The United States government registered a strategic loss in the field of artificial intelligence (AI) this Monday (20), with the departure of Chris Fall from the leadership of the Center for Artificial Intelligence Standards and Innovation (CAISI). He had held the position for only three months, having been appointed by the Donald Trump administration.
While the government has not designated a permanent successor, Arvind Raman, director of the National Institute of Standards and Technology (NIST), will assume the role of interim director. This move comes at a time when Washington is intensifying monitoring of advanced AI technologies and facing growing competition from Chinese companies seeking to penetrate the market dominated by American corporations.
Fall's departure raises questions about who will define the central AI policies in the White House. Furthermore, the Trump administration has yet to appoint a permanent replacement for David Sacks, who left his post in March, having previously been responsible for the government's agenda on AI and cryptocurrencies.
Kristen Eichamer, spokesperson for the Department of Commerce, confirmed that Raman will continue leading NIST while temporarily taking over CAISI. CAISI was established to support the American government in creating evaluation procedures and technical collaboration involving commercial AI systems, contributing with testing and research on the development of these technologies.
This move follows the signing of an executive order by President Donald Trump focused on AI. This document allows model developers to voluntarily offer their systems to the government for capability analysis before full launches. The order also required federal agencies to create an evaluation framework for these technologies within 60 days, generating an uncertain environment for companies launching new models.
Companies such as OpenAI and Anthropic have already demonstrated regulatory impact; OpenAI limited the distribution of its GPT-5.6 series to trusted partners following a request from the US government in June. Previously, Anthropic temporarily suspended access to its Fable 5 and Mythos 5 models due to Department of Commerce export control determinations. The internal reorganization occurs parallel to the advancement of open-source AI models developed in China, such as Moonshot AI's Kimi K3, which has been narrowing the gap with American systems.
Additionally, the Trump administration began implementing measures from the executive order through a system called 'Gold Eagle,' focused on identifying and correcting digital security flaws. According to the White House, this mechanism already receives and prioritizes cybersecurity vulnerabilities, coordinating checks, although CAISI was not cited as a participant in this initiative.
When submitting resumes and tailoring documents for different opportunities, candidates may notice that the responses received are often similar. An investigation conducted by Stanford University suggests that this similarity may not be a coincidence.
The researchers analyzed millions of applications and concluded that various companies may employ artificial intelligence systems that replicate the same acceptance and rejection criteria. The research, titled Algorithmic Monocultures in Hiring, examined about 4 million applications submitted by over 3.4 million individuals to 156 companies across 11 economic sectors.
A crucial factor identified was that all these organizations were using algorithms provided by the same vendor. This detail allowed for the identification of the phenomenon known as 'algorithmic monoculture,' a term inspired by agriculture, where vast areas are dedicated to a single type of crop. When many companies adopt similar tools, the probability increases that they will evaluate applicants following essentially the same logic, which applies to both successes and failures inherent in the models.
Another relevant finding concerns similar candidate profiles. According to the study, individuals with analogous characteristics tend to receive consistent evaluations, even when competing for positions at different corporations. The primary results reveal that approximately 10% of candidates participating in four selection processes are rejected in all of them. Additionally, about 4% of those who apply for ten positions suffer ten consecutive rejections.
The rejections occur more frequently than expected in independently made decisions. It is notable that many resumes are eliminated before even being reviewed by a human recruiter. To confirm whether this behavior was random, the researchers compared the data with a theoretical baseline and previous studies on recruitment without algorithmic centralization, demonstrating that successive rejections reflect a common pattern across different selection processes.
According to the simulations conducted, continuing to submit resumes is still advantageous. The study points out that increasing the volume of applications improves the chances of obtaining an opportunity, although this benefit decreases when companies use identical systems. In a scenario where decisions are autonomous, about ten applications would be sufficient to achieve a high probability of receiving at least one positive recommendation. However, when processes are mediated by centralized platforms, this number rises to approximately 25 applications to ensure a 99.9% probability.
The authors also issued a warning about the concentration of the technology market focused on recruitment. Since few vendors serve many companies, any existing biases or limitations can spread rapidly. Furthermore, the lack of transparency in these platforms hinders independent research and complicates the understanding of how such tools impact employment access, especially since, for many candidates, this entire process occurs without them knowing that an algorithm performed the initial resume screening.
Artificial intelligence (AI) tools have demonstrated great potential to increase productivity in various areas. However, recent research suggests that indiscriminate use of these technologies can lead to the weakening of individuals' cognitive abilities.
Studies analyzed indicate that excessive dependence on these systems can diminish users' aptitude for problem-solving without technological support. This warning arose from investigations conducted with students and professionals, where participants who used AI showed immediate improvements in performance but faced difficulties when performing the same tasks autonomously.
Experiments with workers and students confirmed that AI can optimize results when applied to activities aligned with their competencies. In a study involving hundreds of consultants from the Boston Consulting Group, researchers from the Wharton School noted an increase in the number of completed tasks and a decrease in time spent by those who had access to the tool.
This survey, published in 2026 in the journal Organization Science, revealed that employees assisted by AI produced higher quality work in functions where the technology was most competent. The most significant progress was observed among professionals who had a more modest initial performance.
A similar finding was found in a study conducted by Grace Liu of Carnegie Mellon University, focusing on mathematical problem-solving. By comparing students with and without access to AI, the study identified superior performance among those who could use the resource during exercises.
Despite the instant gains, researchers detected adverse effects after the removal of technological assistance. Individuals accustomed to AI began to show lower performance than those who had never used the resource and demonstrated less resilience in the face of difficulties.
Additionally, another study investigated how excessive trust in responses generated by AI systems affects decision-making. Steven Shaw and Gideon Nave evaluated over 1,300 participants and identified a phenomenon called 'cognitive surrender,' characterized by the user abandoning their own evaluation in favor of the conclusion provided by the machine.
Researchers argue that AI can operate as a third cognitive mechanism, complementing the traditional methods of fast thinking and detailed analysis described by Daniel Kahneman. The problem arises when this tool ceases to be a complement to human reasoning and begins to replace it entirely.
Experts emphasize that the crucial skill in the age of AI will be discerning which tasks should remain under human control and which can be delegated to automated systems. Collaboration is most effective when the user understands the limits of the technology and can judge its outputs.
An analysis from 2024, published in the journal Nature Human Behavior and based on 106 AI experiments, showed that joint performance between humans and machines is optimized when each party acts in its area of greatest advantage. However, when the system demonstrates superiority, the human difficulty in knowing whether to trust or contest the tool can compromise the final result.
The interviewed experts highlighted that processes such as initial idea conception, text writing, and knowledge generation require active human participation. For them, AI is most valuable in phases of review, questioning arguments, and refining existing work.
The concern extends to the educational environment. The cited studies indicated that students may learn less by using AI solely to speed up school assignments. On the other hand, when the tool is used to gain clarification, formulate questions, and assimilate concepts, the damage to the learning process is minimized.
Judy Hanwen Shen and Alex Tamkin, from Anthropic, reported in their research with developers learning a new programming library conceptual difficulties, code reading, and debugging when AI was used as a shortcut to obtain ready-made solutions. The researchers' recommendation is to reconfigure AI as a tool for deepening understanding, stimulating questioning and expanding analytical capacity, rather than simply eliminating the mental effort required to learn.
