Apple is examining the possibility of acquiring specialized chip companies to enhance its artificial intelligence processing capabilities. This move comes due to obstacles encountered with the components currently used in its AI servers, seeking alternatives to keep pace with the rapid evolution of large artificial intelligence models.
According to Reuters reports, Apple has initiated contact with chip startups and discussed potential acquisitions with financial institutions, although no negotiation has been formally confirmed by the company. The company's AI servers use internally developed chips based on the M2 Ultra, but these present performance limitations, as reported by The Information. Furthermore, the project named 'Baltra,' intended for a new generation of processors, has been postponed.
Among the goals set by Apple are the purchase of companies focused on AI chips, increasing its internal processing capacity, reducing dependence on external solutions, and preparing new functionalities based on artificial intelligence.
During the development process of a new version of Siri, Apple conducted tests using the Gemini models, provided by Google, on its servers. However, the Mac-based chips proved incapable of managing the larger models, forcing part of the operation to be transferred to Nvidia chips hosted on a Google cloud infrastructure. This scenario illustrates one of the challenges the company faces in competing for advanced AI resources.
Even while avoiding major acquisitions, Apple purchased Q.ai, an Israeli AI technology company focused on audio, in January. As of March 28, the company held $45.57 billion in cash (equivalent to about R$ 246 billion). Additionally, an agreement was announced with Broadcom, valued at over $30 billion (approximately R$ 162 billion), regarding chip supply.
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.
