Tech Talks Daily

Have you ever looked at a global hiring plan and wondered whether you are building a team, or accidentally buying a bundle of hidden fees, legal risk, and avoidable stress?
In this episode, I'm joined by Oksana Petrus from Alcor, where she leads customer success and operations, helping tech companies build and scale engineering teams across Eastern Europe and Latin America.
If you have ever tried to expand beyond your home market, you know the promise is real, access to great talent, broader coverage across time zones, and the chance to build faster. But the reality can get messy quickly once contracts, compliance, culture, and cost assumptions collide.
Oksana brings a sharp perspective because she has seen both sides. Earlier in her career she worked as a lawyer with outsourcing providers, so she understands how pricing structures and contracts can create surprises once a team is already in motion.
We talk about why so many leaders start out thinking outsourcing will be simple, then discover they cannot clearly see what they are paying for, who is actually doing the work, or how much of the spend is going to overhead.
We also discuss the growing challenge of trust in recruiting, especially as AI tools make it easier to fake profiles, inflate experience, and even perform better in interviews than the person behind the screen can deliver on the job.
Oksana shares how teams are responding with stronger verification, background checks, and a more transparent operating model so hiring managers can feel confident about who they are bringing in.
We also dig into the real cost of global scaling, and why "salary charts" are only the starting point. Oksana explains how benefits, taxes, local customs like a 13th salary, currency controls, and even language realities can derail budgets and slow hiring if teams do not have local insight. The result is often frustration on both sides, candidates lose momentum, managers lose time, and projects drift.
Culture comes through as a theme too, and not in a vague, feel good way. We talk about how different regions communicate, how expectations need to be set early, and why "challenge culture" can be a strength when leaders welcome it.
Oksana shares an example of a CTO who came to value Eastern European teams precisely because they questioned decisions and offered alternatives that improved outcomes.
If you are a founder, CTO, or business leader thinking about scaling an engineering team this year, this episode is a practical look at what tends to go wrong, why it gets expensive, and how to build a smarter path forward without overcommitting too early.
 Where do you think the line is between smart global expansion and taking on complexity before your business is ready for it, and what has your own experience taught you?

How can a world that produces more than enough food still leave millions of people struggling to put a healthy meal on the table?
In this episode of Tech Talks Daily, I speak with Jordan Schenck, CEO of Flashfood, about the growing paradox at the heart of our global food system. Grocery prices are climbing, families everywhere are making harder choices at the checkout, and food banks are seeing rising demand. Yet at the same time, vast quantities of perfectly edible food never make it onto a plate.
Jordan shares the startling scale of the problem. In North America alone, billions of pounds of edible food are thrown away every year, including huge volumes from grocery stores themselves.
Fresh produce, meat, and dairy often end up discarded even though they remain safe and nutritious to eat. The result is a system where food waste and food insecurity grow side by side, despite a supply chain that already produces far more calories than the world needs.
Flashfood is attempting to change that equation with a simple but powerful idea. Through its marketplace app, the company partners with grocery retailers to sell surplus food at steep discounts before it reaches the landfill.
Shoppers gain access to fresh groceries at far lower prices, while retailers recover value from inventory that might otherwise be lost. What emerges is a rare triple win for shoppers, grocers, and the environment.
During our conversation, Jordan explains how consumer behavior, retail expectations, and supply chain logistics have shaped today's food waste problem. She also shares how technology and data are beginning to shift the system in a different direction.
Flashfood is now working with more than two thousand grocery partners across North America and serving over a million users, using data and AI to help retailers price surplus inventory more effectively and move products before they are discarded.But the story behind Flashfood is also personal.
Jordan reflects on her earlier experiences at Impossible Foods and as founder of the beverage brand Sunwink, and how those roles helped her see both the strengths and weaknesses inside modern food production.
Over time, she began to question whether the industry truly needed more products on shelves, or whether the bigger opportunity lay in fixing the inefficiencies that already existed.
Our discussion touches on the psychology of grocery shopping, the economics of surplus inventory, and the cultural expectations that lead retailers to overstock shelves in the first place.
We also explore why many consumers are more open to buying discounted food than retailers once believed, particularly as the cost of living continues to rise.

Perhaps most encouraging of all is the idea that solving food waste does not require entirely new supply chains or radical lifestyle changes. Sometimes it simply requires connecting the dots between food that already exists and the people who need it most.

Is 2026 the year AI finally has to prove it is worth the investment?
In this episode, I'm joined by Chris Riche-Webber, VP of Business Intelligence and Analytics at SmartRecruiters, to explore why so many AI and agentic AI initiatives stall after the pilot phase and what separates the projects that scale from the ones that quietly disappear. With Gartner predicting that more than 40 percent of agentic AI programs could be cancelled by 2027, Chris brings a pragmatic, data-led perspective on what is really happening inside organizations as the hype meets operational reality.
We talk about the fundamentals that have not changed despite the new technology. Influence, clearly defined problems, measurable impact, and adoption still determine success, yet they are often overlooked in the rush to deploy the latest tools. Chris explains why "good vibes" are no longer enough in front of a CFO, how to baseline outcomes properly, and why ownership of results is one of the most common missing pieces in enterprise AI programs.
A big part of the conversation focuses on what Chris calls the "agent washing" problem. Just as products are sometimes marketed with fashionable labels that do not reflect their real value, many solutions are being positioned as agentic without delivering true autonomy or business outcomes. We discuss how leaders can cut through the noise by asking better questions, aligning technology to specific use cases, and recognizing when simple automation is the right answer.
Trust, adoption, and measurable ROI emerge as the three signals that determine whether an AI initiative survives. Chris shares a clear framework for defining these signals in a way that is consistent, comparable over time, and meaningful to the executive team. We also explore how connecting talent decisions to revenue, productivity, and retention changes the conversation, especially in the context of SmartRecruiters' broader SAP ecosystem and the opportunity to link people data directly to business performance.
This is a conversation about moving from experimentation to accountability, from buying narratives to solving real problems, and from technology-first thinking to outcome-first leadership.
So as the window for easy wins closes and the demand for proof of value grows, will your AI strategy be remembered as a pilot that generated excitement or as an initiative that delivered measurable business impact?

What if the real AI race in 2026 isn't about building bigger models, but about where decisions are made, how fast they happen, and whether they deliver measurable value?
In this episode, I'm joined by John Bradshaw, Director of Cloud Computing Technology and Strategy at Akamai, to unpack his predictions for the next phase of cloud, AI inference, and the economics that will shape enterprise technology over the next 12 months. As organizations move beyond experimentation, John explains why the boardroom conversation has shifted from capability to return on investment, and how spiraling compute demands are forcing leaders to rethink the balance between performance, cost, and innovation.
We explore why this new financial scrutiny is not slowing AI adoption, but refining it. John shares how inefficient GPU workflows, centralized inference, and poorly aligned architectures are being challenged by a more disciplined approach that pushes intelligence closer to the edge. This shift is not only about latency and performance. It is about building scalable, value-driven platforms that can support real-time decision-making, agentic workloads, and global user experiences without breaking traditional IT budgets.
Trust is another major theme throughout our conversation. From the rise of everyday AI agents that quietly handle routine tasks to the growing importance of secure, resilient inference pipelines, John outlines how low-latency edge infrastructure, local processing, and hybrid cloud models will redefine reliability for both enterprises and consumers. We also discuss the smart home backlash following recent outages, and why the next generation of connected products will be designed to work even when the network does not.
The episode also looks at the future of streaming, where consolidation, intelligent content delivery, and AI-driven personalization are reshaping both the user experience and the economics behind the platforms. Behind the scenes, orchestration is emerging as a defining capability, with multiple models and services working together to validate outputs, reduce hallucinations, and create more dependable AI systems.
This is a conversation about moving from possibility to production, from experimentation to accountability, and from centralized architectures to distributed intelligence.
So as AI becomes embedded in every workflow and every customer interaction, will the winners be the companies with the biggest models, or the ones that know exactly where their AI should live, how it should be orchestrated, and how it proves its value every single day?

What happens when AI moves from a standalone tool to a teammate that works inside the flow of your organization?
In this episode, I'm joined by Mick Hodgins, General Manager for EMEA at Notion, to explore how the idea of a connected AI workspace is reshaping the way teams collaborate, make decisions, and measure productivity. With a career that includes more than a decade at Google scaling growth across multiple countries, Mick brings a unique perspective on what it takes to build technology businesses across diverse markets and why this moment in AI feels fundamentally different from previous waves of innovation.
We talk about Notion's journey from a flexible, block-based collaboration platform to an AI-native workspace where context is the real differentiator. Mick explains why AI performs better when it understands how work actually happens, and how embedding agents directly into shared workflows allows teams to move from prompting tools to orchestrating outcomes. From automated reporting and knowledge management to self-improving agent loops that learn from their own performance, the conversation brings to life how organizations are already using AI to remove the "work around the work" and focus on higher-value thinking.
A major theme throughout the discussion is return on investment. In a world where many companies are still stuck in pilot mode, Mick shares how leaders can reframe ROI around productivity, speed, and the elimination of repetitive tasks rather than treating AI as a single project with a fixed payback period. We also explore how roles, org structures, and hiring priorities are beginning to shift as agents become extensions of team capability rather than experimental add-ons.
Because Mick leads the EMEA region, we also dive into the differences in adoption between the US and Europe, from regulatory considerations and cultural attitudes to the growing strength of the European startup ecosystem. It's a balanced view that recognizes both the caution and the creativity emerging across the region.
This is ultimately a conversation about friction. What happens to an organization when coordination overhead disappears, when reporting builds itself, and when knowledge stays current without human intervention?
So as AI agents move from novelty to infrastructure, are businesses ready to redesign how work gets done, and what becomes possible when teams stop managing tasks and start compounding impact?