By André Beck
The artificial intelligence sector is facing a multifaceted bottleneck, marked by limitations in computing infrastructure, a shortage of specialized professionals, insufficient databases, regulatory pressures, and increasing development costs. In a highly competitive environment, where thousands of startups vie for attention and investment, a central question arises: what makes an AI solution cease to be just a promising experiment and become a real driver of transformation?
The answer lies less in technical complexity and more in the ability to generate concrete, sustainable, and ethical impact. The startups that have advanced the most in the last year have something in common: they all develop technologies that... solve They address real-world problems with direct application in sectors such as healthcare, public administration, logistics, energy, and education, and deliver consistent long-term value.
The current saturation is not due to a lack of capital. Although the global volume invested in AI continues to expand, a large portion of projects are still stuck in the transition between prototypes and full adoption. The absence of local infrastructure, restricted access to advanced chips, incomplete or biased datasets, and the difficulty in forming mature machine learning teams widen the gap between what is demonstrated in the laboratory and what actually reaches the user. Even initiatives that reach the initial implementation phase often fail to build a solid foundation for continuity.
According to the report The State of AI – 2025, by McKinsey & Company, Many companies are already using AI and beginning to explore autonomous agents, but most are still in the early stages of scaling. Among the main challenges, the study highlights the transition from pilot programs to full deployment, the need to redesign workflows to capture real value, and the increase in initiatives aimed at mitigating risks.
In this scenario, the solutions that become relevant are those that manage to overcome these barriers. Startups that have recently gained prominence have adopted more efficient models, capable of operating with fewer resources, supported by compression techniques, transfer learning, and inference optimization. They have also advanced in technological independence, with architectures suitable for local infrastructure, preservation of sensitive data within their own territory, and less dependence on foreign providers—a movement that reinforces digital sovereignty and expands the strategic autonomy of countries and organizations.
The human impact has become equally crucial. Applications that expand access to healthcare, improve public services, optimize educational journeys, strengthen security measures, or reduce inequalities are the ones gaining traction. The innovations that have stood out in recent years have gone beyond technical performance: they have transformed routines, reduced costs, and demonstrated that technology can be useful, inclusive, and strategically relevant.
Ultimately, what differentiates irrelevant AI from transformative AI is the combination of utility, efficiency, ethics, and impact. The startups that thrived in the last cycle were not those that sought to compete solely on scale, but those that created accessible, responsible solutions aligned with the real needs of the market and governments.
The future of AI will not be defined by who builds the most powerful model, but by who delivers the most useful one. In a rapidly maturing sector, the real challenge lies not only in creating technologies, but in ensuring that each innovation is able to overcome bottlenecks, consolidate relevance, and cease to be just another project along the way.
* André Beck is a Partner and Executive at WideLabs
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
