Large corporations have the money, infrastructure, and data to invest heavily in artificial intelligence. Entrepreneurs have access to tools that can help a small team accomplish work that once required several departments. So who stands to benefit most?
In this episode of Start. Scale. Exit. Repeat., Colin C. Campbell and Michele Van Tilborg opened that question to the Startup Club community. The conversation explored the opportunities AI creates for founders, the advantages large companies still hold, and why access to technology is only part of the equation.
For anyone exploring AI for startups, the discussion offered a useful place to begin: identify where these tools can improve your business, then understand what it takes to use them well.
The startup advantage comes from turning accessible technology into something customers value.
A Smaller Team Can Do More
Colin’s case for entrepreneurs starts with the cost of getting a business off the ground. Research, early development, marketing materials, and administrative work can consume significant time and money before a company earns its first dollar.
AI can help founders move through some of that work faster. Colin described using it to assist with application materials and fundraising documents for a new domain extension venture, reducing the preparation work he previously relied on consultants to complete. He also retained attorneys for the legal work he wanted professionally handled.
That distinction matters. AI can help a founder prepare, organize, and develop ideas while experienced professionals review work that requires specialized judgment.
The opportunity extends to existing businesses, too. A founder might use AI to improve reporting, streamline repetitive tasks, or test a new offering without immediately adding employees. Those improvements can make a meaningful difference to a small company’s capacity and profitability.
Data Still Gives Big Companies an Advantage
Michele raised a concern that ran throughout the conversation: better tools do not automatically give everyone the same resources.
She pointed to reports of Google’s $10 million winning bid for de-identified Spirit Airlines business records. The proposed transaction covered internal business information and excluded customer profiles—an important distinction in understanding the story. It illustrated the value large companies place on data as they develop AI products.
Drawing on her experience with real estate valuation models, Michele emphasized the importance of relevant, current information. Even a sophisticated system can produce disappointing results when its inputs are incomplete or outdated.
For founders, that makes the information already inside the business worth examining. Customer questions, sales records, service requests, and product feedback may reveal opportunities to improve decisions. Using that information effectively requires attention to its quality, permissions, and the problem you are trying to solve.
Implementation Creates Opportunities for Startups
Several contributors saw potential in helping businesses apply AI to specific operational problems.
One participant described providing AI-assisted phone services to small businesses, including service providers that struggle to answer every call. The value was easy to understand: help the owner respond to customers and capture business that might otherwise be missed.
That is a practical model for founders evaluating an opportunity. Start with a recognizable problem, understand the existing workflow, and develop a solution whose usefulness can be demonstrated.
Michele also discussed working with specialists who incorporate AI into their services. Their industry knowledge remains essential, while the tools help them analyze information and execute more efficiently.
For consultants and service businesses, that creates an incentive to keep learning. Clients will judge the quality, cost, and results of the work.
Keep Your Judgment Involved
The discussion also challenged the idea that access to AI makes someone an expert in every field.
Founders still need enough understanding to ask useful questions, recognize weak assumptions, and evaluate the results. A confident answer can be inaccurate. An automated process can repeat an error. A persuasive business plan can depend on assumptions nobody has tested.
Michele emphasized documenting processes, involving the team, and maintaining human oversight. Other participants raised concerns about adoption efforts that overlook the details of how a department actually operates.
A practical starting point is one clearly defined task. Establish what a good result looks like, test the output, and measure whether the process saves time or improves performance. That gives the business evidence to guide its next investment.
Build Advantages Customers Will Recognize
Easier access to technology also means more people can build competing products. During the conversation, Colin described creating an early working version of a game with AI assistance, while acknowledging that it still needed considerable development.
That raises a question for any founder: what will make customers choose your business as competing options appear?
Colin pointed to brand, original products, distribution, and relationships. Reflecting on .CLUB Domains, he described years spent building connections across markets—work that contributed value beyond the underlying technology.
Those advantages require sustained attention. Customer trust, a useful product, dependable service, and strong partnerships give a business more to compete on as the tools become widely available.
Turn Speed Into Measurable Progress
The community did not reach a unanimous verdict on who will win the AI race. Participants raised questions about affordability, competition, corporate incentives, and the concentration of resources.
Colin emphasized the freedom entrepreneurs have to experiment. A small team can make a decision, test a workflow, and change direction quickly. Michele emphasized the learning mindset needed to make that experimentation worthwhile.
For founders, that suggests a concrete approach: choose a customer problem or operational bottleneck, apply AI thoughtfully, and evaluate what improves. Use the results to decide where to go deeper.
You do not need to predict the outcome of the entire AI industry to find an opportunity in your own business. You need a clear problem, a workable solution, and the willingness to keep learning as the tools evolve.