The Agency Eating Machine
Despite a sensory information stream of 1 gigabit per second, humans are only capable of thinking at 10 bits per second.
So even though we can see, feel, and experience a very many things at any moment, we are only capable of consciously thinking in a single thought stream at a time, and only at a rate of 10 bits per second.
This means every person’s daily budget for logical thinking is only around 576,000 bits of unique thought, assuming they’re awake for 16 hours out of the day, and in complete control of their consciousness.
At best, our maximum capacity for thought in a day is a little more than half of the sensory information we receive every single second.
Focused, conscious attention is a very valuable, fragile, and limited resource.
But for most of my life, I’ve been expected to conjure it up on demand, every working day.
How Knowledge Work… Works
That’s because, for the past 20+ years I’ve written software systems.
This has proven challenging, as creating software requires bringing up a special sort of focused attention that can be exhausting to create and maintain, but also very easy to lose.
Successful software development requires consistently blocking distractions, mentally loading problems, and systematically planning a solution, without getting distracted.
Fortunately, in software you can usually tell very quickly whether or not your work has “solved” the problem.
But notably, without a minimum threshold of focused attention, it’s impossible to write any software.
Most other knowledge work is also like this. Without the ability to focus and load up problems into our mental framework, we can’t get anything done. Think of it as a hill. Without the ability to get over the hill of required initial attention, it’s impossible to get any actual knowledge work done.
But, now that’s changing!
Enter the Thinking Machines
Because now, we’ve got a new alien intelligence to work with and against, called LLMs.
Despite being trained on massive, internet scale dataset, LLMs are like us, limited in their ability to hold a finite set of symbols in their mind when solving a given problem. We call these symbols “tokens”.
Each time we interact with them we have a limited window within which to explain and set the stage for our unique problems, using the language of their tokens. This is called a “context window”.
Each LLM is diferent, but for example, Claude has a context window size of 200,000 tokens for Pro users.
This means if we want to extract answers and explore ideas with Claude, we must do so within a 200,000 token context window.
How LLMs Load Context for Your Problems
This context for describing and navigating through our problem is so critical to the peformance of LLMs, that Anthropic has released a whole new protocol called “Model Context Protocol”, to allow developers to empower the LLM to autonomously decide when it needs to to grab more context for any given query.
This is because LLM model weights are “frozen” in production. The LLM doesn’t know of, and can’t reason on things that have changed since it was trained.
Model Context Protocol lets us change that, by exposing Tools, Prompts, and Resources.
The LLM models can use these resources to autonomously decide to search the web, access a database, or create new things like web pages. Again, just as long as each of these actions and their results fit within the Context Window.
This makes the LLMs much more powerful at solving novel problems autonomously.
The Human Model Context Protocol of Media
Similarly, we humans generally rely on media platforms to load new context into our own thinking spaces.
What we read, see, and watch largely explains the context we have for understanding and relating to the world.
So how do we decide what we load into our own personal context?
In 1976, a man named Wilbur Schramm created a theory for how people decide which media to consume:
Expected value divided by the required effort.
At the time, Schramm saw television as a promising new medium.
Television could possibly educate an entire population at once, delivering sensory rich content (critical for effective learning), at a fixed production cost.
This content could be built by world experts, designed to have maximum educational impact, and shape a shared social context and value system for an entire population at a time.
Programs like Sesame Street tried to achieve this goal, and educate the young population.
This dream came with a trade-off: the viewers’ focused attention for 30-minute blocks, interrupted by commercials, in exchange for polished entertainment.
For decades, this agreement worked.
But then social media arrived, and the math on our collective attention spans changed.
Attention at 30 Seconds or Less
Where television asked for thirty minute units of our attention, TikTok and Instagram demanded just thirty seconds for a novel experience.
Better still, once on these platforms, they they elinimate the need for conscious choice. Instead their algorithm can usually find a better piece of content than we’d be able to find ourselves, without any conscious effort on our end.
This might seem like a simple shift in duration and cost of decision, but it fundamentally breaks Schramm’s ratio and lowers our ability to choose media experiences outside of these algorithms.
Traditional media has to create shows and films to target a very large audience, in order for the cost of creation to pay itself back. Social media flipped this approach, making the audience fund their own entertainment, allowing the audience to discover and create much more niche content than previsouyl feasible in mass media.
When the required effort of a media platform approaches zero, and content is infinitely personalized, the other media choices become even less relevant.
Why invest attention elsewhere, for a lower possible reward on average?
TikTok’s unique breakthrough of steering recommendations based upon whether or not we swipe “next” on a video has created an algorithm that now has the average teenager spending 3.5 hours per day, locked in a feed.
(For reference, teenage television viewership peaked in 1995 at around 2.95 hours per day.)
Of course, we should mention there are positive associated benefits:
A majority of adolescents report that social media helps them feel more accepted (58%), like they have people who can support them through tough times (67%), like they have a place to show their creative side (71%), and more connected to what’s going on in their friends’ lives (80%)
But there’s also something fundamentally different about how social media affects their mental health.
Around ⅓ of teens report using social media “almost constantly”, and:
adolescents who spent more than 3 hours per day on social media faced double the risk of experiencing poor mental health outcomes including symptoms of depression and anxiety
(There’s plenty more from the Youth Mental Health Social Media Advisory.)
So what could be responsible for this rapid increase in misery and isolation?
Schramm’s Model of How we Communicate With Each Other
To answer this, we can look another model Schramm created for explaining how communication occurs between people.
Critically, when we want to send a message to someone, we must first encode it, using a our shared experience as a medium.
Platforms like television and books give us a common set of myths, used as a shorthand to tell our inner stories, and relate to one another.
But through this lens, social media seems to be a shared experience lowering tool.
Social media is an isolating system by design, as the feeds become increasingly personalized. The ideal feed shrinks to fit our unique needs, and minimizes the size of our shared collective experience, pushing each us into isolated, self-reinforcing idea bubbles.
Social media customizes itself using our non-verbal, non-thinking cues. Again, these happen quicker than our verbal, conscious thinking process. They continously steer us further away from a shared cultural reference point, and further split us into a set of feedback loops inducing emotional responses and increasing stickiness.
Calling the current social feeds “algorithms” doesn’t really cut it anymore. The recommender systems that being built are advanced forms of artificial intelligence, trained to addict us in exchange for the opportunity to sell some ad space.
Better Living Through LLMs
Because once again, AI has an unequal footing with our biological systems. We can only consciously perceive deliberate thoughts at our 10 bits / second when we given them our full attention, but the software systems built around us operate at a much faster pace.
LLMs and future media could increasingly short circuit our more basic processes and turn us into media addicted zombies if left unsupervised.
We’ve all seen people in ideal sensory zones– the beach, concerts, on dates… choosing to be on their phone, rather than in their current physical space.
But what if we wanted to build a media platform that challenged us, challenged each other to be better, instead?
What might that look like? How might we be pulled away from the endless loop of personal gratification and distraction addiction?
How could we take the positive things from social media, and add them to the emerging, lower cost of thinking powers that LLMs give us?
LLMs are Already a New Media Platform
For me, the video above shows social media at its best.
When we work together to create something new, opening each of us to participate in something absurd and fun.
Although we’re still in the early innings with LLMs, they do seem to be actively rewriting large portions of how we interact with and build a new media.
We can see this when we ask LLMs to do work for us that seems tedious, or when we feel stuck on a problem. They lower the costs of asking a “dumb” question to zero.
This means instead of the avoiding the normal anxiety associated with avoiding a problem, we can ask an LLM to take a first stab at a problems. We can then step in to correct the places that feel wrong.
And LLMs at their best allow us to be more ambitious in our work, writing software we might not have otherwise written, or thinking ideas that would otherwise be inaccessible.
LLMs lower the cost of thinking new ideas.
So what’s the problem?
LLMs by default have the same social problems that we see with social media.
They are fundamentally shared context shrinking.
Again, added to this is the fact that they have a knowledge cutoff date, and are currently “frozen” in time.
Some models can incorporate search results at prompt time, but they cannot “learn” from their conversations with you in real time, and they cannot “learn” from the current cultural climate.
But they have made the process of exploring and linking new ideas together cheaper. LLMs allow us to explore new ideas with a fundamentally lower cost of required attention investment. We can ask an LLM to link together two disparate ideas, have them translated into our knowledge domain, and see whether or not the beginning results match what we’d hoped for.
If we give LLMs the ability to better fill up their context windows with information about us, and about the people we care about, they don’t have to be socially isolating.
This will allow us to collectively think a lot of new thoughts that previously would have had too high of an attention cost to explore effectively, while minimizing the social costs.
I dream of making the tool that would combine the fun of the video above with the power and flexibility of LLMs to explore new ideas. In fact, that’s what I’ve been working on.
Drawing the Owl of the Next Media Platform
Can a better media platform emerge from the best parts of social media and LLMs?
I’ve spent the past year and a half focused on this very idea, trying to come up with something that could be fundamentally better.
One of the last social media platforms I built started with video editing.
It’s become apparent that using LLMs to search through piles of video for us is useful. We can use Model Context Protocol and custom Tools to suss out the interesting bits, or all the times when something related to our ideas happens.
We can also isolate things in video now too. This combined with the idea exploration LLMs are so good at makes for a much more dynamic, interactive, experimental platform.
I’ve started to put these ideas together, but it’s still incredibly early, with a lot of unanswered questions.
For example, what is the LLM medium equivalent of the TikTok recommendation algorithm for ideas?
On the day I published this post, almost every top app in the app store was an LLM chat tool, or some media platform. (Temu and a VPN app were the only two non media platforms.)
LLMs are already a new media platform, as proven by their spots in the app store. The question is how we will decide to use them going forward.
If you’re interested in an alternative, you can sign up for the waitlist for some new tools at video-jungle.com.
You can also reach out, and follow me on the existing media platforms. Because they are where everyone is, for now.
In the meantime, in order to reach people we will need to continue to perform, speak, and act in the ways the addiction algorithms reward.
Until we don’t.
Thanks to Erica Dohring for reading an early version of this blog post.
All the animations on this blog post were made with the help of Claude and Manim, and the source code for them lives on Github.