The New Stack Podcast

WeAreDevelopers, the Berlin-based developer conference founded in 2015, has grown into a major global event, attracting 15,000 developers from over 70 countries each year. In 2026, it expands beyond Europe with new editions in San Jose, California, and Bengaluru, India. Co-founder and CEO Sead Ahmetovic says the conference was created to give developers a stronger voice in an industry where marketers, salespeople, and entrepreneurs often receive more recognition. 

He believes developers, despite being less vocal, build the products that power the modern world. The event began as a small meetup that quickly gained popularity, filling a gap between highly specialized technical gatherings and broader business-focused conferences. Former GitHub CEO Thomas Dohmke highlights another benefit: giving developers a platform to share the stories behind their work and inspire peers. 

Discussing the future of software development, Dohmke predicts AI agents will handle much of the coding, while developers focus on managing ideas, prompts, and workflows. Ahmetovic agrees, arguing that developers will remain essential, spending less time typing code and more time thinking, orchestrating, and creating new solutions. 

Learn more from The New Stack around the latest in developer community growth: 

How Community Helps Developers Grow 

Empowering Developers Is Critical to Drive AI Innovation 

3 Ways Organizations Can Redefine the Developer Experience 

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

At a recent MCP developer summit, The New Stack spoke with Till Döhmen, AI lead atMotherDuck, about the company’s growing role in the evolving DuckDB ecosystem. Backed by investors includingTomasz Tunguz, MotherDuck is commercializing the open-source analytical databaseDuckDBwhile also expanding how employees interact with data through AI agents rather than traditional dashboards.

Döhmen emphasized the company’s close collaboration withDuckDB FoundationandDuckDB Labs. Because MotherDuck operates what he described as the world’s largest fleet of DuckDB databases, the startup regularly pushes the database to its limits and feeds insights back to the core maintainers. Rather than forking DuckDB to create proprietary advantages, MotherDuck instead extends the platform through its existing architecture while contributing core improvements upstream when needed.

The conversation highlighted the delicate but productive relationship between venture-backed companies and the open-source projects they commercialize, positioning MotherDuck as another example of startups driving both OSS adoption and strong business growth simultaneously.

Learn more from The New Stack around the latest in DuckDB:

DuckDB: Query Processing Is King

DuckDB: In-Process Python Analytics for Not-Quite-Big Data

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

JetBrains is positioning itself as the last major independent AI coding-tool vendor in a market increasingly tied to hyperscalers and foundation model labs. Speaking at Google Cloud Next, JetBrains VP of business developmentMikhail Vink argued that competitors such as Microsoft Copilot, Anysphere Cursor, and Windsurfare all tied to either AI labs or cloud providers. By contrast, JetBrains says its independence allows customers to switch freely between models fromOpenAI,Anthropic, andGoogle Cloudwithout being locked into one ecosystem.

That flexibility underpins JetBrains’ broader AI strategy. Rather than building its own foundation model, the company is focusing on orchestration and governance through JetBrains Central, announced in March as a management layer for AI agents, usage controls, analytics, and consumption-based billing. Vink said the company’s profitability, 16 million users, and 300,000 commercial customers from its long-running IDE business have allowed it to remain venture-free and model-neutral. JetBrains argues that as developers increasingly swap between AI models, neutrality may become more valuable than owning the models themselves.

Learn more from The New Stack around the latest in AI coding-tools: 

JetBrains ‘Agentic’ AI Agent Helps Automate Coding Tasks

JetBrains: AI agents are about to repeat the cloud ROI crisis 

JetBrains names the debt AI agents leave behind

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

What began as an internal developer tool atBlockhas evolved into a broader open-source initiative with industry backing. Goose, Block’s AI coding agent, followed a path similar to Amazon’s transformation of internal infrastructure intoAmazon Web Services. After deploying Goose companywide, Block open-sourced the tool under a permissive license, leading to rapid adoption across the developer community.

But according to Manik Surtani, Office of the CTO, Block and Co Founder of Agentic AI Foundation, early momentum exposed governance challenges. Although Goose was technically open source, Block retained trademark ownership, creating concerns for enterprises seeking truly independent governance. To address this, the team partnered with the creators ofAnthropicand the Model Context Protocol community to establish theAgentic AI Foundationunder the umbrella of theLinux Foundation.

Goose, MCP, and Agents.MD became the foundation’s initial projects, chosen largely to accelerate the launch of the new organization and create a collaborative ecosystem around agentic AI development.

Learn more from The New Stack around the latest in open-source AI: 

Anthropic extends MCP with a UI framework

Why the Linux Foundation adopted MCP, with Jim Zemlin and Mazin Gilbert

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.

At Google Cloud Next 2026, Fivetran Chief Product Officer Anjan Kundavaram argued that enterprise data systems are unprepared for the scale of AI-driven analytics. Unlike humans, AI agents can generate exponentially more queries, often routing them through the same expensive compute infrastructure. Kundavaram compared it to “using a Lamborghini to mow the lawn.” To address this, Fivetran introduced its “Open Data Infrastructure” vision and a benchmark designed to expose hidden AI workload costs in closed ecosystems.

Kundavaram said agents can optimize for cost instead of speed, choosing cheaper compute engines when appropriate — but only in open architectures with multiple options. Closed systems force every query through high-cost paths. He also warned that fragmented data and weak context create a “triple whammy” of poor AI responses, soaring analytics bills, and wasted compute. While many organizations respond by tightening controls, Kundavaram argued the better path is investing in open infrastructure, interoperability, and strong semantic data practices before AI costs spiral further.

 

Learn more from The New Stack around the latest in enterprise data systems: 

Enterprise AI Success Demands Real-Time Data Platforms

AI Agents Are Morphing Into the 'Enterprise Operating System'

Join our community of newsletter subscribers to stay on top of the news and at the top of your game.