In this episode of the Earley AI Podcast, host Seth Earley welcomes Krishna Rangasayee, Founder and CEO of SiMa.ai, for a grounded conversation on what it takes to make AI work in real world environments. The discussion focuses on moving beyond hype to address the practical challenges of deploying AI systems that are efficient, scalable, and reliable at the edge.
Krishna brings decades of experience across hardware, software, and AI systems design. He shares why many AI initiatives struggle outside controlled environments and how organizations must rethink architecture, performance, and context when deploying AI closer to where data is created and decisions are made. The episode explores why efficiency is not just a cost concern but a core enabler of real time intelligence across industries.

Key Takeaways from this Episode:
Common misconceptions about AI readiness and why scaling models alone does not lead to success

Why edge AI is critical for real time decision making, latency reduction, and operational reliability

How efficiency at the hardware and system level unlocks new AI use cases
The importance of aligning AI architecture with real world constraints such as power, bandwidth, and deployment conditions

Why organizations must rethink the balance between cloud and edge computing

How leadership and culture influence whether AI experimentation turns into production impact

Insightful Quotes from the Show:
"AI success is not about chasing bigger models. It is about understanding the environment where AI actually has to operate and designing systems that work within real constraints." - Seth Earley

"If you want AI to deliver value in the real world, efficiency has to be designed in from the start. Otherwise, intelligence never makes it past the lab." - Krishna Rangasayee

Links
LinkedIn: https://www.linkedin.com/in/krishnarangasayee/
Website: https://sima.ai

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

In this episode, host Seth Earley welcomes Brandon Lucia, CEO of Efficient Computer, for a deep dive into how AI advancements are reshaping the future of computing—particularly with a focus on energy efficiency, sustainable infrastructure, and real-world applications.

Brandon Lucia brings almost 20 years of experience in computer architecture, having served as an academic at Carnegie Mellon University and led significant research at the boundary of hardware and software innovation. He and his team have pioneered a new kind of hardware architecture designed to drastically reduce power consumption for AI workloads without sacrificing performance or versatility. Their work has far-reaching implications for data centers, edge AI, robotics, automotive, and large-scale infrastructure monitoring.

Key Takeaways from this Episode:

• AI’s energy demands are accelerating rapidly and require rethinking not just bigger models, but architectural efficiency at every level.
• Effective AI infrastructure goes beyond mathematical optimization (like linear algebra); it includes real-world complexity and physical deployment.
• Specialized hardware architectures (CPU, GPU) are evolving, but general-purpose solutions with built-in efficiency—like those from Efficient Computer—can unlock new application domains.
• Edge computing and “physical AI” (as distinguished from legacy IoT) require extremely efficient processing to enable long device lifetimes and advanced capabilities.
• Efficient Computer’s chips offer exponential gains in energy efficiency compared to market-leading CPUs and embedded GPUs—sometimes up to hundreds of times better.
• Enterprises should focus on hardware-software co-design and apply principles like Amdahl’s Law: you are limited by what you can’t optimize, so balancing all types of computation is critical.
• Fine-grained personalization and retraining of AI at the edge will be increasingly important for future applications.
• Organizations that deal in manufacturing, logistics, automotive, infrastructure, or robotics stand to benefit greatly from advances in efficient hardware and architecture.

Insightful Quote from the Show:

"We're not going to meet these energy requirements with the existing hardware and software—we have to change." - Seth Earley

"We are vastly ahead of our competition when it comes to energy consumption. Batteries last longer. You can do more under a power cap. You're not limited by thermal constraints. Those convert directly into capabilities into lifetime. So you can do more than you could do today." - Brandon Lucia

Tune in for a conversation that not only explores the technical side of AI hardware, but also the practical, business, and societal impacts of powering tomorrow’s intelligent systems with greater efficiency.

Links

LinkedIn: https://www.linkedin.com/in/brandon-lucia-0767792/

Website: https://www.efficient.computer/

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

In this episode of the Earley AI Podcast, host Seth Earley sits down with Forrest Zeisler, co-founder and Chief Technology Officer at Jobber. With years of experience building technology for service professionals, Forrest Zeisler has played a pivotal role in empowering small businesses—from landscapers and plumbers to cleaners and contractors—to harness AI and automation for streamlined operations and growth.

Discover how Forrest Zeisler and his team scaled Jobber from three customers to over 300,000, delivering more than $100 billion in services, and learn how their journey demonstrates the transformative impact AI can have on businesses of all sizes.

Key Takeaways:

• Small businesses can benefit enormously from AI, especially for reducing administrative tasks and boosting productivity.
• Adopting new technology isn't just about features—it's about building trust and reliability for the end user.
• Jobber’s growth began with direct customer conversations, leading to a highly configurable platform supporting over 55 industry verticals.
• The journey from manual onboarding and white-glove service to sophisticated self-serve and AI-driven automations took years of iteration and customer feedback.
• Integrating AI isn’t just about chatbots or flashy features; the real impact comes from making technology disappear in the background, allowing users to focus on their craft.
• Reliable automation, rooted in real customer behavior and best practices, is key to driving widespread adoption of AI across industries.
• Building trust with AI systems should mirror how you onboard new employees: review, supervise, and gradually increase autonomy as reliability is proven.
• Orchestrating multiple AI models and agents allows platforms like Jobber to deliver context-aware, intelligent assistance that feels human and personalized.

Insightful Quotes:

"AI is beginning to simplify that work and reduce administrative overhead and reduce those efforts and help small companies provide more consistent and more efficient and more reliable results." - Seth Earley

“Our goal is not to stick a lot of chatbots in front of our customers. It's to make Jobber just magically always seem like it knows what you need when you need it. We want to measure our success by how little we're sticking in front of our customers.”- Forrest Zeisler

Tune in for a behind-the-scenes look at building scalable, reliable AI for small business—and the lessons you can apply whether you're an entrepreneur or driving digital transformation in a larger enterprise.

Links

LinkedIn: https://www.linkedin.com/in/forrestzeisler/

Website: https://www.getjobber.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book