An Interview With Chad Silverstein
What I find most interesting is how LLMs and Generative AI has democratized aspects of expertise previously financially unattainable to most of the world.
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 Chad Vavra.
A design executive with twenty-plus years of experience, Chad has worked with Cisco, Pixar, Meta, Google, NBC Universal, the US Air Force, Samsung, and many more. He is the creator and cofounder of Chat Agency AI, a platform that leverages advanced AI to create conviction for entrepreneurial concepts by automating and streamlining the ideation-to-execution process.
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?
I have a degree in Fine Art, and I focused on computer-aided industries, such as video gaming. With that, I found work at start-ups and enterprises during the early 2000s. Being a deeply curious person and having a DIY ethos drove me to learn to code. This gave me the understanding and ability to communicate systems.
Being able to empathize in both creative and technical roles enabled me to lead User Experience teams at several agencies in New York. I had the privilege of working with incredible people and some of the biggest companies in the world as clients, which gave me a broad set of experiences to apply AI.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
What I find most interesting is how LLMs and Generative AI has democratized aspects of expertise previously financially unattainable to most of the world — whether it is technical and allows almost anyone to create and launch an application, or how LLMs distill vast amounts of information into advice — it was something that specialized consultants were previously required for and commonly cost six figures in the world of digital products.
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?
Curiosity: There are so many tools and new AI technologies coming out so quickly that it’s easy to be overwhelmed by options, shut down, and fall back on either OpenAI, Microsoft, or Anthropic. While those models are incredible, they are also limited by their competitiveness and closed systems. But when you combine them so they work together and add open-source models, you get results that stand out. We do this, and our customers tell us that they get better results than they can using a single model license.
Optimism: AI has already been hugely disruptive, and that disruption can lead to significant negativity. Some deserved, some not. One of the criticisms is how the information used to train models was obtained and whether its use is legal or fair to the original authors. In 1984, at the first Hackers Conference, Stewart Brand said, “Information wants to be free,” referring to the disruption caused by how cheap it was to copy, recombine, and distribute digital information. He also said “Information wants to be expensive” because creators still need to earn a living, and the value of the information to its recipient is high. He goes on to say, “That tension will not go away. It leads to endless wrenching debate about price, copyright, ‘intellectual property,’ the moral rightness of casual distribution, because each round of new devices makes the tension worse, not better.” Forty years later, through the birth of the web, e-commerce, social media, and the globalization of supply chains, we have managed to survive. Some better than others, but overall, humans have prospered. Now that AI is amplifying that tension more than ever, I still have to believe that we will continue to prosper. The genie is out of the bottle with AI, and I believe that it will be a gift that ultimately helps us create more good than bad.
Critical Thought: To paraphrase Maria Popova, “Optimism without critique is naiveté.” When I decided to build Chat Agency AI, I intentionally wanted humans in the loop to decide when each action was necessary. I did this for two critical reasons. First, my experience as a consultant told me that it’s important to understand how a system’s parts work together. If you let AI build a system for you, without any review or input, you won’t be very invested in it. All you’ll know is that someone else made many important decisions without you, and now you are responsible for them. For that reason, I ask users to initiate and review each step in the process. I consider this good user friction. Second, running queries uses processing power, which in turn uses energy. AI is already energy hungry. I felt that it was more ethical to minimize use to only what users want to run.
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?
Sure, I love this question because it humbles my soul. Our business was coded in partnership with Claude, before Claude Code was a product. We tested and found that Claude was proficient in React and PostgreSQL — more so than the other AI models in early 2024. The truth is, I had almost no experience with SQL, but I did have enough technical background to know what was needed to scale the product from prototype to production.
With intentional prompting, forums like Stack Overflow, and a lot of iterations, we ended up with a platform that a 3rd party technical assessment said “The architecture, code, and technical stack are high quality and scalable” and “The quality of code is very high, with good structure and consistent patterns”. This would not have happened without using AI as a trusted coder.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
I see a lot of people publicly launching ‘vibe’ created things. When I say ‘vibe’, I’m referring to tools with a single-input form where you can tell it what you want, sit back, and wait for a final product to be presented to you. The misconception is that the AI somehow predicted all the context needed to produce a product aligned with the underlying problem and the user expectations you are trying to solve. Chat Agency AI is meant to validate and build conviction for an idea, so that if you do choose to accelerate development with AI, you have the information you need to know if the product is really ready.
The other misconception is that AI is a dishonest way of creating deliverables, akin to paying someone to take a test for you, and that the result is less productive because your workforce isn’t learning for themselves. The first way I deal with this is to demonstrate that critique and cynicism aren’t the same thing. Critique is about improving things. Cynicism is about destroying them.
I find myself quoting Stephanie Tyler a lot regarding AI cynicism.
“You know what I think is actually inauthentic? Pretending that suffering through an inefficient creative process somehow makes your work more noble.”
If you are passing AI responses off as your own, that is inauthentic. But if you are in the loop and working with AI to do more with less time and resources, great.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
It can reduce or eliminate mundane tasks that don’t inspire your workforce.
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. Customer-first, data-driven decisions.
AI is great at keeping specific goals at the forefront of its answers. For instance, NPS isn’t just a number. There are also verbatim comments associated with each NPS score that commonly aren’t read or understood by the people using them. AI can consume information faster and keep more information in memory than an individual; it’s perfect for augmenting the understanding of unstructured data.
2. The number of meetings is inversely related to an employee’s satisfaction.
AI is great at summaries, action items, follow-up emails, etc. Don’t just turn on an AI notetaker and never look at the output. Make it part of the process and expect the same contribution from it as you would from anyone else.
3. QA testing.
OpenWork AI is an open-source alternative to Claude Work. It has a great feature that can automatically detect broken links on a website. (and you can watch it work) With a little bit of curiosity, I would expect anyone to be able to get it to do spelling and punctuation checks, and more. Consider this automated User Adherence Testing (UAT)
4. Social listening.
The biggest brands have social listening teams that constantly monitor the web, respond to customers and prospects, and protect their reputations. Every brand can now have that capability at minimal cost. A quick search will provide a number of ‘AI social listening tools’ to try for free.
5. Great ideas come from anyone.
AI can actually make that a reality. We’ve proven with www.chatagency.ai that AI can deliver top-tier consultant-quality solutions when working in tandem with a person.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
There is a saying in change management. “Work with the willing”. While it’s more obvious across enterprise silos, I think it applies everywhere. If you want to integrate AI, but need it to be grassroots, support the curious people who are championing it rather than trying to convince everyone all at once.
Give those champions guidelines, but don’t slow them down with red tape. Give them the time and space to share what they learn.
If you want to be prescriptive, you can suggest they start with open source to minimize costs. I began my exploration with:
- Ollama — a tool that allows you to run open source AI models on your own computer. No risk of secrets getting out.
- AnythingLLM — creates a local application with an interface similar to ChatGPT and integrates with Ollama
- HuggingFace.co — a website and community for AI models and tools. You can find open source models here and test community-created experiments.
- YouTube.com — there is endless free instruction on using all of the above.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
I suspect many business leaders are too young to remember that cloud computing was once considered crazy. Who in their right mind would risk putting their data on someone else’s computers? Now, you would be hard-pressed to find a company that doesn’t rely on AWS, Google Cloud, or Microsoft Azure in some way. I billed a lot of hours helping companies catch up with cloud computing in the last decade.
We’re at that same stage with AI. It feels crazy to put too much into it. Consider, though, one of the leading academic voices on AI, Ethan Mollick, describes AI as having a jagged edge. It is really sophisticated in some areas (like coding) and very rudimentary in other areas (try using AI to put a paragraph of text on an image).
Almost no matter what you do, AI can help you do it better. If you are a roofer, AI can help you create estimates more quickly and accurately. If you are a student, AI can build study guides and sample tests. If you are a laborer, AI can help determine if that pile of sand will fit in your truck. And yes, if you are a coder, AI can do almost (and the key is almost) as much coding as you can.
You need to know where the edge is, stay up to date on what’s working, and be aware of what’s improving, or you’re going to risk falling behind.
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?
I touched on data [NPS] earlier, but I think it will continue to shape things more than anyone realizes. Future advances in AI will rely on machine-generated data, as publicly available human-generated data has all but been exhausted.
Autonomous AI with a physical presence will generate volumes of data we can’t yet comprehend. We already have self-driving vehicles, full of sensors and cameras. Humanoid robots with AI capabilities are closer than ever and will be able to walk among us and capture memories (data).
Companies that embrace these new data sources and learn to integrate them into their existing data will have huge advantages. It’s actually possible to imagine AI solving its own energy problems. AI is already designing parts for the aerospace and automotive industries. I will be shocked if AI can’t come up with novel materials to create and further optimize those designs.
That said, access to this data will determine the pace of cross-industry innovation and will likely lead to an entirely new economy controlled by a few big corporations. [Similar to Google’s search data]
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
Right now, businesses are looking to AI to do more with fewer resources. Whether it’s people, time, or money, the hope is that AI will help to create new levels of abundance. On the surface, that sounds like a widening divide, with margins increasing and being distributed to fewer people. That would be the worst of abundance.
Personally, I don’t think it’s going to be that dystopian. I think that AI, especially open-source AI, has given customers, employees, and the broader community more power than anything in the last hundred years. Now, more people can solve problems in their own ways. They can start new businesses on their own and take on significantly less risk. An example of how we support this is the Co-Founder feature we built into chatagency.ai. It uses AI to play the roles of your cofounders, filling in gaps where you need advice.
I think more with less applies to everyone who is willing to try, and that is pretty exciting.
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. 🙂
I would want people to believe and demand that AI can provide healthcare to everyone as a basic human right. AI should refuse to turn anyone away from care or medicines that they need. Similar to Asimov’s first rule of robotics [A robot may not injure a human or, through inaction, allow a human to come to harm], AI should not be made to require payment for services that could cause harm through inaction.
How can our readers further follow you online?
We have a newsletter and blog at www.chatagency.ai. You can also find me on LinkedIn.
Thank you so much for the time you spent sharing with us.
Thank you. I really enjoyed the conversation.
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.
Chad Vavra of Chat Agency AI 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.