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The Human-in-the-Loop is Tired

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Yet another thought piece about LLMs. I know. Bear with me.

This is an attempt to put words around something I think most developers are experiencing right now but haven't had time to make sense of. Programming with LLMs is genuinely useful and genuinely destabilizing. These two things coexist. If we pretend the second one isn't happening, we will all burn out.

At Pydantic, we build tools that developers use to validate data, build AI agents, and observe what their systems are doing in production. We are, quite literally, in the business of making LLM-powered software more reliable. And we are also having a weird time.

This isn't a thinkpiece about whether AI will replace programmers. It's not a doomer essay and it's not a hype piece. It's an honest account of what it feels like to be a developer right now, from someone inside it, and some thoughts on what might actually help.

Hands in the fabric

When I was first learning to code in my early twenties, I remember having this distinct sensation that programming let me dip my hands into the fabric of the universe and shape it to my will. This was, of course, before I'd hit too many compile errors. But that feeling of touching some deep fundamental layer of abstraction, of being able to make things from nothing but logic, has always stuck with me.

I'm not a Computer Science graduate. I'm a designer and a programmer — formally trained in the first, self-taught in the second. I came to the formalisms of software engineering through painful experience rather than academic instruction. If anything, that made me take those principles more seriously once I understood them. When you've earned your opinions about architecture and code quality the hard way, they feel less like textbook rules and more like scar tissue.

That primal feeling of creation? It's the same promise that the low-code and no-code tools of the 2010s kept making but never quite delivered on. I'm old enough to remember building web pages in Dreamweaver, watching Adobe spruik zero-code design tools that generated absolute spaghetti under the hood. It was always almost there, just good enough to hint at a future that was just around the corner (if only you were smart enough to grasp it).

If you're cynical about the current wave of AI tools, I get it. We've been promised this before. But this time the gap between promise and reality has actually, finally, narrowed to something meaningful. And that's exactly what makes it so unsettling.

What "the code writes itself" actually feels like

Yes the code (sorta) writes itself, but the human reviewing, directing, and course-correcting feels worse, not better.

I recently had a conversation with my colleague Douwe, who maintains the Pydantic AI framework and has been one of the most thoughtful people I know about integrating LLMs into open source workflows. He described waking up to thirty PRs every morning, each one pulled overnight by someone's AI, and needing to make snap judgment calls on every single one. The temptation to delegate the review itself to an AI was enormous. But, as he put it: "at that point, what am I still doing here?".

The honest truth is that in the last few months, there have been days when I have spent close to two full days writing a plan for an LLM to execute: obsessively clarifying, specifying, re-specifying, only to have it still do something inexplicably stupid. Port a React hook into a Storybook story file. Read from the wrong plan. Invent components that don't exist. And these aren't errors of capability; they're errors of coherence. The models are smart enough to produce plausible code, but not always smart enough to maintain a coherent intent across a complex change.

This creates a peculiar new kind of fatigue, the fatigue of supervision: of holding the intent in your head while the machine generates volumes of mostly-correct output that still needs your eyes, your judgment, and your taste. Douwe put it well: he used to get a dopamine hit from collaborating with a real person on a cool feature in open source. Helping someone become better at their craft. Now, he said, "everything I write goes into some AI black hole. There's no person on the other side actually learning anything." That loss is real and it's worth naming.

The intensity trap

Simon Willison recently highlighted a Berkeley Haas study which describes how AI usage increases the intensity of work. The constant pull of "one more prompt at the end of the day, one more feature that could make this perfect." I felt that one in my bones. I was up until nearly 2am recently, prompting, because I was so close to getting a plan right. Or so I thought.

It's all a part of the plan

Marcelo, another Pydantic colleague, when asked about his Claude Code session freezing said: "just open 5 claude sessions. You'll never notice because you're busy giving feedback to the others." He was joking. I think. But it captures something true about the current moment. The parallelism is exhilarating and kind of feral. The number of things you can start has dramatically increased. The number of things you can thoughtfully finish hasn't changed at all, because that part still requires the one resource we can't parallelise: your brain.

Here's a term for what I think is happening: the human reward function problem. In machine learning, a reward function tells an agent what good looks like. Writing code by hand was never easy, but it was full of small rewards. Solving a problem in your head. Understanding a gnarly bit of logic. Watching the code compile. The feeling of control. LLM-assisted programming has automated much of the work that generated those dopamine hits and replaced it with the cognitive load of review and supervision. The satisfying part shrank. The exhausting part grew. And there are no new rewards to fill the gap.

If you're feeling like your work is simultaneously more productive and less satisfying, you're not broken. The feedback loop is broken. And I think we need to start treating that as an engineering problem in its own right, not a personal failure.

It's also, frankly, quite lonely. Programming with an LLM is an intensely solitary activity.

You and the machine, going back and forth, refining and prompting and reviewing. The natural moments where you'd turn to a colleague to ask a question, to rubber-duck a problem, to share the small victory of something finally clicking. Those moments get quietly replaced by another prompt. In a team without a strong existing culture of collaboration, this has a tendency to further separate people, to chill communication at precisely the moment when you most need the reassurance that other humans are finding this hard too.

And it's addictive in a way that makes the isolation worse. Sometimes you get something brilliant, sometimes garbage, and you never quite know which. Textbook Skinner Box. It can be genuinely hard to step back and remember that you're allowed to just... write code. But switching between LLM-assisted and manual work is jarring and uncomfortable, two very different modes of thinking, and it takes a kind of maturity and confidence to give yourself permission to switch.

Breakpoints

This moment brings to mind the fear and angst caused by responsive design. I was working as a designer and frontend developer at the time, following Ethan Marcotte and the Zeldman / A Book Apart crowd like everyone else, and I remember how unsettling it felt to be told that the fixed-width layouts we'd all mastered were basically over.

For the younger devs: there was a genuine cultural moment around 2009 when websites moved from fixed, pixel-perfect, magazine-style layouts to fluid, responsive ones. And designers hated it. The loss of control was existential for people whose entire identity was built around precise layouts and perfect grids. You're telling me the user might see my design at any width? On any device? That the layout I crafted would... flow?

Responsive design animation

Image design by Jyotika Sofia Lindqvist

The resistance was intense. And it was understandable. People had built real expertise in a paradigm that was being fundamentally disrupted. The designers who thrived through that transition were the ones who reframed their skills. The eye for proportion still mattered. The understanding of hierarchy still mattered. The craft didn't die, it evolved. What became less relevant was the obsession with pixel-level control. What became more relevant was understanding systems, adaptability, and designing for uncertainty.

I don't want to oversell this parallel. Responsive design played out over years. The current shift is measured in months. Agencies lost clients and designers lost gigs over the responsive transition, but it didn't carry the same existential dread. The stakes are materially different, and the pace is genuinely exhausting in a way that the responsive transition never was. But the underlying pattern, of craft evolving rather than dying, of the core skills mattering more not less, I think that holds.

Working with LLMs on code feels like a similar inflection point. The skill isn't gone, it's shifting. You're not less of an engineer because you didn't hand-write every line. But you do still need to know what good looks like, arguably more than ever, because you're now the quality gate for a much higher volume of output.

What survives

In an era when anyone can produce reasonable-looking UI and code that compiles, the distinguishing markers become: taste, nuance, mature architectural opinions, and the contrarian calls that come from genuine expertise rather than pattern-matching.

It's noticeable to me that we are most successful guiding LLMs in the domains where we understand the code, the decisions, and the trade-offs most deeply. As we venture into the shallow ends of our skill sets, the outputs become markedly more impressionistic. Further from production-ready. More plausible-looking, less actually correct. The model doesn't know what it doesn't know, so it fills the gaps with confidence. Sound familiar? It's a very human failure mode, too.

But new skills are also emerging. I've started running what I call pre-mortems on complex plans: asking a fresh LLM session to assume the plan has catastrophically failed and diagnose why. It catches specification gaps that I miss after two days of being too deep in the details. One of our engineers built a tool that extracts rules from thousands of his past code review comments to seed an AGENTS.md file, essentially encoding years of implicit engineering judgment into instructions an LLM can follow. That's not the death of expertise. That's expertise being distilled.

The people who are finding their footing right now seem to share a few traits: they have strong opinions earned through practice, they can distinguish between principles that still apply and habits that were just bandwidth constraints, and they're willing to evolve their workflow without abandoning their standards.

A view from inside the loop

I don't think the current wave of AI represents the end of software engineering as a profession. I do think it represents a serious contraction and a fundamental reshaping of what the work is. The fear of obsolescence is legitimate. The fear of skill rot is legitimate. And the fear that if you don't go fast enough you'll be left behind is — while often overstated — not entirely unfounded.

But the bottleneck was never the code. It was always the human attention, the engineering judgment, the ability to hold a coherent vision for a system. We just didn't notice because writing code felt like the hard part. Now that it's being automated, those human capacities are revealed as the actual scarce resource. And scarce resources are valuable.

So if you're feeling overwhelmed, destabilized, simultaneously more productive and less happy, know that you're not alone. The team building the tools you're probably using to navigate this moment is feeling it too. We're debugging our reward functions in real time, same as you.

The code is changing. What we do with it is changing. How it feels is... a work in progress.

But the humans are still in the loop. We're just tired. And that's worth talking about.


We're building tools to make this less chaotic: Pydantic AI and Logfire. We're also hiring.

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emrox
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No, People Don’t Want More AI In Their Life

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Many companies silently assume that everybody wants more AI in their lives. That people are craving new AI features, new AI products, new AI workflows — that would all magically replace all existing outdated practices and broken ways of working.

But in reality, it seems like people don’t want more AI at all — at least not in the way most AI leaders envision it. Unsurprisingly, many AI features have low adoption and retention — at a very high cost of delivery, and a high risk of reputation damage.

The AI People Don’t Need

It’s remarkably difficult to make a strong argument with senior leadership, but AI is not a value proposition. New AI features don’t magically make for happy or excited customers. Because AI features are often bolt-ons and separate tools for employees to use, they typically take people out of their regular way of working.

AI is pretty good at amplifying shortcuts and shortcomings in organizations — from data quality to decision making. It can’t magically fix years of accumulated quick patches, technical debt, broken culture and internal politics. If anything, they become more visible with AI as inconsistencies or conflicting priorities and get handed directly to users, who are then left to make sense of the mess themselves.

Because in most organizations, work typically requires hopping on and off between plenty of disconnected and fragmented systems, with a new AI tool, they now have yet another system that they also need to hop on and off. Often it produces more work, and typically it’s not particularly rewarding work either.

On top of that, people are very much aware of the cost of finding and fixing AI hallucinations. Asking AI to generate a response might feel easier than writing from scratch, but it has a cost:

  • Skim through the entire AI output,
  • Spot key points to focus attention on,
  • Review/verify key points, one-by-one,
  • Check rationale for what follows next,
  • Articulate corrections + regenerate,
  • Review the response (a number of times).

For many people, AI isn’t something they can proactively choose and explore on their own — it arrives uninvited, at someone else’s pace. On top of that, plenty of messages amplify fears and worries about AI replacing work — so it’s hardly surprising that the perception of AI isn’t excitement. It’s resistance to change and deep anxiety about one’s place in a world that seems to be changing without them.

At best, AI features might be silently accepted or nodded away. At worst, AI raises concerns, doubts, caution — and calls for a healthy dose of skepticism. And sometimes it’s perceived as a threat or liability — because unlike other features, AI is neither predictable nor reliable.

People don’t dream of AI art museums or AI fridges or AI hotel reception or AI-narrated children’s books. They don’t want their children to have romantic AI partners. Most people don’t want to actively manage (and clean up after) a swarm of AI agents roaming in their bank accounts and acting on their behalf in the real world. And most notably, people don’t really want a magical box to speak to or type into all the time.

The AI People Actually Need

I’m always puzzled by the comparison of AI features with how unreliable humans are. But people don’t compare software with other people. They compare features with features — and if one feature in one product is unreliable, while a similar feature works flawlessly in another, they choose the latter. It’s not about AI or not AI, but rather what works consistently and reliably, and what doesn’t.

Many conversations about AI are conversations about the speed of delivery. But to many people, there is little value in increasing the speed of delivery. They want to do things well, with enough time to think and make good decisions. They also want to enjoy the time they spend working on things, rather than just ship faster. There is an enormous feeling of reward and achievement that slowly disappears, one vibe-coded change at a time.

People don’t change much. And after all these years, they (still) want features that are fast, accessible, reliable, predictable and useful — every single time. And ideally not the ones that replace their entire workflow, but that augment their way of working — and that take over the most mundane, annoying, and boring tasks that they find no pleasure in.

Many jobs are exposed to AI automation, but in many of them there is a rewarding, unique, creative part that requires taste, point of view, and perhaps even human intuition. And if AI automates boring parts of it, that’s an advantage for everyone. That’s also what enhances productivity and brings more joy in daily life.

When AI automates tedious and mentally exhausting tasks, its value is much easier to grasp. But for that, AI shouldn’t feel like a bolt-on. It should be deeply integrated into people’s existing workflows. It must also match existing mental models that they have developed and fine-tuned for years or decades. AI should adapt to how people think and make decisions, not the other way around.

And it doesn’t really matter if these features are branded as “AI”, “smart” or “automation”. However, they must work well for people using them. And that means that people must be aware of use cases where it actually helps them, and be inspired to find more use cases on their own.

Ironically, tools that work well there aren’t “AI-first” — they are “AI-second”. Subtle, humble, calm, ambient, taking a supportive role in the background for work that otherwise is remarkably dull and unnecessary.

I don’t want to read books written by AI. I don’t want to gaze upon paintings by AI. I don’t want AI to teach my children. I don’t want to have an AI therapist. I don’t want AI making my medical decisions. I want AI to do all the physical and mental labor that taxes me so I can read books written by humans and go to art galleries to engage with art made by humans. I want AI that makes my life easier rather than forces me to change myself.

Bo Young Lee
Wrapping Up

Perhaps I’m missing a bigger picture, and perhaps I’m just old school — but I really do like people. Their stories, their thinking, their emotions, their enthusiasm, their laughing. AI can be remarkably helpful in many situations, but so are people. And between the two, I would favor spending time with a human — however imperfect they are — every single time.

No, people don’t need more AI in their lives — they need AI to automate all the boring stuff they have to deal with every day, so they have more time and headspace to do things that they actually love and enjoy doing. That doesn’t mean spending more time with AI — but spending more time with people they love.

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emrox
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July 2026 Security Release

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The July 2026 security release for Next.js is now available
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Beyond the Prompt: The Social Costs of Generative Artificial Intelligence

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The Business, Entrepreneurship & Tax Law Review

Abstract

The rapid commercialization of generative artificial intelligence has produced extraordinary value for technology companies while systematically externalizing costs onto society. This Article provides a review of these externalities across three domains: environmental, human health, and digital infrastructure. The environmental burden includes massive energy and water consumption alongside ecologically destructive mining for rare earth elements and accelerating electronic waste. The human toll encompasses a documented pattern of AI-induced mental health crises, including suicide and self-harm linked to anthropomorphic chatbot design, raising novel questions of product liability in cases like Garcia v. Character.AI. The digital commons face degradation through industrialized data scraping enabled by the Ninth Circuit’s hiQ Labs v. LinkedIn decision, which effectively stripped platforms of their primary defense under the Computer Fraud and Abuse Act. This Article argues that the AI industry’s business model is predicated on a fundamental market failure: the privatization of benefit and socialization of cost. Until the costs of extraction, defense, and human harm are borne by those who generate the risk, the AI revolution will continue to levy an uncompensated tax on the broader economy.

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Ableton Live and Push can now run on Linux, unofficially

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Finally, proof that there's life for Live after Apple and Microsoft. It's unofficial and totally unsupported, but a set of patches for the free Wine compatibility environment lets you run Ableton Live 12, Max for Live, and Push 2 and Push 3 on Linux. And the result is just about indistinguishable from native support.

The post Ableton Live and Push can now run on Linux, unofficially appeared first on CDM Create Digital Music.

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emrox
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Stop saying that AI is just a tool and it only matters how it is used

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[2026 Update]

This post has garnered significant traffic to my blog as well as discussion on and offline over the last 10 months. For some google search queries (such as “ai just a tool”), this ranks in the top-2 search results consistently. So for folks finding this post, and my thoughts on the matter, I want to note that I’ve given a keynote on “tool-making” (which is the subject of my PhD) and I encourage you to read this blog and then consider watching the first 10 minutes (or so) of my talk. The introduction is a concrete walk-through of some of the ideas I discuss here in this blog. These two complement each other well:

And I have also recently written more, about very similar problems, in terms of how modern generative AI influences prototyping, and threatens what prototyping should be about. So if you’re interested in further reading after this, consider checking out that post on genAI+prototyping.

Original post start:

I’ve been thinking constantly about the common and casual phrase I’ve heard so often, “AI is just a tool - it matters how you use it.” This has been the rallying cry of tech-loving academics who no longer do their own research, tech bros who salivate over generative images of criminal depictions of people without their consent, and business-minded folks who actually don’t care about AI but see this as an opportunity to rake in more and more money for themselves.

The phrase is deceptively simple and deceptively misleading. Yes, AI is a tool. And yes, it is important how we choose to use tools.

But the phrase’s core reasoning is insultingly naive. It doesn’t work well for most things: “A car is just a tool, it matters how you drive it.” Well… oil and gas is destroying the climate, seatbelts help save lives whether or not someone is a good driver, and since the invention of cars, American city design has become utterly unwalkable and unlivable.

So there is much more to tools than how we use them. And since I have seen this phrase used by award-winning, highly successful HCI researchers, I can’t help but wonder if some people really just want to shut up folks who disagree with them. Are these academics just afraid their ethics are being interrogated? Or do some people believe so strongly in the benefits of AI that they really don’t care for the downsides? I’m not sure why some cling so feverishly to this childish mantra that “AI is just a tool,” but I certainly lose respect any time I see someone who should know better use it.

Have we not talked about how all artifacts have politics in our discipline for decades and decades? Tools are massively impactful on our environment, law, policy, and what it means to be human. Believing that AI is “just a tool” is naive at best and dismissive at worst because nothing about tools is “just” anything. They are highly complex parts of life and culture.

The last part of the phrase, “it matters how you use it” is also deceptively misleading and overly simplistic. Oh really? The entirety of all ethics involved in modern technological ecosystems and infrastructures rests solely on how a singular person chooses to use something? Individual action won’t solve all of our problems. Some ethical issues are systemic and require more than just one person choosing the right method for using a piece of technology.

The reason people say something like this is because it immediately invites solutionism. “It matters how you use it” is an intellectual half-gesture. The audience who hears that phrase will sagely agree, “ah, of course, in my wisdom I know how to use things well. And this means that is all there is to it!” It turns people into fools, thinking they are wizards. “It matters how you use it” is then a glaringly simple, solveable problem space: well, some people just don’t know. “All we need to do is teach people how to swing a hammer, and then hammers are ethically good!” Nonsense.

Even a hammer, made of wood and iron, requires trees to be cut down and earth to be mined up. A simple hammer requires laws to be written about fair treatment of workers in multiple industries, sustainability of various biological and geological environments, and regulation about the sale and use of the hammer. “It matters how you use it,” in regards to artificial intelligence ignores the reality that it also matters how AI is made, how AI is disseminated, the waste AI produces, the damage AI causes to economies and environments, and the overall impact that AI has on human life and culture.

“It matters how you use it” is something that an immature and self-absorbed young child would say, a child who has yet to reckon with the reality that they live in a society full of other people and other living organisms and participates in a system of entities that are all constantly fighting for fairness, dignity, and survival.

I loathe the phrase, “AI is just a tool, it matters how you use it.”

And tools use us by their design. This is Heidegger’s Gestell (“en-framing”): the notion that technologies shape who we are because of their design and use. A hammer isn’t just made of wood and iron, then. A hammer is a hammer because of what it does and who we become when we use it.

Tools, then, aren’t “neutral” in any way.

My dissertation centers on this tension and builds on it: well, if tools aren’t neutral - then what? In my thesis, I focus on the accessibility of visualizations, with tool design as an intervention. But the concepts, imperatives, and calls to action in my dissertation can be applied more broadly:

We must interrogate and reshape our technologies. We need to fight back against design that flattens our humanity at the benefit of efficiency and productivity. We need to question how our tools have created infrastructures and landscapes that are hostile to human existence. And of course:

We must interrogate how tools shape us, by their design.

Take the “chair:”

Anna Gyllenklev writes, “Ever feel like your chair is bossing you around? “Sit still. Face forward. Behave.”

A chair orders you to sit and sit in a particular way, by its design.

Your being is intended through the tool: you are intended to sit still, face forward, and behave. Artificial intelligence works in exactly the same way. We might use these tools believing that “it’s all in how you use them” - and yet, still, our tools are using us. Our being is, perhaps more now than it has ever been, intended to become reliant on our tooling. All tools do this, it isn’t new.

But artificial intelligence, far more than any tool we’ve ever created, intends us not just to sit forward and behave, but to cease to think critically, to cease to imagine, and, most temptingly, to cease to feel struggle and pain.

Knowing the difference between drudgery and meaningful struggle

illustration of Miyazaki drawing with cigarettes in his mouth in profile side with the caption "If life's hassles disappeared, you'd want them back." "If life's hassles disappeared, you'd want them back," - Hayao Miyazaki. (Also, this is real human art!) Credit: Sam Yang, @samdoesarts.bsky.social

The greatest selling point of automation has always been to remove drudgery. And at the heart of drudgery is a certain variety of struggle and pain.

Artificial intelligence in our modern imagination and material reality is sold to consumers as a solution to all struggle: we can simply ask for art and it materializes before us. There is no struggle at all involved, thus the terrible labor of being an artist is removed!

But is all struggle the same thing as drudgery?

And AI is not new, in this regard. The flattening of all pains into a total loss of pain has previously been the job of recreational drug use or theology. So AI is therefore more like an opiate than anything else. Or perhaps, given the fervor of its modern supplicants, it is more like a religion on drugs.

Modern automation of everything, including art, thinking, and writing, numbs who we are. Total automation softens our ability to discern between struggle that makes and pain that takes.

How you answer these two questions should inform how you treat the use of AI:

If it was possible: Should we climb a mountain, or flatten it? And should we climb a curb, or cut it?

Climbing a mountain is the point: the struggle and overcoming it is what matters. But a curb? A curb is a barrier to access. The struggle against a curb shouldn’t exist. This is why, in accessibility, we try to cut curbs and flatten barriers whenever we can.

Take the gym, for example: struggle against the pain of exercise is rewarding and uplifting. The weights don’t have to be moved, lifting them isn’t a required task of us. It would be nonsense to ask a robot to lift weights for us at the gym.

However, tools and technologies that improve how we lift weights are a recognition of our love of lifting. Newer, safer weight lifting machines, protections from dropped weights, stronger cables, mirrors in front of the dumbells, and so on. Many technologies exist to enhance our human love of struggle.

But we cease to feel struggle when we use AI. We don’t need to write our mothers a well-meaning email on her birthday, we don’t need to make the case for our promotion to our bosses, we don’t need to think through the hard parts of an algorithm we are writing, and, when it comes to art, we don’t need to feel the pain of improving our craft. We simply prompt, and (optionally) we could choose to do the work of validating whatever it came up with. But of course, automating validation is just another thing that modern AI-dreamers dream of.

Artificial intelligence is the quintessential tool-as-a-drug. It operates with an economy of infinity, as if there is no downside to any interaction and no risk or cost involved in anything we do.

But the greatest cost comes in how our tooling shapes us and “flattens our being” (as Heidegger writes). This is because truly feeling and experiencing pain and struggle is central to our humanity. We are both unique individuals and collectively unified through struggle. So a tool that intends for us to never struggle is at fundamental odds with the pains that shape us and our ability to understand each other.

And on the chair analogy: we can refuse to use chairs as they are designed (or even entirely). And we can use chairs for more than sitting. And we can design new chairs and non-chairs that do any sort of thing. We have the power and the responsibility to make our technologies shape humanity into something good and meaningful.

So what do we do with AI?

For a student who used AI to write a paper: Now I let it fall back in the grasses. I hear you. I know this life is hard now. I know your days are precious on this earth. But what are you trying to be free of? The living? The miraculous task of it? Love is for the ones who love the work. Presently, my favorite poem. Credit: Joseph Fasano, @josephfasano.bsky.social

Tools are immensely influential: they have the ability to mold humanity, to include and exclude, to define what matters, and to literally shape the climate and environments we live in. “Tools” are radically powerful extensions of human will.

I want to argue that AI agents (as the corporate-controlled transformer and diffusion based models of our modern day) are largely bad to use, especially now, and in most all contexts. Their dangers are environmental, economic, and existential. As a “tool” they are far too destructive.

On the environment: modern AI agents have accelerated climate change and come at an immense cost to our already precarious world. Continuing to use them is actively consenting to their ongoing destruction of our fresh water and energy resources. However, like many environmentally destructive industries, we could reign them in with policy and better, more efficient tech and infrastructure. Maybe someday the environmental damage will be under control and AI will be truly “sustainable.”

On the economics of AI: Modern multi-billion parameter AI models are scaffolded on and made possible by the largest heist in human history: theft of everything that could be scraped from every corner of the digital spaces we share. Without prevention of and justice for this damage caused by current models, their use is highly fraught, ethically. We, as human beings, have developed complex social forms of intelligence when it comes to dealing with things like credit and provenance, two things that modern models are incapable of. And without monetary and policy recognition of the entire global economy of labor that enabled current AI models, using them is active permission given to the theft of all human art and knowledge.

On our existence:

Tina He writes on our ontological crisis with modern AI, “we are awakened to the danger precisely through contact with it. The same algorithmic indifference that unsettles us may also jolt us into a higher vigilance, a refusal to hand over the entirety of our experience to optimization, market logic, or digital control. The very anxiety these systems produce is a clue: something vital, unquantifiable, and irreducibly human still resists.” He continues, “This isn’t about throwing away the tools, but about wrestling them into alignment with what we find sacred or essential.”

So that is our charge. Our job now is the same as it always as been: to fight for our own humanity and for the health of the world, to not use tools uncritically, and to shape our tools before they shape us into flat nothingness. We can turn these modern models into things that mean something to us, but we need policy, economic justice, and guardrails in place. We need to reimagine what they should be for and continue to explore and innovate ways that we can continue to create and experience meaningfully.

Go and do what machines cannot: advocate and fight for policy change, resist and refuse unjust systems, recognize by name those who taught and inspired you, “appreciate [your] predecessors and fellow-workers in the saltmines of literature,” as Le Guin remarks, and feel the good kind of pain that gives us shape and meaning; become.

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6 days ago
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Hamburg, Germany
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