Containerized RTB and How It Shifts the Programmatic Space

Containerized RTB and How It Shifts the Programmatic Space

By Dan Jaye, CTO The Google AdEx privacy settlement and its introduction of RTB Control made one thing clear: identity enforcement is moving into the core pipes of programmatic. When consumers can opt out and strip identifiers at the auction level, the open bidstream...
Most AI Projects Fail. Here are Ways to Fix That.

Most AI Projects Fail. Here are Ways to Fix That.

We’ve suspected for a while that 95% of AI initiatives were silent failures. The research just confirmed it. It’s not a lack of effort or investment – it’s irrational optimism. Teams believe a shiny tool will be enough, but without learning, without consistency, it isn’t. The big question is, why?

Why Native API Development Won’t Survive the AI Era

Why Native API Development Won’t Survive the AI Era

Dynamic, MCP-driven negotiation opens up a much bigger attack surface. We’ve already seen large language models capable of social engineering and misusing authorization. With MCP, those risks multiply. And because the “edges” of these new systems are fuzzy, traditional defenses won’t always work.

The Agentic Mesh Is a Mess, But…

The Agentic Mesh Is a Mess, But…

Nate’s takedown of the mesh struck a nerve because it spoke the quiet part out loud: most companies aren’t ready. But that doesn’t mean a fluid and dynamic agentic future is nonsense. It just means we need to focus less on diagrams – and more on the plumbing.

The real future of AI in the enterprise won’t be defined by self-directing agents hammering on a million keyboards instead of monkeys, but by reliable foundations for collaboration. Autonomous agents with exponential relationships are not the answer. However, in case I’m wrong, let me be the first to welcome our robot overlords.

Recent Acquisitions Advance the AI Infrastructure – But We’re Still Missing a Critical Layer

Recent Acquisitions Advance the AI Infrastructure – But We’re Still Missing a Critical Layer

The acquisitions made by Snowflake, Databricks, and Salesforce are smart bets on where the AI infrastructure stack is going. But they also create an opportunity (and a need) to build out capabilities that turn data into understanding.

This means helping enterprise teams move beyond real-time ingestion and toward real-time interpretation. For marketing use cases, that means AI systems that understand customer history, resolve identity with confidence, and avoid the blind spots that lead to wasted spend or flawed personalization.