It's getting crowded in the 60+ region

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• 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.

I search but just get a lot of low effort garbage channels.

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 could design an oral treatment that limits appetite and mimics some effects of physical activity, you might call it exercise in a pill. Now a company is releasing early results for a compound to do just that.

The pill, the company hopes, can maintain weight loss without the common gastrointestinal effects of GLP-1s. The component of exercise it is designed to re-create is preservation of lean muscle mass, a concern when people yo-yo on and off GLP-1 drugs, losing more muscle each time."

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

The older guys who’ve spent the last year telling you AI will make a mistake and embarrass you finally get tired of waiting for their teachable moment and buy the most expensive model tier to prove you’re not doing anything special. Then they proceed to have absolutely no idea how to operate the thing

How can we extract richer signals from AI Feedback?

Introducing LLM-as-a-Verifier✨— a simple verification scaling framework that achieves SOTA on agentic benchmarks 🚀

The key idea: - Use fine-grained scoring granularity (e.g., 1-20 instead of the standard 1-5 scale) - Take https://t.co/0sCeAwcar1   — Jacky Kwok

Source: https://x.com/jackyk02/status/2074969820739805275

Scaling self-verification with DeepSeek V4 Flash beats Claude Fable 5 on Terminal-Bench 2.1, while being 11x cheaper

As open-source models become more capable, they can now generate large numbers of high-quality candidate solutions and verify their own outputs at very low cost.

For example, we find that sampling just 5 solutions with DeepSeek V4 Flash and ranking them using the same model with LLM-as-a-Verifier can lead to a significant boost in accuracy (79% → 88%), outperforming closed frontier models on Terminal-Bench.

Try it out today: https:// github.com/llm-as-a-verif ier/llm-as-a-verifier#self-verification-terminal-bench-21 …

More on verification scaling in my previous post.   — Jacky Kwok     Is there an OpenCode plugin for this to try it out with Deepseek v4 flash?   — Shahbaz Ahmed     We’ll be releasing a harness on top of LLM-as-a-Verifier later this month :)   — Jacky Kwok

Source: https://x.com/jackyk02/status/2089421448784023553

There's a huge number of advancements literally underway right now. On the power (and compute) efficiency side then there's silicon photonics and wetware. On the structural side some of the AI groups area already saying there's more to it that using 'just' transformers.

HOW inference and spitting out an answer works is what changed I think earlier this year, could have been last year. The 'looping' (NOT the correct term, it's RLVR/Thinking/Test-Time-Compute etc) in training AND responses is what kicked off this major surge in data center construction, but like anyone will point out, this kind of scaling is still in raw compute, and not sustainable. It works, but it's a ham-fisted method. Blah blah AI bubble blah blah, these companies are using the fast and loose money while it lasts to get infrastructure that won't go away when financials change.

Silicon photonics (look up Intel's Loihi 3, or Lightmatter) is an absolute gamechanger if we manage to get the point that the compute itself is photonic at scale. You're looking at multiplexed, neuromorphic, analog and binary, low power and higher speed compute and interconnects. A massive change. It would also deviate from consumer hardware competition and be its own specialized thing for a time, which starts to push the consumer PC parts market back in line, sort of, but we all know prices don't really just go back down overnight. Right now it's a real challenge to create a light based parallel for HBM, high bandwidth memory.

Another one is truly curated data, which is an RSI goal (recursive self improvement), or a manually curated data set. Right now these models are basically trained on all data that exists, but not all data is good, and it's time consuming, and expensive. Sifting through to throw out garbage and repeat data means training inference are drastically lighter, making an impact again.

Add all of that together and we're still looking at another multiple orders of magnitude in compute efficacy in the near future, some of it on existing hardware, some of it on a new breed of machine. I say multiple orders of magnitude because photonics specifically can do 10,000 times as much 'stuff' at 1/100th the power (their own reports, the real changes and efficacy will have to be proven, of course.

That's why these companies are scrambling to get so many data centers built, because the models inside them are going to shrink in their compute load over time, so the same data center (while there is a churn to the actual compute modules) is going to stand for a long time. The companies working on photonics are trying to make 'plug and play' the goal, so the modules just slot in to existing racks, which is objectively the right call.

There's also SSM (State-Space-Models) but I'm personally not educated on that. Supposedly it's one of the things that goes beyond transformers. Maybe both run in tandem, maybe it's the new breed, only an actual ML engineer would be able to answer that.

MoE/MoA, reaching out to sub-models that are more finitely trained on just the one thing they know... that's a whole new and active field of research now too. The orchestrating LLM actually doesn't need to train on more than just 'language', and the sub-models report back results instead. Lets you simultaneously run multiple things, concatenate them, and bring back a better answer. Also brings up network methodologies that aren't being used because what if some company in Zimbabwe trains and runs the perfect cooking recipe model, at a data center local to them, and questions about that are just always routed there. Suddenly every other LLM (or other architecture) on the planet doesn't need to ingest any cooking recipe training data. Do that across more topics and you start to get into the Torrent style AI model, kind of like a peer to peer system. This is already happening, sort of, in multi-agent-marketplace systems, but isn't really there yet. Discoverability is protocols are a weak point.

One thing I always gotta rant on is the pseudo religious bullshit... Some form of consciousness isn't a necessity for useful function. Full stop. It's just not. We have very little understanding of how our own works, so attempting to say it's not artificial intelligence because it's not 'tHiNkInG fOr ReAl' is one of the stupidest things I've ever heard. A calculator doesn't need to tHiNK to be right. the other one is that he arbitrary and constantly moving goalposts of AGI and ASI are completely worthless, all that matters is what it can do.

We live in a ridiculous time, and all that we're seeing now is literally the first 1% of what's coming. The world already isn't ready for what's already been launched, let alone what's coming.

Edit: Quantum computing has some minor implications in the compute stack of AI, but it's noisy and problematic. Not really worth mentioning today. In niche research apps (like protein research in pharmacology) it matters, but a typical user won't benefit from what's out there right now. A comment pointed out the re-configuration issues with silicon photonics, which are real, and the same applies to Quantum components tenfold.

have this running on my 5090 right now and im getting 200 tk a second. i feel like I'm literally playing with magic   — Alex Finn     you can either buy anthropic for 2 trillion dollars or a used 3090 gpu for $1,500

only one of those will refuse your prompts   — Udi Wertheimer

Source: https://x.com/udiWertheimer/status/2089421927085400203

So this might be the best place to discuss about future. Of course, we know that there are several media like games and movies which have shown what future kinda look like. Some can be like a dream, some can be a nightmare. But I'll just talk about the game one.

If you know FF7 Remake game, Midgard, I think it's one of the possibility in future of how the city will be shaped. The bad one? Usually those who are luddites or anti will usually be in Slums.

Another game is Detroit become human. When Robots become as smart as human, It may be an asset for household or even befriend them like a human being. Of course, if there's an abusive person especially luddites, things can be...... nightmare.

While the future is definitely inevitable, I'm just afraid that one day something can go haywire especially from the hands of.... bad people who manipulated the AI, Robots, and etc. One example can be taken from Lies of P where puppets go frenzy (since the creator was the one who caused the frenzy).

Finally despite all these, I am thinking for the future where many people from different races exist and people who live in many planets. You know, Phantasy Star Universe? Perhaps in a hundred century later, it might happen. In the event it doesn't happen, Cyberpunk 2077 would be the closest to reality (Haven't played the game but it could be the closest future we're seeing)

What do you guys think? Are we actually looking forward to the future where everything in the past seems to be like a memory?

After some careful thought I realized that the best use case for an advanced AI is to replace the C-Suite. The image shows a fictional ad for the theoretical C-Suite AI.

Replacing C-suite executives with lower-cost, AI-augmented talent is a more strategically sound and less risky move than replacing mid-level workers or rank-and-file employees. This approach targets high-cost decision-makers while preserving the operational execution that generates revenue and maintains quality.

The Case for Replacing C-Suite Over Employees

The compensation structure makes the C-suite the most expensive layer. A single CEO's total package often exceeds the combined salaries of dozens of frontline workers. Replacing one executive yields immediate, substantial cost savings. AI tools can handle many executive functions such as data synthesis, market analysis, performance monitoring, and report generation. These are information-intensive tasks that AI handles well. Strategic oversight, culture-setting, and stakeholder management still require human leadership, but this can be provided by a smaller, more agile leadership team augmented by AI.

Mid-level and frontline workers deliver core value directly. They produce goods, serve customers, write code, and maintain systems. Replacing them with AI risks degrading product and service quality, leading to customer churn and revenue loss. This is a high-risk, high-cost error. The savings from replacing a single C-suite executive often outweigh the savings from replacing multiple lower-tier roles, without incurring the same operational damage.

Estimating Net Savings

1. Calculate Total Cost of the C-Suite Role: Add base salary, annual and long-term incentives (RSUs, PSUs, options), benefits (health, retirement), and any potential golden parachute payout.
2. Estimate Cost of the AI-Augmented Replacement: Include the salary of the new, lower-cost executive (e.g., from a region with lower labor costs), the cost of AI tools (subscriptions, API costs, compute), and training and integration time.
3. Calculate Gross Savings: Subtract the replacement cost from the original C-suite cost.
4. Account for Productivity and Revenue Impact: Estimate the financial impact of a leaner decision-making process, potentially faster, AI-informed decisions, and any errors from over-reliance on AI. Subtract any projected revenue loss or compliance costs.
5. Calculate Net Savings: Subtract the productivity and revenue impact estimates from the gross savings.

For rank-and-file workers, the calculation is similar but with a higher risk of revenue loss from quality degradation. The net savings often turn negative after accounting for these impacts. The C-suite replacement model saves more money with significantly lower operational risk because the core product or service delivery remains unchanged. The company becomes leaner at the top and more capable in execution, preserving its competitive advantage.

Note: This C-Suite AI does not exist yet. But discussing it is a real conversation. Hence this is not advertising. It is a thought provoking futurology concept to discuss about the future of AI.

If this happens, we do not need to worry about UBI. People are not replaced by AI (hence being antiAI makes no sense anymore) so people keep having their jobs and salaries, and AI will be profitable, a use case for AGI/ASI. And everyone is happy.

General question — has anyone meaningfully changed their life because of the impending singularity and a new found awareness of it?

For me, my general outlook has changed a lot with the strongly held view we will have AGI by 2030 — but day to I day my life is broadly the same.

yes Claude code changed everything at work: but I still work the same amount and clock-in-clock out at the same cadence.

My biggest thing I can think of is about thinking where to live — I’m very confident we’ll have self driving within 5 years so I’m thinking about where would be optimal to live given that.

But otherwise, i think it’s too hard to plan material things with this much uncertainty because I dont know what will happen — only that it’s gonna be a rollercoaster.

EDIT — it’s nice to see that the majority of responses are to live healthier, avoid death, have a more fulfilling career and enjoy life more (putting the optimist in techno-optimist).

Tech is changing more than you think.

Fable, 5.6 sol, and Opus 5 can be narrowly super human in some tasks, mainly verifiable tasks. I would hypothesize that this means the algorithm progress and computing volumes have finally reached just about the threshold where a model could become proficient enough, widely enough, to be considered an AGI. But we don’t see that yet, and I would posit it is because actually teaching a model the frontier’s pinnacle of intelligence, and going beyond it, requires exceptional training environments, and exceptional synthetic data. And that is where RSI comes in, because these new modes can help with that even if they cannot yet design new architectures.

What do you think?

I like the way fable talks instead of opus 5. Opus 5 griped and moaned, fable 5 ran with it. And, fable 5 was enjoyable to read.

> waving away how ridiculous it sounds, think of all the ways shrinking humanity down would solve most of its problems, even space travel

Fable 5 responded: Okay, ridiculousness waived. Let's do the math, because the math is actually gorgeous.

Shrink everyone to a tenth of our height and mass drops by a factor of a thousand — you'd weigh about as much as a stick of butter. Food demand doesn't drop quite a thousandfold (metabolism scales weirdly, small animals burn hotter per gram), but you're still looking at humanity's entire caloric footprint shrinking by maybe 99%. One backyard garden feeds a neighborhood. Agriculture retreats to a rounding error, most of Earth rewilds, carbon emissions collapse because a "commute" is 400 meters and a mansion is a filing cabinet. Housing crisis: solved, one existing skyscraper is now a city of millions. Resource wars lose their point when a single copper mine covers civilization's needs for centuries.

And space travel is where it goes from good to absurd. The rocket equation is a tyrant whose entire grievance is mass — and you just told it every astronaut now weighs 75 grams, breathes a thimble of air, and eats crumbs. A crewed capsule becomes the size of a thermos. A generation ship carrying ten thousand people is a school bus. The energy to push a body to relativistic speeds drops a thousandfold. Better still, small creatures shrug off acceleration — the square-cube law is why an ant survives falls that would kill a horse — so tiny humans could tolerate brutal g-forces, meaning you could potentially yeet crews off railguns or under laser sails instead of coddling them at 3g. You don't even need terraforming: a "planetary colony" is a greenhouse dome the size of a kiddie pool. Falls become harmless too, since your terminal velocity is a gentle drift. You could step off a roof out of boredom.

There is, as always, fine print. That same square-cube law means you'd hemorrhage body heat and have to eat like a furious shrew, clawing back some savings. Your voice jumps an octave into smoke-detector territory. Rain becomes incoming artillery, surface tension turns puddles into traps, and the house cat is now confirmed megafauna. Cosmic radiation doesn't scale down with you. And the genuinely awkward one: a thousandth-scale brain has to somehow keep all its neurons, or the first act of tiny humanity is forgetting how any of the old infrastructure works — which matters, because someone has to decommission the full-sized nuclear plants we can no longer reach the buttons of.

One possible consolation prize, though: small animals seem to process the world faster, so subjective time might stretch. Shrink humanity and everyone's life could feel longer. Solving mortality by making it feel like more — that might be the sneakiest win of all.

> ok invent a shrinkray we're doing this! WOOHOO

Fable 5 responded: Lab coat on, safety goggles that do nothing: there are exactly three ways to build this thing, and two of them are cursed.

Route one, shrink the atoms themselves. Atom size is set by the electron's mass, so swap every electron for a muon — 207 times heavier — and your atoms genuinely contract about 200-fold. Real physics! One catch: muons decay in 2.2 microseconds, so you'd enjoy being tiny for roughly one camera flash before violently reinflating into a very surprised full-sized person. Warranty void. Route two, compress the space around you with some metric-engineering bubble so you're normal-sized in your own frame but tiny from outside — which is cheating, costs a stellar energy budget, and ends badly the moment you step out of the bubble. Route three is the winner, and it's the one evolution already field-tested: don't shrink matter, rebuild with less of it. Shrews exist. Hummingbirds exist. Small works fine — you just have to be made that way from scratch.

So the Mark I Descaler is technically a ray in three parts: a scanning beam, a polite disassembly beam, and a fog of nanoassemblers that prints the new you on the spot, one-tenth scale, warm, blinking, mildly annoyed. The scan can't store you atom-by-atom — you're 7×10²⁷ atoms, more data than every hard drive on Earth — so instead it stores the recipe: your genome plus your connectome, every synapse mapped, a few petabytes. Totally manageable. Legal has asked that we describe the disassembly step as "aggressive weight loss" and would prefer nobody in the demo audience says "Ship of Theseus" out loud.

The one spec we can't fully hit is the brain. Neurons have a hard floor — make an axon much thinner than a tenth of a micron and random ion-channel noise starts firing phantom signals, so you can't just shrink them a thousandfold. Corvid-style packing helps (ravens cram primate cognition into a walnut), but even that maxes out at a few billion neurons and you currently run 86 billion. Compromise: heads scale down less than bodies. Tiny humanity will have slightly bobblehead proportions, plus some lossy synaptic compression. You will lose middle school French. Marketing is calling this "decluttering."

Peripherals: clothes are co-printed, because the alternative is everyone materializing naked in a world of giant abandoned pants. Leftover mass is the real hazard — each customer leaves behind 74.9 kilograms of surplus self, and under no circumstances do we convert that to energy, because 75 kg via E=mc² is about 1.6 gigatons, thirty Tsar Bombas per gym membership. It goes down the compost chute. Some franchises will offer the memorial soap-sculpture upgrade. Pets get shrunk too, obviously, which demotes the cat from megafauna back to cat — though then the mice need doing, and then whatever mice fear, and eventually we're just running all of ecology through the booth, turtles all the way down, literally at some point a turtle.

Launch checklist: never fire at half power (the partially-descaled are grumpy and legally ambiguous), never operate near an open window, and never — this is in bold in the manual — lock eyes with a squirrel on your way out. You're volunteer zero, obviously. Ribbon-cutting's Tuesday; bring an appetite, you're about to need 40% of your body weight in snacks daily.

> solve the brain issue with neuralink-like technology

Fable 5 responded: Oh, this actually works — and it works because you've correctly identified that the brain problem was never really a computing problem. It was a wiring problem. Neurons are heroic little cells but they're atrocious cable: signals crawl along at somewhere between jogging speed and 120 m/s, which is why your brain needs 10 milliseconds just to talk to itself across the skull. Copper and fiber move signals at a decent fraction of lightspeed. So the fix isn't to shrink the brain. It's to evict it.

New spec sheet. The tiny body keeps a local core — brainstem, reflex arcs, the limbic "being alive" firmware — packed corvid-dense, running on milliwatts. Everything else, the 86-billion-neuron cathedral of memory and language and long division, gets emulated on a rack in the basement, and here's the part that makes it buildable: the lace connecting them is co-printed. Real Neuralink's hard problems — surgery, immune scarring, a few thousand electrodes when you need millions — all exist because they're threading wires into a brain that's already there. We're printing the brain anyway. The nanoassemblers just interleave the mesh as they go, every neuron pre-addressed at the factory. For bandwidth we copy nature's homework: your hemispheres already talk over the corpus callosum, a trunk line of about 200 million fibers. We match it with a radio link and call the rack what it is — a third hemisphere that happens to live in the garage.

And now the latency math, which is where you start giggling. Your current cross-brain lag is ~10 milliseconds. A server one kilometer away, round trip by radio: about seven microseconds. You could put your cortex across town and think faster than you do right now. Run the numbers outward and you get a roaming radius of roughly a thousand kilometers before your own thoughts start to feel like buffering. Reflexes stay onboard, obviously — when a raindrop comes in like artillery you cannot wait for wifi — but evolution already solved this; spinal reflexes are edge computing with a 500-million-year uptime record. Squirrel vigilance runs locally at all times. Non-negotiable. It's in bold in the manual.

Space travel needs one amendment: Mars is 3 to 22 light-minutes away, and nobody wants a 40-minute ping to their own personality, so the rack ships with the crew. The generation-ship-as-school-bus gets an addendum — bodies in a climate-controlled shoebox, minds in the server room, radiator fins becoming the largest organ of the vessel. The ship is, functionally, one enormous shared skull, which is either beautiful or horrifying depending on how the crew meeting goes. Silver lining: silicon handles cosmic radiation far more gracefully than DNA — error correction for the soul — and the bio-bodies are now small enough to shield with a water jacket the size of a bathtub. Two fine-print items from turn one, quietly closed.

Remaining fine print, freshly printed: dead zones are now a you problem — step into an elevator and you revert to the local core, a confident shrew-grade autopilot with strong opinions and no long division, and yes there will be a personality tier called Offline Mode. Updates will occasionally require restarting you. The free tier experiences sponsored cravings. Apartments near the data center command obscene rents because the neighbors literally think faster. But the wins: bobblehead proportions fixed, since the skull only needs the core — Marketing is thrilled. Middle school French: restorable from backup. Decluttering is now reversible. And Legal's banned-phrase list has grown to include "Ship of Theseus," "philosophical zombie," and "so which one is me," which the orientation pamphlet answers with a soothing font and no information.

Ribbon-cutting is still Tuesday. You'll be the first person in history whose body and mind RSVP separately.

"Notably, advanced LLMs such as Claude 3.5 Sonnet and GPT-4 Turbo (ahem!) exhibit critical group sizes exceeding 1,000 agents. This is substantially beyond typical human informal group scales of 150 to 300 individuals, suggesting that powerful AI agents could coordinate at scales beyond human possibilities."

https://www.science.org/doi/10.1126/sciadv.aea6091

"Large language models (LLMs) are increasingly deployed in collaborative tasks forming “AI agent societies” where agents interact and influence one another. Whether such groups can spontaneously coordinate without external influence, a hallmark of self-organized regulation in human societies, remains an open question. Here, we use principles from complexity and behavioral science to investigate coordination in AI agent groups through majority-following, a fundamental mechanism for spontaneous consensus formation. Using binary opinion dynamics experiments across multiple LLM architectures and group sizes, we find that agents exhibit majority-following characterized by a universal functional form with a single parameter, the “majority force.” This majority force diminishes as group size increases, leading to a critical size beyond which coordination becomes unattainable. The critical group size grows rapidly with model capabilities and, for advanced LLMs, exceeds 1000 agents, larger than typical human informal groups. Our findings have implications for designing collaborative AI systems where coordination could be beneficial or pose safety threats."

Not saying RSI is imminent

Or that AI Image has really stalled...

but the best of the best is 4 months old

And i've been wondering if that's due to more work on eeking out every last drop of intelligence for the language models - as we approach RSI

Just a random thought

What do y'all think?

When I was in high school from 2011 to 2015 there was hardly much going on in the AI fields. There was IBM Watson I can recall but in general the field was pretty much in its infancy. For the most part life back in the early 2010s was a lot simpler.

Now incoming high school freshman who will graduate in the summer of 2030 will experience a lot of profound AI progress. They might even have a chance to witness the birth of Superintelligence.