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Howardism
Howardism · Vol. 03Plate II · No. 02

Superintelligence Trajectory, in order.

Notes23DomainSuperintelligence TrajectoryOpen Qs69Newest11 Aug 2026Oldest7 Jun 2026

Recursive self-improvement, scaling limits, and the path to ASI.

Map of Content for the superintelligence-trajectory domain — 23 concepts. The path from AGI to ASI: recursive self-improvement, intelligence-explosion dynamics, ASI theory and limits, and frontier governance. Curated entry point; see Home for all domains.

  • The Abstraction Barrier — Lerchner's hypothesis that AI trained on human concepts may be unable to discover genuinely novel conceptual primitives from raw data — capping single instances near AGI — and the embodied bottleneck that grounds concept validation in real-world experiment speed, converting recursive self-improvement into a process paced by empirical science
  • Advantages of Digital Intelligence — The six properties (Table 1) that follow from knowing an AI's source code — I/O speed, processing speed, working memory, substrate independence, lossless replication, high-bandwidth experience sharing — each of which scales with compute in ways biological intelligence cannot, widening the human–AI gap
  • AGI-to-ASI Pathways — DeepMind's four non-exclusive, parallel technological routes from human-level AGI to superintelligence — scaling, algorithmic paradigm shifts, recursive self-improvement, and multi-agent group agency — plus the six frictions (data wall, economics, paradigm-insufficiency, research-gets-harder, abstraction barrier, deliberate slowdown) whose impact is the report's central set of open research questions
  • AI Accelerating AI Development — The empirical core of When AI builds itself: measured evidence AI already speeds AI R&D at Anthropic — >80% of merged code Claude-authored, ~8× code/engineer/day vs 2024, a kernel-optimization eval going 3×→52× in a year, an automated researcher recovering 97% of a weak-to-strong gap, and model next-step judgment beating humans 64%
  • AI R&D Autonomy Evaluation (AECI) — How Anthropic measures whether a model can automate or dramatically accelerate AI research — the capability that drives recursive self-improvement; tracked via the AECI capability index plus concrete shortcomings vs. human researchers; Opus 4.8 sits below the frontier and is not close to substituting for research staff
  • Artificial Superintelligence (ASI) (hub) — DeepMind's informal characterization of ASI as a system that exceeds large, well-coordinated human-expert collectives across virtually all domains — distinct from human-level AGI below it and the incomputable Universal AI limit above it, all points on the Legg–Hutter intelligence continuum
  • Autonomous Scientific Discovery — Mythos-class models now conduct novel science with limited human input — autonomous protein/drug design (~10× faster, matching skilled humans), molecular-biology hypotheses preferred ~80% over Opus-class (one E. coli mechanism independently corroborated), and week-long genomics that beat a Science-published model at 100× smaller; the wet-lab analogue of AI-driven formal proof search, and fresh evidence in the research-taste debate
  • Balance-of-Power Superintelligence — Zuckerberg's thesis: distribution of personal superintelligence to individuals — not centralized control — is the safety mechanism; anti-singleton alignment argument (humanity isn't a monoculture); jobs optimism conditional on the automation-vs-empowerment balance. The August 2026 Meta manifesto is the full statement, adding an RSI compute-allocation rule, alignment redefined as alignment-to-the-person, and a lab-government checkpoint proposal
  • Capability-Gated Model Fallback — Fable 5's safeguard architecture: classifiers detect cyber / bio-chem / distillation queries and route the response to a less-capable model (Opus 4.8) instead of refusing — 'fallback, not refusal'; >95% of sessions never trigger; conservative tuning, robust to 1,000+ hours of jailbreak testing; a new point on the safeguard spectrum for capabilities past a risk threshold
  • Cross-Lab Pre-Release Review — Musk's proposal that frontier labs get 1–2 weeks of competitor API access to test each other's models before release, with government reserved for the case where a lab refuses to act on a flagged danger — competitors as the technically-capable honest brokers, on the MPAA self-rating model; the Mythos cyber-risk incident is the informal precedent
  • Effective Compute Scaling — DeepMind's framing of compute growth as ~10×/year of 'effective compute' — the product of hardware improvement (~1.5×/yr), compute investment (~2.5×/yr), and algorithmic efficiency (~3–6×/yr) — and the data-wall and economic frictions that determine how long the scaling pathway to ASI can be sustained
  • Frontier Pause Verification — The arms-control problem of a credible, verifiable slowdown or pause of frontier AI: detectability is harder than for other technologies (training runs are easier to conceal than missile silos), so the Anthropic Institute aims to build the verification systems a multilateral pause would require
  • Fundamental Limits of ASI — Even far-superhuman AI is bound by hard physical (Landauer, Bremermann, Bekenstein, light-speed), complexity-theoretic (P vs NP), and logical (Gödel, Halting) limits — but these negative results are often 'vacuous' in practice because good heuristic approximations exist below the worst case
  • Government Checkpoint Sharing — Zuckerberg's August 2026 proposal that frontier labs hand governments intermediate training checkpoints plus technical staff — capability transfer to the defender instead of a release-gating review — designed so oversight adds zero delay to public release; the acceleration-compatible pole of the pre-release-oversight design space
  • Intelligence Explosion Dynamics — The growth-curve question behind recursive self-improvement: whether AI-accelerating-AI produces exponential, super-exponential/hyperbolic (singularity-in-finite-time), or S-curve dynamics — and the four mechanisms (genetic, cultural, cooperative, data) plus the physical/economic frictions that bound it
  • Multi-Agent Collective Intelligence — DeepMind's fourth pathway to ASI: superintelligence as an emergent property of many coordinated AGI agents — group agents, virtual agent economies, and centrally-steered super-collectives — governed by hoped-for 'multi-agent scaling laws' and the open question of when a homogeneous LLM collective actually becomes more than the sum of its parts
  • Open-Weight Elicitation Irreversibility — A wiki-drawn synthesis of Brown and Gemma 4: if dangerous capability scales with inference budget, then an open-weight release fixes the model's safety evaluation at one budget forever while leaving elicitation budget unbounded and recall impossible — the closed-weight mitigations (classifier fallback, suspension, retention) all require a server the vendor controls
  • Recursive Self-Improvement (hub) — An AI system autonomously designing and developing its own successor; Anthropic Institute's When AI builds itself argues AI is already accelerating AI development (engineers ship ~8× more code/quarter) and lays out three futures — stalled-but-diffused, compounding-efficiency, and full RSI
  • Research Taste as the Human Bottleneck — The narrowing human role as AI absorbs execution: choosing which problems matter, which results to trust, and when an approach is a dead end; the top rung of the autonomy ladder, and the open question of whether taste is 'just another capability' AI fails at then masters
  • Researcher Uplift from Code Output — Thomas Kwa (METR) translates Anthropic's reported 8× code-per-engineer-per-day into serial researcher uplift with production functions: Cobb-Douglas gives U = M^β = √8 ≈ 2.83, CES stays within ±3% of that across elasticities because 8 ≈ e², and a low-stakes-code-discounted model still lands [2.33, 2.66] — so researcher uplift from coding agents alone is plausibly >2×, reconciled with Anthropic's 'well short of 2× overall R&D uplift' because R&D speedup also depends on compute (Greenblatt: labor^0.55 × compute^0.45)
  • Responsible Scaling Policy Evaluations — Anthropic's RSP gates deployment on pre-release capability evaluations in CBRN, automated AI R&D, and high-stakes misalignment; the Opus 4.8 determination is that it does not advance the frontier beyond Mythos Preview and that catastrophic risk remains low given current mitigations
  • Transformative Creativity — Boden's three-level model of creativity (combinational, exploratory, transformative) used to locate today's AI achievements — Move 37, AlphaFold, theorem-proving — at the exploratory level within human-given conceptual spaces, and to frame Boden level-3 (creating new conceptual spaces, à la Hassabis's 'could AI rediscover general relativity?' test) as a hallmark requirement of true ASI
  • Universal AI (AIXI) (hub) — Hutter & Legg's formal upper bound on machine intelligence: AIXI, the incomputable agent optimal on average over all computable environments under Solomonoff's universal prior; the theoretical endpoint of the intelligence continuum that ASIs approximate from below

Open questions 69 open

  • Advantages of Digital Intelligence2 open
    • SourceDoes training on human data suffice to give digital intelligence human-grade abstractions, or does the low embodiment factor cap concept formation? (The crux shared with The Abstraction Barrier.)
    • WaitWhat do ASI "societies" actually look like — homogeneous super-collectives, market ecologies, or compute-tethered virtual worlds?
  • AGI-to-ASI Pathways3 open
    • SourceFor each friction: is it a fundamental blocker (multi-year plateau) or a mere friction (slows, doesn't halt)? The report's central unresolved question. Partially answered (synthesis against Anthropic): RSI Growth Curves: Which Friction Binds First? — data-wall and research-gets-harder demote themselves into compute; economics and neural-paradigm are pathway-conditional; the abstraction barrier is the candidate fundamental (re-pacing) blocker; and deliberate slowdown is the only exogenous friction — the one Anthropic wants to install and this report doubts can be made to bind. Retagged #oq/now → #oq/source 2026-08-10: the synthesis over existing pages has been run, and what remains is a weight DeepMind itself calls "an open research question" — it needs external evidence, not another /query.
    • SourceDo the four pathways compound multiplicatively when run in parallel, and how would we detect that early?
    • WaitCan benchmarking methodology that doesn't saturate at human level be built before it's needed for ASI?
  • AI Accelerating AI Development3 open
    • LOC, self-reports, and headroom-dependent multiples all overstate; what unbiased throughput metric would Anthropic's promised shift to "direct measurement of AI R&D acceleration and researcher uplift" (AI R&D Autonomy Evaluation (AECI)) actually use? Partially answered: Researcher Uplift from Code Output — Kwa argues code output (the 8× itself) beats per-hour code uplift because output already prices in marginal value through time reallocation and is robust to production-function assumptions; but it stays corrupted by verbosity, barely-useful "Cadillac" code, and fun-driven time-allocation shifts — so the metric it really points to is quality-adjusted code output, which still needs internal data LoC can't supply.
    • SourceThe W2S result didn't transfer to production-scale models. Is that a temporary scaling artifact or a structural limit on autonomous research?
    • SourceThe next-step judgment trend (51%→64%) is measured only on weak-human-move slices. What does the curve look like on a representative sample of research decisions?
  • AI R&D Autonomy Evaluation (AECI)3 open
    • Source"Not close to substituting for senior researchers" is a subjective, internally-sourced judgment. What objective signal would replace it as models approach the threshold?
    • SourceAECI is a single scalar fork of an external index; how sensitive is the 155.5 / frontier-not-advanced conclusion to the choice of the n=11 evaluation set? Partially answered: the Claude Opus 5 card discloses that every snapshot refits the ECI globally, so values move as the benchmark set changes (n=11 → n=40 → n=67 across recent cards) and "do not exactly match the values of previous AECI reports," though the shifts stay "well within our reported error bars." The index is robust enough for within-card ranking and explicitly not a cross-card time series — which is a partial answer for sensitivity and a caution against reading generation-over-generation AECI deltas.
    • SourceThe shift to "direct measurement of AI R&D acceleration and researcher uplift" is announced but not yet operationalized in this card — what does that measurement look like? Sharpened: Researcher Uplift from Code Output — one external answer: translate a measured code-output multiplier into serial researcher uplift with a production function (Cobb-Douglas/CES), preferring code output over per-hour uplift because output prices in time reallocation. It also splits the target quantity in two — serial researcher uplift (labor only) vs Anthropic's overall R&D speedup (labor × compute) — so a rigorous internal measure must state which it reports.
  • Artificial Superintelligence (ASI)3 open
    • SourceCan we even recognize ASI? We lack benchmarks for general superhuman performance (only narrow ones like chess), and the tasks must be abstract/open-ended enough to reveal it.
    • SourceIs the jaggedness of capabilities a fundamental theoretical property, or an artifact of comparing against human performance? (Open question 6d in the report.)
    • SourceWhere does practical ASI plateau relative to the hard limits — how much slack is there?
  • Autonomous Scientific Discovery3 open
    • WaitEvery result is Anthropic-reported and example-selected; the genomics "100× smaller beats Science" claim is "intend to publish" — what survives external peer review?
    • SourceScience's verification gap: the formal-proof loop self-validates; here a wrong-but-confident hypothesis costs a wet-lab cycle to falsify. Does autonomy without a fast verifier increase the verification bottleneck rather than relieve it?
    • SourceIf hypothesis-generation is genuinely at ~80% preference, how much of "research taste" is left as a distinctively human function — and how would you measure the residue?
  • Balance-of-Power Superintelligence3 open
    • WaitThe conditional jobs claim is testable: does widely-distributed AI shift employment toward small businesses and new-firm formation? Trigger: firm-size and new-business-registration data through 2027–28. Partially answered: Firm AI-Spend Intensity and Headcount Growth measures headcount growth gated on adoption intensity (~10% for high-intensity adopters, none for low), which tests the employment half but not the firm-size half.
    • SourceDoes the superintelligent-lawyer equilibrium survive capability asymmetry — when access is symmetric but compute, complements, and skill are not? The wiki's organizational-complements evidence suggests realized advantage concentrates even under equal access.
    • SourceDoes the RSI compute-allocation rule have any operational form? The manifesto names no threshold fraction, no measurement, and no binding mechanism — and a "significant majority of intelligence directed by people" is not observable from outside a lab.
  • Capability-Gated Model Fallback4 open
    • SourceThe >95%/<5% figures are session-level; what's the false-positive rate for legitimate security researchers and biologists, whose benign queries are exactly the ones most likely to trip the conservative classifiers? Partially answered: on FrontierBench (74 hard science/engineering terminal tasks), Claude Opus 5's classifiers flagged 5% of API calls in 4% of trials where Fable 5's flagged 42% in 26% — so the over-broad tuning was costing roughly a quarter of trials on exactly this kind of legitimate technical work, and has been substantially narrowed. Still a benchmark proxy, not measured professional traffic. Further evidence (2026-07-30): a competitor's benchmark runs (Kimi K3 card) put Fable 5's fallback rate at 35% of SWE-Marathon tasks, 17.5% of Kimi Code Bench tasks and 40% "downgraded" on Agents' Last Exam — corroborating the order of magnitude from outside Anthropic, and locating it on plain software engineering rather than only on science-adjacent work. Still benchmarks, still not professional traffic.
    • NowFallback-not-refusal preserves UX but means the real general-access model for security/bio-adjacent work is Opus 4.8, not Fable — does that quietly cap Fable's value for whole professional segments until the trusted-access programs open? Partially answered (anecdote, 2026-07-24): Cline abandoned a 17-hour autonomous evals-research campaign on Fable 5 because the classifier "kept downgrading the model to Opus-4.8," and ran it on a competitor's model instead — the first instance in this corpus of the cap being paid as a lost workload rather than as a lower benchmark score, and from outside Anthropic. One vendor's passing remark with no rate attached; it establishes the failure mode exists in the wild, not its frequency.
    • SourceThe UK AISI's "progress toward a universal jailbreak" is disclosed but not quantified — and the post-launch access suspension (see Claude Fable 5) raises the question of whether a safeguard failure forced it.
    • SourceDoes swapping to a weaker model on flagged topics create an exploitable oracle (probe which queries trigger fallback to map the classifier's boundary)?
  • Cross-Lab Pre-Release Review3 open
    • SourceDid the Mythos cyber-risk escalation actually run Amazon → White House → export-control threat, as Musk states? Single-source and checkable against Anthropic's and the US government's own records.
    • WaitDoes a competitor with pre-release access over-report danger to delay a rival's launch? The proposal's incentive argument only models under-reporting, and the mechanism has no adjudication step.
    • SourceHassabis's late-July 2026 public-private-regulator proposal is referenced but not in the wiki — ingest it and compare its adjudication design against this one.
  • Effective Compute Scaling3 open
    • SourceWhen does more compute reliably yield more intelligence — only for some problem classes, or generally? Can quantitative and qualitative scaling be traded off?
    • NowCan data generation (synthetic, simulated, interactive) actually keep pace with model-size growth, or does the data wall bind first?
    • WaitWhen (if ever) does scaling become economically unviable, and how do hardware/software-efficiency trends move that point?
  • Frontier Pause Verification3 open
    • SourceWhat does an AI-training "verification regime" concretely consist of — compute-accounting, datacenter inspection, hardware attestation, on-chip telemetry? The essay names the problem, not the mechanism.
    • SourceDetectability < verifiability: can detection even be made reliable when training runs leave no physical signature and inputs are dual-use?
    • WaitWho adjudicates triggers and lifts? No institution currently holds that mandate, and standing one up is itself a decade-scale task.
  • Fundamental Limits of ASI2 open
    • SourceCan we develop theory for "hard and inapproximable" problem classes — the only negatives with practical bite?
    • SourceHow much slack sits between these fundamental limits and the practical ceiling of AGI/ASI systems?
  • Government Checkpoint Sharing2 open
    • SourceHas any frontier lab actually transferred a pre-release checkpoint to a government body, on any terms? The proposal is stated as a recommendation to the industry, including implicitly to Meta itself; whether Meta has done it is not claimed.
    • WaitDoes a government holding a frontier checkpoint mid-training, in practice, stay out of the release decision? The proposal's zero-latency property depends entirely on the answer and offers no mechanism to secure it. Trigger: any first instance of such an arrangement being disclosed.
  • Intelligence Explosion Dynamics3 open
    • SourceCan "recursive improvement scaling laws" be formulated — predicting self-improvement curves (and their plateau point) from early-onset datapoints?
    • SourceHow far can a fixed model's performance be pushed with test-time search alone, and under what conditions does recursive distillation degenerate vs. compound?
    • SourceWhich binds first — algorithmic ceilings, the embodied bottleneck, or compute/energy supply — determining exponential vs. hyperbolic vs. S-curve? Partially answered: RSI Growth Curves: Which Friction Binds First? — both this report and Anthropic's locate the binding constraint outside cognition (the slowest un-acceleratable step coupling the loop to reality); the embodied bottleneck re-paces rather than halts, data-wall/research-harder demote into compute, and the abstraction barrier is the one candidate fundamental blocker. Retagged #oq/now → #oq/source 2026-08-10: the candidate is named but unranked, and ranking it needs external evidence rather than further synthesis.
  • Multi-Agent Collective Intelligence4 open
    • SourceDo homogeneous LLM collectives produce real synergy, or only humans-with-human-limits benefit from division of labor? Partially answered on the parallelization half (2026-08-03): OrchBench holds workers perfectly homogeneous and non-specializing (they are simulated), so it isolates parallelization from specialization cleanly — and finds the collective's advantage over a single serial agent is a context-capacity effect, not a coordination one: +0.302 quality at a 16k per-agent limit, +0.007 at 128k, with the single agent ahead on 82% of model-problem pairs at 128k and on every problem size below 100 subtasks. Synergy in the homogeneous case is what you get for not overflowing a window, and it is bought at ~1.5× the tokens. The specialization half stays open by construction: simulated workers cannot specialize, so nothing here speaks to whether prompt- or finetune-differentiated agents produce genuine division-of-labor gains. First datum on the specialization half (2026-08-03), and it is an efficiency answer: Cursor's production swarm runs role-differentiated agents (planner never implements, worker never plans) across four planner/worker model assignments at matched task and matched time budget — quality came out similar in all four while total cost spanned ~8× and worker spend 23×. Division of labor bought economics, not capability; the arm that moved quality was the coordination machinery, with models held fixed. Bounded to one task, one vendor, two roles, case-study, and role-differentiated by prompt and architecture rather than by finetuning.
    • SourceWhat's the actual shape of "multi-agent scaling laws," and does it depend on organization form (homogeneous collective vs. heterogeneous market) or task complexity? Partially answered on the homogeneous-vs-heterogeneous axis: Shi et al. hold group size, task and horizon fixed and vary only composition, and heterogeneity is costly rather than synergistic in social dilemmas — mixed-provider groups split on announcement semantics and produce persistent payoff asymmetries (up to −2.60 in Diners) present from Round 0. Bounded hard: six canonical games with explicit payoffs and a payout-maximizing instruction, three models, five agents, 10 rounds, and the effect only appears in games where compliance redistributes payoff — nothing here speaks to whether heterogeneous cooperative collectives on open-ended tasks scale better or worse. Also partially answered on the group-size axis (2026-08-03): OrchBench varies population from 1 to 100 agents over workflows of 10 to 1,000 subtasks and finds the curve is flat-to-negative, not linear or superlinear — raising the agent cap from 16 to 64 more than doubles the agent count and moves the score by ~0.01, and at 100 subtasks agent count correlates -0.021 with quality. The variable that does scale with capability is transfer coverage, and it degrades discontinuously (two of three frontier planners fall from 0.981 to ~0.42 coverage between 500 and 1,000 subtasks while a third holds). So if a multi-agent scaling law exists in this regime, its argument is information routed, not agents added. Bounded: simulated workers, fixed task decomposition, plan-only variation.
    • SourceIs running more instances more compute-efficient than making individual models larger (up to a single monolithic system)?
    • SourceHow do humans meaningfully interact with and steer very large agent groups operating at superhuman speed and output volume?
  • Open-Weight Elicitation Irreversibility4 open
    • SourceWhat would an open-weight safety evaluation even report? A single number is meaningless per premise 1. A curve of dangerous capability against elicitation budget is publishable — and is also a roadmap. Is there a disclosure regime that is informative to auditors and not to attackers?
    • SourceDoes the "everybody can audit" advantage actually materialize? Who has funded a serious post-release dangerous-capability audit of any open-weight model, and at what budget?
    • SourceGemma 4's safety section reports no numbers. Is that a deliberate non-disclosure, a judgment that the model is far from any threshold, or simply a technical report's genre convention? The document does not say, and the distinction matters.
    • SourceAnthropic's answer to a threshold-crossing model was a safeguarded SKU and an unsafeguarded one (Claude Fable 5 / Mythos 5), both hosted. What is the open-weight equivalent of shipping the safeguarded SKU?
  • Recursive Self-Improvement3 open
    • WaitIs "research taste" a true ceiling (future 1) or just the next capability to fall (futures 2–3)? The essay frames this as the single load-bearing uncertainty.
    • SourceThe RSI extrapolation rests on trends staying exponential rather than S-curving — but the essay concedes it cannot rule out an architectural ceiling or a compute/energy supply-chain constraint. Which binds first? Partially answered (synthesis against DeepMind): RSI Growth Curves: Which Friction Binds First? — the three futures map one-to-one onto DeepMind's three growth shapes; the first friction to bind is the already-binding one (Amdahl's-law verification/oversight = DeepMind's embodied bottleneck), and the abstraction barrier supplies the mechanism Anthropic lacks for whether taste is a real ceiling (Future 1). Retagged #oq/now → #oq/source 2026-08-10: which friction actually binds is now an empirical question about the next capability generation, not a synthesis gap.
    • SourceIf misalignment compounds through self-improvement (future 3), is AECI-gated RSP review fast enough to catch it before control is lost?
  • Research Taste as the Human Bottleneck3 open
    • WaitIs research taste a genuine ceiling (an architectural capability scaling can't reach) or the next jagged valley to fill? The essay calls this the decisive unknown. Contested premise: Ng argues the question presupposes taste is a capability at all.
    • WaitIf taste is automatable, what — if anything — remains a durable human comparative advantage in AI development?
    • SourceHow do you measure rubber-stamping? "Humans set direction" can be true on paper while real judgment quietly transfers to the model.
  • Researcher Uplift from Code Output3 open
    • SourceThe whole chain rests on β = 0.5 (pre-AI coding time share), fixed "for simplicity." Kwa flags substantial uncertainty; how much does the 2.3–2.9× band widen once β is varied and measured against Anthropic's actual time-use data?
    • WaitVerbosity and value-per-line are the load-bearing unknowns, and both are "at least partially resolvable with internal Anthropic data." Will any lab publish quality-adjusted (not just LoC) code-output measures?
    • SourceGreenblatt's 0.55/0.45 labor/compute split is itself an assumption. Is the true R&D production function really that insensitive to labor — and if so, does labor uplift matter far less than the RSI discourse assumes?
  • Responsible Scaling Policy Evaluations3 open
    • WaitThe RSP determination leans heavily on "we use it daily and it doesn't substitute for our researchers." How well does that subjective judgment scale as models approach the threshold? Partially answered: Claude Opus 5 extends the same judgment from AI R&D to the CB domain — the CB-2 call rests on an n=3 protein-design experiment overriding an automated portfolio that read as frontier-level — and simultaneously drops the saturated AI R&D rule-out suite from the determination. The judgment is not scaling down as models approach the threshold; it is carrying more weight as the quantitative evidence loses discriminating power.
    • SourceThe two new general-access risk pathways (other AI developers; major governments) are newly in scope but lightly evaluated — what would a positive finding there even look like?
    • SourceHow does the RSP brake interact with Recursive Self-Improvement: is AECI-based gating fast enough if acceleration compounds, and does single-lab gating even matter without the multilateral pause-verification regime?
  • The Abstraction Barrier3 open
    • SourceIs the current paradigm of large-scale pretraining on human data fundamentally bounded by human conceptual frameworks, and by how much? (Report open question 1i.)
    • SourceDoes the embodied bottleneck reduce the intelligence-growth rate to empirical-science speed, and can that be modelled?
    • SourceCan a system be built that does grounded concept discovery from raw sensor data — and is collective ASI a way around an individual cap?
  • Transformative Creativity3 open
    • SourceDoes increasing intelligence inherently produce increasing creativity, or do transformative leaps require something (grounded discovery) the current paradigm lacks?
    • SourceIs the AlphaGo→AlphaFold class strictly exploratory, or are there early signs of transformative (new-conceptual-space) creativity?
    • SourceCould transformative artistic creativity ever emerge from optimization power without lived cultural grounding?
  • Universal AI (AIXI)3 open
    • SourceDoes modern agentic scaffolding (or RL-tuned implicit decision-making) actually satisfy the AIXI planning ideal, or only superficially resemble it?
    • SourceCan the embedded/multi-agent AIXI extension produce practical insight for real multi-agent ASI (Multi-Agent Collective Intelligence), or does it remain a theoretical patch?
    • WaitWill a fundamental shortcoming of the current paradigm (vs. the AIXI ideal) surface before ASI is reached — i.e. is the "no theoretical blocker" conjecture safe?