The TechEd Podcast

Nikki Gonzales has built a career at the intersection of industrial automation, software, and systems thinking, and in this episode, she makes the case that the next chapter of manufacturing won’t be defined by AI alone. It will be defined by how well people understand process, data, machines, and the interfaces that connect them. The future of automation is as much about human judgment and lifelong learning as it is about smarter technology. 
A big part of that story runs through the human-machine interface. The HMI has evolved from a control screen into a communication layer between machines, operators, plant systems, and increasingly, AI-enabled tools. The conversation explores how open standards, AI assistants, scripting support, and emerging protocols like MCP could expand what industrial systems can do, while also lowering the barrier for more people to work with them. 
But the episode is not a story about technology replacing expertise. We also discuss technology raising the premium on real understanding. Gonzales argues that even as AI becomes more capable, foundational knowledge of physics, process, controls, and manufacturing systems still matters. She also makes the case that careers in this space are built not just through technical skill, but through curiosity, relationships, mentorship, and the willingness to keep learning.
In this episode:
How NVIDIA’s Inception program is helping a 30-year-old HMI company innovate like a startup
Why HMIs are a great starting point for applied AI projects
What MCP can make possible in industrial automation that a standard API connection cannot
How AI could lower the barrier to entry in automation while raising the bar for process knowledge
Will the future of skills be more specialized, or more generalized?
3 Big Takeaways:
The HMI may be one of the best places to start with applied AI in manufacturing. The HMI already sits at the intersection of the machine, the PLC, plant systems, and operator decision-making, which makes it a natural place to aggregate data and connect AI tools. In that sense, the future of applied AI in manufacturing is about smarter interfaces that can translate, contextualize, and move information where it needs to go. 
AI will make automation more accessible, but not less demanding. Nikki argues that AI can reduce the barrier to entry by helping newer users with scripting, debugging, and development workflows, especially on the HMI side. But she is equally clear that these tools raise the premium on people who understand process, physics, controls, and how manufacturing systems actually work, because the consequences of getting it wrong are too high. 
The future automation workforce will be built as much through community as through technology. Through Automation Ladies and OT SCADA CON, Nikki makes the case that technical careers are shaped not only by tools and training, but also by mentorship, relationships, and exposure to the full range of roles in the industry.
Resources in this Episode:
Connect with Nikki on LinkedIn
Learn more about Automation Ladies
More links & resources: https://teche
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What skills actually matter in technical careers now that the work is more digital, more automated, and more interconnected?
Industrial employers are not asking schools to choose between hard skills and soft skills. They're asking for both, and they still need the hard skills to come first. At Bosch, Justin Allen sees that every day: teamwork, drive, and professionalism matter, but technical problems don't get solved unless people understand the systems, tools, and engineering underneath them.
In this episode:
The hard skills vs. soft skills debate: soft skills matter, but technical work can't get done without hard skills
Are digital skills now a soft skill?
What employers really mean when they say they want drive, work ethic, and teamwork
How MAGMA has figured out how to successfully re-skill the current workforce
How technical careers are shifting from narrow expertise to systems thinking
3 Big Takeaways from this Episode:
1. Industrial employers still expect hard skills for all technical positions, not just "soft" or "employability" skills. Justin says it directly: while companies like Bosch value teamwork, drive, and professionalism, technical problems don't get solved unless people understand the systems, tools, and engineering behind the work. It's an important distinction for schools that hear employers talk about soft skills and assume the technical bar has somehow been lowered.
2. Digital fluency is moving from specialized skill to baseline expectation. Justin argues that younger workers are already showing up comfortable with digital tools, automation, scripts, and AI, while many employers are still adjusting to how fast that shift is happening. In technical roles, that means software awareness and digitalization are becoming part of the expected skill stack.
3. Schools and workforce programs need tighter alignment with industry's talent and skill needs. Justin shares how he's working directly with universities to help shape curriculum, evaluate where students are still missing key competencies, and bringing real engineering problems into capstone projects so learning stays connected to actual technical work. He also points to MAGMA and Michigan’s workforce ecosystem as examples of how employers, public partners, and training providers can help incumbent workers build new skills, retrain for technical roles, and stay aligned with what industry needs now.
Resources in this Episode:
Learn more about Bosch: https://www.bosch.us/
Learn more about MAGMA: https://miautomobility.org/
More links & resources on the episode page: https://techedpodcast.com/allen
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Imagine being able to track your brain health before serious decline sets in, understand your personal risk factors, and intervene early enough to help prevent dementia rather than simply react to it.
In this episode of The TechEd Podcast, Matt Kirchner talks with Christin Glorioso, MD, PhD, founder and CEO of NeuroAge Therapeutics, about one of the most intriguing frontiers in health and longevity right now: whether brain aging can actually be measured, influenced, and in some cases pushed back. If you’ve paid attention to the rise of biohacking, brain games, cognitive optimization, or the broader longevity movement, this conversation gets underneath the trend and into the science.
Glorioso explains how brain MRI, cognitive testing, blood biomarkers, genetics, and AI can be used together to create a more individualized view of brain health. More broadly, the conversation shows how brain health is starting to shift from late-stage treatment toward earlier measurement, prevention, and ongoing optimization.
In this episode:
Why one of the biggest assumptions in dementia research may have been wrong for years  
The 65% number that could completely change how you think about Alzheimer’s risk
Why brain aging starts earlier than almost anyone realizes, and what that means for prevention  
Whether you can actually make your brain younger, not just protect it from decline
The 9 daily factors that may matter more than any miracle supplement or trend  
How AI is enabling faster research and more personalized intervention
A roadmap for accessible tests + personalized recommendations for a healthier brain
3 Big Takeaways from this Episode:
1. Dementia is not one disease, and treating it like one has held the field back. Glorioso argues that Alzheimer’s has been approached too narrowly for too long, with the pharmaceutical industry spending decades focused almost entirely on amyloid. Her point is bigger than one protein: brain decline is multifactorial, which means future progress will depend on earlier diagnosis and more personalized intervention.  
2. Brain aging starts earlier than most people realize, but it is not a fixed downhill slide. In the episode, Glorioso says measurable shrinkage in the hippocampus begins in the mid-20s and accelerates with age. She also points to evidence that targeted exercise can increase volume in that same brain region, making the conversation far more hopeful than most people expect.  
3. You're more in control of your brain health than you may realize. Glorioso cites research suggesting that up to 65% of Alzheimer’s cases may be preventable through lifestyle interventions, then grounds that claim in concrete areas like exercise, sleep, stress, diet, metabolic health, and social connection. That turns brain health from an abstract fear into something people can measure, manage, and improve over time.
Resources in this Episode:
NeuroAge Therapeutics
Follow Christin on Substack
Visit the episode page for the rest of the resources: https://techedpodcast.com/glorioso/
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Artificial intelligence is moving from novel feature to core infrastructure, and that shift is forcing companies, schools and regulators to confront a harder question than how to use the technology: how to govern it.
In this episode of The TechEd Podcast, Matt Kirchner talks with Patrick Sullivan, Vice President of Strategy & Innovation at A-LIGN, about the emerging rules of the AI economy. From the EU AI Act and a patchwork of state-level regulation in the U.S. to new standards like ISO 42001, Sullivan explains why AI is beginning to look less like a software feature and more like a system that carries real operational, security and compliance risk.
The conversation also gets into the practical tension leaders are facing now. How do you innovate without creating blind spots? How do you use AI to improve decisions without mishandling student data, exposing customers or introducing risk you do not fully understand? Sullivan’s argument is that done right, governance is not the brake pedal. It is the structure that allows organizations to move faster without losing control.
In this episode:
Why AI products are starting to face the kind of scrutiny manufacturers already know well
What the EU AI Act reveals about where regulation can quickly become burdensome
Why adding AI without a clear value proposition is becoming an expensive mistake
All about new ISO standards for AI
How bias, de-identification, and prompt injection are reshaping AI risk
3 Big Takeaways from this Episode:
1. AI is forcing leaders to think about products, risk, and compliance in a new way. Patrick draws a sharp distinction between how the U.S. often treats AI as software and how the EU increasingly treats AI as part of a broader product, including embedded systems like medical devices. That shift matters because it changes how organizations think about safety, conformity, and responsibility before a product ever reaches the market. 
2. The AI race is producing a lot of motion, but not always much value. Many organizations are adding AI because the market expects it, not because the business case is strong. One MIT study suggests only a small share of enterprises surveyed were realizing meaningful ROI. Leaders need to ask whether the technology creates real value or simply creates new cost, risk, and complexity. 
3. Good governance is not a brake on innovation; it's what makes innovation durable. Patrick’s most effective metaphor is the football field: the lines are not there to punish you, but to show where you can move fast and where you are out of bounds. That idea comes through again when he discusses ISO management systems, lifecycle thinking, investor expectations, and enterprise buyers who increasingly want proof that AI is being developed and used responsibly.
Resources in this Episode:
Follow Patrick on LinkedIn
ISO 42001 - AI Management Systems
More links & resources on the episode page: https://techedpodcast.com/sullivan/
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Are employers hurting themselves by only asking for 'soft skills' and ignoring their real technical needs? How can homeschool students get the same access to STEM labs as students in traditional schools? And what can education leaders learn from the way the Manhattan Project mobilized talent and innovation to solve an enormous problem?
These questions (and more!) came directly from the you, and we're answering them on this episode of Ask Us Anything. Entrepreneurship, career strategy, workforce skills, and the rapidly evolving role of AI in modern organizations - we cover them all!
In this episode:
The best times to take entrepreneurial risks
Why your professional network is probably bigger than you think
The "soft skills" issue and why employers are actually hurting themselves by asking educators to teach them
Rapid-fire real examples of AI being used in business
3 Big Takeaways from this Episode:
1.Early-career risk can be an advantage for entrepreneurs. Matt explains that early in your career the consequences of failure are often much smaller, which makes it an ideal time to experiment with starting a business or pursuing a bold opportunity. With fewer financial obligations and more flexibility, young professionals often have the greatest ability to take meaningful risks.
2. Industry is hurting itself by only asking schools for soft skills instead of technical ones. Businesses frequently tell educators they want graduates who communicate well, collaborate, and show initiative. So why is industry so shocked that there aren't enough students with any technical background or interest? Employers: take a look at your job postings and start asking education to teach all the skills for those jobs: soft and technical (hard) skills alike.
3. Private schools have a unique opportunity to innovate in STEM education. Because they often have more flexibility than traditional public systems, private schools can move quickly to adopt emerging technologies, modern equipment, and new instructional models. That freedom creates an opportunity to design programs that expose students to advanced STEM fields earlier and more creatively.
Resources in this Episode:
Jack Dorsey’s Block to Lay Off 40% of Its Workforce in AI Remake
Oppenheimer movie
Developing an AI Strategy: Best Practices for Business Leaders - Todd Wanek, CEO of Ashley Furniture Industries
Using AI to Build Better Relationships with Your Network - Canay Deniz, CEO of Ren
More notes & resources on the episode page!
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