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Thomson Reuters is making a significant bet on proprietary AI, announcing the launch of its own frontier model built on the company's deep reserves of legal, tax, and financial data. Rather than licensing someone else's technology, Reuters is wagering that domain-specific training data is the real competitive moat in this industry.
Shifting to the people building with these tools, a new survey finds that eighty percent of developers describe AI coding assistants as more addictive than genuinely helpful — a striking distinction. They keep reaching for the autocomplete even when it slows them down or produces code they have to untangle. Convenience, it turns out, can quietly erode craft.
And Meta is taking another step toward hardware independence with the MTIA three hundred, its first training chip to integrate network interface controllers directly onto the silicon. By collapsing networking and compute into a single package, Meta is trying to cut the latency and overhead that quietly bleeds performance in massive AI training clusters. It is the kind of unglamorous infrastructure work that tends to matter enormously at scale.
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