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The Global AI Insights Dashboard is AI360’s analytics hub, presenting three live embedded charts sourced from Our World in Data to help you visualize artificial intelligence’s expanding footprint across global markets. Each chart loads directly in the browser as a responsive, full-width embed, giving you an interactive lens into training compute growth, international AI development, and shifting public opinion — all from a single /dashboard page.

What’s on the Dashboard

AI Training Computation Growth

Tracks the exponential increase in the amount of computation used to train notable AI models, spanning from 2005 to the present day. Data is sourced from ourworldindata.org/grapher/artificial-intelligence-training-computation and rendered as a time-series chart. The embed is animated with an opacity and y-axis entrance transition via Framer Motion (duration: 0.8 s).

Cumulative AI Systems by Country

Compares the cumulative count of large-scale AI systems built across leading nations, including the USA, China, UK, France, Canada, India, Germany, Israel, UAE, Finland, Hong Kong, and multinational collaborations. Sourced from ourworldindata.org/grapher/cumulative-number-of-large-scale-ai-systems-by-country and rendered at a fixed height of 450 px. The embed uses loading="lazy" and supports web-share and clipboard-write permissions.

Public Views on AI's Impact (Next 20 Years)

Displays international survey data on how the public expects AI to affect society over the next 20 years — covering both optimistic and cautious perspectives. Sourced from ourworldindata.org/grapher/views-ai-impact-society-next-20-years and rendered at 500 px height. This chart animates in last with a 1.2 s Framer Motion transition.

How to Access

Navigate directly to /dashboard in the app, or click the View Dashboard button on the AI360 homepage hero section. The button is prominently placed above the fold and links to this route via Next.js <Link>.
https://<your-domain>/dashboard

Technical Details

DetailImplementation
Chart rendering<iframe> embeds inside shadcn/ui <Card> components
Responsive sizingFirst chart uses a pt-[56.25%] aspect-ratio wrapper; subsequent charts use fixed heights (450 px / 500 px)
Lazy loadingloading="lazy" attribute on the country and sentiment embeds
Entrance animationsFramer Motion (motion/react) — each card fades in and slides up from y: 40 with staggered durations (0.8 s, 1.0 s, 1.2 s)
Page client directive"use client" — the page is a Next.js Client Component
All three charts rely on live data served by Our World in Data. An active internet connection is required for the iframes to render. If the charts appear blank, verify your network connection or check that the Our World in Data domain is not blocked by a firewall or content policy.

Key Insights

AI Training Computation Growth

Training compute for state-of-the-art AI models has roughly doubled every six to ten months since 2010 — a rate that significantly outpaces Moore’s Law. By examining this chart, you can see when inflection points occurred, such as the rise of large language models post-2020, and anticipate the infrastructure investments needed to sustain frontier research. Understanding this curve is essential for anyone forecasting AI hardware demand or energy consumption trends.

Cumulative AI Systems by Country

The United States has historically produced the largest share of large-scale AI systems, but China has closed the gap rapidly since 2017, and multinational collaborations are increasingly common. This chart reveals how geopolitical priorities, research funding, and talent distribution shape global AI leadership. For policymakers and investors, the relative trajectories of each country signal where regulatory frameworks and competitive dynamics will be most consequential.

Public Views on AI’s Impact (Next 20 Years)

Survey respondents in different countries hold markedly different expectations about AI’s societal impact, with some regions expressing strong optimism about economic growth and productivity, while others lean toward caution regarding job displacement and safety risks. This divergence has direct consequences for how governments regulate AI and how companies communicate its benefits. Tracking sentiment over time can also reveal whether public trust grows or erodes as AI systems become more pervasive.

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