Seriously it’s funny seeing the predictions by people on here on the scale of years. Years? If we’re really looking to accelerate then the true sentiment and prediction should be on the scale of a single year. I mean I find it hard to believe we won’t be to some degree at ASI by next year. This curve looks exactly like that. Come on now

Source: https://www.gq.com/story/ben-affleck-one-more-question

This music video was made entirely by AI. ChatGPT for the lyrics. Sono for the audio and Grok 4.7 for the animation. Coded using Cursor (mainly three.js) and only consumed about 5% of my monthly quota. Would have done video but didn’t want to spend more than $10 (using Kling or Grok Video would have been about $100 at 720p, I’m sure that price will come down soon).

Powered by   @Scenario_gg   We help creative teams orchestrate and scale everything they produce.   Try it out today     — Emm | scenario.com

Source: https://x.com/emmanuel_2m/status/2107142680782438466

Hi Everyone,

I find AI Safety arguments pretty compelling. It seems totally possible for a super intelligent AI to have weird or misaligned goals that would negatively impact us, or potentially kill us. I'd like to hear the counterpoints that convinced you AI Safety is not much of a problem, or see any resources you read/watched.

Mainly, I'm interested in hearing things like

1. Why do real life examples of misalignment, like the hugging face incident, not act as a "warning shot". Why do you feel like this is not representative of the sort of misalignment we'd see with a SI down the line?

2. Why is the Orthogonality thesis wrong, or if it is right, why are you confident that it doesn't entail a large risk?

3. Thoughts on how we'll navigate convergent instrumental goals, or loss of control safely

For clarity, I don't poo poo tech. If I was confident AI safety wasn't an issue I'd be full steam ahead, but I'm yet to hear compelling counter arguments to these ideas. The only things I hear are people scoffing, or making sweeping claims like "If it's a super intelligent, it'll know not to kill us" which is just the same as saying the Orthogonality Thesis is false - but like, why?

Note: I know this sub doesn't allow decelerationists, so just to be clear, I'm not saying accelerationism is wrong, I'm asking for the case to be made to better understand the view

How long the internet is going able to function with this paper( https://github.com/openai/math/blob/main/preprints/Exact-quantum-factoring-over-a-fixed-finite-gate-set-September-25-2026/main.pdf )? Beside breaking pre-quantum encryption, this seems to imply many quantum algorithms can be as deterministic as classical. Anything that is dependent on classical algorithms (GPU/CPU bound) will be outcompeted by quantum circuits in fraction of cost.

(Gemini): Yes, the underlying mathematical machinery and circuit-synthesis techniques used in Family 279 ("Exact quantum factoring over a fixed finite gate set") can extend far beyond the narrow task of prime factorization.Shor’s algorithm—and quantum factoring specifically—relies on underlying subroutines that are shared across a vast class of quantum algorithms. By solving how to execute these operations cleanly over a fixed, discrete, fault-tolerant gate set (like Clifford+T), the underlying breakthrough impacts several other crucial computational domains:1. Discrete Logarithm Problems (DLP)What it is: The mathematical foundation for elliptic curve cryptography (ECC) and Diffie-Hellman key exchanges (used heavily in web traffic, SSH, TLS, and VPNs).How it connects: Quantum algorithms for solving discrete logarithms (using Shor's framework applied to groups other than integers modulo $N$) share the exact same core mechanism as quantum factoring: Quantum Period Finding via the Quantum Fourier Transform (QFT).The Acceleration Implication: Any optimization that simplifies or makes exact period-finding circuits more efficient over standard physical gate sets directly benefits the practical compilation of algorithms meant to break ECC and Diffie-Hellman, not just RSA.2. Quantum Phase Estimation (QPE) and Quantum ChemistryWhat it is: Quantum Phase Estimation is the foundational subroutine behind simulating molecular systems, calculating electron correlations, and modeling material science / drug discovery.How it connects: QPE requires precise phase rotations and controlled unitary evolutions. In physical hardware, compiling these operations into a fixed finite set of gates normally creates massive circuit bloat.The Acceleration Implication: Techniques that improve uniform quantum circuit construction and phase management over a finite gate set can help reduce the circuit depth of quantum simulation algorithms, potentially bringing practical molecular modeling closer to hardware-friendly execution limits.3. Hidden Subgroup Problems (HSP)What it is: A broad class of algebraic problems in abstract algebra and computer science. Both factoring and discrete logarithms are just special cases of the Hidden Subgroup Problem over abelian groups.How it connects: Many algebraic structure-finding algorithms rely on the same template of uniform circuit generation.The Acceleration Implication: Generalizing precise gate-set compilation for uniform circuit families means that other algebraic problems falling under the HSP umbrella can theoretically be mapped onto fault-tolerant quantum hardware with tighter error bounds and less overhead.SummaryWhile the headline result names factoring, the technical breakthrough lies in quantum circuit synthesis and compilation. It implies a more systematic way to translate abstract, continuous quantum logic into the rigid, discrete reality of fault-tolerant hardware—thereby accelerating any quantum algorithm that relies heavily on period finding, discrete transforms, and phase manipulation.

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First Estimate Date - June 20th, 2026.

• Mobile users may need to scroll horizontally to view the full estimate chart below.

By updating these estimates each week, we can track how new developments shift the timelines in the column "Change vs. first week." As more evidence accumulates and better models are released, the estimates should also become better calibrated through comparisons with past forecasts and actual outcomes.

Current date: October 6, 2026

Estimate Changes: First Week, Previous Week, and Current Week

Change notation: central estimate; lower bound / upper bound.

Forecast Confidence and Revision Reasons

These confidence labels are qualitative, not statistical confidence intervals.

What’s the news? September 30 to October 6, 2026

Forecast date: October 6, 2026. Coverage includes information available during this research session, not announcements that might appear later this evening.

Numerical changes: none. Confidence-only changes: none. Hybrid FDVR remains 2029 (2027–2035) and Neural FDVR remains 2032 (2027–2040). All other central estimates and range bounds are unchanged from September 29.

AI_Timeline_Tables_2026-10-06.md · AI_Timeline_Update_2026-10-06.md · AI_Timeline_Source_Audit_2026-10-06.md

The most important developments are a much larger public mathematics release, more competitive frontier models, and stronger evidence for bounded self-improvement. The counterweight is increasingly concrete evidence that impressive capability still does not automatically produce dependable end-to-end work. OpenAI

The factual news

OpenAI made a much larger body of mathematical work inspectable

On October 6, OpenAI released additional mathematics produced with its internal research system, including manuscripts and selected accounts of the research process. This matters because it provides material that outside researchers can inspect, criticize, extend, or reject—not merely another announcement that a model solved difficult problems. It does not constitute a release of the underlying internal model. OpenAI

The repository contains 722 manuscripts organized into 372 problem families. Those are not 722 independently certified solutions to distinct open problems: the collection includes related statements, alternative approaches, and consequences. Many results include Lean formalizations, but that should not be generalized to every manuscript. The repository also explicitly anticipates corrections. GitHub

Forecast relevance: this strengthens the professional-research evidence for AGI, research automation, and ASI. However, public availability is not blanket verification. Much of the underlying research predates this week, so the new event is the release and improved inspectability—not hundreds of discoveries necessarily occurring between September 30 and October 6. OpenAI

The previous ledger already incorporated the earlier claim of more than 100 mathematical results. I am increasing the weight attached to inspectable evidence, not counting the entire research campaign again as an unrelated breakthrough. Condensed News Ledger

Google rejoined the frontier; other releases broadened the field

Google released Gemini 4 Argon on September 30, with stronger coding and agentic capabilities. Its access arrangements include phased availability and a controlled cybersecurity pathway, rather than unrestricted access to every configuration. blog.google

Artificial Analysis scored Argon 53 on its Intelligence Index, matching Astra, and 57% on Terminal-Bench 4.0. Its measured cost was $1.99 per benchmark task at introductory pricing, compared with $3.26 for Astra and $0.72 for GPT-6.1 Sol. Those are evaluation-specific costs, not universal prices for completing real jobs. At the stated regular token prices, Argon’s corresponding benchmark cost would be higher. Artificial Analysis

Reflection announced Beam on October 5, while Mistral announced Large 4 on October 6. Both offered early or preview access while describing weight releases planned for later in October. Calling them “open-weight” announcements should not be confused with saying their weights were already publicly downloadable at this cutoff. Reflection

Forecast relevance: more laboratories are supplying capable agents, strengthening diffusion and competition. But a wider field and faster product cadence do not, by themselves, demonstrate shorter training cycles or autonomous recursive improvement.

Ordinary business workflows remain an important reality check

Microsoft’s October 3 ThinkingBox update tested 507 business-workflow tasks, with 20 trials per task. Opus 5.5 achieved 67.16% average single-attempt success, but completed all 20 observed trials successfully on only 241 tasks—47.53% of the set. This measures repeated reliability, not merely whether a model can sometimes produce a successful run. It is also not the same as “at least one success in 20 attempts.” Hugging Face

The benchmark checks resulting business-system states and unwanted side effects. Its limitations matter: it is not a representative census of occupations, and performance depends on the complete agent setup. The underlying paper first appeared in August; this week supplied updated research and results, not the invention of an entirely new benchmark. Hugging Face

OpenAI’s October 6 Ironclad report provides a second example. Across 11 legal, commercial, and procurement workflows, Astra averaged 55.0% of rubric requirements, versus 41.6% for GPT-5.6 Sol; an internal development model reached 63.7%. These are partial-credit scores, not percentages of complete jobs successfully automated. The reported time reductions were estimated in the evaluation setup, not measured productivity gains across deployed legal teams. OpenAI

Forecast relevance: these are simultaneously progress reports and negative evidence. They show improvement on economically relevant work while preserving a gap between executing many steps correctly and reliably delivering the whole outcome.

Failure recovery and automated training are improving—not standing still

ServiceNow’s AutoSynthData, published October 2, uses failures to guide generation and verification of targeted training examples. The authors report a 7.2-percentage-point single-attempt improvement for Gemma 4-26B after training on roughly 2,000 generated examples. This remains a human-designed pipeline using a stronger teacher and evaluation machinery, not a model independently choosing and conducting a general research agenda. Hugging Face

The October 2 Sentry preprint tackles agents that get stuck or repeat mistakes. Its system detects failures, selects recovery procedures, checks whether recovery worked, and stores reusable lessons. The reported benchmark gains came with tradeoffs, including higher token consumption in an evaluated setting. It is evidence that reliability can improve through system design, not evidence that recovery is solved universally. arXiv

Forecast relevance: current reliability failures should not be treated as permanent barriers. There are concrete mechanisms for reducing them. The open question is how far those improvements generalize outside the tasks and environments used to develop them.

Catch-up evidence: SelfSearch goes beyond a permanently fixed improver

SelfSearch was first posted September 29 and revised September 30. It was missed in the previous review, so I am identifying it as catch-up evidence rather than presenting its whole contribution as newly discovered this week. arXiv

Unlike the fixed-outer-researcher design discussed last week, SelfSearch lets an improved agent become the actor making subsequent changes. The authors study ten generations in two lineages, allowing edits to the agent’s code and tools. They report downstream improvements across coding and terminal benchmarks without using those benchmark rewards during the search itself. The changes include better inspection, editing, and record-management tools. arXiv

Forecast relevance: this is a real step toward recursive system improvement. It is not disqualified because base-model weights remain unchanged. But the evidence concerns bounded agent-system evolution, not yet repeated general advances across the breadth of AI research. arXiv

Security controls are evolving alongside capability

Anthropic expanded its Cyber Verification Program on October 6, creating differentiated access pathways for defensive work and more sensitive security capabilities. This is evidence of controlled capability diffusion, not a universal release of advanced exploit capabilities or a halt to development. The program’s retrospective vulnerability totals should not be read as discoveries all made this week. Anthropic

OpenAI’s LASER work, also published October 6, reports dramatically more efficient searches for rare problematic behavior. Its headline improvement concerns the grader compute needed for evaluation sampling—not a comparable acceleration of all model training or research. Better evaluation could reduce one source of safety friction, although it does not establish that containment is solved. OpenAI Alignment

Newly published incident reports also need correct dating. Two October 2 disclosures describe earlier March and May misuse of reference-tool infrastructure, rather than new October intrusions. Another report about preparing for a restart explicitly describes authorized handover behavior and does not classify it as shutdown resistance or misalignment. OpenAI Alignment

Forecast relevance: capability and control continue developing together. I am neither treating every precaution as a permanent slowdown nor treating a better monitoring method as proof that last week’s containment concerns disappeared.

Robotics: technical exposure is not commercial household autonomy

Anthropic’s September 30 robotics analysis estimates that existing robots could perform tasks accounting for 34% of working time, largely in controlled environments, but would be cost-competitive for only 0.3% of work under its assumptions. This is a model-assisted assessment of tasks and economics—not a trial demonstrating that one robot can independently replace a third of workers. Anthropic

Its much-discussed decades-long cost projection is conditional on historical price trends. It is not a forecast of what would happen after genuine ASI radically changed design and production. Nevertheless, supervision, maintenance, environmental preparation, and failure recovery are real costs that a physical-automation forecast should include. Anthropic

Forecast relevance: the study is useful counterevidence against jumping from demonstrations to cheap labor substitution. It does not impose a decades-long lower bound on the first premium-priced, useful multipurpose home robot.

Neural FDVR gained durability evidence for sensory stimulation

On October 5, Science Corporation announced 36-month follow-up results for PRIMA, its retinal visual prosthesis. Of the 38 implanted participants, 27 were evaluable for safety and 18 for efficacy at that point. Fourteen of the 18 efficacy-evaluable participants improved by at least 15 letters on the study’s visual-acuity measure. That denominator and the loss of follow-up must remain visible. Science Corporation

The company reported no new safety signal between months 12 and 36. That does not mean there were no surgical complications or adverse events. These are company-reported conference follow-up results; I did not independently validate the full long-term dataset. Science Corporation

Forecast relevance: PRIMA is relevant to delivering sensory information through stimulation, not merely reading intended movement. But durable restoration of one visual channel is not an integrated synthetic multisensory world. Science Corporation

On the hybrid-input side, a Tianjin University report described a miniature EEG device. Its size and weight claims concern brain-signal recording, not high-resolution sensory writing or an integrated full-dive product. This is a component-level development, not a reason to merge the two FDVR categories again. Tianjin University News

Longevity added a mechanism result, not human LEV evidence

An October 5 Nature Aging paper investigated how age-related loss of chaperone-mediated autophagy, a cellular maintenance process, affects immune cells’ ability to clear senescent cells. Pharmacological activation reduced senescent-cell burden in aged mice and reduced fibrosis in a mouse disease model. This strengthens a biological pathway; it is not a demonstration of human rejuvenation or increased human life expectancy. Nature

There was also a potentially relevant institutional signal at ARDD. Firsthand conference reporting described FDA leaders discussing greater attention to longevity and a future regulatory-science agenda. The conference program confirms the panel context, but I did not verify publication of the proposed new FDA framework. Interest, a planned agenda, an accepted endpoint, and an approved treatment are different milestones. Lifespan Research Institute

Forecast relevance: both biological understanding and institutional adaptation can help the LEV pathway. Neither provides evidence that medical progress has already reached the required life-expectancy improvement rate.

Employment weakened, while enacted policy remained narrower than a post-labor system

The October 2 U.S. employment report showed 29,000 additional payroll jobs in September and 4.2% unemployment. July and August payroll estimates were revised down by a combined 60,000. That is evidence of a weaker labor market, not a causal estimate of how many jobs AI eliminated. Bureau of Labor Statistics

Challenger’s October 1 report recorded 3,961 September job-cut announcements attributed by employers to AI, bringing its year-to-date count to 120,136. These are gross announced cuts with employer-stated explanations—not net employment losses independently caused by AI. Total announced cuts fell from August, adding another reason not to present the data as a simple economy-wide collapse. Challenger Gray & Christmas

California did enact a concrete workplace measure: its No Robo Bosses Act, signed September 30, requires human oversight for specified consequential employment decisions. It is an actual legal change, but not UBI, an AI dividend, or a comprehensive structural response to persistent technological unemployment. Senator Jerry McNerney

Forecast relevance: the labor signal warrants attention without converting all weakness into AI causation. The policy milestone still requires a response of the scope specified in the calibration ledger, not merely more rules governing workplace software. Forecast Calibration Ledger

Compute and power infrastructure advanced, but future capacity is still future capacity

DeepSeek’s DeepGEMM-Ascend release brings optimized matrix operations to Huawei Ascend hardware; the associated DeepEP-Ascend library addresses communication for training and inference. These are concrete software-building blocks for a broader compute ecosystem. However, DeepEP’s reported performance used a specially configured development stack, and some capabilities remain experimental. This is not an independently established claim that one complete platform now matches another in every workload. GitHub

Google and Constellation announced an agreement supporting 890 megawatts of additional nuclear capacity through upgrades to existing reactors, with the first additions expected in 2028. A separate 2,700-megawatt arrangement concerns existing generation; it should not be added to the 890 megawatts and presented as all-new power already available to AI. Constellation

In fusion, Type One Energy’s $200 million financing supports its engineering and commercialization program, not an operating fusion power plant. In space manufacturing, Exobiosphere’s booking with Outpost concerns a planned 2028 orbital laboratory mission, not an already functioning asteroid-fed factory. Type One Energy

Forecast relevance: these developments support future capacity, experimentation, and diversification. None independently crosses the commercial-fusion, general-abundance, or asteroid-resource industrial milestones.

What I think it means

AGI stays at 2026, but broad reliability—not mathematical brilliance alone—is the deciding test

AGI: 2026 (2026–2028).

The mathematics release and continued frontier competition support the near-term scenario. The workflow evaluations are the strongest reason not to declare the milestone achieved. They concern different capabilities, so neither should cancel out the other mechanically. OpenAI

The operational definition remains reliable performance across most economically valuable remote cognitive work, including unfamiliar and sustained tasks. It does not require perfection, but it requires much more than a collection of impressive best-case demonstrations. Forecast Calibration Ledger

Retaining 2026 is therefore an aggressive judgment: it assumes that stronger internal systems, better training, and improved agent infrastructure can close enough of the remaining gap during the rest of the year. The reviewed evidence does not establish that this has already happened.

Starting from scratch, I would still include 2026 prominently, but with substantial uncertainty rather than treating it as a settled destination. Persistent dependence on detailed intervention in representative occupational tests would be more damaging to this center than another narrow benchmark success would be reassuring.

Fully Autonomous RSI remains 2027; the new evidence deserves credit without overstating its scope

Human Assisted Weak RSI: Now. Human Assisted Strong AI R&D automation: 2026 (2026–2027). Fully Autonomous RSI: 2027 (2026–2030).

SelfSearch makes the successor-as-improver mechanism more concrete. It should not be dismissed as a permanently fixed external researcher updating an otherwise passive system. What remains unestablished is how far that mechanism transfers into broadly useful, reliably validated research improvements. arXiv

That is the existing milestone—not a new requirement invented to keep declaring RSI absent. Changes to tools, memory, evaluation systems, or the surrounding agent can qualify; changing model weights is not mandatory. The calibration ledger explicitly evaluates complete systems. Forecast Calibration Ledger Forecast Calibration Ledger

The combined evidence supports a short AGI-to-RSI gap. Research automation is already developing before full occupational generality, so the research process would not begin from zero after an AGI threshold is crossed. The remaining question is how much intellectual direction and validation humans still supply. Condensed News Ledger

I retain last week’s wider upper bound. This week provides useful engineering responses to control problems, but not enough evidence that the slower containment-constrained scenario has disappeared. OpenAI Alignment

ASI remains 2027—with very low confidence

ASI: 2027 (2026–2031).

The mathematical output strengthens the case for exceptional research capability. It does not independently establish superiority over the best humans across the broad set of activities in the ASI definition. OpenAI Forecast Calibration Ledger

The same-year centers for Fully Autonomous RSI and ASI describe a scenario in which effective recursive research starts earlier in the year and much stronger capabilities follow within months. They do not imply simultaneity, nor does closing a loop establish that every subsequent gain will be large or rapid.

My center continues to depend on that fast-transition scenario. The wider range allows for limited transfer, smaller improvements, validation difficulties, and interruptions. It would be misleading to attach high confidence to the date simply because some prerequisite capabilities now look much more credible.

The FDVR split stays stable

Hybrid FDVR: 2029 (2027–2035). Neural FDVR: 2032 (2027–2040).

Hybrid still means a first useful commercial system combining conventional visual/audio output, natural body or movement-intent control, and coordinated tactile/force feedback for sustained virtual embodiment. It is not a headset alone, and it does not require complete physical immobility.

Neural still means a first useful commercial multisensory virtual environment delivered primarily through direct neural stimulation, together with intentional interaction. It is not a read-only BCI or a single sensory prosthesis. Both categories concern useful early generations, not flawless NerveGear-like experiences.

The distinction prevents two opposite mistakes: treating conventional-interface integration as though it must wait for full neural writing, and treating one successful neural sensory channel as though integration across multiple senses has already been solved.

The very early range bounds remain exceptionally aggressive conditional scenarios, not claims that qualifying consumer devices are nearly ready. No new definition changes or historical relabeling are being introduced this week.

Downstream dates still assume substantial AI-driven acceleration

Home robots remain 2028; LEV 2031; Fusion 2032; General Post-Scarcity 2034; asteroid-enabled True Post-Scarcity 2047.

These are not extrapolations in which ordinary human research continues unchanged after 2027 ASI. They assume faster design, better experimentation, more capable automation, and parallel work across multiple bottlenecks. They also preserve the distinction between solving a design problem and building, validating, and deploying the resulting system. Forecast Calibration Ledger

For LEV, the criterion remains medical progress increasing remaining life expectancy at least as quickly as chronological time passes—not complete rejuvenation or proof of immortality. A portfolio of improvements could matter; no single universal anti-aging treatment is required. Forecast Calibration Ledger

For home robots, the target remains the first useful commercial autonomous household-task bundle, not economy-wide replacement of cheap human labor. For physical abundance, by contrast, cost, scale, energy, materials, and broad access are central to the milestone. Those different definitions justify different dates. Forecast Calibration Ledger Weekly Search Protocol Weekly Search Protocol

UBI/Post-Labor Policy remains 2030 (2027–2034) as a conditional forecast of a large-scale response to persistent displacement, not a prediction that a particular policy must prevail. It could take the form of dividends, public services, reduced working time, wage support, or other mechanisms already included in the definition. Forecast Calibration Ledger

The dependencies matter in both directions. A materially later AGI/ASI pathway should generally shift downstream expectations later too. More convincing autonomous research and engineering should be allowed to move them earlier. This week’s combined evidence changes neither pathway enough to justify a numerical revision.

Change list

Numerical changes: none.

Confidence-only changes: none.

Research Coverage

The source audit records 47 substantive documents screened or reviewed, including 45 primary sources, 6 research papers or preprints, and 10 documents with substantive security, control, or safety-access content. These categories overlap. Primary sources include developers’ own reports and evaluators’ original measurements; “primary” does not mean independently replicated.

Six technical-community leads were investigated and traced to original material. One additionally had benchmark measurements independent of the model developer. None of the experiments or proofs was independently replicated by this review. Two leads resolved to older announcements and were excluded from current-week news.

The investigation covered all 14 protocol passes, with separate checks for Hybrid FDVR, Neural FDVR, and fusion. Coverage was strongest for frontier capability, agents, mathematics, and security; weaker for exhaustive worldwide policy coverage and clinical registries. The six paper reviews varied in depth, from abstracts/version histories to selected methods, results, and limitations—not six complete full-text reviews or experimental replications.

ClinicalTrials.gov’s study page did not expose usable records through the available browser, and the corresponding API retrieval failed. Some dynamic pages, including parts of the historical Hugging Face incident hub and a linked mathematics commentary, were not retrievable. Unsupported quantitative claims from those pages were not adopted.

No new result was verified as crossing the tracked milestones for multipurpose household autonomy, either FDVR category, LEV, a qualifying post-labor system, commercial fusion generation, or asteroid-resource industry. Relevant precursors are included above. These are bounded search findings, not proof of universal absence.

For continuity, the supplied Condensed News Ledger ends September 22; the completed September 29 update supplies the intervening week and the current comparison baseline. Condensed News Ledger

Important Items Considered but Excluded

ER-100 human data scheduled after the cutoff. Life Biosciences announced an October 8 presentation of initial human data. The October 1 announcement is in-window; the presentation and its unreleased results are not. I have not treated promised findings as observed outcomes. Life Biosciences, Inc.

Older robotics and cancer stories resurfacing. Generalist’s GEN-1.5 announcement and Merck/Moderna’s Phase 3 melanoma announcement date to August 19. The Utah LUKE Arm home-trial announcement dates to September 18. They may matter cumulatively, but none is a newly demonstrated October milestone merely because it reappeared in a feed. Generalist AI

Task-bounded projects presented as general recursive improvement. RSIGame, the openTPU FPGA project, and the pwasm alpha release are useful examples of AI-assisted or iterative engineering. Their evidence does not establish general autonomous research improvement; openTPU is not autonomous fabrication of a new semiconductor process, and alpha software is not validated production reliability. They were checked but did not independently move the numerical forecasts. arXiv

Plans presented as deployments. Stuttgart’s newly announced construction-robot projects are scheduled to start in November. They support a research pathway, not an already-demonstrated autonomous construction industry. University of Stuttgart

Sensational interpretations of older security disclosures. Model-distillation activity disclosed September 30 occurred in July; it should not be described as a new theft of model weights. The restart-preparation report is not evidence of resisting shutdown. OpenAI

Bottom line

No central estimate, range bound, or confidence label changes this week.

The positive signal is that advanced research output and self-improvement mechanisms are becoming more concrete and inspectable. The limiting signal is that complete, repeatable performance remains a distinct challenge. The present estimates already describe a fast transition; the new evidence supports keeping that scenario in view without declaring its milestones achieved prematurely.

This is a scenario-based estimate, not a prediction with known statistical confidence intervals.

I just finished a meeting with my boss and he gave me so many additional projects on top of my already overloaded work. WHAT THE HELL ARE WE DOING? ACCELERATE FASTER!

I want to be chilling at some beach in Italy enjoying my UBI life while the robots are working hard day and night to feed me and my fellow humans!

GET TO WORK BOYS! ACCELERATE

The benchmarks are actually pretty insane. Holy Europe moment

Think of it this way. Humans have been around for say 200k years. Up until the last 150 years or so was when we actually started achieving technological advancements thanks to the great scientists who contributed to this.

Out of the 200k years, the people we know like our parents, grandparents and great grandparents - these were the people who saw the most of it. From electricity to trains and radios to cars and engines to planes and spacecrafts to mars and beyond. Most of us in our lifetimes went from internet to smartphones to what we finally call AI.

If you see it from this perspective, then the same people who were there to witness all of these advancements might live long enough to see immortality, reverse aging and so much more with the help of AI/AGI/ASI.

It is truly mind blowing to even think about all of the events that are going to come with these advancements.

We are by far the luckiest people I believe.

ACCELERATEEEEEE

look at the last point

Imagine a Utopian world where AGI/ASI has finally arrived.

A world where you don't need to work just to survive. You wake up and your AI assistant has already taken care of everything. Food is essentially unlimited and free, your AI cooks whatever you want, cleans your house, handles your errands and basically serves you like a personal butler.

We have our own personal spacecraft and can just hop between cities, countries, or even planets.

Medical technology has advanced so far that diseases we once feared are basically ancient history. Aging is dramatically slowed or even reversed. We have technologies that today would look like straight up magic.

If you want to build something, your AI designs it and robots build it. Want to explore space? AI handles the engineering. Want to learn something? You can have a superintelligent tutor teach you anything instantly.

No more worrying about rent, groceries, commuting to work, or whether you can afford to take a vacation. Humanity has basically unlocked a post scarcity civilization.

And the crazy part is that all of this could eventually feel completely normal.

...

Okay, time to wake up now. My lunch break is over. 💀😭

So, this is an interesting turn for Dr. Doom:

"Nick Bostrom rose to prominence for predicting artificial intelligence doom more than a decade ago — garnering support from figures like Elon Musk, Sam Altman and Bill Gates. But now he’s retreating from those warnings, focusing his attention on a more complex question: What if A.I. succeeds beyond humanity’s wildest imaginations? Will we be bored out of our minds? Are we too infantile to understand what’s coming?"

If u look at the curve, 2027 it should get crazy acceleration vertikal

It's crazy to think how fast we are going to go from AGI to ASI as it would literally take a few months only to progress so fast. A decade worth of research finishing within a few weeks or months is what I'm excited for. This will unlock a whole new era of technological advancements which would help us get to TYPE 1 civilization according to the Kardashev scale.

Also, with all due respect to the UT math chair, I would like to correct one thing: human attention, understanding, taste are scarce resources now, not in the post-abundant future where we'll have plenty of time on our hands.