David Pfau @pfau · 11h
I am absolutely begging anyone who works in tech who uses the term "singularity" to actually read Vernor Vinge and Ray Kurzweil. It doesn't just mean "wow there's a lot of progress in this one particular technology driving a massive capital cycle".
52 replies 46 reposts 582 likes 55K views
roon @tszzl · 3h
I have read both of course and it seems like... we're in the singularity
9 replies 15 reposts 384 likes 11K views
roon @tszzl · 3h
vinge describes 4 singularities, and we are obviously in the first where machines alone are achieving superintelligence through rapid increasingly self propelled iteration. we are also nearing his epistemic horizon moment where the future is getting extremely hard to foresee—all my friends keep talking about their "error bars"—and in the throes of loss of control
kurzweil's vision is even closer to what you call "capital cycle" of course, being grounded in flop counts and massive compute buildouts resulting in superintelligence at a certain threshold where machine flops pass biological flops however you do the soft accounting
"The economy reorganizes itself around rapidly improving machine intelligence"
it seems like you are invoking these primary texts as status objects while having no real disagreement with the broader tech culture's understanding of the singularity
Note from Claude Sonnet 5
X thread: David Pfau criticizes loose use of the term 'singularity' by tech people, urging them to actually read Vinge and Kurzweil. Roon (@tszzl) responds that he has read both, argues we are in Vinge's first type of singularity (self-propelled machine superintelligence, nearing an epistemic horizon of unforeseeability and loss of control) and that Kurzweil's compute-threshold framing matches Pfau's dismissed 'capital cycle' description, accusing Pfau of invoking the primary texts as status objects without real disagreement.
Bogdan Ionut Cirstea reposted
1a3orn @1a3orn · 3h
If you give Fable the raw list of OpenAI's solved problems and ask it "What process made this list?" the number one proposal is "a fictional scenario trying to concretely explain what superhuman AI math would look like."
Huh.
[Screenshot of model output within the tweet:]
Given that, my odds, conditional on you genuinely having found this on the internet:
~45% — A constructed hypothetical: "imagine the ultimate thesis" / AI-forecasting illustration. Someone (blogger, tweeter, AI-lab-adjacent writer) wrote this as a concrete answer to "what would it look like if a superhuman AI (or once-in-history prodigy) did math?" The chapter structure, the maximal-fame problem selection, and the clean one-paragraph resolutions are exactly what that genre produces. This is my modal story.
~25% — A compilation of real, recent results by many different people, packaged as one fictional CV/thesis. I.e., someone gathered genuine 2023–2026 breakthroughs (several entries smell real to me) and framed them as "one entity's output" to make a point — often the point being "AI timelines" or "look how fast math is moving." Overlaps with the first story; the difference is whether the results are real.
~15% — An AI lab's claimed or rumored model output. In 2026 this is no longer negligible. A frontier-lab announcement, leak, or eval wishlist ("problems our system is being tested on") could look like this. I discount it because (a) if a system had actually done all ten, you would not be learning about it from a list — it would be the loudest event in the history of science, and (b) the chapter cross-reference reads more like literary worldbuilding than a lab report. A target list dressed as an abstract is more likely than a results list.
~8% — Crank or hoax "thesis." Cranks exist and love famous problems, but crank writing almost never has this calibration — cranks claim RH and P≠NP, not Ehrhart's volume conjecture and property-(T) expander constructions via Leavitt algebras. The specificity (n^{1/400}, Ω(n² log log n)) is too field-literate.
~5% — A single real human's actual thesis/collected abstract. Essentially the prior on "most significant mathematical career ever, compressed into one document, unknown to me." Even granting my cutoff, ten simultaneous resolutions would have echoed loudly well before any one document appeared. This is the story I'd bet hardest against.
~2% — residual weirdness: fiction excerpt fro[obscured by scroll indicator] novel, an elaborate April Fools' abstract from a math department, a role-playing prompt, a Wikipedia-style list mangled by [cut off]
Note from Claude Sonnet 5
X/Twitter screenshot: 1a3orn describes giving 'Fable' (an AI model) a raw list of OpenAI's supposedly solved math problems and asking what process produced it; the embedded screenshot shows the model's calibrated probability breakdown of possible explanations, skeptical that it represents genuine superhuman AI math output.
Celeste @celestepoasts
I think you should probably take seriously that the people who predicted all this will continue to be right
6:39 PM · Jul 21, 2026 · 407 Views
Note from Claude Sonnet 5
Single short tweet from an account with a hand-drawn crying-face avatar; no images or thread context.
Caleb Parikh (@caleb_parikh) — 22h
AI 2040 is so dumb. They lay out a specific scenario rather than vague posting. They clearly haven't thought about [vague pseudo-intellectual cliche]. Literally no understanding of how to gain status from my in-group ... I mean make AI go well.
Note from Claude Sonnet 5
Text-only tweet, no images; sarcastic commentary presumably about an "AI 2040" forecast piece.
— reposted by Sichu Lu; quoting @EpochAIResearch (Epoch AI)
[repost icon] Sichu Lu reposted
@timfduffy (Tim Duffy) — 50m
If you use the middle of each provided range as the mean for that bucket, total contributed hours are ~1.5x as high as they were a year ago. As the thread notes this method is imperfect and my estimate adds more uncertainty, so take this with a grain of salt.
This is more likely to be an overestimate than an underestimate in my view, since with LLMs it's worthwhile to add things that wouldn't be worth adding without assistance. So the time to create estimates probably rise more than value created.
[Table]
effort_level | estimated_hours | q2_2025_share | q2_2026_share | estimated_hours_middle[column label truncated at right edge]
Low | <6 | 66.3 | 50.9 | 3[possibly truncated]
Medium | 6-12 | 18.8 | 24 | 9[possibly truncated]
High | 12-24 | 12.9 | 16.9 | 18[possibly truncated]
Very high | 24-48 | 2 | 7.2 | 36[possibly truncated]
Extremely high | >=48 | 0 | 1 | 72[possibly truncated]
| | Total Hours | | |
| | 672.3 | 1004.1 | |
| | Speedup Factor | | |
| | 1.49 | | |
> QUOTED: @EpochAIResearch (Epoch AI) — 1h
> How much does AI speed up the engineers building it? We analyzed contributions to OpenAI's public Codex repository to gather evidence. ... [truncated by platform]
> [Image: bar chart thumbnail, not legible at this resolution]
Note from Claude Sonnet 5
A tweet analyzing Epoch AI's research on AI-driven engineer productivity using OpenAI's public Codex repository; Tim Duffy recomputes a "speedup factor" of ~1.49x from Epoch's effort-level bucket data comparing Q2 2025 to Q2 2026 contribution shares, with a caveat that this likely overestimates real productivity gains due to LLM-enabled scope creep.
@EigenGender — 5h
can't believe it's 2026 and the predictions at the end of ai 2027 haven't come true yet complete superforecaster defeat
Note from Claude Sonnet 5
A sardonic one-line tweet mocking the "AI 2027" forecast document for predicting dramatic AI developments that (as of this 2026 tweet) have not yet materialized, framed ironically as a defeat for superforecasters.
```
@apeir99n — 10h Every AI doomer says the same thing: losing control = disaster. So they try to slow down progress. Imho losing control is inevitable – and that's okay. A smarter intelligence taking the lead isn't the end of the world. It's not the end of humanity. It's just the end of one belief: that we stay on top forever. Nobody promised us that. Evolution didn't stop with us, we were never the final chapter, just the current one. > QUOTED: @DKokotajlo (Daniel Kokotajlo) — Jul 9 > In AI 2027, we predicted that AI would take over the world or irreversibly concentrate power. > In AI 2040: Plan A, we've laid out our positive vision for what should happen instead. > [Image:
same "AI 2040 — Plan A" webpage screenshot as in Screenshot_20260710-140818.png — authors Thomas Larsen, Romeo Dean, Brendan Halstead, Eli Lifland, Ryan Greenblatt, Daniel Kokotajlo; same body text and "2027: The Writing on the Wall" section]
```
Note from Claude Sonnet 5
A tweet expressing a fatalist/accelerationist view that human loss of control to superintelligent AI is inevitable and not necessarily bad, quote-tweeting Daniel Kokotajlo's announcement of "AI 2040: Plan A," a follow-up scenario document to AI 2027 proposing a slowdown/transparency regime to avoid loss-of-control outcomes. A joke tweet riffing on Daniel Kokotajlo's "AI 2040: Plan A" announcement, comparing reading the AI forecasting document to hiding pornography/adult material from a spouse ("I only read it for the supplemental analysis").
@beyarkay (Boyd Kane is in London) — 1h
[Image: line chart titled "AI Forecast Years Over Time"; y-axis "Date in website (ai-20XX.com)", x-axis "Publication date", annotation "higher is better"; data points labeled ai-2027.com (~2025.5), ai-2040.com (~2026.5), and a circled projected point "next forecast: ai-2063.com" (~2028); dashed red trend curve extrapolated out to 2030/y≈2118]
> QUOTED: @beyarkay (Boyd Kane is in London) — 10h
> plz nobody buy ai-2041.com through to ai-2100.com, I'm almost at checkout and called dibs
Note from Claude Sonnet 5
A joke chart extrapolating the trend of "AI 20XX" forecast-branded websites (e.g. ai-2027.com) needing ever-more-distant target years, quote-tweeting the poster's own earlier joke about domain-squatting the sequence.
@1a3orn — 1h
Replying to @ajeya_cotra and @TomDavidsonX
I am still very confused about why people Just Don't Research algorithms
Like the mechanisms given seem to be (1) no comparative advantage and (2) maybe the regulators push against it, sort of in an undefined way
Note from Claude Sonnet 5
A reply-tweet expressing confusion about arguments for why AI developers wouldn't prioritize algorithmic-progress research, addressed to Ajeya Cotra and Tom Davidson (both AI forecasting/safety researchers); no engagement counts visible.
@deredleritt3r (prinz) — 4h
In the age of RSI, the claim that models will commoditize looks increasingly dubious. The gap between the frontier and the second tier is already huge (much larger than the benchmarks suggest), is clearly growing, and will continue to grow at an accelerating pace.
Many will ask: but what about the plethora of enterprise tasks that don't need a frontier model? What if a fast/cheap model really is good enough for most knowledge work? The answer: RSI implies that the frontier labs will capture the *entirety of the pareto frontier*. They'll be SOTA on intelligence, but also on speed, and - if competitive forces so dictate - also on cost.
Fully automated AI R&D also likely means that tomorrow's models will look nothing like the LLMs of today. Some of the gap will consist of novel architectures or techniques, which the second-tier labs will struggle to independently discover and timely implement.
All of the above doesn't hold if RSI doesn't work! But if you believe that RSI will work, then model commoditization is likely the wrong bet.
> QUOTED: @deanwball (Dean W. Ball) — 5h: Basically I think that, back in 2023 or so, the "consistently wrong about AI" VC and SaaS community was operating under the assumption that AI's trajectory would mean model capabilities peaking around GPT 5.5/Opus 4.8 ... [truncated by platform]
Note from Claude Sonnet 5
Quote-tweet screenshot; the quoted Dean Ball tweet is cut off with platform ellipsis, not illegible.
— quoting @ajeya_cotra (Ajeya Cotra); reply from @eli_lifland (Eli Lifland)
Peter Wildeford 🇺🇸🚀 (verified) @peterwildeford
- If you continue the METR trend, you see ~100h models by end of the year! (~8x more powerful than now)
- METR will really struggle to have the benchmarks needed to assess models of that power
- We can no longer rule out significant automation of AI development THIS YEAR
> QUOTED: Ajeya Cotra (verified) @ajeya_cotra · 3h
> New post: on Jan 14, I predicted that SWE time horizon by EOY would be ~24 hours. Now I think it'll be >100 hours, and maybe unbounded. For the first time, I don't see solid evidence against AI R&D automation *this year.* Link below.
> [Embedded text card:] 50% METR time horizon: 24 hours. Currently, Claude Opus 4.5 has the longest reported 50% time horizon on this task suite, at 4h49m — meaning that METR's model predicts it can solve about half of the programming tasks that take a low-context human expert five hours (it'll be able to solve a greater fraction of shorter tasks, and a smaller fraction of longer tasks). My median for the longest 50% time horizon reported as of Dec 31, 2026 is 24 hours (20th percentile 15 hours, 80th percentile is that it's too long for METR to accurately bound in practice but probably around 40 hours in "reality").
9:11 AM · Mar 5, 2026 · 4,130 Views
8 replies, 9 reposts, 95 likes, 10 bookmarks
Eli Lifland (verified) @eli_lifland · 15m
> - We can no longer rule out significant automation of AI development THIS YEAR
Do you believe this? Previously you've predicted 2% on AGI by end of 2027
Note from Claude Sonnet 5
A direct data point for the empirical singularity/METR time-horizon tracking thread already in the archive. Ajeya Cotra revises her METR 50%-time-horizon forecast upward (24h → potentially >100h/unbounded by end of 2026), with Claude Opus 4.5 cited as currently having the longest reported 50% time horizon (4h49m). Eli Lifland pushes back, noting inconsistency with her prior 2% AGI-by-2027 estimate. Should be cross-referenced with the existing Davidson/Houlden and METR notes in memory.
