- Lumiera
- Posts
- 🔆 Shift Left: Reactive to Proactive Leadershift
🔆 Shift Left: Reactive to Proactive Leadershift
Solid AI development practices, sneaky Soundcloud updates and good pasta 🍝
Emma G Moller & Allegra Guinan
May 15, 2025
Was this email forwarded to you? Sign up here
🗞️ Issue 70 // ⏱️ Read Time: 5 min
In this week's newsletter
What we're talking about: How shifting left, i.e., moving testing, validation, and quality assurance earlier in the development process, transforms AI system reliability, security, and performance by catching issues when they're still manageable.
How it's relevant: As AI systems handle more critical functions, the cost of post-deployment failures grows exponentially. Organizations implementing comprehensive early testing see 60% fewer production incidents and significantly lower operational overhead.
Why it matters: The systems that succeed long-term aren't necessarily the most accurate in initial testing: They're the ones that degrade gracefully, adapt to new conditions, and fail safely. These properties must be built in, not bolted on.
Hello 👋
When an AI-powered trading system loses millions in minutes due to market volatility it wasn't trained for, when a content moderation model starts flagging legitimate posts as harmful, when a recommendation engine creates filter bubbles that hurt user engagement, the root cause isn't algorithmic bias or ethics violations: It's a failure to catch fundamental system weaknesses early enough to address them cost-effectively.
Imagine being the person held responsible for these failures: All of a sudden, you’re scrambling to explain algorithmic decisions to regulators, retrain models, and manage a PR crisis. This reactive approach to AI responsibility is costing organizations more than just money: It's eroding trust and hindering innovation.
Software engineering learned this lesson decades ago: Finding bugs in production costs more than catching them during development. AI systems face the same reality, but with added complexity. They're probabilistic, data-dependent, and operate in dynamic environments where "correct" behavior isn't always well-defined.
The False Economy of Late-Stage Validation
Most AI teams still follow a waterfall approach to quality: Train the model, validate on held-out data, deploy, then react to issues.
It’s kind of like forgetting to add salt to your pasta water and realising it as you’re about to plate it for your friends, trying to compensate by adding extra salt on top. If you have ever cooked pasta, you know that it doesn’t really give the same results. The conclusion tends to be: The only thing to add on top of pasta is the parmesan. For a nice meal, be proactive and add the salt earlier in the cooking process.