Dave Rubinstein discusses with Tim Qi, lead data analyst at Linear, how AI is changing who builds software. Linear's study found that AI is accelerating team velocity and changing behavior on platforms. Companies are adopting AI in two main ways: top-down, tightly controlled rollouts, and bottom-up, allowing experimentation. Training varies, often evolving with use cases. AI adoption is similar across company sizes. The study also revealed that AI tools are making previously cumbersome tasks more manageable, leading to higher quality outputs. The industry is still figuring out how to manage the shadow AI issue.

Dave Rubinstein and Matt Whitbourne discuss the evolution and potential of mainframe modernization, particularly with the integration of AI. They highlight the long-standing challenge of maintaining legacy systems, such as COBOL, and the role of AI in enhancing innovation and efficiency. Whitbourne emphasizes the importance of understanding business logic before modernizing and the potential for AI to assist in this process. He also notes the benefits of AI in improving code serviceability, risk management, and compliance. The conversation underscores the need for a strategic approach to mainframe modernization, focusing on business goals and the unique characteristics of mainframe workloads.

Dave Rubinstein interviews Ilya Kabanov, a former Google AI security manager, about the disconnect between AI's rapid code generation and security measures. Kabanoff, who leads the Weather Report and is a research affiliate at MIT, discusses the significant progress AI has made in writing functional code, citing a 4x increase in security over a year. However, security checks cost five times more than code writing, posing economic challenges. Kabanov emphasizes the need for education, understanding AI's limitations, and prioritizing fundamental security practices to ensure secure AI-generated code.

Dave and Ipek Ozkaya discuss the AI adoption maturity model created by Carnegie Mellon's Software Engineering Institute and Accenture. The model addresses issues like mismatched expectations, untested implementations, and misaligned applications that hinder AI investment returns. It includes eight core dimensions: organizational change, workforce and culture, workflow reengineering, risk and governance, data engineering, operations, technology ecosystem, and AI lifecycle engineering. The model progresses from exploratory AI to scaled AI, emphasizing the importance of strategic alignment, workflow reengineering, and ecosystem partnerships.

SD Times editor-in-chief Dave Rubinstein and Barun Singh of tech talent company Andela discuss the impact of AI on nurturing future developers. Singh emphasizes that while AI automates coding basics, core knowledge and critical thinking are still essential. He highlights the importance of code review, planning, and security, which require senior experience. Singh believes colleges should focus on foundational knowledge, and boot camps remain relevant due to their immersive learning approach. He notes a shift towards retraining existing talent rather than just recruiting new workers. 

In this episode, David Rubinstein talks with Craig Kerstiens, Snowflake's Director of Software Engineering on Postgres, about the enduring popularity of Postgres, a 30-year-old database technology, due to its reliability and recent innovations like JSON support and vector store databases. He highlighted the challenge of integrating operational (OLTP) and analytical (OLAP) data for AI applications, noting the importance of reducing latency and operational burden. Kerstiens introduced PG Lake, an open-source tool that embeds Iceberg in Postgres, simplifying data movement and querying. He also mentioned Snowflake's mirroring feature and the future of Postgres, emphasizing its continuous improvement and extension ecosystem.

Dave Rubinstein and Jonathan Macoskey discuss the limitations of current AI models in understanding context, particularly in enterprise settings. Macoskey explains that while language models have improved with internet connectivity, they still struggle with outdated information and proprietary data. He highlights Lovelace's context engine, which integrates data from various sources into a knowledge graph, addressing issues like entity resolution and recall. This system ensures AI agents can provide accurate, cited information, enhancing trust and decision-making. The conversation also touches on the challenges of scaling large language models and the importance of grounding AI responses in reliable data.

Dave Rubinstein, Editor in Chief of SD Times, discusses with Kumar Vikesh, Senior Product Owner at Progress Data Direct, the challenges of REST connectivity and the introduction of an AI-driven solution to automate the creation of model files for the Autonomous REST Connector. Progress Data Direct offers a standard connectivity platform with over 60 connectors and 100 data sources. The new AI-driven approach uses three agents: a generator, validator, and launcher, to create, validate, and test REST model files, reducing manual effort from days to hours. The solution is currently in an early access program, with plans for future general availability and integration with Visual Studio Code.

Dave discusses real-time voice AI with Mike Pappas, CEO of Modulate. Pappas explains that voice AI involves three key technologies: speech-to-text, language models, and text-to-speech. This technology is valuable in detecting fraud, ensuring policy adherence, and routing calls efficiently. 

In this episode, Dave speaks with John D'Emic, cofounder and CTO at Revenium, about the challenges organizations face in managing costs and ROI of agentic AI initiatives. John explains that the transition from pre-production to production often reveals gaps in economic visibility, leading to "agent debt" due to unanticipated costs from API calls and tool usage.