AI Pulse — Signals

The developments that actually move the frontier

A curated read on the most consequential recent developments across the AI ecosystem — models, software and agents, labs, hardware, compute, energy, and the concepts under serious consideration at the edge. Every signal shows its source, date, and data class, so you can stay oriented in ten minutes a month without doomscrolling.

Learn to work with the frontier Back to the dials

Models

Models2026-05-08

Frontier agents reach ~16-hour autonomous task horizons

METR's time-horizon tracking shows frontier models at roughly 12–16 hour 50%-reliability horizons, with METR cautioning that measurements above 16 hours exceed what its current task suite can reliably measure. The doubling pace accelerated to roughly 4 months in 2024–2026 from a 7-month historical trend.

Why it matters: Task length, not chat quality, is the best single proxy for how much real work can be delegated.

METR — Time HorizonsMeasured

Models2026-03-25

ARC-AGI-3 launches; frontier models score under 1%

The ARC Prize Foundation released an interactive-reasoning benchmark of hand-built game environments with no instructions. Humans solve 100%, frontier LLMs scored below 1% at launch, and the best purpose-built agent reached 12.58%, with over $2M in 2026 prizes.

Why it matters: The gap isolates novel-skill acquisition — the capability UofAi's Superintelligence Horizon Index weights heavily.

ARC Prize FoundationMeasured

Software & Agents

Software & Agents2026-02-05

Inference cost at fixed capability keeps halving every ~2 months

Epoch AI's tracking shows the cost to run an LLM at a fixed capability level falling roughly two orders of magnitude per year, though unevenly across task types.

Why it matters: Capabilities that are demo-expensive today become workflow-cheap within quarters — the core reason professionals should learn transferable workflows, not tools.

Epoch AI — TrendsMeasured

Frontier Labs

Frontier Labs2026-06-16

Hyperscaler AI capex on pace to outrun cash inflows by end of 2026

Epoch AI analysis finds hyperscaler capital expenditure trending above operating cash inflows by late 2026, with compute the majority expense of AI companies.

Why it matters: The financing structure of the buildout is a live risk factor for the pace of frontier progress.

Epoch AIExtrapolated

Frontier Labs2026-01-15

Humanity's Last Exam published in Nature; expert gap persists

The CAIS/Scale benchmark of 2,500 expert-written questions was published in Nature; human domain experts average ~90% while frontier SOTA sits in the mid-40s to low-50s percent (VERIFY against the official Scale/CAIS leaderboard at refresh; aggregator figures conflict).

Why it matters: It is the current canonical yardstick for expert-level knowledge and a core SHI input.

Nature / CAIS & Scale AIMeasured

Emerging & Independent Labs

Emerging & Independent Labs2025-12-05

Independent team sets ARC-AGI-2 state of the art at 54%

Poetiq's open-source system, verified on the semi-private set, reached 54% at roughly half the per-problem cost of the prior 45% best — without training its own frontier model.

Why it matters: Scaffolding and test-time methods from small teams can leapfrog raw model scale; the frontier is not only a big-lab game.

Poetiq (ARC Prize-verified)Measured

AI Hardware & Robotics

No curated signals in this category yet.

Chips & Compute Infrastructure

Chips & Compute Infrastructure2026-06-11

Single-data-center compute records double every ~7 months

Epoch AI reports the record computing capacity in a single AI data center has doubled roughly every 7 months, tracked via satellite and permit data.

Why it matters: Physical build-out, not algorithmic ideas, is currently the pacing constraint on frontier training.

Epoch AI — Data centersEstimated

Chips & Compute Infrastructure2026-02-05

Frontier training compute keeps growing ~5× per year

Epoch AI's dashboard shows frontier training compute growing 4–5× annually since 2020 (largest known run ≈ 5×10²⁶ FLOP), global AI chip stock growing ~3.4×/year, and the largest single data center at roughly 800,000 H100-equivalents.

Why it matters: These curves set the ceiling for everything else on this page.

Epoch AI — TrendsEstimated

Power & Energy

Power & Energy2025-08-11

Frontier training runs pass 100 MW; multi-GW runs projected by 2030

Epoch AI and EPRI project frontier-training power demand growing 2.2–2.9× per year, with the largest single runs reaching 4–16 GW by 2030 — offset partly by ~40%/year GPU energy-efficiency gains.

Why it matters: Energy is becoming AI's binding constraint and a board-level topic in every industry UofAi serves.

Epoch AI / EPRIExtrapolated

Frontier Concepts

Frontier Concepts2026-04-13

The benchmark treadmill: saturation as a structural feature

MMLU, MMLU-Pro, and GPQA Diamond have reached or are approaching saturation (frontier models clustering above ~90%), pushing measurement toward harder instruments like HLE and interactive suites like ARC-AGI-3 — and METR openly notes its own suite can't measure beyond ~16-hour tasks.

Why it matters: This is why the UofAi Pulse has a saturation-rotation rule instead of a fixed benchmark list.

Kili Technology review / METRMeasured

Curated under the UofAi Benchmark Standard — see methodology.

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