again.

So what’s the takeaway for those who are concerned about remaining relevant in this messy, blurry future?

Virtually everybody I run into today has this nagging anxiety, wondering “Am I still going to have a job five years from now? If not, what new skills do I need to learn to remain relevant?” We all have to replace that anxiety with action. Learn from what’s known, and extend that out into the unknown future. Whatever your profession, what can you learn from its historical context? How will the future be different as exponential technologies emerge and converge? We can’t just read about these technologies. We need to touch them, experiment, and figure out how to apply them to the problems we’ll face in the future.

I like to say we shape our future, and our future shapes us. The more time you spend visiting the future and thinking about potential scenarios, the more comfortable you will feel in preparing for it. Think on a global scale, and pay attention to the accelerating rate of change driven by technologies. One common mistake people make is underestimating just how quickly our lives, business, and every industry are changing.

Finally, I would say that there’s never been a better time to find your tribe. Join future-focused communities. Attend events, mastermind groups, hackathons, and professional associations. None of us can know the future, but all of us can empower ourselves to know what’s coming and to create our own fulfilling futures.

This post was originally published on the Singularity University Blog on June 27th, 2019.

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By Futurist Thomas Frey, author of 'Epiphany Z – 8 Radical Visions Transforming Your Future'

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25 Questions to Help You Understand How AI will Affect Your Job in the Future

25 Questions to Help You Understand How AI will Affect Your Job in the Future

25 Questions

1. Which aspects of my current job could potentially be automated by AI?

2. What unique human skills do I possess that AI may struggle to replicate?

3. How might AI augment or enhance my current role rather than replace it?

4. What new job opportunities might emerge as AI becomes more prevalent in my industry?

5. How can I adapt my skill set to remain relevant in an AI-driven job market?

6. Which industries are likely to be most disrupted by AI, and which might be more resilient?

7. How might AI impact the demand for emotional intelligence and interpersonal skills in the workplace?

8. What ethical considerations might arise in my field as AI becomes more integrated?

9. How could AI affect wage structures and income inequality in my profession?

10. What new collaborations between humans and AI systems can I envision in my line of work?

11. How might AI influence the gig economy and freelance work opportunities?

12. What potential biases in AI systems should I be aware of in my industry?

13. How might AI change the geographic distribution of jobs in my field?

14. What new regulations or policies might be necessary as AI becomes more prevalent in the job market?

15. How could AI impact work-life balance and job satisfaction in my profession?

16. What new safety considerations might arise with increased AI integration in my workplace?

17. How might AI affect the power dynamics between employers and employees?

18. What new training or educational programs might emerge to prepare workers for an AI-driven job market?

19. How could AI impact job security and employee turnover rates in my industry?

20. What new metrics for job performance might emerge as AI becomes more integrated into work processes?

21. How might AI affect the pace of work and productivity expectations in my field?

22. What new forms of human-AI teamwork might develop in my profession?

23. How could AI impact the hiring and recruitment processes in my industry?

24. What new job titles or roles might emerge in my field as AI becomes more prevalent?

25. How might AI affect the retirement age and career longevity in my profession?

Final Thoughts

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In Case You Didn’t Get the Memo, the Nature of Work Has Just Changed

In Case You Didn’t Get the Memo, the Nature of Work Has Just Changed

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