This morning it finally came home to me. I will probably never write another program in C again. Which is a big deal. I've been writing C since 1983. 43 years is a long time and a lot of skill investment. It's not like I didn't see this coming. I've been writing about the decline of C for years, predicting that it would be displaced by newer systems languages with better safety guarantees and more coherent designs. It wasn't better language design alone that did it, though. It's how good LLMs now are at generating code from specs and prompts. This means I can speak a program into existence in Rust or Go without any more effort than would be required to conjure it up in C. What makes this particularly dramatic is that I never learned to hand-code in Rust. And now - I don't need to. What a long, strange trip it's been.

This is the case against AI doom. Pass it on First we recaptulate standard x-risk argument: we build AI substantially smarter than humans → it becomes an autonomous optimizing agent → its goals are misaligned → instrumental convergence makes it seek power and resist correction → superintelligence lets it acquire decisive strategic advantage → humans lose control permanently. Here are the main counterarguments: * Intelligence does not imply agency: A system can be extraordinarily capable at prediction, theorem proving, engineering, programming, etc. without having persistent goals, self-preservation drives, or an independent desire to act. Present LLMs are much more naturally described as systems that produce outputs in response to inputs than as organisms pursuing long-term objectives. Doomers incorrectly ascribe to them drive to replicate and seize resources because they're smuggling in premises from observing biological systems. * Agency does not imply a single, stable utility function: Much alignment theory reasons about agents as expected-utility maximizers with coherent preferences. Actual AIs systems need not resemble that abstraction, and currently do not. They have context-dependent behavior, conflicting heuristics and corrigibility produced by training. Increasing competence doesn't necessarily turn such a system into a paperclip-maximizing von Neumann–Morgenstern agent. * Capability and motivation are being conflated: Being able to formulate a plan for escaping a sandbox doesn't entail wanting to escape it. Being able to manipulate humans doesn't imply spontaneously deciding to do so. Critics argue that some doom scenarios slide from “an AI could do X if instructed” to “therefore a sufficiently capable AI will do X.” In present reality, AIs don't do anything a human doesn't tell them to do. This seems unlikely to change. * Recursive self-improvement doesn't entail an intelligence explosion: “AI can improve AI” establishes a positive feedback loop, but positive feedback needn't be explosive. Semiconductor design software already helps design better computers; compilers can compile better compilers. Feedback loops encounter diminishing returns and external bottlenecks. * Intelligence may have sharply diminishing returns: There may be no meaningful scalar quantity corresponding to arbitrarily large “general intelligence.” Even if there is, going from IQ-equivalent 150 to 1,500 need not produce the sort of qualitative advantage that separates humans from chimpanzees. Human dominance may depend heavily on language, accumulated culture, institutions and cooperation rather than merely individual cognitive horsepower. * Superintelligence isn't omnipotence: Intelligence cannot repeal physics or eliminate uncertainty. A brilliant AI still needs processors, electricity, network access, money, factories, robots and people willing or tricked into doing things. The physical world has latency and friction. Recent criticism of biological-doom scenarios makes this point particularly clearly: designing a hypothetical pathogen digitally is very different from successfully producing and deploying one. * Humans retain numerous intervention points: The doom narrative sometimes jumps from “AI behaves dangerously” to “humanity is helpless.” In reality there may be many checkpoints: developers can notice anomalous behavior, revoke credentials, shut down servers, change architectures, restrict networks, regulate deployment, physically seize data centers, and learn from less-catastrophic failures. This has been formalized as the checkpoints-for-intervention argument. * Alignment may not get harder with intelligence: A smarter system might understand human intentions better. Much doom reasoning distinguishes knowing what humans want from wanting it, correctly, but this still leaves open the empirical question of whether training increasingly capable systems to behave as intended becomes harder or easier. Alignment might turn out to be an ordinary, albeit difficult, engineering discipline rather than an insoluble philosophical problem. * Current empirical evidence for the strongest mechanism is thin: We have abundant evidence for hallucination, specification gaming, reward hacking and undesirable model behavior. We don't yet have comparable public empirical evidence of an AI independently pursuing a sustained strategy of acquiring power against humanity. A 2023 evidence review characterized the evidence for extreme misaligned power-seeking as concerning but inconclusive and noted the absence, at that point, of public empirical examples of it. * The argument compounds uncertain premises: Suppose, illustratively, that five necessary steps each seem 50% likely. Their conjunction is only about 3%. One can't simply assign numbers this way when the premises are correlated, but the underlying criticism is important: “AGI seems plausible,” “superintelligence seems plausible,” and “misalignment seems plausible” do not by themselves imply a high P(doom). The entire causal chain has to work. Current attempts to quantify P(doom) consequently operate under severe epistemic uncertainty and little direct empirical evidence. * Anthropomorphic analogies probably mislead: Arguments like “an inferior species couldn't control a superior species” import assumptions from biological evolution. Humans and chimpanzees are autonomous organisms produced by competition for reproductive success. Software is engineered, copied, permissioned, sandboxed and run on hardware controlled by other agents. The analogy establishes that intelligence can confer power, not that artificial intelligence will reproduce interspecies competition. Summary: The doom case consists of a long chain of individually contestable extrapolations—scaling → AGI → superintelligence → agency → misalignment → power-seeking → uncontrollability → decisive strategic advantage → extinction—and present evidence doesn't establish the whole chain with enough confidence to justify a high P(doom). (ChatGPT 6 Astra assisted with the research for this post.)