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Sam Altman, co-founder and CEO of OpenAI and co-founder of Tools for Humanity, participates remotely in a discussion on the sidelines of the IMF/World Bank Spring Meetings in Washington, D.C., April 24, 2025.

Brendan Smialowski | AFP | Getty Images

Not long ago, Silicon Valley was where the world’s leading artificial intelligence experts went to perform cutting-edge research. 

Meta, Google and OpenAI opened their wallets for top talent, giving researchers staff, computing power and plenty of flexibility. With the support of their employers, the researchers published high-quality academic papers, openly sharing their breakthroughs with peers in academia and at rival companies.

But that era has ended. Now, experts say, AI is all about the product.

Since OpenAI released ChatGPT in late 2022, the tech industry has shifted its focus to building consumer-ready AI services, in many cases prioritizing commercialization over research, AI researchers and experts in the field told CNBC. The profit potential is massive — some analysts predict $1 trillion in annual revenue by 2028. The prospective repercussions terrify the corner of the AI universe concerned about safety, industry experts said, particularly as leading players pursue artificial general intelligence, or AGI, which is technology that rivals or exceeds human intelligence.

In the race to stay competitive, tech companies are taking an increasing number of shortcuts when it comes to the rigorous safety testing of their AI models before they are released to the public, industry experts told CNBC.

James White, chief technology officer at cybersecurity startup CalypsoAI, said newer models are sacrificing security for quality, that is, better responses by the AI chatbots. That means they’re less likely to reject malicious kinds of prompts that could cause them to reveal ways to build bombs or sensitive information that hackers could exploit, White said.

“The models are getting better, but they’re also more likely to be good at bad stuff,” said White, whose company performs safety and security audits of popular models from Meta, Google, OpenAI and other companies. “It’s easier to trick them to do bad stuff.”

The changes are readily apparent at Meta and Alphabet, which have deprioritized their AI research labs, experts say. At Facebook’s parent company, the Fundamental Artificial Intelligence Research, or FAIR, unit has been sidelined by Meta GenAI, according to current and former employees. And at Alphabet, the research group Google Brain is now part of DeepMind, the division that leads development of AI products at the tech company.

CNBC spoke with more than a dozen AI professionals in Silicon Valley who collectively tell the story of a dramatic shift in the industry away from research and toward revenue-generating products. Some are former employees at the companies with direct knowledge of what they say is the prioritization of building new AI products at the expense of research and safety checks. They say employees face intensifying development timelines, reinforcing the idea that they can’t afford to fall behind when it comes to getting new models and products to market. Some of the people asked not to be named because they weren’t authorized to speak publicly on the matter.

Mark Zuckerberg, CEO of Meta Platforms, during the Meta Connect event in Menlo Park, California, on Sept. 25, 2024.

David Paul Morris | Bloomberg | Getty Images

Meta’s AI evolution

When Joelle Pineau, a Meta vice president and the head of the company’s FAIR division, announced in April that she would be leaving her post, many former employees said they weren’t surprised. They said they viewed it as solidifying the company’s move away from AI research and toward prioritizing developing practical products.

“Today, as the world undergoes significant change, as the race for AI accelerates, and as Meta prepares for its next chapter, it is time to create space for others to pursue the work,” Pineau wrote on LinkedIn, adding that she will formally leave the company May 30. 

Pineau began leading FAIR in 2023. The unit was established a decade earlier to work on difficult computer science problems typically tackled by academia. Yann LeCun, one of the godfathers of modern AI, initially oversaw the project, and instilled the research methodologies he learned from his time at the pioneering AT&T Bell Laboratories, according to several former employees at Meta. Small research teams could work on a variety of bleeding-edge projects that may or may not pan out.  

The shift began when Meta laid off 21,000 employees, or nearly a quarter of its workforce, starting in late 2022. CEO Mark Zuckerberg kicked off 2023 by calling it the “year of efficiency.” FAIR researchers, as part of the cost-cutting measures, were directed to work more closely with product teams, several former employees said.

Two months before Pineau’s announcement, one of FAIR’s directors, Kim Hazelwood, left the company, two people familiar with the matter said. Hazelwood helped oversee FAIR’s NextSys unit, which manages computing resources for FAIR researchers. Her role was eliminated as part of Meta’s plan to cut 5% of its workforce, the people said.

Joelle Pineau of Meta speaks at the Advancing Sustainable Development through Safe, Secure, and Trustworthy AI event at Grand Central Terminal in New York, Sept. 23, 2024.

Bryan R. Smith | Via Reuters

OpenAI’s 2022 launch of ChatGPT caught Meta off guard, creating a sense of urgency to pour more resources into large language models, or LLMs, that were captivating the tech industry, the people said. 

In 2023, Meta began heavily pushing its freely available and open-source Llama family of AI models to compete with OpenAI, Google and others.

With Zuckerberg and other executives convinced that LLMs were game-changing technologies, management had less incentive to let FAIR researchers work on far-flung projects, several former employees said. That meant deprioritizing research that could be viewed as having no impact on Meta’s core business, such as FAIR’s previous health care-related research into using AI to improve drug therapies.

Since 2024, Meta Chief Product Officer Chris Cox has been overseeing FAIR as a way to bridge the gap between research and the product-focused GenAI group, people familiar with the matter said. The GenAI unit oversees the Llama family of AI models and the Meta AI digital assistant, the two most important pillars of Meta’s AI strategy. 

Under Cox, the GenAI unit has been siphoning more computing resources and team members from FAIR due to its elevated status at Meta, the people said. Many researchers have transferred to GenAI or left the company entirely to launch their own research-focused startups or join rivals, several of the former employees said. 

While Zuckerberg has some internal support for pushing the GenAI group to rapidly develop real-world products, there’s also concern among some staffers that Meta is now less able to develop industry-leading breakthroughs that can be derived from experimental work, former employees said. That leaves Meta to chase its rivals.

A high-profile example landed in January, when Chinese lab DeepSeek released its R1 model, catching Meta off guard. The startup claimed it was able to develop a model as capable as its American counterparts but with training at a fraction of the cost.

Meta quickly implemented some of DeepSeek’s innovative techniques for its Llama 4 family of AI models that were released in April, former employees said. The AI research community had a mixed reaction to the smaller versions of Llama 4, but Meta said the biggest and most powerful Llama 4 variant is still being trained.

The company in April also released security and safety tools for developers to use when building apps with Meta’s Llama 4 AI models. These tools help mitigate the chances of Llama 4 unintentionally leaking sensitive information or producing harmful content, Meta said.

“Our commitment to FAIR remains strong,” a Meta spokesperson told CNBC. “Our strategy and plans will not change as a result of recent developments.”

In a statement to CNBC, Pineau said she is enthusiastic about Meta’s overall AI work and strategy.

“There continues to be strong support for exploratory research and FAIR as a distinct organization in Meta,” Pineau said. “The time was simply right for me personally to re-focus my energy before jumping into a new adventure.”

Meta on Thursday named FAIR co-founder Rob Fergus as Pineau’s replacement. Fergus will return to the company to serve as a director at Meta and head of FAIR, according to his LinkedIn profile. He was most recently a research director at Google DeepMind.

“Meta’s commitment to FAIR and long term research remains unwavering,” Fergus said in a LinkedIn post. “We’re working towards building human-level experiences that transform the way we interact with technology and are dedicated to leading and advancing AI research.”

Demis Hassabis, co-founder and CEO of Google DeepMind, attends the Artificial Intelligence Action Summit at the Grand Palais in Paris, Feb. 10, 2025.

Benoit Tessier | Reuters

Google ‘can’t keep building nanny products’

Google released its latest and most powerful AI model, Gemini 2.5, in March. The company described it as “our most intelligent AI model,” and wrote in a March 25 blog post that its new models are “capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.”

For weeks, Gemini 2.5 was missing a model card, meaning Google did not share information about how the AI model worked or its limitations and potential dangers upon its release.

Model cards are a common tool for AI transparency.

A Google website compares model cards to food nutrition labels: They outline “the key facts about a model in a clear, digestible format,” the website says.

“By making this information easy to access, model cards support responsible AI development and the adoption of robust, industry-wide standards for broad transparency and evaluation practices,” the website says.

Google wrote in an April 2 blog post that it evaluates its “most advanced models, such as Gemini, for potential dangerous capabilities prior to their release.” Google later updated the blog to remove the words “prior to their release.”

Without a model card for Gemini 2.5, the public had no way of knowing which safety evaluations were conducted or whether DeepMind checked for dangerous capabilities at all.

In response to CNBC’s inquiry on April 2 about Gemini 2.5’s missing model card, a Google spokesperson said that a “tech report with additional safety information and model cards are forthcoming.” Google published an incomplete model card on April 16 and updated it on April 28, more than a month after the AI model’s release, to include information about Gemini 2.5’s “dangerous capability evaluations.” 

Those assessments are important for gauging the safety of a model — whether people can use the models to learn how to build chemical or nuclear weapons or hack into important systems. These checks also determine whether a model is capable of autonomously replicating itself, which could lead to a company losing control of it. Running tests for those capabilities requires more time and resources than simple, automated safety evaluations, according to industry experts.

Google co-founder Sergey Brin

Kelly Sullivan | Getty Images Entertainment | Getty Images

The Financial Times in March reported that Google DeepMind CEO Demis Hassabis had installed a more rigorous vetting process for internal research papers to be published. The clampdown at Google is particularly notable because the company’s “Transformers” technology gained recognition across Silicon Valley through that type of shared research. Transformers were critical to OpenAI’s development of ChatGPT and the rise of generative AI. 

Google co-founder Sergey Brin told staffers at DeepMind and Gemini in February that competition has accelerated and “the final race to AGI is afoot,” according to a memo viewed by CNBC. “We have all the ingredients to win this race but we are going to have to turbocharge our efforts,” he said in the memo.

Brin said in the memo that Google has to speed up the process of testing AI models, as the company needs “lots of ideas that we can test quickly.” 

“We need real wins that scale,” Brin wrote. 

In his memo, Brin also wrote that the company’s methods have “a habit of minor tweaking and overfitting” products for evaluations and “sniping” the products at checkpoints. He said employees need to build “capable products” and to “trust our users” more.

“We can’t keep building nanny products,” Brin wrote. “Our products are overrun with filters and punts of various kinds.”

A Google spokesperson told CNBC that the company has always been committed to advancing AI responsibly. 

“We continue to do that through the safe development and deployment of our technology, and research contributions to the broader ecosystem,” the spokesperson said.

Sam Altman, CEO of OpenAI, is seen through glass during an event on the sidelines of the Artificial Intelligence Action Summit in Paris, Feb. 11, 2025.

Aurelien Morissard | Via Reuters

OpenAI’s rush through safety testing

The debate of product versus research is at the center of OpenAI’s existence. The company was founded as a nonprofit research lab in 2015 and is now in the midst of a contentious effort to transform into a for-profit entity.

That’s the direction co-founder and CEO Sam Altman has been pushing toward for years. On May 5, though, OpenAI bowed to pressure from civic leaders and former employees, announcing that its nonprofit would retain control of the company even as it restructures into a public benefit corporation.

Nisan Stiennon worked at OpenAI from 2018 to 2020 and was among a group of former employees urging California and Delaware not to approve OpenAI’s restructuring effort. “OpenAI may one day build technology that could get us all killed,” Stiennon wrote in a statement in April. “It is to OpenAI’s credit that it’s controlled by a nonprofit with a duty to humanity.”

But even with the nonprofit maintaining control and majority ownership, OpenAI is speedily working to commercialize products as competition heats up in generative AI. And it may have rushed the rollout of its o1 reasoning model last year, according to some portions of its model card.

Results of the model’s “preparedness evaluations,” the tests OpenAI runs to assess an AI model’s dangerous capabilities and other risks, were based on earlier versions of o1. They had not been run on the final version of the model, according to its model card, which is publicly available.

Johannes Heidecke, OpenAI’s head of safety systems, told CNBC in an interview that the company ran its preparedness evaluations on near-final versions of the o1 model. Minor variations to the model that took place after those tests wouldn’t have contributed to significant jumps in its intelligence or reasoning and thus wouldn’t require additional evaluations, he said. Still, Heidecke acknowledged that OpenAI missed an opportunity to more clearly explain the difference.

OpenAI’s newest reasoning model, o3, released in April, seems to hallucinate more than twice as often as o1, according to the model card. When an AI model hallucinates, it produces falsehoods or illogical information. 

OpenAI has also been criticized for reportedly slashing safety testing times from months to days and for omitting the requirement to safety test fine-tuned models in its latest “Preparedness Framework.” 

Heidecke said OpenAI has decreased the time needed for safety testing because the company has improved its testing effectiveness and efficiency. A company spokesperson said OpenAI has allocated more AI infrastructure and personnel to its safety testing, and has increased resources for paying experts and growing its network of external testers.

In April, the company shipped GPT-4.1, one of its new models, without a safety report, as the model was not designated by OpenAI as a “frontier model,” which is a term used by the tech industry to refer to a bleeding-edge, large-scale AI model.

But one of those small revisions caused a big wave in April. Within days of updating its GPT-4o model, OpenAI rolled back the changes after screenshots of overly flattering responses to ChatGPT users went viral online. OpenAI said in a blog post explaining its decision that those types of responses to user inquiries “raise safety concerns — including around issues like mental health, emotional over-reliance, or risky behavior.”

OpenAI said in the blogpost that it opted to release the model even after some expert testers flagged that its behavior “‘felt’ slightly off.”

“In the end, we decided to launch the model due to the positive signals from the users who tried out the model. Unfortunately, this was the wrong call,” OpenAI wrote. “Looking back, the qualitative assessments were hinting at something important, and we should’ve paid closer attention. They were picking up on a blind spot in our other evals and metrics.”

Metr, a company OpenAI partners with to test and evaluate its models for safety, said in a recent blog post that it was given less time to test the o3 and o4-mini models than predecessors.

“Limitations in this evaluation prevent us from making robust capability assessments,” Metr wrote, adding that the tests it did were “conducted in a relatively short time.”

Metr also wrote that it had insufficient access to data that would be important in determining the potential dangers of the two models.

The company said it wasn’t able to access the OpenAI models’ internal reasoning, which is “likely to contain important information for interpreting our results.” However, Metr said, “OpenAI shared helpful information on some of their own evaluation results.”

OpenAI’s spokesperson said the company is piloting secure ways of sharing chains of thought for Metr’s research as well as for other third-party organizations. 

Steven Adler, a former safety researcher at OpenAI, told CNBC that safety testing a model before it’s rolled out is no longer enough to safeguard against potential dangers.

“You need to be vigilant before and during training to reduce the chance of creating a very capable, misaligned model in the first place,” Adler said.

He warned that companies such as OpenAI are backed into a corner when they create capable but misaligned models with goals that are different from the ones they intended to build.

“Unfortunately, we don’t yet have strong scientific knowledge for fixing these models — just ways of papering over the behavior,” Adler said. 

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Tesla must pay portion of $329 million in damages after fatal Autopilot crash, jury says

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Tesla must pay portion of 9 million in damages after fatal Autopilot crash, jury says

A jury in Miami has determined that Tesla should be held partly liable for a fatal 2019 Autopilot crash, and must compensate the family of the deceased and an injured survivor a portion of $329 million in damages.

Tesla’s payout is based on $129 million in compensatory damages, and $200 million in punitive damages against the company.

The jury determined Tesla should be held 33% responsible for the fatal crash. That means the automaker would be responsible for about $42.5 million in compensatory damages. In cases like these, punitive damages are typically capped at three times compensatory damages.

The plaintiffs’ attorneys told CNBC on Friday that because punitive damages were only assessed against Tesla, they expect the automaker to pay the full $200 million, bringing total payments to around $242.5 million.

Tesla said it plans to appeal the decision.

Attorneys for the plaintiffs had asked the jury to award damages based on $345 million in total damages. The trial in the Southern District of Florida started on July 14.

The suit centered around who shouldered the blame for the deadly crash in Key Largo, Florida. A Tesla owner named George McGee was driving his Model S electric sedan while using the company’s Enhanced Autopilot, a partially automated driving system.

While driving, McGee dropped his mobile phone that he was using and scrambled to pick it up. He said during the trial that he believed Enhanced Autopilot would brake if an obstacle was in the way. His Model S accelerated through an intersection at just over 60 miles per hour, hitting a nearby empty parked car and its owners, who were standing on the other side of their vehicle.

Naibel Benavides, who was 22, died on the scene from injuries sustained in the crash. Her body was discovered about 75 feet away from the point of impact. Her boyfriend, Dillon Angulo, survived but suffered multiple broken bones, a traumatic brain injury and psychological effects.

“Tesla designed Autopilot only for controlled access highways yet deliberately chose not to restrict drivers from using it elsewhere, alongside Elon Musk telling the world Autopilot drove better than humans,” Brett Schreiber, counsel for the plaintiffs, said in an e-mailed statement on Friday. “Tesla’s lies turned our roads into test tracks for their fundamentally flawed technology, putting everyday Americans like Naibel Benavides and Dillon Angulo in harm’s way.”

Following the verdict, the plaintiffs’ families hugged each other and their lawyers, and Angulo was “visibly emotional” as he embraced his mother, according to NBC.

Here is Tesla’s response to CNBC:

“Today’s verdict is wrong and only works to set back automotive safety and jeopardize Tesla’s and the entire industry’s efforts to develop and implement life-saving technology. We plan to appeal given the substantial errors of law and irregularities at trial.

Even though this jury found that the driver was overwhelmingly responsible for this tragic accident in 2019, the evidence has always shown that this driver was solely at fault because he was speeding, with his foot on the accelerator – which overrode Autopilot – as he rummaged for his dropped phone without his eyes on the road. To be clear, no car in 2019, and none today, would have prevented this crash.

This was never about Autopilot; it was a fiction concocted by plaintiffs’ lawyers blaming the car when the driver – from day one – admitted and accepted responsibility.”

The verdict comes as Musk, Tesla’s CEO, is trying to persuade investors that his company can pivot into a leader in autonomous vehicles, and that its self-driving systems are safe enough to operate fleets of robotaxis on public roads in the U.S.

Tesla shares dipped 1.8% on Friday and are now down 25% for the year, the biggest drop among tech’s megacap companies.

The verdict could set a precedent for Autopilot-related suits against Tesla. About a dozen active cases are underway focused on similar claims involving incidents where Autopilot or Tesla’s FSD— Full Self-Driving (Supervised) — had been in use just before a fatal or injurious crash.

The National Highway Traffic Safety Administration initiated a probe in 2021 into possible safety defects in Tesla’s Autopilot systems. During the course of that investigation, Tesla made changes, including a number of over-the-air software updates.

The agency then opened a second probe, which is ongoing, evaluating whether Tesla’s “recall remedy” to resolve issues with the behavior of its Autopilot, especially around stationary first responder vehicles, had been effective.

The NHTSA has also warned Tesla that its social media posts may mislead drivers into thinking its cars are capable of functioning as robotaxis, even though owners manuals say the cars require hands-on steering and a driver attentive to steering and braking at all times.

A site that tracks Tesla-involved collisions, TeslaDeaths.com, has reported at least 58 deaths resulting from incidents where Tesla drivers had Autopilot engaged just before impact.

Read the jury’s verdict below.

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Crypto wobbles into August as Trump’s new tariffs trigger risk-off sentiment

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Crypto wobbles into August as Trump's new tariffs trigger risk-off sentiment

A screen showing the price of various cryptocurrencies against the US dollar displayed at a Crypto Panda cryptocurrency store in Hong Kong, China, on Monday, Feb. 3, 2025. 

Lam Yik | Bloomberg | Getty Images

The crypto market slid Friday after President Donald Trump unveiled his modified “reciprocal” tariffs on dozens of countries.

The price of bitcoin showed relative strength, hovering at the flat line while ether, XRP and Binance Coin fell 2% each. Overnight, bitcoin dropped to a low of $114,110.73.

The descent triggered a wave of long liquidations, which forces traders to sell their assets at market price to settle their debts, pushing prices lower. Bitcoin saw $172 million in liquidations across centralized exchanges in the past 24 hours, according to CoinGlass, and ether saw $210 million.

Crypto-linked stocks suffered deeper losses. Coinbase led the way, down 15% following its disappointing second-quarter earnings report. Circle fell 4%, Galaxy Digital lost 2%, and ether treasury company Bitmine Immersion was down 8%. Bitcoin proxy MicroStrategy was down by 5%.

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Bitcoin falls below $115,000

The stock moves came amid a new wave of risk off sentiment after President Trump issued new tariffs ranging between 10% and 41%, triggering worries about increasing inflation and the Federal Reserve’s ability to cut interest rates. In periods of broad based derisking, crypto tends to get hit as investors pull out of the most speculative and volatile assets. Technical resilience and institutional demand for bitcoin and ether are helping support their prices.

“After running red hot in July, this is a healthy strategic cooldown. Markets aren’t reacting to a crisis, they’re responding to the lack of one,” said Ben Kurland, CEO at crypto research platform DYOR. “With no new macro catalyst on the horizon, capital is rotating out of speculative assets and into safer ground … it’s a calculated pause.”

Crypto is coming off a winning month but could soon hit the brakes amid the new macro uncertainty, and in a month usually characterized by lower trading volumes and increased volatility. Bitcoin gained 8% in July, according to Coin Metrics, while ether surged more than 49%.

Ether ETFs saw more than $5 billion in inflows in July alone (with just a single day of outflows of $1.8 million on July 2), bringing it’s total cumulative inflows to $9.64 to date. Bitcoin ETFs saw $114 million in outflows in the final trading session of July, bringing its monthly inflows to about $6 billion out of a cumulative $55 billion.

Don’t miss these cryptocurrency insights from CNBC Pro:

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Google has dropped more than 50 DEI-related organizations from its funding list

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Google has dropped more than 50 DEI-related organizations from its funding list

Google CEO Sundar Pichai gestures to the crowd during Google’s annual I/O developers conference in Mountain View, California, on May 20, 2025.

David Paul Morris | Bloomberg | Getty Images

Google has purged more than 50 organizations related to diversity, equity and inclusion, or DEI, from a list of organizations that the tech company provides funding to, according to a new report.

The company has removed a total of 214 groups from its funding list while adding 101, according to a new report from tech watchdog organization The Tech Transparency Project. The watchdog group cites the most recent public list of organizations that receive the most substantial contributions from Google’s U.S. Government Affairs and Public Policy team.

The largest category of purged groups were DEI-related, with a total of 58 groups removed from Google’s funding list, TTP found. The dropped groups had mission statements that included the words “diversity, “equity,” “inclusion,” or “race,” “activism,” and “women.” Those are also terms the Trump administration officials have reportedly told federal agencies to limit or avoid.

In response to the report, Google spokesperson José Castañeda told CNBC that the list reflects contributions made in 2024 and that it does not reflect all contributions made by other teams within the company.

“We contribute to hundreds of groups from across the political spectrum that advocate for pro-innovation policies, and those groups change from year to year based on where our contributions will have the most impact,” Castañeda said in an email.

Organizations that were removed from Google’s list include the African American Community Service Agency, which seeks to “empower all Black and historically excluded communities”; the Latino Leadership Alliance, which is dedicated to “race equity affecting the Latino community”; and Enroot, which creates out-of-school experiences for immigrant kids. 

The organization funding purge is the latest to come as Google began backtracking some of its commitments to DEI over the last couple of years. That pull back came due to cost cutting to prioritize investments into artificial intelligence technology as well as the changing political and legal landscape amid increasing national anti-DEI policies.

Over the past decade, Silicon Valley and other industries used DEI programs to root out bias in hiring, promote fairness in the workplace and advance the careers of women and people of color — demographics that have historically been overlooked in the workplace.

However, the U.S. Supreme Court’s 2023 decision to end affirmative action at colleges led to additional backlash against DEI programs in conservative circles.

President Donald Trump signed an executive order upon taking office in January to end the government’s DEI programs and directed federal agencies to combat what the administration considers “illegal” private-sector DEI mandates, policies and programs. Shortly after, Google’s Chief People Officer Fiona Cicconi told employees that the company would end DEI-related hiring “aspirational goals” due to new federal requirements and Google’s categorization as a federal contractor.

Despite DEI becoming such a divisive term, many companies are continuing the work but using different language or rolling the efforts under less-charged terminology, like “learning” or “hiring.”

Even Google CEO Sundar Pichai maintained the importance diversity plays in its workforce at an all-hands meeting in March.

“We’re a global company, we have users around the world, and we think the best way to serve them well is by having a workforce that represents that diversity,” Pichai said at the time.

One of the groups dropped from Google’s contributions list is the National Network to End Domestic Violence, which provides training, assistance, and public awareness campaigns on the issue of violence against women, the TTP report found. The group had been on Google’s list of funded organizations for at least nine years and continues to name the company as one of its corporate partners.

Google said it still gave $75,000 to the National Network to End Domestic Violence in 2024 but did not say why the group was removed from the public contributions list.

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