AI was supposed to make work easier. So why are companies racing to cut people before the results even exist?
In this episode of Let’s Solve IT!, NetApp’s Matt Brown sits down with Dave Blodgett, VP, Global Head of Infrastructure, to unpack the uncomfortable truth behind AI hype, skyrocketing infrastructure costs, and the growing fear that “productivity” is becoming corporate code for layoffs.
Because the future of work may not be what the AI evangelists promised.
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AI infrastructure, enterprise AI, AI costs, future of work, workforce automation, cloud infrastructure, AI productivity, generative AI, AI strategy, IT operations, cloud operations, artificial intelligence, AI adoption, enterprise technology, AI investment, digital transformation, automation, infrastructure scaling, tech layoffs, AI and jobs, operational efficiency, CIO strategy, infrastructure management, NetApp, cloud computing, AI hype, business transformation, IT leadership, AI governance, productivity gains
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Transcript Episode overview:
Is AI being built to replace people—or to help IT teams move faster, work smarter, and focus on the problems that actually differentiate the business?
In this episode of Let’s Solve IT!, host Matt Brown sits down with Dave Blodgett, NetApp’s VP of Cloud Infrastructure and Operations, for a direct conversation about one of the biggest questions facing CIOs, CTOs, and IT leaders today: how do you harness AI without losing the trust, judgment, and innovation that only people bring?
If your organization is under pressure to deliver AI-driven productivity gains, this conversation reframes the issue. The real opportunity is not replacing people. It is using AI to unlock the work IT teams have been too constrained to do—work that improves operations, accelerates delivery, and helps the business compete.
At the center of the discussion is a practical leadership challenge: AI can increase human velocity, but only if teams understand the strategy, trust the intent, and have real access to the tools. Dave argues that AI is already delivering meaningful gains in areas such as software development, code quality, operational triage, and NOC services. But he is equally clear that complex engineering work still depends on human judgment, context, and innovation.
If your team is still asking whether AI is coming for their jobs, this conversation offers a better question: how can AI help people move faster, solve harder problems, and focus on work that humans are uniquely equipped to do?
The interview was framed by the current climate of public and employee anxiety surrounding AI-driven job displacement [8:05]. This context was explicitly established by the interviewer's reference to recent layoffs at the "magnificent seven" tech companies, who are also making massive investments in AI [0:42]. The conversation also acknowledged that while the concept of AI is old, dating back to 1953, the recent advancements have renewed these concerns [0:19].
Related Events
The primary related events referenced were the widespread layoffs in the tech industry, which some companies have linked to their AI investments [1:21]. Blodgett also mentioned an internal company hackathon as a specific event that spurred the creation of a valuable AI tool, the "autonomous Knock" [8:33].
Potential Impact
Blodgett's statements could have a reassuring effect on engineers and other IT professionals, reframing AI as a tool for empowerment and career enhancement rather than a threat [12:55]. His focus on using AI for competitive differentiation could influence business leaders to adopt a value-creation mindset for their AI strategies, rather than one purely focused on cost reduction [3:04]. Furthermore, his practical advice on fostering adoption through transparency and hands-on experimentation offers a tangible model for other managers and executives navigating the same challenges [11:58] [11:12].
The interview began with a direct, challenging question about whether AI is being built to fire people [1:16]. Dave Blodgett addressed this head-on, establishing a pragmatic and reassuring tone that he maintained throughout the conversation [1:21]. The discussion flowed logically from this central fear to the practical applications of AI in IT [6:36], leadership strategies for encouraging innovation and managing employee concerns [8:05], and finally to a broader philosophical view on AI's role in augmenting human ingenuity [12:45]. There were no significant shifts in Blodgett's calm and authoritative tone.
How do leading IT organizations get real value from AI?
Start by putting AI where the work is measurable, repetitive, and operationally constrained:
Dave gives a concrete example from his team: an autonomous NOC model where the observability fabric detects an incident, routes a ticket, and allows an AI agent to perform triage, correlate indicators, identify likely root cause, and recommend next steps. By the time the human engineer receives the ticket, the work has already been enriched with context. That is the difference between AI as a vague productivity promise and AI as an operational capability that can be measured.
But Dave is careful not to overstate what AI can do. He draws a clear line between automation that supports engineering work and the idea that AI can replace engineers outright. His own experimentation with vibe coding tools reinforced that technical complexity still requires engineering expertise. A non-engineer can generate a basic utility, but complex systems quickly demand architecture, reasoning, validation, and judgment.
That distinction matters for leaders. If AI is framed only as a cost-cutting mechanism, teams will resist it. If it is framed as a way to remove operational drag, accelerate learning, and create space for more meaningful work, teams are more likely to engage.
A major theme throughout the episode is trust.
Moving from AI fear to AI fluency requires leaders to:
Dave compares today’s AI adoption curve to the early days of the PC. The technology may be available, but broad adoption depends on fluency, tools, supporting frameworks, and a culture that knows how to use it. The difference is speed: what took years with the PC will happen much faster with AI.
Practical advice for IT leaders:
Ultimately, this episode reframes AI as a leadership and trust challenge as much as a technology challenge. The organizations that succeed will not be the ones that simply deploy the most AI. They will be the ones that help their teams understand it, use it, measure it, and apply it to the work that matters most.
The interviewer's questions were effective and well-structured. They started with the broad, fear-based premise common in public discourse about AI and progressively narrowed the focus to specific business expectations, leadership tactics, and real-world applications [1:16] [6:24] [8:05]. Questions like "How does that shape the way you lead your team today?" prompted Blodgett to draw insightful comparisons between past and present technology waves [4:31] [4:43]. Blodgett's responses were direct and substantive, often supported by specific examples from his own experience or his team's work, such as the "autonomous Knock" project, which added credibility and depth to his arguments [8:33].
