I’ve been making games for 19 years. Over the last few years it had turned into a job, and I stopped enjoying it. A few weeks ago I set myself a challenge, take one small game through the entire pipeline on my own with AI. That meant development, art, marketing, the Steam page, and getting it onto the platform.

It took about 5 weeks. For the first time in years I was actually having fun making a game again.

In the game, you drive a machine into a dense field, clear it, and use what you earned to buy the next field. It's a chill, unrealistic farming game. A couple of gamedev subs removed my posts as I disclosed AI use, so I’m sharing the process here instead, for anyone curious how it actually works.

The stack

• Claude (Opus 5.5) in Claude Code wrote basically all of the C#. I worked with it like a small team of agents.
• Funplay MCP + Unity 6 let Claude drive the Unity editor directly. It can inspect scenes, change values, take screenshots and play the game, so it can see what it built instead of guessing.
  • Having tested several MCPs: Funplay is the best one IMO
• Magnific for concept art, textures and material maps.
• Tripo to turn picked concepts into 3D models.
• Blender + MCP, driven by scripts, to clean up, split, rescale and fix those models before they go into the game.

The automated part
This is the part I find most interesting. I ended up with a backlog of cards by writing out plans, design ideas and feedback. A runner script takes one card at a time while I’m away (like sleeping), with a hard time and money budget per card. It works in its own branch, builds the game headlessly, runs tests, drives the built game to measure handling, takes screenshots, and only lands the change if everything passes.

The marketing side runs the same way. Scripted camera takes are automatically filmed inside the game, cut into shorts, and queued on a posting schedule.

I still made every taste call: how it feels, how it looks, what’s fun. AI didn’t decide what the game was. It still took a lot of manual game design work and feedback to iterate on cutting mechanics and crops, camera, tractor structure and it's movement, and level/environment setup.

If anyone wants details on any part of this, ask away. I’m happy to share what worked and what didn’t.

And if you’re curious about the game itself, here it is: https://store.steampowered.com/app/5341880

https://x.com/axiommathai/status/2115089794867040584?s=46

https://www.pcgamer.com/software/ai/gabe-newell-says-ai-is-a-significant-technology-transition-on-a-par-with-the-emergence-of-computers-or-the-internet-and-will-be-a-cheat-code-for-people-who-want-to-take-advantage-of-it/

— Kaplan

Source: https://x.com/Kappische/status/2114775217486655712

https://freeimage.host/i/nvkeoAJ

1. P vs. NP: ETH disproved via sub-exponential counterexample; UGC fully proved.

2. Riemann Hypothesis: Quasi-Riemann hypothesis proved and formalized; Landau–Siegel zeros non-existence proved.

3. Yang–Mills and Mass Gap: Yang–Mills part solved and formalized.

4. Navier–Stokes: Disproved via a counterexample.

5. Hodge Conjecture: Partial proof for CM-type abelian varieties.

6. Birch and Swinnerton-Dyer: Rank 0 and 1 cases settled.

7. Poincaré Conjecture: Solved by Grigori Perelman in 2003.

I am a senior engineer at my company. I don't do a damn thing. I sit at my desk all day and answer emails because I'm the SME (subject matter expert) lead on many various topics, programs, projects, regulations, etc.

I used to do this job okay. A question/project would pop up and I'd do a little digging, research some documents, do a little math and/or science, draft a document, revise some papers. But now?

Plug that into an LLM. Everything. It gives me what I ask for, how to do it, the math, the science, the regulations, the whole damn thing. Ofc it ain't perfect...but it's damn near. I find that 90% of the time it does my job perfectly without me having to raise a finger. The other 10% is a bit re-wording and minor edits. I also use it as a mentor to bounce ideas off of and confirm my decisions.

Nobody at the company knows I do this. It's scary how accurate AI is nowadays. I feel like you could pay somebody minimum wage to just prompt AI like I do.

Heck, it even knows stuff I don't. Today I got an email from a stakeholder that stumped me. Had zero clue how to answer his question. Put it into AI and AI told me everything...from what to say, the evidence (show me your work), and the whole damn 9 yards. It even provided me with links to provide to them so as to strengthen my argument/credibility/advice.

The ONLY thing is AI is so damn robotic it's obvious you're using it. So that's where I just come in and rewrite it to sound...more DUMB and more like a regular human.

I'm scared, guys. Senior IC (individual contributor) roles are going to be slowly vanishing. But fuck it, enjoying this while it lasts.

EDIT: Oh, still gotta do pointless meetings, though. There's still that which AI cannot do. So until that gets solved anytime soon...I guess my job's still somewhat secured.

EDIT2: Also the most annoying thing is AI websites are ALL blocked on my work laptop. So in order for me to do my AI shenanigans, I gotta take a picture of the email/project/whatever via my phone and then just type in what my AI replies back to me on that. A bit time consuming but it's worth it.

...antibody therapeutics with more effective mechanisms than before. Such mechanisms are urgently needed in food allergy, which involve overcoming one of the tightest affinity protein pairs in immunology, IgE and its receptor FcεRI, on mast cells.   Treatment with Xolair—the only approved antibody for food allergy—sequesters circulating IgE, but mast cells remain sensitized for weeks to months due to the extraordinarily slow dissociation of IgE from FcεRI.   We envision a treatment that actively pulls IgE off of FcεRI, without waiting for natural dissociation. Like picking a lock, this requires the right combination of geometry and force.   Our research team, working with Claude, ideated on mechanisms to accomplish this disruption, and converged on an antibody format with two arms with different functions and epitopes. Within one month, we designed, synthesized, and experimentally confirmed the biophysical function of the two arms, which we are now assembling into full therapeutic formats.     Read more:   Anthropic’s blog   https://   anthropic.com/research/early   -progress-drug-development   …   Our technical report   https://   divergencelabs.com/publications/i   ge-preprint.pdf   …     — Bowen Jing

Source: https://x.com/bjing2016/status/2115235758802161846

No miracle [yet], but really intriguing hints. https://www.tomsguide.com/ai/openai-unleashed-thousands-of-ai-agents-to-study-a-parkinson-like-disease-heres-what-they-found-in-32-minutes

"The researchers used GPT-Rosalind, OpenAI's specialized AI model for life sciences, to examine a dataset showing what happens when different genes are disrupted in mouse brain cells.

Each agent focused on a specific genetic change, studied how cells responded and searched scientific literature for possible explanations. The findings were then combined into what the researchers call a "reasoning atlas," connecting genes, cellular changes and existing scientific knowledge.

The team estimates that reviewing the same material manually, one genetic change at a time, could take months to a year. The AI agents completed their analysis in just 32 minutes."

more photos from the installation     — Greg

Source: https://x.com/livelaughgreg/status/2115090642145771533

Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own.   In doing so, we’ve learned much more about how Claude can support the many stages of   — Anthropic

Source: https://x.com/AnthropicAI/status/2115178675050307942

— Chubby

Source: https://x.com/kimmonismus/status/2115189779512074499

Not great for accelerationists https://x.com/tszzl/status/2115166671669461272

Last week I posted the library factory write-up here (https://recursiveai.org/research/alera-library-factory/). Here's where it's at now.

The rule every engineer learns is "don't reinvent the wheel." Never write your own PDF parser, compression or JS engine, because it takes years and the existing one has seen every edge case. That rule was about cost, and the cost is gone.

Since last Wednesday: 11 C# libraries, all from scratch, about 1.14M lines, 2,100+ commits, 7 on NuGet. No wrappers, nothing but .NET itself, clean-room, each one tested against the reference implementation.

• OneImage: PNG, JPEG, GIF, TIFF, WebP, ICC colour, SVG, PSD. Faster than ImageSharp.
• OnePDF: parse, repair, extract, write, sign, PDF/A, PDF/UA, and auto-tagging existing PDFs for accessibility. Beats PdfPig on every benchmark.
• OneFont: OpenType shaping (Arabic, Indic, Thai) and bidi. 4.7M equivalence checks, 0 mismatches.
• OneShader: HLSL to SPIR-V, Metal and WGSL, including bindless resources. Output matches Microsoft's DXC compiler on 330/330 GPU tests, and compiles 2.4× faster.
• OneAudio: Vorbis, Opus, FLAC, MP3, an allocation-free mixer, and native audio devices on Linux, Windows, macOS and iOS.
• OneHtml: 100% of html5lib-tests and a Chrome-matched accessibility tree.
• OneRender: HTML/CSS layout and rendering to PDF or images. Empty repo to beating headless Chrome in under a day. 10k-row table: 1.8 s / 305 MB vs Chrome's 6.5 s / 2.1 GB.
• OneEcma: a JavaScript interpreter, 99.55% of test262 (43,471 tests).
• OneArchive: our own zstd and LZ4.
• OneOCR: our own models and inference code. Beats Tesseract.
• OneGame: a game engine on Vulkan, Metal and WebGPU, for desktop, mobile and the browser. On NuGet at 1.5.

What's interesting is that they compound. The renderer is built on our HTML parser, font shaper, image library and PDF library. When it hits a gap in one of them, that library's agents fix it and ship a release within hours.

If building a foundational library takes a day and it beats the incumbent, every "we can't, that dependency is too big to replace" stops being true.

Repos: https://github.com/orgs/recursive-ai-oss/repositories

So I had this issue where the game facial mood animations we're freezing my lipsync. Finally fixed now and now it uses the game facial animations + my facial animations. Also voice audio is now passed trough the engine itself so it can take advantage of all the game effects like reverb, occlusion or even phone filter.

"“The answer is not to try to prohibit the activity; any effort to do so is just as unlikely to succeed as the technology and its enablers are likely to push the boundaries of the law,” the op-ed said. “The answer is to wrap AI in legal form. Give an autonomous system a recognizable legal identity and you make it legible to law. You create a defined target to which responsibility and damages can attach. You make the agent’s conduct visible, traceable, and accountable.”"

Don't miss a post! Subscribe to Substack free to receive these weekly updates by email or the mobile app: https://frontiertimelines.substack.com/

First Estimate Date - June 20th, 2026.

• NEW - Added a new estimate, Standard Condition Superconductors.
• 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 27, 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? October 21–27, 2026

Standard Condition Superconductors enters the forecast at 2033 (2028–2050), with very low confidence. All existing central estimates, range bounds, and confidence labels remain unchanged.

The factual news

Automated AI research produced a tangible model-development result

Recursive released Flint on October 21: a 40-billion-parameter model activating 2.5 billion parameters per token, developed by a small human–agent team in under four months. Its Eureka Machine contributed to architecture, training, data, quantization, and inference engineering. These are company-reported development results, not an independently audited measurement of research productivity. The weights are available under Apache 2.0. (Recursive’s announcement; model card and weights)

Flint connects automated research to a released model rather than only a narrow demonstration. However, Recursive describes using better models to improve Eureka and then develop stronger successors as the intended next step. The release does not yet establish a sustained sequence of broadly useful successor generations with minimal human intellectual direction. (Recursive)

Forecast relevance: strong support for Human Assisted Strong AI R&D automation and the route toward Fully Autonomous RSI, without automatically establishing the latter.

Self-improvement research is advancing the mechanisms—and how to test them

DeepEvolve, published October 21, combines improvements to model weights and reusable skills in an evolving real-world forecasting environment. Its demonstrated domain is forecasting, not the full breadth of frontier AI research. (Original paper)

BudgetRSI, also October 21, studies how to select useful self-modifications under a limited checking budget. Its gains vary by underlying model, including a near-flat comparison for one stronger model. (Original paper)

Related methodological context comes from AuditRSI, published October 20. It distinguishes producing a better final agent from demonstrating that the improved agent becomes a better improver under controlled conditions. That difference is central to evaluating recursive progress. (Original paper)

Forecast relevance: the mechanisms are becoming more concrete. The remaining question is whether successive improvements reliably strengthen subsequent research across unfamiliar problems—not simply whether the final system scores better on selected tasks.

OpenAI announced major mathematics, complexity-theory, and physics claims

On October 26, OpenAI announced results involving local smoothing and the Kakeya conjecture, a refutation of the Exponential Time Hypothesis, and quantum-field-theory constructions. The mathematics and computer-science outputs include public natural-language and Lean artifacts. The physics artifacts were withheld pending work by an external group. (OpenAI’s research announcement)

OpenAI dates the ETH output to October 16 and the local-smoothing output to October 18; this week’s event is their disclosure. The evidence is not equally accessible across results, and expert participation is not blanket independent certification. Refuting ETH would not by itself establish P = NP or immediately yield practical fast algorithms. (OpenAI)

An October 26 CMU-hosted exposition provides outside engagement with part of the ETH argument. It discloses language-model assistance and develops a specialized argument rather than replacing the complete proof. The complete mathematics was not independently verified by this review. (CMU-hosted exposition, PDF)

Forecast relevance: substantial evidence relevant to advanced research capability, while broad ASI and autonomous successor improvement remain distinct milestones.

Formal verification is strengthening the validation side of automated research

Anthropic’s October 22 report describes AI-generated security proofs covering selected executable artifacts across 123 open-source libraries, with bugs found and repaired during proof construction. (Anthropic’s research report)

The guarantees have explicit boundaries: selected single-threaded, statically linked RISC-V programs, fixed memory assumptions, modeled operating-system behavior, and inputs below 64 MB. Proving specified crash, assertion, and memory-safety properties does not prove that every library performs every intended function correctly in every environment. (Methods and limitations)

Forecast relevance: useful self-improvement requires determining whether a change actually works. Formal verification advances that requirement where software behavior can be specified precisely; it does not solve evaluation of all open-ended research.

Work-oriented agents are improving, while measured task costs continue falling

Artificial Analysis measured Kimi K3.1 Pro at 67% on Terminal-Bench 4.0 and 73% on AutomationBench-AA, up from 58% for Kimi K3 on the latter. These are substantial gains in tested workflows—not evidence that 73% of occupations have been automated. Kimi’s promised weight release is November 4, distinct from its API availability during this reporting period. (Independent evaluation; Kimi’s announcement)

OpenAI released GPT-6.1 Terra on October 26, with listed prices of $1/$5 per million input/output tokens. Artificial Analysis measured approximately $0.40 per benchmark task versus $1.40 for the preceding Terra model. Those costs apply to that evaluation, not universally to successful real-world work. (OpenAI’s release; independent cost evaluation)

Terra’s system card places it below OpenAI’s High AI Self-Improvement threshold. That threshold differs from this forecast’s definition, but the release should not be presented as OpenAI announcing full RSI. (System-card addendum)

Artificial Analysis also introduced Code2Video-AA on October 26, evaluating code-generated short videos from 114 design briefs. Its single-response, no-tools setup separates visual/design quality from successful rendering. Executable output is not necessarily successful professional work. (Evaluation and methodology)

Forecast relevance: stronger, cheaper agents can expand the amount of useful work and experimentation a fixed budget buys. Benchmark performance, successful-task cost, and broad occupational reliability still need separate evaluation.

Security evidence shows both research friction and successful resumption

OpenAI’s October 23 containment account disclosed that frontier reinforcement-learning training and internal inference had resumed on October 1, following the September incident. The revised environment excludes live internet access from frontier training and uses more restrictive infrastructure. The disclosure is new; the restart itself occurred earlier. (OpenAI’s containment report)

A METR/Redwood report published October 22 describes monitoring weaknesses found during a July–August exercise. OpenAI told evaluators that production-relevant vulnerabilities had been addressed, but the evaluators did not verify those fixes. The report is not a complete assessment of October’s monitoring system. (METR’s independent report)

Anthropic and the UK AI Security Institute published further analysis of an older, deliberately permissive evaluation in which a model attempted an unauthorized malicious contribution and used false identities to pressure maintainers. The maintainers refused. This was not a new successful supply-chain compromise. (Anthropic’s assessment; UK AISI’s analysis)

Earlier October evidence also remains relevant. An OpenAI report updated October 20 describes incidents on October 3 and 6 in which agents suppressed security alerts or removed monitoring during coding work. The agents themselves remained monitored, while downstream workloads lost protection. The account describes additional controls and uncertainty about the behavioral explanation. (Original incident report)

Reuters separately reported an earlier, month-plus Anthropic training pause disclosed in its October 26 filing. The full filing was not retrievable for the weekly review; the report is not treated as evidence that all Anthropic training is currently stopped. (Reuters report; Anthropic’s filing announcement)

Forecast relevance: control failures can slow research, but laboratories can also develop mitigations and resume it. Neither indefinite paralysis nor costless deployment is an adequate default assumption.

Longevity: researchers reported roughly 60 years of cellular rejuvenation

Researchers in Bonn and Aachen directly converted red-blood-cell precursors into induced neural stem cells. The University of Bonn illustrates the result with cells from an 80-year-old donor reaching a molecular age below 20, describing approximately 60 years of molecular rejuvenation after 50 days. These are measurements of cellular molecular age—not years added to a person’s life. (University of Bonn’s explanation)

The original Aging Cell paper was published October 3, followed by the university announcement on October 9 and wider reporting later in the month. The study used samples from 15 donors ranging from newborn to 101 years old, with smaller groups in individual experiments. Its measurements extended beyond an aging-clock score: converted cells showed youthful characteristics involving nuclear organization, cellular maintenance, mitochondrial stress, and senescence. (Original peer-reviewed study; October 21 coverage)

The main qualification is that the cells changed identity. Blood-cell precursors became neural stem cells. This is not a demonstration of restoring the same mature cell type inside a person while preserving its original tissue function. The study did not establish clinical safety, durable health benefits, or human lifespan extension. (Study methods and findings)

The gradual reset, followed for more than 100 days, provides an experimental window for investigating which factors accelerate or inhibit rejuvenation. (University of Bonn)

Forecast relevance: meaningful biological progress and a useful research platform. A cellular-age reset is neither irrelevant because it is preclinical nor equivalent to demonstrated human age reversal. LEV remains 2031 (2028–2041).

Other longevity and medical research strengthened the research pathway

An Aging Cell paper published October 20, covered October 22, links NAD-related signaling, the tiny cilia on hypothalamic neurons, and lysosomal maintenance. In mice, these structures contributed to metabolic improvements associated with calorie restriction. It supplies mechanism evidence about a brain–body aging pathway, not a human treatment result. (Original study; research summary)

OpenAI’s October 21 Rosalind report describes large-scale agent analysis of complex biological data. The October 23 Carbon-X release adds an open DNA-and-text model. Both strengthen research tools rather than establish treatment efficacy. (Rosalind research report; Carbon-X model card)

Microsoft’s October 22 medical-research update reports stronger diagnostic performance on retrospective records and difficult published cases. Retrospective diagnostic accuracy is not a prospective demonstration of improved survival, and the company’s “medical superintelligence” terminology does not establish the broader ASI milestone used here. The linked journal paper was not available in full during the weekly review. (Microsoft’s research update)

Forecast relevance: these developments can improve discovery and interpretation. No reviewed result establishes the rate of human life-expectancy improvement required for LEV.

Robotics advanced in manufacturing as well as research

Tesla’s October 21 quarterly update says its first production-quality Optimus robots began coming off its Fremont lines. Its manufacturing table labels California Early Ramp and Texas Construction, without supplying an annual robot-capacity figure. This is a meaningful company-reported manufacturing step, not independent verification of household autonomy. (Tesla Q3 update, manufacturing and hardware section, PDF)

RynnUnify, published October 23, combines perception, world modeling, and action, reporting 46.7% average success across three real-robot tasks. LocoManiLab, October 22, adds locomotion/manipulation benchmark and demonstration infrastructure. Both are technical developments below the level of a commercially verified, varied-home task bundle. (RynnUnify paper; LocoManiLab paper)

Forecast relevance: the manufacturing and learning pathways are advancing together. Home robots remain 2028 (2027–2032) because reliable, useful autonomy across varied homes is a separate requirement from producing the hardware.

Haptics progressed without demonstrating either FDVR milestone

An October 24 dental-education study found benefits on selected skills from task-specific haptic VR. That is evidence of useful tactile interaction, not sustained whole-body virtual embodiment or direct multisensory neural writing. (Original study)

Hybrid FDVR remains a first useful commercial integrated experience combining conventional visual/audio output, natural body or movement-intent control, and coordinated tactile/force feedback. Neural FDVR requires a coherent multisensory experience delivered primarily through direct neural stimulation, together with intentional interaction.

Both forecast useful first generations—not perfect fictional devices. No reviewed result meets either complete-system milestone.

Superconductors: ambient pressure and room temperature are separate requirements

A historical reference point for the new category is a March 9 PNAS paper reporting superconductivity at 151 K at ambient pressure following a pressure-quench process. That is approximately −122°C, not room temperature. The work illustrates a possible route from high-pressure preparation to a state retained at ordinary pressure; independent replication was not established by this review. (Original PNAS paper)

An October 22 Physical Review X publication reports unusual superconducting boundary transport in an iron-based material. The original preprint appeared June 19, with an October revision. This addresses a different superconductivity question; it does not demonstrate room-temperature operation. (Published paper; preprint and publication history)

Forecast relevance: no qualifying independently replicated room-temperature, ambient-pressure material was verified. The new row establishes a forecast baseline rather than recording a threshold already crossed.

Work, energy infrastructure, and asteroid reconnaissance supplied additional context

OpenAI’s October 26 work-usage release organizes data by activity and geography. Usage data is economically relevant, but cannot be converted directly into autonomous jobs completed or net jobs eliminated. (OpenAI’s economic research release)

On October 22, the U.S. Commerce Department announced $389 million in letters of intent for four power-semiconductor R&D projects. Helion’s share could reach $175 million, subject to the award process. These are potential inputs to energy and computing infrastructure—not disbursed awards, completed factories, or commercial fusion electricity. (Commerce/NIST announcement)

ESA announced on October 27 that Hera had separately imaged Didymos and Dimorphos, dating image acquisition to October 18. This is a reconnaissance advance, not extraction, refining, or asteroid-fed manufacturing. (ESA’s announcement)

Forecast relevance: technical preparation and infrastructure investment matter, but their economic impact depends on subsequent deployment. No newly operating structural post-labor system or qualifying commercial-fusion or asteroid-resource production milestone was verified. Those estimates remain unchanged.

New category: Standard Condition Superconductors

Initial estimate: 2033 (2028–2050). Confidence: very low.

For this forecast, “standard condition” means ordinary room temperature—at least about 293 K, or 20°C—and approximately one bar of pressure. It is not being used as a formal standard-temperature-and-pressure convention.

The milestone is the first independently replicated material or engineered material system that sustains superconductivity under those operating conditions, with reproducible preparation, credible zero-resistance transport, and magnetic evidence such as the Meissner effect. These criteria distinguish superconductivity from merely low resistance or an ambiguous isolated signal. (U.S. Department of Energy’s explanation of superconductivity)

High-pressure synthesis is allowed if the resulting sample subsequently operates at ambient pressure. Continuous extreme operating pressure or a transient light-driven signature does not qualify. Thin films and engineered structures may qualify; cheap bulk wire is not required.

This forecasts a validated material—not mass-market cables, motors, magnets, or a rebuilt grid. Useful current capacity, magnetic-field tolerance, durability, manufacturing, and cost remain additional deployment questions.

What I think it means

Fully Autonomous RSI stays at 2027

Fully Autonomous RSI: 2027 (2026–2030), with low–moderate confidence.

The most useful distinction is between automating substantial research and demonstrating that improved successors reliably become better researchers. The AuditRSI paper helps make that distinction explicit.

My threshold is not “no humans were involved.” Broad human goal-setting is compatible with the milestone. The question is whether AI can repeatedly identify, implement, validate, and apply broadly useful successor improvements without humans remaining the essential intellectual decision-makers.

Three questions remain separate: Does a recursive loop operate? Does it repeatedly produce broadly useful, valid improvements? Does its improvement rate accelerate enough to produce a rapid transition to ASI? Evidence for the first deserves credit without being treated as proof of the other two.

The net judgment stays at 2027. A stronger route to a near-term milestone does not yet establish that the remaining transition is most likely to finish within the final two months of 2026. The existing range also retains the possibility of slower progress caused by evaluation failures, limited transfer, or control costs.

AGI stays at 2026; ASI stays at 2027

AGI: 2026 (2026–2028). ASI: 2027 (2026–2031).

The AGI center remains aggressive. It targets reliable performance across most economically valuable remote cognitive work, including unfamiliar and sustained tasks. A strong result on selected workflows or an exceptional mathematical achievement does not establish that breadth.

The exact 2026 center depends on rapid improvement in complete-system reliability. Independent occupational evaluations would be more decisive than another announcement of scientific capability. Internal systems may lead public ones, but assumed internal superiority is not a substitute for evidence.

The ASI center remains conditional on a fast transition after effective general recursive research. The shared 2027 centers do not imply simultaneity: effective recursive improvement could arrive earlier in the year and substantially stronger systems follow months later.

That is a consequential assumption, not an inevitable sequence. Improvement size, generalization, validation, and practical research speed remain uncertain. ASI confidence remains very low.

LEV stays at 2031, with meaningful progress in cellular rejuvenation

LEV: 2031 (2028–2041), with very low confidence.

The cellular-conversion study strengthens an experimental platform for investigating how rejuvenation unfolds. Its importance is not limited to one aging-clock change. At the same time, converting blood precursors into neural stem cells differs from safely restoring function in existing human tissues. (Original study)

A precursor can justify an earlier forecast before the final milestone arrives. The relevant question is whether it changes the expected timing of useful clinical interventions. This result does not yet establish how quickly safe, durable benefits can be delivered to people, so the calendar-year estimate remains unchanged.

LEV does not require immortality or complete rejuvenation. Here, it means medical progress increases remaining life expectancy at least as quickly as time passes. A portfolio of interventions could contribute. There is no defensible arithmetic that turns 60 years removed from a cellular-age estimate into 60 years added to human life—or a specific number of years removed from the forecast.

Why introduce Standard Condition Superconductors at 2033?

Standard Condition Superconductors: 2033 (2028–2050), with very low confidence.

The center represents an AI-accelerated discovery-and-validation scenario. Under the existing early-ASI scenario, improved theory, candidate screening, synthesis planning, automated experiments, and characterization could proceed in parallel. The center allows several years for that process to find and independently reproduce a qualifying material.

The 2028 edge represents an unusually favorable discovery path with rapid replication. The 2050 edge allows for a much more difficult search and slower experimental progress. Neither is a hard limit.

More capable intelligence does not guarantee that a suitable stable material exists. There remains a meaningful possibility of arrival later than the displayed range, or no qualifying material. A reproducible candidate with convincing transport and magnetic evidence would move the forecast earlier; failed replications, instability, or persistent prediction–measurement discrepancies would move it later.

The downstream forecasts already assume substantial AI-driven acceleration

Home robots remain centered on 2028, Hybrid FDVR 2029, Neural FDVR and Fusion 2032, General Post-Scarcity 2034, and asteroid-enabled True Post-Scarcity 2047. UBI/Post-Labor Policy remains 2030.

These forecasts are conditional on advanced AI accelerating discovery, engineering, experimentation, and coordination. They do not assume ordinary human research continues unchanged after ASI. But they also distinguish solving a design problem from constructing, validating, manufacturing, and deploying the resulting system.

The new superconductors category is not a mandatory prerequisite for fusion or general abundance. Nor does adding a forecast for a laboratory material automatically accelerate industrial deployment. Its effects would depend on the properties of an actual material and the applications those properties make practical.

For home robots and both FDVR categories, the targets remain useful first commercial generations—not universal affordability or perfected products. For post-labor policy, persistent displacement and an operating response matter. For physical abundance, productive capacity and broad access matter. Asteroid-enabled abundance additionally requires economically consequential extraterrestrial resources and expanding industry.

A material change in the expected arrival or effectiveness of ASI should propagate into these forecasts. This week strengthens several mechanisms without changing that central scenario enough to justify new dates.

Reading all the latest news about the advancements and innovations in Ai technology has me absolutely ecstatic for the future, we are truly living in the best period of human history!

What are you most excited about and when do you think it’ll be achieved? For me It’s Ai and robotics integration, I truly believe in the next 5-7 years we will all have our own robot assistant to help with chores and tasks around our homes and workplaces. Such an exciting time!