The main thing that AI has brought to business is fundamentally changing competition. When AI is in the hands of experts, it effectively reduces the cost of feedback, which is essential for building better products, to almost zero.
The rise of AI is similar to the famous “Pottery Experiment” made famous by James Clear, an anecdote that illustrates the power of quantity over perfection. Atomic Habits. The ceramics professor divided the students into two groups. The first group was evaluated only on the quality of one perfect pot produced during the semester. The second group was scored solely based on the amount of weight (measured by weight) of the finished pot. Surprisingly, the quantity-oriented students produced the highest quality work because they learned through rapid repetition and mistakes, while the quality-oriented group wasted time theorizing.
Before AI, companies valued resources and well-structured processes to deliver their products. These factors have kept the business afloat, enabled growth, supported new product launches, and helped attract customers.
Now, the game has taken a radical turn. AI is more than just a feature. It provides lightning-fast market feedback that has become the ultimate moat. It requires organizational flexibility, non-obvious thinking, and adherence to true product-market fit.
Process and resource burden
Processes and resources that were once essential to growth are becoming increasingly taxed. Businesses necessarily involve bureaucracy and require processes designed to maintain resources and grow. These processes require multiple layers of approval, and larger decisions involve more people. This no longer makes sense. The resources needed to fund product development are no longer a competitive advantage, as intelligently applied AI can achieve comparable results for a fraction of the money and labor costs. Importantly, these decisions can now be made quickly by a single founder.
Can you meaningfully compare a product developed by AI to a product built by a large specialized department and tested by another department? There may be nuances and differences, but the person paying for the product is unlikely to notice any significant differences in how well the product meets their needs. In many cases, the quality of products built with AI tools can be even higher. Key points of the competition are: AI negates the resource advantage and shifts attention to the second advantage: flexibility and process rigor.
When large companies identify a market opportunity and decide to launch a new product, they can face months of bureaucratic work. Processes are designed to carefully manage resources, and quick results are unrealistic when development, testing, and human time from multiple departments are involved.
To meet market demands, companies often have to contend with their own rules. Their process does not allow them to act in ways that are not formally documented.
Startup reactions vary widely. AI allows you to launch products and test market hypotheses as quickly as possible. We are not constrained by formality or rigid processes. The focus is on quickly finding out whether people need the product and, if so, what needs to change to make it better. Achieving product-market fit is where small startups focus their resources. In extreme cases, highly successful projects consist of just one person who is highly skilled at managing AI.
The third drawback of large amounts of resources is that they consume resources. Departments require salaries, which are reflected in the price of the final product. Startups using AI can significantly reduce costs, offer lower prices to customers, attract more users, and continually improve their products based on feedback.
The pottery effect for startups
If AI-enabled startups and businesses identify the same market opportunity at the same time, the results will be clear a year from now. A company might use 100 engineers to test two hypotheses, while a startup might use just two engineers to test 100 hypotheses. As a result, the startup's product aligns more closely with market demand and, in some cases, even creates demand that didn't exist before. This benefit comes from the dramatically increased amount of feedback and corresponding flexibility.
A natural limitation of this model is capital-intensive industries such as space, defense, and healthcare, where experimentation costs are too high to obtain customer feedback quickly and cheaply. The same applies to B2G sales and other capital-intensive sectors.
How should companies adapt?
If large companies want to compete with fast-moving AI-powered rivals, they need to emulate their approaches, rather than the other way around. First, we must recognize that slow traditional bureaucracies are a major stumbling block. Enterprise models must evolve to prioritize speed and experimentation.
A key step is establishing an autonomous AI team that operates within a managed sandbox. This means a controlled environment with simplified compliance requirements. These teams must be completely separated from standard bureaucratic decision-making processes. This enables rapid iteration and deployment of AI prototypes without the need for long approval cycles.
New key performance indicators measure the speed of learning and technology adoption, marking a clear shift away from traditional metrics tied to slow, long-cycle projects. Companies also need to create a comprehensive catalog of internal data so that model training can be easily accessed.
The essence of competition using AI
New competitive models will not immediately disrupt traditional businesses or stop them from growing. The larger the organization, the slower the decline. That's exactly the problem. Even though annual reports still show positive growth, companies fail to adapt because they do not recognize the immediate threat. Growth slows down gradually, first by a percentage and then by a total percentage. On the other hand, new businesses can grow by thousands of percent per year and ultimately challenge incumbent companies that are completely unprepared.
This dynamic is well illustrated by the relationship between Intel and Nvidia, even though AI experimentation was not a factor at the time. “If you don't have a microprocessor, what else do you have to sell?” Intel CEO Paul Otellini said in 2009, denying Nvidia's claims that the industry was moving toward graphics chips. Nvidia's success was fueled by Intel's dominance, allowing its rival to quietly grow and eventually overtake it a decade later.
With the power of AI, this shift in dominance will happen faster.
