epoch ai

FleetingBits @fleetingbits

2026-10-05 — quoting @EpochAIResearch — saved image

FleetingBits @fleetingbits · 15m

by the end of 2027, the world will have enough compute to do approximately 300,000-600,000 navier-stokes-sized agent runs a year.

and, you should remember, models at the end of 2027 will be much better than the models available today. it's going to be wild.

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Epoch AI @EpochAIResearch · Oct 2
We estimate AI infrastructure could soon support hundreds of millions or even billions of AI agents. At the high end, that's enough to rival the working hours of the global human workforce.... [cut off]

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Andrew Curran @AndrewCurran_

2026-10-02 — quoting Epoch AI (@EpochAIResearch) — saved image

Andrew Curran (verified) @AndrewCurran_ · 1h
From the blog:
'AI chips shipped through 2027 could run tens to hundreds of millions of concurrent frontier-model agents. Running nonstop, these agents would supply as many weekly working hours as about 140–720 million full-time employees.

More efficient models could potentially support billions of agents on the same hardware. Applying DeepSeek V4 Pro serving benchmarks to the projected hardware supply yields approximately 1.9 billion concurrent agents supplying as many weekly working hours as 8 billion people each working 40 hours.

[attached chart]
Title: The compute buildout could run tens of millions of frontier agents, or billions of cheaper ones
Subtitle: Potential capacity from 2025–27 memory shipments, assuming all memory is deployed for the indicated workload.
Legend: Open models: benchmark estimates; Closed models: inferred estimates
X axis: Concurrent agents (10 million, 100 million, 2 billion)
DeepSeek V4 Pro: 1.9 billion
GLM-5.2: 571 million
Kimi K3: 240 million
GPT-5.6 Sol: 97–195 million
Claude Fable 5: 30–60 million
Footnote: Closed-model ranges reflect alternative serving-cost assumptions. Memory supply for 2026-27 is projected.
Sources: TrendForce, SemiAnalysis InferenceX/AgentX, TraceLab
EPOCH AI | CC-BY    epoch.ai

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Epoch AI (verified) @EpochAIResearch · 1h [cut off]

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Thomas Wolf [verified] @Thom_Wolf · 1h

on the scale of cosmic history, the declining price of intelligence might be one of the most profound things happening

both ends of the universe's timeline are empty of knowledge:
- the big bang: low entropy, no complex structure
- heat death: maximum entropy, no usable energy gradients

everything we build happens on the slope between them

but the universe is generally a poor converter of its free energy into anything constructive. most of its free energy just radiates away as heat or collapses into black holes

life, brains and now AI models are structures that sit in the entropy flow and divert a portion of it through constructive paths, building and maintaining low-entropy structures (machines, cells, crystals, memories)

increasingly cheaper intelligence diverts increasingly more of that gradient flow through knowledge-building structures, complex, self-maintaining, predictive structures, filling the window in which understanding exists

[Quoted post]
Epoch AI [verified] @EpochAIResearch · Sep 22
AI is getting cheaper more quickly than any other transformative tech in history. At a given level of performance, cost has fallen ~47%/quarter since 2023. [cut off]

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Andrew Curran @AndrewCurran_

2026-09-10 — saved image

Andrew Curran @AndrewCurran_ · 22h
According to new estimaees from Epoch OpenAI quadrupled its computing power in both 2024 and 2025, for a roughly 17× increase over two years.

[Embedded Epoch AI chart]
OpenAI saw 17× increase in compute capacity over 2 years
Estimated compute capacity in H100 equivalents with 90% confidence intervals

H100 equivalents (H100e); y-axis 500K to 2.5M
Bars: 2023 = 103K; 2024 = 386K; 2025 = 1.7M (each with error bars)

Estimates of the AI compute capacity rented or used by OpenAI, in the equivalent number of Nvidia H100 GPUs, as of each date.
EPOCH AI | CC-BY

epoch aiopenai computeh100 equivalentscompute scaling

Epoch AI @EpochAIResearch · 53m
We've updated the MirrorCode leaderboard with results for Claude Fable 5 and GPT-5.6 Sol.

Claude Fable 5 leads with a 64% solve rate, followed by GPT-5.6 Sol at 20%.

[Chart: "AI models can autonomously complete some large software projects" — Overall score on MirrorCode (ML, +Private, 2L). Even when they fail to reimplement targets, AIs typically make substantial progress, passing 90% or more of tests. Bar chart, solve@100% rate (per-target mean), whiskers ±1 SE: Claude Fable 5 64%, GPT-5.6 Sol 20%, GPT-5.4 16%, GPT-5.5 10%. EPOCH AI | CC-BY, epoch.ai]

13 replies, 32 reposts, 213 likes, 8.3K views

Epoch AI @EpochAIResearch · 53m
MirrorCode tests whether AI agents can reimplement software projects from scratch. A solve requires passing 100% of visible and hidden tests.

Claude Fable is the first model we evaluated to solve the C preprocessor and Pkl tasks in at least one run.

[Chart: "The hardest MirrorCode targets remain unsolved" — Per-target solve rates for MirrorCode (ML, +Private, 2L), for full reimplementation (100% of tests passed). Table by model x target:
Claude Fable 5 (avg 64% ±10pp): tssql 100, private_M 100, texmacros 100, wren_cli 100, bib2json 100, nonogrid 83, brotild 83, gotree 0, sed 67, mailauth 83, giac_subset 0, cprepro 83, pkl 25, private_L 33, ruff 0.
GPT-5.6 Sol (avg 20% ±9pp): tssql 100, private_M 50, texmacros 0, wren_cli 33, bib2json 100, nonogrid 0, brotild 17, gotree 0, sed 0, mailauth 0, giac_subset 0, cprepro 0, pkl 0, private_L 0, ruff 0.
GPT-5.4 (avg 16% ±8pp): tssql 100, private_M 50, texmacros 0, wren_cli 0, bib2json 50, nonogrid 0, brotild 33, gotree 0, sed 0, mailauth 0, giac_subset 0, cprepro 0, pkl 0, private_L 0, ruff 0.
GPT-5.5 (avg 10% ±6pp): tssql 83, private_M 0, texmacros 0, wren_cli 0, bib2json 33, nonogrid 0, brotild 33, gotree 0, sed 0, mailauth 0, giac_subset 0, cprepro 0, pkl 0, private_L 0, ruff 0.
Footnote: Each model was run three times in each of the two implementation languages for a target (six planned attempts per target). Attempts with infrastructure errors and no valid final evaluation are excluded; scored sample counts are shown in tooltips. Overall scores average the 15 per-target solve rates, weighting each target equally. ± values show 1 SE across target programs. EPOCH AI | CC-BY, epoch.ai]

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Tim Duffy @timfduffy

2026-07-10 — reposted by Sichu Lu; quoting @EpochAIResearch (Epoch AI)

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@magnushambleton

2026-06-27

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Jaime Sevilla @Jsevillamol

2026-01-20 — quoting Epoch AI (@EpochAIResearch)

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Aidan McLaughlin @aidan_mclau

2025-08-29

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