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Selected on · Four signals worth your attention.

  1. Dubai launches an AI-agent program for public services.

    Dubai Future Foundation launched an accelerator to develop government services using AI agents. Its first phase will assess more than 300 public-facing use cases across 33 government entities. The program moves the question beyond chatbots toward systems that can carry out administrative tasks. Those numbers describe the scope of an assessment, not 300 deployed services or demonstrated improvements in speed and reliability.

    Dubai Future Foundation ·

  2. Panasonic plans to start making humanoid robots by 2029.

    Panasonic Holdings plans to develop its own humanoid robots, chief AI officer Akira Sakakibara told Nikkei in an interview published today. The company aims to begin making the machines by 2029, targeting factories and warehouses. That adds an established electronics and battery manufacturer to the field, but the statement is a development plan, not an available robot or proof of reliable factory work.

    Nikkei interview ·

  3. GPT-Rosalind reaches its API billing start date.

    OpenAI lists October 5 as the billing start date for GPT-Rosalind's research API. Published rates per million tokens are $5 for input, $0.50 for cached input and $25 for output. Access remains limited to approved internal research through its trusted-access program. This is a scheduled cost change for eligible research teams, not a new model launch; the API's availability was announced in September.

    OpenAI API pricing ·

  4. Axiom orders solar wings for its second station module.

    Redwire announced a follow-on contract to develop and deliver two roll-out solar-array wings for Axiom Space's second station module. The company is already building arrays for the first module. Power hardware is a concrete step toward the planned commercial station, but the launch schedule remains ahead: Axiom targets 2028 for its first module and less than a year later for the second.

    Axiom Space / Redwire announcement ·

A World in Perspective

In Charts.

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The Scale of AI Training

Training Compute, Selected AI ModelsFLOP · LOGARITHMIC SCALE
10¹⁸10²⁰10²²10²⁴10²⁶2012201620202024AlexNet: 4.7e+17 FLOPAlexNetGPT-3 175B: 3.14e+23 FLOPGPT-3 175BPaLM 540B: 2.5272e+24 FLOPPaLM 540BLlama 3.1-405B: 3.8e+25 FLOPLlama 3.1-405BModel Publication Year10¹⁸10²⁰10²²10²⁴10²⁶2012201620202024AlexNet: 4.7e+17 FLOPAlexNetGPT-3 175B: 3.14e+23 FLOPGPT-3 175BPaLM 540B: 2.5272e+24 FLOPPaLM 540BLlama 3.1-405B: 3.8e+25 FLOPLlama 3.1-405BModel Publication Year

Selected historical models · estimates. Each vertical step is 100×.

Data: Epoch AI · Chart: VastkindModels: 2012–2024 · Dataset Accessed: 5 Oct 2026

What It Shows

Epoch’s estimates put the training computation for Llama 3.1-405B at about 80 million times that of AlexNet. That is a comparison of computation, not capability: these models were built for different tasks.

What It Does Not Show

Four selected historical models are not a complete trend or a ranking of today’s frontier. Epoch classifies all four records as “Confident”; its guidance still allows roughly a factor of three of uncertainty. This label covers the least certain populated quantity, not a separately measured error bar for each compute value.

Sources, Method & Data

Points use the dataset’s publication dates and training-compute estimates. The vertical axis is logarithmic: equal distances represent equal multipliers. No fitted trend line is shown. Selection and visualization by Vastkind.

Data: Epoch AI, Data on AI Models, accessed 5 October 2026. CC BY 4.0. Dataset Documentation · Original CSV.

Values Used in This Chart
ModelPublication DateTraining Compute (FLOP)
AlexNet2012-09-304.7e+17
GPT-3 175B2020-05-283.14e+23
PaLM 540B2022-04-042.5272e+24
Llama 3.1-405B2024-07-233.8e+25

From AlexNet to Llama 3.1: selected milestones reveal an extraordinary rise in training compute. More computation does not mean an equivalent rise in intelligence.

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In Charts

The Cost of New Renewable Power

Global Weighted-Average Generation Cost2025 USD / MWh · NEW PROJECTS
0255075100125Onshore Wind33Solar PV44Hydropower62Offshore Wind78Bioenergy86Geothermal89Concentrated Solar1150255075100125Onshore Wind33Solar PV44Hydropower62Offshore Wind78Bioenergy86Geothermal89Concentrated Solar115

Levelized cost of electricity. Different technologies supply power differently.

Data: IRENA · Chart: VastkindData: 2025 · Report: Jul 2026

What It Shows

Onshore wind and utility-scale solar had the lowest global average generation costs among these renewable technologies in 2025. Their position is striking, but an average is not a quote for a specific project.

What It Does Not Show

Levelized cost spreads generation costs over a project’s lifetime. It is neither a household tariff nor a measure of the full electricity system, including storage and grid needs. Geography, financing and when power is available all matter.

Sources, Method & Data

Values from Figure S1, page 6, of IRENA’s Renewable Power Generation Costs in 2025 (2026). Units are 2025 US dollars per megawatt-hour. Bars begin at zero and are sorted from lowest to highest. “Concentrated Solar” means concentrated solar power (CSP). Data © IRENA 2026; selected and visualized by Vastkind.

Values Used in This Chart
Technology2025 USD/MWh
Onshore Wind33
Solar PV44
Hydropower62
Offshore Wind78
Bioenergy86
Geothermal89
Concentrated Solar115
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In Charts

The Scale of China’s Robot Rollout

New Industrial Robot InstallationsSELECTED ASIAN MARKETS · 2025
0100k200k300k400kChina354,000Japan36,219South Korea30,0000100k200k300k400kChina354,000Japan36,219South Korea30,000

Selected markets, not a global ranking. Figures as reported by IFR.

Data: IFR · Chart: VastkindData: 2025 · Source Published: 24 Sep 2026

What It Shows

China installed almost ten times as many industrial robots as Japan in 2025, according to the International Federation of Robotics. The comparison shows the size of its annual factory automation rollout.

What It Does Not Show

These are new industrial robot installations, not humanoid sales, the total installed stock or robots per worker. Country totals do not adjust for the size of manufacturing. Values retain the precision of the source; China and South Korea are reported as rounded totals.

Sources, Method & Data

Three public figures from IFR’s 24 September 2026 release. Bars share a zero baseline; countries are ordered by installations. This is an original Vastkind visualization, not a reproduction of an IFR graphic.

Values Used in This Chart
MarketInstallations in 2025
China354000
Japan36219
South Korea30000
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