Throughout this post, the second in our AI and human thought series, we explore the implications of a future world where AI has reached omnipresence, examining how deeply integrated AI will change the course of daily life, human thought, and phenomenological experience. We argue that while such a world may appear dystopian in nature, AI literate individuals will be able to navigate it healthily and prosperously by understanding when reliance on AI is appropriate. We conclude by offering a fictional narrative that illustrates how AI literacy can help individuals maintain autonomy and critical thinking in a world increasingly driven by AI technologies.
In our previous post on this topic, we examined a relatively narrow yet related sub-domain: how humans can leverage large language models (LLMs) to enhance (or diminish) the way we think.
We chose to focus on LLMs because of their popularity, versatility, accessibility, and ease of use, not because we believe that other forms of AI are somehow less useful or inferior, especially in consideration of ongoing generative AI (genAI) advancements, and the increasing notoriety of Mixture-of-Experts (MoE) architectures. In other words, the vast majority of people can find some value in leveraging LLMs in their daily lives, regardless of whether use cases are personal, creative, or work-related.
In this post, we’re going to expand our perspective significantly, venturing into the future and exploring the nature, structure, and impacts of a world where AI is embedded in virtually everything we know, from our toasters to our toothbrushes. In this context, we’ll no longer be considering the question of how AI can change the way we think, but rather, how it will change the way we think. To the skeptics, such a world may be decades away and fall within the realm of science-fiction, but to those who use AI and other state-of-the-art digital and information technologies on a regular basis, it may feel as though this world is already knocking on our door.
Nonetheless, this discussion will be driven by pragmatism and realism, and even if the skeptics find themselves unconvinced by our arguments, we urge them to at least consider their importance as thought experiments. A world where AI is omnipresent—not omnipotent—is possible, and even if the probability of it coming to fruition is small, considering its impacts on the human experience, namely what it means to think as and be a human is crucial.
A world where AI is omnipresent—not omnipotent—is possible, and even if the probability of it coming to fruition is small, considering its impacts on the human experience, namely what it means to think as and be a human is crucial.
In essence, what we’re getting at here is the idea that in the near future, AI will be doing most of our thinking for us, irrespective of who we are, what we do, or where we live. To many, this future may be deeply concerning and even terrifying—rightly so—but we’re here to offer a glimmer of hope and guidance. The current trajectory of AI innovation and proliferation suggests that we are on a path toward omnipresent AI—a claim we’ll discuss in the following section—and if we proactively navigate this path we’ll be able to steer the AI trajectory in positive and impactful ways. But, this will require AI literacy.
Here, we’ll consider two core arguments substantiating the claim that we’re on a path toward omnipresent AI. The first of these two arguments centers on the idea of digitalization, namely as it concerns AI integration with edge devices or technologies that, in the past, didn’t possess advanced computational characteristics and capabilities, like a fridge or mirror. The second argument will address the forces inspired by the engine of capitalism in relation to the role that AI does and will continue to play in increasing the ease and efficiency with which we execute many of our daily tasks and decisions.
Ultimately, we hope that in making these arguments, we’ll convince readers of two things: 1) the path toward omnipresent AI is unavoidable, and 2) successfully navigating a world in which AI is omnipresent necessitates at least some degree of AI literacy.
AI is no stranger to the digital ecosystem, having made itself at home in many different sectors long before the emergence of LLMs and other forms of advanced AI. Google began integrating machine learning features into its search engine as early as 2001, Facebook has been leveraging AI since its inception in 2006, and Amazon claims that its use and development of AI spans over two decades. But, it’s not just Big Tech that’s found immense value in AI since the dawn of the 21st century.
AI first entered the financial sector in 1982, in the form of expert systems, which were leveraged to provide personalized financial advice. Similarly, AI applications designed for knowledge acquisition and sharing have been around in the manufacturing industry since the 1980s. Many other sectors including but not limited to robotics, scientific R&D, military operations, surveillance and intelligence, law enforcement, and industrial engineering have also been utilizing AI for quite some time.
Looking back on the history of AI, a clear trend emerges: AI proved most valuable either in situations where lots of data needed to be quickly and accurately classified, synthesized, and interpreted or in cases where humans were required to perform undesirable, dangerous, or time-consuming tasks. In other words, AI’s value lies in its ability for automation.
However, when most people think of AI-driven automation, they reasonably gravitate toward the obvious—autonomous vehicles, weapons, or robots, real-time credit score and insurance premium adjustments, fraud detection and prevention, recommender systems or targeted advertising, surveillance and profiling, and so on. Make no mistake, these kinds of use cases are currently plentiful and continually evolving, but the increasing rate at which AI is being integrated at the edge, not only with sophisticated digital tech like smartphones and wearables but also mundane household tools and appliances, suggests that a less obvious but equally impactful series of AI use-cases is poised to emerge. In fact, many such cases already exist in the form of “smart” appliances, several of which we describe below:
AI-driven automation isn’t just going to replace all the things we don’t like to do, it’s also going to enhance all the things we enjoy, from making a delicious breakfast to never missing out on the latest news.
What each of these rather “unexpected” use cases highlights is that AI can and will generate value in virtually any domain in which a human must make a decision or execute a task, regardless of whether it’s mundane or pleasant. AI-driven automation isn’t just going to replace all the things we don’t like to do, it’s also going to enhance all the things we enjoy, from making a delicious breakfast to never missing out on the latest news.
The engine of capitalism is fueled by the hedonic treadmill—when we get what we want, we might feel better for a little while, but ultimately, we’ll always return to our baseline satisfaction, especially when we see that other people have what we want. In other words, give a man a crumb, and he may be satisfied. But give his neighbor a slice, and the man will come back asking for a loaf, and then eventually, the whole bakery.
As humans, it’s within our nature to always crave more because we think it’ll make our lives easier, and this is a major reason for why we build tools—one loaf of bread can be baked over a fire, but an oven can hold many more. An easier life means that there’s more time for us to solve other less serious problems or do the things we find pleasurable and worthwhile, and our creation of technology is what makes this venture possible.
In the past, humans have typically created technologies to help them solve their most pressing problems. For example, to avoid the problem of water scarcity in densely populated urban areas, the Romans perfected vast infrastructures of aqueducts that supplied cities with fresh water from outside sources. Similarly, the compass was invented to overcome the challenge of navigation when the stars and sun weren’t visible, becoming a major technological factor during the Age of Exploration. Jumping forward, Henry Ford created the moving assembly line to eliminate automobile manufacturing inefficiencies, which ultimately allowed him to make the Model T much more affordable.
All the examples above represent technologies of necessity—while there may have been additional factors motivating their development, each was created out of necessity, to address some problem or provide a novel solution. Today, these kinds of technologies are still critical, but we’ve undeniably entered a new age, no longer solely defined by technologies of necessity, but also, by technologies of convenience—things that we don’t necessarily need but desire because they make our lives easier, like a microwave or coffee machine.
AI can be both a technology of necessity and convenience.
Interestingly, AI can be both a technology of necessity and convenience. It can help us derive solutions to the world’s most significant problems, from sustainable agriculture and renewable energy to curing previously incurable diseases and making education accessible to all. However, to those of us whose fundamental needs (food, water, shelter, livable wage, healthcare, education, etc…) have already been met, the only thing left for us to seek is an even higher degree of comfort, and this is where capitalism has made its stronghold.
It’s no surprise that the wealthiest nations on earth—China, Japan, the US, EU, UK, and so on—are leading the race for AI development and innovation. While each of these nations undeniably has its own problems, many of which are unfortunately still being overlooked, many of their citizens enjoy a much higher standard of living than the rest of the world, which translates to more excess time and money at the population scale.
Capitalism, however, is mostly indifferent to national origin, values, interests, and culture, and responds to one thing only: money. As long as wealthy nations continue to exist, so will wealthy individuals, and as long as wealthy individuals are willing to spend their money, capitalism will continue churning out both technologies of necessity and convenience. Importantly, as the world gets richer, more and more problems will be solved, and eventually, technologies of convenience will dwarf technologies of necessity, even for those who could benefit from them enormously—sadly, money speaks louder than ethics.
Before we move on, let’s consider a concrete example. To date, 15 versions of the iPhone have been released, each one supposedly superior to the last. But if we take a moment to consider the core capabilities of the iPhone, not much has changed—like today’s models, the first iPhone had a simple touch-screen interface, could surf the web, run various kinds of applications, make calls and send messages, and take pictures. So, why do new versions keep coming out and why do people keep buying them when their core communication needs have already been met? Because the bar keeps going up, not out of necessity, but out of convenience—sure, you could keep using your current iPhone, but if you’re eligible, why not upgrade to one with more storage and processing power, a far better camera, bigger screen, and more built-in features?
The problem is that as people “upgrade” their lives with new technologies, they eventually become dependent on them, which means that what used to be desires have now reverted back to needs.
In a nutshell, capitalism works because it teaches us to be unsatisfied with the things we already have, even if all our imminent needs are met. In other words, capitalism creates the illusion that we have other needs that aren’t being met, which can more accurately be described as desires, like a better phone, bigger house, or faster car. The problem is that as people “upgrade” their lives with new technologies, they eventually become dependent on them, which means that what used to be desires have now reverted back to needs—initially, upgrading to the latest iPhone might not be necessary, but once you find out the all the apps you like are only compatible with the latest software, which your phone can no longer support, your only choices are to upgrade or abandon the iPhone, and if everyone is using one, why would you be the one to give it up?
Making mundane technologies “smart” is the next phase in the evolution of technologies of convenience, and AI will be the driving force in this process. Technologies of convenience might not be inherently good or bad, but they do increase our vulnerability to dependency and/or greed, both of which can motivate collectively detrimental selfish behavior and poor real-world decision-making.
Making mundane technologies “smart” is the next phase in the evolution of technologies of convenience, and AI will be the driving force in this process.
To protect ourselves from these risks, we need to begin cultivating AI literacy by developing an understanding of how to use various AI tools, features, and applications alongside an awareness of their limitations and capabilities as well as their potential risks, benefits, impacts, and use cases. This kind of knowledge is instrumentally valuable for two reasons: 1) it allows us to identify which mundane technologies AI will convert into “smart” technologies, and 2) how to leverage “smart” technologies for convenience without risking dependency.
Let’s consider an example that’s likely to become a reality in the near future: fully autonomous vehicles (AVs). The first users of AVs, having grown up using regular cars, will still know how to drive. However, for those who are brought up in the age of AVs, learning to drive won’t be a necessity, even more so for those who live in urban areas with access to public transportation. But, what happens when an AV fails, which it undoubtedly will? The early versions of true AVs will surely allow users to take control of the vehicle, but if a user is literally incapable of controlling the vehicle because driving is no longer unnecessary, then catastrophe is unavoidable.
When AI reaches omnipresence, only those who are AI literate will be able to understand which technologies of convenience they can safely rely on vs. which technologies of convenience they can use but not depend on.
When AI reaches omnipresence, only those who are AI literate will be able to understand which technologies of convenience they can safely rely on vs. which technologies of convenience they can use but not depend on. In simple terms, relying on a smart toaster to get your toast right every morning is fine—if it fails, nothing bad actually happens. But, if you rely on your smart pet feeder while you’re on vacation and it fails, you may return home to a grim scene. The latter of these two examples is fairly low stakes, especially when considering what smart technologies might look like at scale—imagine what could happen if a smart home or even a smart city fails.
Knowing what AI does for you, in terms of the daily decisions you make and tasks you execute, will be essential to ensuring that you don’t become overly dependent on it.
On a related but equally crucial note, AI literacy will also allow people to envision how AI-powered technologies of convenience will fundamentally alter the way they think. Knowing what AI does for you, in terms of the daily decisions you make and tasks you execute, will be essential to ensuring that you don’t become overly dependent on it. However, it will also be the driving factor in allowing you to allocate time and energy to other forms of thinking and being that are more meaningful, important, and impactful, like thinking critically about world events or maintaining vibrant relationships with close friends.
In this final section, we’ll begin with a fun fictional story of the future that chronicles a morning in the life of “X,” a person living in a world where AI has reached omnipresence. Following this, we’ll discuss which components of human thought and experience are most likely to change if such a world comes to fruition.
At precisely 7:00 am, X’s bedroom is aggressively thrust into daylight as the blinds open themselves, the lights come on, and the playful chirps of an artificial bird cacophony are relayed from speakers in the walls and ceiling. Shortly thereafter, a humanoid voice emerges and exclaims, “Good morning X! Today is July 7th, 2044. It’s 7:05 am. You can expect partly cloudy skies, 72% humidity, and a comfortable temperature of 77 degrees. I’ve already begun brewing your coffee, is there anything else I can help you with this morning?”
X, now partly awake, groans and turns to his nightstand. He picks up and puts on a pair of glasses with an earpiece attached to them, after which he taps the left arm twice with his index finger to activate them. He then shifts his view to the ceiling, where the glasses project an augmented reality display visible only to him. Coarsely, X remarks, “Slept like crap last night. Alexa, show me sleep insights.”
The voice returns and calmly explains, “According to your smart bed and wearable data, you obtained a sleep score of 73 out of 100 last night. You spent approximately 1.5 hours in REM, 2 hours in deep sleep, and 3 hours in light sleep, with occasional moments of wakefulness. Would you like to hear additional insights on your metabolic rate, blood oxygen saturation, and heart rate variability?
X responds, “No, I’m good, just show me the visual.” Alexa then projects a graph that outlines X’s sleep patterns on the glasses’ virtual display, after which X grunts and questions, “What’s in the books for today, Alexa?”
“You don’t have to be at the office until 9 am this morning, and traffic is light. I’ve calculated the optimal travel route and hailed your vehicle for you—it’ll be waiting downstairs by the front entrance at 8:30. You have three meetings today, one with marketing at 11 am, another with Nina at noon, and finally a sales team meeting at 3 pm, each of which is scheduled for a 45-minute window. You also have a 7 pm dinner date with your friends Mark and Nadia at the Red Line Bistro. All the details have already been added to your calendar with reminders.”
X nods in approval, removes his glasses, gets up from bed, and goes to the bathroom. Upon entering, the bathroom mirror display activates autonomously. X grabs his control bracelet off the counter and clips it onto his right hand—with a series of elegant no-touch wrist flicks, and while brushing his teeth with his other hand, X navigates through the various interactive mirror displays, taking another look at the weather, then live news, and eventually, his email and text notifications.
Once X finishes brushing his teeth, he taps the vertical touch screen on the shower handle, which then shows him three options, each of which optimizes water usage in accordance with X’s pre-set shower preferences: 1) a 7-minute long hot shower, 2) a 10-minute long warm shower, or 3) a 15-minute long cold shower. X chooses option 1, and once he finishes his shower, integrated air dryers activate immediately, drying him off in under two minutes. Now, it’s time to get dressed and have some breakfast.
As X travels through his apartment to his walk-in closet, the lights above him progressively turn on with every step he takes. Once he reaches his closet, the doors open themselves, and a rack with all his work outfits, accompanied by a human-sized mirror, slides out before him. X, still wearing his control bracelet, activates the mirror with a quick swipe-up movement—the mirror takes about 10 seconds to calibrate as a virtual halo-like outline begins to form around X’s body. Once calibrated, the mirror presents X with three wardrobe selections: 1) work, 2) personal, 3) adventure. X selects option 1, and almost instantly, an outfit, composed of various clothes from his work-specific wardrobe, is projected onto his reflection in the mirror. After some deliberation and wrist-flick scrolling, X eventually settles on an outfit he likes, keeping it simple with a matte gray suit and a white dress shirt.
Now dressed and almost ready for work, X heads to the kitchen, where he finds a warm pot of coffee ready for him. He pours himself a cup and takes a seat at the breakfast table, “Alexa, what can I have for breakfast today?”
“This morning, you can have two hard-boiled eggs with buttered rye toast or vanilla and maple-flavored overnight oats with blueberries.” X goes to the fridge and grabs the overnight oats, after which the voice chimes in again, “It seems like you’re low on groceries. Would you like me to create a shopping list and forward it to your phone?”
“Yes,” X replies, “but let me see it first.” The touch screen fridge display activates with X’s grocery list—X scrolls through the list, swiping right to remove any food items he doesn’t need until he’s satisfied, “Looks good, you can send it.”
It’s now 8:20 am, X finishes his breakfast, grabs his coat and briefcase, and right before exiting his apartment he asks, “Alexa, is there anything else I should know for today?” Alexa answers, “Your vehicle will be ready at 8:25, I hailed it earlier due to a minor change in traffic conditions. Also, I’ve re-optimized your heating and cooling schedules to account for the oncoming cold front and the increase in natural gas prices. Additionally, your smart lock and alarm system is now equipped with the most recent software package, which I downloaded and installed last night after your approval. You shouldn’t have any more problems operating the system remotely moving forward.”
“What’s my daily reminder?” questions X. A small pause ensues, before Alexa says, “Remember, I’m just an AI.”
“Great!” X exclaims as he rushes out of his apartment on his way to work.
The story we’ve just told represents what we think is a realistic approximation of the near future—as the costs of edge AI integration decrease over time, especially if steady increases in compute power per unit cost and investments in the AI ecosystem continue, it’s likely that these kinds of technologies of convenience will eventually become commonplace in most people’s homes (most notably those that live in wealthy nations with vibrant AI innovation landscapes, after which trickle down effects will take hold). So, if this future does emerge, how will it affect us?
In terms of how we think, one obvious yet broad negative consequence emerges: AI-powered technologies of convenience will make us lazy. Most of these technologies will probably target the automation or streamlining of mundane daily tasks and decisions, fueled by the assumption that most people don’t find considerable value in the completion of their low-level daily responsibilities. Once such technologies are adopted at scale, the idea that mundane daily activities lack value will be continually reinforced, despite the fact that many individuals might still find meaning in ordinary daily rituals like brewing their morning coffee or selecting their outfits by hand—these very rituals are often at the heart of our sense of purpose, and if they are usurped by machines, we may find ourselves lost and unmotivated to pursue anything worthwhile.
Moreover, the execution of daily tasks and decisions requires discipline—if people no longer need to be disciplined about the trivial parts of their lives, how can we expect them to be disciplined about the serious ones, like work projects, staying healthy, or maintaining family ties? This is just one of the many questions to consider, however, below we expand on some additional negative consequences that may arise due to the widespread integration of such technologies:
On a different note, we might expect the emergence of a more subtle trend in terms of how we experience the world: things will move much faster on a daily basis and the pace of regular life will increase. For example, today, it may take you roughly one hour to get ready for work each morning. But, in a world where AI-powered technologies of convenience automate and/or streamline roughly 80% of the tasks involved in your morning routine, one hour could quickly turn into 15 minutes.
At first, it might be great. You have more time to sleep in, finally get in that morning meditation, or even go for a run. But, as time passes, and more people adopt these technologies, a shift in the collective mindset could occur. If there’s more time for people to do stuff, then people will begin expecting others to do more stuff in shorter amounts of time—this mindset won’t just apply to other people, but also institutions. In fact, for this shift in mindset to happen, people don’t even need to do anything more with the extra time they have—the belief that others will is enough to motivate the emergence of the collective social expectation.
In essence, this represents an abstracted version of a psychological collective action problem: it’s in all our best interests to do more meaningful things with the extra time we have, and while some of us will, most of us won’t simply because it’s easier not to when benefitting from technologies of convenience is an option. But, we’ll still be under the illusion that everyone is doing more, and so we’ll hold others to this expectation without consistently applying it to ourselves. The result will be a deep disconnect between what happens in the real world vs. the idealized world. Initially, this disconnect will permeate our personal lives—we’ll conveniently forget to bring dishes to family potlucks because we assume other family members can pick up the slack or we’ll keep delaying vacation plans with friends because we think each of them can easily make last-minute adjustments to their schedules.
Eventually, the expectation that people can do more things in less time will transition from the personal to the professional realm, and this is where the real problems will start to pop up. We won’t go into depth on this point since an entire book could probably be written on the topic—especially in consideration of some current AI applications and models, which, as technologies of necessity, are specifically designed to address workflow inefficiencies—but it’s not hard to envision how this dynamic could lead to major misalignments between managers and their teams, educators and their pupils, governments and their civil servants, and so on. In this respect, we’ll leave it up to readers to imagine what these impacts could look like.
We’ve now explored several of the negative externalities that could exist in a world where AI is omnipresent. However, as readers have likely noted, the fictional story we told earlier is closer to a utopian worldview, but this is only because we intentionally left out a key assumption: X, our main character, is AI literate. Now that we have this context, we’ll highlight three key moments in the story that demonstrate why X’s AI literacy is crucial to helping him healthily and prosperously navigate the world he’s a part of.
The first of these moments is when X decides not to view the additional health data Alexa suggests. X does this not because he’s uninterested in this data, but because he’s had a tough night, and he knows that if he hears it, it will make him anxious and worried for the rest of the day. X also doesn’t want to succumb to a lifestyle where all his daily decisions and tasks are predominantly motivated by data, since it feels robotic and inhuman. X knows that people need to be allowed to exercise their own judgment, which forms an integral dimension of human autonomy and agency.
AI can and will get things wrong sometimes, and even if it doesn’t, the information it churns out may not always align with your preferences, regardless of how well it “knows” you.
This brings us to the next moment, where X decides to double-check the grocery list that appears on the fridge display. Not only is this a direct exercise of X’s autonomy, but also a recognition of the fact that AI can and will get things wrong sometimes, and even if it doesn’t, the information it churns out may not always align with your preferences, regardless of how well it “knows” you. Moreover, X realizes that by not considering the nature of the information that AI feeds him, he makes himself vulnerable to superficial thought processes, where one can easily begin taking all things in life for granted.
Finally, we arrive at the final moment, where X asks Alexa for his daily reminder. X has instructed Alexa to issue the same reminder every day as a prompt that forces him to think critically about the role AI plays in his life, the hedonistic and materialistic temptations he might be experiencing, and perhaps most importantly, the tendency to imbue smart technologies—particularly those that are interactive—with human characteristics.
As our future becomes progressively AI-driven, AI literacy will transition from an asset to a necessity.
Ok…what’s the point of all this? The vast majority of the world’s citizens are regular people, and unfortunately, they will only be able to marginally influence what their future looks like. In reality, these kinds of decisions will fall with major technology companies, politicians, and quite likely, other wealthy and powerful members of society. So, if we regular people are fundamentally limited in our abilities to create a future that aligns with our values, interests, and expectations, how can we ensure that we benefit from it? AI literacy is the answer, and as our future becomes progressively AI-driven, AI literacy will transition from an asset to a necessity. Be like X. Work with AI, learn from it and with it, experiment, take risks, and most importantly, remember that AI isn’t going anywhere and neither are you.
No hype. No doom. Just actionable resources and strategies to accelerate your success in the age of AI.
