MIT Technology Review AI (1 hour ago)
The next competitive advantage in enterprise AI has less to do with which model you choose than with what you build around it. That’s an important shift.
Read the full story on MIT Technology Review AI.
Canva is diving even deeper into AI with the launch of an image generation model that competes directly with those from OpenAI, Microsoft, and Google.
Datacenter and chip costs fueled sharp decrease in AI sector although non-AI stocks still had a strong showing The latest shudder for stocks in the AI industry pulled markets lower worldwide on Thursday, while oil prices hit their highest levels since May on worries about the war with Iran .
Almost every part of the process changed, except the end result
1Password is expanding its AI integrations beyond Claude, allowing agents like ChatGPT Work, Dots, Meta Muse, and Grok Bot to securely sign in to websites on your behalf.
It’s nothing we haven’t seen before, but it’s hyper specificity toward business owners and professionals.
A scalable AI system requires a strong foundation. Data, engineering, platforms, operating models, and governance must converge to work as one.
Enterprise AI can generate recommendations, automate tasks, and help organizations make decisions faster. But without an understanding of how a business actually operates, even powerful models can miss the constraints and relationships that determine whether a decision is the right one.
Ask most executives to picture the cost of AI and they’ll likely see GPUs—racks of accelerators with gigawatts of power—and a waitlist for silicon. But that picture leaves something out: Data storage costs. What happens to data after a model responds?
Imagine your company just discovered electricity. At first only a few machines run on it, and nobody worries about efficiency — consumption is low and the value is obvious. But once electricity becomes infrastructure, every process depends on it, and the question changes from “can we use it?”
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