Google Bets $9B on South Carolina for AI Future
Google’s $9B South Carolina AI Investment: Data Center Expansion & Economic Impact Google invests $9 billion in South Carolina data…
Google’s $9B South Carolina AI Investment: Data Center Expansion & Economic Impact Google invests $9 billion in South Carolina data…
Microsoft has introduced GitHub Copilot integration in SQL Server Management Studio 22 Preview 3, bringing AI-powered coding assistance to database developers. This highly-requested feature helps users write T-SQL more efficiently while maintaining full context awareness of connected databases. The integration represents Microsoft’s ongoing commitment to enhancing developer productivity through artificial intelligence tools.
Microsoft has delivered a significant enhancement to its flagship database management tool with the integration of GitHub Copilot into SQL Server Management Studio (SSMS) 22 Preview 3. This move responds to growing developer demand for artificial intelligence assistance within database development workflows, particularly following last year’s SQL Server 2025 preview that introduced native vector data support for improved AI workload performance.
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PepsiCo’s Chief Strategy Officer reveals how the company buys AI tools while demanding influence over vendor roadmaps. The beverage giant maintains ownership of core AI-augmented processes while leveraging external technology partnerships for scale and innovation.
As enterprises worldwide accelerate their artificial intelligence adoption, PepsiCo has developed a distinctive approach that balances external technology acquisition with internal process ownership. The company’s strategy, articulated by Chief Strategy and Transformation Officer Dr. Athina Kanioura at Salesforce Dreamforce 2025, emphasizes owning core AI-augmented processes while strategically partnering with technology providers.
AI agents are revolutionizing business operations with machine-speed execution, but their autonomous nature creates unprecedented security challenges. Traditional human-oriented permission models fail to contain AI-driven actions, requiring fundamental rethinking of authorization frameworks.
As AI agents transition from experimental projects to production environments, organizations are discovering both the tremendous efficiency gains and significant security risks these systems introduce. The fundamental challenge lies in applying human-designed permission models to machine-speed operations, creating a dangerous mismatch between capability and control.
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