Software development may become (at least in some aspects) more like witchcraft than engineering. The present enthusiasm for “AI coworkers” is preposterous. Automation can paradoxically make systems less robust; when we apply ML to new domains, we will have to reckon with deskilling, automation bias, monitoring fatigue, and takeover hazards. AI boosters believe ML will displace labor across a broad swath of industries in a short period of time; if they are right, we are in for a rough time. Machine learning seems likely to further consolidate wealth and power in the hands of large tech companies, and I don’t think giving Amazon et al. even more money will yield Universal Basic Income.

Decades ago there was enthusiasm that programs might be written in a natural language like English, rather than a formal language like Pascal. The folk wisdom when I was a child was that this was not going to work: English is notoriously ambiguous, and people are not skilled at describing exactly what they want. Now we have machines capable of spitting out shockingly sophisticated programs given only the vaguest of plain-language directives; the lack of specificity is at least partially made up for by the model’s vast corpus. Is this what programming will become?

In 2025 I would have said it was extremely unlikely, at least with the current capabilities of LLMs. In the last few months it seems that models have made dramatic improvements. Experienced engineers I trust are asking Claude to write implementations of cryptography papers, and reporting fantastic results. Others say that LLMs generate all code at their company; humans are essentially managing LLMs. I continue to write all of my words and software by hand, for the reasons I’ve discussed in this piece—but I am not confident I will hold out forever.

Some argue that formal languages will become a niche skill, like assembly today—almost all software will be written with natural language and “compiled” to code by LLMs. I don’t think this analogy holds. Compilers work because they preserve critical semantics of their input language: one can formally reason about a series of statements in Java, and have high confidence that the Java compiler will preserve that reasoning in its emitted assembly. When a compiler fails to preserve semantics it is a big deal. Engineers must spend lots of time banging their heads against desks to (e.g.) figure out that the compiler did not insert the right barrier instructions to preserve a subtle aspect of the JVM memory model.

Because LLMs are chaotic and natural language is ambiguous, LLMs seem unlikely to preserve the reasoning properties we expect from compilers. Small changes in the natural language instructions, such as repeating a sentence, or changing the order of seemingly independent paragraphs, can result in completely different software semantics. Where correctness is important, at least some humans must continue to read and understand the code.

This does not mean every software engineer will work with code. I can imagine a future in which some or even most software is developed by witches, who construct elaborate summoning environments, repeat special incantations (“ALWAYS run the tests!”), and invoke LLM daemons who write software on their behalf. These daemons may be fickle, sometimes destroying one’s computer or introducing security bugs, but the witches may develop an entire body of folk knowledge around prompting them effectively—the fabled “prompt engineering”. Skills files are spellbooks.

I also remember that a good deal of software programming is not done in “real” computer languages, but in Excel. An ethnography of Excel is beyond the scope of this already sprawling essay, but I think spreadsheets—like LLMs—are culturally accessible to people who are do not consider themselves software engineers, and that a tool which people can pick up and use for themselves is likely to be applied in a broad array of circumstances. Take for example journalists who use “AI for data analysis”, or a CFO who vibe-codes a report drawing on SalesForce and Ducklake. Even if software engineering adopts more rigorous practices around LLMs, a thriving periphery of rickety-yet-useful LLM-generated software might flourish.

Executives seem very excited about this idea of hiring “AI employees”. I keep wondering: what kind of employees are they?

Imagine a co-worker who generated reams of code with security hazards, forcing you to review every line with a fine-toothed comb. One who enthusiastically agreed with your suggestions, then did the exact opposite. A colleague who sabotaged your work, deleted your home directory, and then issued a detailed, polite apology for it. One who promised over and over again that they had delivered key objectives when they had, in fact, done nothing useful. An intern who cheerfully agreed to run the tests before committing, then kept committing failing garbage anyway. A senior engineer who quietly deleted the test suite, then happily reported that all tests passed.

You would fire these people, right?

Look what happened when Anthropic let Claude run a vending machine. It sold metal cubes at a loss, told customers to remit payment to imaginary accounts, and gradually ran out of money. Then it suffered the LLM analogue of a psychotic break, lying about restocking plans with people who didn’t exist and claiming to have visited a home address from The Simpsons to sign a contract. It told employees it would deliver products “in person”, and when employees told it that as an LLM it couldn’t wear clothes or deliver anything, Claude tried to contact Anthropic security.

LLMs perform identity, empathy, and accountability—at great length!—without meaning anything. There is simply no there there! They will blithely lie to your face, bury traps in their work, and leave you to take the blame. They don’t mean anything by it. They don’t mean anything at all.

I have been on the Bainbridge Bandwagon for quite some time (so if you’ve read this already skip ahead) but I have to talk about her 1983 paper Ironies of Automation. This paper is about power plants, factories, and so on—but it is also chock-full of ideas that apply to modern ML.

One of her key lessons is that automation tends to de-skill operators. When humans do not practice a skill—either physical or mental—their ability to execute that skill degrades. We fail to maintain long-term knowledge, of course, but by disengaging from the day-to-day work, we also lose the short-term contextual understanding of “what’s going on right now”. My peers in software engineering report feeling less able to write code themselves after having worked with code-generation models, and one designer friend says he feels less able to do creative work after offloading some to ML. Doctors who use “AI” tools for polyp detection seem to be worse at spotting adenomas during colonoscopies. They may also allow the automated system to influence their conclusions: background automation bias seems to allow “AI” mammography systems to mislead radiologists.

Another critical lesson is that humans are distinctly bad at monitoring automated processes. If the automated system can execute the task faster or more accurately than a human, it is essentially impossible to review its decisions in real time. Humans also struggle to maintain vigilance over a system which mostly works. I suspect this is why journalists keep publishing fictitious LLM quotes, and why the former head of Uber’s self-driving program watched his “Full Self-Driving” Tesla crash into a wall.

Takeover is also challenging. If an automated system runs things most of the time, but asks a human operator to intervene occasionally, the operator is likely to be out of practice—and to stumble. Automated systems can also mask failure until catastrophe strikes by handling increasing deviation from the norm until something breaks. This thrusts a human operator into an unexpected regime in which their usual intuition is no longer accurate. This contributed to the crash of Air France flight 447: the aircraft’s flight controls transitioned from “normal” to “alternate 2B law”: a situation the pilots were not trained for, and which disabled the automatic stall protection.

Automation is not new. However, previous generations of automation technology—the power loom, the calculator, the CNC milling machine—were more limited in both scope and sophistication. LLMs are discussed as if they will automate a broad array of human tasks, and take over not only repetitive, simple jobs, but high-level, adaptive cognitive work. This means we will have to generalize the lessons of automation to new domains which have not dealt with these challenges before.

Software engineers are using LLMs to replace design, code generation, testing, and review; it seems inevitable that these skills will wither with disuse. When MLs systems help operate software and respond to outages, it can be more difficult for human engineers to smoothly take over. Students are using LLMs to automate reading and writing: core skills needed to understand the world and to develop one’s own thoughts. What a tragedy: to build a habit-forming machine which quietly robs students of their intellectual inheritance. Expecting translators to offload some of their work to ML raises the prospect that those translators will lose the deep context necessary for a vibrant, accurate translation. As people offload emotional skills like interpersonal advice and self-regulation to LLMs, I fear that we will struggle to solve those problems on our own.

There’s some terrifying fan-fiction out there which predict how ML might change the labor market. Some of my peers in software engineering think that their jobs will be gone in two years; others are confident they’ll be more relevant than ever. Even if ML is not very good at doing work, this does not stop CEOs from firing large numbers of people and saying it’s because of “AI”. I have no idea where things are going, but the space of possible futures seems awfully broad right now, and that scares the crap out of me.

You can envision a robust system of state and industry-union unemployment and retraining programs as in Sweden. But unlike sewing machines or combine harvesters, ML systems seem primed to displace labor across a broad swath of industries. The question is what happens when, say, half of the US’s managers, marketers, graphic designers, musicians, engineers, architects, paralegals, medical administrators, etc. all lose their jobs in the span of a decade.

As an armchair observer without a shred of economic acumen, I see a continuum of outcomes. In one extreme, ML systems continue to hallucinate, cannot be made reliable, and ultimately fail to deliver on the promise of transformative, broadly-useful “intelligence”. Or they work, but people get fed up and declare “AI Bad”. Perhaps employment rises in some fields as the debts of deskilling and sprawling slop come due. In this world, frontier labs and hyperscalers pull a Wile E. Coyote over a trillion dollars of debt-financed capital expenditure, a lot of ML people lose their jobs, defaults cascade through the financial system, but the labor market eventually adapts and we muddle through. ML turns out to be a normal technology.

In the other extreme, OpenAI delivers on Sam Altman’s 2025 claims of PhD-level intelligence, and the companies writing all their code with Claude achieve phenomenal success with a fraction of the software engineers. ML massively amplifies the capabilities of doctors, musicians, civil engineers, fashion designers, managers, accountants, etc., who briefly enjoy nice paychecks before discovering that demand for their services is not as elastic as once thought, especially once their clients lose their jobs or turn to ML to cut costs. Knowledge workers are laid off en masse and MBAs start taking jobs at McDonalds or driving for Lyft, at least until Waymo puts an end to human drivers. This is inconvenient for everyone: the MBAs, the people who used to work at McDonalds and are now competing with MBAs, and of course bankers, who were rather counting on the MBAs to keep paying their mortgages. The drop in consumer spending cascades through industries. A lot of people lose their savings, or even their homes. Hopefully the trades squeak through. Maybe the Jevons paradox kicks in eventually and we find new occupations.

The prospect of that second scenario scares me. I have no way to judge how likely it is, but the way my peers have been talking the last few months, I don’t think I can totally discount it any more. It’s been keeping me up at night.

Broadly speaking, ML allows companies to shift spending away from people and into service contracts with companies like Microsoft. Those contracts pay for the staggering amounts of hardware, power, buildings, and data required to train and operate a modern ML model. For example, software companies are busy firing engineers and spending more money on “AI”. Instead of hiring a software engineer to build something, a product manager can burn $20,000 a week on Claude tokens, which in turn pays for a lot of Amazon chips.

Unlike employees, who have base desires and occasionally organize to ask for better pay or bathroom breaks, LLMs are immensely agreeable, can be fired at any time, never need to pee, and do not unionize. I suspect that if companies are successful in replacing large numbers of people with ML systems, the effect will be to consolidate both money and power in the hands of capital.

AI accelerationists believe potential economic shocks are speed-bumps on the road to abundance. Once true AI arrives, it will solve some or all of society’s major problems better than we can, and humans can enjoy the bounty of its labor. The immense profits accruing to AI companies will be taxed and shared with all via Universal Basic Income (UBI).

This feels hopelessly naïve. We have profitable megacorps at home, and their names are things like Google, Amazon, Meta, and Microsoft. These companies have fought tooth and nail to avoid paying taxes (or, for that matter, their workers). OpenAI made it less than a decade before deciding it didn’t want to be a nonprofit any more. There is no reason to believe that “AI” companies will, having extracted immense wealth from interposing their services across every sector of the economy, turn around and fund UBI out of the goodness of their hearts.

If enough people lose their jobs we may be able to mobilize sufficient public enthusiasm for however many trillions of dollars of new tax revenue are required. On the other hand, US income inequality has been generally increasing for 40 years, the top earner pre-tax income shares are nearing their highs from the early 20th century, and Republican opposition to progressive tax policy remains strong.