An Interview With Chad Silverstein
Coding Assistants as Democratizers. Coding assistants like GitHub Copilot and Cursor are transforming who can develop applications. Anyone can now quickly build custom solutions without specialized training. This democratization means businesses can rapidly author solutions for business processes without purchasing off-the-shelf products or hiring additional headcount.
In today’s tech-driven world, artificial intelligence has become a key enabler of business success. But the question remains — how can businesses effectively harness AI to address their unique challenges while staying true to ethical principles? To explore this topic further, we are interviewing Alex Thaman.
Alex Thaman is the Chief Technology Officer of Andesite. Over a 20+ year career, Alex has been an engineering leader at Microsoft, Unity Software, and Scale AI. At Microsoft, Alex worked on compiler technologies before transitioning to AI, helping to develop Xbox Kinect, Hololens, and Microsoft’s Speech platform. As Chief Architect and Manager for Computer Vision at Unity Software, he developed and led an engineering and product team that worked to simplify the creation of synthetic data to train and test computer vision models. Alex holds a BS with a double major in Computer Science and Math from Purdue University.
Thank you so much for joining us in this interview series. Before we dive into our discussion, our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path in AI?
My journey into AI wasn’t exactly a straight line, which is probably true for many people in this field. It began when I took a 500-level classical AI course at Purdue. There was something captivating about teaching machines to solve problems that typically required human intelligence.
My early professional years were at Microsoft, where I worked on Visual Studio, focusing on compiler technology. I then branched out to work on the user interface and other aspects of the product. It was technically challenging, but I gradually realized I was more drawn to the human-computer interaction side of technology and how people and machines communicate and work together. This led me to Xbox Kinect, which was an exciting opportunity to work on a team pioneering consumer computer vision and speech and gesture recognition at a time when the technology wasn’t widely available in the consumer space.
From there, I transitioned to developing speech interfaces for Hololens and other devices across Microsoft. This deepened my experience working on training data for LLMs and in user experience design, understanding how to create AI systems that feel natural and responsive to users.
Through these experiences, I discovered my sweet spot was an AI leader building products that leverage AI, which requires understanding machine learning, product strategy, and data engineering. Every day brings new challenges that require both technical knowledge and a clear vision of how technology can serve real human needs.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
Working on data collection for AI systems, you often deal with collection strategies and policies around sensitive data.The most memorable story involved a bug in our audio collection system for voice activation models.
We had this system where our internal testing devices collected audio data to improve our voice activation accuracy. When devices weren’t being used, the display would shut off and the audio collection was supposed to stop at the same time. However, there was a bug where the audio collection kept running for a few minutes longer.
We only found this when someone on my team was evaluating audio samples and heard things they absolutely shouldn’t have: a conversation with someone from HR and a leadership team member discussing performance feedback about my own manager! Talk about an awkward discovery.
Thankfully, this was only on our internal testing devices and never made it to production. We implemented much more rigorous safeguards after that, including visual indicators when any recording was happening and multiple redundant checks for collection shutoff.
You are a successful leader in the AI space. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
First, I’m always learning. I’m not a researcher by trade, but I’ve spent countless nights and weekends poring over research papers. This has given me an intuition about AI that you just can’t get from summaries or podcasts. It also helps me spot opportunities others miss and builds credibility when I’m working with technical teams. We’ve embedded a culture of continuous learning at Andesite with weekly research paper discussion sessions.
Secondly, I’ve always balanced theory with practical application. Understanding how AI works conceptually is one thing, but applying it to solve real problems within real-world constraints is totally different. I remember during the Xbox Kinect days, we built this slider UI control you could grab in mid-air. The gesture recognition actually worked pretty well technically, but users hated it because grabbing something without any tactile feedback just felt wrong. We wasted months optimizing a hand-open-versus-closed detector when that wasn’t even what we needed, not to mention we’d blown past our compute budget. That painful lesson taught me more than years of successful projects.
Additionally, I maintain a level of healthy skepticism. It’s easy to put together a flashy demo or talk track for an AI product, but creating something people actually love using daily is incredibly hard. I start from the assumption that what we’ve built probably doesn’t work well enough yet. At Unity, instead of spending years in stealth mode, we released rough open-source tools early and saw how customers would use them in order to get real feedback. Yes, it was uncomfortable showing early versions, but it forced us to confront reality instead of “drinking our own Kool-Aid.” That approach kept us honest about how we stood compared to competitors and prevented us from celebrating too early based on internal metrics that didn’t matter to users.
Let’s jump to the primary focus of our interview. Can you share a specific example of how you or your organization used AI to solve a major business challenge? What was the problem, and how did AI help address it?
We’re currently solving one of the most meaningful challenges I’ve encountered in my career: building a product that scales the efficiency, and therefore effectiveness, of cyber Security Operations Centers (SOCs) by up to 100x. Today’s security teams waste enormous time switching between tools, face difficulties sharing tribal knowledge, and cannot accurately assess organizational risk, putting defenders at a disadvantage against attackers.
Our solution uses agentic AI to reason across complex data landscapes. When a security bulletin arrives, our system can search internal and third-party data, analyze findings, and produce actionable results within minutes. This process typically takes elite teams hours to complete manually.
A human analyst still reviews and refines the findings, but the heavy lifting is automated. We’re essentially making every security analyst dramatically more effective while making the digital world safer.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
The most common misconception I encounter is fear of catastrophic AI failures. The “What if your AI misses a major security threat and reports everything’s fine?”
I address these objections reminding people they already face this problem. Humans make plenty of mistakes too. Then I focus on reality. These worst-case scenarios are relatively rare.
Building trust is crucial, which is why I emphasize appropriate human oversight. The most effective approach is human+AI collaboration, leveraging the strengths of both. This hybrid model addresses most concerns while delivering substantial benefits that neither humans nor AI could achieve independently.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
I believe AI’s greatest business potential is in upskilling and training. The most visible example is how it’s democratizing coding ability. We’re seeing a wave of applications developed by people who don’t have traditional software backgrounds. Experienced developers can now deliver complex projects with much smaller teams, sometimes even solo.
This pattern will extend across all industries. When specialized knowledge becomes more accessible through AI assistance, companies can operate more efficiently and innovate faster with their existing workforce. The productivity multiplier effect is substantial and immediate.
Ok, let’s dive deeper. Based on your experience and research, can you please share “5 Ways AI Can Solve Complex Business Problems”? These can be strategies, insights, or tools that companies can use to make the most of AI in addressing their challenges. If possible, please share examples or stories for each.
1. Coding Assistants as Democratizers. Coding assistants like GitHub Copilot and Cursor are transforming who can develop applications. Anyone can now quickly build custom solutions without specialized training. This democratization means businesses can rapidly author solutions for business processes without purchasing off-the-shelf products or hiring additional headcount.
2. AI Chatbots and Browsers as Productivity Multipliers. The business value of tools like Claude, ChatGPT and Perplexity is primarily speed. It’s not always easy to map foundational technology or software to business outcomes. recently drafted a job description from three bullet points with only three minutes of editing, which previously took 30+ minutes. This 10x productivity gain allows your workforce to focus on more complex challenges that truly need human insight.
3. Strategic Commitment to Integration. Effectively integrating AI into products or business processes requires persistence. The most optimal implementation rarely happens immediately. Organizations need to commit to a learning curve, allowing teams time to experiment, adjust approaches, and discover where AI delivers maximum value.
4. Data Expertise as Foundation. AI’s effectiveness ultimately depends on data quality and accessibility. For specialized use cases like risk assessment, the system needs to understand how your organization conceptualizes risk and other contextual factors. Having someone who understands data structures remains essential despite advances in AI capabilities.
5. AI Co-pilots as Force Multipliers. AI co-pilots represent the next evolution in human-machine collaboration. Rather than just executing commands, these systems actively participate in problem-solving. In customer service, for example, co-pilots can simultaneously pull relevant account information, suggest resolution paths, and draft responses while the human agent maintains the relationship. This partnership approach preserves human judgment while dramatically expanding capability.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
Small businesses and startups are actually ideally positioned to integrate AI precisely because of their limited budgets. When resources are tight, you’re constantly hunting for ways to be faster and more efficient. This is the perfect mindset for AI adoption.
The nimbleness of smaller organizations is also a huge advantage. You can change processes quickly without navigating layers of approval or legacy systems. Highly-capitalized companies tend to resist change, while smaller operations can pivot rapidly.
If you only have one person handling sales, you can transform your entire sales operation by bringing AI to just that individual. Start there, prove the value, and then expand to other areas. The initial investment might feel relatively significant, but the cost savings or realized revenue increase will quickly fund additional AI initiatives in other departments.
Focus on high-impact, low-complexity use cases first. Build confidence and competence before tackling more ambitious projects.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
Start simple. Pick one small problem that’s particularly expensive or tedious and solve just that first. It might be drafting a product strategy, developing a hiring plan, or writing job descriptions. It should be concrete with clear value. Expect some rough edges initially, but stick with it. Identify the evangelists in your team and let them lead by example. As people see real results, they’ll develop intuition for where AI can help.
Your organization will naturally learn how to apply this technology to different use cases and cross-train their teams. Eventually, people will want to integrate it more deeply into workflows, either building solutions themselves or finding products that already do what they need.
This technology spreads best organically and iteratively, not through top-down mandates. Small wins build confidence and spark creativity far better than grand blue-ribbon AI initiatives.
In your opinion, how will AI continue to shape the business world over the next 5–10 years? Are there any trends or emerging innovations you’re particularly excited about?
There’s so much industry buzz around AI agents right now, without a clear consensus on definition. For me, it’s about AI that can perceive, reason, AND take action. That action is what will unlock transformational business value. Think about when computer software first appeared. The step-change came from machines accepting human-written programs and executing them repeatedly. With AI agents, we’re seeing a similar shift, but those “programs” won’t require specialized skills to create.
In many cases, your AI “program” might just be a brief description of what you want or a simple demonstration. This represents a dramatic leap in our ability to scale and rapidly adapt business processes to our fast-changing world. The democratization of process automation through natural interfaces will transform organizations in ways we’re only beginning to understand.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
I think we’re in for some bumpy roads as AI rapidly expands across businesses. We can learn a lot by looking at previous tech transformations. Take chatbots. When they first appeared, companies raced to implement them without considering the user experience. They made customer support worse for most people. It took years to iron out the problems, and I rarely met anyone who preferred those early bots over talking to a real person.
We’ll see similar frustrations from employees as new AI tools roll out across organizations. People tend to resist unfamiliar technology that disrupts their established workflows. We’ve seen this pattern with countless IT implementations.
That said, the short-term pain will likely be worth the long-term gain. These systems naturally improve over time, and humans are pretty adaptable once they see tangible benefits. As AI becomes more refined and people develop new habits and expectations around working with these tools, we’ll reach a better equilibrium.
The key is managing expectations during the transition and designing AI implementations with genuine human needs in mind, rather than just chasing the technology for its own sake.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people through AI, what would that be? You never know what your idea can trigger. 🙂
For me, it all comes back to using AI to support and empower humans rather than replace them. I believe AI has incredible potential, if used right, to level the playing field across the world and improve the lives of hundreds of millions of people.
This is personal for me. Our family sponsors four children in countries where access to quality education is extremely limited. Some of these kids are as young as 8 and already working to help support their families, which obviously cuts into any time for learning. It breaks your heart to see that cycle perpetuating itself. I can see AI that delivers truly personalized education accessible anytime, anywhere. Not just static content, but adaptive teaching that understands each child’s strengths, weaknesses, and learning style. Because it’s personalized, it’s more time-efficient.
Imagine a child being able to access a world-class education through a smartphone, learning at their own pace, in their own language, with content that connects to their lived experience. The system adjusts to different literacy levels, incorporates local cultural nuances, and provides immediate feedback without judgment.
This could completely transform opportunities for millions of children and adults. We could see a whole new generation of entrepreneurs emerging from places that have historically been left behind. People with brilliant ideas but no access to conventional education could build skills, start businesses, and create solutions for problems we’ve never even considered.
That’s the potential catalytic impact of AI I want to spark: using AI to break down barriers and unleashing human potential that’s currently going untapped. Technology can, and should, be a great equalizer.
How can our readers further follow you online?
Find me on LinkedIn, where I post regularly about AI and cyber topics: https://www.linkedin.com/in/alex-thaman-93436659/
Stay tuned for future articles on Medium and Substack!
This was great. Thank you so much for the time you spent sharing with us.
About the Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead, advises Authority Magazine’s Thought Leader Incubator.
Alex Thaman of Andesite On How Artificial Intelligence Can Solve Business Problems was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.