AI goes beyond the trend: how the rules of the game are changing for the tech business

In this new reality, demand for developers is growing like never before. But the old SaaS rules no longer work — AI companies now have to build their businesses according to entirely new laws.

From impressive demos to real products

When ChatGPT launched in 2022, it seemed that all AI applications would quickly become identical — simple “wrappers” around models that were easy to copy. But over three years, it became clear: making a demo is easy, but creating a stable product is hard.

Users are unpredictable, data is messy, and models sometimes give random answers. A mistake can cost millions — as in the case of the Air Canada bot, which invented non-existent ticket refund rules.

Therefore, AI teams are learning to balance model power with reliability. They test dozens of solutions, build complex processes, train their own models, and adapt them to customer needs. This is no longer about pretty demos, but about engineering work — integrations, security, context. It is precisely here that true value is born, which cannot be replaced by a single API call.

Time is growing faster and the bar is higher

Just a few years ago, a startup with $1 million in annual revenue could confidently go for a Series A round. Today, that is not enough. AI companies are showing 10x year-over-year growth, and leaders like Cursor are becoming the fastest-growing software companies of their generation.

The reason is simple: corporate clients are now coming for AI themselves. They have budgets, top-down mandates, and specific implementation tasks. Sales are moving faster, and contracts are larger.

AI products often do not just help people work; they replace the work itself. Therefore, budgets come not from IT departments, but from payroll funds. This opens the door to deals many times larger than those of traditional SaaS.

The barrier to entry has almost disappeared

The cost of creating AI products is falling rapidly: from $30 to less than $5 per million tokens. OpenAI recently reduced the price of the o3 model by 80% overnight. At the same time, tools like Cursor, Lovable, and Replit are making development accessible even to non-technical specialists.

The result is a true “democratization” of AI development. Now it is possible to create applications that were previously economically meaningless: narrow tools for specific tasks, “one-person programs,” personal dashboards, or microservices for specific business processes.

For companies, this is a chance to automate what was previously done manually or through fragile RPA scenarios. Hundreds of new niches, previously considered “too small,” are becoming markets.

Speed is everything

Today, corporate clients are drowning in offers. To break through, a startup needs not just innovation — it needs to be first. Development speed and time-to-market have become the main advantage.

Cursor, Decagon, ElevenLabs, Hebbia, and Harvey all used this momentum to become leaders in their categories. When a product is the first to occupy a place in users’ minds, it becomes the standard, even if large players enter the market later.

Even giants like Microsoft and OpenAI with their GitHub Copilot and Codex could not stop Cursor’s growth. Startups win through focus: they do one thing better than anyone else and grow faster than corporations burdened by internal bureaucracy and parallel initiatives.

How to maintain the advantage

AI itself does not create a protective barrier. To maintain leadership, a company must embed itself into the client’s infrastructure — technically, operationally, and culturally.

1. Become a system of record
When a product becomes the primary source of truth for data — it cannot be ripped out. Startups like Eve or Salient start with an “entry point,” such as voice call analysis, and gradually build solutions downstream until they become an integral part of processes.

2. Embed into workflows
Habit is more powerful than marketing. Products embedded into users’ daily work create workflow lock-in. For example, Decagon combines support automation with an intuitive interface, keeping human oversight. Users do not just depend on AI; they depend on a specific UX.

3. Build deep integrations
Complex enterprise systems are rarely compatible with each other. Startups that take on this pain become indispensable. Tennr integrates with legacy medical systems, HappyRobot connects to logistics platforms, and Glean works through dozens of enterprise APIs. This is labor-intensive, but almost impossible to copy.

4. Build relationships, not just sell.
AI providers are increasingly becoming strategic partners, helping companies shape their own AI strategies. In this role, trust and human relationships prove to be no less valuable than the product itself.

The era of creators

AI has opened a new era for entrepreneurs. The cost of technology is falling, implementation speed is increasing, and the market is expanding beyond the imagination of old-school SaaS.

But to survive this race, making an impressive demo is not enough. You need to build real products, deeply integrated into clients’ businesses, act faster than everyone else, and defend your hard-won position with thoughtful barriers.

AI is not just a tool. It is a new infrastructure for building companies with lasting value.

Source: a16z.com