For years, much of the technological conversation revolved around what seemed like an inevitable idea: the more advanced digital tools became, the greater humanity’s ability would be to produce, automate and solve problems at unprecedented speed.
In many ways, that has already happened.
Generative Artificial Intelligence can now write texts, create images, program software, edit video, analyze data and execute complex tasks in a matter of seconds. Tools that only a few years ago required specialized teams, expensive infrastructure and long technical processes can now be used by millions of people from a phone or laptop.
Yet as technological speed accelerates, another discussion begins to emerge — less visible, but considerably more uncomfortable.
The ability to build is no longer the main differentiator.
The real challenge now seems to lie somewhere far more complex: deciding what is actually worth building.
This reflection does not come exclusively from external analysts or technological critics. Hearst Communications — one of the largest media conglomerates in the United States, owner of newspapers, magazines and digital platforms with decades of editorial experience — recently published an internal analysis on Artificial Intelligence, product development and digital experimentation that ultimately opened a much broader conversation than initially expected.
In the piece, Freyja Balmer, senior vice president of product at Hearst Newspapers, warned that current tools allow organizations to execute projects, launch products and conduct experiments at unprecedented speed. However, she also pointed to a growing problem: organizations are learning to build faster than they are learning to reflect on what they are building.
The observation may sound corporate, but it actually touches a much deeper cultural tension.
For decades, the internet primarily rewarded acceleration: more content, more automation, more speed, more publishing, more responsiveness, more production.
Today, however, excess capacity is beginning to generate new questions around judgment, experience and decision-making.
Converging Signals
Different academic institutions and international organizations are already pointing toward the same phenomenon from distinct perspectives.
MIT Sloan Management Review recently published an analysis on the future of professional expertise in the age of AI. Its conclusion was revealing: as Artificial Intelligence democratizes access to information and technical execution, true human value begins shifting toward abilities that are far more difficult to automate — interpreting context, asking better questions, recognizing gray areas and synthesizing complex information.
London Business School has expressed something similar, warning that human judgment remains indispensable even within highly automated environments, particularly in areas related to ethics, interpretation and social context.
Meanwhile, publications far removed from traditional technological discourse are beginning to detect the same transformation. Vogue, for example, recently explored how concepts such as taste, intention and creative sensitivity are gaining new value in a world where visual production is becoming massively accessible through generative tools available to almost anyone.
The conversation is no longer confined to the technical sphere, it is becoming a cultural phenomenon.
As production becomes cheaper and speed increases, accumulated experience and the ability to discern regain relevance in unexpected ways.
The phenomenon is also reshaping perceptions around creativity and specialized knowledge. Business Insider recently published an analysis in which several experts argued that AI does not necessarily replace human expertise. In many cases, it simply exposes who truly understands what they are doing — and who merely depends on superficial automation.
The distinction may appear subtle, but it is not.
For years, the internet lowered many barriers related to publishing, communication and digital creation. Artificial Intelligence pushes that process even further. Today, it is possible to generate complex images, long-form texts or audiovisual pieces without necessarily going through the traditional learning processes historically associated with those disciplines.
That does not automatically imply cultural or artistic degradation.
But it does alter the historical relationship between experience, effort and production.
Invisible — and Expensive — Infrastructure
Digital abundance is also beginning to reveal another less visible paradox: much of the technology perceived as instantaneous or immaterial depends profoundly on physical infrastructure.
Every interaction with AI systems activates globally distributed data centers, massive energy consumption, industrial cooling systems and complex material networks sustained by very tangible physical resources.
The apparent lightness of digital life increasingly depends on water, electricity, minerals and industrial supply chains that remain invisible to most users.
In recent years, multiple investigations have begun warning about the accelerated growth of energy consumption associated with generative models and AI-specialized data centers. Technology companies, universities and international organizations are already discussing the impact that expanding these infrastructures may have on energy availability, water consumption and environmental pressure.
The issue extends far beyond image or text generation.
Training advanced models requires enormous computational capabilities sustained over long periods of time, while the massive everyday use of assistants, generative engines and automation systems continuously multiplies operational demand.
The conversation becomes especially relevant because much of this infrastructure remains outside the daily experience of users.
For millions of people, interacting with AI simply means typing a prompt and receiving an almost instantaneous response. Yet behind that apparent simplicity lie industrial chains, processing centers and planetary-scale energy systems.
Various technology analysts and sustainability specialists have begun warning that the current acceleration could create major tensions between digital growth and the availability of physical resources.
Water used for cooling systems, rising energy demand and dependence on strategic minerals are all part of a conversation that is only beginning to gain public visibility.
In that context, the discussion around judgment ceases to be exclusively philosophical or editorial.
It also becomes a conversation about scale, purpose and direction.
Rediscovering Essentials
Technological acceleration seems to have partially displaced a question that remained implicit for a long time: not only what can be done, but what should be done, what deserves to be sustained, and what meaning there is in producing almost infinitely within an environment already saturated with information, stimuli and automated content.
The paradox is difficult to ignore.
Humanity has achieved many of the technological capabilities it imagined for decades:
automation, intelligent assistance, instant production, mass access to information,
and highly sophisticated creative tools.
Yet at the same time, it is beginning to discover that expanding capability does not necessarily solve problems related to depth, clarity, meaning or collective direction.
Perhaps that is why human judgment is regaining value.
Not as a romantic resistance against technology, nor as nostalgia for slower processes,
but as an increasingly scarce capacity within an environment where almost anything can be produced rapidly.
Because while Artificial Intelligence continues reducing technical barriers, the question of what is truly worth building seems increasingly difficult to answer.
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