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Based on "We Automated Everything With AI and Tripled Our Headcount" from Every Watch the original video

The AI Paradox: Why Automation is Creating More Human Work, Not Less

Episode summary

Every's Dan and Brandon challenge the prevailing fear that AI will eliminate jobs, arguing that automation actually triples human work. They cite Every's own experience, growing from four to 30 people since GPT-3, despite being "as AI native as it gets" with agents in Slack and daily use of tools like Cloud Code. Their core argument: AI makes "yesterday's expert competence cheap," flooding the zone with "close but not quite right" work, which then increases demand for human experts to build systems, refine outputs, and create entirely new things that were previously impossible, like an inbox built end-to-end in a month.

The practical takeaway is that while AI excels at defined tasks, it constantly asks, "What should I do next?" This "looking back" for human direction, even with future AGI, means humans remain essential for deciding what truly matters and adapting to ever-changing situations. While some companies like ClickUp have fired staff due to AI, Every argues these are often poorly implemented, and customer resistance to machines (e.g., in call centers) will slow adoption, proving that genuine human "agency" – the self-motivated will to act – remains distinct and invaluable.

The advent of artificial intelligence has sparked widespread fear: "AI is coming for our jobs." Images of robots replacing humans, mass unemployment, and a future devoid of meaningful work dominate headlines and conversations. Yet, for some, the reality on the ground is starkly different. At Every, a company deeply integrated with AI, the experience has been a surprising paradox: the more they automate, the more human work emerges, and their headcount has tripled since the early days of advanced AI models.

This counter-intuitive truth challenges the prevailing narrative, suggesting that AI isn't a job destroyer but a catalyst for a new era of human expertise and creativity.

The Automation Paradox: When AI Makes Yesterday's Expertise Cheap

The core of this paradox lies in how AI functions within the workplace. As Dan, the author of a recent piece on the subject, explains, AI excels at making "yesterday's expert competence cheap." Think about it: AI models are trained on vast datasets of human outputs—code, writing, design, decision-making. This means that tasks once requiring years of specialized training can now be performed by anyone with a well-crafted prompt, at a fraction of the cost and time.

Suddenly, non-experts can generate sophisticated code, draft comprehensive reports, or design compelling visuals. This democratizes skills, leading to what Dan describes as "flooding the zone" with work that is "close but not quite right." While AI can produce impressive initial outputs, these often lack the nuanced understanding, specific context, or truly innovative spark that only a human can provide.

"Everyone's making pull requests," Dan recounts from Every's internal experience. "Ops people are making pull requests, and you know, engineers are like writing essays." This blurring of traditional roles, where non-experts cross lines into specialist domains, initially feels threatening to established experts. What becomes of their value when their unique skills are commoditized?

The answer, surprisingly, is that experts become more indispensable than ever. This glut of "okay" but not "great" AI-generated content creates a massive new demand:

  1. Refinement and Elevation: Experts are needed to take the "close but not quite right" work produced by AI and non-experts, and transform it into something truly excellent, appropriate for the specific situation, and genuinely impactful.
  2. System Building: To manage the sheer volume of AI-assisted output, experts are vital in designing and implementing systems, guidelines, and review processes that ensure quality and efficiency. For example, Every has repo rules and editorial guidelines to shepherd AI-assisted work to a high standard.
  3. Pushing Boundaries: With AI handling the foundational, repetitive, and "yesterday's competence" tasks, experts are freed up to tackle problems that were previously impossible. Dan cites Kieran at Every, who built an entire inbox end-to-end in just a couple of months—a feat that would have been "completely impossible" before AI.

Every's own growth trajectory serves as a powerful testament to this phenomenon. Since the early days of GPT-3, the company has expanded from four people to over 30 and continues to hire. Despite being "as AI-native as it gets"—where a stick swung in their Slack might hit an AI agent as often as a human—they find "more human work to do than ever."

The Unyielding Need for Human Direction: Beyond Autonomy

The fear that AI will become fully autonomous and render humans obsolete misunderstands a fundamental aspect of its current and foreseeable capabilities. As powerful as AI models are, they invariably reach a point where they "stop working and look back at you and say, 'What should I do next?'"

This is captured in a poignant analogy to Zeno's paradox of Achilles and the tortoise: you prompt AI, it blows your mind, you feel inadequate, like it's sprinting ahead of you. But then, it halts, awaiting your direction. The "human connection with an agent to actually do the work is the most important thing for making it work well," Dan emphasizes. "The further away an agent gets from a human, the less valuable it is."

A critical distinction is drawn between "autonomy" and "agency":

The incentive structure for AI development also works against true agency. Who wants an AI that refuses to work because it "feels like playing"? Until AI can genuinely reject human commands based on its own internal motivations—a capability far removed from its current reliance on training data—it will always be looking back at us for direction. Even in a theoretical AGI future, if we built it, it would be to serve our purposes, not its own.

Challenging the AI Job Armageddon Narrative

The mainstream media is rife with "doomers" predicting massive job losses. Figures like Ray Dalio suggest half of entry-level white-collar jobs might be wiped out, and even financial titans like Ken Griffin express shock at AI's capabilities. However, Dan argues that many of these conclusions come from individuals experiencing AI's "curve of improvement" for the first time, without the context of its limitations or the long-term trends seen by early adopters like Every.

The narrative of AI-driven layoffs, such as the widely publicized tweet from the ClickUp CEO about firing thousands, is also met with skepticism. Dan suggests that such events are often more indicative of poorly managed companies, existing bloat, or strategic shifts rather than AI being the sole or primary cause. "I really don't think it's very tastefully done," he remarks, calling such announcements "self-serving." As Jensen Huang of Nvidia aptly put it, "if your answer to progress is firing people, you're not a very creative CEO."

The real-world adoption of AI is also slower and more complex than often assumed. Take customer service: companies have indeed tried to automate, laying off call center staff, only to realize months later that customers actively resist talking to machines and prefer human interaction. Poor AI implementation also yields poor results, forcing companies to backtrack. The world is complicated, and human preferences act as a significant brake on rapid, wholesale AI adoption.

AI will undoubtedly change workflows and company structures, making some tasks easier and others harder. This will necessitate "broad reorganizations of companies," but it's a far cry from a complete elimination of human work. The critical challenge is managing this transition thoughtfully and humanely, rather than using AI as a convenient scapegoat for poor business decisions.

Reimagining Value and Compensation in the AI Era

In a world increasingly shaped by AI, the definition of "what matters" is constantly evolving. Humans retain the crucial role of determining this value. AI, in a recursive loop, influences the world, which in turn changes what's valuable, placing "more onus on us to like update and decide what matters because AI is going to wait for us to be like what matters?"

This dynamic shift in value could lead to entirely new models of compensation. Dan muses about the return of "pensions" or a "last job you'll ever have" agency model, where individuals are paid based on their unique contribution to training data, which then generates revenue over time. A recent initiative for publishers, paying them based on their unique contribution to AI training corpuses, offers a glimpse into this future. The more generic, AI-like content a publisher produces, the less they get paid; the more unique and human-generated, the more valuable it becomes.

This highlights a key insight: AI companies are "hunting for net new unique data." While AI can synthesize existing information, its outputs quickly become generic and devalued. The truly valuable contribution, therefore, is fresh, unique, human-generated insight that can't be found elsewhere.

Riding the Models: Your Path to a More Ambitious Future

The core message is one of hope and empowerment. The future isn't about fearing AI; it's about embracing it. Dan's ultimate call to action is simple and profound: "If you just ride the models, you're going to be fine."

This means actively engaging with new AI tools as they emerge, learning how to integrate them into your workflow, whatever your profession. By doing so, individuals can unlock new levels of productivity, creativity, and fulfillment. AI can make an "ambitious life" more possible for more people, enabling them to do more, better, and more meaningful work than ever before. While it's possible to opt out, those who choose to "ride the models" will find themselves at the forefront of this evolving landscape, equipped to thrive.

The Craft of Ideas: Writing an 8,000-Word Thesis with AI

Even the process of articulating these complex ideas benefited from AI. Dan's journey to write his 8,000-word piece, "After Automation," was a grueling intellectual marathon. He described the difficulty of longer pieces, where "if you change something here, it changes four other things over here," making them exponentially harder than shorter articles. The challenge was to articulate an underlying feeling—a "ground truth" observed daily—that couldn't quite be cleanly put into words.

AI became an indispensable partner in this deep thinking process:

This personal anecdote underscores the very point of the article: AI isn't replacing human creativity or deep thought, but augmenting it. It's a powerful tool that enables humans to push their own boundaries, refine their ideas, and achieve intellectual feats that would be far more challenging, if not impossible, on their own.

In conclusion, the future of work isn't a zero-sum game between humans and AI. It's a collaborative dance where AI automates the predictable, making "yesterday's expert competence cheap," while simultaneously elevating the demand for uniquely human skills: direction, refinement, system-building, and the continuous definition of what truly matters. So, as Dan confidently asserts, "If you ride the models, you're going to be okay. You're going to have a job. You're going to do great work. And you don't have to worry."

Based on "It's giving incel: The evolution of internet slang | Code Switch" from NPR Podcasts Watch the original video

The Secret Lives of Slang: How Internet Algorithms and Subcultures Are Rewriting Our Language

Episode summary

This episode of Code Switch explores the evolution of internet slang, particularly how terms like "it's giving" and "maxing" filter from niche online communities into mainstream culture. Linguist Adam Alex, author of Algo Speak, explains that "maxing" originates from Dungeons and Dragons' "min-maxing" and later evolved in incel forums like 4chan to "looks maxing," a nihilistic ideology centered on optimizing one's appearance. The internet, through algorithms and "clip farming," rapidly decontextualizes and spreads these terms, often stripping them of their problematic origins, as seen with "unalive" replacing "kill" due to censorship.

The practical takeaway is to be mindful of word origins, especially as terms like "chud" and "foy" (dehumanizing terms for women) gain traction, even if used ironically. While language constantly evolves and some problematic origins fade from memory (like "long time no see" from mocking Chinese Pidgin English), the hosts and Alex argue for an "existentialist" approach: to imbue language with meaning and kindness, rather than succumbing to the "nihilistic" online view that nothing matters, especially given the internet's tendency to blur irony and authenticity, potentially normalizing harmful ideologies.

Language is a living, breathing entity, constantly shifting and evolving. But in the age of the internet, its evolution has accelerated to a dizzying pace, shaped by algorithms, subcultures, and a curious blend of irony and earnestness. From the vibrant ballrooms of New York City to the dark, often misogynistic corners of online forums, words embark on unexpected journeys, eventually landing in the mouths of mainstream culture, often stripped of their original context or even their unsettling origins.

NPR's Code Switch recently delved into this linguistic phenomenon with linguist, author, and TikToker Adam Alex, known online as "The Etymology Nerd." Alex, a Harvard-trained linguist and author of Algo Speak: How Social Media is Transforming the Future of Language, sheds light on the twin currents shaping modern slang: the appropriation of language from marginalized communities and the alarming mainstreaming of vocabulary born from extremist online spaces.

The Dance of "It's Giving" and Cultural Currents

The conversation kicks off with a seemingly innocuous anecdote. Code Switch host BA Parker recounts hearing a young white child at a Broadway show exclaim, "It's giving Christmas" in response to festive stage decorations. Co-host Gene Demby immediately recognizes the phrase's roots. "It's giving," he explains, "comes from the ballroom scene in places like New York City, and the people in that space are mostly queer, Black and Latino folks."

This is a classic example of linguistic appropriation, a process that has long been a hallmark of language evolution. Phrases and terms originating within specific, often marginalized, cultural groups are adopted by broader society. While sometimes innocuous, this process often decontextualizes the language, severing its ties to the community that created it and sometimes diminishing its original power.

"Cool" and the Echoes of Appropriation

The phenomenon isn't new. Adam Alex points to the word "cool" as a prime historical example. Its earliest records trace back to the 1880s in African-American English, later permeating jazz culture and then being embraced by beatniks before becoming a ubiquitous term of approval. Originally denoting a "nonchalance under pressure" or a form of resistance, its meaning broadened as it traveled to "increasingly peripheral groups capitalizing on the underlying idea without perhaps embodying the idea to the same extent."

The internet, however, has amplified this cycle. Ballroom slang like "slay" or "serve," once confined to specific queer Black and Latino spaces, now proliferates across TikTok and mainstream media, often used by those completely unaware of its origins. As Alex notes, "Once you see a white girl saying it on TikTok, you completely have lost the context of where it came from... but it does take power out of that original community as well." This rapid spread, driven by the internet's viral mechanisms, means that cultural artifacts are consumed and re-shared at an unprecedented rate, often losing their historical and social anchors in the process.

When Algorithms Shape Our Words: The Rise of "Algo Speak"

Beyond cultural appropriation, Alex introduces the concept of "Algo Speak," explaining how social media algorithms actively sculpt our language. This isn't just about what goes viral; it's about the subtle, and sometimes not-so-subtle, ways platforms influence how we communicate.

A classic example is the word "unalive" instead of "kill" or "suicide." Historically, platforms like TikTok have censored or suppressed certain keywords to control content, leading users to invent euphemisms to bypass these restrictions. But Alex argues that the algorithm's influence runs deeper, incentivizing influencers to adopt specific words and pushing the replication of trending keywords, whether for humor, engagement, or to navigate content moderation.

Decoding "Maxing": From D&D to the "Black Pill"

This algorithmic influence, combined with the rapid spread of online subcultures, brings us to the second, more unsettling stream of internet slang: vocabulary emerging from extremist online communities. Parker observes the ubiquity of the suffix "-maxing" in mainstream culture—from "fiber maxing" to "nothing maxing" to even "lethality maxing" (optimizing to kill as many people as possible). But where did this playful, yet sometimes sinister, linguistic trend begin?

Alex traces "maxing" back to the "min-maxing" strategies in Dungeons and Dragons and video games, where players optimize their characters for specific stats or abilities. This concept then migrated to the "looks maxing" forums of the 2010s. These forums, often tied to the "black pill" ideology, promoted the idea that one could "optimize their looks" based on a nihilistic framework where "attractiveness is the sole determiner of your sexual success and your position in society." The "black pill," contrasted with the "red pill" (associated with right-wing political awakening), posits that one's appearance dictates their destiny, leading to a fatalistic view of social interactions.

Figures like the streamer Clavicular, featured in The New York Times, exemplify this looks-maxing influencer culture, demonstrating how these niche, often toxic, ideas can bubble up from "festering in this small corner of the internet in this case 4chan" to mainstream visibility. While terms like "whimsy maxing" or "nothing maxing" may seem harmless, their linguistic parentage is rooted in these darker online spaces.

The Incels' Lexicon: "Chud," "Foy," and the Shifting Overton Window

The origins of "maxing" lead directly to the incel community—involuntary celibates. Alex explains that the term "incel" began innocently in 1997 with "Alana's Involuntary Celibate Project," an online space for individuals struggling to find romantic partners. However, it soon fractured, with a "more misogynistic group of users that broke off and created their own forums" on platforms like 4chan. This evolution led to the development of a specific lexicon that is now seeping into broader internet culture.

Alex warns of terms like "chud" and "foy" gaining traction. "Chud," which describes a "loser character in his mother's basement," harks back to the 1984 horror film Cannibalistic Humanoid Underground Dwellers and was notably used in Donnie Darko. "Foy," on the other hand, is a "dehumanizing term for women." Another term, "mog" (from "alpha male of the group"), describes looking or performing better than someone else, again rooted in incel ideology.

The danger, Alex explains, lies in how these words, often initially used ironically, contribute to shifting the "Overton window"—the range of politically acceptable discourse. "The more a certain idea gets represented," he states, "the more acceptable it is to articulate it authentically." When problematic language, even if used in jest, becomes normalized, it paves the way for the underlying, often harmful, ideologies to gain wider acceptance.

Irony, Memes, and the Perils of Decontextualization

The internet thrives on irony, and much of this problematic language initially spreads through humor and memes. "Language travels when it we see it as funny often," Alex notes. Memes born from image boards, while genuinely funny to some, often carry the baggage of their origins. The line between ironic use and earnest adoption becomes increasingly blurred online, creating a "plausible deniability" for extreme language. Someone might say something offensive and then claim, "Oh no, I was joking," allowing more radical ideas to filter through.

This blurring is exacerbated by the internet's tendency towards decontextualization. The rise of "hood irony memes"—memes "making fun of how people talk in the hood"—is another example of language being stripped of its cultural context and used for ironic effect. This creates a virtual world where, as Alex describes, "everyone's just talking in quotation marks," seeing the world through a "camp lens" where performance and humor overshadow actual effect.

The Case of "Wahi" and "Bumbleclot"

The decontextualization of words is vividly illustrated by terms like "wahi" and "bumbleclot." "Wahi," an Arabic colloquialism meaning "on God" and used with deep religious sincerity in some Muslim communities, has been adopted by Gen Z as a casual, funny interjection. Similarly, "bumbleclot," a Jamaican Patois expletive, is now often used as a humorous interjection.

The culprit here is often the "clip"—short, decontextualized snippets of videos. Streamers like IShowSpeed, who famously uses "Say wahi, bro!" in a comedic, out-of-context manner, contribute to this phenomenon. "You see something without context," Alex explains, "and you scroll and then you don't question it. You don't question the original where it came from." This "clip farming," where armies of creators intentionally seek out sensational snippets for viral potential, is a structural problem inherent to social media platforms, which prioritize profit over fostering kindness and understanding.

Why Origins Matter: A Call for Linguistic Consciousness

So, what is the impact of forgetting or never knowing the origins of words? Is it truly serious? Alex admits to the tension: "In a way, it is in a way nothing serious. You know, it's uh what what is even meaning, right? Perhaps there is no meaning whatsoever and we're just grasping at sound waves and we think this matters."

Yet, he quickly pivots to an existentialist perspective: "In another sense maybe everything matters." This stance contrasts with the nihilism prevalent in some online spaces, which declares that nothing has value. Instead, Alex argues for "imbuing everything with meaning" and embracing the "important human instinct" to build a better internet and spread kindness.

While some problematic words, like "long time no see" (which originated from mocking Chinese pigeon English), might lose their hurtful connotations over time, Alex advocates for awareness. "It is good I think to be aware of this stuff and be aware of when words have the potential to hurt people." He personally stopped using "long time no see" after learning its origin, emphasizing that "ideally if you know we all just are thoughtful about it we end up choosing the kinder option."

The evolution of internet slang is a complex tapestry woven from cultural exchange, technological innovation, and human psychology. It demonstrates how language can build bridges and reinforce divisions, how it can be a tool for humor and a vector for hate. As our digital lexicon expands at warp speed, understanding the origins and trajectories of our words becomes not just an academic exercise, but a vital act of cultural and ethical consciousness. In a world where meaning itself can feel fluid, choosing to engage thoughtfully with language is perhaps one of the most powerful ways we can shape the future of our online, and offline, interactions.