Artificial Intelligence From Early Research to Its Next Phase

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The early idea

In the summer of 1956, researchers met at Dartmouth College. The meeting helped establish artificial intelligence as a field of study. Early work focused heavily on rules, logic, search, and symbolic reasoning.

Progress came in uneven waves. Computing power, data, and training methods kept changing what machines could do. In 2012, AlexNet showed how deep neural networks and GPUs could improve large scale image recognition. In 2017, the Transformer paper introduced an attention based architecture that later became central to language models.

By the middle of the 2020s, AI had moved beyond research labs. Systems could generate text, code, images, audio, and video. They could also process several forms of information inside one model.

AI in 2026

Stanford’s 2026 AI Index reports AI use in 88 percent of surveyed organizations. Generative AI appears in at least one business function at 70 percent. Yet AI agents remain early, with deployment in single digits across most business functions.

Technical capability has also risen faster than many evaluation methods. Some benchmarks now become outdated within months. At the same time, reliability remains uneven. Stanford recorded 362 documented AI incidents in 2025, compared with 233 in 2024.

This tension defines the present phase. AI can handle more tasks, yet performance still depends on context, data, tools, and human checking.

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What is changing underneath

The largest systems now depend on large amounts of computing infrastructure. Stanford reports global AI compute capacity grew about 3.3 times per year since 2022. Industry produced more than 90 percent of notable AI models in 2025.

That concentration matters. Future progress will depend on chips, energy, data access, efficiency, and capital. It will also depend on whether smaller models can perform specific tasks with lower computing requirements.

Where the research points next

AI agents are likely to receive more attention because they can use tools and complete longer tasks. Their current failure rates show why reliability research still matters. Robotics is another direction, although machines continue to struggle in unstructured physical settings.

Science may become a larger test case. In 2025, AI related publications in natural sciences reached about 80,150, up 26 percent from 2024. Yet research agents still scored far below PhD experts on full research tasks.

Energy will shape the pace too. The International Energy Agency projects data centre electricity use at about 945 TWh by 2030. That would be just under 3 percent of global electricity consumption.

The next phase will therefore be measured by dependable use, not capability alone. A useful habit is watching what systems can do repeatedly under real conditions.

This newsletter is a regular research check, not a complete answer. It can flag what changed and what deserves attention. The useful part begins outside the email, in how you learn, test, and work. Reading over time gives those decisions better context.