Why Project with Serenity in the AI Era?

A Look at the Accelerated Technological Evolution

Since the advent of the Internet in the 1990s, technology-driven business opportunities have grown at a staggering pace. Entry barriers have gradually diminished, giving rise to a new wave of entrepreneurs eager to create disruptive solutions without requiring significant initial resources.

The speed at which ideas could turn into billion-dollar companies—once unthinkable before the digital age—fueled a startup culture where innovating quickly and capturing the market early became essential. The success stories of tech giants that started in a garage and reached the top only reinforced the belief that “those who don’t take risks don’t win.”

The Startup Culture: Avoiding the “Next Big Miss”

The cases of Blockbuster and Kodak have become cautionary tales in business schools and entrepreneurial circles. Both companies relied too heavily on their traditional models and failed to adapt to technological shifts: the former ignored the rise of video streaming, and the latter underestimated the potential of digital photography.

Such stories instilled in new entrepreneurs the fear of “falling behind,” leading to a mindset of “launch now, improve later”:

Rapid iteration: Developing minimum viable products (MVPs) to test ideas with real users as soon as possible.
Fearless pivoting: Changing direction quickly if the market does not respond.
Scaling up: If a product works, the next goal is aggressive expansion to capture market share before competitors do.

The Leap into the AI Era: Unprecedented Speed

Until recently, this mentality worked relatively well. Conventional technological evolution (new apps, online services, social networks, etc.) was fast but still allowed time for iteration and refinement before becoming obsolete.

However, the arrival of artificial intelligence, particularly large language models (LLMs), has introduced an unprecedented pace:

What once took months to learn can now be instantly processed by a well-trained model.
Continuous updates shift the boundaries of what is “possible” almost weekly.
Open-source solutions and automated tools enable global competitors to replicate or even surpass functionalities in record time.

The Risk of Becoming Obsolete in Weeks

With this acceleration, launching an AI venture without a future-proof vision and a clear area of value poses significant risks:

Opportunity cost: Investing time and resources into a product that could soon be equaled or surpassed by a generic model.
False differentiation: Many startups rely on a single AI feature that soon becomes standard or available for free.
Competitive speed: With the proliferation of open-source repositories and AI models, a similar or superior solution can emerge overnight.

As a result, the tech community faces heightened sensitivity: no one wants to repeat the fate of Blockbuster or Kodak, but now obsolescence can strike in weeks or even days instead of years.

The Importance of Foresight and Observation

In this new landscape, the key is not just “launching fast” but understanding the trajectory of AI and the infrastructure that supports it. Essential strategies include:

Practicing foresight: Analyzing trends, anticipating future developments, and focusing on solutions that can escape immediate commoditization.
Enhancing observation capabilities: Closely monitoring AI model evolution, platform changes, and shifts in user demand.
Differentiation through integration: Having a model is not enough—it must be embedded within systems and workflows to create lasting value.

With this foundation, an AI startup can focus on providing real value instead of building something that a “boosted model” could surpass within a month at a lower cost.

Autonomous Agents and AI: The Next Strategic Step

Given this scenario, the responsible approach is to design business structures based on autonomous agents that can be deployed within AI-driven operating environments. Such agents, operating in local or hybrid (cloud and on-premise) settings, enable deeper customization, better adaptation to diverse contexts, and significantly reduce dependence on a single AI model.

This approach allows businesses to update or replace models as new technologies emerge—without dismantling their core infrastructure.