Tech Talks Daily

At Google Cloud Next in Las Vegas, I sat down with Granville Valentine to talk about one of the biggest shifts happening in business technology right now, the move from isolated AI experiments to orchestrated, production-scale agentic systems.
Granville leads Google Cloud's AI Go-to-Market organization across North America, working directly with major enterprises on adopting Gemini, customer experience AI, and multi-agent workflows. That puts him right at the center of how businesses are actually deploying AI in the real world, and where many are still getting stuck.
In this conversation, we explore why so many companies discovered in 2025 that standalone chatbots were failing to deliver measurable ROI, and how orchestration-based AI systems are changing that. Granville explains why the future belongs to multi-agent workflows built around business outcomes rather than technology demos, with different agents collaborating around customer experience, commerce, upselling, support, and personalization.
We also discuss the rise of proactive "digital concierges" that unify search, commerce, maps, personalization, and customer service into a single intelligent journey rather than the fragmented app experiences consumers are used to today. Granville shares practical examples from companies like The Home Depot and explains how businesses are using Gemini Enterprise for Customer Experience to create more natural and effective customer interactions.
Another major theme in this episode is data. We explore how cross-cloud connectivity and universal context engines are helping organizations query data across multiple cloud environments without moving everything into a single platform first, dramatically reducing friction for companies trying to build agentic workforces.
The conversation also touches on generative media, from video and image creation to interactive shopping experiences, and how businesses are using these tools to drive real engagement, customer retention, and revenue growth rather than simply producing flashy content.
Most importantly, this episode cuts through the hype and focuses on execution. Granville explains why businesses need to stop thinking about AI as a standalone feature and start thinking about it as an operating model built around outcomes, experimentation, and continuous learning.
Are businesses finally ready to move from AI experimentation to the agentic enterprise?
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What happens when AI growth collides with the physical limits of power, materials, and global supply chains?
In this episode of Tech Talks Daily, I speak with Matt Kelly, CTO and Vice President of Technology and Standards at the Global Electronics Association, about the growing pressure on AI infrastructure and the supply chains that support it.
Drawing on insights from thousands of member organizations across manufacturing, automotive, and electronics, Matt offers a practical look at what business and technology leaders should really be preparing for in 2026 and beyond.
Our conversation begins with the shift from cost optimization to resilience and system-level performance. Matt explains why the old procurement mindset of chasing the lowest-cost supplier is rapidly being replaced by what he calls confidence-based sourcing. In a world shaped by geopolitical disruption, pandemic aftershocks, and surging demand for AI, organizations are discovering that cheap sourcing means little if critical components fail to arrive on time.
We also discuss why dual sourcing has evolved from a procurement strategy into a business continuity requirement. Matt shares real-world examples of how something as small as a missing capacitor can prevent the delivery of million-dollar AI infrastructure systems. That single point of failure has pushed resilience metrics such as recovery time, geographic diversity, and validated backup suppliers into boardroom discussions.
Another major focus of the episode centers on AI infrastructure itself. 
While many conversations around AI focus on software models and automation, Matt argues that the true bottleneck may soon become power availability. From server cooling and energy consumption to sustainable hardware design and material shortages, the industry now faces challenges that stretch far beyond compute performance alone.
Matt also explains why fully localized supply chains remain unrealistic for the electronics industry. Instead, he advocates for a balanced model that combines trusted global partnerships with strategic regional sourcing for critical components and security-sensitive technologies.
One of the strongest takeaways from this conversation is that AI infrastructure must now be approached as a system problem. Silicon design, packaging, thermal management, power delivery, sustainability, and supply chain strategy cannot be treated as separate conversations.
As organizations race to scale AI capabilities over the next few years, are business leaders truly prepared for the infrastructure realities sitting behind the AI boom, or are we about to discover that resilience and energy matter just as much as innovation itself?
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What happens when the pace of AI innovation collides with the realities of semiconductor development?
In this episode of Tech Talks Daily, I speak with Faraj Aalaei, CEO of Cognichip and a semiconductor industry veteran with more than 25 years of experience spanning engineering, venture capital, and two successful IPOs. Faraj joins me to discuss why the future of artificial intelligence may depend on radically rethinking how chips are designed, manufactured, and scaled.
Cognichip recently emerged from stealth with $33 million in seed funding and a bold ambition to create the world's first Artificial Chip Intelligence, or ACI®. The company is developing a physics-informed foundational AI model purpose-built for semiconductors, with the goal of reducing the enormous time, cost, and complexity associated with chip design.
Faraj explains how the semiconductor industry now faces a growing bottleneck. While AI software can evolve at remarkable speed, chip development often still takes between three and five years and costs more than $100 million. That mismatch is becoming increasingly difficult to sustain as demand grows for specialized AI hardware, edge computing systems, and next-generation infrastructure.
Our conversation also explores the geopolitical and economic shifts reshaping the semiconductor industry. Faraj shares his perspective on the emerging concept of "Pax Silica," the growing effort by governments to restructure global chip supply chains and reduce reliance on China. While many policymakers see this as a matter of national security and resilience, Faraj warns there may also be unintended consequences, including rising AI infrastructure costs, engineering shortages, and slower innovation cycles.
One of the most interesting parts of our discussion centers on the idea that AI itself may become the missing scaling factor for semiconductor development. Instead of relying solely on larger engineering teams and longer development cycles, Cognichip believes AI-designed chips could dramatically accelerate innovation and make advanced hardware development accessible to far more companies and researchers.
Faraj also reflects on his career journey from entrepreneur to investor and back again, sharing lessons from decades spent helping build the modern semiconductor ecosystem. From supply chain realities to the growing pressure on engineering talent, this episode offers a rare insider perspective on the technologies quietly powering the entire AI economy.
As AI systems continue to demand faster, more specialized hardware, are we reaching the limits of traditional chip development, and could AI itself become the tool that reshapes the future of semiconductors?

What happens when a country moves from cash-only transactions to instant digital payments that work for everyone?
In this episode of Tech Talks Daily, I sit down with Steve Haley, Director of Market Development at The Mojaloop Foundation, to discuss how open and interoperable payment systems are helping reshape financial inclusion across Africa and other emerging markets.
For many listeners in Europe or North America, instant payments and digital banking are often taken for granted. But Steve explains how millions of people around the world still live in economies where cash dominates daily life, and where even those with mobile money accounts remain disconnected from the wider financial system. In some countries, people have even been forced to carry two phones because competing mobile payment providers could not communicate with each other.
Our conversation focuses heavily on Liberia, where the Liberian Inclusive Instant Payments System was deployed in just 73 business days. Built using Mojaloop technology in partnership with the Central Bank of Liberia, ThitsaWorks, and AfricaNenda, the system now allows interoperable mobile money transfers between major operators, including MTN and Orange Liberia.
Steve shares why this matters far beyond convenience. Removing barriers between providers means people no longer need money trapped across separate accounts, merchants can accept digital payments more easily, and governments can distribute payroll and public payments through faster and more transparent systems.
We also discuss how mobile wallets are helping expand account ownership across Liberia, which now exceeds 50 percent according to World Bank data, and why interoperability may become the missing piece that transforms access into meaningful financial participation.
Another fascinating part of our discussion centers on the future of cross-border payments in Africa. Steve explains how many transactions between neighboring African countries still route through systems in the United States, increasing both cost and complexity. He believes interoperable instant payment systems across the continent could dramatically lower those barriers and unlock new levels of regional trade.
This episode offers a thoughtful reminder that digital transformation is not always about the latest AI model or enterprise software platform. Sometimes it is about giving people the ability to send money, pay merchants, receive salaries, and participate in the economy with the same ease many of us already expect every day. 
So how different would life feel if digital payments finally became accessible to everyone, regardless of where they live or who they bank with?
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How do AI agents safely access the data they need without exposing the business to risk?
In this episode, I speak with Alex Gallego, CEO and founder of Redpanda, about why streaming data is becoming such an important foundation for enterprise AI. Redpanda began as a high-performance streaming data platform, but the company is now building what it calls the Agentic Data Plane, a governed access layer designed to connect AI agents with enterprise data and systems.
Alex shares the story behind Redpanda's journey, from solving a personal engineering frustration to powering mission-grade systems for some of the world's largest organizations. We discuss why many enterprises are racing toward agentic AI while still lacking the permissions, controls, context, and observability needed to make agents safe in production.
One of the standout moments in our conversation is Alex's comparison between hiring AI agents and forgetting to onboard them. Businesses are deploying accounting agents, coding agents, customer success agents, and security agents, yet many still lack a reliable way to decide what those agents can access, what actions they can take, and how to prove what happened when something goes wrong.
We also talk about explainability, agent transcripts, and why enterprises need a full record of agent behavior across complex chains of activity. Alex explains how this matters in regulated sectors such as banking, where organizations may need to prove that an AI agent is acting helpfully and responsibly, and in manufacturing, where a faulty agent action could affect months of production.
Alex also shares Redpanda's work with NVIDIA Vera, where benchmarks showed 5.5 times lower latency and 73% higher throughput. For business leaders, that means faster systems, lower costs, better customer experiences, and the ability to monitor agent behavior in real time.
This conversation is a practical look at what enterprise AI needs next. 
Speed matters, but governance, trust, and control may decide which companies can move AI agents from experiments into real operations. So, are we ready to give AI agents access to the enterprise, or do we first need to learn how to manage them like part of the workforce?
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