I watched Peter Leyden’s Big Think video and it gave me a useful name for a feeling I have not been able to shake.
Not a prophecy. Not “2025 is the single most important year in human history.” That kind of certainty always makes my teeth hurt.
One way to read this moment is that a bunch of systems are changing at the same time, and the simultaneity is the point.
AI, media, software, economics, work, trust, energy, biology, creative tools, personal infrastructure. None of these are isolated lanes anymore. They keep crashing into each other. That is why everything feels so weirdly compressed.
The exact year matters less than the compression of change.
The Useful Part of Leyden’s Frame
Leyden’s big historical pattern is that America periodically hits reset moments where old systems stop working, people fight over the broken machinery, and then new institutions get built. In the video, he points to the founding era around 1787, the post-Civil-War era after 1865, and the post-World-War-II era after 1945.
I don’t know if the 80-year-cycle thing is “true” in any clean historical sense. History is not a metronome. The danger with these frameworks is that they make messy human choices look like physics.
But as a mental model, it helps.
The useful part is not “this exact year is definitely the turning point.” The useful part is that old operating systems are failing at the same time new tools are becoming usable.
That feels right to me.
You can see it everywhere. The media business is melting. Search is changing. Trust in institutions is shot. Hollywood is trying to figure out whether AI is a tool, a legal exposure, a labor threat, or all three. Software teams are trying to decide what junior work even means when an agent can scaffold a feature, write tests, and explain the code back to you.
Meanwhile, individuals are rebuilding their own personal infrastructure because the public information environment feels like a busted pipe.
This is not one thing. It is a pile-up.
The Convergence Is the Story
Leyden talks about three big technology tipping points: AI, clean energy, and bioengineering. OpenAI introduced ChatGPT on November 30, 2022. The 2020 Nobel Prize in Chemistry went to Emmanuelle Charpentier and Jennifer A. Doudna for developing a method for genome editing. The International Energy Agency describes solar PV and lithium-ion batteries as learning-curve technologies, with costs falling as cumulative capacity doubles.
That is the clean futurist version.
The messier version is more interesting.
AI is not just a chatbot. It changes software, research, design, writing, customer support, search, scheduling, legal review, coding, editing, and the basic expectation of how fast a thought can become a draft.
Media used to be easier to talk about as “content,” which was always a dumb word but at least pointed at something. Now it is distribution, trust, identity, recommendation systems, fandom, propaganda, newsletters, podcasts, YouTube clips, TikToks, Discord servers, and whatever the hell X is today.
Work gets flattened into remote versus office because that is the argument people know how to have. The real shift is deeper: meetings, email, project management, hiring, training, documentation, review cycles, approvals. All the boring plumbing is being renegotiated.
Economics is the same. Inflation and interest rates matter, obviously, but the deeper question is whether people believe the deal still works. Housing, healthcare, education, subscriptions, software rent, creator income, job security, retirement, the cost of being a functioning adult. The spreadsheet does not feel sane to a lot of people.
Trust is not just misinformation. It is the loss of shared reality. Screenshots can be fake. Audio can be fake. Expertise is politicized. Institutions overplayed their credibility. Random people on the internet are sometimes right before the official channels are. That breaks your brain a little.
Creative tools are crossing old boundaries. Unreal Engine is not just for games. It is previs, post-vis, virtual production, digital puppetry, and real-time filmmaking. AI image tools are not just making pretty mood boards. They are becoming part of pitch decks, look development, location planning, costume visualization, and concept exploration.
Then there is personal infrastructure, which used to sound like productivity nerdery and now feels closer to survival gear. Notes, archives, backups, automations, password managers, local files, RSS feeds, read-it-later queues, personal websites, media libraries, AI assistants, private search across your own stuff. When the outside world gets noisier, your own system matters more.
That is the compression.
The new tools do not arrive politely. They land in the middle of broken incentives, exhausted workers, fragile institutions, declining trust, and industries that already felt like they were duct-taped together.
So the feeling is not “AI exists.” The feeling is “AI exists while everything else is already unstable.”
That is different.
Production Is a Good Example
Production makes this easier to see because production is where abstract technology either helps or gets laughed out of the room.
On a real show, nobody cares that your AI tool can make a beautiful impossible room. Can construction build it? Can the camera move through it? Can VFX use it? Does it match the budget? Does legal approve the training data? Does the union care? Does it save the art department time, or does it create a thousand new little cleanup tasks?
That is where the hype gets useful or dies.
The practical stuff is much less glamorous and much more important: searching a video archive with natural language, roughing out a shot list, tone-checking an email, generating a first-pass lookbook, turning a location photo into a rough 3D reference, testing furniture in a virtual set, cleaning plates, doing roto prep, comparing schedule options, summarizing production notes.
That is not the robots making the movie.
It is the workflow getting compressed.
The distance between idea, reference, test, revision, and decision gets shorter. That changes the work even when the crew size does not change. It changes expectations. It changes what “fast” means. It changes who can make a credible pitch. It changes how much polish people expect before a thing is even real.
Same with Unreal. The interesting part is not that a game engine can make pretty images. The interesting part is that film people, game people, editors, directors, VFX supervisors, and production designers can all start touching the same virtual object earlier in the process.
That is a systems change hiding inside a tool change.
Software Is the Same Pattern
Software is going through its own version of this.
The first-order take is “AI writes code.” Fine. True sometimes. Overstated often. Also not the whole story.
The better take is that software work is being reorganized around faster loops.
You can ask an assistant to inspect a repo, find the relevant files, make a scoped change, write the tests, run the checks, explain the diff, and leave documentation. Not perfectly. Not magically. But well enough that the shape of the job changes.
The bottleneck moves.
It used to be “can I produce the code?” More and more, it becomes “do I understand the system well enough to ask for the right change, review the result, and know what failure would look like?”
That is a different skill stack.
The same thing is happening in media. The bottleneck used to be access to publishing. Then it was attention. Now it is trust, taste, verification, and persistence. Anyone can publish. Anyone can generate. Anyone can clip. That makes actual judgment more valuable, not less.
The Old Machinery Is Still Here
This is where Leyden’s historical frame is helpful without needing to be gospel.
When old systems stop working, the people who benefited from them do not usually say, “Fair enough, let’s redesign this thoughtfully.” They defend the old system. Sometimes because they are cynical. Sometimes because their whole identity is built on it. Sometimes because the replacement really is undercooked and dangerous.
That part is not mysterious.
You can see it in media companies trying to defend distribution models that no longer make sense. You can see it in studios trying to use new tools without triggering labor blowback. You can see it in universities trying to respond to AI with plagiarism panic instead of redesigning assignments. You can see it in software orgs pretending nothing has changed while individual engineers quietly rebuild their workflows. You can see it in politics, where institutions built for a slower information environment are getting battered by real-time outrage machines.
Again, not prophecy. Just pattern recognition.
The old machinery is still powerful. The new machinery is still immature. We are stuck in the ugly middle where both are true.
What I’m Doing Differently Because of This
I am taking my own infrastructure more seriously.
Not in a bunker way. In a “the defaults are not neutral anymore” way.
I want my notes searchable. I want my files backed up. I want my important work in formats I can move. I want my website to be mine. I want my media library to survive platform churn. I want AI in the workflow, but I also want receipts, sources, diffs, tests, and human judgment around it.
I am paying more attention to production workflows than AI demos.
The demo is usually the least interesting part. The real question is where the tool lands in the pipeline. Who uses it? What does it replace? What does it make faster? What new mess does it create? What approval process does it need? What happens when it is wrong?
I am treating trust like a design problem.
Screenshots, citations, provenance, version history, reproducible checks, clear authorship, local archives. This stuff used to feel like extra credit. Now it feels load-bearing.
I am trying to stay flexible without becoming gullible.
Some of this is hype. Some of it is real. The trick is not to pick a team and defend it forever. The trick is to keep updating as the tools hit actual work.
The Point
Leyden may be right that this is one of those rare historical transition windows. He may be forcing the pattern too hard. Both can be true.
I do not need 2025 to be “the most pivotal year” for the frame to be useful.
The year is not the point.
The compression is the point.
When enough systems start moving at once, the lived experience changes. It gets harder to tell which disruption belongs to which category. AI changes software. Software changes work. Work changes media. Media changes trust. Trust changes politics. Pretty quickly, people need different tools, different communities, and different ways to keep themselves oriented.
That is one way to read the moment we are in.
Not the apocalypse. Not the promised land. Not a neat historical cycle clicking into place.
Just a lot of old assumptions expiring at the same time the new tools are becoming usable.
That is enough to pay attention.