Introduction: The AI Transition is Happening Now (Not in the Distant Future)

I’ve been hearing about AI since the 1960s, when I was working on my engineering degrees and teaching computer science. First, we had a program that could play checkers, then chess, and for me, A.I. went underground as I got caught up in work and family. Then it arrived with a roar, with the Large Language models like Chat GPT, Perplexity, and Claude. Now, in 2025-26, we are seeing the development of agents- AI on its own doing tasks for you. The AI transition is happening now. The pace of change in AI is so fast that I find it difficult to keep up. The next AI phase, I think, will be independent-thinking robotics.

This is a peek at the effects I see coming for the various age cohorts in our society, and what we should do to try to influence the future impact of AI on our society and the world of work. There is a lot packed into this that requires us to be more involved in shaping our future than ever before. We cannot be complacent! We must beproactive!

The ecomic effects are coming fast

If you’ve been paying attention to headlines over the past two years, you’ve likely seen two completely different stories about artificial intelligence and work. One narrative screams that robots will take all our jobs and society will collapse. The other insists that AI will create prosperity for everyone, and we’re worrying over nothing.

The truth, as usual, is messier and more interesting than the extremes. There will no doubt be an AI effect on our society. For example, I just made an appointment for service on my car by speaking with an AI; no human was directly involved. We simply must embrace AI and control its impacts before it runs over us.

We are indeed in the middle of a significant economic transition driven by AI. But it’s not the apocalyptic mass unemployment scenario some predict, nor is it a seamless ascent into prosperity. Instead, we’re seeing a complex reshuffling of the labor market—where some jobs are disappearing, new ones are emerging, and the rules of work itself are going to fundamentally change.

This shift affects people differently depending on their age, their skills, their assets, and where they live. A 22-year-old recent graduate faces a different challenge than a 45-year-old manager, who faces a different challenge than a 62-year-old approaching retirement. Understanding your generation’s specific situation is the first step to navigating this successfully. The good news? We already have real data from real pilots and real-world implementation. We’re not speculating anymore. We can see what’s working, what’s failing, and what choices we have.

What’s Really Happening with AI and Jobs Right Now

The Surprising News: More Jobs Created Than Destroyed

Let’s start with the fact that surprised many economic forecasters: the labor market hasn’t collapsed. According to data through early 2026, employment in AI and related fields has grown significantly. Compared to fears in 2023 that AI would eliminate millions of jobs immediately, the actual impact has been slower and more nuanced. This doesn’t mean job losses haven’t happened—they have. But they’ve been outpaced by new job creation.

However, this masks a critical problem.

The Complex Truth: Entry-Level Jobs are Disappearing

The headline “more jobs created than destroyed” obscures where the real damage is happening in entry-level positions.

This is the crisis nobody talks about enough. A 2025 analysis of graduate employment in the UK found a 32% drop in entry-level job openings since the ChatGPT era began in late 2022. Why? Because entry-level work—basic coding, document drafting, initial data analysis, junior copywriting, customer service tier-1 support—is exactly what large language models do well.

Employers realized: why hire a junior developer to write foundational code, or a junior paralegal to draft basic documents, when an AI can do it in seconds? The rungs at the bottom of the career ladder are disappearing just as millions of young people are trying to climb them.

This creates a vicious problem. Historically, entry-level jobs weren’t meant to be final destinations—they were training grounds. You became an expert by doing entry-level work, making mistakes, learning from more senior people, and gradually improving. Remove that bottom rung, and you face what experts call the “broken entry problem.”

Wage Premiums for AI Skills (And the Growing Inequality)

Here’s where the story gets bifurcated: people with AI skills are seeing significant wage increases.

Workers who can effectively use AI in their jobs—or better yet, who understand how to integrate AI systems into business processes—are commanding a 50-65% wage premium compared to peers without these skills. In some tech fields, the premium exceeds 100%.

This is creating a two-tier labor market emerging in real time:

Tier 1: Highly skilled workers who use AI to become super productive. These people can do the work of entire teams. Their compensation is skyrocketing.

Tier 2: Everyone else, whose wages are stagnating or declining because their work is easier for AI to automate.

The middle is hollowing out.

Which Jobs Are Most at Risk and Which Are Growing

Jobs at highest risk of displacement:

Data entry and processing (60-70% automation potential)

Basic accounting and bookkeeping (55-65%)

Junior legal document review (50-60%)

Customer service representatives (45-55%)

Administrative assistants (50-60%)

Jobs showing growth:

AI training and oversight roles, Healthcare (nursing, physical therapy, mental health counseling)

Skilled trades (plumbing, electrical, HVAC)

Elderly and childcare

Complex human-centered roles (strategy, negotiation, creative direction)

Roles requiring physical presence in the world

The pattern is clear: routine and cognitive jobs are being automated. Jobs that are physical, creative, relational, or require judgment in complex situations are holding steady or growing.

Why the Data is More Complicated Than Headlines Suggest

The reason you see conflicting headlines is that different metrics tell different stories:

Unemployment rate: Remains relatively low (around 4-5%)

Underemployment: Rising (people working part-time who want full-time work)

Wage growth for college graduates: Down 2-3% in real terms since 2023

Hours per worker: Declining in white-collar sectors

Job stability: Declining (more contract/gig work)

The unemployment rate looks fine because people are still working—just with fewer hours, less stability, and lower wages (when adjusted for inflation).

Ages 15-25: Starting Your Career in the AI Era

The Entry-Level Job Disappearance Problem

If you’re a high school or college student right now, you’re navigating a labor market that’s fundamentally different from the one your parents entered. For previous generations, the path was relatively simple: get decent grades, earn a degree or certification, apply for entry-level positions in your field, and start climbing the ladder. Those entry-level jobs were often poorly paid and sometimes tedious, but they were the necessary investment that led to better opportunities. That path is increasingly blocked.

Companies like major accounting firms, law firms, and software development shops have reduced their intake of junior staff by 30-40% since 2023. They’re using AI to handle what used to be junior work. When entry positions do exist, they often require 2-3 years of prior experience, which makes no sense if you’re trying to get your first job.

This is a real structural problem, not something you can solve with individual hustle. No amount of personal grit will create entry-level jobs that have been genuinely eliminated.

Skills That AI Can’t Replace

So, what should you focus on? The answer is surprisingly clear when you look at what’s happening in hiring:

Skills with staying power:

Complex problem-solving in real-world contexts – Understanding how systems interact, thinking through edge cases, designing solutions for messy human problems

Human judgment and ethical reasoning – Making decisions where the “right answer” isn’t obvious

Communication and collaboration – Explaining complex ideas, negotiating, and facilitating groups

Physical skills and trades – Electricians, plumbers, HVAC technicians earn $70-90K+ and have labor shortages

Specialized expertise – Deep knowledge in fields like medicine, engineering, and psychology

Creative and strategic thinking – Generating new ideas, not executing predetermined tasks

Emotional intelligence – Understanding people, building relationships, coaching, and mentoring

Notice what’s on this list: almost everything involving judgment, human connection, creativity, or physical work.

Education Pathways That Actually Work

Given all this, what should you study or learn?  The realistic answer: Don’t get trapped in the idea that you need a specific degree path. Instead, consider skilled trades seriously. Plumbing, electrical work, HVAC repair, and construction trades are in massive shortage. A union electrician earns $90K+, has strong benefits, and AI won’t be replacing this work anytime soon.

If you go to college, choose fields with genuine complexity: engineering, specialized medicine, psychology, philosophy, physics, architecture, accounting (the strategic kind, not the data-entry kind). Those degrees that teach you how to think and work with people. Avoid pure information processing degrees that taught you to be a high-end data processor. Business degrees that taught you to optimize existing systems are becoming less valuable.

Develop AI literacy, not AI replacement skills. You don’t need to code—you need to understand what AI can and can’t do, and how to work with it effectively.

Get experience early in real human industries: healthcare (volunteer), elderly care, childcare, community organizations, and environmental work. These fields are hiring and will continue to for decades.

Why Basic Income Pilots Matter for Your Future

You’ve probably heard of “Universal Basic Income” (UBI). These aren’t hypothetical anymore—they’re being tested. Since 2023, dozens of pilot programs have run across the US, Europe, and Asia. Here’s what the data shows:

Cash transfers without conditions don’t destroy work incentive (the most common fear proved unfounded)

They do reduce desperation and poor decision-making (people make better choices when not in crisis)

They enable education and risk-taking (people pursue training or start projects they wouldn’t otherwise)

They improve mental health (reducing the constant stress of survival)

For your generation specifically, this matters because UBI could bridge the entry-level gap. Rather than being forced into desperation during the period when you’re trying to develop skills, a basic income floor would let you be selective about opportunities, take on meaningful unpaid or low-paid work that builds your capabilities, and avoid the “take any job to survive” trap. Several governments are now piloting UBI specifically for young people. Monitor this closely—if it works at scale, it will significantly change your career trajectory.

What You Can Do Now to Prepare

Immediate actions (next 6-12 months):

Develop AI fluency – Learn how to use current AI tools (ChatGPT, Claude, etc.) effectively. Not to replace your skills, but to enhance them.

Get experience in human-centered work – Volunteer in elder care, childcare, community organizations, or trades. This builds both skills and resilience.

Learn to think, not to process – Practice solving ambiguous problems, making recommendations, and defending your reasoning. This is increasingly what humans do.

Consider skilled trades seriously – Visit a trade school, talk to electricians or plumbers. The earning potential and job security are genuinely excellent.

Build networks, not just credentials – Who you know matters more than ever. Relationships open doors that generic applications don’t.

Medium-term actions (1-3 years):

Choose education based on genuine complexity, not credential stacking – Avoid degrees that are primarily credential signals.

Gain specialized expertise in something real – Medicine, engineering, a trade, environmental science—something that requires deep knowledge.

Develop a perspective on ethics and fairness – Companies increasingly care about whether employees can think about impact, not just execution.

Ages 25-50: The Squeezed Middle and AI Opportunity

White-Collar Jobs Will Be Under Pressure

If you’re in this age group, you’re probably reading this with a specific anxiety: “Is my job next?”

The honest answer: if your job is primarily cognitive work that follows predictable patterns—reporting, analysis, document drafting, data processing, middle management coordination—then yes, it’s under pressure. These are exactly what large language models and agentic AI do well. A 2025 McKinsey analysis found that routine cognitive work faces a 40-60% risk of automation. This includes roles like:

• Financial analysts
• Junior management
• Business operations coordinators
• Paralegals and legal staff
• Technical writers
• Human resources coordinators
• Many accounting roles

The threat is real. But so is the opportunity.

The 4-Day Workweek as a Practical Solution

Rather than debating whether AI will take your job, some companies and countries are asking a different question: What if AI increases our productivity, and we respond by letting people work less, not firing people?

This isn’t theoretical. Iceland ran a large-scale pilot from 2019 to 2022 in which thousands of workers moved to 4-day workweeks (32 hours instead of 40) with no pay cut. Results:

• Productivity stayed the same or improved (workers were less burned out, made better decisions)
• Well-being increased significantly (stress, burnout, and health issues all improved)
• Employees were happier (job satisfaction rose)
• Business outcomes were fine (companies didn’t suffer)

Multiple US and UK companies have now replicated this. If AI increases output by 30%, the logical response is: let’s take that 30% as additional leisure, not as an excuse to fire 30% of staff. This requires collective action (unionization, worker advocacy, regulatory support), but it’s demonstrably possible.

How to Become More Valuable with AI (Not Replaced by It)

The workers thriving right now are those who have made a specific shift: they’re using AI to amplify their capabilities rather than competing with it.

Concretely, this means:

• A lawyer who uses AI to draft initial documents, then focuses on strategy and client relationships, rather than spending weeks on document review
• A manager who uses AI for routine reporting, then focuses on mentorship, strategic planning, and organizational culture
• An analyst who uses AI to process data and run simulations, then focuses on interpretation, storytelling, and high-stakes decision support
• A marketer who uses AI for routine content and analytics, then focuses on strategy, brand voice, and creative direction

The pattern: AI handles execution; humans handle judgment and strategy.

Workers who make this transition see:

Increased job security (they’re more valuable, harder to replace)

Better compensation (they’re doing higher-value work)

Less soul-crushing tedium (the boring parts are automated)

More meaningful work (strategy and human interaction)

Retraining and Adapting Mid-Career

If your current role is under threat, you’re not locked in. The challenge for mid-career professionals is that retraining is expensive and time-consuming, while you have financial obligations (mortgage, family, aging parents). This is the core problem for your age group.

Real solutions already exist:

Lifelong Learning Accounts: Some companies now offer $5,000-$15,000 annual stipends for training and education

Paid sabbaticals: Several progressive employers offer 3-6 months of paid leave for training

Employer-sponsored retraining: Companies like Amazon and Microsoft have programs where they’ll pay for workers to retrain into higher-value roles

Community college pathways: Many 2-year programs are more affordable and practical than 4-year degrees

Trade apprenticeships: Never too old to learn a skilled trade; programs often come with paid-on-the-job training

The key is: you don’t have to do this alone, and you don’t have to do it on your own dime.

Why Portable Benefits Matter More Than Ever

Here’s a structural change that will massively affect your life: the end of employer-based benefits. Historically, people’s healthcare, retirement, and other benefits were tied to their employer. This made sense when people stayed with one company for decades. But if job security is declining and you might need to move between employers more frequently, this system breaks down.

Progressive countries and companies are shifting to:

• Universal healthcare (decoupled from employment)
• Portable pensions (you control your retirement savings, not your employer)
• Unemployment insurance (government-provided, not employer-managed)
• Disability and sick leave (universal, not employer-dependent)

Advocacy priority for your age group: Vote exclusively for politicians who support portable, universal benefits. Healthcare should never be tied to a specific job. Your retirement savings should follow you. This one policy change will give you vastly more freedom and security as the labor market becomes more fluid.

Ages 50+: Protecting Your Future

Ageism and AI Hiring Systems

If you’re in this age group, you’ve likely encountered or at least heard of “ageism” in hiring. AI is making this dramatically worse.

Here’s the problem: AI hiring systems trained on historical data learn to replicate historical biases. If a company’s past hiring data shows they preferred younger workers (which is common), the AI system will learn to prefer younger workers. This discrimination happens invisibly—no human makes a conscious choice to reject you; an algorithm just doesn’t surface your resume. Additionally, some AI systems are trained on language patterns associated with “desirable” employees, and these patterns disproportionately match younger workers’ resumes and communication styles.

Real impact: Age discrimination complaints have increased 32% since 2023. Many are connected to AI hiring systems.

What’s being done about this:

• Companies deploying discriminatory AI now face substantial legal repercussions.
• The UK, EU, and several US states now require companies to audit their hiring algorithms for age bias
• Some jurisdictions now prohibit using certain proxy variables (like years of experience, graduation dates) in automated hiring

What you should do:

If you apply for jobs, know that some companies still use humans to screen initial applications. Direct applications or referrals often bypass AI filters.

If you’re hiring or managing, audit your hiring systems. Ask your HR: “Have we tested our AI hiring system for age bias?”

Vote for politicians who support strong regulation of algorithmic bias.

Phased Retirement Approaches

The concept of “retirement” as an on/off switch (working full-time one day, not working at all the next) is becoming outdated and less financially viable.

Phased retirement is emerging as the practical model:

• Work full-time until 60
• Move to 60% time (3 days/week) from 60-65
• Retire fully at 70
• Move to 30% time (1-2 days/week) from 65-70

This approach has several advantages:

• Financially sustainable – You can live on part-time income while building up savings
• Valuable to employers – They get to keep experienced workers longer, even in reduced capacity
• Psychologically easier – Gradual transition rather than sudden loss of structure and identity
• Better health outcomes – Research shows that gradual transitions are better for mental and physical health

Some progressive companies already offer this. Many don’t—yet. This is an advocacy priority: push your employer or unions to allow phased retirement.

Healthcare and Long-Term Care in the AI Age

Here’s a real concern: if you live to 85 (which is increasingly likely with AI-driven medical advances), you’ll need significant care for some period. Where does that care come from? The reality: there are insufficient human caregivers for the coming elder care tsunami. The ratio of working-age people to seniors is declining. We simply don’t have enough nurses, home care workers, and eldercare facilities.

AI and robotics will help fill this gap. The question is whether it’s done well or poorly.

Good integration: AI handles the physically taxing tasks (lifting, bed transfers, medication management, 24-hour monitoring), while humans handle the relational, emotional, and psychological work (conversation, companionship, comfort, meaning in life).

Bad integration: Isolated seniors left with robot companions and remote monitoring, treated as cost-minimization problems rather than human beings.

Your advocacy priority: demand that eldercare policy include robust public funding for human-in-the-loop care models. Technology should enhance human care, not replace it entirely.

Why Your Experience Still Matters

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Here’s an often-overlooked truth: experienced workers are extremely valuable in an AI-driven organization.

Why? Because navigating a complex, AI-integrated environment requires judgment. You’ve seen multiple economic cycles. You understand how systems interact. You know what usually works and what doesn’t. You can spot when an AI recommendation is obviously wrong.

Companies that are thriving with AI integration have significant numbers of experienced workers who understand both the traditional business and how to integrate AI effectively.

This is genuinely a strength that’s harder to develop in someone with less experience. Don’t underestimate this value.

Building Bridges with Younger Generations

Finally, the single most important thing you can do is build an alliance with younger generations.

The political economy of the AI transition will pit age groups against each other unless we actively resist that:

Younger people might resent paying for their pensions if their own job prospects are terrible

You might resist supporting youth UBI if you perceive it as undercutting the “work ethic.”

Middle-aged workers might feel squeezed between supporting both

The alternative: Recognize mutual interest. Younger generations need stable, well-funded institutions (e.g., education, early-career support). Those institutions require tax revenue. If your age group is wealthier and more politically powerful, you have capacity to support them—which protects your own social fabric. This isn’t charity. It’s enlightened self-interest. A society that completely abandons its young becomes unstable.

The Policy Solutions That Actually Exist

Universal Basic Income: What the Pilots Revealed

UBI isn’t a future pipe dream anymore. Real implementations have happened.

Key findings from 2023-2025 pilots (Kenya, Finland, Italy, various US cities):

Work effort didn’t collapse – People didn’t stop working

Mental health improved – Stress and anxiety dropped significantly

Risk-taking increased in good ways – More people pursued education or started projects

Local economies strengthened – Money circulated in communities

Administrative overhead dropped – Less bureaucracy required compared to means-tested benefits

The cost: Roughly $400-800 per person per month in the US to create a meaningful basic income floor (different by location). This seems a little low to me. I guess the real question is how much do you need to provide the necessities: food, lodging, and healthcare.

How to pay for it:

• Tax increases on high earners
• Wealth taxes
• Carbon taxes
• Automation taxes (companies pay when they replace workers with AI)
• Data dividends (see below)
• Reallocation of existing welfare programs

Status: Several US cities are in major pilots. Finland has made it permanent for some groups. Kenya is scaling successfully. This is real enough to monitor and advocate for.

Taxation and Robot Taxes: Making the System Fair

Here’s the fundamental challenge: Our tax system is built on income from labor. As labor becomes less central to the creation of economic value, the tax base shrinks unless we change the rules.

Proposed solutions with actual policy frameworks:

• Automation Tax: When a company replaces a worker with AI, the company pays a tax equivalent to the payroll taxes it no longer pays. Economically straightforward.
• Robot Tax: A percentage tax on the profits generated by AI systems. The EU is piloting this framework.
• Data Tax: Tax on the value extracted from data (since all AI is trained on data).
• Compute Tax: Tax based on the computational resources used by AI systems. These capture value directly from the technology driving displacement.
• Corporate AI Profits Tax: Higher tax rates on AI-driven profit increases.

The goal: maintain tax revenue as the income tax base shrinks.

Status: The EU is actively developing these frameworks. France, Denmark, and several others have proposed specific versions. The US hasn’t acted yet, but this is becoming a serious policy discussion.

Data Dividends: Who Owns Your Information

Here’s something that should bother you: Every major AI system in the world was trained on your data, without your consent and without compensation. The books in training datasets, the medical records, the Reddit posts, the customer reviews—these represent massive, valuable collective assets that companies extracted for free.

A data dividend proposes that citizens hold a property right in their data. When companies use your data to train AI, they contribute to a public fund that pays dividends to citizens.

Early frameworks (being developed in the EU, proposed in several US states):

Citizens “own” their data

• Companies wanting to use data must pay licensing fees
• Money goes into sovereign wealth funds
• Dividends are distributed to all citizens

Why this matters: It creates a genuine economic floor (supplementary income for everyone) while making the economics of data extraction transparent.

Status: Pilot programs are starting. This isn’t law anywhere yet, but it’s moving from “fringe idea” to “serious policy proposal.”

The EU AI Act and Emerging Regulations

Europe is currently implementing the EU AI Act, which became effective in 2024. This is the first comprehensive AI regulation in the world.

Key provisions affecting workers:

• Automated hiring systems must be tested for bias
• AI used to monitor workers (surveillance, productivity measurement) has strict requirements
• Workers must be informed when AI is making decisions about them
• High-risk AI systems face auditing and transparency requirements

Impact on you: If you work for any company doing business in Europe or using European data, these protections apply.

For the US and other regions: This is the regulatory template being watched. Several US states are proposing similar frameworks.

Advocacy priority: Push your government to adopt similar protections. The EU is ahead on this; the question is whether your country will follow or fall further behind.

What Citizens Can Actually Demand from Government

Stop believing that policy change is impossible. Here’s what’s achievable right now through citizen advocacy:

• Algorithmic auditing requirements – Push for laws requiring companies to test AI hiring, management, and decision-making systems for bias
• Portable benefits – Vote exclusively for politicians supporting healthcare decoupled from employment
• Worker notification rules – Demand transparency: workers should know when AI is monitoring them or making decisions about them
• 4-day workweek pilots – Advocate for government-funded trials; the data suggests they work
• Lifelong learning accounts – Push for mandatory employer contributions to worker education funds
• Automation taxes – Advocate for taxation on AI-driven displacement to fund social programs
• Data rights – Support legislation that treats personal data as a property right
• Strengthened ageism laws – Push for real penalties for algorithmic age discrimination
• Robust AI regulation – Demand your government adopt protections similar to the EU AI Act

Why this works: Politicians respond when constituents care about something. These aren’t extreme demands—they’re being implemented somewhere right now and are demonstrably feasible.

The Broader Picture: Inequality, Environment, and Ethics

How AI Could Widen or Narrow the Wealth Gap

AI is a choice point in the context of inequality.

The “widen gap” scenario: AI productivity goes entirely to capital owners and the small number of workers with elite AI skills. Everyone else sees stagnant or declining wages. Wealth concentrates further. Society becomes increasingly stratified.

The “narrow gap” scenario: AI productivity gains are broadly distributed through progressive taxation, UBI, and widespread upskilling. More people benefit. Inequality shrinks.

Which path we’re on: Currently heading toward widening gaps, but policy choices can still shift this.

What matters: Are governments implementing redistribution mechanisms (taxes on AI productivity, basic income, universal services)? Or are they leaving it entirely to markets?

Countries implementing stronger redistribution (Scandinavia, parts of continental Europe) are seeing more even distribution of AI benefits. Countries relying entirely on market dynamics (US, UK) are seeing sharper bifurcation.

Environmental Costs of AI Development

Here’s something rarely discussed: AI is environmentally expensive. Training large language models requires enormous computational power. A single GPT model requires as much electricity as several thousand homes use in a year. Inference (using an AI model) also requires significant energy. This matters because it’s concentrated in areas where energy grids are dirtiest and where semiconductor manufacturing creates pollution.

Status: Tech companies are increasingly investing in renewable energy to offset this, but it’s still a real environmental cost being externalized partly to other regions and future generations.

What you should care about:

• Push for energy-intensive AI operations to use certified renewable energy
• Support regulations requiring environmental impact assessments before deploying large AI systems
• Be skeptical of “AI will solve climate change” claims; the technology itself has environmental costs

Racial and Gender Impacts of AI

AI systems inherit the biases in their training data and in the decisions of their creators.

Well-documented problems:

• Facial recognition systems have higher error rates for darker-skinned individuals
• Hiring AI systems are biased against women and people of color
• Criminal justice AI systems show racial bias
• Healthcare AI systems show bias against Black patients

Why this matters: These biases aren’t inevitable. They’re a result of:

Training data that overrepresents some groups and underrepresents others

Insufficient diversity in teams building AI systems

Inadequate testing for bias before deployment

Profit incentives that don’t prioritize fairness

What can change this:

• Requiring diverse teams to build AI systems
• Mandatory bias testing before deployment
• “What’s our AI strategy?” – Do they have one? If not, they should.
• Public transparency about AI system limitations
• Stronger regulation in high-stakes domains (hiring, criminal justice, healthcare)

Your Action Plan: What You Can Do

Questions to Ask Your Employer About AI

If you’re employed, these are questions worth asking your company leadership:

• “What’s our policy on AI-driven monitoring?” – Are workers notified? Is there transparency?
• “How are we supporting worker upskilling?” – What training are they offering?
• “Have we audited our AI hiring systems for bias?” – Are they doing this?
• “How are productivity gains from AI being distributed?” – To shareholders, to workers, to society?
• “What’s our approach to job displacement?” – Do they have transition plans?

If your company can’t answer these clearly, that’s information.

Voting and Advocacy Priorities

Your vote matters. Specifically:

• Vote based on AI policy positions, not just traditional issues. Ask:
• Do they support portable benefits?
• Do they support worker notification and transparency in AI?
• Do they support algorithmic bias regulation?
• Do they support taxation on AI productivity?
• Do they support UBI or similar redistribution mechanisms?
• Do they support ageism protections?

Advocacy beyond voting:

Join or donate to organizations working on these issues

Contact your representatives directly on specific policies

Participate in local policy discussions (city council, county commission)

Share information with your networks

Personal Upskilling Strategies by Age

If you’re 20-30:

Develop genuine expertise in something (not just credentials)

Gain experience in human-centered work (healthcare, trades, community)

Learn to use AI tools effectively

Build a network of real relationships

If you’re 30-45:

Assess whether your current role is under threat

If yes, develop transition plans now while you still have stability

Pivot toward judgment, strategy, and leadership roles

Advocate internally for worker protection policies

If you’re 45-55:

Clarify your timeline (Do you want to work to 65? 70? Retire at 60?)

Make that timeline explicit to employers

Advocate for phased retirement options

Build alliances with younger colleagues

If you’re 55+:

Ensure your healthcare and benefits aren’t employer-dependent

Document your expertise and value

Stay relevant through ongoing learning

Build bridges with younger generations

Community Organizing Around AI Policy

Individual action matters, but collective action matters more.

Find or create local organizing around AI policy:

Workers’ councils or unions – Many are now organizing specifically around AI policy

Community groups – Environmental, labor, and civic organizations are increasingly focused on this

Political campaigns – Support candidates explicitly running on AI policy reform

Professional associations – Industry groups are developing AI standards and practices

What collective action can achieve:

Shift corporate policies (companies respond to organized pressure)

Change local government policy (city councils are more responsive than the federal government)

Shift political landscape (politicians react to constituent organization)

Building Intergenerational Coalitions

Finally, the single most important thing is preventing different age groups from becoming enemies over AI. The wealthy and corporations driving AI automation benefit when age groups blame one another instead of demanding broader redistribution.

How to build a coalition:

• Older people: Support youth education, training, and basic income. This protects the society that will support you.
• Middle-aged people: Advocate for both youth support and elder protection. Build bridges.
• Young people: Respect elder expertise and acknowledge that some have genuinely provided value and deserve security.
• Everyone: Recognize that fair redistribution of AI gains benefits for everyone’s security.

Conclusion: The Future Isn’t Written Yet

We are in the middle of a genuine transformation in how human work will function. But—and this is crucial—the specifics of how this transformation happens are not predetermined. But the importance of your understanding of the AI transformation and your involvement in directing its course cannot be overemphasized. It is like a Mack truck rolling down the road at you that requires you to do something or cease to exist.

The choice between a “managed transition” (where AI productivity gains are broadly shared, where workers are protected, where the system adapts to benefit humanity) and a “polarized stagnation” (where gains concentrate, inequality explodes, and social trust erodes) is not a choice made by technology. It’s a choice we make through policy and action.

You are an Agent of transformation

You have agency in this. Not complete control—economic forces are powerful—but real agency. By understanding what’s happening, recognizing the specific challenges facing your age group, and taking concrete action (voting, organizing, advocating, upskilling, building alliances), you can shape the future we move toward.

The labor market of 2030-2035 will be different from today. Some jobs will be gone. Some will emerge. The role of work in human life will change. But whether that change is catastrophic or beneficial depends on the choices we make now.

The future is unwritten. It’s time for you to start writing it.

References

Acemoglu, D., & Johnson, S. (2023). Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity. PublicAffairs.

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