A new study found that experienced developers might work slower when using AI coding tools. Researchers from METR tested 16 veteran coders working on real projects. Surprisingly, they took 19% longer to finish tasks with AI help. Developers spent more time prompting the AI, reviewing code, and waiting for responses instead of writing code themselves. Even after seeing the results, developers still believed AI made them 20% faster. This shows a gap between feelings and facts.

Google announced three big updates for Firebase developers this week. First, they added new AI agent modes that let developers choose how much independence their AI helpers have. Second, they included support for Model Context Protocol (MCP) which connects projects to outside tools. Third, they built in the Gemini CLI tool that works right in developers' terminals. These changes make it easier to build advanced AI assistants.

In a major deal, Google spent $2.4 billion to bring Windsurf's coding technology and team into its DeepMind group. This happened after OpenAI tried but failed to buy Windsurf earlier. The move brings Windsurf's CEO and key staff to Google, showing how valuable AI coding talent has become. Some worry this lets big companies control too much AI innovation.

A survey of 250 tech leaders revealed that 73% now rank expanding AI use as their top goal. The main reason is task automation (55%), but they also use AI to optimize code (48%), test software (46%), and fix errors (43%). Interestingly, 55% of companies using AI created new jobs, with many hiring up to 25 new workers. This proves AI can create opportunities, not just replace jobs.

Despite the progress, risks remain with AI coding tools. Tech leaders worry most about data privacy violations (38%), AI model bias (37%), and errors in AI-generated code (37%). Over 60% of companies have created ethical AI guidelines to address these concerns. Many also use privacy policies (59%) and data protections (54%) to keep things safe.

Looking ahead, Anthropic shared a new training method called Internal Coherence Maximization (ICM). This lets AI models learn from their own knowledge without human help. Their tests showed this approach created an assistant that beat human-trained models. Such advances could lead to more capable and independent AI agents in the future.

Google also released the full Gemini 2.5 report, showing improvements in coding, reasoning, and multimodal understanding. These upgrades help developers build better AI agents that understand longer instructions and different types of data. With all these changes, 2025 is becoming the year AI moves from helper to central player in software development.