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compute scaling

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@jon_ghoh

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Jongho Park @jon_ghoh

We did run those in the arc-agi-3 ablation here! See the solid green line. Anecdotally speaking, with enough compute, single agent with more budget seemed to plateau faster than best-of-N with 1/N budget.

[chart: "Performance under varying compute allocation for games SB26/LP85" -- two panels, y-axis "SB26 solve rate by level" and "LP85 solve rate by level", x-axis Level 0-7. Legend: Team@5 (1x budget per agent); Team@5 (0.2x budget per agent); Best@5 (1x budget per agent); Best@1 (5x budget per agent); Best@1 (1x budget per agent)]

10:28 AM · Sep 21, 2026 · 5,409 Views
1 reply, 1 repost, 12 likes, 6 bookmarks

Dimitris Papailiop... @DimitrisP... · 20h
MNIST also seems that one can infer an upper bound from the curves too. Takes 10-100x for the single best agent to get to the team's level.

[chart: "MNIST Compression vs. Time" -- y-axis "Compression size (bytes)" log scale 10^3 to 10^6, x-axis "Time (hours)" log scale 10^-1 to 10^2. Legend: Single-agent runs; Team@4 GPT-5.6 Sol; Best human compression (dotted line, labelled 2,461 B). Team line ends at 1,957 B.]
Note from Claude Sonnet 5

Quote-tweet style screenshot of two X posts about team-of-agents versus single-agent compute scaling, each with an attached chart (solve rate by level on ARC-AGI-3 games SB26/LP85; MNIST compression size vs time with a team line dropping below the best human compression).

agentscompute scalingbest-of-narc-agi-3multi-agent teamsmnist compression

@MTSlive

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MTS @MTSlive

SITUATION DETECTED: Anthropic told investors it plans to scale its available compute to 5 gigawatts by the end of this year, up from 1.5 gigawatts last year, and to double that again to 10 gigawatts by the end of 2027, per NYT.

9:07 AM · Sep 18, 2026 · 18.9K Views
Note from Claude Sonnet 5

Post by MTS relaying an NYT report that Anthropic told investors it plans to scale compute to 5 gigawatts by end of this year (from 1.5 GW last year) and 10 GW by end of 2027.

anthropiccompute scalinggigawattsnytinvestors

Andrew Curran @AndrewCurran_

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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
Note from Claude Sonnet 5

Tweet by Andrew Curran citing Epoch AI estimates that OpenAI quadrupled compute in 2024 and 2025 (about 17x over two years), with the Epoch bar chart of H100-equivalents: 103K in 2023, 386K in 2024, 1.7M in 2025. The tweet's own typo 'estimaees' is preserved.

epoch aiopenai computeh100 equivalentscompute scaling

Zeeshan Patel @zeeshanp_

Zeeshan Patel @zeeshanp_ · 1h after leaving frontier labs, many technical staff choose to build new companies. you'd imagine that it would be hard without large-scale data and compute to work on frontier research. if anything, we're seeing the complete opposite play out. there are several "neolabs" working on novel research and making good progress at small scale. even neolabs that raise hundreds of millions usually only have a few thousand chips at most, which is trivial compared to the incumbents. the key insight is that you don't need large data or compute to make meaningful progress. it's easy to forget that the core breakthroughs powering the industry today were discovered with extremely scarce resources by modern standards. to make fundamental developments, it's important to spend time finding more effective ways of utilizing compute rather than just scaling existing paradigms. many times, this is best executed under tighter resource constraints. it's very exciting to see so many talented folks taking courageous next steps towards researching new frontiers, which will hopefully bring upon new paradigms in deep learning.
Note from Claude Sonnet 5

A tweet arguing that compute-constrained "neolabs" (small AI research startups) can still drive fundamental deep learning progress, since historic breakthroughs happened under resource scarcity. Resonates with Nathan's own brain_graph_1 thesis that architectural/wiring innovation can match larger-scale approaches at a fraction of compute.

twitterai researchcompute scalingneolabsdeep learning paradigmsresource constraints