AI Angst

My input stream is full of it: Fear and loathing and cheerleading and prognosticating on what generative AI means and whether it’s Good or Bad and what we should be doing. All the channels: Blogs and peer-reviewed papers and social-media posts and business-news stories. So there’s lots of AI angst out there, but this is mine. I think the following is a bit unique because it focuses on cost, working backward from there. As for the genAI tech itself, I guess I’m a moderate; there is a there there, it’s not all slop. But first…

The rent is too damn high · I promise I’ll talk about genAI applications but let’s start with money. Lots of money, big numbers! For example, venture-cap startup money pouring into AI, which as of now apparently adds up to $306 billion. And that’s just startups; Among the giants, Google alone apparently plans $75B in capital expenditure on AI infrastructure, and they represent maybe a quarter at most of cloud capex. You think those are big numbers? McKinsey offers The cost of compute: A $7 trillion race to scale data centers. ¶

Obviously, lots of people are wondering when and where the revenue will be to pay for it all. There’s one thing we know for sure: The pro-genAI voices are fueled by hundreds of billions of dollars worth of fear and desire; fear that it’ll never pay off and desire for a piece of the money. Can you begin to imagine the pressure for revenue that investors and executives and middle managers are under?

Here’s an example of the kind of debate that ensues.

I suggest that when you’re getting a pitch for genAI technology, you should have that greed and fear in the back of your mind. Or maybe at the front.

And that’s just the money · For some reason, I don’t hear much any more about the environmental cost of genAI, the gigatons of carbon pouring out of the system, imperilling my children’s future. Let’s please not ignore that; let’s read things like Data Center Energy Needs Could Upend Power Grids and Threaten the Climate and let’s make sure every freaking conversation about genAI acknowledges this grievous cost. ¶

Now let’s look at a few sectors where genAI is said to be a big deal: Coding, teaching, and professional communication. To keep things balanced, I’ll start in a space where I have kind things to say.

Coding · Wow, is my tribe ever melting down. The pro- and anti-genAI factions are hurling polemical thunderbolts at each other, and I mean extra hot and pointy ones. For example, here are 5600 words entitled I Think I’m Done Thinking About genAI For Now. Well-written words, too. ¶

But, while I have a lot of sympathy for the contras and am sickened by some of the promoters, at the moment I’m mostly in tune with Thomas Ptacek’s My AI Skeptic Friends Are All Nuts. It’s long and (fortunately) well-written and I (mostly) find it hard to disagree with.

it’s as simple as this: I keep hearing talented programmers whose integrity I trust tell me “Yeah, LLMs are helping me get shit done.” The probability that they’re all lying or being fooled seems very low.

Just to be clear, I note an absence of concern for cost and carbon in these conversations. Which is unacceptable. But let’s move on.

It’s worth noting that I learned two useful things from Ptacek’s essay that I hadn’t really understood. First, the “agentic” architecture of programming tools: You ask the agent to create code and it asks the LLM, which will sometimes hallucinate; the agent will observe that it doesn’t compile or makes all the unit tests fail, discards it, and re-prompts. If it takes the agent module 25 prompts to generate code that while imperfect is at least correct, who cares?

Second lesson, and to be fair this is just anecdata: It feels like the Go programming language is especially well-suited to LLM-driven automation. It’s small, has a large standard library, and a culture that has strong shared idioms for doing almost anything. Anyhow, we’ll find out if this early impression stands up to longer and wider industry experience.

Turning our attention back to cost, let’s assume that eventually all or most developers become somewhat LLM-assisted. Are there enough of them, and will they pay enough, to cover all that investment? Especially given that models that are both open-source and excellent are certain to proliferate? Seems dubious.

Suppose that, as Ptacek suggests, LLMs/agents allow us to automate the tedious low-intellectual-effort parts of our job. Should we be concerned about how junior developers learn to get past that “easy stuff” and on the way to senior skills? That seems a very good question, so…

Learning · Quite likely you’ve already seen Jason Koebler’s Teachers Are Not OK, a frankly horrifying survey of genAI’s impact on secondary and tertiary education. It is a tale of unrelieved grief and pain and wreckage. Since genAI isn’t going to go away and students aren’t going to stop being lazy, it seems like we’re going to re-invent the way people teach and learn. ¶

The stories of students furiously deploying genAI to avoid the effort of actually, you know, learning, are sad. Even sadder are those of genAI-crazed administrators leaning on faculty to become more efficient and “businesslike” by using it.

I really don’t think there’s a coherent pro-genAI case to be made in the education context.

Professional communication · If you want to use LLMs to automate communication with your family or friends or lovers, there’s nothing I can say that will help you. So let’s restrict this to conversation and reporting around work and private projects and voluntarism and so on. ¶

I’m pretty sure this is where the people who think they’re going to make big money with AI think it’s going to come from. If you’re interested in that thinking, here’s a sample; a slide deck by a Keith Riegert for the book-publishing business which, granted, is a bit stagnant and a whole lot overconcentrated these days. I suspect scrolling through it will produce a strong emotional reaction for quite a few readers here. It’s also useful in that it talks specifically about costs.

That is for corporate-branded output. What about personal or internal professional communication; by which I mean emails and sales reports and committee drafts and project pitches and so on? I’m pretty negative about this. If your email or pitch doc or whatever needs to be summarized, or if it has the colorless affectless error-prone polish of 2025’s LLMs, I would probably discard it unread. I already found the switch to turn off Gmail’s attempts to summarize my emails.

What’s the genAI world’s equivalent of “Tl;dr”? I’m thinking “TA;dr” (A for AI) or “Tg;dr” (g for genAI) or just “LLM:dr”.

And this vision of everyone using genAI to amplify their output and everyone else using it to summarize and filter their input feels simply perverse.

Here’s what I think is an important finding, ably summarized by Jeff Atwood:

Seriously, since LLMs by design emit streams that are optimized for plausibility and for harmony with the model’s training base, in an AI-centric world there’s a powerful incentive to say things that are implausible, that are out of tune, that are, bluntly, weird. So there’s one upside.

And let’s go back to cost. Are the prices in Riegert’s slide deck going to pay for trillions in capex? Another example: My family has a Google workplace account, and the price just went up from $6/user/month to $7. The announcement from Google emphasized that this was related to the added value provided by Gemini. Is $1/user/month gonna make this tech make business sense?

What I can and can’t buy · I can sorta buy the premise that there are genAI productivity boosts to be had in the code space and maybe some other specialized domains. I can’t buy for a second that genAI is anything but toxic for anything education-related. On the business-communications side, it’s damn well gonna be tried because billions of dollars and many management careers depend on it paying off. We’ll see but I’m skeptical. ¶

On the money side? I don’t see how the math and the capex work. And all the time, I think about the carbon that’s poisoning the planet my children have to live on.

I think that the best we can hope for is the eventual financial meltdown leaving a few useful islands of things that are actually useful at prices that make sense.

And in a decade or so, I can see business-section stories about all the big data center shells that were never filled in, standing there empty, looking for another use. It’s gonna be tough, what can you do with buildings that have no windows?

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