Task Time-Horizon Scaling

Sources#

• Claude Fable 5 and Claude Mythos 5
• When AI builds itself

Summary#

The external-benchmark trendline behind Recursive Self-Improvement: the length of task an AI can complete reliably on its own is doubling roughly every four months — accelerated from an earlier ~seven-month doubling. The metric, from METR's time-horizons work, reports the duration over which a model is 50%-reliable at a basket of tasks (the curve looks the same at 80%). It is the quantitative spine of When AI builds itself: where AI Accelerating AI Development shows AI speeding up AI work inside Anthropic, this shows the underlying capability rising on public benchmarks.

The doubling curve#

Model	~Date	Reliable task length
Claude Opus 3	Mar 2024	~4 minutes
Claude Sonnet 3.7	~Mar 2025	~1.5 hours
Claude Opus 4.6	~2026	~12 hours
(projected)	this year	days
(projected)	2027	weeks

Mythos Preview is at the edge of measurability: METR found it could work for "at least" 16 hours and was "at the upper end of what [METR] can measure without new tasks." The trend's acceleration (7-month → 4-month doubling) is the part that matters — it is why the essay argues the loop may close "sooner than most institutions are prepared for."

The June 2026 Mythos-class release pushes further still: Fable 5 / Mythos 5 "can work autonomously for longer than any previous Claude models," and the concrete datapoint is over a week of largely autonomous genomics work (assembling data, designing and training a model, beating a published baseline — see Autonomous Scientific Discovery). A week-long autonomous research run is well past METR's measurable task basket — the metric is now chasing the capability rather than bounding it.

Benchmark saturation as the corroborating signal#

The same pattern appears as benchmarks going from near-zero to "saturated" (≈100%, allowing for errors that cap many benchmarks below 100%):

• SWE-bench — hands a model a real open-source codebase + bug report and asks for a change that passes the project's own tests. Low single digits → saturated in two years. (Cf. Claude Opus 4.8: 88.6 on SWE-bench Verified.)
• CORE-Bench — reproduce a published paper's results from its code and data; a prerequisite for conducting original research. ~20% (2024) → saturated in fifteen months.

Saturation is why time-horizon length, not single-benchmark accuracy, has become the more informative capability axis — and why Anthropic retired its task-based AI-R&D benchmarks once models crossed the top human baselines (see AI R&D Autonomy Evaluation (AECI)).

Caveats#

• Infrastructure strain is a leading indicator, not just trivia. GitHub saw ~1B commits in all of 2025; by mid-2026 it saw ~275M/week (~14B/year pace) and is "pushing incredibly hard" on capacity — a downstream signature of the same throughput surge.
• Time-horizon numbers are a 50%-reliability statistic on a basket of tasks; the jaggedness within the basket is real — a model that handles a 12-hour task can still fail a trivial one.
• Whether the curve is a true exponential or an S-curve approaching its bend is the explicit uncertainty of Recursive Self-Improvement's first future.

Connections#

• Recursive Self-Improvement — this curve, extrapolated, is the quantitative case that the loop could close soon
• AI Accelerating AI Development — the internal-throughput companion to this external-benchmark evidence
• Jagged Intelligence (Ghosts, Not Animals) — the within-basket caveat: long-horizon competence coexists with trivial failures
• The Bitter Lesson — rising capability on general benchmarks is what makes hand-built scaffolding a shrinking advantage
• AI R&D Autonomy Evaluation (AECI) — why saturated task-based benchmarks were retired from RSP determinations
• Build for the Next Model — the forecastable capability curve this measures is what makes "bet on the next release" a rational product strategy rather than a gamble
• Autonomous Scientific Discovery — Mythos 5's week-long autonomous genomics run is a concrete long-horizon datapoint past Mythos Preview's measured 16h ceiling
• Effective Compute Scaling — the compute-side curve this capability-side trendline complements; Whitfill et al. model time-horizon growth under compute projections
• AGI-to-ASI Pathways — a concrete capability trendline feeding the report's quantitative-forecasting and benchmarking-beyond-human agenda
• Intelligence Explosion Dynamics — the metric that makes "is the curve bending toward a singularity, or S-curving?" empirically checkable
• Deep Research Agents — deep research is a long-horizon autonomous task of exactly the kind this metric measures; DRACO grades the report quality at that horizon
• DRACO Benchmark — a sibling capability benchmark (quality of agentic research reports vs. the task length a model sustains); both face benchmark-saturation pressure (DRACO discards >90%-solved tasks)
• Production-Sourced Evaluation — the refresh-from-live-usage method that answers this page's open question of what replaces a saturated task basket
• Repository Exploration Subagent — the SWE-bench family (Multilingual / Pro / Verified / SWE-QA) is FastContext's evaluation surface; the longest-horizon variant (SWE-bench Pro) shows the largest gains, consistent with exploration cost compounding over task horizon
• Planning / Execution Division of Labor — the ceiling (what models can do autonomously, measured here) vs. the realized autonomy users actually grant in practice; Anthropic cites METR's horizons as the rising ceiling its usage data sits below
• Agentic Coding Work-Composition Shift — the rising reliable-task-length ceiling is the upstream cause of usage moving from debugging toward operating/analyzing whole workflows end-to-end
• Conversation-to-Delegation Shift — the usage-side reading of this ceiling: OpenAI's Codex study finds the share of individual users delegating a task estimated at >8 experienced-human-hours rose 2.1%→25.6% since Dec 2025 — task complexity climbing right under METR's measured horizon
• Parallel Agent Orchestration — the long-running-agent runtime margin (p99 OpenAI users ~71 agent-hours/day) is single-agent task duration sitting below this reliable-length ceiling
• Exposure Taxonomy: Observed, Theoretical, Reported, Anticipated — theoretical exposure (what an LLM could do) is bounded above by this reliable-task-length ceiling; the survey's reported/anticipated exposure sit below it

Open questions#

• Is the 4-month doubling a stable regime or a local steepening? The trend's shape (exponential vs S-curve) is undetermined.
• Time horizon is measured on task baskets that themselves saturate; what replaces them once weeks-long tasks become measurable — and who builds those tasks?

Sources#

• When AI builds itself — §"Evidence from the outside world" (METR time horizons; SWE-bench / CORE-Bench saturation; GitHub commit-volume footnote)
• Claude Fable 5 and Claude Mythos 5 — "work autonomously for longer than any previous Claude models"; week-long autonomous genomics