Tech Talks Daily

Perform 2026 felt like a turning point for Dynatrace, and when Steve Tack joined me for his fourth appearance on the show, it was clear this was not business as usual. 
We began with a little Perform nostalgia, from Dave Anderson's unforgettable "Full Stack Baby" moment to the debut of AI Rick on the keynote stage. But the humor quickly gave way to substance. Because beneath the spectacle, Dynatrace introduced something that signals a broader shift in observability: Dynatrace Intelligence.
Steve was candid about the problem they set out to solve. Too much focus on ingesting data. Too much time spent stitching tools together. Too many dashboards. Too many alerts. The real opportunity, he argued, is turning telemetry into trusted, automated action. And that means blending deterministic AI with agentic systems in a way enterprises can actually trust.
We unpacked what that looks like in practice. From United Airlines using a digital cockpit to improve operational performance, to TELUS and Vodafone demonstrating measurable ROI on stage, the emphasis at Perform was firmly on production outcomes rather than pilot projects. As Steve put it, the industry has spent long enough in "pilot purgatory." The next phase demands real-world deployment and real return.
A big part of that confidence comes from the foundations Dynatrace has laid with Grail and Smartscape. By combining unified telemetry in its data lakehouse with real-time topology mapping and causal AI, Dynatrace is positioning itself as the engine behind explainable, trustworthy automation. When hyperscaler agents from AWS, Azure, or Google Cloud call Dynatrace Intelligence, they are expected to receive answers grounded in causal context rather than probabilistic guesswork.
We also explored what this means for developers, who often carry the burden of alert fatigue and fragmented tooling. New integrations into VS Code, Slack, Atlassian, and ServiceNow aim to bring observability directly into the developer workflow. The goal is simple in theory and complex in execution: keep engineers in their flow, reduce toil, and amplify human decision-making rather than replace it.
Of course, autonomy raises questions about risk. Steve acknowledged that for now, humans remain firmly in the loop, with most agentic interactions still requiring checkpoints. But as trust grows, so will the willingness to let systems self-optimize, self-heal, and remediate issues automatically.
We closed by zooming out. In a market saturated with AI claims, Steve encouraged listeners to bet on change rather than cling to the status quo. There will be hype. There will be agent washing. But there is also real value emerging for those prepared to experiment, learn, and scale responsibly.
If you want to understand where AI observability is heading, and how deterministic and agentic intelligence can coexist inside enterprise operations, this episode offers a grounded, practical perspective straight from the Perform show floor.

What happens when the noise around AI starts to drown out the actual business value it is meant to deliver?
In this episode of Tech Talks Daily, I sat down with Adam Field, Chief AI and Product Officer at Tungsten Automation, fresh from the conversations unfolding at Davos.
While headlines continue to celebrate agentic AI and sweeping automation claims, Adam offered a grounded perspective shaped by decades of experience turning AI pilots into measurable, ROI-driven deployments. His view is simple. The hype cycle may be accelerating, but many organizations still struggle with the fundamentals.
Adam described a common boardroom dynamic. "What do we want? AI. What do we want it to do? We're not sure." That pressure to move fast often collides with a deeper reality. Software has shifted from deterministic to probabilistic. Leaders who grew up expecting the same inputs to always produce the same outputs now face systems that behave differently by design. Measuring value in that environment requires a different mindset.
One of the most compelling ideas in our conversation was Adam's concept of "boring AI." While splashy announcements about replacing hundreds of employees grab attention, he argues that real returns often come from quieter use cases. At Tungsten Automation, that means intelligent document processing, extracting trusted, AI-ready data from the 80 percent of enterprise information that is unstructured. Contracts, invoices, transcripts, compliance paperwork. The work may not trend on social media, but it saves time, improves accuracy, and fits directly into daily workflows.
We also explored accountability. AI can compress output, but it concentrates responsibility. When generative tools make architectural or compliance decisions, the liability does not shift to the model. Organizations remain accountable for privacy, ethics, and customer trust. Adam shared his own experience rebuilding a legacy application in days using AI code generation, only to discover licensing and compliance nuances that required human judgment. The lesson was clear. AI amplifies capability, yet human oversight remains essential.
For leaders searching for signals that an AI strategy will actually deliver long-term returns, Adam pointed to two patterns from the small percentage of projects that succeed. First, integration into daily workflows drives adoption. Second, partnering with trusted vendors often reduces risk compared to attempting everything in-house. In a world flooded with open-source experiments and "X is dead" headlines, discipline and focus still matter.
Tungsten Automation has spent four decades evolving alongside automation technologies, previously known as Kofax. Today, the company applies large language models and agentic workflows to transform unstructured data into decision-ready insights across finance, logistics, banking, and insurance. It is a reminder that the future of AI may be less about replacing people and more about removing friction so humans can do the work they were actually hired to do.
So as AI investment continues to grow and pressure for returns intensifies, the question becomes harder to ignore. Are we chasing the headlines, or are we building systems that quietly deliver value where it counts?
Useful Links
Connect with Adam Field
Learn more about Tungsten Automation
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How do you build a $30 million ARR business with just three people and a fleet of AI agents doing the heavy lifting?
In this episode of Tech Talks Daily, I connected with Amos Joseph, CEO of Swan AI.
From the moment we joked about AI notetakers silently observing our conversation, it was clear this discussion would go beyond surface-level automation talk. Amos is attempting something bold. He is building what he calls an autonomous business, one designed to scale with intelligence rather than headcount.
Amos has already built and exited two B2B startups using the traditional growth-at-all-costs model. Raise early, hire fast, expand the vision, chase valuation. This time, he is rewriting that script entirely. Swan AI is built around ARR per employee, human-AI collaboration, and what he describes as scaling employees rather than scaling the org chart. With more than 200 customers and only three founders, Swan is already testing whether AI agents can run real go-to-market operations autonomously.
We explored why over 90 percent of AI implementations fail and why grassroots experimentation consistently outperforms executive mandates. Amos argues that companies looking outward for AI solutions before understanding their internal bottlenecks are simply scaling chaos. The organizations that succeed start with process clarity, define what humans should do versus what should be automated, and then allow AI to execute within that structure. It is a powerful reminder that becoming AI-native has less to do with tools and more to do with operational self-awareness.
We also unpacked the difference between automation and agentic AI. Traditional automation follows deterministic steps coded in advance. Agentic AI shifts decision-making power to the model itself. The AI decides what to do next, introducing statistical reasoning rather than predefined logic. That shift in agency changes everything about how workflows operate and how leaders think about control.
Perhaps most fascinating is how Swan generates pipeline entirely through LinkedIn. No paid ads. No outbound. Amos has built an AI-driven engine that creates content, monitors engagement, qualifies prospects, and nurtures relationships at scale. It is an experiment in trust-based distribution powered by agents, not marketing budgets.
This conversation reframes what growth can look like in an AI-native world. If scaling no longer equals hiring, and if every employee becomes a manager of AI agents, what does leadership look like next? How do founders build organizations that amplify human zones of genius rather than bury them under coordination overhead?
If you are questioning long-held assumptions about team size, growth, and AI adoption, this episode will give you plenty to think about.

Is Bitcoin still just a digital store of value, or is it quietly evolving into the financial engine of a new on-chain economy?
In this episode of Tech Talks Daily, I sat down with Callan Sarre, Co-Founder of Threshold Labs, to explore what happens when the world's most recognized crypto asset stops sitting idle and starts becoming programmable capital. We recorded against the backdrop of a sharp market correction that wiped out value across crypto and traditional assets alike, making for a timely and honest conversation about volatility, maturity, and why Bitcoin's next chapter may be defined by utility rather than price speculation. 
Callan explains how the rise of ETFs and institutional flows is reshaping ownership, while decentralized infrastructure is working to ensure users can still access the asset's underlying power.
At the heart of our discussion is tBTC, a trust-minimized bridge that moves native Bitcoin into DeFi without handing control to centralized custodians. Callan breaks down how Threshold's decentralized custody model works in practice and why removing single points of failure matters in a post-FTX world. We also explore the behavioral barriers that have kept long-term holders from putting their BTC to work, the real risks behind Bitcoin yield strategies, and the infrastructure required to make these tools accessible to a broader audience through familiar Web2-style experiences.
The conversation also takes a global turn as we look at why Asia is accelerating Bitcoin innovation, how regulation is driving institutional adoption in Western markets, and what the shift from DAO-led governance to a lab execution model reveals about the realities of building at scale. 
Looking ahead five years, Callan paints a picture of an integrated on-chain financial system where Bitcoin can be borrowed against, deployed, and settled instantly across shared liquidity rails, while still preserving the principles that made it attractive in the first place.
So if Bitcoin becomes productive capital and the majority of financial activity moves on-chain, what does that mean for traditional finance, for long-term holders, and for the next wave of builders? And are we ready for a world where the most secure monetary asset also becomes the most composable?

What actually happens when AI stops being a cloud-only experiment and starts running on desks, in labs, and inside real teams trying to ship real work?
In this episode, I sit down with Logan Lawler, Senior Director at Dell Technologies, to unpack how AI workloads are really being built and supported on the ground today. Logan leads Dell's Precision and Pro Max AI Solutions business and hosts Dell's own Reshaping Workflows podcast, giving him a rare vantage point into how engineers, developers, creatives, and data teams are actually working, not how marketing slides suggest they should be.
We start by cutting through the noise around AI PCs. At every conference stage, Logan breaks down what genuinely matters when choosing hardware for AI work. CPUs, GPUs, NPUs, memory, and software stacks all play different roles, and misunderstanding those roles often leads teams to overspend or underspec. Logan explains why all AI workstations qualify as AI PCs, but not all AI PCs are suitable for serious AI work, and why GPUs remain central for anyone doing real model development, fine-tuning, or inference at scale.
From there, the conversation shifts to a broader architectural rethink. As AI workloads grow heavier and data sensitivity increases, many organizations are reconsidering where compute should live. Logan shares how GPU-powered Dell workstations, storage-rich environments, and hybrid cloud setups are giving teams more control over performance, cost, and data. We explore why local compute is becoming attractive again, how modern GPUs now rival small server setups, and why hybrid workflows, local for development and cloud for deployment, are becoming the default rather than the exception.
One of the most compelling parts of the discussion comes when Logan connects hardware choices back to business reality. Drawing on real-world examples, he explains how teams use local AI environments to move faster, reduce cloud costs, and avoid getting locked into architectures that are hard to unwind later. This is not about abandoning the cloud, but about being intentional from the start, mainly as AI usage spreads beyond developers into marketing, operations, and everyday business roles.
We also step back to reflect on a deeper challenge. As AI becomes easier to use, what happens to critical thinking, curiosity, and learning? Logan shares a candid perspective, shaped by his experiences as a parent, technologist, and podcast host, raising questions about how tools should support rather than replace thinking.
If you are trying to make sense of AI PCs, local versus cloud compute, or how teams are really reshaping workflows with AI hardware today, this conversation offers grounded insight from someone living at the center of it. Are we designing systems that genuinely empower people to think better and build faster, or are we sleepwalking into decisions we will regret later? How do you want your own AI workflow to evolve?
Useful Links
TLDR AI newsletter and the Neurons.
The Reshaping Workflows podcast
Connect with Logan Lawler
Follow Dell Technologies on LinkedIn