Ep. 9 - Are you building AI to fire me?

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. 

 

You’ll hear: 

 

• Why companies are investing billions into AI before provingreal businessvalue 
• How “productivity gains” are becoming justification for workforce cuts
• The hidden infrastructure and cloud costs powering enterprise AI
• Why AI hype is colliding with operational reality inside IT organizations
• What the future of work couldlooklike as automation accelerates 
• Why the biggest AI challenge may not be technology but trust

 

Because the future of work may not be what the AI evangelists promised. 

You are not alone. Let’s Solve IT! 

Episode keywords:  
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 

 

Learn More 

IT case studies | NetApp 

 

  

Connect with us!  

https://www.linkedin.com/in/cmattbrown 

Dave Blodgett | LinkedIn 

 

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? 

Topics covered: 

• Why AI should be treated as a force multiplier, not simply a workforce reduction tool 

• How AI can help IT teams shift attention from keeping the lights on to strategic, differentiating work 

• The limits of “vibe coding” and why engineering judgment, nuance, and expertise still matter 

• What makes this AI wave different from previous automation and cloud transformations 

• How autonomous NOC workflows, AI agents, event correlation, and root cause analysis can materially reduce time to resolution 

• Why AI adoption requires transparency, hands-on exposure, business-value metrics, and team trust 

• How leaders can help employees move from fear to fluency by making AI part of the engineering reflex 

Episode themes 

• AI as Augmentation, Not Replacement: The idea that AI will enhance and assist human workers, particularly skilled ones like engineers, rather than replace them, was a consistent theme throughout the interview [1:57] [12:55] [13:07] (1:21, 11:58).  

• Efficiency Driving Differentiation: Blodgett repeatedly connected the operational efficiencies gained from AI to the opportunity for teams to focus on higher-value, "differentiating work" that improves a company's competitive edge [2:28] [3:04] (1:21).  

• Transparency and Trust: The importance of leaders being transparent with their teams about AI initiatives to manage fear and foster trust was emphasized at both the beginning and end of the conversation [8:18] [11:58] (8:18, 11:58).  

• Adoption Through Exposure: The belief that practical, hands-on experience with AI tools is more critical for adoption and assimilation than formal training was a key theme [11:04] [11:12] (11:04, 11:12).  

Key takeaways  

• AI's primary purpose is to act as a "force multiplier" to increase efficiency, not to facilitate mass layoffs [1:57] (1:21). Blodgett argued that tech company layoffs were a correction for overhiring, with AI being used as a convenient narrative [1:21] (1:21).  

• Increased efficiency from AI will allow IT organizations to shift their focus from essential but non-differentiating work like maintenance and patching to strategic initiatives that make the company more competitive [2:48] [3:04] (1:21).  

• While some lower-skilled, repetitive roles may be reduced, engineering jobs are safe from wholesale replacement due to the complexity and need for nuance in their work [3:38] [4:14] (1:21).  

• Successful adoption of AI requires moving beyond abstract concepts to hands-on exposure, which helps build fluency and makes its use an "engineering reflex" [10:06] [11:28] (9:41, 11:12).  

• Leadership must operate with high disclosure and transparency regarding AI strategies to build team trust and mitigate fears of job displacement [8:18] [11:58] (8:18, 11:58).  

Context and background  

• Contextual Information  
  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].  

 

Interview flow  

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.  

Episode description 

How do leading IT organizations get real value from AI? 

Start by putting AI where the work is measurable, repetitive, and operationally constrained: 

• Development acceleration through tools like GitHub Copilot, Cursor, and Claude Code, especially for repetitive coding patterns, code generation, and code quality checks 

• Low-variability operational workflows, such as NOC services, where incidents can be detected, triaged, correlated, and enriched before human intervention 

• Observability and event correlation that help teams move faster from incident detection to root cause understanding 

• Measurable business outcomes, including reduced time to resolution, faster time to market, improved code quality, and better operational efficiency 

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: 

• Operate with high disclosure so employees understand the intent behind AI investments 

• Share concrete examples of what teams are building and where AI is producing value 

• Give people access to tools so they can experiment, tinker, and discover relevant use cases 

• Connect AI to real workflows, not abstract hype or generic training 

• Track business outcomes so investment can be tied to measurable improvements 

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: 

• Be transparent about AI strategy and acknowledge employee concerns directly 

• Focus first on use cases where AI can safely reduce operational friction and produce measurable outcomes 

• Give teams hands-on access to AI tools and examples so adoption becomes practical, not theoretical 

• Use AI to free engineers from repetitive work and redirect capacity toward competitive differentiation 

• Build guardrails for non-deterministic AI systems, especially where agents are making recommendations or taking action 

• Measure AI value through outcomes such as time to resolution, code quality, operational efficiency, and faster time to market 

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. 

Supporting evidence  

• To support the continued relevance of engineers, Blodgett cited his personal experimentation with "three or four different vibe coding platforms," where he observed that users without an engineering background "very quickly get in trouble" [3:58] (1:21).  

• He provided a concrete example of AI augmenting work by describing a hackathon project that produced an "autonomous Knock," where an AI agent triages incidents, performs root cause analysis, and enriches tickets before a human engineer intervenes, materially decreasing resolution times [8:33] [9:02] (8:18).  

• He pointed to existing tools like GitHub Copilot, Cursor, and ClaudeCode as real-world examples that "absolutely three, five, 10X engineers" by handling basic, repetitive coding tasks [6:36] [7:02] (6:36).  

• Blodgett used the historical analogy of the PC's introduction in the 1980s, noting it took 15 years for broad adoption because the surrounding ecosystem didn't exist [9:41]. He suggested AI faces a similar, though "dramatically compressed," adoption curve [9:57] [10:06] (9:41).  

 

Question Analysis  
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].  

Notable quotes: 

• “Ultimately AI is a force multiplier and we hear these terms 3X, 5X, 10X, 100X, so forth.” (1:57) — Dave uses this phrase to explain that AI can increase the output of engineers, but he cautions against the simplistic conclusion that more productivity automatically means fewer people. In IT, demand already exceeds supply, and AI can help teams finally get to the backlog of valuable work that has been pushed aside. 

• “The stuff that gets sacrificed is the differentiating work, the stuff that makes you as a company more competitive.” (3:04) — Said while describing how basic operational hygiene often consumes IT capacity. AI can create room for the strategic work that moves the business forward: better products, faster delivery, and more competitive capabilities. 

• “We have this whole universe of non-deterministic artificial intelligence where you input a bunch of things and you’re really not sure what you’re going to get.” (5:08) — Dave contrasts today’s AI with earlier rules-based automation. This new wave opens up enormous opportunity, but also requires clear guardrails so AI agents do not overstep, overdeliver, or create unintended consequences. 

• “First of all, we’ve been very transparent with the team.” (8:18) — Dave explains that leaders cannot “lurk in the shadows” when it comes to AI. Transparency, open communication, and active contribution from engineering teams are essential to reducing fear and building trust. 

• “So, we’re seeing our time to resolution metrics decrease materially.” (9:08) — This was said while describing an autonomous NOC workflow where an AI agent triages incidents, correlates events, performs root cause analysis, and enriches tickets before a human engineer takes action. The point is clear: AI value must be measurable. 

• “AI solution patterns have to become part of the engineering reflex.” (11:28) — Dave argues that adoption cannot remain an abstract concept or occasional experiment. Teams need exposure, access, examples, and practice until AI becomes a natural part of how they approach technical problems. 

• “I have not seen any indication that AI will out-innovate people. I can’t imagine that ever happening.” (12:45) — Dave closes the conversation by reinforcing that AI interpolates human-produced data. It can accelerate work, but it does not replace human nuance, judgment, creativity, or innovation. 

• “I think it is to increase human velocity, not supplant human innovation, human contributions.” (13:07) — This becomes the core takeaway of the episode: the best AI strategy is not about removing people from the equation. It is about helping people move faster, make better decisions, and focus on higher-value work. 

Follow-Up Questions:  

• You mentioned the need for "guardrails" to ensure AI agents don't become "rogue actors" [6:02]. What specific types of technical or ethical guardrails are you implementing for your AI systems?  

• You gave the example of the "autonomous Knock" reducing resolution times [8:33] [9:02]. Can you share another specific project where AI has been implemented and what the measurable business outcomes were?  

• You contrasted the slow adoption of the PC with the "dramatically compressed" timeline for AI [9:57] [10:06]. What are the biggest cultural or technical obstacles you see to this compressed adoption, and how is your organization addressing them?  

• You described the application of AI for creative work and ideation as a "big gray zone" [7:36]. In what ways is your team experimenting with or evaluating the use of AI for these less-defined, more creative tasks?  

• While you don't see engineering roles being supplanted, you acknowledged that lower-skilled functions might see a reduction [13:07]. What is your organization's strategy for reskilling or transitioning employees in those potentially affected roles?  

• You emphasized the importance of making AI an "engineering reflex" [11:28]. What specific metrics or qualitative indicators do you use to measure this cultural shift and the level of AI fluency within your teams?