What happens when the technology helping you build your business also threatens the way customers discover it?
That question is becoming increasingly important for entrepreneurs as artificial intelligence changes software development, content creation, search, marketing, and online discovery.
In Part 2 of our conversation with Amit Zandberg, Rob Broadhead and Michael Meloche explore what it means to build trust in the AI era while creating a review platform for online mentors and high-ticket educational programs. The discussion moves beyond using AI as a productivity tool and examines a larger challenge: AI can simultaneously be a business tool, a competitor, and a new distribution channel.
For Amit, adapting to that reality means thinking beyond traditional software features and search rankings. The long-term opportunity may be to build something both people and AI systems recognize as a trusted source of information.
About Amit Zandberg
Amit Zandberg is an entrepreneur focused on improving trust and transparency in online education. His work centers on helping people make better-informed decisions about online mentors, content creators, courses, coaching programs, and other high-ticket educational products.
Through his review-focused platform, Amit is working to create a more reliable source of information for prospective students while giving quality creators an opportunity to establish credibility through meaningful learner feedback. His approach combines detailed reviews, structured ratings, AI-assisted analysis, and validation mechanisms designed to make manipulation more difficult.
Amit’s work sits at the intersection of online education, entrepreneurship, artificial intelligence, consumer trust, and digital discovery. As AI changes both how information is created and how people find it, his longer-term strategy is evolving beyond traditional search. The goal is to build trusted information valuable enough to serve not only people searching for courses and mentors, but also the AI systems increasingly helping them make those decisions.
Building a Marketplace Around Trust
Amit describes his platform as something similar to Yelp for online education. Rather than attempting to review every inexpensive online course, the focus is primarily on creator-led education where the financial decision is considerably larger. These are mentors and content creators who build audiences on platforms such as LinkedIn, YouTube, Instagram, and TikTok before offering courses, coaching programs, masterminds, or other educational products. The buying decision changes significantly as the price increases. Spending $10 on a course may require little research. Spending several thousand dollars should require considerably more.
A review platform serving this market therefore needs to provide more than a simple star rating. Potential students need information about price, mentor quality, learning experience, reported results, and whether the program is appropriate for someone with their particular goals. This makes trust more than a feature of the platform. Trust becomes part of the product itself.
Trust Has to Be Built Into the Product
A review platform does not simply provide information. It also asks users to have confidence in that information. If users believe reviews are purchased, selectively collected, generated by AI, or manipulated by creators, the value of the platform quickly disappears. Trust cannot simply be a marketing claim placed on a landing page. It has to be supported by the way the system operates.
Amit discussed several mechanisms for making review manipulation more difficult, including detailed review requirements, account validation, suspicious activity monitoring, AI-assisted detection, manual investigation, and the ability to request additional verification. No individual control guarantees that every review is legitimate. Instead, the objective is to create several layers of validation that make large-scale manipulation increasingly difficult.
That principle applies to more than review platforms. Anyone building an AI-enabled product should consider what information would need to be manipulated to cause the system to produce an incorrect or misleading result. Those potential failure points should become part of the product’s quality and validation strategy.
Recent Information Can Be More Valuable Than Old Data
Reviews introduce another challenge because products change. A mentor may launch a program and initially receive poor feedback. The creator might then respond to that feedback, improve the material, change the delivery process, and produce a much better experience for future students.
Should reviews from several years ago continue to define the program? Amit’s approach gives greater importance to recent information. He discusses using a recent group of reviews or reviews from a defined period rather than allowing every historical review to carry the same weight indefinitely. The idea allows creators to improve while still providing prospective students with information that reflects the product they are considering today. It also illustrates an important principle for AI and data systems: more data is not automatically better data.
Historical information has value, but relevance and recency can sometimes be more important than volume. The correct balance depends on the decision the system is trying to support.
AI Is Changing the SEO Equation
One of the largest challenges facing Amit’s business is not necessarily another review platform. It is the changing nature of search itself. The platform relies partly on people searching for information about specific mentors and courses. Someone considering an expensive program might search for the creator’s name followed by the word “reviews.” Traditionally, that search creates an opportunity for a review site to rank in Google and receive a visitor.
AI-generated search experiences can change that relationship. Instead of requiring the user to visit several websites, an AI-generated search result may summarize information directly on the search page. The search engine can potentially consume information from publishers while reducing the need for users to visit the original source. For businesses that depend heavily on organic search traffic, that creates a significant challenge.
AI is not simply changing how content is produced. It is changing how that content is discovered and consumed.
Moving Beyond Traditional SEO
Amit’s response to this change is particularly interesting because he does not view blocking AI as the long-term answer. Instead, he wants his platform’s information to become valuable enough that AI systems use it as a trusted source. The traditional SEO objective has been straightforward: rank highly enough that Google sends users to your website.
The emerging objective may be different. Businesses may need to become authoritative enough that AI systems use their information when answering questions. Whether the industry ultimately calls this GEO, AEO, AI search optimization, or something else, the strategic question is similar: When an AI system answers a question about your industry, where does its information come from?
Businesses need to begin thinking about whether they are simply creating content for search engines or building information valuable enough to become part of the answers AI systems provide.
Becoming the Source Is a Competitive Advantage
This idea connects directly to Amit’s longer-term strategy. AI makes software increasingly easy to reproduce. A competitor can potentially analyze a website, recreate many of its features, generate similar content, and launch an alternative faster than would have been possible only a few years ago. However, recreating an application is not the same as recreating a business. A competitor still needs users. It needs creators willing to participate. It needs trustworthy reviews, search authority, historical information, distribution, and enough marketplace activity for people to consider the platform credible.
As AI reduces the cost of producing software, competitive advantages may increasingly move away from the code itself. Trusted proprietary data, customer relationships, brand authority, historical information, distribution, network effects, and integration with other ecosystems become more difficult to reproduce than an application’s interface. Amit’s strategy is to make the platform’s review information valuable enough that it becomes an authoritative source for both people and AI systems.
AI Makes Software Easier to Copy
Michael raised an important question during the conversation: What prevents someone from using AI to recreate the platform? For many technology companies, the uncomfortable answer is that software features alone may provide less protection than they once did. AI-assisted development dramatically reduces the effort required to create applications. Competitors may be able to reproduce interfaces and common features much faster than before.
The important distinction is that copying an application does not automatically copy the ecosystem surrounding it. A clone does not inherit the original company’s reputation, relationships, users, data, search authority, or history. Those assets take time to establish. This changes how entrepreneurs should think about defensibility. The software remains important, but the application is only one component of the business.
Distribution May Be Harder Than Development
One of Amit’s primary goals for the remainder of the year is increasing distribution. That challenge should be familiar to almost anyone who has launched a product. Creating something useful is difficult. Getting people to consistently discover and use it can be even harder. AI can accelerate development and content production, but it does not automatically create demand. It does not guarantee search visibility, establish trust, create customer relationships, or prove product-market fit.
As software becomes easier to build, these nontechnical elements of business become increasingly important. The competitive question is no longer simply, “Can we build this?” More organizations will be able to answer yes. The more difficult questions are whether people will find it, trust it, use it, and continue returning to it.
Trusted Data Becomes More Valuable in an AI World
There is an interesting paradox emerging around artificial intelligence. AI can produce enormous amounts of information, but that abundance may make trustworthy information more valuable. As generated content becomes increasingly common across websites, search engines, social networks, reviews, and educational products, both people and AI systems need better methods for determining which sources deserve confidence. That creates an opportunity for businesses capable of building specialized, trustworthy datasets.
Some of the most valuable AI businesses may not necessarily be the companies creating the largest models. They may be organizations that provide the trusted information those models need to produce useful answers. Instead of asking only how a business can survive AI disruption, Amit is asking a different question: How can the business become useful enough that AI systems need its information?
That shift turns AI from a competitor into a potential distribution channel.
Choosing the Right Mentor
At the end of the interview, Amit offered practical advice for anyone considering a significant investment in a mentor or coaching program. Before investing a large amount of money, prospective students should first develop enough knowledge about the industry to evaluate what they are purchasing. A mentor should not be expected to magically solve every problem simply because the program is expensive. Amit also recommends looking for insight that cannot easily be obtained elsewhere. A strong mentor should provide experience, perspective, or knowledge that changes how the student understands the problem.
The right question is not simply whether the mentor knows more than the student. It is whether the mentor possesses knowledge and experience relevant to the student’s specific goals. The value of mentorship ultimately depends on fit.
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