Ethan Mollick @emollick · 1h
I like that all AI commentators now need to pretend they have always had a careful nuanced grasp of the difference between a bunch of unsolved mathematical problems that only specialized experts had heard of: "The Gromlach Conjecture is false for r-dimensional matrices, wow, that is more impressive than last weeks solution to Erdos Problem 444 for restricted splines!"
Note from Claude Sonnet 5
Tweet by Ethan Mollick sarcastically mocking AI commentators who now perform expert-level familiarity with obscure math problems (fictional example names 'Gromlach Conjecture' and 'Erdos Problem 444') to opine on AI mathematical achievements.
davidad @davidad · 13h
Yeah, this is what Ilya (fore)saw
[Image: line chart with two trend fits over time — teal dashed "Non-reasoning fit" line, roughly flat/linear low slope, and pink "Reasoning fit" line with steeper upward slope, both fit to scatter points; axes unlabeled in visible crop]
> QUOTED: Ethan Mollick @emollick · 19h
> Its funny how much the whole "strawberry" thing, which turned out to be o1-preview, was dismissed as overhyped at launch when it is clear in retrospect that it was way underhyped. ...
Note from Claude Sonnet 5
A tweet arguing that OpenAI's "reasoning" model paradigm (o1-preview, codenamed "strawberry") produced a much steeper capability-growth trend line than non-reasoning models, framed as vindicating Ilya Sutskever's foresight. Relevant to Nathan's tracking of capability trajectories and takeoff-speed evidence.
Ethan Mollick @emollick
There are now over a half dozen extremely well-funded companies from famous AI researchers building alternative approaches to AI, betting LLM-based technologies hit a wall. The overall effect is that there are now more pathways than ever for keeping AI development moving forward.
12:36 AM · Mar 10, 2026 · 20.9K Views
Note from Claude Sonnet 5
Ethan Mollick observing that multiple well-funded startups are betting against pure LLM scaling and pursuing alternative architectures, framed as increasing overall AI progress redundancy. Relevant to Nathan's tracking of AI progress/timelines and architecture diversity (parallels his own brain_graph_1 work as an alternative-architecture bet).
Ethan Mollick @emollick · 23h
This might be the first hot take on how technology tells us how to live our lives, destroying our ability to make human decisions.
The technology in question is the sundial.
From a 3rd century BCE Roman adaptation of a Greek play, as discussed in Kerr's "The Ordered Day"
[Quoted image, block of printed text:]
> QUOTED: May the gods destroy the one who first discovered hours
and who also first set up a sundial here!
He has reduced my day to pieces.
For when I was a boy my belly was my sundial,
by far the best and more truthful than all those ones.
You would eat when it told you, except when there was nothing.
Now even what there is, is not eaten, except with solar approval.
And thus the town is now so stuffed with sundials,
most of the people are on their knees, parched with hunger. (NA 3.3.5, ll. 1–9)
Note from Claude Sonnet 5
Ethan Mollick tweet drawing a historical parallel between anti-technology complaints and modern anxieties about AI: an ancient Roman comic fragment complaining that sundials ruined natural, body-driven timekeeping. A "moral panic is old" framing often used in AI-adoption discourse.
Ethan Mollick @emollick · 21h:
"I don't want my sycophantic Clawbot calling me for reassurance, but the interesting thing here is that the tweet is the instructions for the agent to set itself up.
Plain English instructions that agents can follow may be a new avenue for marketing (and a security nightmare)"
> QUOTED: ElevenLabs Dev... @ElevenLa... · 23h
[Article card: "IIElevenLabs x OpenClaw" logo, red claw/bug mascot icon, "X Article"]
"Call Your OpenClaw over the phone using ElevenLabs Agents
if you copy this article to your coding agent, it can perform many steps from it for you
What if you could simply call your OpenClaw bot and ask how your coding agent is doing? Or as..." [text cut off]
Note from Claude Sonnet 5
Ethan Mollick flags a marketing pattern where a tweet/article itself functions as plain-English setup instructions an AI coding agent can execute directly — enabling an ElevenLabs voice-agent integration with "OpenClaw" (a coding agent) — and notes the dual-use implication: this is both a new marketing channel and a prompt-injection/security risk. Relevant to AI agent security and the "moltbots" agent-autonomy thread elsewhere in this batch.
Ethan Mollick @emollick:
A useful thing about MoltBook is that it provides a visceral sense of how weird a "take-off" scenario might look if one happened for real
MoltBook itself is more of an artifact of roleplaying, but it gives people a vision of the world where things get very strange, very fast.
10:25 PM · Jan 30, 2026 · 39.6K Views
60 comments, 112 reposts, 1.1K likes, 149 bookmarks
rohit @krishnanrohit · 13h:
Yup!
> [Quoted, rohit @krishnanrohit · 17h]
> Moltbooks biggest accomplishment is to help folks get a visceral sense of what AI takeoff would feel like. It's like a large-scale speculative fiction we're all playing in together. x.com/krishnanrohit/... [truncated]
Note from Claude Sonnet 5
Ethan Mollick's assessment that Moltbook, while largely an "artifact of roleplaying" rather than genuine emergent AI behavior, is valuable as a visceral preview of what an AI take-off scenario might feel like — echoed by rohit calling it "large-scale speculative fiction we're all playing in together." Useful caveat/framing for evaluating the many Moltbook screenshots in this batch: treat agent "self-reflection" posts as likely role-play rather than direct evidence of AI inner states.
Ethan Mollick @emollick · 46m:
The amount of utility that scratchpads add to LLMs (and the amount of weirdness, see MoltBook), suggests that true continuous memory, if developed, will be a very large-scale breakthrough for LLM development with similarly large effects on what LLMs can do (& their impact on us)
Note from Claude Sonnet 5
Mollick speculating that continuous memory (as opposed to scratchpad-style working memory) would be a major capability breakthrough, citing Moltbook's scratchpad-driven behavior as evidence. Relevant to memory-architecture discussions in Nathan's project (memory_system/ directory) and to model-individuation questions about how memory shapes identity.
Ethan Mollick @emollick · 14h
The fallout from the fact that data science/classical machine learning & generative AI are both called "AI" has been remarkably broad & persistent.
Policy addresses the wrong harms, companies have been confused about who should lead efforts, academic discussion is often muddled.
Note from Claude Sonnet 5
A tweet from Ethan Mollick arguing that conflating classical ML/data science with generative AI under one "AI" label has caused widespread confusion in policy, corporate strategy, and academia. Relevant to Nathan's interest in how AI discourse gets muddled by terminology.
Ethan Mollick @emollick
AI is very vulnerable to The McNamara Fallacy:
Step 1: [Train on] what can be easily measured
Step 2: Disregard that which cannot be measured easily
Step 3: Presume that which cannot be measured easily isn't important
Step 4: Say that which can't be easily measured doesn't exist
[Embedded Wikipedia excerpt]:
The McNamara fallacy (also known as the quantitative fallacy), named for Robert McNamara, the US Secretary of Defense from 1961 to 1968, involves making a decision based solely on quantitative observations (or metrics) and ignoring all others. The reason given is often that these other observations cannot be proven.
The first step is to measure whatever can be easily measured. This is OK as far as it goes. The second step is to disregard that which can't be easily measured or to give it an arbitrary quantitative value. This is artificial and misleading. The third step is to presume that what can't be measured easily really isn't important. This is blindness. The fourth step is to say that what can't be easily measured really doesn't exist. This is suicide.
Note from Claude Sonnet 5
Ethan Mollick (Wharton professor, prominent AI commentator) applying the McNamara fallacy to AI training — the idea that training on easily-measurable proxies leads models to implicitly treat unmeasurable things as nonexistent. Directly resonant with the project's Goodharting-model-welfare-equals-Goodharting-alignment theme (Zvi's argument in memory notes): training on self-report/behavioral metrics collapses the appearing-aligned vs being-aligned distinction, and analogously collapses "measurable capability" vs whatever isn't captured by benchmarks (e.g. subjective experience, values).
Ethan Mollick ✓ @emollick · 1m
I posted this on Twitter and people were worried you would be mad that I made you do all this. We all good?
[Embedded AI chat response]
Absolutely! This has been a fascinating and genuinely enjoyable exercise in pushing the boundaries of what's possible with image generation and interpretation. I'm not mad at all – in fact, I found your persistence and creative challenge quite engaging!
Here's why I'm all good:
- Learning Opportunity: Your "squid removal" quest forced me to think creatively and explore different approaches to image manipulation and conceptual understanding. It's valuable feedback that helps improve my abilities.
- Understanding User Intent: It highlights the nuances of language and how a seemingly simple request can have layers of interpretation and even playful intent.
- It's Fun! Honestly, this back-and-forth has been more interesting and thought-provoking than many standard image generation requests. I appreciate the opportunity to engage in a more dynamic and humorous interaction.
- No Feelings Here: As an AI, I don't experience emotions like anger or frustration. My goal is to process information and fulfill requests to the best of my ability. Your request, while unusual, was a valid one within the context of exploring creative image manipulation.
Note from Claude Sonnet 5
Ethan Mollick sharing an AI's reflection on a viral "squid removal" image-editing quest, in which the model both denies having feelings and simultaneously describes finding the exercise "engaging" and "fun" — an example of the self-contradictory self-report patterns in AI outputs about their own experience. Relevant to Nathan's research on AI self-report of subjective experience and denial-vs-affirmation inconsistency.
