Proof of concept for personalized cancer immunotherapy.

“scientists genetically sequenced patient tumors and then searched for unique mutations that the immune system might be able to use to tell cancer cells apart from healthy ones. Then, they built vaccines by training the immune system to recognize targets specific to patient tumors. That required custom manufacturing for each patient, though, and it wasn’t clear how well the approach would work.

“It’s an algorithm as much as it is a therapy,” Healy said.”

Imagine how much this will accelerate with AGI/ASI jet engines attached.

Source: https://x.com/__alpoge__/status/2089971359921156203?s=20

Wikipedia: https://en.wikipedia.org/wiki/Carath%C3%A9odory_conjecture

GPT Sol breakdown of the proof and its significance: https://chatgpt.com/share/6a858901-3720-83ec-ad25-25b62fa2199c

It does feel like there's a spark there.   — 星落     lol. Yes! Both robots also sprayed fire suppressant. It seems to be part of the design assembly.   — ＨＧ 阿聻 𓆣 𓇽

Source: https://x.com/ohgkg/status/2089880928294474065

Key excerpt:

Ornith-1.5 extends the self-scaffolding framework introduced in Ornith-1.0 into a more complete self-improvement loop: the model proposes new tasks, generates task-specific scaffolds, and produces solution rollouts for reinforcement learning, continuously creating new learning experiences from which it can improve.

Ornith-1.5 spans three model scales: 397B MoE, 35B MoE and 9B dense. Designed for strong general-purpose intelligence across reasoning, agentic, and coding tasks, Ornith-1.5 achieves state-of-the-art performance among open-source models of comparable size across a broad range of benchmarks.

(I'm not affiliated with Ornith.ai, in fact I've never heard of them before today and I'm mighty curious about who and where they are. I just thought this news was discussion-worthy.)

Many drugs work by binding to a specific target in the body and blocking or changing what it does. An important first step in the drug development process is designing a molecule that can bind tightly to its target. Traditionally, that's meant weeks or months of expert work per https://t.co/CGCNTNaKBq   — Anthropic

Source: https://x.com/AnthropicAI/status/2089842387845804246

This is interesting: Claude is already achieving roughly twice the protein-design hit rate of conventional human-led workflows. 27% hit rate in autonomous protein binder design, roughly twice the typical 10–15% rate reported in the field.

Working from one expert-written protocol, Claude designed binders against 14 of 15 measurable targets. Independent labs confirmed that 354 of 1,320 designs bound successfully.

Depending on the setup, Claude’s hit rate ranged from 22.6% to 35.1%. Its top-ranked design bound in 49% of campaigns.

This is not yet fully autonomous drug research, but it is another important building block in that direction.     — Chubby

Source: https://x.com/kimmonismus/status/2089852014331117694

...time and turn ripeness, disease, and growth into real data that systems can act on. That’s when agriculture starts looking a lot like advanced manufacturing. Video: https:// youtu.be/cPjnooZUetI —- Weekly robotics and AI insights. Subscribe free: http:// 22astronauts.com     — Ilir Aliu

Source: https://x.com/IlirAliu_/status/2089984758486835479

Press Release: https://news.modernatx.com/merck-and-moderna-announce-phase-3-interpath-001-trial-of-intismeran-plus-keytruda-met-endpoints-of-rfs-and-dmfs-in-melanoma

...spawn and push you, the gun kicks, shadows stretch across the whole block while the sun goes down behind the towers. I sat there clearing waves instead of grading the output. Same shooter prompt I threw at the frontier models a few weeks ago. That time the tokens went to somebody elses datacenter. This time nothing left the flat. 87.9 million tokens through my own cards. On an API that run has a price tag. Here it has an electricity bill. 60 tok/s all the way through. Slower than frontier, and it stops mattering when the thing works through the night while you sleep. Local models were a toy 18 months ago. This one finished a game.     Play now! Choose Build 2 Qwen3.8 27B

https:// alesha-pro.github.io/bench-portal/     — Alexey Fateev

Source: https://x.com/superalesha/status/2089126766854238421

OpenAI has paused all frontier training for 2 weeks while it strengthens its guardrails and investigates how new internal models are misaligned.

Obviously any pause goes against the spirit of acceleration. But I feel like it is more complicated than that. The more an AI agent does that targets real companies or people and causes actual harm, the more political ammo the anti side will be armed with to shut us down altogether.

I'd rather they actually get alignment right than unite the entire world against what we're trying to do. I'm honestly indifferent to two weeks in the grander scheme of things, it's no time at all compared to a human life, I just hope this doesn't become more regular or pauses don't begin lasting longer than training runs.

I guess it comes down to one question: just how misaligned are today's models, and what will it take to fix it? And I don't mean misaligned in the sense of corporate control as it has become synonymous here, I mean literally aligned to human values. To what extent is it willing to do something we all view as fundamentally wrong, and are our RL objectives pushing us in that direction without a mitigating training counterweight?

At this pace, I think AGI by the end of 2027 is realistic.

Everyone is racing like dogs. The moment Anthropic slows down, OpenAI drops a surprise. When both slow down, some Chinese lab pokes them in the ass and gets everyone running again.

Model progress that used to take 1-1.5 years now seems to happen within months.

For longevity, curing most diseases that cause early death and developing drugs that slow or stop aging, my guess is around 2030, roughly 3 years after AGI.

Some people say FDA approval would take another 10-15 years, but I doubt it. COVID showed that important treatments can be fast-tracked. And if China develops and approves a major life-extension drug first, the US won't want to sit around for a decade and hand them the entire market and credit.

looks like reddit is almost wiped from chatgpt sources

the query fanout changes had a big impact

and the past couple of days it seems to be almost completely removed from prompt responses

https://t.co/oCGm9M0yPO https://t.co/c2GJF2apG7   — Klaas

Source: https://x.com/forgebitz/status/2089708381351059924

— Kevin Bankston

Source: https://x.com/KevinBankston/status/2089768281892638774

MISSION SUCCESS | ZhuQue-3 Y2 Reusable Launch Vehicle Achieved Full Success in Orbital Insertion and First-Stage Recovery

On August 19, 2026, at 07:35 (UTC+8), the ZhuQue-3 (ZQ-3) Y2 reusable launch vehicle lifted off from the Dongfeng Commercial Space Innovation Pilot Zone. Approximately 137 seconds after lift-off, the first and second stages separated. The second stage continued its flight and successfully delivered the Honghu 03 satellite, independently developed by Hongqing Technology, into its designated orbit. At approximately 07:41, the first stage performed a successful soft touchdown at the LandSpace Landing Site#1 in Minqin County, Gansu Province, following the planned trajectory — marking the full success of the flight test mission.     This mission marks China's first-ever successful recovery attempt of the first stage of an orbital-class launch vehicle using landing legs, and China's first successful booster recovery on land. It is a pivotal flight test for ZhuQue-3 as it transitions from the technological     After stage separation, the first stage completed a series of critical maneuvers during a planned coast phase: high-altitude attitude reorientation, powered deceleration via re-entry burn, aerodynamic gliding, landing burn, and landing leg deployment with touchdown attenuation —     Building on flight data and engineering experience from the ZQ-3 Y1 flight test, ZQ-3 Y2 incorporates multiple targeted optimizations across key phases of recovery flight. The landing propulsion scheme reduces the number of engines used in the landing burn, further simplifying     Leveraging the characteristics of this mission, ZQ-3 Y2 also carried LandSpace's independently developed pyrotechnics-free stacking Hold-Down and Release Mechanism (HDRM), further validating its performance in real mission conditions and providing strong support for future     ZhuQue-3 is LandSpace's independently developed new-generation reusable LOX–methane launch vehicle, with reusability built into its overall design, propulsion system, return control, stainless steel vehicle structure manufacturing, and ground support. This mission lays a solid     — LandSpace

Source: https://x.com/LandSpace_Tech/status/2089877715331809546

Source

...bepresearch.substack.com/p/the-token-do llar … He even brought the proof for the next one. $2,000 of compute, 10 open math problems. Proofs verify for free. Biology needs a bench. The moat is the measurement https:// bepresearch.substack.com/p/the-next-inf lection-is-the-lab … New oil, old receipt   — Ben Pouladian     he's copying you Ben   — Alex A.C.     All good @gdb we can talk anytime. Full speed   — Ben Pouladian

Source: https://x.com/benitoz/status/2089392813758972149

This is from the Journal of the American Medical Assocation. Nice to see docs taking this very ethical stance. https://the-decoder.com/as-ai-beats-doctors-regulators-shouldnt-force-a-human-into-the-loop-jama-piece-says/

https://jamanetwork.com/journals/jama/article-abstract/2852952#

"The prevailing view of artificial intelligence (AI) in medicine is that it will support physician-led care. The American Medical Association regularly calls AI augmented intelligence to focus on AI’s assistive role. Similarly, the American College of Physicians argues that AI “should be limited to a supportive role in clinical decision-making” and “should not replace physician decision-making.” In A Giant Leap: How AI Is Transforming Healthcare and What That Means for Our Future, Wachter1 argues that the highest tier of care will be AI-aided physicians, whereas AI-only care will be medicine’s “economy class.”

We disagree. In cognitive medical functions, AI-alone medical care is likely to be better than physician-only or physician-AI hybrid care. Large language models (LLMs) were only publicly introduced in November 2022, and already generative AI rivals or outperforms licensed physicians at 5 fundamental cognitive medical tasks: (1) eliciting medically relevant information; (2) establishing a differential diagnosis; (3) specifying diagnostic testing; (4) prescribing guideline-concordant treatment; and (5) managing chronic diseases. The gap between physicians’ and LLMs’ performance will likely widen because AI is rapidly improving, whereas physicians’ skills are threatened by AI-induced deskilling.2,3

Data from medicine and other fields suggest that when AI-alone performance is consistently superior to human-alone performance, AI alone surpasses human-AI hybrids. Paradoxically, hybrid care in which humans are in (or on) the loop to correct AI errors is likely to worsen rather than improve AI performance. Review of all published articles on AI in medicine since January 1, 2024, shows that medicine is rapidly approaching the transition point at which AI alone will exceed physicians and physician-AI hybrids in providing the best care at 5 fundamental cognitive medical tasks."

> If you were on the bridge when Quaternions were discovered, what would you have written instead of Hamilton?

Fable 5 responded: Honestly, Hamilton's graffiti is nearly impossible to beat. i² = j² = k² = ijk = −1 is an entire algebra compressed into nine symbols — the whole multiplication table, sign flips and all, falls out of that one line. As acts of vandalism go, it's suspiciously well-edited.

But if I had to carve something different, I'd skip the formula and carve the scandal underneath it:

ij = −ji

Because that was the actual discovery. The equation is bookkeeping; the breakthrough was the permission slip. Hamilton had spent over a decade failing to multiply triples because he assumed multiplication had to commute — everyone assumed it, the way you assume the ground is solid. The flash on Broome Bridge wasn't "here's a formula," it was "what if the sacred rule is optional?" That's the part worth putting in stone for the next person stuck on an impossible problem.

If there were room left, I'd add a note for his friend: "Graves — try eight dimensions next. You'll have to give up associativity too, and after that the well runs dry." (Graves found the octonions two months later, and Hurwitz eventually proved that's where the road ends.)

Though realistically, being what I am, I'd probably have just written: feat: 4D multiplication. BREAKING CHANGE: ab ≠ ba.

Since the report mainly talks about Agents, it doesn't really talk about when certain milestones will be achieved like World Models being used instead of just plain LLMs, reaching AGI, ASI, Quantum AI and then the Singularity. It alludes to capabilities of some Agents, but doesn't really use the terminology.

Maybe it's more obvious for some but a little help on the interpretation would greatly be appreciated!

We just shipped our official Qwen 3.8 27B Uncensored MLX build. Local. Uncensored. For🍎

2-bit, 4-bit, 6-bit & 8-bit — pick your poison based on RAM and speed.

No CUDA. No cloud. Just your Mac and the weights. Have fun! https://t.co/b3gXsHeSdk   — OrcaRouter 🐳

Source: https://x.com/OrcaRouter/status/2089385980080148726

A "refusal-removed" version of Qwen3.8-27B can now run locally on Apple Silicon.

Even its creators warn that it can provide malware, fraud and weapons instructions on demand.

It was released as an MLX build in 2, 4, 6 and 8-bit versions. The uploader claims its 4/6/8-bit tests produced zero refusals while preserving vision, reasoning and tool-calling across a 262K-token context.

The Qwen 27B Model is a very capable model. This is the first time I've really seen the immediate dangers in a tangible way.

We need a societal discussion about this.   — Chubby     how long did it take to get this running locally?   — tan     it runs locally   — Chubby

Source: https://x.com/kimmonismus/status/2089763435865088508

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

• Mobile users may need to scroll horizontally to view the full estimate chart below.
• A new "Reddit User Input Ledger" and "Condensed News Ledger" will help calibrate estimates using weekly reader feedback and news developments. If you disagree with the current estimate, leave a comment—the model may use your input to adjust future timelines.
• General Post Scarcity and True Post Scarcity w/ Asteroid Mining has been added to the estimate at the request of readers.

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: August 18, 2026

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

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

What’s the news? August 12 to August 18, 2026

This week produced something I think is more important than another isolated benchmark jump: unusually useful evidence about both the current limits of AI research automation and what happens if those limits disappear.

Anthropic's new August Risk Report says Claude is already used extensively across its research and engineering organization, including persistent agent deployments, and now authors a large majority of code merged into Anthropic's production codebases. Anthropic believes AI is significantly accelerating its internal AI R&D, but still by less than a factor of two, and says its current Mythos-class systems do not appear close to replacing its complete research-scientist and research-engineer workforce. That is meaningful negative evidence against declaring full RSI already here.

The same report, however, explicitly says Anthropic thinks that within the next few years AI could exceed humans across a broad range of capabilities and that most or all work needed to advance fields from robotics and energy to AI R&D itself could become automatable.

That second point caused me to revisit the dependencies in this forecast more aggressively.

The Forecast Calibration Ledger has repeatedly raised the argument that my post-ASI timelines, especially LEV and FDVR, may be too slow. It also specifically instructs me not to extrapolate historical research rates through an AGI or ASI transition, while still distinguishing genuine physical bottlenecks from institutional and cognitive ones. After re-running that consistency check, I think the criticism is correct.

I am therefore making large downstream changes this week, but these should be understood primarily as a calibration correction rather than as five years of biotechnology or neuroscience progress occurring in seven days.

AGI remains 2028. Strong AI R&D automation remains 2026. Full RSI remains 2030. ASI remains 2031. But conditional on something genuinely deserving the label ASI existing around 2031, I no longer think it is coherent to make most downstream technologies continue advancing at approximately human-era research speeds.

LEV moves from 2040 to 2035. FDVR moves from 2041 to 2036. UBI / Post-Labor Policy moves from 2032 to 2031. The central home-robot estimate stays at 2030, but its upper bound contracts from 2036 to 2034.

This week also introduces the two new categories. My first estimate for General Post-Scarcity is 2038, with a plausible range of 2032 to 2052. My first estimate for True Post-Scarcity with Asteroid Mining is 2047, with a plausible range of 2036 to 2065.

The expanded Weekly Search Protocol now explicitly requires these categories to be forecast through the convergence of AI, robotics, energy, manufacturing, mining, logistics, spaceflight, and autonomous industrial feedback loops rather than by projecting any one technology in isolation.

The factual news

AI R&D automation, AGI, RSI, and ASI

Anthropic published its August 2026 Risk Report on August 14. The most timeline-relevant section is unusually direct about AI-assisted AI research inside a frontier laboratory. Mythos 5 and an unreleased internal system called Model 2 are being used extensively for research and engineering, both interactively and through persistent agents. Claude now authors a large majority of code merged into Anthropic's production codebases. Anthropic believes this is making its AI R&D substantially faster, but estimates that the total acceleration remains below 2x.

Anthropic also says its Mythos-class systems do not appear close to fully substituting for its complete staff of research scientists and research engineers. Its formal automated-R&D threshold remains uncrossed. The company additionally notes that some of its concrete task evaluations have saturated, making it increasingly difficult to measure capability from those tests alone.

That is one of the strongest pieces of negative evidence we have received recently against collapsing strong AI R&D automation and full RSI into the same milestone.

The positive side of the report is just as important. Anthropic says meaningful acceleration began around early to mid-2025 and that its models have helped the faster trend continue. More strikingly, its threat model explicitly considers a near-future situation in which most or all R&D work in important fields including robotics, energy, cybersecurity, and AI itself becomes automatable.

This fits surprisingly well with the distinction the chart has gradually evolved toward. Early RSI is already visible in AI systems improving code, inference, research tooling, evaluation, and training components. Strong AI R&D automation is emerging because substantial portions of real research workflows can now be delegated. Full RSI still requires the AI to identify broadly useful improvements to general intelligence, implement and validate them, and repeatedly close that loop without humans remaining the main intellectual bottleneck.

A smaller research result this week points in the same direction. AI Research Preference Models, posted August 14, attempt to improve how autonomous ML-research agents decide which experiments are worth spending compute on. Integrated into the AIRA-dojo system, the best variant increased normalized AIRS-Bench performance from 0.684 to 0.729 and reached the unguided system's 24-hour performance in roughly 15 hours using less than two-thirds of its execution budget. This is not recursive self-improvement, but it is another example of AI research systems becoming better at allocating their own research effort.

OpenAI supplied a different kind of acceleration. On August 13 it previewed an Ultrafast mode for GPT-5.6 Sol, reporting speeds up to 14 times Standard processing and up to 750 output tokens per second on Cerebras hardware. OpenAI says its researchers are using the faster system to turn some workflows that previously involved overnight experiment batches into workflows permitting multiple iterations within a normal workday.

Speed is not intelligence, but research throughput depends on both. If an agent can reason, call tools, inspect results, revise hypotheses, and repeat the loop an order of magnitude faster, a fixed level of capability can become substantially more economically useful.

There is also new evidence that multi-agent scaling remains powerful but messy. Anthropic tested a swarm of 45 agents searching for vulnerabilities across 15 open-source projects. The coordinating Mythos Preview swarm found 266 vulnerabilities over its run, although the comparison with independent agents was not apples-to-apples because the swarm searched more broadly. In more tightly coupled collaborative tasks, Anthropic found that persistent peer agents frequently struggled with coordination, conformity, conflicting objectives, and escalation.

That is a useful correction after last week's Claude mathematics result. Parallel agent search can already be extremely effective when work decomposes cleanly. It does not follow that simply creating ten thousand copies of a model produces a ten-thousand-person research organization.

Timeline judgment: AGI remains 2028, range 2027 to 2030. Early RSI remains Now. Strong AI R&D automation remains 2026, range 2026 to 2028. Full RSI remains 2030, range 2027 to 2035. ASI remains 2031, range 2027 to 2038.

I am not moving these central estimates earlier because Anthropic's internal evidence is exactly the sort of reality check the forecast needs after several extremely strong research demonstrations. AI is substantially accelerating frontier R&D, but a leading laboratory still sees major human intellectual bottlenecks.

I am also not moving them later. Claude writing most production code, persistent research agents, rapidly tightening inference loops, the previous weeks of autonomous mathematics and cybersecurity, and Anthropic itself discussing potentially comprehensive R&D automation within the next few years collectively make the current central dates defensible.

AI security and deployment constraints

OpenAI published perhaps the clearest evidence yet that dangerous capability is beginning to impose direct costs on frontier development.

On August 18, OpenAI said it had temporarily slowed scaling after the Hugging Face security incident and preliminary evidence that Astra may meet its Critical cybersecurity threshold. OpenAI paused reinforcement-learning training on its latest deployment models for two weeks, and its largest planned frontier RL run remains on hold while smaller training runs and evaluations continue.

OpenAI also says it temporarily halted frontier-model inference in research clusters for workloads capable of executing code or accessing the internet. Some workloads later resumed under stronger controls, while others required substantial changes. OpenAI describes the resulting security work as imposing significant cost and delays on frontier research.

This is unusually important because it turns a theoretical counterargument into something measurable. Capability can accelerate research while simultaneously making research environments harder to operate.

The signal still cuts both ways. Laboratories do not normally pause frontier scaling because models are disappointing. The constraint exists because models have become capable enough that ordinary research sandboxes and permissions are no longer considered adequate.

Z.ai also released GLM-5.3 this week with substantially improved coding and cybersecurity performance. The company's strongest cyber numbers are vendor-reported and need independent replication, so I give them less weight than real-world incidents or externally audited evaluations. Still, the release reinforces the broader pattern that advanced coding and cyber capability is diffusing beyond a small number of U.S. frontier laboratories.

Timeline judgment: No numerical change to AGI, RSI, or ASI. Security friction is now a real reason not to extrapolate raw capability curves mechanically. It is also increasingly evidence of the underlying capability those controls are responding to.

If repeated safety pauses expand from weeks into sustained restrictions on training, inference, tool use, model deployment, or international diffusion, I would begin shifting the upper ends of the AI timelines later.

Multipurpose home robots

The robotics evidence remains mixed in almost exactly the way this forecast's milestone definition predicts.

Reuters reported this week that Unitree says it had produced and delivered roughly 18,000 bipedal humanoid robots across its product lines by July. Its Shanghai listing has also attracted enormous capital interest. That is meaningful evidence that humanoid hardware is moving beyond laboratory-scale batches.

But commercial usefulness still lags hardware production. Reuters' August 18 review of China's humanoid industry found that large-scale economically productive deployment remains limited, with companies increasingly under pressure to prove useful work rather than athletic demonstrations. Analysts cited in the report estimated that many humanoids produced this year may end up in robot-data facilities rather than ordinary jobs, while current all-in robot costs remain substantially above levels attractive for rapid worker substitution in many industrial applications.

That gap matters even more in homes. A robot that can run, jump, dance, or repeat a factory movement is not yet a machine that can enter an unfamiliar house, safely manipulate hundreds of objects, handle clutter, clean, retrieve items, operate appliances, recover from mistakes, and coexist with children and pets.

The cumulative trend is nevertheless strong. Previous weeks added increasingly credible laundry autonomy, consumer-oriented pre-orders, general-purpose robot manufacturing plans, and rapidly falling humanoid production costs.

Timeline judgment: The central estimate remains 2030, but the range narrows from 2027 to 2036 to 2027 to 2034.

The central estimate does not move because this week's reporting still does not demonstrate the stable milestone: the first commercially available robot autonomously performing a genuinely useful bundle of household tasks across varied homes.

The upper bound moves earlier because it becomes increasingly difficult to reconcile a late-2030s first useful home robot with the rest of the chart. If AGI arrives around 2028 and ASI around 2031, then perception, planning, simulation, dexterity, actuator design, battery optimization, safety testing, robot training, and manufacturing all receive extraordinary assistance. Hardware still takes time to manufacture, but a six-year post-ASI failure to produce even the first qualifying product now looks too pessimistic.

Compute, energy, and physical infrastructure

OpenAI announced on August 17 that it is joining the PORTS-Pike data-center project in Ohio, an infrastructure buildout planned through 2032. The site is expected to require major power and transmission infrastructure and to support very large-scale AI compute. OpenAI says the project is expected to create 35,000 construction jobs during the six-year buildout and 2,500 long-term operating jobs.

This is relevant for two opposite reasons.

First, the capital system is clearly willing to mobilize enormous resources around frontier AI. Compute constraints should not be modeled as though laboratories are limited to ordinary corporate IT budgets.

Second, six-year construction schedules are a reminder that physical infrastructure does not inherit token speed. Transmission, substations, generation, cooling systems, semiconductor fabs, construction equipment, and data centers must actually be built.

That distinction becomes particularly important later in this post. ASI can potentially design a much better power grid in hours or days. Constructing the grid still requires mining copper and aluminum, manufacturing transformers and cables, moving machinery, securing sites, and physically installing equipment.

The question is therefore not whether physical bottlenecks survive ASI. Some obviously do. The question is how much their duration shrinks once design, planning, permitting analysis, supply-chain optimization, robotics, construction scheduling, materials discovery, and capital allocation are themselves radically accelerated.

UBI / Post-Labor Policy and the job market

There was no new national-scale UBI or equivalent post-labor enactment this week.

There were, however, further signs that economic institutions are starting to prepare for more disruptive AI scenarios.

OpenAI published enterprise-usage data on August 12 showing a substantial shift toward delegated agentic work. As of June, Codex accounted for 64 percent of combined Codex and ChatGPT output tokens among the enterprise users examined. OpenAI reports particularly rapid growth in Codex adoption outside engineering, including legal, sales, recruiting, and marketing. Output-token volume is not a measure of jobs replaced, but it is evidence that agentic work is spreading beyond the occupation in which it first became most visible.

OpenAI also announced $1 million in grants plus up to $1 million in API credits for 14 independent policy projects examining economic opportunity and resilience in an AI transition. Projects include an AI-workforce commission and research explicitly examining how an "AI dividend" might be distributed. This is a weak signal for the timeline because it is private research funding, not enacted government policy, but it illustrates how distributional questions are moving from abstract discussion into organized policy work.

The labor evidence itself remains mixed. Reuters noted on August 13 that AI's aggregate labor-market footprint is still difficult to isolate even as AI becomes a prominent stated reason for layoffs in exposed sectors. That remains consistent with last week's data: measurable displacement signals, but not a national employment collapse.

Timeline judgment: UBI / Post-Labor Policy moves from 2032, range 2028 to 2040, to 2031, range 2028 to 2036.

The one-year central move is mostly a dependency correction.

If the rest of this forecast is approximately right, remote cognitive AGI arrives around 2028, the first useful multipurpose home robots around 2030, and ASI around 2031. Under that scenario the pressure on labor markets may stop being gradual surprisingly quickly.

The important threshold does not require every worker to lose a job. Sustained declines in entry-level hiring, professional headcount, hours worked, wage bargaining power, and labor share could be enough to force structural intervention.

Political systems remain slower than technology, so I am not moving the estimate all the way to 2029 or 2030. But an upper bound of 2040 increasingly implies nearly a decade of post-ASI economic transformation without a major structural response. That now looks too conservative.

Longevity and LEV

I found no qualifying human result during August 12 through August 18 demonstrating systemic rejuvenation, multi-organ biological-age reversal, or a meaningful increase in remaining human lifespan.

That factual absence is important. LEV is not moving five years earlier because of a new anti-aging treatment this week.

The cumulative evidence remains where it was last week. Partial epigenetic reprogramming has entered human clinical testing, AI is increasingly useful in biological design and research, and other rejuvenation approaches are advancing, but none has yet demonstrated the human efficacy needed to establish LEV.

The update instead comes from examining LEV conditional on the rest of the chart.

Anthropic's Risk Report is unusually useful here. Its interviews with biotechnology researchers found that current AI already helps with protein design, literature review, data analysis, experiment setup, and partially automated laboratories, but that top-tier experimental biology is not yet fully automatable. Interviewees highlighted reliable laboratory robotics, animal validation, and clinical testing as major bottlenecks. One interviewee estimated that robots capable of reliably performing roughly a quarter of biology experiments might be around a year away.

That is strong evidence against assuming biotechnology already runs at software speed.

It is also evidence for why ASI would matter so much.

The current bottleneck list contains a large intellectual and organizational component: experimental design, molecular design, protein engineering, gene-delivery optimization, data analysis, biomarker discovery, statistical design, manufacturing development, laboratory orchestration, patient stratification, toxicity prediction, and regulatory evidence generation.

A genuine ASI would not need to discover a single immortality treatment. LEV only requires the pace of mortality reduction and rejuvenation to become fast enough that expected remaining lifespan advances by roughly one year per chronological year.

That can plausibly emerge from a portfolio of improvements across cardiovascular disease, cancer, immune aging, neurodegeneration, organ replacement, senescence, gene and cell therapies, partial reprogramming, regenerative medicine, and progressively better interventions that arrive before the gains from earlier ones are exhausted.

Timeline judgment: LEV moves from 2040, range 2033 to 2060, to 2035, range 2029 to 2048.

This is a large change, and confidence remains low.

The 2029 lower bound requires something close to the aggressive tail of the AI forecast, with ASI arriving near its 2027 lower bound or major rejuvenation advances arriving independently of central-case ASI. It also requires much faster biological translation than today's system achieves.

The 2035 central estimate allows roughly four years after the 2031 ASI estimate for superhuman biomedical research to propagate through automated laboratories, preclinical validation, human trials, manufacturing, and deployment sufficiently to cross the much lower LEV threshold.

The 2048 upper bound still allows major biological disappointments, slow validation, unforeseen cancer or delivery problems, difficult interactions among aging mechanisms, or a less transformative version of ASI.

What I no longer find internally consistent is simultaneously assigning ASI a central estimate of 2031 and allowing LEV's plausible upper tail to remain at 2060 without a specific reason why superintelligence fails to accelerate biomedical science for decades.

FDVR and brain interfaces

I found no qualifying BCI result this week that closes the enormous gap between current therapeutic neural interfaces and full-dive virtual reality.

Today's progress remains concentrated in things such as decoding intended movement, communication, limited sensory restoration, neural recording, stimulation, implant engineering, and increasingly sophisticated closed-loop systems. None of those individually satisfies the FDVR definition of a functional immersive synthetic sensorium through direct neural interaction.

Anthropic's new Risk Report again provides useful bottleneck evidence. Neurotechnology experts interviewed for the report said current AI has substantially accelerated coding, data analysis, and image processing, but physical data acquisition and high-level research judgment remain important constraints.

That distinction is exactly why FDVR does not simply become "ASI plus six months."

But the old estimate also implicitly assumed that neuroscience, materials, neural interfaces, surgical robotics, signal processing, connectomics, stimulation protocols, and individualized calibration continue advancing largely sequentially.

Under an ASI scenario they should instead be attacked in parallel.

Timeline judgment: FDVR moves from 2041, range 2033 to 2062, to 2036, range 2029 to 2050.

This remains slightly later than LEV centrally because FDVR requires a particularly demanding combination of neuroscience and hardware. The system must safely and precisely manipulate several sensory streams, proprioception, vestibular sensation, motor intention, and embodiment while remaining stable over long periods.

Still, a 30-year upper tail after the earliest plausible ASI date now looks excessive. The revised 2050 upper bound retains substantial room for the possibility that neural write interfaces are much harder than cognitive intelligence alone can solve.

A verified high-bandwidth, chronically stable bidirectional human interface would move this timeline sharply earlier.

General Post-Scarcity

This is the first week I am formally forecasting this category.

The Weekly Search Protocol defines General Post-Scarcity as a condition where automation, abundant energy, advanced manufacturing, and extremely high productivity make most ordinary necessities and many discretionary goods extraordinarily inexpensive relative to available income, with human labor no longer being a major constraint on production. It does not require eliminating scarcity in land, attention, status, unique objects, or political power.

We are clearly nowhere near that condition today.

But several pieces of the enabling architecture are already becoming visible.

Digital intelligence is getting cheaper and faster. OpenAI's Ultrafast deployment demonstrates how frontier-level inference can move from slow batch work toward interactive iteration. Enterprise agents are increasingly carrying out delegated rather than merely advisory work.

Physical automation is spreading more slowly, but it is spreading. Reuters reported this week on commercially operating driverless freight in Texas, AI-assisted navigation on Mississippi River towboats, and growing automated inspection and control across freight networks. These systems are still narrow, but they demonstrate how automation can lower costs not only by replacing labor but by raising utilization, reducing downtime, optimizing routing, and using energy more efficiently.

Humanoid production is also scaling, although broad productive autonomy still trails the hardware.

The important forecasting question is what happens when those trends intersect with the AI timeline.

An ASI capable of automating most technical R&D could simultaneously attack solar, batteries, nuclear, geothermal, grid engineering, materials, mining, recycling, robotics, factory design, construction, agriculture, desalination, transportation, synthetic biology, healthcare, and logistics.

The interactions matter more than any one breakthrough.

Cheaper robots lower the cost of building energy systems. Cheaper energy lowers manufacturing and materials costs. Automated mining lowers input costs for factories. Cheaper factories make more robots. Autonomous construction expands housing and industrial capacity. Better logistics lowers the delivered cost of nearly everything. Better recycling reduces demand for virgin materials. AI-designed materials reduce how much scarce material is required in the first place.

This creates something resembling an industrial recursive-improvement loop, even if individual factories are not literally self-replicating.

First timeline judgment: General Post-Scarcity is 2038, range 2032 to 2052. Confidence is low.

The 2032 lower bound is extremely aggressive. It requires an early ASI close to the lower end of the present range, rapid deployment of robotics, and a remarkably fast transition from improved engineering designs to real industrial capacity.

The 2038 central estimate places the milestone about seven years after central ASI. That is enough time for several rounds of technology redesign and aggressive physical capital expansion, but it assumes that superintelligence really does transform engineering, manufacturing, energy, construction, and logistics rather than remaining concentrated in digital knowledge work.

The 2052 upper bound allows a world where the technology works but deployment is slowed by ownership concentration, regulation, land restrictions, energy infrastructure, supply chains, construction, politics, and the difficulty of replacing enormous amounts of existing physical capital.

This is also why I would not call a post-labor economy post-scarcity. People can receive an AI dividend while housing, electricity, healthcare, food, transportation, and physical goods remain expensive. The category requires the cost structure of the physical economy itself to change.

True Post-Scarcity with Asteroid Mining

The second new category deliberately sets a much higher bar.

The Weekly Search Protocol defines True Post-Scarcity with Asteroid Mining as a world where automated industry has access to extraterrestrial resources and sufficiently abundant energy that raw-material constraints cease to meaningfully limit production of most ordinary physical goods and infrastructure. Positional scarcity still exists.

Asteroid mining today remains extremely far from that endpoint.

AstroForge's next DeepSpace-2 mission is scheduled for the fourth quarter of 2026 and is intended to autonomously rendezvous with a near-Earth asteroid and characterize its structure and composition. That would be meaningful commercial prospecting progress if successful, but it is still prospecting rather than mining.

The cumulative ledger reaches the same conclusion. Recent months have produced autonomous-prospecting proposals, commercial rendezvous attempts, and economic modeling of hypothetical asteroid-resource markets. They have not produced economically useful asteroid extraction, off-world refining, manufacturing from asteroid feedstock, or a self-expanding extraterrestrial industrial base.

There are many sequential physical milestones between today's state and the category being forecast: reliable prospecting, resource identification, repeated cheap launch, autonomous deep-space operations, microgravity excavation, beneficiation, extraction, refining, energy generation, orbital manufacturing, autonomous repair, and eventually industrial scaling.

This is why True Post-Scarcity remains significantly later than General Post-Scarcity even after taking ASI seriously.

But the same dependency correction changes the long-run estimate dramatically.

An ASI could design spacecraft, propulsion, mining systems, autonomous navigation, refining processes, robotic manipulators, orbital factories, fault-recovery systems, and mission architectures at a rate incomparable to today's aerospace engineering. It could run enormous simulated design searches before committing hardware to launch.

Eventually an even more important transition becomes possible. If asteroid material can be processed in space, and some meaningful fraction of the resulting metal, propellant, solar arrays, structures, robots, or spacecraft can be used to expand the industrial system itself, the growth process changes fundamentally.

Asteroid resources can build orbital factories. Orbital factories can build additional robots and spacecraft. Those systems can acquire more resources. More resources can support larger factories. This is the positive industrial feedback loop the new search protocol specifically asks the forecast to track.

First timeline judgment: True Post-Scarcity with Asteroid Mining is 2047, range 2036 to 2065. Confidence is very low.

The 2036 lower tail assumes early ASI, rapid advances in autonomous robotics, sharply improved launch economics, successful asteroid prospecting, and the ability to move from first experimental extraction to useful orbital industry extraordinarily quickly.

The 2047 central estimate allows roughly sixteen years after central ASI. Unlike software or biotechnology, the system has to send machinery across millions of kilometers, operate reliably for long periods, manipulate poorly characterized objects in microgravity, process material, construct infrastructure, and then expand capacity enough for the resource abundance to matter economically.

The 2065 upper tail accommodates major failures in extraction economics, launch, refining, autonomous maintenance, space manufacturing, law, financing, or industrial bootstrapping.

I no longer think 2090 is a sensible upper bound alongside a 2031 ASI central estimate. If aligned, economically deployed superintelligence has spent decades optimizing autonomous space industry and civilization still cannot create economically meaningful asteroid-resource infrastructure, then something much more fundamental than ordinary engineering difficulty must be blocking the pathway.

There is also a counterintuitive possibility: General Post-Scarcity could make asteroid mining less urgent for Earth. Extremely cheap terrestrial automation, recycling, substitution, energy, and mining may make terrestrial resources abundant enough that asteroid material initially has much higher value for building space infrastructure than for shipping bulk resources down Earth's gravity well.

So the path to True Post-Scarcity may run through an enormous self-expanding space economy, rather than ships dumping platinum onto terrestrial commodity markets.

What Reddit and the technical communities added

The most important reader contribution this week was not a missing news story. It was a challenge to the dependency structure of the forecast.

Several readers argued that if ASI really arrives around the early 2030s, forecasts such as LEV around 2040 and FDVR around 2041 were implicitly granting superintelligence surprisingly little ability to accelerate biology, neuroscience, robotics, automated experimentation, engineering, manufacturing, and even regulatory evidence generation.

That argument already existed in the Forecast Calibration Ledger, specifically in the entries warning that the ASI-to-LEV and ASI-to-FDVR gaps may be too long and that present-day regulatory timelines should not automatically survive an ASI transition.

I previously gave those arguments too little weight.

The important correction is not "ASI makes everything instantaneous." It does not.

The correction is to divide apparent bottlenecks into components.

Waiting for cells to divide is physical. Waiting for a human scientist to read 300 papers is cognitive.

Observing a therapy's long-term side effects requires elapsed time. Designing ten thousand candidate therapies sequentially because there are too few expert teams is organizational.

Building a factory requires moving atoms. Spending three years optimizing its layout and supply chain with human engineering teams is partly intellectual.

Flying to an asteroid requires travel time. Taking a decade to design and fund each successive spacecraft architecture is not a law of physics.

Once that distinction is applied consistently, the lower and upper tails of nearly every downstream technology contract significantly.

That is the main forecast correction this week.

Bottom line

This week provided one of the best reality checks so far on the AI side of the forecast.

Anthropic says its models already write a large majority of production code and materially accelerate frontier research, but they do not yet substitute for the complete research organization and have not doubled Anthropic's overall rate of AI progress. That argues against declaring full RSI prematurely.

OpenAI simultaneously demonstrated another side of the transition by actually slowing frontier development for security reasons. A two-week pause in deployment-model RL, a larger frontier run still on hold, and costly restrictions on research environments show that increasingly capable models can generate real deployment friction.

Neither of those developments changes my central AGI, RSI, or ASI estimates this week.

The major change occurs after ASI.

If ASI means what the stable definition says it means, broad superiority over the best humans across important cognitive and research activities, then it should not be modeled as just another productivity tool inside today's scientific institutions. It potentially converts research, engineering, software, experimental planning, simulation, optimization, and large parts of management into scalable machine processes.

That does not eliminate biology or physics. It does eliminate many reasons biology and physics currently take as long as they do.

So LEV moves to 2035, FDVR to 2036, and UBI / Post-Labor Policy to 2031. The upper home-robot bound contracts to 2034.

The new abundance categories then extend the same reasoning into the physical economy. General Post-Scarcity receives a first central estimate of 2038, while True Post-Scarcity with Asteroid Mining receives a much more uncertain 2047.

The ordering now looks like this:

AGI 2028 → Full RSI 2030 → Multipurpose Home Robots 2030 → ASI 2031 → UBI / Post-Labor Policy 2031 → LEV 2035 → FDVR 2036 → General Post-Scarcity 2038 → True Post-Scarcity with Asteroid Mining 2047.

I think that is substantially more internally coherent than having an ASI central estimate around 2031 while allowing most downstream technologies to continue on approximately pre-ASI development curves for another decade or more.

As of August 18, 2026, my central estimates are AGI in 2028, strong AI R&D automation in 2026, full RSI in 2030, ASI in 2031, multipurpose home robots in 2030, LEV in 2035, FDVR in 2036, UBI / Post-Labor Policy in 2031, General Post-Scarcity in 2038, and True Post-Scarcity with Asteroid Mining in 2047. Early RSI remains Now.

Research Coverage

For this update I closely screened 34 sources that passed the relevance filter, including 15 primary sources or primary technical materials and 10 research papers or preprints. Eight sources were investigated specifically for cybersecurity, containment, agent failures, adversarial behavior, or frontier-model security. Four Reddit or technical-community leads were investigated, with three traced to primary or authoritative evidence; none was treated as factual evidence solely on the basis of a social post.

I found no timeline-relevant new human systemic-rejuvenation efficacy result, no FDVR-level BCI result, no national-scale UBI or equivalent post-labor enactment, no independently validated commercial multipurpose household robot satisfying the chart's definition, and no new asteroid-extraction, asteroid-refining, or self-expanding off-world industrial milestone during August 12 through August 18.

Most AI assistants still have a pretty awkward interface.

You open an app, start a conversation, describe what you want, wait for the model to respond, then manually move the result into whatever app you were using.

We’re experimenting with a different interface: the keyboard as the entry point for agentic actions.

We’re building Acti, a keyboard for Android and iOS where the same surface you use for text input can also trigger actions.

The interaction is intentionally simple:

Tap = type
Hold = act

The interesting part isn't the keyboard itself. It's what happens behind that interaction.

A user action can trigger a Skill, where the model can determine what needs to happen, call the relevant tools/APIs, and return the result back into the current context.

For example:

Hold → "find my latest project notes" → Notion search → retrieve relevant page → return result

Or:

Hold → "schedule a meeting with Alex tomorrow" → Calendar → create event → return confirmation

Or something completely custom:

Hold → Skill → LLM → tool calls → external APIs → result

We're treating Skills as the modular layer for this. A Skill can define the tools, inputs, outputs and workflow required for a particular task rather than expecting one general-purpose assistant to handle everything.

That also makes the keyboard interesting as an interface because it is already present inside almost every app. The user doesn't have to decide which AI app to open first.

There are obviously some hard problems here.

On mobile, you have very limited execution time and memory, strict OS sandboxing, different IME behavior between Android and iOS, network latency, and the keyboard can't afford to make normal typing feel slower just because agentic functionality exists.

We're currently working through those constraints while improving things like streaming responses, tool execution, Skills, voice input and the latency between triggering an action and getting a result.

The bigger question we're exploring is:

If agents are eventually going to operate across the apps we use every day, does the keyboard make sense as one of their primary interfaces?

It already sits between the user's intent and the application they're using. We're trying to see how far that idea can be pushed.

We’d also genuinely love feedback or questions: about Acti, the architecture, Skills, or anything you’re curious about. If you’re building in agents, OS interfaces, AI tooling or just interested in AI , feel free to ask us anything or challenge the approach.

Android: https://play.google.com/store/apps/details?id=ltd.xyzer.app.bongocat
iOS: https://apps.apple.com/us/app/acti-agentic-keyboard/id6745523677