NaNLABS's Podcast

AI Is Changing Developers. Here’s What Actually Changes.

In this episode, Matías, CEO of NaNLABS, joins Daniela to explore how AI is reshaping the role of software engineers. Beyond the hype of “AI replacing developers,” this conversation dives into what’s actually changing inside real teams: shifting responsibilities, evolving skill sets, and new expectations for engineers and CTOs alike. Learn why coding is no longer the main differentiator, how the most effective teams are breaking traditional roles, and what leaders should do today to stay competitive in the AI era. ✅ Why AI isn’t replacing developers—but shifting where value lives ✅ The rise of ownership, product thinking & communication skills ✅ How engineering roles are expanding beyond traditional boundaries ✅ What actually matters when hiring developers in 2026 ✅ From individual AI usage to team-level systems ✅ A practical roadmap for CTOs to adopt AI without overhauling everything

Rolling Out Enterprise AI: from tools to trust

In this NaNTalk, Daniela and Matías explore one of the biggest challenges enterprises face today: moving from AI pilots to real adoption. As NaNLABS prepares to sponsor the 0111 CTO Conference in San Diego, this episode takes a deep look at what it really takes to roll out AI at scale: from choosing the right tools to building the trust, policies, and alignment that make adoption stick. ✅ Why AI adoption often stalls after the pilot phase ✅ Empowering teams beyond tools: the human side of rollout ✅ “Technology doesn’t fail — rollouts do”: what that means in practice ✅ The rise of AI governance and why every company needs a policy ✅ Technical foundations for successful enterprise AI systems ✅ One piece of advice for CTOs planning their 2026 AI strategy

Crossing the GenAI Divide: Why 95% of AI Projects Fail and How to Be the 5%

In this episode, Matías, CEO of NaNLABS, unpack the “GenAI Divide.” MIT’s 2025 report revealed that 95% of enterprise AI initiatives stall before delivering value. So what separates the 5% that succeed? From workflow integration to shadow AI, we explore why most pilots fail, what winning teams do differently, and how enterprises can embed AI into real business transformation. ✅ Why 95% of projects stall in pilots & demos ✅ The “integration cliff” and shadow AI economy ✅ What the 5% winning companies do differently ✅ LLM limits vs. Small Language Models & agentic AI ✅ First steps for CTOs to move from pilot to production ✅ GenAI opportunities in EV charging & automotive

What breaks first when you scale 30,000 chargers: architecture, ocpp, and AI readiness

In this episode, Matías, CEO of NaNLABS, unpacks the hidden cracks that appear when Charging Point Operators (CPOs) scale from a few thousand chargers to 30,000+. Legacy OCPP stacks, monolithic backends, and brittle data pipelines start to collapse under the pressure—long before AI can deliver value. Learn what tends to break first, how to future-proof without a full rewrite, and what “AI readiness” really means when scaling EV infrastructure for the next decade. ✅ Scaling challenges: OCPP, sockets & monoliths ✅ Load testing & stress simulations at scale ✅ AI readiness through clean, streaming data pipelines ✅ Smart abstractions: adapters, proxies & modularization ✅ Lessons from high-growth CPOs in Europe & the US

Your chargers are smart. Is your infrastructure smarter? How Cloud-Native Data Architecture makes or breaks AI in EV Charging

In this episode, Matías, CEO of NaNLABS, goes under the hood of AI in EV charging to reveal the real make-or-break factor: data infrastructure. While many Charging Point Operators rush to add AI features, the truth is that poor data foundations kill performance before models even get started. Learn why AI readiness starts with real-time telemetry, multi-source data fusion, and robust orchestration—and how future-proof architectures are enabling predictive maintenance, dynamic pricing, and even AI copilots for operations. ✅ Real-time telemetry ingestion & normalization ✅ Multi-source data fusion (energy, weather, usage) ✅ AI readiness: clean, connected, actionable data ✅ Modern, cloud-native architectures for scale