Google Pushes Database Engineers to Rely Heavily on AI Coding Tools for PostgreSQL

Sailesh Krishnamurthy has a direct message for his database teams. Use AI heavily. The vice president of databases at Google Cloud delivered the guidance as the company accelerates its open-source contributions to PostgreSQL.

Productivity gains inside Google Cloud have proven substantial. Yet accountability stays firmly with individual engineers. No matter how much code an AI drafts or suggests, the human signs off on every line that ships upstream.

“We do encourage folks to use AI heavily,” Krishnamurthy told The Register. “We are seeing huge amounts of productivity improvements internally. In the end, we have individual engineers take accountability for our contributions.”

He describes a spectrum. Some code arrives fully drafted by AI. Other changes involve no AI at all. The full range sits in between. Responsibility never shifts.

Open-source codebases offer a natural advantage here. PostgreSQL’s public repository has trained the models. Generative tools grasp its patterns far better than they understand proprietary systems locked behind firewalls. “That’s how models have a better sense of the code, as opposed to many proprietary pieces of code, which are inside the firewall,” Krishnamurthy explained.

PostgreSQL’s extensibility makes it especially attractive for this approach. Developers can isolate experiments. They prototype academic concepts as extensions with limited blast radius. AI interprets the familiar codebase quickly. Ideas move from concept to working code in far less time. Engineers at Google apply the tools heavily in these scenarios. They also apply them judiciously.

The broader industry has bet big on PostgreSQL. Migrations pour in from Oracle, Microsoft SQL Server, IBM Db2, Sybase and Informix. New applications choose it from the start. Cloud providers and digital-native companies treat it as a universal data layer regardless of where information actually resides.

Gartner research from earlier this year underscores the trend. Among the dominant database vendors from 15 years ago — Oracle, IBM, Microsoft and SAP — only Microsoft has expanded market share. AWS leads overall. Oracle sits third. Google trails but contributes steadily to the open-source momentum that slowly shifts power.

Google’s own recent PostgreSQL work targeted logical replication. The team added Automatic Conflict Detection so replication workers can spot and handle conflicts between incoming changes and local state. They also enabled logical replication of sequences. These enhancements arrived alongside the AI-assisted development push.

AI assistance now reaches far beyond code generation at Google Cloud.

The company’s Database Migration Service made Gemini generally available for Oracle-to-PostgreSQL projects. The AI does not simply translate PL/SQL syntax. It re-architects logic into clean, idiomatic PostgreSQL functions. Nested cursor loops become recursive Common Table Expressions. Ambiguities receive flags along with multiple modern alternatives and trade-off explanations.

One logistics company handed over a 20,000-line PL/SQL codebase for global inventory reconciliation. What once looked like an 18-month, multimillion-dollar death march shrank to six weeks of review and refinement. “It wasn’t a crude, literal translation,” the Google Cloud blog reported. “The AI hadn’t just swapped syntax. It had re-architected the logic into clean, idiomatic PostgreSQL functions.” The project liberated trapped business logic and let engineers focus on application improvements instead of manual rewrites.

Developers increasingly pick PostgreSQL as the foundation for AI applications themselves. Structured enterprise data serves as the essential ground truth that keeps large language models and agents from hallucinating. Nvidia CEO Jensen Huang called it exactly that in a recent keynote.

“An enterprise needs the agents and AI applications that they built to reference the data that they already have,” Phillip Merrick, co-founder and chief product officer at pgEdge, told The New Stack. “So it [structured data] really is the ground truth for AI… It’s how you make sure your LLM [large language model] and your agents aren’t hallucinating; you point them at the actual data. And Postgres is the best place to point them for that data.”

The pgvector extension lets Postgres store, index and query embeddings alongside traditional relational data. No need for a separate vector database. Performance matches specialized systems while preserving a single codebase, security model and operational practice. Yet vectors require maintenance. Change the source document and embeddings can drift unless tools automatically refresh them. Extensions such as pgEdge Vectorizer address exactly that gap.

Sixty-six percent of respondents to the Stack Overflow 2025 Developer Survey reported working in Postgres in the past year and wanting to continue. The database’s community-driven development, deployment flexibility and enterprise-grade features explain much of its appeal for both operational systems and AI workloads.

Operational support tools have followed the same AI wave. In early May, pgEdge released its AI DBA Workbench as an open-source co-pilot for PostgreSQL administrators. The tool continuously monitors query performance, vacuum activity, connection health, WAL throughput and replication lag. A three-tier anomaly detection system combines statistical baselines, vector similarity pattern matching and AI classification to surface problems before they escalate.

An integrated assistant named Ellie runs EXPLAIN ANALYZE on slow queries, inspects schemas, reviews historical metrics and walks administrators through diagnostic workflows. It suggests specific SQL fixes. Humans must review and approve every change. The workbench builds on pgAdmin, works with any Postgres version 14 or newer, and supports cloud services including Amazon RDS and Supabase as well as on-premises deployments.

“Database teams are being asked to do more with less, and the tools most of them rely on were designed for a simpler era,” David Mitchell, president and CEO of pgEdge, said in the PostgreSQL.org announcement. “The AI DBA Workbench gives teams an operational co-pilot that doesn’t just show you an alert and leave you to figure out the rest. It understands your environment, catches issues early, and helps you work through problems step by step.”

Google Cloud itself promotes conversational agents on PostgreSQL and MySQL, AI-driven query optimization in services such as AlloyDB, and best practices for generative AI applications that pair Cloud SQL for PostgreSQL with Vertex AI. The message is consistent. AI augments every layer of the database lifecycle.

Yet the core principle from Krishnamurthy holds. Humans retain accountability. AI accelerates contributions to PostgreSQL’s logical replication engine. It converts decades of Oracle stored procedures into modern functions. It monitors production fleets and proposes fixes. In every case the engineer, the DBA, the reviewer makes the final call.

That balance — aggressive adoption paired with clear ownership — defines how Google approaches AI inside its database organization today. Other teams watch closely. The productivity numbers are hard to ignore. The risks of unchecked automation are equally obvious. So far the company reports no major incidents tied to AI-generated contributions. Individual accountability appears to be working.

PostgreSQL’s momentum shows little sign of slowing. Major cloud vendors contribute code. Enterprises migrate legacy systems. Developers build new AI-native applications on its extensible foundation. And the tools that help them do all three now carry a heavy dose of artificial intelligence.

The humans remain in charge. They just move faster.