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AI, Nuclear Energy, and Data Management Emerge as Key Tech Trends in 2026
Developments in artificial intelligence, nuclear energy, and data management are rapidly changing the technology landscape in early 2026. From new AI models tackling tabular data to advancements in nuclear reactor technology and novel approaches to data storage, these areas are seeing significant innovation.
Fundamental, a San Francisco-based AI firm founded by DeepMind alumni, launched NEXUS, a native foundation model designed to bypass manual ETL (extract, transform, load) processes for tabular data, according to VentureBeat on February 5, 2026. This aims to address the "blind spot" in the deep learning revolution, where structured, relational data in systems like ERP and CRM has been treated as just another file format. Fundamental hopes to replace the labor-intensive data science processes currently used for forecasting business outcomes with this new technology.
Meanwhile, nuclear power remains a hot topic, with next-generation reactors requiring different fuel sources than conventional reactors, MIT Technology Review reported. Many of these reactors do not use low-enriched uranium. The report addressed audience questions about the supply chain challenges associated with these new fuel needs.
In AI model evaluation, METR, or Model Evaluation Threat Research, is closely watched by the AI community. MIT Technology Review noted that METR updates a graph that tracks the development of AI capabilities, particularly after the release of new large language models from companies like OpenAI, Google, and Anthropic. The graph suggests that certain AI capabilities are developing at an exponential rate. The latest version of Anthropic's most powerful model, Claude Opus 4.5, outperformed previous trends, the report stated.
An article on Hacker News on February 2, 2026, argued for the increased use of PostgreSQL in 2026, advocating that it is time to "just use Postgres." The article, titled "It’s 2026, Just Use Postgres | Tiger Data," suggested that specialized databases may not always be necessary in the AI era, and that the hidden costs of using them can add up. The author, Raja Rao DV, highlighted the modern Postgres stack and provided a quick start guide for adding extensions.
In a separate development, a GitHub repository highlighted a potential privacy concern related to LinkedIn. The repository, titled "GitHub - mdp/linkedin-extension-fingerprinting," revealed that LinkedIn silently probes for 2,953 Chrome extensions on every page load. The repository provides a list of these extensions, along with their names and Chrome Web Store links. This discovery raises questions about user privacy and the extent to which LinkedIn monitors browser extensions.
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