#scaling

#scaling

Let's be real-it's not easy. Scaling a manufacturing company is messy, unpredictable, and often brutal. My job as CEO is not to hide the chaos, it's to cut through it and get to the finish line. We've built systems, habits, and a culture that make the hard things manageable. From the outside that might look like ease. But every single day there is grit.

Just recently SVPG Partner Christian Idiodi hosted Shreyas Doshi on his Product Therapy podcast, where they discussed the role of product leadership. If you haven't yet listened to this interview, I would strongly encourage it, as I loved hearing Shreyas' thoughts on this critically important topic. Shreyas described three different archetypes of product leaders: the craftsperson ("it's all about the product"), the operator ("it's about scale"), and the visionary ("it's about the future"). I hope you listen to the full interview as Shreyas shared valuable context and nuance that I consider essential for this foundational topic.

The talk covers how leading firms orchestrate agent workflows to handle complex tasks and deliver business value, with examples across telecom, finance, retail, and more. Importantly, it dives into practical considerations for scaling AI agents: establishing governance frameworks to ensure agents behave reliably and transparently, and implementing observability tools to monitor, debug, and optimize these dynamic systems in real time.

Brands are doubling down on spend and scale, with some even spending up to 50% of their marketing budgets on influencers. Against this backdrop, the inaugural gathering of the Global Influencer Council highlighted both the promise and the challenges of scaling influencer marketing. The council is a new body of marketing leaders from some of the world's most iconic brands aiming to set standards in the creator economy through collaboration, transparency, and shared best practices.

"We scaled Tesla in 30 months from $2 billion in revenue to $20 billion in revenue," Jon McNeil stated. Understanding how to measure product-market fit helps assess scaling potential effectively.

Each new invocation of an AWS Lambda function may spin up a new environment or reuse an existing one, leading to a surge in database connections.