Synthetic Memories: What Happens When AI Edits the Photos We Remember By

Every photograph is a negotiation. Long before generative AI, the frame decided what was in and what was out, the darkroom adjusted what the negative captured, and the person in front of the lens chose which version of themselves to offer. Photography has never been a neutral record of reality. What it has offered, for most of its history, is stability: once a print existed, changing it took effort, skill, and intent.

In episode 110 of Modem Futura, Andrew Maynard and I explore what happens when that stability disappears.

The conversation centers on research from MIT Media Lab, "Synthetic Human Memories: AI-Edited Images and Videos Can Implant False Memories and Distort Recollection," presented at the ACM CHI conference in 2025. In a pre-registered study of 200 participants, people who later viewed subtly AI-edited versions of images reported significantly more false memories about the originals. The strongest effect came from short AI-generated videos made from the edited images: false memories roughly doubled compared with the control group, and confidence in those false memories was higher too.

Memory is already a reconstruction

The finding lands because of how memory works. Recalling an event is not retrieval from storage; it is a rebuild, and every rebuild draws on whatever cues are at hand. Increasingly, those cues live on a phone. Most people take several photos of a moment, edit the best one, share it, and rarely look at the others again. The edited version becomes the reference point, for the person who took it and for everyone who saw it.

Whose story does the picture tell?

That raises questions that are more human than technical. If a group photo is quietly adjusted so that everyone is smiling, whose story is being told: the subject's, or the photographer's? A portrait has always been a collaboration. AI editing tools make it easy to change the terms of that collaboration after the fact, often without the subject ever knowing.

The liquid past

The idea extends well beyond images. Digital publishing makes it simple to backdate a post, quietly correct a record, or retitle an episode. Archives such as the Wayback Machine help, and watermarking efforts like SynthID label some AI-generated content, but none of them restore the friction that once made the past hard to rewrite. Provenance increasingly has to be established rather than assumed.

Marshall McLuhan argued in 1967 that media shape people so thoroughly that little is left unaltered. What is new is a medium that can revise itself, and with it the memories built on top of it.

One possible future is a world of gentler, happier, better-curated archives. Another is a world where people slowly lose track of which memories are their own. Neither is inevitable. The open question is what each of us chooses to hold onto as a touchstone.

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Not the Audience: Reading Ethan Mollick's Co-Existence

There is a particular kind of frustration that comes from reading a good book that wasn't written for you. Ethan Mollick's Co-Existence: The Next Phase of AI produced exactly that feeling, and it became the starting point for a review conversation with Andrew Maynard on Modem Futura.

The book picks up where Mollick's 2024 Co-Intelligence left off. That book framed AI as a collaborator, with humans at the center and chatbots as helpers. This one turns to coexistence with systems that are sometimes, but not always, better than we are. The chapters are short and the tone is warm and generous, and Mollick is candid about what confuses him, which makes the book a useful counterweight to the faux-certainty that saturates so much public talk about AI.

The case for a gentle book

Andrew's argument is that accessibility is the contribution. Most people meet AI through a chat window or a pop-up in a familiar app. They need a guide they trust more than a technical download, and a slim book that gets read does more good than a definitive one that doesn't.

The case for going deeper

My own reading kept snagging on ideas the book raises and then leaves behind. Mollick's advice to evaluate AI output is sound, but evaluation depends on expertise and judgment built through effort, which is precisely the friction AI tools are designed to remove. His four human advantages (deep knowledge, wide knowledge, taste, and agency) offer a helpful starting map, yet each opens onto long traditions of scholarship, from embodied cognition to aesthetics, that readers will need to find elsewhere. The book is at its best on work, where Mollick treats jobs as bundles of tasks rather than single units a machine can take. That lens clarifies a great deal, even if it can't settle what happens to people whose bundles are small, or who are caught in the years between old work disappearing and new work arriving.

What a book is for now

The most interesting question the book raised sits just outside its pages. Mollick's book website includes a version written for AI agents reading on behalf of humans. If AI becomes an author's most attentive reader and summaries replace reading for many people, the lasting value of a book like this may lie in the voice itself: the doubt, the self-questioning, the sense of walking alongside someone who is still figuring it out. That voice does not survive summarization well.

So read it, especially if the pace of AI has left you disoriented, and treat it as a set of starting points rather than a destination. Then pick an island and go deeper.

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The Permission to Be Ridiculous: Flying Cars, Play, and the Future of Innovation

In October 2026, Tesla revealed a redesigned version of its long-delayed Roadster at a SpaceX test facility in Texas. The car climbed a curved ramp until it hung upside down, held itself in place with an underbody fan system, and then used cold-gas thrusters developed with SpaceX to flip back onto its wheels. Tesla says production is planned for 2028. Whether the most extreme versions ever reach public roads is another matter.

On Modem Futura, Sean Leahy and Andrew Maynard treat the moment less as car news than as a prompt for a broader question: what is the value of ideas that look ridiculous?

Playpens and playgrounds

Modern education and work are full of language celebrating creativity and boundary-pushing. Yet creativity is often expected to happen within limits set by someone else, closer to a playpen than a playground. Sean describes a high school project to build a mousetrap-powered car, which he completed by having the mousetrap trigger a model rocket engine. It outdistanced every other car in the class and earned a D for not following the expected path. The lesson many people absorb from experiences like this is not "be creative," but "be creative in approved ways."

Spectacle, spinoffs, and power

A long-standing argument holds that ambitious, seemingly frivolous projects pay off indirectly; NASA's research budget is often defended through the technologies that trickle out into everyday life. Fan systems that let a vehicle grip a surface at any speed might find uses far beyond sports cars, perhaps in specialized rescue vehicles. Yet there is an uncomfortable complication. When the most visible acts of technological play are funded and fronted by some of the world's most powerful people, celebrating the play can feel inseparable from endorsing the power. One response the hosts return to is widening access: more of this kind of experimentation in universities and public research labs, where results can be shared openly.

The flying car as a borrowed future

Futurist Jim Dator's second law holds that any useful idea about the future should appear to be ridiculous. Flying cars complicate that. They still look ridiculous, yet they are also among the most familiar images of the future, inherited from more than a century of inventors, industrialists, and cartoons. Futurists sometimes describe this as a "used future": a vision borrowed rather than imagined. The challenge is distinguishing ridiculousness that opens new possibilities from ridiculousness we have simply grown used to.

The more durable question is not whether any of us will drive an upside-down car, but how to create places where students, researchers, and teams have room to pursue the improbable without needing a billionaire's budget to do it.

Listen to the full conversation on Modem Futura, Episode 108.

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The Syllabus Is Not the Course: AI, Course Design, and the Junk Playground

Generative AI can produce a "complete-looking" university course in minutes: learning outcomes, weekly topics, readings, assignments, rubrics. For anyone who designs courses, that is both an extraordinary capability and a quiet trap. In Episode 107 of Modem Futura, Sean Leahy and Andrew Maynard reflect on more than a year of using AI as a private instructional designer, and on why the most important part of a course may be the part these systems are least able to build.

From sounding board to finished product

Used as a thinking partner, AI can be remarkably helpful. It can test whether a sequence holds together, surface readings no single person would find, and catch overlap between weeks. It is also good at turning existing material into worksheets or self-contained interactive pages that work around the limits of a learning management system. The trouble starts when the tools move from responding to finishing. Ask for help thinking through a course and, increasingly, they return a complete one: plausible, polished, and conventional. These systems are very good at reflecting what has worked before and much weaker at imagining what hasn't been tried (which is no surprise, but increasingly frustrating).

Content is abundant; journeys are designed

The distinction the hosts keep returning to is between content and journey. What remains scarce is the intentional design of experience, the sequence of challenges and moments of realization that carries a learner somewhere new. It echoes John Dewey's argument that education should build on learners' natural impulses toward inquiry, communication, construction, and expression rather than fill them with facts. A course that a single AI prompt could reproduce may be telling its designer something.

A junk playground for the AI age

The episode's most generative idea comes from 1943, when a playground designed by Danish landscape architect Carl Theodor Sørensen opened in Emdrup, Copenhagen. Sørensen had noticed that children were more drawn to piles of scrap than to the playgrounds he designed, so he gave them a space full of loose materials and a play leader to support rather than direct them. He later described it as both the ugliest and the best of his works. Andrew is now exploring what a digital junk playground might look like: a curated space where students build with whatever AI tools they choose and learn through the making.

For educators, that points to a different role, less authority over learning and more what Sean calls an elevated co-participant in it. When nobody yet knows the rules for learning alongside AI, play, experimentation, and care may be among the most rigorous approaches available.

Listen to the full conversation on Modem Futura.

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Megatrends 2026 Explained: The 11 Forces Shaping our Futures

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

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

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.

Listen to the full conversation on Modem Futura, wherever you get your podcasts.

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

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

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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The Right to Be Unpredictable

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.

Listen to the full episode wherever you get your podcasts, or watch it on YouTube.

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🎧 Apple Podcast: https://apple.co/46cmohk

🎧 Spotify: https://open.spotify.com/episode/3BcLR19gSOYvg1F6k8Imuc?si=8QQEKsocSt-K3dOQZzUlxg

📺 YouTube: https://youtu.be/JNjtU5uP1e4

🌐 Website: https://www.modemfutura.com/