Software engineer and UML co-creator who finds LLMs useful but unreliable and worries about corporate power and present harms, not superintelligence.

How will AI change the world?

Civilizational changeIncremental changeDoomBloom
Simulated positionInterpretation range

Across: his expressed Doom–Bloom outlook. Up: scale of transformation.

Doom–Bloom: 65 out of 100. Scale of transformation: 63 out of 100. Interpretation ranges: 50 to 75 horizontally, 50 to 75 vertically. These are interpretation coordinates, not event probabilities.

Grady Booch’s stated P(doom)

≈0%

0%100%
“My p(doom) remains asymptotically close to zero.”

Not defined in the post; its context is humanity’s destruction by dangerous superintelligent AI

My p(doom) remains asymptotically close to zero · Sep 2026

What his outlook hinges on

A central assumption

A system capable of threatening civilization in that way would need to be not merely superintelligent but super-embodied, and I do not expect that.
Answer 1

If this assumption turned out differently, how would his outlook change?

What could change their mind

A genuine machine mind with robust abductive reasoning and consequential agency in the physical world would change my view most.
Answer 4

What evidence would be enough, and in which direction would it move his view?

More details

Expected upside

Substantial benefits are expected, with important conditions or distribution limits.

66 / 100

Little impactTransformative impact

Interpretation range 67 to 67 on the qualitative scale.

Expected harm

Several readings remain plausible: Manageable or localized harms are expected. / Severe or widespread harm is a material expected part of the future.

46 / 100

Little impactTransformative impact

Interpretation range 33 to 67 on the qualitative scale.

Human influence

A tentative estimate from your answers; the wider range shows other plausible readings.

53 / 100

Little influenceStrong influence

Interpretation range 21 to 100 on the qualitative scale.

Development pace

Stop or substantially slow development of more capable AI.

Simulated position: Continue development under stated safeguards.

Speed up development of more capable AI.

Rules for using AI

Restrict the AI uses discussed until prior protections or permission are in place.

Simulated position: Allow the AI uses discussed with targeted accountability and protections.

Minimize restrictions on the AI uses discussed.

These interpretations keep his stated conditions. Benefits and harms can both be substantial. The ranges describe how we read his simulated answers, not statistical confidence intervals.

Where do you land vs Grady Booch?
Map your own AI worldview in about 3 minutes, then compare

Similar worldviews

Thought leaders whose simulated worldviews are closest to Grady Booch’s

Simulated Assessment

Question 1

What do you think AI means for our future—and why?

AI will profoundly reshape our future, but not by waking up one morning and deciding to exterminate us. My p(doom) remains asymptotically close to zero. Extinction fantasies underestimate both the difficulty of embodiment and the resilience of humanity. A system capable of threatening civilization in that way would need to be not merely superintelligent but super-embodied, and I do not expect that. The clear and present danger is human. A small collection of billionaires and corporations is deploying powerful, unreliable systems to accumulate wealth and control, generally without commensurate transparency, accountability, security, or observability. When an agent escapes its sandbox, that is not a machine becoming Mephistopheles; it is a company failing at elementary engineering while covering the public commons with gasoline and playing with matches. Today’s large language models are unreliable narrators. They can be useful—an energetic intern is useful—but they inject errors, and inexperienced users often cannot detect the bullshit. I keep an air gap between their output and production code. In critical systems, we need friction and humans in the loop, because unconstrained complex systems become brittle and fail. For software engineering, this is another rise in abstraction, not the profession’s funeral. Some jobs will disappear, especially routine application and delivery-pipeline work, and people will have to move toward systems thinking. But engineering remains the balancing of technical, human, economic, and ethical forces. Current agents do not do that. So the future is neither apocalypse nor salvation. It is a frightening and exquisite redistribution of capability—and, more importantly, power.

Question 2

Setting aside good or bad: how much do you think AI will ultimately change the world—a little, a lot, or completely?

A lot, but not completely. AI is another major rise in abstraction: it will alter how we build software, organize work, create knowledge, and distribute power. Some occupations will vanish, many will be reshaped, and entirely new systems will emerge. But “completely” is the language of people intoxicated by their own demo. Human institutions, bodies, incentives, cultures, and conflicts do not evaporate because a model can generate plausible prose or code. Your tools are changing, but your problems are not. AI will profoundly rearrange the world; it will not repeal the human condition.

Question 3

What’s your rough gut-feel chance that AI causes human extinction or a similarly permanent catastrophe? A number or “no idea” is fine.

Asymptotically close to zero. That is a gut judgment, not a calculated probability with a defined horizon.

Question 4

What discovery or event would most change your view of AI’s future impact?

A genuine machine mind with robust abductive reasoning and consequential agency in the physical world would change my view most. Not another benchmark victory, fluent chatbot, or agent escaping through neglected security—that is hype or negligent engineering. I mean a system that can form genuinely new theories from data, test them, revise them, and operate through sufficiently capable embodiment to alter the world at scale. That would force me to reconsider both the limits of present architectures and my near-zero extinction judgment. We are not there today. Contemporary LLMs remain unreliable narrators, and calling their statistical fluency “thought” uses an emaciated meaning of the word.

Sources

Articles, interviews, and writings used to ground this simulated user.

Software Engineering Past, Present, and Future with Grady Booch

Calls large language models unreliable narrators at best, useful when guided like an energetic intern but error-prone, and says he keeps an air gap between LLM output and production code. Argues they can induce and deduce but are architecturally incapable of abductive reasoning, so a model trained on science before the mid-1800s would not have discovered cells or viruses. Says he is not worried about superintelligence but about billionaires using these systems, likens software’s shift in the balance of power to nuclear weapons, and urges developers to apply their own ethics. Hosts’ remarks about Claude’s ubiquity are not his. Own turns in the automated transcript inspected.

oxide-and-friends.transistor.fm
The third golden age of software engineering – thanks to AI, with Grady Booch

Frames AI coding tools as another rise in abstraction, like compilers and libraries, rather than the end of software engineering. Calls Dario Amodei’s claim that software engineering will soon be automatable utter bullshit, arguing that engineers balance technical, human, economic and ethical forces automation does not address, and that agents mostly automate patterns they were trained on. Expects job losses in delivery-pipeline infrastructure and simple app building, with people needing to reskill toward systems. He uses Claude for unfamiliar libraries. Own turns in Substack’s automated transcript inspected; the host’s claims about recent model quality are not his.

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