According to nearly 200 economists and technology company executives, including 15 Nobel laureates, immediate action is necessary. This signal reflects concerns that artificial intelligence (AI) may transform the economy more aggressively than during the Industrial Revolution.
According to the International Labour Organization's (ILO) forecasts for 2026, there is a significant threat of labor market changes influenced by AI.
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.
The progress of artificial intelligence (AI) is at the center of discussions about the professional future. However, a new OECD report indicates that this technology has not yet triggered a wave of unemployment among the organization's member countries.
The document, titled Employment Outlook 2026, from the Organisation for Economic Co-operation and Development (OECD), reveals that the unemployment rate remains at 4.9%, a figure very close to the historical low of 4.8%, reached in June 2023. Projections point to an increase of 0.3% in employment this year and 0.6% in the following year.
These indicators confirm that AI development, so far, has not reduced job availability in the examined nations. Mathias Cormann, Secretary-General of the OECD, stated during the report's presentation that there is no evidence that intensive use of AI by corporations is causing a broad contraction in labor demand.
Although the overall picture remains positive, young people's entry into the labor market remains more complex. The OECD suggests that recent advances in generative AI contribute to this scenario. Simultaneously, companies are placing greater emphasis on specific skills required for various roles.
The report details several important points: greater valuation of competencies linked to new technologies; modification of skills required by companies; greater difficulty for young people to access the market; and the continuation of job growth despite transformations.
Mathias Cormann emphasized that AI is promoting a metamorphosis in work, not just a reduction, ensuring that the technology has not yet compromised employment expectations for both young people and workers in general.
The study also observed the resilience of the labor market, even in the face of the impact of the war in the Middle East on energy costs. Job creation remained robust, and the volume of vacancies, although lower than the post-pandemic peak, has stabilized since the beginning of the conflict.
Additionally, the report points out that many employees have not yet noticed improvements in their income. In about one-third of OECD countries, real wages remain below levels recorded five years ago. Final data indicate that AI already influences the skills sought by companies, but, according to the OECD itself, there are no signs of a generalized decrease in jobs in member countries.
High-income families in the United States are investing significant amounts to enroll their children in institutions that integrate artificial intelligence (AI) as a central element of the teaching process. This new model transforms students into active participants in testing various educational tools.
This trend is gaining momentum among entrepreneurs and investors in the technology sector, notably in Silicon Valley. Companies like Forge Prep and Alpha School have begun offering private programs that replace part of the conventional school structure with AI-based tutors and project-oriented activities.
This movement occurs amid intense debate about the ethical and pedagogical limits of AI in education. Although some parents show great enthusiasm, none of these companies has presented concrete proof that this method yields superior academic results.
The schools that have adopted this methodology present AI as a way to reform traditional teaching. The expectation is to form children better equipped to solve problems and handle real-life scenarios, rather than being limited to content memorization.
Shaun Johnson, a venture capitalist based in San Francisco, is a proponent of this perspective. He argues that traditional education has flaws and that new approaches can foster skills such as independent thinking and adaptability. In a statement to The Wall Street Journal, he mentioned that his goal was to provide his son with an education focused on challenges, not just the repetition of facts.
Despite the argument presented, there are criticisms regarding the premise that AI systems can fully develop such competencies. Evaluators point out that these tools may induce overly conformist behavior without necessarily stimulating children's critical reasoning.
Another issue raised concerns the handling of sensitive topics. MacKenzie Price, co-founder of Alpha School, stated the intention to exclude controversial political and social issues from classrooms. The TWSJ report questioned whether this exclusion could restrict crucial discussions about history and society, especially at the high school level.
Additionally, there is a lack of transparency regarding the achieved results. As reported, companies like Forge do not disclose performance metrics that allow one to assess whether their methods truly improve student learning. The lack of comparable data constitutes another significant obstacle to measuring the impact of these institutions, since, unlike public schools, these private entities are not obligated to submit educational indicators to state authorities, hindering independent analyses of the effectiveness of the methods employed.
Among the highlighted institutions is Alpha School, established in Austin, Texas. This network, primarily focused on elementary education, has expanded its operations across the United States, offering units in various regions of the country, in addition to having a homeschooling platform with a curriculum focused on skill development.
The school's growth reflects a shift in the profile of parents interested in educational alternatives. Many believe that AI will play a predominant role in the future economy, leading them to question whether traditional teaching models are sufficient to prepare students for a constantly changing job market.
In this context, Alpha combines technology with in-person activities. Its system monitors student interactions and uses this information to modulate subsequent content, building a personalized learning path according to each student's performance.
The proposal has also attracted families willing to make large investments in their children's education. Shaun Johnson opted for the institution for his son after expressing dissatisfaction with the locally assigned public school and finding conventional private options insufficient. The unit attended by Johnson's son costs approximately $75,000 annually.
For the investor, the main attraction lies not only in the presence of AI but also in the possibility of offering an educational trajectory tailored to the unique needs of each child.
The expansion of this model has generated interest from researchers and educators. Caroline Hoxby, a professor at Stanford University, acknowledges the long history of project-based learning but emphasizes that integration with AI systems is a recent novelty. She observes that parents connected to the tech sector tend to accept new educational technologies more easily because they expect AI to take over repetitive tasks. However, she warns that the scarcity of solid studies prevents definitive conclusions about the benefits of these experiences.
The discussion also covers the identity of professionals in these schools. Victor Lee, a professor at the Stanford School of Education, assesses that replacing the term 'teacher' with designations such as 'guide' or 'mentor' may diminish the value placed on the preparation and skills required for teaching. Alpha, for its part, justifies this nomenclature by stating it was a decision made by the network's own professionals, who voted against using the traditional title of teacher, according to spokesperson Anna Davlantes.
Another example of the model's dissemination is Renzi Stone, a businessman from Oklahoma City, who began using Alpha's homeschooling platform for his two children. After investing over $300,000 in private education over the years, he saw AI as a chance to make screen time more productive.
At Forge Prep, demand for the proposal has also grown. Anand Sanwal, founder, reported receiving hundreds of applications for new classes, although the school initially maintains a limited number of students. The institution plans to gradually increase its capacity to accommodate high school students. In addition to focusing on practical skills, Forge has created an incentive for students to start businesses after graduation, potentially receiving financial support from the school itself, according to the proposal.
Sanwal advocates that technology should serve as a tool for creation, and not merely as a means of consuming information. For him, the speed of current transformations demands a restructuring of education to prepare students for a landscape distinct from that experienced by past generations.
