How do global companies make confident decisions when supply chains are constantly disrupted by tariffs, geopolitical tension, shifting consumer demand, and unpredictable global events?

In this episode of Tech Talks Daily, I sat down with Dr. Ashwin Rao, EVP of AI and R&D at o9 Solutions, to talk about how artificial intelligence is changing the way organizations plan, forecast, and respond to uncertainty. Ashwin brings a fascinating mix of experience to the conversation. After earning a PhD in mathematics and computer science, he spent fifteen years on Wall Street working on derivatives trading strategies at Goldman Sachs and Morgan Stanley before moving into the world of enterprise technology. Today, he operates at the meeting point between business and academia as both a senior AI leader and an adjunct professor at Stanford University.

Our conversation begins with Ashwin's unusual career path and how those early experiences in finance shaped the way he thinks about risk, decision making, and real world AI deployment. The journey from theoretical mathematics to trading floors and eventually into Silicon Valley offers an interesting lens on how analytical thinking can travel across industries and still remain highly relevant.

We then move into the work happening at o9 Solutions, where AI is helping organizations make smarter decisions across supply chain planning, demand forecasting, and inventory management. In a world that Ashwin describes using the acronym VUCA, volatility, uncertainty, complexity, and ambiguity, businesses are under pressure to react faster and make better informed decisions. He explains how enterprise AI platforms can connect fragmented data across departments and create a more complete view of the business.

One example he shares brings the concept down to earth. Even predicting how many bananas a grocery store should stock on any given day requires analyzing internal sales trends alongside external signals such as weather, social media trends, and economic conditions. Machine learning systems can now process those signals in real time and continuously update forecasts so businesses can respond quickly to changes.

We also explore the rise of neuro- and symbolic AI, a concept Ashwin believes represents the next stage in enterprise decision-making. Rather than relying only on large language models, this approach blends the structured reasoning of symbolic systems with the pattern recognition of neural networks. The result, he suggests, feels less like a chatbot and more like having an expert coach embedded inside the decision-making process.

Along the way, we also discuss why many organizations still struggle to embed AI successfully. Technology is only one piece of the puzzle. Ashwin believes the toughest obstacle is organizational change management, bringing teams together, connecting data across silos, and helping leaders guide their organizations through transformation.

If you have ever wondered how AI moves beyond chatbots and into the systems that quietly power global supply chains, this conversation offers a thoughtful and practical perspective.

So, how prepared is your organization to make decisions in a world defined by volatility and uncertainty, and could AI become the trusted partner that helps guide those choices?

Useful Links

• Ashwin's blog
• Ashwin's LinkedIn
• o9 Solutions Website
• o9 LinkedIn

What does it take to design a data center for a world where the technology inside it may change several times before the building even opens?

In this episode of Tech Talks Daily, I sit down with Jackson Metcalf, Principal at Gensler, to talk about how AI is forcing a complete rethink of data center design. Jackson has spent nearly two decades working on critical facilities, and in our conversation he explains how the shift from traditional cloud workloads to dense AI environments is changing everything from building form and cooling strategy to long-term infrastructure planning.

What struck me most in this conversation is the sheer mismatch in timescales. Data centers can take two and a half to three years to design and build, while chip and GPU roadmaps are evolving in cycles of months. Jackson explains why that means designing for a fixed end state no longer makes sense. Instead, the future may belong to facilities built with flexibility at their core, spaces that can be reconfigured, upgraded, and even conceptually rebuilt over time rather than treated as static assets.

We also talk about what hyper-flexibility actually means in practice. This is not just a buzzword. It is about designing buildings with enough structural and engineering headroom to support very different cooling and power models over their lifespan. As AI workloads push cabinet densities to levels that would have sounded impossible only a few years ago, the need for plug-and-play mechanical and electrical infrastructure becomes far more than a design preference. It becomes essential.

Another fascinating part of the conversation centers on sustainability. Jackson shares why durable, well-built structures can create long-term environmental value, even in an industry often criticized for its energy demands. We discuss embodied carbon, adaptive reuse, and why a high-quality building may have a much better second life than something built purely for short-term speed. That leads into a wider conversation about repositioning underused real estate, from former industrial facilities to vacant office buildings, as potential digital infrastructure.

We also get into the growing energy challenge behind AI. With demand for power rising fast, and the US grid under increasing pressure, many operators are now weighing options such as on-site natural gas generation while waiting for cleaner long-term alternatives to mature. Jackson offers a thoughtful perspective on the tension between urgent infrastructure needs and environmental responsibility, as well as the uncertainty surrounding future energy roadmaps.

Looking further ahead, I ask Jackson what will define a successful data center campus in the years to come. Will it be raw megawatts, adaptability, carbon intensity, location strategy, or something else entirely? His answer opens up a much bigger conversation about whether these buildings can become more connected to the communities around them, and what role they may play in a future where digital infrastructure is no longer hidden in the background, but central to how society functions.

So if AI is pushing data center design to extremes, how do we build facilities that are ready for what comes next without becoming obsolete almost as soon as they open? And what does sustainable, adaptable digital infrastructure really look like in practice?

How close are we to the moment when quantum computing moves from scientific curiosity to real-world infrastructure? In today's episode of Tech Talks Daily, I speak with Christian Weedbrook, Founder and CEO of Xanadu, a company pushing the boundaries of what quantum computers might soon achieve.

Xanadu has taken an unconventional route in the race to build practical quantum systems. Instead of relying on electronic approaches used by many others in the field, the company builds quantum computers using photonics, effectively computing with particles of light. Christian explains why this matters and how working with photons could unlock advantages in energy efficiency, scalability, and networking as quantum machines grow into large data center–scale systems.

The conversation also arrives at a fascinating moment for the company. Xanadu has announced plans to go public through a SPAC deal that values the company at around $3.1 billion. Christian shares what that milestone means, not only for Xanadu but for the broader quantum ecosystem. According to him, the excitement surrounding quantum computing is no longer limited to research labs. Governments, enterprise partners, and investors are increasingly paying attention as the technology edges closer to commercial relevance.

One of the most engaging parts of our conversation is Christian's own journey into the world of quantum physics. Before earning a PhD in photonic quantum computing, he began as a film student who admits he once dreamed of becoming a filmmaker. That winding path eventually led him into physics and entrepreneurship, where he founded Xanadu in 2016 with a mission to make quantum computers useful and accessible to everyone.

We also discuss PennyLane, the open-source quantum programming framework developed by Xanadu that has quietly become one of the most widely used tools in the quantum developer community. Now taught in universities across more than 30 countries, PennyLane plays an important role in building the next generation of quantum talent.

Christian also shares a realistic timeline for where the industry stands today. Quantum computers already exist, but they remain smaller than what is needed for commercial breakthroughs. Xanadu's roadmap points toward large-scale quantum data centers by the end of the decade, systems capable of tackling problems in drug discovery, materials science, logistics, and finance that traditional computers struggle to simulate.

For enterprise leaders listening today, the message is clear. The quantum future is closer than many people assume, and organizations that begin exploring use cases now will be far better prepared when these systems mature.

So how should businesses prepare for a computing paradigm based on the mathematics of quantum physics rather than traditional software logic? And what lessons can founders learn from a journey that began with filmmaking ambitions and led to building one of the most ambitious quantum companies in the world? Let's find out together.