Six Shifts For 2026 (And The Next Six Years)

The Future Is TBD, But The Signals Are Emerging Today

It’s that time of year—the time where things slow down a little bit before they accelerate again, then we begin to shift focus toward next year, or in the case of this newsletter, the next six. I have this theory that I talk about from time to time; in short, we still haven’t fully processed how the world has changed since the pandemic—and if we are to glean insights into where things are going, we’ll need to take a brief pause to reflect on what has been, as we ponder what will be:

Work: The Great Reordering

In early 2020, the workplace as we knew it came undone. Offices emptied overnight, and what was meant to be a temporary measure became a seismic shift. Remote work blurred into hybrid models, leaving leaders scrambling to define what “back to normal” even meant. The Great Resignation followed—tens of millions of workers rethinking their priorities, walking away from roles that no longer served them.

Gen Z entered the workforce amid this turbulence with new expectations—flexibility and transparency. Values alignment wasn’t a perk; it was table stakes. Employers responded with progressive policies, lavish signing bonuses, and culture-driven retention strategies. But as economic uncertainty grew, the power pendulum swung back. Return to Office struck back while rolling layoffs reminded employees that security was never guaranteed and employers aren’t families, it’s business.

This ongoing push-and-pull between autonomy and accountability has created a new reality: a workplace where stability feels elusive. We are now managing a multi-generational workforce with radically different definitions of success, navigating leadership challenges in real time, and rewriting the social contract of work as we go, all against the backdrop of AI and a Wall Street/Shareholder/CEO-driven movement pushing for extreme efficiency and a “do more with less” mandate. If the pre-pandemic years were business as usual, then the last six years have been “business as unusual”.

Life: The No Normal

Outside of work, life has felt just as unstable. Political polarization continues to fracture communities, leaving people unsure whom—or what—to trust. The world order itself has shifted. The pandemic revealed the interconnectedness (and fragility) of global systems. Conflicts, supply chain breakdowns, and energy crises have only reinforced this sense of vulnerability.

At the same time, AI has quietly woven itself into our personal lives. Recommendation algorithms shape the content we consume and the products we buy. Chatbots like ChatGPT help with everything from drafting emails to planning vacations, leaving us simultaneously amazed and uneasy. We’re adapting to AI without a collective conversation about what it means for identity, creativity, or connection.

Inflation, an affordability crisis, and a general sense that post-pandemic life feels “different” even if we can’t quite put a finger on why. The result is a lingering sense that life is perpetually in flux. The ground beneath us never quite settles, and uncertainty has become a baseline condition we’re learning to live with.

Technology: The Acceleration Loop

Then there’s technology—the accelerant in this “no normal” era. The explosion of generative AI in late 2022 felt like a switch flipping overnight. Suddenly, anyone could generate images, code, essays, or business plans with a few prompts. Tools like GitHub Copilot and Midjourney made creativity and engineering more accessible, compressing development cycles and lowering barriers to entry.

This acceleration has fueled a wave of new AI-native companies—emerging unicorns like Lovable are rethinking product design with “vibe coding” and other AI-enabled approaches. The ecosystems around these tools have matured just as quickly. GitHub, once a niche platform for developers, is now the backbone of open-source collaboration, powering AI advancements at scale.

Technology no longer evolves in linear increments. It moves in loops—iterating, compounding, and spawning new platforms faster than businesses and societies can adapt. AI Automation and augmentation are no longer theoretical. They’re here, forcing organizations and individuals alike to ask: what is uniquely ours to do?

This brief look into where we’ve been can help us see where we are going. The past six years didn’t just disrupt routines; they rewired expectations for work, life, and technology. Under the surface, new patterns have taken shape. If we zoom out, these patterns point toward six shifts already forming in the near distance:

Shift 1: From Co-Intelligence To Co-Dependance

In his timely and groundbreaking book, Academic Ethan Mollick framed the generative AI revolution as “co-intelligence” – humans and AI working together, each amplifying the other. That still holds. The shift over the next six years is less philosophical and more behavioral: we start to need AI to function at work and in everyday life.

Right now, that dependence is already visible in the numbers. Microsoft’s latest Future of Work research shows that almost a third of information workers use generative AI several times a week, and that Copilot users lean on it for high-complexity tasks at nearly three times the rate of traditional search. A recent workplace study shows AI assistants increasingly handling “mental load” work like scheduling, travel booking, and task management, offloading cognitive overhead from humans to software. And let’s not forget human nature. Many workers are hiding their use of AI either because of perception or company governance. An Anthropic study found that most workers use AI at work, but 69% are actively hiding their AI use.

The pattern looks familiar. First, we used GPS as a handy tool, then many of us forgot how to read a map. In the same way, we’ll move from “AI helps me do my job” to “I can’t actually do my job without AI.” You see it in how managers let Copilot triage their inbox, how analysts ask models to interpret messy spreadsheets, and how students offload first drafts and study guides. The work still gets done, but the scaffolding shifts from human memory and skill to a human-AI loop.

This “GPS effect” cascades through the coming agent economy: AI not just as a tool on your desktop, but as a swarm of agents acting on your behalf, end to end. Co-intelligence becomes co-dependence once those agents are wired into everything: your calendar, your CRM, your financial systems, your kid’s school portal. Humans provide intent and guardrails. AI handles the rest. When those systems go down, it will feel less like losing an app and more like losing electricity. AI needs humans to exist, and we need AI to function.

The risk isn’t just job replacement; it’s skill atrophy, judgment erosion, and a quieter question: what parts of being human do we want to outsource, and what parts do we hold back, on purpose? These are the big questions we will be wrestling with over the next six years.

Shift 2: White Collar FTEs Become Gray Collar Gig Workers

White-collar work spent decades optimizing for one main thing: stable, full-time employment: the org chart, the benefits package, the annual review cycle. AI is colliding with that model right as Wall Street keeps pressing for productivity growth without proportional headcount growth.

The labor data already points to something wobbling. Recent research estimates that around 64 million Americans did some form of freelance work in 2023, about 38% of the workforce. Other surveys estimate that total gig or “non-standard” workers range from 25% to 43% of the workforce, which translates to at least 42 million people in the U.S. alone, depending on definitions. More recent estimates suggest over 70 million Americans participate in freelance work in some capacity – roughly one in three workers.

Inside companies, AI is starting to carve up what used to be full-time jobs into task portfolios. Microsoft’s Copilot research is already mapping real AI usage against the U.S. Department of Labor’s job taxonomy, showing that many occupations are clusters of AI-addressable tasks rather than indivisible roles.

That’s a hint of what’s coming: a world where “fractional” becomes the default, not the exception.

Artificial intelligence can do the work currently performed by nearly 12% of America’s workforce, according to a recent study from the Massachusetts Institute of Technology.

This is where “gray collar” comes in. We already have this term for people who sit between white-collar and blue-collar (ie, nurses, dental assistants, etc.). Think fractional CMOs, contract data scientists, part-time product leaders, gig-based UX teams, and AI-augmented copywriters selling their time in slices to multiple clients. Employers get flexibility and cost control. Workers get freedom AND fragility at the same time. The social contract of full-time white-collar work shifts from “we’ll take care of you” to “we’ll give you a platform.” Historically, pensions were replaced by 401(k)s; the next phase replaces job titles with personal operating systems and portable professional reputations. It is with some irony that many late-stage career knowledge workers (with gray hair) are finding themselves transitioning into gray-collar work after a layoff. Still, gray-collar gig work is becoming more common across multiple generational cohorts. Boomers and Gen Xers who age out, Gen Zers who opt out, and even millennials who burn out are finding themselves in the gray-collar class, either by choice or necessity.

Shift 3: Higher Education Transforms Into Adult Preparation

Higher ed is under pressure from three sides: AI in the classroom, fewer traditional entry-level roles, and an escalating student debt problem.

On the debt side, the numbers speak for themselves. About 42.3 million Americans hold federal student loan debt, with total federal balances around $1.67 trillion and roughly $1.81 trillion when you include private loans. The Federal Reserve reports that for those who still owe money for their own education, the median debt sits between $20,000 and $24,999. Some borrowers, especially those in certain professions or with advanced degrees, carry balances averaging over $80,000.

At the same time, policy around repayment keeps shifting. The U.S. Department of Education’s SAVE income-driven plan, which enrolled roughly 7.7 million borrowers, is now being phased out after a legal challenge, forcing those borrowers into less generous options. That unpredictability only amplifies skepticism from younger generations who already watched older siblings or parents struggle under loan burdens.

Layer AI on top of this. Students are quietly using models to generate essays, outline projects, and “study” by outsourcing the hard thinking. Many teachers use AI to create assignments or grade them. Yet graduates still walk into a job market where classic entry-level roles are thinning out, and basic knowledge work is the first thing AI can automate.

So the unmet need isn’t just “education” – it’s preparation for adult life in a TBD world. Four years dedicated not only to conventional learning methods, but to:

• Working with AI instead of around it

• Building hands-on skills in trades and tech where shortages persist

• Learning how to navigate gig-based, non-linear careers

• Developing social, emotional, and decision-making muscles that AI can’t replicate

This opens the door for alternatives: hybrid programs that blend apprenticeship with classroom learning; AI-aware bootcamps; community college systems that connect directly to local employers; even “adulting studios” where students learn finances, caregiving, and real-world collaboration alongside code and theory. Higher ed pivots from “buy a degree” to “train to be an adult in a world where the old playbook is gone.”

Shift 4: Human Experiences Shift From Common Commodity To Scarce Luxury

As AI-generated everything becomes cheap and abundant, the scarcest resources in many categories will be human attention, effort, and presence.

Research in areas like fashion design already shows a consistent preference for human-created work over AI-generated output when people are aware of the difference. Luxury analysts keep pointing out that what makes high-end brands feel premium is not the logo but “human genius and attention to detail,” from craft to service. Newer commentary in the sector argues that in an AI-saturated market, what commands a premium is emotional relevance and original ideas that stand apart from what models can easily copy.

If AI-authored content, AI-rendered imagery, and AI-driven service flows are everywhere, then a real person answering the phone becomes a status marker. Your airline may offer a free AI chat interface and a paid tier that guarantees access to a human. Hospitality brands can lean into “100% human-hosted” as a feature.

Neighborhoods and new housing communities are already experimenting with designs that prioritize proximity and interaction, trading a bit of privacy for connection in a culture starving for it. I’ve often spoken about how the employees at Trader Joe’s make the experience feel special, premium, and differentiated. In South Texas, where I live, a new Trader Joe’s just opened—an outlier for the H-E-B-dominated geography. If you’re not familiar with H-E-B, it enjoys an almost fanatical following and has a virtual monopoly in the market. Most grocers don’t even attempt to compete here.

On the demand side, you can already see the conversation forming. Comment threads and think pieces ask whether human-made goods will become luxuries reserved for a few, or whether ordinary people will still carve out small “human only” spaces in their lives.

The next six years likely bring tiered experience stacks:

• Default: automated, synthetic, efficient

• Premium: curated or checked by humans

• Luxury: fully human, bounded, sometimes even “no screens allowed.”

For brands and employers, the challenge becomes: where do you deliberately keep the human in the loop, and how do you design those touchpoints so they feel like a treat rather than an afterthought?

Shift 5: Big Tech Becomes Big Intelligence

Big Tech already owns the pipes (cloud), the platforms (apps, operating systems), and the eyeballs (attention). The next phase is about owning the intelligence layer that sits on top of everything.

Every prompt you type into an LLM, every correction you give a chatbot, every thumbs-up or “regenerate” is training data. Analysts point out that a handful of AI players are already pulling ahead because of this data advantage – not just the size of their models, but the richness of interaction logs and behavioral signals they control.

Extensive studies based on millions of conversations between users and tools like Bing Copilot show how deeply these systems are woven into actual work tasks, mapped directly against government job taxonomies. Market overviews of the LLM space note rapid adoption by businesses across sectors, turning providers like OpenAI, Microsoft, Google, Meta, and others into infrastructure-level intelligence utilities. And the trend is not limited to hyperscale data centers; newer models are pushing intelligence to devices and the edge, broadening the reach of that data loop.

So Big Tech evolves from “we host your data” to “we see how you think.” They don’t just store documents; they observe how those documents are written, summarized, translated, and remixed. They don’t just run your search; they watch how you delegate decisions to AI, where you hesitate, where you override.

In geopolitical terms, this becomes a new form of soft power. Countries and companies that control the largest and most diverse streams of human-AI interaction data hold an advantage in everything from product design to information warfare. You already see this with the U.S. and China racing to blend AI, robotics, and industrial capability at scale.

Big Tech becomes Big Intelligence when models, data, and agents fuse into a global nervous system that knows more about how humans work, shop, learn, and vote than any institution in history.

Shift 6: Infrastructure Becomes Influence

The AI race is about brains and muscle. The brains get the press. The muscle sits quietly in the background: data centers, chips, cooling, energy, copper, water.

McKinsey estimates that by 2030, data centers will require about $6.7 trillion in global investment to keep up with compute demand, with roughly $5.2 trillion of that tied to AI-optimized facilities. The International Energy Agency projects that electricity demand from data centers will more than double by 2030, reaching about 945 terawatt-hours – roughly the current electricity use of Japan. Goldman Sachs forecasts that power demand from data centers could climb by up to 165% by the end of the decade.

This isn’t just about servers. Analysts warn that the build-out of AI-heavy data centers is pushing copper toward structural shortages. AI facilities can consume 27 to 33 tonnes of copper per megawatt, and some sites are being built at 150 MW scale, which means thousands of tonnes per complex. Current production trends suggest only about 70% of copper demand might be met by 2035 if nothing changes.

Regions are responding like it’s a new oil rush. In the U.S., places like Pennsylvania are becoming data-center hubs, adding gigawatts of capacity in a single year, often tethered to gas or nuclear plants, with grid operators warning of strain and trade-offs. Internationally, sovereign wealth and infrastructure funds are pouring billions into AI infrastructure joint ventures to position their countries as compute hubs for entire regions.

So “infrastructure becomes influence” in at least three ways:

• Compute: Who has the GPUs and the fabric to run state-of-the-art models at scale?

• Power: Who can feed those data centers with reliable, affordable electricity without breaking their grid or their climate commitments?

• Materials: Who controls the copper, rare earths, and manufacturing capacity required to build and refresh the physical stack?

Policies, partnerships, and even local zoning decisions start to look like foreign policy. Cities bid for data centers the way they once did for factories or stadiums. Companies that used to compete on software features now fight over grid connections and substation capacity. The visible AI story is chatbots and copilots; the hidden one is transformers made of concrete, copper, and megawatts.

In terms of power production and access to critical materials, China’s influence potential is undeniable. The next six years will be vital in determining where the power balance between the US and China will land on infrastructure as a means of global power and influence.

The thread running through these six shifts is simple: the past six years didn’t just test our systems; they reshaped our instincts. Work fractured then reassembled itself in new patterns. Life kept moving even as the ground felt unstable and technology compressed time, taking us from experimentation to mass adoption before most of us caught our breath. These shifts aren’t forecasts carved in stone. They’re signals rising from the turbulence we’ve been navigating since early 2020.

They point toward a future where our relationship with intelligence continues to tighten, where careers drift toward portability, where education must produce real-world readiness, where the human touch becomes a rare premium, and where power resides in the infrastructure behind the scenes. Each shift hints at what’s forming beneath the noise.

And still, the story isn’t finished. Ultimately, the future is TBD, but these shifts tap into emerging trends still in development and signal where things may go over the next few years. The world ahead feels dynamic, uneven, and full of open questions.

Which leads to the real closing idea: in a future that’s TBD, it’s not just about AI, the technology. It’s about AI as in Adapting Intelligently. That becomes the human factor in a world where uncertainty is certain. The people and organizations that learn, adjust, and respond with awareness, not fear, are the ones who shape what comes next.

Visually yours,

David Armano is a futurist, strategist, and Enterprise AI transformation leader who helps his colleagues, clients, and community solve intricate business challenges and see a clear path forward.

He’s known for his unique approach to visual thinking and insightful, yet grounded, takes on intelligent experiences, culture, and leadership. In addition to his day job, he writes David by Design to translate complex shifts into actionable ideas.