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Modern Software Development For Today's Systems & Applications
Why Token-Maxxing Is the Wrong Way to Measure AI Success
Silicon Valley has been measuring AI success by token consumption. The more tokens, the more AI transformation. Right? Wrong. Andi Gutmans, Vice President and General Manager for Data Cloud at Google, joins Cindi Howson on the podcast to share that the best context is the context that drives the outcomes you need with the least amount of tokens and processing. Efficiency, not volume, is where the real value is.
How to Accelerate Vulnerability Remediation with AI
Perforce QAC and Klocwork's new AI-assisted code remediation capabilities combine deep static analysis with AI-guided fix recommendations, helping developers resolve issues faster while maintaining compliance, security, and code quality. In this webinar, you'll see a live demo of how teams can accelerate remediation, reduce rework, and enable flexible AI-powered workflows directly within their development environment.
AI Transport v0.6.0: mid-run steering
Steering lets a client redirect an agent while the agent is still working, so a follow-up message reshapes the answer in flight instead of cancelling the Run and starting over, or waiting for it to finish. In the AI Transport SDK, a Run encapsulates the agent's output for a single turn (including multiple iterations around an agentic loop). The last release, v0.5.0, made a single agent turn survive a crash by splitting a Run into re-attemptable Steps.
Data Mage: meet our AI data analyst that lives in Slack
As many other data teams do, we have a dedicated Slack channel (#data) where the whole company can ask data-related questions that get picked up by a designated, weekly-rotated data analyst (data mage). It can become tiresome quickly as the questions are fairly repetitive and it takes away precious time from other high-impact tasks. The team built clean, well-documented marts and reports in Metabase that are used regularly, but these were still missing something that makes reporting truly self-service.
Ep 82 | Behavior Change: How Startups Are Making AI Stick
Behavior change is the biggest hurdle in AI adoption. AI only creates value when people make it part of their everyday work. In this episode of The AI Forecast, Paul Muller sits down with Varun Puri, CEO and co-founder of Yoodli, to discuss why successful AI adoption starts with changing how people work. Drawing on his experience at Google, Google X, and as the founder of an AI startup, Varun shares practical lessons on embedding AI into everyday workflows and building habits that stick.
A Google Data Cloud Leader's Formula for Token-Efficient AI
For most enterprises, autonomous AI agents still feel like a risk waiting to happen. Andi Gutmans, Vice President and General Manager for Data Cloud at Google, joins Cindi Howson to explain what it takes to build the data foundation that trustworthy agentic AI depends on. He breaks down how organizations can finally activate the 90 percent of enterprise data that's unstructured, why tokenmaxxing is the wrong way to measure AI value, and how open standards like Apache Iceberg are helping leaders tear down fragmented, multi-cloud data silos and unify data across clouds.
Securing Non-Deterministic Systems: Comprehensive AI Security Testing for Enterprises
Enterprise QA teams are discovering that deploying machine learning models breaks their existing validation pipelines. Legacy testing environments rely on a simple truth: fixed inputs must produce predictable outputs. Because intelligent architectures operate on probabilistic distributions, deterministic testing alone can no longer guarantee reliability. When conducting a code review or architectural risk assessment, treating an active model as a standard black-box API leaves critical flaws unaddressed.
Why Integration and MCP Are the New Foundation of Your Agentic AI Strategy
If you've been following the agentic AI wave, you've probably noticed that the conversation tends to center on the agents themselves: which LLM to use, which orchestration framework to pick, which use cases to tackle first. But a growing body of analyst research is pointing to a different bottleneck, one that's hiding in plain sight: integration. Forrester's David Mooter argues that integration must sit at the center of your AI strategy — not as plumbing, but as a strategic capability.
The AI Opportunity Gap Is Real. It's Growing. And It Is Not About Access to Tools.
In the first part of this series, I argued that discernment, the ability to recognise when an AI-generated answer is wrong, is becoming one of the most valuable capabilities inside an organisation. The question this piece addresses is simpler and harder: who is actually being given the opportunity to develop it? The AI opportunity gap is real. It is not primarily a gap in access to tools. It is a gap in permission. And I believe that gap starts earlier than most leaders realise, often in school.