Igor believes AI is changing what it means to start a business. For the first time, starting a company is no longer limited by money, teams, or access. It can begin with just an idea that is tested right away.

He argues that the way startups are built has shifted. Before, founders would raise money, build a product, and then find out if people wanted it. Now, AI allows ideas to be tested much earlier. It can help check demand, explore assumptions, and show whether something is worth building before a lot of time or money is spent. This democratizes access to entrepreneurship, even without traditional resources.

Igor believes the old Silicon Valley model of raise, build, test, and scale is being replaced by a simpler flow of test first, then build. Instead of committing early, ideas are tested in small steps until there is clear proof of demand. This lowers both cost and risk because mistakes are caught early.

Through Ventora, Igor is building a system that follows this approach. It helps take ideas, test them quickly, and only move forward when there is a real signal from the market.

Igor argues that the next wave of companies will not come from individual founders building single startups, but from systems that continuously create startups. He even refers to them as “Startup Factories”.  Instead of one idea becoming one company, the model shifts toward platforms that generate and test many ideas in parallel.

In his view, this changes how scale works. In the past, growth meant hiring more people, raising more capital, and expanding teams. Now, a large part of that scale can come from systems that handle idea creation, testing, and early validation. This reduces the need for large teams or heavy operational structures just to explore new opportunities.

He sees venture factories as the next step in this evolution. These are systems that can repeatedly generate startups without starting from scratch each time. Ventora is built around this idea, using AI to structure ideas, test them quickly, and move forward only with those that show real demand.

From this perspective, entrepreneurship becomes less about building a single company and more about designing systems that consistently produce and validate new ones.

Igor argues that the role of investors is changing as the cost of starting and testing companies continues to drop. In the past, capital was needed early because building a product required teams, infrastructure, and long development cycles. Investors played a central role in making that possible.

He explains that AI has removed much of that early cost. Ideas can now be structured, tested, and even turned into simple products without large teams or significant funding. This means founders can reach real market signals before needing outside capital, which changes when and how investment becomes relevant.

Igor believes the real shift is that validation now comes before funding. Instead of investing in ideas based on assumptions, more of the early risk can be reduced through automated testing and real user feedback. This reduces the dependency on early-stage investors to fund unproven concepts.

Igor argues that in 2026, building a product is no longer the difficult part. AI can already generate code, design interfaces, and help launch tools quickly. The real challenge is not execution, but knowing what is actually worth building in the first place.

He explains that most ideas still fail for the same reasons: they are based on assumptions, built too early, and only reach real users after too much time has already been spent. In his view, speed without validation only leads to faster failure, not better outcomes.

Igor defines validation not as feedback or opinions, but as behavior. What matters is whether users actually take action:  sign up, click, use the product, or pay. If there is no real user behavior, the idea is not validated, no matter how good it sounds.

He believes the process should be simple and structured: define a narrow use case, create a basic entry point, drive real traffic, and measure real signals like conversion, activation, and retention. From there, decisions should be made quickly: build if there is strong demand, iterate if signals are weak, or drop the idea entirely if there is no response.