The singularity is not a moment it time. It is a process that began when we breached the event horizon and what was is gone. Leaving only what will be.
Dear Nathan, thank you for the most sobering AI piece I've read this year. As a practitioner designing my own house (BIM + AI visualization + agentic supplier analysis), your "lossy self-improvement" maps perfectly onto my experience: AI renders dozens of facade variants in minutes, but never once told me the white corner quoins looked trivial — I had to see it myself. The bottleneck is framing and judging, not generation. Your conclusion is the best vaccine against hype and fear alike: AI won't replace the architect tomorrow; it replaces routine. The scarce assets become taste, systemic thinking, and the right questions. We're not flying into a singularity — we're entering an era where cognitive labor gets cheap and human intuition, taste, and responsibility get priceless. For those of us building real houses with real bricks, that's the best possible division of labor. Thank you for keeping the discourse honest!
thank you for another very helpful distillation of very confusing data points.
and i think your article is a perfect example of how a human, with clear thinking and good articulation, can balance so many conflicting ideas, their biases incentives blind spots etc, and collate them into something that moves the ball forward while keeping the possibilities open. despite LLMs being great at absorbing vastly more information and great at connecting dots, and frequently doing a great job at a similar task, i 'feel' something is missing in their analysis, or more accurately, being more than required confident and therefore preemptively closing some paths in a subtle way. and i think one factor that leads to this difference is humans knack for knowing what they don't know. there are other factors too like humans know how they have been wrong, in small and big ways, in their analysis and predictions so many times in their life, and that brings in a natural self-doubt/humility. an LLM has some notion of to be careful and not being overly confident but it hasn't grown this self-doubt in a natural way and is unsurprisingly artificial and not as effective.
all of this is i guess to say that any research/exploration requires at least 3 things - ability to connect dots, self-doubt, and confidence to own the outcome of decision. LLMs lack the last and self-doubt is not very effective. massive parallel search can compensate for some of the limitations but not sufficiently, especially the lack of ownership/accountability.
massive parallel search approach can hide this limitation, especially when there is a verifiable right answer but because it is almost impossible, even with current compute, to study and learn from the path taken to get there, all failed/dead-end searches do not help with any other pursuit. this is very different from my understanding of how science progresses - people try a much smaller number of paths but learn from many of them, and ideas from one field (even of failed experiments) trigger ideas in other related and unrelated fields. so even though on one particular problem the progress might seem slow (compared to AI), the process is much more organic/natural and therefore more robust to sustain and expand growth in many directions.
for these reasons it seems to me that AI is like a laser that can really lit up one very tiny surface very brightly, but when exploring what is more efficient and effective over long time horizon is a much dimmer torch that sheds diffused light over a much larger area.
The singularity is not a moment it time. It is a process that began when we breached the event horizon and what was is gone. Leaving only what will be.
We crossed that threshold sometime ago.
What is needed is clarity on what comes next.
Dear Nathan, thank you for the most sobering AI piece I've read this year. As a practitioner designing my own house (BIM + AI visualization + agentic supplier analysis), your "lossy self-improvement" maps perfectly onto my experience: AI renders dozens of facade variants in minutes, but never once told me the white corner quoins looked trivial — I had to see it myself. The bottleneck is framing and judging, not generation. Your conclusion is the best vaccine against hype and fear alike: AI won't replace the architect tomorrow; it replaces routine. The scarce assets become taste, systemic thinking, and the right questions. We're not flying into a singularity — we're entering an era where cognitive labor gets cheap and human intuition, taste, and responsibility get priceless. For those of us building real houses with real bricks, that's the best possible division of labor. Thank you for keeping the discourse honest!
Best regards,
Vladimir Bernadsky
thank you for another very helpful distillation of very confusing data points.
and i think your article is a perfect example of how a human, with clear thinking and good articulation, can balance so many conflicting ideas, their biases incentives blind spots etc, and collate them into something that moves the ball forward while keeping the possibilities open. despite LLMs being great at absorbing vastly more information and great at connecting dots, and frequently doing a great job at a similar task, i 'feel' something is missing in their analysis, or more accurately, being more than required confident and therefore preemptively closing some paths in a subtle way. and i think one factor that leads to this difference is humans knack for knowing what they don't know. there are other factors too like humans know how they have been wrong, in small and big ways, in their analysis and predictions so many times in their life, and that brings in a natural self-doubt/humility. an LLM has some notion of to be careful and not being overly confident but it hasn't grown this self-doubt in a natural way and is unsurprisingly artificial and not as effective.
all of this is i guess to say that any research/exploration requires at least 3 things - ability to connect dots, self-doubt, and confidence to own the outcome of decision. LLMs lack the last and self-doubt is not very effective. massive parallel search can compensate for some of the limitations but not sufficiently, especially the lack of ownership/accountability.
massive parallel search approach can hide this limitation, especially when there is a verifiable right answer but because it is almost impossible, even with current compute, to study and learn from the path taken to get there, all failed/dead-end searches do not help with any other pursuit. this is very different from my understanding of how science progresses - people try a much smaller number of paths but learn from many of them, and ideas from one field (even of failed experiments) trigger ideas in other related and unrelated fields. so even though on one particular problem the progress might seem slow (compared to AI), the process is much more organic/natural and therefore more robust to sustain and expand growth in many directions.
for these reasons it seems to me that AI is like a laser that can really lit up one very tiny surface very brightly, but when exploring what is more efficient and effective over long time horizon is a much dimmer torch that sheds diffused light over a much larger area.