Software Engineering Daily

TypeScript is a programming language that builds on JavaScript by adding a system of types. Those types let developers describe the shape of their data and catch mistakes before code ever runs, while also powering the autocompletion and editor tooling that many developers now rely on every day. It was first released in 2012, and has since become one of the most widely used tools in web development. TypeScript recently underwent one of the most significant changes in its history with the release of version 7.

Daniel Rosenwasser is the Principal Product Manager of TypeScript at Microsoft, where he began as an engineer on the team just weeks after the TypeScript 1.0 release. In this episode, Daniel joins Josh Goldberg to talk about the features of TypeScript 7. They discuss the TypeScript team’s approach to tooling, TypeScript’s relationship with the TC39 standards process behind JavaScript, the new API and IPC boundary, how LLMs could reshape type checking and linting, and more.

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It is widely reported that a gap has emerged between enterprise spending on AI and the durable value captured from that spend. Individual employees have enthusiastically adopted coding assistants and chatbots, yet those gains do not seem to be transforming businesses at an organizational level. One of the most important questions in the tech industry today is understanding why AI is not yet delivering returns that match the investment, and what separates the small number of enterprises succeeding from the many that are not.

Scale AI is known for supplying the human-labeled data behind many frontier models. It now also builds AI applications and agents for large enterprises. That combination of working alongside frontier labs and inside enterprise deployments gives the company a rare view of why enterprise AI may be stalling.

Emily Xue is the Head of Enterprise AI at Scale AI, and previously spent over a decade at Google. In this episode, Emily joins Kevin Ball to discuss the three layers where enterprise AI breaks down, why frontier model benchmarks miss what enterprises actually need, the data foundation problem, how the most successful companies combine internal domain expertise with outside AI specialists, and more.

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The conversation about AI often focuses on software, automation, and the race between attackers and defenders in code. However, some of the most consequential risks lie further afield, in domains where a mistake is measured in human lives. Advanced models can now offer step-by-step guidance toward chemical and biological weapons, and militaries are already folding AI into targeting and battlefield assessments. These are no longer speculative fears confined to the AI doomer crowd. They are documented in red team disclosures, government legislation, and events unfolding on real battlefields.

Gordon M. Goldstein is an adjunct senior fellow at the Council on Foreign Relations, where he focuses on the convergence of technology and US foreign policy. He previously spent nearly a decade as a managing director at Silver Lake, and he is the author of Lessons In Disaster: McGeorge Bundy and the Path to War in Vietnam, which is a study of national security strategy and White House decision-making. In this episode, Gordon joins Kevin Ball to discuss the credibility of AI-enabled chemical and biological weapon threats, the ad hoc safeguards meant to contain them, and the rise of autonomous warfare and its implications for human control and nuclear deterrence. Gordon speaks on his own behalf, not on behalf of the Council on Foreign Relations.

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News feeds and recommendation systems have long relied on deep learning architectures that score each candidate item independently. As LLMs have matured, they have opened up a fundamentally different approach, where a system can reason about content the way it reasons about language. However, that power comes with a fresh set of engineering challenges around cost, scale, and evaluation.

LinkedIn recently rebuilt its news feed to treat content recommendation as a sequence modeling problem. The general approach is to predict what a user will want next, much like an LLM predicts the next token in a sentence.

Tim Jurka has worked at LinkedIn for 13 years and is currently a VP of Engineering. In this episode, Tim joins Matt Merrill to discuss how LinkedIn re-engineered its feed, how the team combines LLMs with traditional signals, managing inference costs at massive scale, steering content quality using natural language policies, and more.

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For two decades, the cloud has been shaped by human developers writing code and managing its deployment. Now a growing share of production code is generated by LLMs with little human review. Because that code is not fully trusted, it increasingly runs in isolated, sandboxed environments. Meanwhile, AI agents are starting to operate infrastructure directly, by spinning services up and tearing them down on their own. Together these shifts raise the question of whether the cloud needs to be rebuilt for machine operators rather than humans.

Render is a cloud platform designed for application deployment, by handling scaling, self-healing, and security to reduce operations work. Render has been adapting to the AI era by building tools that let agents deploy and debug applications directly, with added guardrails and security.

Anurag Goel is the founder and CEO of Render. In this episode, Anurag joins Sean Falconer to discuss why so many teams end up rebuilding the same infrastructure on top of Kubernetes, what changes when AI agents become first-class users of infrastructure and the guardrails that shift demands, and why the economics of AI are pushing developers toward higher-level platforms that trade fine-grained control for speed and safety.

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