What's Up with Tech?

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Speed used to be the headline for broadband. Now the real question is tougher: did your internet actually feel reliable all day, on every device, in every room? We talk with Janelle Morse from SCTE, an industry insider helping shape Tech Expo 26 in Atlanta, about why the broadband world is pivoting toward end-to-end customer experience and what that means for operators, engineers, and anyone building modern networks.

We break down the event’s theme, “convergence powered, experience protected,” and why convergence is no longer a buzzword. Fiber, HFC, DOCSIS, Wi-Fi, wireless broadband, and even satellite increasingly have to operate as one coordinated system. That pushes the industry toward cloud-native architectures, more virtualization, and more intelligence at the edge, because the network boundary now extends into the connected home where “my Wi-Fi is my internet” becomes the customer’s reality.

AI is the accelerant. Instead of vague promises, we get into concrete use cases like network diagnostics, predictive service assurance, fault detection, and optimization, plus the next step: autonomous networks and self-healing operations that can sense issues, predict failures, and take action before customers ever notice. We also talk about how Tech Expo is structured, what to prioritize when you attend, why the expo floor matters, and how workforce skills and upskilling fit into an AI-driven future.

If you care about broadband innovation, network reliability, and what’s next for customer experience, hit play, share it with a colleague, and subscribe for more. If you’ve got a strong opinion on fiber vs DOCSIS or where AI should help first, leave a review and tell us what you’re seeing.
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100G Ethernet quietly crossed a line from “carrier-only” to “normal enterprise,” and the operational fallout is real: bigger links mean bigger outages, higher stakes, and far less room for sloppy assumptions. We sit down with NetAlly CEO Jeff O’Mara and Product Lead Thomas Doumas to break down why 100G networking is showing up in enterprise environments now, how AI-driven demand is accelerating the bandwidth curve, and what network teams need to do differently when the speed jumps beyond 10G.

We get specific about what actually gets harder at 25G, 40G, and 100G. Auto-negotiation habits that feel natural at lower speeds often do not apply, so both ends of the link can require careful hard-coded settings, with many possible port modes and only a few that will work. Add the “fussy” world of fiber optics at 100G, where connector cleanliness and tiny physical issues can become real failures, and you can see why misconfiguration and optical problems dominate modern troubleshooting.

We also unpack NetAlly’s approach to AI-assisted troubleshooting through what they call Ally Intelligence: guided, field-tested workflows that turn broad complaints into checkable root causes, without relying on generic chat outputs. Then we talk economics and accountability, including how to validate that a 100G service or upgrade is performing to spec, reduce guesswork, and shorten “time to innocence” when the network gets blamed. We close with the upgrade reality of old and new running together, classic landmines like spanning tree, and what comes next as the industry heads toward 400G.

If you want practical, enterprise-focused guidance on testing, troubleshooting, and validating high-speed networks, hit subscribe, share this with your network team, and leave a review. What’s the hardest part of supporting 100G where you work?
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Your AI budget isn’t blowing up because someone asked too many questions. It’s blowing up because AI agents turn one request into a chain reaction of inference calls, and the infrastructure underneath can’t deliver fast tokens efficiently at scale. We sit down with Vasanth Mohan from SambaNova to get specific about what changes when AI moves from “chat” to “do,” and why enterprises suddenly feel latency, throughput, and cost as operational constraints.

We break down the difference between training and AI inference, then zoom into the agentic reality: swarms of agents, model tiering, and the constant balancing act between tokens per second per request and tokens per second per chip. You’ll hear how model size drives memory pressure, why batching affects your bill, and how to decide when “fast enough” is actually costing you productivity, especially for coding workflows and other tight feedback loops.

Then we go hardware-deep in plain language. Vasant explains SambaNova’s reconfigurable dataflow unit (RDU), why mapping dataflow graphs to silicon can change performance, and how disaggregated inference splits prefill from decode. We also talk about GPUs working alongside other chips, extending the life of existing deployments, and why energy efficiency and air-cooled data centers matter for sovereign AI and global rollouts. We close with a reality check on benchmarks, “benchmaxing,” and why private evals and testing on your own workloads are the only trustworthy path.

If you care about enterprise AI, AI agents, inference optimization, and the real economics of tokens, hit subscribe, share this with a builder on your team, and leave a review with the toughest inference problem you’re trying to solve.
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Enterprise tech is having a weird moment: innovation is real, but so is the noise. AI shows up in every pitch, “leading” is used in every sentence, and buyers who actually have budgets and deadlines are stuck asking the same question: what’s genuinely worth my time? I’m joined by Rose Ross, founder and chief Trailblazer at the Tech Trailblazers Awards, to talk about how credible startup awards can act as a practical filter for enterprise IT leaders and a meaningful growth lever for founders.

We dig into what makes an enterprise tech awards program valuable instead of performative: clear criteria, a respected panel of judges, and an emphasis on proof over polish. Rose shares how the Tech Trailblazers Awards grew into its 15th edition across categories like cybersecurity, networking, telecoms, containers, sustainability, AI in a B2B context, plus newer frontiers like robotics and quantum computing. We also talk about why customer impact matters more than “sexiness,” and how buyers can use awards as a shortlist without outsourcing their judgment.

If you’re a founder or operator, we get very specific on the practical side: who can enter (nine years or younger and not beyond Series C), the Firestarter subset (three years or younger with no Series A), how to think about the entry like an investor pitch deck, and what judges hate most. The simplest advice wins: answer the question, drop the vague buzzwords, and back every claim with evidence.

Subscribe for more founder and enterprise tech conversations, share this with a builder or buyer who needs a better filter, and leave a review with the best buzzword you never want to hear again.
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Frontier AI is changing the rules of security, and enterprises feel it first. From the floor at VMware Explore, we sit down with Purnima Padmanabhan
Vice President and General Manager, Tanzu Division, to unpack what customers are asking for as AI agents move into real systems and real data. The headline: private AI is getting a serious bump, not as a trend, but as a control strategy for regulated industries and anyone worried about risk, governance, and cost. 

We walk through the “threat side” of AI, where advanced models can amplify attacks and push open source security to a breaking point. Purima explains why so many enterprise applications depend on open source, especially Spring and Java, and what happens when vulnerability reports spike. We also dig into TrueSource by Broadcom, the idea of a secure software supply chain for curated open source packages, libraries, and images that enterprises can trust and operationalize. 

Then we shift to the “opportunity side”: moving beyond chat and RAG into workflow automation with enterprise AI agents. That raises hard questions about sandboxing, limiting autonomy, and keeping agents secure. Just as important, we talk about why data projects slow everything down, and why governed access to the right data is what makes agents useful at scale. 

Finally, we get practical about developer experience and SDLC automation, including Purima’s example of running a swarm of 21+ agents across planning, Jira creation, coding, and testing, plus the caution that not all AI-generated code is equal. If you’re building on VMware Tanzu, thinking about private cloud AI, or trying to balance security, sovereignty, and TCO, this is for you. Subscribe, share with a builder on your team, and leave a review. What would make you trust an AI agent in production?
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