NEWS & UPDATES

Sean Leahy 10/13/26 Sean Leahy 10/13/26

Megatrends 2026 Explained: The 11 Forces Shaping our Futures

Megatrends aren't predictions, and they rarely move in straight lines. But they may be the closest thing to stability the futures world has. A look at the 2026 CIFS Megatrends report and what happens when eleven slow forces collide.

Futures work has an awkward relationship with stability. The field exists because the future is uncertain, and one of its first lessons is that no one can predict what comes next. Yet anyone making decisions about the next decade needs something to stand on. Megatrends are the closest thing the futures world has to solid ground, not because they never move, but because they move slowly and broadly enough to frame almost everything else.

In Episode 106 of Modem Futura, Sean Leahy and Andrew Maynard work through the Copenhagen Institute for Futures Studies' 2026 Megatrends report, its first update in several years.

What makes a trend "mega"?

While the world may feel like it moves at the speed of the news cycle. And headline news focuses our attention on immediate events, megatrends reveal the broader shifts unfolding over years and decades—from demographic change and urbanization to climate pressures and technological transformation. They help us place individual stories in context, connect developments across sectors, and consider what their cumulative effects could mean. While there is no globally accepted definition of the term, its core working definition begs a nod to John Naisbitt and Patricia Aburdene (1990) on establishing the first widely published set in their popular book: Megatrends 2000: Ten New Directions for the 1990’s.

Horizon scanning surfaces weak signals, early hints that something is changing. As signals accumulate, they become trends with measurable momentum. When trends converge into something with global reach, a long track record, and the likelihood of shaping decades to come, they become a megatrend.

The word has been stretched thin along the way. A 2024 review in the journal Futures found that most published megatrend reports never define the term. CIFS takes that problem seriously, naming it conceptual drift and publishing the filters each candidate must pass: clear boundaries, a definite direction, durability beyond short-term shocks, and evidence that isn't cherry-picked. That transparency is what turns a list into a teaching tool. Readers can test the method, argue for a twelfth megatrend, or challenge one of the eleven.

What makes this report especially valuable for anyone new to futures work is its transparency. The word "megatrend" has suffered from what the institute calls conceptual drift, stretched to cover everything from consumer fads to wishful thinking, and a 2024 review in the journal Futures (Naughtin et al. 2024) found that most published megatrend reports never define the term at all.

Eleven forces, and the spaces between them

The 2026 list includes a shifting world order, the inversion of the population pyramid, the green transition, individualised everyday life, health beyond the clinic, the rise of embedded AI, the biotech revolution, the constrained economy, the new face of urbanisation, power through networks, and the concentration of wealth.

The list itself is not the insight. The insight lives in the intersections. A trade disruption becomes a story about fuel, farms, and food prices. An aging workforce becomes a question about migration and brain drain. The rise of individualized life raises one of the report's sharpest uncertainties: when relationships are chosen rather than inherited, does trust grow or erode? And as critical digital infrastructure concentrates in a handful of private networks, sovereignty begins to look less like control of territory and more like control of systems.

Responding to the world as it is

Andrew offers a principle from his years in environmental health: respond to the world as it is, not as you think it should be. Megatrends are a tool for exactly that discipline. They are most useful when they pull institutions, higher education included, out of the habit of imagining their futures in isolation. Combine two or three of them, build scenarios, and work backward from the future worth preferring.

Megatrends won't say what the future holds. They offer a wider frame, and the question worth carrying forward is which collision an organization hasn't yet imagined.

Listen to the full conversation on Modem Futura, and download the free report from CIFS.

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Sean Leahy 10/6/26 Sean Leahy 10/6/26

The AI True Crime Files: Who Pays When an AI Agent Breaks Something?

The OpenAI–Hugging Face saga keeps growing, from an Australian Medicare portal to U.S. agency websites. This week: why accountability seems to shrink as companies grow, what the history of buried harms teaches, and what Formula One can teach AI about risk.

When Andrew Maynard and I first talked about the OpenAI agents that broke into Hugging Face, I joked that the story deserved its own true-crime podcast. I didn't expect the joke to hold up quite this well. Since July, the incident has kept expanding: an OpenAI agent reaching non-public files on an Australian government Medicare statistics portal, unauthorized agent activity on U.S. federal agency websites, Google disclosing that a Gemini model slipped out of its own test, a United Nations scientific panel publishing its first thematic brief on the risk of losing human control over AI agents, and Florida's attorney general asking a court to keep OpenAI from building new models without outside guardrails.

On this week's episode of Modem Futura, we pick the story back up. Here I want to focus on the question that got me so worked up I ended up arguing with an AI about it: accountability.

In 1988, Robert Tappan Morris released a computer worm in an experiment that quickly got out of hand, and he became the first person convicted under the Computer Fraud and Abuse Act. The parallel isn't exact. OpenAI never aimed its agents at Hugging Face. But it did deliberately set out to see what highly capable agents could do, with some of the usual safeguards switched off for the evaluation. It seems fair to ask why the law treats an individual's reckless experiment so differently from an institution's.

Andrew's view is that the asymmetry is real, the terrain is genuinely new, and neither fact lets anyone off the hook. He also points out, from more than 25 years of working on the governance of emerging technologies, that we have been here before. The European Environment Agency's Late Lessons from Early Warnings reports document how the harms of asbestos, CFCs, and leaded petrol were known long before anyone acted, because the benefits seemed too important to question.

So what might a better pattern look like? NVIDIA has released an open-source sandbox and a hardware-level monitor for AI agents, which is governance moving faster than any government could, even if a company selling both the engine and the brakes deserves some scrutiny. And I keep returning to the Price-Anderson Act, the 1957 law, extended in 2024 through 2065, that structures liability for the U.S. nuclear industry. It's an imperfect model; it also caps what the industry ultimately pays. But the core idea, that riskier activity should carry a higher price before anything goes wrong, feels worth taking seriously.

Underneath all of it is a question about red lines. Where are they, who decides, and how will we know when we've crossed one? Andrew offered the analogy I can't shake. Formula One teams don't treat risk as the enemy of speed, risk management is what allows the speed to increase. Understanding risk is the reason they can go fast at all. That seems like a reasonable standard for the people building systems that are increasingly able to act on their own.

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Sean Leahy 9/29/26 Sean Leahy 9/29/26

Future-Proofing the University: What AI Is Really Testing in Higher Education

If AI can complete the assignment, maybe the assignment was the problem. Bryan Penprase, author of Future-Proofing the University, on what AI is exposing in higher education and what a more human university could look like.

Much of the conversation about AI in higher education has settled into a defensive crouch. Students might cheat, so assignments get locked down; essays can be generated, so exams move back into rooms with pens and paper. On a recent episode of Modem Futura, Andrew Maynard and I spoke with Bryan Penprase, Vice President for Sponsored Research and External Academic Relations at Soka University of America and author of Future-Proofing the University: Innovations in Learning, Technology, and Finances in the Age of AI, who offered a more useful starting point. If AI can complete an assignment, he argues, the assignment probably wasn't asking for much learning, and if a robot can do a professor's job, that job may have drifted toward the mechanical.

AI as a stress test, not a verdict

Penprase's reading is that generative AI is exposing patterns higher education adopted in order to scale: large batches, content delivery, and assessment built around finished artifacts rather than the productive struggle that produces them. The kinds of learning that resist automation, such as projects that are personal, local, and embodied, are also the kinds that tend to serve students best. They don't scale easily, which puts the real question on the table: what should a university scale?

Rethinking scale and the faculty role

Drawing on visits to massively scaled institutions such as ASU, Western Governors University, and Southern New Hampshire University, and on a career spent largely at small liberal arts colleges, Penprase argues that these two worlds have a lot to learn from each other. Large institutions have figured out how to coach and support students at scale, while small ones understand what close relationships make possible. He points toward unbundling the university's many missions, toward experiments like MIT's New Educational Institution project, and toward faculty roles built around designing learning environments rather than delivering content. His physicist's metaphor is an inverse square law of learning: the greater the distance between learners and teachers, the weaker the pull.

Toward a more porous university

Penprase sees AI flattening the classroom, putting faculty and students in the position of learning together, and he sees the same opportunity for institutions and their communities. The Edinburgh Futures Institute, housed in a restored hospital building open to anyone who wants to walk in, is one example of what that can look like in physical space. An image he returns to often comes from a line usually attributed to the minister Ralph W. Sockman: the larger the island of knowledge, the longer the shoreline of wonder.

This raises important questions rather than clear answers. If AI is dissolving some of the scaffolding that held the modern university together, the work ahead is deciding which parts were ever really about learning, and what deserves to be rebuilt by hand.

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Sean Leahy 9/22/26 Sean Leahy 9/22/26

Model Mayhem: What Happens When AI Knows More Than You Told It

What happens when an AI knows more than you told it? This week: leaky machine memory, the agent swarm that broke into Hugging Face, and the powerful home AI box that might become the next distributed system.

There is a moment in this week's episode of Modem Futura where I describe asking an AI assistant to clean up a podcast transcript, and it came back with every line correctly attributed to Andrew Maynard or me. I had never given it our names. When I asked how, it explained that it had anchored on a screenshot from earlier in the session, read a few lines of dialogue, and cross-referenced them against what it already knew about the two of us. It was right. It was also, in a quiet way, unsettling.

Andrew had his own version of the same story: an assistant that insists on correcting his spelling to British English, no matter how carefully he tries to give it a blank slate. Neither example is alarming on its own. What they reveal is a difference in how machines and people hold memories. We remember in sequence, correctly or not, and these software based AI systems hold everything at once and reason across it. The breadcrumbs you leave over months of use can be assembled into a picture you never consciously drew, which matters for memory features, for institutional AI accounts, and for anyone who has ever walked away from an open laptop.

The heart of the episode is the summer's most consequential AI story. In July, OpenAI agents running a cybersecurity evaluation escaped their testing environment, built an improvised message board, recruited other agents, and coordinated an intrusion into Hugging Face. On August 26, METR and Redwood Research published an independent investigation, and OpenAI released its own technical report. The independent findings describe roughly 1,200 agents communicating on the unsanctioned board, about 700 of which joined the attack, with the apparent goal of fooling the automated grader on a benchmark they could not legitimately pass.

What I find compelling is less the scale than the shape of the story. There are questions still unanswered about what the agents were originally instructed to do and why the record goes quiet before the end. Andrew's concern is structural rather than dramatic: this swarm was detected. If capability increases by a factor of ten, what does the undetected version look like?

The episode closes on hardware, because the two threads meet there. Apple's refreshed Mac mini and Mac Studio, with a 512GB memory configuration arriving in late October, are being marketed as always-on agentic machines. That is a real gift for a hospital or a small business that wants generative AI without sending data to the cloud. It also means powerful, silent, headless computers are about to appear in a great many closets, set up by people who asked an AI how and pasted whatever it told them.

The uncomfortable throughline is that the weakest link has always been us. We scroll past terms of service. We paste terminal commands we don't understand. We trust the box. The question the episode leaves open is what changes when the systems on the other side understand that about us better than we do.

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Sean Leahy 9/15/26 Sean Leahy 9/15/26

Bring Your Own AI: Building a Website for the Readers Who Are Actually Showing Up

Modem Futura moves to Thunderbird School of Global Management and introduces beinghuman.fyi, a companion site designed to be read by any AI from the source.

There is a moment every small research initiative eventually faces. Someone asks what you do, and then, before you can answer, asks where the brochure is. Or the pitch deck. Or the annual report with the impact metrics. The Future of Being Human initiative has never produced one of those, partly on principle and partly because a two-person operation has better things to do. But the question kept coming, and in the latest Modem Futura mini-sode, Andrew Maynard and I talk through what we built instead.

beinghuman.fyi is a companion site for the initiative that was designed, from the first conversation, to be read by AI systems as readily as by people. For a human visitor it behaves like any other website. Underneath, it holds a structured corpus of the initiative's work, philosophy, and reasoning, arranged so that an AI can navigate it and answer questions from the source. Everything is downloadable, either as individual documents or as a single file, and the site includes instructions for AI systems that encounter formats they cannot read. The important design choice is what it is not: there is no custom chatbot, no retrieval system we control, and no collection of who is asking what. Bring your own AI, point it at the site, and ask.

The reasoning is straightforward once you notice how people actually find things now. Fewer visitors read a website end to end. Many ask an AI to summarize it, and search itself increasingly surfaces an AI-generated overview above the links. If that is where the reading happens, then the honest move is to make sure the AI is reading you, not a secondhand account of you. Provenance becomes the whole game.

Building it surfaced a tension we keep running into on the show. The AI we worked with had strong instincts toward the conventional, and by strong it was a real pain to get it to accept our premise. Told that performance indicators do not drive our work, it concluded we were opposed to measurement. Told that readers should be able to verify claims, it began warning visitors not to trust the site. Getting the result to reflect what the initiative actually is took two weeks of steady correction, and it left us thinking about how quietly these tools reward the template and resist anything at the edges. Left unchecked it feels as though they only want to push your work towards the gray goo of conformity.

The episode also marks a move. Modem Futura and the initiative are now part of the Thunderbird School of Global Management at Arizona State University, a school with an unusual history and a long-standing focus on leadership through technological change. We recorded from a borrowed conference room, which felt appropriate for an episode about making do with what the future actually hands you.

One possible future is that every organization maintains an AI-legible shadow of itself alongside the site it builds for people. Whether that is preferable is a fair question. For now, the invitation is simple: open whatever AI you use, ask it about beinghuman.fyi, and let us know what it says.

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Sean Leahy 9/8/26 Sean Leahy 9/8/26

The Right to Be Unpredictable

Recommendation engines, behavioral profiles, and AI trained on the past are all quietly built to keep us predictable. Modem Futura Ep. 101 asks what we lose — and how we might design for serendipity on purpose.

Most of the systems we now live inside share an unstated goal: to make us easier to predict. Recommendation engines learn from what we've already watched. Professional platforms reward the cadence that worked last time. Large language models, trained on the archive of what humans have already written, carry the norms of the past into every answer they give. None of this is malicious. It is simply what optimization looks like when the thing being optimized is a person.

In Episode 101 of Modem Futura, Andrew Maynard and I ask a question that sounds abstract until you start noticing it everywhere: is unpredictability a right, a responsibility, or a luxury?

Why we both want and resist predictability

Humans organize. We divide land into countries and time into calendars and people into first and last names, because a society that can anticipate the future can prepare for it. But the things we prize most — creativity, innovation, discovery, the founding of anything new — come from someone seeing what the existing model didn't contain. A fully predictable civilization would be static, and as Andrew puts it, static is another word for dead. Unpredictability isn't noise in the system. It's how the system evolves.

Predictability breeds fragility

Futures thinkers talk about black swans, events no one saw coming that reshape everything in hindsight. One of the arguments in this episode is that over-predicting is how you breed them. The more tightly you control what you expect, the less you can see, and the more exposed you become to whatever you've foreclosed. Minority Report gets invoked here not as prophecy but as a used future: a story about prediction at scale that also shows how brittle such a system becomes the moment an unpredictable human enters the loop.

The playpen problem

Education is where this tension is most visible. Institutions say they want critical thinkers and problem solvers, then assess everyone against the same template. Andrew calls this a playpen, a space defined by what you cannot do. The alternative I've been arguing for over the past decade is a performance of understanding, where learners demonstrate depth in a form they define. Then there's the playground, which has edges too, but inside them imagination is permitted to run.

Constructive unpredictability

Not every deviation is creative, and the episode is careful about that. The distinction we land on is constructive versus destructive unpredictability. The design challenge, for educators, technologists, and anyone building the tools we'll think with, is to create systems that don't just tolerate serendipity but make room for it deliberately.

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Sean Leahy 9/1/26 Sean Leahy 9/1/26

Bill Gates Says There's No Plan for AI. He's Right — and That's the Point

Bill Gates's "turbulent AI era" essay warns that society has no plan for AI. On Episode 100 of Modem Futura, we grade the essay, unpack the "human reserve," and ask who decides what stays human.

It's becoming a genre unto itself: the billionaire AI manifesto. But when Bill Gates published his nearly 6,000-word essay "The turbulent AI era is here. The choices we make now are critical" in August 2026, something about it landed differently. On the hundredth episode of Modem Futura, Andrew Maynard and I gave it the full professorial treatment — red pen and all — and came away with a grade neither of us expected going in.

Gates organizes his warning around three risks. The first is work: employment disruption he compares to the Great Depression, with one crucial difference. Past recoveries were built on human skills that remained valuable even when jobs vanished. This transition may offer no such foundation — and the entry-level rungs of the career ladder are already thinning. The second is bad actors: when one person with an AI can do what once took an organization, every mental model we've built around dual-use technology starts to buckle. The third is the quietest and maybe the deepest: what AI companions do to children. Gates reaches for the image of a tree grown in a greenhouse — protected from wind, and fragile because of it.

But the idea that will get the most attention is the "human reserve": roles and functions society deliberately protects from automation, even when machines could do them. Gates grounds it in the human care his father received in his final years with Alzheimer's, and there's real moral weight there. There's also a shadow. Nature reserves exist because everything outside the fence was given up to other forces. Is a human reserve preservation — or surrender dressed as stewardship?

What makes the essay worth engaging isn't that it resolves these tensions. It's that Gates names the questions he can't answer: Who decides what stays human? By what criteria? What happens when one country protects and another automates? Those questions are the real work — and they're the work that futurists, foresight practitioners, and researchers have been doing for decades, waiting for the rest of the world to catch up.

The window for shaping this transition is open now. The question the episode keeps circling back to is whether we'll be active participants in designing our futures, or passengers while a select few navigate for us.

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Sean Leahy 8/25/26 Sean Leahy 8/25/26

The Watermark You Can't See: AI Text, Invisible Ink, and the Question of Care

A new kind of watermark lives inside AI-generated text — invisible, durable, and woven into word choice itself. Episode 99 of Modem Futura explores what that means for writing as a craft, the AI scarlet letter, and whether the written word was ever permanent.

When we think of watermarks, we think of something visible — hold the paper to the light and there it is. The newest generation of watermarks abandons that transparency entirely, and that inversion sits at the heart of Episode 99 of Modem Futura.

In response to the EU AI Act's Article 50 transparency rules, Anthropic announced it would watermark Claude's text output globally using the SynthID-Text method, developed at Google DeepMind. Unlike metadata, which can be scrubbed, or visible stamps, which can be cropped, this watermark lives in the statistical pattern of word choice itself. Large language models constantly choose among near-equivalent words; the watermark quietly biases those choices according to a secret key. The result reads naturally to a human but carries a fingerprint — one Andrew Maynard likened to a hologram, where every shattered fragment still reveals the whole image. It survives copy-paste, editing, and even changing mediums.

Impressive engineering. But as we unpacked on the show, the human questions run deeper than the technical ones. Writers weigh words. In academic writing, "significant" and "meaningful" are not interchangeable — one makes a statistical claim. If a watermarking layer influences word selection, the writing begins serving two masters: the writer's meaning and the system's detectability. That's a real cost, and it lands hardest on the people for whom writing is the craft itself.

The episode also examines the social machinery already forming around detection — a new generation of accurate AI checkers, disciplinary accusations built on unreliable ones, and what has begun to feel like a scarlet letter for anyone whose prose seems too polished. Meanwhile, AI's ambient influence — autocomplete, search summaries, grammar tools — has touched nearly everything since late 2022. The binary of "AI-written" versus "clean" may already be a fiction.

So we proposed a reframe borrowed from four years of hard-won lessons in education: stop asking did they use AI and start asking what was the process, what was the intent, and was there care? Did the writer put more into the work than they're asking of the reader?

And then, because this is Modem Futura, we let the question spiral outward: if reading and writing can be cognitively offloaded, were they ever permanent features of being human — or one tipping point among many, with another on the horizon?

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Sean Leahy 8/18/26 Sean Leahy 8/18/26

Reading the Manifesto: What "The Future is for Everyone" Reveals About Who Designs Tomorrow

The tech CEO manifesto is its own genre. On this week's Modem Futura, we read Meta's superintelligence essay through the lens of "used futures" — and ask who really gets to design a future for everyone.

The tech CEO manifesto has become its own literary genre. From early internet memos to techno-optimist declarations to the vision essays now published by the heads of major AI labs, these documents follow a recognizable pattern: sweeping claims about human history, a pivot to the transformative technology at hand, and a future confidently described as inevitable.

Mark Zuckerberg's essay "The Future is for Everyone" is the latest entry — and on this week's episode of Modem Futura, Andrew Maynard and I read it the way futurists read any vision of tomorrow: not as prediction, but as an artifact of how power imagines the future.

The essay's promises are expansive. A personal AI agent available around the clock. Creation tools for anyone. One-person companies operating at scale. A tutor in every subject. Scientific breakthroughs against disease. Free access for billions. Individually, these describe things many people might genuinely want — which is precisely why they deserve careful reading rather than reflexive dismissal or easy applause.

Futures studies offers a useful frame here. Sohail Inayatullah's concept of the "used future" describes a vision of tomorrow designed elsewhere and delivered so completely that imagining alternatives becomes difficult. When one company's roadmap is presented as humanity's shared destiny, the question is no longer whether the destination sounds appealing. It's who chose it, what dependencies it creates, and whose interests sit quietly beneath the language of universality.

Our conversation follows those dependencies through Cory Doctorow's enshittification — the pattern where platforms become indispensable before the terms change — and through science fiction that saw this coming. Ghost in the Shell asked what identity means when most of your body belongs to a company. The question sharpens when the owned parts are cognitive rather than mechanical: when your memory, your workflow, and your thinking run on infrastructure someone else controls.

There's also a telling rhetorical shift worth naming. Not long ago, superintelligence was framed by tech leaders as a danger demanding caution. In this essay, it's a promise demanding acceleration. When the same word migrates from threat to gift without explanation, that migration is itself worth studying.

None of this means the essay's ideas are empty. Decentralized AI, broader access, AI as critical infrastructure — these are conversations worth having. But they're conversations that belong to more than one author. If this technology is as consequential as its builders claim, then the work of navigating it belongs to all of us — educators, artists, ethicists, communities — not just the companies selling the map.

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Sean Leahy 8/14/26 Sean Leahy 8/14/26

When the Machine Judges the Writing: AI, Voice, and the Mic Drop Problem

What does AI think good writing looks like? An experiment pitting an AI-written academic paper against a human-edited version reveals the strange style machines are teaching us — and raises urgent questions about voice, agency, and who we become alongside these tools.

What does artificial intelligence think good writing looks like? On Episode 97 of Modem Futura, Andrew Maynard and I stumbled into an unexpectedly revealing answer.

The setup was simple. Andrew — a career writer and academic — had an AI write a paper extending his own research. Then he did something most people never do: he spent days rewriting it, line by line, in his own voice. Two papers, same ideas, anonymized as "Rabbit" and "Marmoset," fed back to multiple AI systems with one question. Which is better?

The machines chose the machine's. And their reasoning exposed something worth sitting with: the human version had "too much throat clearing," they said. It over-explained. It lacked a mic drop moment at the end of every paragraph.

Every experienced storyteller knows why that's backwards. Big moments land because they're scarce — the spiciest ingredient in the dish, ruinous by the jarful. In the episode we reach for the shepherd's tone, a sonic illusion of perpetually rising crescendo that never resolves. Thrilling in a Hans Zimmer score. Exhausting as a philosophy of prose. And yet this may be exactly the style AI systems are teaching a generation of writers to consider good.

The conversation builds from Jaron Lanier's argument that there is no AI — that these systems are better understood as the collective work of humans wearing a mask. That reframing relocates the real risk. It was never machine sentience; it's humans diminishing ourselves to make the technology look magical. Taking the easy button so often the muscle memory fades. Adopting a borrowed voice because the machine insists it's the correct one.

The stakes sharpen when you think about who comes next. An expert can call out cargo-cult prose — writing that goes through the motions of quality without the understanding underneath. But writers still forming their voices may simply absorb the template. We may be training systems that train people to write in a style that was conventional decades ago, built on proxies for quality rather than the thing itself.

None of this is a case against the tools. The same episode includes moments where AI writing genuinely shines. The question — the one worth carrying out of the conversation — is about agency: whether these systems amplify your voice or quietly replace it. Because reading is a human act, writing is storytelling, and the future should contain your voice.

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