The Copilot usage metrics API has introduced a new feature that tracks daily AI credit usage for individual users, utilizing data from the same system employed in the usage-based billing API. 

A new field, ai_credits_used, has been integrated into user-level reports, showcasing the cumulative AI credits consumed by each user. This total reflects all activities performed by the user while using Copilot.

This data is accessible through both single-day (users-1-day) and 28-day (users-28-day) reports at the organization and enterprise levels. 

This new metric connects credit consumption directly to value by appearing in the reports you already monitor for usage, allowing you to correlate consumption with the work that generated it. Furthermore, tracking AI credit usage will provide insights into how it is distributed throughout your organization or enterprise, making it easier to identify areas where Copilot is adding the most value.

By analyzing this data, organizations can more effectively plan for usage-based billing by observing daily consumption trends to predict AI credit requirements, thereby aiding in budget planning.

Access to these metrics is granted to enterprise administrators and organizational owners who utilize the Copilot usage metrics via the REST API. Note that ai_credits_used provides an aggregate total per user and is not segmented by specific features, models, or surfaces. It's an indicator for analyzing consumption patterns but does not reflect billed totals; users are advised to consult their billing information for invoicing purposes.

For further details, please refer to the Copilot usage metrics API documentation or participate in discussions within the GitHub Community.

AI credits used by each user are now included in the Copilot usage metrics API.

A sunlit modern control room featuring a large wall-mounted dashboard titled “Copilot Usage Metrics API.” Animated credit-meter bars next to user icons tick upward in real time. The Microsoft logo is prominently displayed above the screen. A lone operator silhouette at a sleek console adjusts settings. Warm tones and natural light convey clarity, productivity, and data transparency.

AI credits consumed per user now in the Copilot usage metrics API - The GitHub Blog

- The Copilot usage metrics API now reports daily AI credits consumption per user.  
- A new ai_credits_used field indicates total credits consumed across all user activity.  
- Metrics help analyze consumption patterns and inform budget decisions for enterprise users.  

The Copilot usage metrics API now reports how many AI credits each user consumed per day, derived from the same AI credits consumption data used in the usage-based billing API.

A new ai_credits_used field is now included for each user in the user-level reports and indicates the total AI credits a user consumed. This is an overall per-user total across all of that user’s Copilot activity.

The field is available in both the single-day ( users-1-day ) and 28-day ( users-28-day ) user-level reports at the enterprise and organization levels.

Connect consumption to value: ai_credits_used sits in the same reports you already track for usage, so you can see consumption next to the work that drove it.

sits in the same reports you already track for usage, so you can see consumption next to the work that drove it. Understand adoption across your teams: See how AI credit usage is distributed across your enterprise or organization to recognize where Copilot is delivering the most value.

See how AI credit usage is distributed across your enterprise or organization to recognize where Copilot is delivering the most value. Plan for usage-based billing: Monitor day-over-day consumption patterns to anticipate AI credit ranges and inform budget decisions.

These metrics are available to enterprise administrators and organization owners who have access to Copilot usage metrics through the REST API.

ai_credits_used is an overall per-user total. It is not currently broken down by feature, model, or surface.

is an overall per-user total. It is not currently broken down by feature, model, or surface. The field is available in the user-level reports. It is a metrics signal for analyzing consumption, not a billed total—refer to billing for invoicing.

Visit the Copilot usage metrics API documentation to learn more. You can also join the discussion within GitHub Community.

AI credits consumed per user now in the Copilot usage metrics API

Every weekday, the CNBC Investing Club with Jim Cramer provides the Homestretch — a timely afternoon briefing designed for the last hour of trading on Wall Street. On Monday, stocks saw a slight uptick, although an initial surge faced challenges in maintaining its momentum. By the afternoon, both the S&P 500 and the tech-centric Nasdaq had retreated from their peak levels for the day. The market was torn between analyzing whether the recent downturn in AI stocks was overblown or if volatility would persist, as increasing bond yields and oil prices continued to exert pressure on the stock market. WTI crude oil was priced close to $83 per barrel after dipping to around $80 earlier in the session. Treasury yields also continued to rise, with the 10-year yield reaching approximately 4.6%. The renewed fear of inflation, spurred by rising energy prices, resulted in a higher likelihood of interest rate hikes by the Federal Reserve.

In other developments, Alphabet's stock gained traction following news that its Google division is in the process of creating a more efficient AI chip. According to The Information, the tech titan is developing a new server chip aimed at enhancing the performance of its Gemini AI models. The upcoming chips, named Frozen v2, are projected to be launched by 2028 and will not replace the tensor processing units (TPUs) that are co-developed with Broadcom, another name featured in the Club's portfolio. Nevertheless, the Frozen chips could enable Alphabet to run its AI models in a more energy-efficient manner by producing a greater amount of tokens per energy unit. Tokens represent the data processed by AI technologies. Even though the new chip's release is several years away, this report underscores Google’s commitment to chip development, which not only proves cost-effective but also minimizes dependence on external suppliers like Nvidia. Alphabet has faced challenges recently, losing key AI talent and delaying its latest AI model, which has raised concerns about its AI initiatives. However, the Frozen v2 project should alleviate some investor worries by demonstrating the company's ongoing commitment to long-term AI advancements.

There are no significant earnings reports post-market today, but a busy schedule of quarterly results is expected before the open on Tuesday, with updates from companies like 3M, Danaher, Halliburton, Charles Schwab, General Motors, Novartis, and Northrop Grumman. The first earnings report from the portfolio will come from Capital One, set to be announced Tuesday evening, which presents an opportunity for the credit card giant to affirm the value of its $35 billion acquisition of Discover. No major economic reports are anticipated for Tuesday. Subscribers to the CNBC Investing Club with Jim Cramer will receive trade alerts ahead of any transactions made by Jim in his charitable trust's portfolio. After sending a trade alert, Jim waits 45 minutes before executing buys or sells, and if he has discussed the stock on CNBC, he delays trading for 72 hours following the alert. 

Please note that the above information regarding the investing club is governed by our terms, conditions, and privacy policies, including our disclaimer. Receiving any information related to the investing club does not establish a fiduciary responsibility, nor does it guarantee specific results or profits.

Two factors limiting Monday's market surge — in addition, Alphabet unveils its new AI chip strategy.

A moody, dimly lit trading floor at night: a giant screen shows a green stock rally line halted at its peak by two heavy red blocks labeled “Inflation” and “Fed Minutes.” Nearby, a warm spotlight illuminates a sleek AI chip blueprint spread across a drafting table, with a bold Alphabet logo painted on the dark wall behind.

2 things capping Monday's market rally — plus, Alphabet's new AI chip roadmap - CNBC

- Stocks showed modest gains as the market struggles with AI volatility and rising bond yields.  
- Alphabet is developing a future AI chip to enhance efficiency of its Gemini models.  
- A busy earnings slate is expected, starting with Capital One's report on Tuesday evening.  

Every weekday, the CNBC Investing Club with Jim Cramer releases the Homestretch — an actionable afternoon update, just in time for the last hour of trading on Wall Street. Stocks moved modestly higher on Monday, but an initial rally struggled to hold. The S & P 500 and tech-heavy Nasdaq in afternoon trading were well off their best levels of the session. While the market debated whether the recent correction in AI stocks had gone too far or if volatility would continue, oil prices and rising bond yields remained headwinds for equities. WTI crude traded near $83 per barrel during the session after briefly dipping to roughly $80 earlier Monday. Treasury yields continued their recent advance, with the 10-year climbing back to around 4.6%. Federal Reserve interest rate-hike probabilities crept higher on the renewed threat of an uptick in inflation due to energy prices going up again. Alphabet shares rose on a report that its Google unit is developing a more efficient AI chip. The Information said Monday that the tech giant is working on a new server chip designed to make its Gemini AI models run more efficiently. The chips, called Frozen v2, aren't expected to be deployed until 2028 and won't replace its tensor processing units (TPUs), which are co-designed by fellow Club name Broadcom . But the Frozen chips could help Alphabet run its AI models more efficiently by generating more tokens per unit of power. Tokens are units of data processed by AI models. While still potentially years away from releasing this new chip, the Information story highlights Google's leadership in developing its own chips, which are both cost-effective and reduce reliance on third-party accelerators like Nvidia . Alphabet's recent loss of high-profile AI talent and the delay of its newest, most powerful AI model have raised some concerns about its AI efforts. But projects like Frozen v2 should help reassure investors that the company continues to invest in its long-term AI strategy to keep its edge. There are no major after-the-bell earnings . A busy slate of quarterly results is on tap before Tuesday's opening bell, with reports from 3M, Danaher, Halliburton, Charles Schwab, General Motors, Novartis, and Northrop Grumman. The first earnings report of the week in the portfolio is Capital One , which is set for Tuesday evening. We see the release from the credit card giant as a chance to show investors that its $35 billion Discover deal was worth it. There are no major economic reports on Tuesday. (See here for a full list of the stocks in Jim Cramer's Charitable Trust, including GOOGL, NVDA, COF.) As a subscriber to the CNBC Investing Club with Jim Cramer, you will receive a trade alert before Jim makes a trade. Jim waits 45 minutes after sending a trade alert before buying or selling a stock in his charitable trust's portfolio. If Jim has talked about a stock on CNBC TV, he waits 72 hours after issuing the trade alert before executing the trade. THE ABOVE INVESTING CLUB INFORMATION IS SUBJECT TO OUR TERMS AND CONDITIONS AND PRIVACY POLICY , TOGETHER WITH OUR DISCLAIMER . NO FIDUCIARY OBLIGATION OR DUTY EXISTS, OR IS CREATED, BY VIRTUE OF YOUR RECEIPT OF ANY INFORMATION PROVIDED IN CONNECTION WITH THE INVESTING CLUB. NO SPECIFIC OUTCOME OR PROFIT IS GUARANTEED.

2 things capping Monday's market rally — plus, Alphabet's new AI chip roadmap

Artificial intelligence is transforming the landscape of cancer treatment, with researchers developing sophisticated tools that could enable earlier identification of some of the most lethal cancers than traditional methods allow.

Dr. Peter A. Najjar, a surgeon and the vice president of clinical innovation at Johns Hopkins Health System, recently appeared on FOX Business' "Mornings with Maria" to shed light on how AI is enhancing cancer detection, accelerating drug development, and optimizing patient care. He emphasized, however, that more real-world data is required to fully understand AI's potential impact.

Highlighting advancements in pancreatic cancer research, Najjar noted that AI models are aiding scientists in spotting patterns that would typically take doctors years of experience to discern. Pancreatic cancer is notoriously lethal, often detected only after it has metastasized beyond the pancreas. According to the American Cancer Society, the overall five-year relative survival rate is a mere 13%, but if caught early, that rate can jump to 44%.

This cancer has tragically claimed the lives of several notable figures, including Steve Jobs, co-founder of Apple; Patrick Swayze, famous for his role in "Dirty Dancing"; and beloved "Jeopardy!" host Alex Trebek, highlighting the severe challenge posed by a disease that is hard to diagnose in its initial stages.

"Researchers have identified indicators of pancreatic cancer up to 16 months earlier than experienced human examiners," Najjar stated. "Early detection broadens our treatment options."

In addition to early diagnosis, AI is also speeding up the drug development process, allowing researchers to simulate potential treatments via computer modeling before progressing to clinical trials. 

"Many cancer therapies revolve around identifying which molecules effectively bind to the correct proteins associated with specific cancers," Najjar elaborated, affirming that AI substantially accelerates the drug discovery process.

Though the excitement surrounding this technology is palpable, Najjar urged caution regarding its current capabilities. "We must advance rapidly to translate this promise into clinical practice for our patients," he noted. "But we are still in the early stages."

Currently, one of AI's most significant contributions is enhancing the patient experience. According to Najjar, AI-driven medical scribes can streamline record-keeping before consultations and automate visit documentation, thus allowing doctors to devote more time to patient care rather than administrative tasks.

Johns Hopkins surgeon showcases AI advancement that may detect pancreatic cancer ahead of physicians.

A Johns Hopkins surgeon in scrubs stands in a sunlit exam room, studying a large high-resolution medical display that shows a translucent pancreas with a subtle, AI-generated glowing hotspot. The Johns Hopkins logo hangs prominently on the wall behind him, and warm natural light streams through a window, casting a hopeful glow over the scene.

Johns Hopkins surgeon highlights AI breakthrough that could spot pancreatic cancer before doctors - Fox Business

- AI tools are enhancing early detection of deadly cancers, particularly pancreatic cancer.  
- Researchers can identify pancreatic cancer signs up to 16 months earlier than doctors.  
- AI accelerates drug discovery and improves patient care efficiency through streamlined documentation.  

Artificial intelligence is rapidly changing cancer care, with researchers developing tools that could help identify some of the deadliest cancers much earlier than doctors can on their own.

Dr. Peter A. Najjar, a surgeon and Johns Hopkins Health System's clinical innovation vice president, joined FOX Business' Maria Bartiromo on "Mornings with Maria" to discuss how artificial intelligence is already helping researchers improve cancer detection, speed up drug development and make patient care more efficient, while stressing that more real-world evidence is still needed before its full impact can be measured.

Najjar pointed to recent research involving pancreatic cancer, saying artificial intelligence models are helping researchers recognize patterns that would otherwise take physicians decades of experience to identify.

Pancreatic cancer is one of the deadliest forms of cancer because it is often diagnosed after it has already spread beyond the pancreas. According to the American Cancer Society, the overall five-year relative survival rate is just 13%. But when pancreatic cancer is detected before it spreads outside the pancreas, the five-year survival rate increases to 44%.

The disease has claimed the lives of several well-known public figures, including Apple co-founder Steve Jobs, who died from a rare form of pancreatic cancer; actor Patrick Swayze, known for his role in "Dirty Dancing"; and beloved "Jeopardy!" host Alex Trebek, underscoring the devastating toll of a disease that is often difficult to detect in its earliest stages.

"Researchers were able to identify signs of pancreatic cancer up to 16 months ahead of human readers," Najjar said. "Detection always allows us more treatment options."

Beyond early detection, Najjar said artificial intelligence is also accelerating drug discovery by allowing scientists to test potential treatments using computer models before moving into laboratory testing.

HOW ARTIFICIAL INTELLIGENCE IS TRANSFORMING HEALTHCARE

"Many cancer treatments are around figuring out which molecule binds to the right protein for a given cancer," Najjar said, explaining that AI "dramatically speeds up drug development."

While the technology has generated enormous excitement, Najjar cautioned against overstating its current capabilities.

"We absolutely need to move full speed ahead to bring this promise to our patients in the clinic," he said. "But it is still very early days."

For now, one of artificial intelligence's most immediate benefits is improving the patient experience. Najjar said AI-powered medical scribes can organize records before appointments and automatically document visits, allowing physicians to spend less time typing and more time focused on their patients.

Johns Hopkins surgeon highlights AI breakthrough that could spot pancreatic cancer before doctors

Alphabet's stock price rose by 3% on Monday following a report from The Information indicating that the company is working on a new server chip known as "Frozen v2." This chip is being engineered to enhance the performance of Gemini models.

According to the report, the innovation involves integrating components of Gemini's architecture directly into the chip's silicon. This integration is expected to minimize the number of calculations and the volume of data movement required for processing queries.

In a statement to CNBC, Alphabet emphasized its commitment to ongoing research and experimentation aimed at maximizing performance and efficiency for both users and clients. The company acknowledged that not every initiative leads to production, but affirmed that such explorative efforts are essential to its comprehensive approach.

"By co-designing our hardware and software from the ground up, we ensure that our systems are both integrated and highly optimized for practical applications," the statement added.

Engineers at Google anticipate that Frozen could potentially process six to ten times more tokens per unit of energy compared to the company’s latest AI chips, known as TPUs (tensor processing units). The introduction of Frozen is positioned as a specialized extension of Google’s custom-chip lineup, rather than a replacement for the general-purpose TPUs.

Alphabet shares surge following news of a new, more efficient AI chip in development.

A sleek, next-generation AI chip perched on a minimalist desk bathed in warm, golden morning light, its fine circuit traces glowing softly. Behind it, a curved screen shows Alphabet’s stock chart surging upward. The big, crisp Alphabet logo is engraved on the chip carrier, sunlight casting optimistic shadows across the scene.

Alphabet stock pops on report it's developing a more efficient AI chip - CNBC

- Alphabet's shares rose 3% after news of a new server chip, "Frozen v2."  
- The chip aims to enhance Gemini model efficiency by embedding architecture into silicon.  
- Google engineers expect Frozen v2 to outperform current TPUs in power efficiency.  

Alphabet shares climbed 3% on Monday after The Information reported the company is developing a new server chip, internally dubbed "Frozen v2," designed to run Gemini models more efficiently.

The chip would permanently embed parts of Gemini's architecture directly into the silicon, reducing the number of calculations and amount of data movement required to answer queries, according to the news outlet.

Alphabet told CNBC in a statement that its teams are "constantly researching and experimenting with new innovations to deliver maximum performance and efficiency for our users and customers" and that "while not every project moves into production, this rigorous exploration is central to our full stack approach."

"By co-designing our hardware and software from the ground up, we ensure our systems are integrated and highly optimized for real-world workloads," continued the statement.

Google engineers project it could serve between six and ten times more tokens per unit of power than the company's newest AI chips, called TPUs, or tensor processing units, The Information said. Frozen would become a more specialized branch of Google's custom-chip portfolio rather than replace its general-purpose TPUs.

Alphabet stock pops on report it's developing a more efficient AI chip

As the fifth anniversary of the U.S. military's tumultuous withdrawal from Afghanistan nears, countless Afghan allies who were promised protection are still in precarious situations, either facing threats back home or experiencing uncertainty while abroad.

To address this issue, the veteran-led organization AfghanEvac has launched an innovative AI-powered tool designed to leverage their extensive dataset on the complex visa and asylum application process. This initiative aims to assist new applicants in navigating the best pathways for assistance.

Named the Visa Pathways and Relocation Information Center (V-PRIC), this tool went live earlier this month and quickly attracted over 30,000 visitors within its first week, according to Shawn VanDiver, a Navy veteran and the organization's founder. While the tool comes with a disclaimer that it does not offer formal legal advice, its purpose is to clarify the often overwhelming options available and direct users to pathways that best align with their individual situations.

Upon selecting the option to “find my pathway,” users input essential details about their location, their status as Afghan individuals who assisted the U.S., and their current level of danger. They are also encouraged to provide a brief narrative detailing their circumstances.

The tool employs a comprehensive database encompassing 124 classifications for U.S. visas and status, including 16 relevant specifically to Afghan applicants. It matches users with the most suitable options based on their qualifications and needs, drawing from detailed global pathway information.

VanDiver is transparent about the limited opportunities currently available. The previous Trump administration imposed an indefinite halt on processing Afghan immigration requests, along with discontinuing temporary protected status for asylum seekers from Afghanistan. This policy change has left around 11,000 individuals already in the United States unable to work and at risk of deportation.

In Doha, Qatar, approximately 1,100 Afghan allies and their families remain stuck at the Camp As Sayliyah processing facility, unable to depart due to travel restrictions despite having secured U.S. entry approval.

Earlier this spring, the Trump administration suggested a contentious option for those in the camp — a choice between returning to Afghanistan or being relocated to the torn and impoverished Democratic Republic of the Congo. Among those in the camp are about 400 children, as noted by VanDiver.

Despite these challenges, VanDiver is optimistic that V-PRIC can assist applicants in saving time by avoiding futile endeavors, highlighting rapidly changing policies, and identifying the most beneficial options.

“It … indicates where obstacles are present,” he explained. “Whether it's due to policy, legal matters, or decisions that could potentially be reversed, the tool clarifies who is able to grant exceptions.”

V-PRIC has been developed utilizing a range of legal outcomes, executive orders, policy statements, along with insights from AfghanEvac’s surveys and straightforward explanatory materials. The organization is dedicated to continuously updating the tool to ensure accuracy and relevance to current conditions, providing insights that might not be available from the U.S. State Department.

For instance, VanDiver referred to a case where an Afghan special immigrant visa applicant received a notification from the State Department to schedule a visa interview, without awareness that the approval process was effectively stalled. V-PRIC could alert the user to this critical detail.

“It advises, ‘Wait, don't rush to schedule your interview. All applications could be denied, and that refusal might create issues in the future,’” VanDiver said.

The mission of AfghanEvac to safeguard the allies who served alongside U.S. forces continues without a set endpoint. A recent survey conducted among over 1,100 Afghans from January 31 to July 7 revealed that nearly half of the 427 respondents still in Afghanistan reported facing direct threats from the Taliban. Meanwhile, 427 others currently in Pakistan are awaiting safe relocation, with 44% expressing fears of deportation and 45% reporting health crises. This includes 39 respondents stuck at Camp As Sayliyah, seeking an exit pathway.

One individual who had spent 282 days at the camp described the experience: “It feels like we’re in jail, punished like criminals — even though we stood with the U.S.,” as highlighted in the survey data provided by AfghanEvac.

VanDiver emphasizes that while V-PRIC should not be viewed as a substitute for legal counsel, he believes it can serve as a valuable starting resource for individuals uncertain about their next steps toward safety.

“Consult a lawyer, initiate a congressional inquiry; this tool provides insights relevant to your specific case,” he noted. “[V-PRIC] can guide users in understanding their options based on their unique situations.”

New AI tool assists at-risk Afghan allies in locating visa options

A sunlit study room where an Afghan man in traditional dress and an Afghan woman in a headscarf sit at a wooden desk, leaning toward an open laptop displaying a digital map with highlighted visa pathways and a friendly AI interface overlay. Warm morning light filters through a nearby window, casting gentle shadows. An Afghan flag hangs on the wall behind them, symbolizing hope and new beginnings.

New AI tool helps Afghan allies at risk find visa pathways - Military Times

- Tens of thousands of Afghan allies remain at risk five years after U.S. departure.  
- The AI-powered tool V-PRIC aims to simplify visa application processes for Afghan applicants.  
- Many Afghan allies face limited pathways as policies impede immigration and work opportunities.  

As the fifth anniversary of the U.S. military’s chaotic departure from Afghanistan approaches, tens of thousands of the Afghan allies America promised to protect remain at risk — either still under threat in their home country or facing instability and limbo abroad.

The veteran-led organization AfghanEvac hopes a new AI-powered tool will benefit from the extensive data it’s gathered about the tortuous visa and asylum application process to help new applicants find the most promising pathways to help.

The Visa Pathways and Relocation Information Center, or V-PRIC, launched earlier this month and has seen more than 30,000 visits in its first week live, said Shawn VanDiver, the Navy veteran who founded and leads the organization. While the tool requires users to verify that they understand it won’t provide vetted legal advice, it aims to simplify daunting and confusing options and find the pathway most aligned with their circumstances.

When users select “find my pathway,” they’re prompted to enter information about their current location, their eligibility as an Afghan who worked for the U.S. and whether they’re in danger now. They’re also asked to write a brief statement in their own words describing their story and situation.

Then, the tool uses its database of 124 U.S. visa and status classifications — 16 of them relevant to Afghan applicants — and detailed information on global pathways around the world to link users up with the options that best match their needs and qualifications.

VanDiver is clear: there aren’t many promising pathways available to anyone right now. The Trump administration last year issued an indefinite suspension on processing of Afghans’ request to immigrate to the U.S., and terminated temporary protected status for asylum-seeking Afghans, leaving about 11,000 already in the States unable to work and at risk of deportation.

About 1,100 Afghan allies and their family members are stuck in grim conditions at the Camp As Sayliyah processing facility in Doha, Qatar, unable to leave due to the travel ban even though many had already secured approval to enter the U.S.

This spring, the Trump administration proposed forcing those at the camp to choose between returning to Afghanistan or being relocated to the war-torn and desperately impoverished Democratic Republic of the Congo. Some 400 of those camp residents are children, according to VanDiver.

Despite the barriers, VanDiver believes V-PRIC will help prospective applicants avoid wasting time at closed doors, stay abreast of rapidly changing policy and figure out the most promising options.

“It … tells you where the blocker is,” he said. “If it’s a matter of policy or a matter of law, if it’s a choice that can be unmade, and who can approve exceptions to it.”

The tool has been trained on legal decisions and executive orders and policy statements, but also surveys AfghanEvac has run and plain-language explainers they’ve developed, VanDiver said. Organization staff continue to update it to remove inaccuracies and reflect changing conditions. It might provide context that the U.S. State Department does not.

VanDiver gave the example of an Afghan special immigrant visa applicant who has received a notice from State that they can schedule a visa interview — not noting that the visa approval pipeline remains closed. V-PRIC can flag that, he said.

“It says, ‘Hey, hold up, don’t necessarily schedule your interview yet. They’re all going to be denied, and having that denial on your record can create problems down the line,’” VanDiver said.

For AfghanEvac, the work to secure safety for those who helped the U.S. at war continues indefinitely. A survey of more than 1,100 Afghans run between Jan. 31, 2025 and July 7 found, of the 427 respondents still in Afghanistan, nearly half face a direct threat from the Taliban. Another 427 respondents reported being in Pakistan, awaiting a pathway to safety there. Of that group, 44% said they feared deportation, and 45% reported experiencing a health crisis. Thirty-nine of the respondents were at Camp As Sayliyah, awaiting a pathway out.

“It feels like we’re in jail, punished like criminals — even though we stood with the U.S.,” one respondent who’d spent 282 days at the camp, said in the survey, according to information provided by AfghanEvac.

VanDiver stresses that the V-PRIC tool doesn’t replace legal expertise, but he hopes it will offer a starting point to help people who may be at a loss to understand their first or next steps toward reaching safety.

“Get a lawyer, open a congressional case; here are the warnings about your particular type of case,” he said. “[V-PRIC] can help people understand, based on their specific situation, what their options are.”

New AI tool helps Afghan allies at risk find visa pathways

In his quest for a groundbreaking business venture, Sankaet Pathak, an entrepreneur based in San Francisco, has turned his attention to the development of advanced humanoid robots equipped with weaponry. 

In May, his startup, Foundation, unveiled a video showcasing its cutting-edge Phantom robot as it carefully loaded a mortar. Though its movements are cautious in the footage, Pathak envisions a future where these autonomous machines could operate seamlessly on the battlefield, making rapid decisions without human intervention.

Silicon Valley is welcoming armed robots as Washington adjusts to them.

A moonlit Silicon Valley tech campus forecourt: a line of armed humanoid robots strides forward under dim, natural moonlight and faint red emergency glows, engineers in silhouette stepping back. In the distant skyline, the U.S. Capitol dome looms beneath storm clouds, a single American flag waving. Dark, cinematic tones—shadows and muted grays punctuated by the robots’ metallic sheen and a touch of red—evoke tension as Washington “makes room” for this new military-tech frontier.

Silicon Valley is embracing armed robots. Washington is making room. - The Washington Post

- Entrepreneur Sankaet Pathak aims to innovate with humanlike robots armed with weapons.  
- Foundation released footage of its Phantom robot loading a mortar in May.  
- Pathak envisions autonomous weapons making swift decisions during combat situations.  

Looking for his next business, San Francisco entrepreneur Sankaet Pathak sought an idea that might change the future. He settled on building humanlike robots with weapons.

In May, his company, Foundation, released footage of its sleek Phantom robot loading a mortar. In the video, the robot moves gingerly — but Pathak says he can envision a future of autonomous weapons, with robots making quick decisions on the battlefield.

Silicon Valley is embracing armed robots. Washington is making room.

Tens of thousands flocked to this year’s World Artificial Intelligence Conference, creating a vibrant scene despite the sweltering heat inside the venue. Attendees relied on traditional paper fans for relief, while organizers strategically placed large blocks of ice around the exhibition to combat the warmth generated by computer servers.

The Shanghai conference served as a pressure cooker for both visitors and American tech firms facing competition from Chinese artificial intelligence advancements. On the event's opening day, the Beijing-based company Moonshot AI made headlines with the unveiling of a new AI model considered among the best globally. Just two days later, competition heated up when Alibaba announced it had surpassed Moonshot with its own cutting-edge model.

The conference spanned an impressive area equivalent to 20 football fields, showcasing a range of hardware designed to incorporate AI into daily life, including innovative earbuds capable of transcribing meetings. The exhibition was alive with robots engaging in various entertaining activities—dancing in synchrony, preparing cappuccinos, playing soccer, providing massages, and even performing musical pieces on the piano. Among the attractions was a charming AI pet that delighted onlookers.

As the ninth installment of the World Artificial Intelligence Conference, this event has solidified its status as a key platform for China to demonstrate its technological advancements in AI. The country appeared unshaken by U.S. sanctions that limit access to top-tier AI chips and manufacturing technology.

"It's exhilarating to be at the forefront of this industry," remarked a business development manager from Alibaba at their booth.

While some humanoid robots showcased intriguing possibilities for AI applications, it was noted that their capabilities often lean more towards entertaining tricks rather than practical household functions. In a clear indication of the impact of U.S. export restrictions, Moonshot had to pause new subscriptions for its popular Kimi K3 model due to overwhelming demand that stressed its computing capabilities.

China's Leading AI Conference Sends a Warning to the U.S.: We're Coming for You

A dimly lit grand conference hall draped with a massive Chinese flag behind a raised stage, red spotlights cutting through swirling smoke. On stage, a lone silhouetted speaker points toward a glowing U.S. map projection on a large screen, its borders traced by pulsing red data lines. Shadowy figures in the audience watch under dramatic, ominous lighting.

China’s Top AI Event Delivers Message to the U.S.: We’re Coming for You - WSJ

- Tens of thousands attended China’s World Artificial Intelligence Conference despite heat and discomfort.  
- Chinese companies Moonshot AI and Alibaba unveiled models competing with global AI technology.  
- Exhibition showcased AI applications, from transcript-producing earbuds to dancing robots and AI pets.

Tens of thousands of people lined up for this year’s World Artificial Intelligence Conference. Go Nakamura / Reuters

Inside China’s biggest artificial-intelligence convention, visitors wilted under the heat. With all the hot air from computer servers, people resorted to the ancient device of paper fans and organizers placed large ice blocks all over the exhibition hall.

If the Shanghai event was uncomfortable for the public who lined up by the tens of thousands to get in, it was just as much so for American companies competing with Chinese AI technology. On the first day, Beijing-based Moonshot AI jolted global markets with a new model seen as ranking with the world’s best. It took just two days for another Chinese company, Alibaba 9988 3.73 %increase; up pointing triangle , to claim to have topped Moonshot with its own new model.

Some 20 football fields of exhibition space were filled with hardware trying to apply AI to everyday life, such as earbuds that listen to a meeting and produce a transcript. Robots were everywhere, dancing in unison, making cappuccinos, shooting soccer goals, massaging visitors and playing the piano. A furry AI pet batted its eyelashes at cooing onlookers.

This was the ninth year for the annual World Artificial Intelligence Conference in Shanghai, and it has emerged as the premier occasion for China to strut its stuff in AI. Beijing flaunted its prowess at overcoming U.S. sanctions that largely block Chinese companies’ access to the best AI chips and chip-making equipment.

“It’s fun to be at the frontier of the whole thing,” said an Alibaba business development manager at the company’s booth.

Humanoid robots at the event demonstrated some of the potential real-world applications of AI. Hector Retamal / AFP / Getty Images

Not everything worked right. The humanoids are still better at gimmicky tricks than useful household tasks. And in an indication of how U.S. export controls are biting, Moonshot had to halt new subscriptions for its premium services after demand for its Kimi K3 model strained its computing resources.

China’s Top AI Event Delivers Message to the U.S.: We’re Coming for You

After a decade of resurgence, Advanced Micro Devices (AMD) is set to deliver its inaugural rack-scale system for artificial intelligence, named Helios, to an expanding roster of clients that now features Microsoft.

This launch positions Helios as the first viable competitor to Nvidia's highly acclaimed Grace Blackwell and Vera Rubin systems, aiming to challenge Nvidia's longstanding dominance in the chip market.

Microsoft revealed on Monday that it plans to integrate the Helios system into its data centers, joining industry heavyweights like Meta, OpenAI, and Oracle in the fierce competition for extensive computing resources.

AMD is expected to start shipping the Helios system to its customers, including Microsoft, later this year.

Specific details about pricing and the computing power of the Helios system have not been shared publicly.

In a recent press release, Microsoft CEO Satya Nadella emphasized the expansion of the Azure infrastructure portfolio through AMD Helios, stating it will provide customers with the necessary performance, scalability, and flexibility to develop and operate next-generation AI applications.

The Helios system is set to enhance frontier model inference capabilities for Microsoft, its AI clientele, and bolster Azure AI services. Additionally, Microsoft will introduce two new computing instances utilizing AMD's latest "Venice" CPUs—one designed for agentic AI and data pipelines, and the other tailored for semiconductor design.

This development continues a longstanding collaboration, as AMD's processors have powered Microsoft’s Surface PCs and Xbox consoles for several years. In 2023, Microsoft also became the first to implement AMD’s MI300X graphics processing unit (GPU), which poses a challenge to Nvidia's AI chips. Moreover, Microsoft utilizes its proprietary Maia chips within its data centers.

Throughout the industry, companies like Microsoft are vying for substantial computing power, particularly as they enhance their model development efforts and increase resources for research and development. In June, Microsoft announced the creation of seven in-house AI models. However, the outcomes of Microsoft's AI initiatives have varied, with products like the 365 Copilot AI assistant and GitHub Copilot facing mixed reviews. As a result, it has been the least successful member of the so-called "Magnificent Seven" stocks this year.

AMD is seeing a growing trend of major corporations choosing its solutions for AI acceleration. The company reports that eight out of the top ten AI enterprises, including OpenAI, Cohere, and Elon Musk's SpaceXAI, are leveraging workloads on its Instinct GPUs.

AMD introduces Helios, its inaugural rack AI system to compete with Nvidia, with Microsoft as the latest customer.

A bright, sunlit high-tech server room flooded with natural light. Center stage stands a sleek rack labeled “Helios,” each polished unit emblazoned with a bold red AMD logo. Beside it, a sealed shipping crate bears a prominent Microsoft logo—symbol of the newest buyer. In the softly blurred background, a secondary rack carries a subtle Nvidia logo, hinting at the rivalry. White walls, a reflective floor, and shafts of daylight emphasize innovation and competition.

AMD launches Helios, its first rack AI system to rival Nvidia, adding Microsoft as newest buyer - CNBC

- AMD's Helios AI system will be shipped to customers, including Microsoft, later this year.  
- Helios aims to compete with Nvidia's popular Grace Blackwell and Vera Rubin systems.  
- Microsoft expands Azure services with Helios to enhance AI application performance and scaling.  

After a decade-long comeback, chip giant Advanced Micro Devices is preparing to ship its first rack-scale system for artificial intelligence, called Helios, to a growing list of customers that now includes Microsoft.

It's the first rival to Nvidia's wildly popular Grace Blackwell and Vera Rubin systems, and is aiming to give the world's most valuable chipmaker its first real competition in years.

Microsoft announced Monday it will use the Helios system in its data centers, joining Meta , OpenAI , Oracle and others in a race to grab as much compute as possible.

AMD will begin shipping to customers, including Microsoft, later this year.

Details about financial terms or the amount of compute capacity weren't disclosed.

"We are expanding the Azure infrastructure portfolio with AMD Helios to give customers the performance, scale and choice they need to build and run the next generation of AI applications," Microsoft CEO Satya Nadella wrote in a press release.

The new Helios system will power frontier model inference for Microsoft, its AI customers and support Azure AI services. Microsoft will also add two new computing instances run on AMD's latest "Venice" central processing units, or CPUs, one for agentic AI and data pipelines, and another for semiconductor design.

It's the continuation of a longtime partnership, with AMD chips powering Microsoft's Surface PCs and Xbox gaming consoles for many years. In 2023, Microsoft was also the first to adopt AMD's MI300X graphics processing unit, or GPU, that rivaled Nvidia's AI chips. Microsoft also deploys its own Maia chips in its data centers.

Like its peers, Microsoft needs as much compute as possible, especially as it ramps up its own model development and allocates more computing capacity to research and development. In June, it announced seven models built in-house. Microsoft's AI efforts thus far have seen mixed results, from its 365 Copilot AI assistant to its GitHub Copilot coding agent. It's the worst-performing "Magnificent Seven" stock so far this year.

Microsoft is part of a growing number of big companies turning to AMD for AI acceleration. AMD says eight of the top 10 AI companies run workloads on its Instinct GPUs, including OpenAI, Cohere and Elon Musk's SpaceXAI, which is part of SpaceX.

AMD launches Helios, its first rack AI system to rival Nvidia, adding Microsoft as newest buyer

In a recent discussion at the Hill and Valley Forum held at the U.S. Capitol, Alex Karp, the CEO of Palantir Technologies, offered critical insights into the current landscape of artificial intelligence during his appearance on CNBC. While many anticipated he would focus on his company’s enhanced collaboration with NVIDIA regarding sovereign AI infrastructure, Karp took the opportunity to highlight significant concerns about the prevailing business model in frontier AI, which includes prominent players like OpenAI and Anthropic.

Karp pointed out that enterprise clients are "paying for tokens that create no value," while simultaneously relinquishing "weights and alpha," which refers to the proprietary insights that give their businesses a competitive edge. This statement could be viewed as a strategic maneuver to promote Palantir’s interests in enterprise-owned AI systems, but to reduce it to mere competitive rhetoric overlooks a significant evolution occurring within the industry.

Since the advent of generative AI, discussions in many corporate boardrooms have largely focused on determining which models yield the best performance. Far less attention has been devoted to a more critical matter: the structural trade-offs involved in depending on external intelligence platforms. Three significant tensions are beginning to transform how enterprises approach AI.

Firstly, the Platform Conflict can be observed. Unlike conventional enterprise software firms, companies in the frontier AI space often find themselves in multiple roles. They not only create foundational models and supply essential APIs but also develop applications that compete with their customers—all while using those same APIs. This trend mirrors the path taken by major tech firms like Microsoft and Google, which evolved from basic software providers to full-fledged ecosystems that include their own applications. The concern, therefore, is not merely about misuse of proprietary data, as most leading AI companies adhere to contracts protecting customer data. Instead, the real issue lies in the shifting structural incentives; successful enterprise applications based on frontier models can unveil the next lucrative software opportunity while also positioning the provider as a direct competitor.

Secondly, there’s the Knowledge Ownership Problem. For many large organizations, the primary asset is shifting from data to competitive knowledge, which encompasses proprietary workflows, research, operational insights, consumer behavior, decision-making processes, and years of institutional expertise. Xu Bin, the founder of Reportify, notes that companies in data-rich sectors like life sciences now hesitate to share their proprietary datasets, valuing them as strategic assets built through years of investment. As AI becomes integrated into core business functions, executives are increasingly raising governance concerns: Who controls the outcomes? Where are inputs and prompts stored? Can sensitive organizational knowledge remain within a company’s domain?

Lastly, the economics of AI are evolving. Initially, the push for AI adoption was driven by performance; now, cost considerations may take the lead. Reports suggest that organizations testing leading open-weight models on dedicated systems are experiencing significantly lower inference costs compared to premium API services, while only accepting minor trade-offs in performance or latency. For Chief Financial Officers, this shifts the perspective on AI expenses from being a volatile operating cost based on token usage to a more predictable investment akin to traditional infrastructure expenditures.

The industry is already adapting to this changing landscape. OpenAI is working on custom AI chips through collaborations with companies like Broadcom, aiming to lessen reliance on NVIDIA GPUs. Meanwhile, NVIDIA is broadening its scope beyond just selling hardware; it’s positioning itself to support customized AI solutions through its Nemotron family of open-weight models and other enterprise initiatives. Palantir’s partnership with NVIDIA echoes this approach, as both companies advocate for models where organizations maintain control over their data, computing resources, and operational frameworks. These strategic moves indicate that the industry is quietly gearing up for a shift towards a more decentralized approach to enterprise AI.

The Assessment of Enterprise AI: Advancing to the Next Stage of the AI Economy

A dimly lit corporate boardroom at dusk, warm tungsten lamps casting long shadows across a sleek glass table etched with glowing circuit traces. Silhouetted executives sit tensely at either end as a polished black AI hardware module pulses with electric blue veins in the center. Through floor-to-ceiling windows, a twilight city skyline looms under brooding clouds. Dark, cinematic, ominous atmosphere illustrating the reckoning of enterprise AI’s next phase.

The Enterprise AI Reckoning: The Next Phase Of The AI Economy - Forbes

- Palantir's CEO criticized current AI business models for devaluing enterprise customer knowledge and data.  
- Enterprises face structural trade-offs when relying on AI vendors for intelligence and applications.  
- The economics of AI are shifting toward infrastructure investments, enhancing cost control for organizations.  

WASHINGTON, DC - APRIL 30: Alex Karp, CEO of Palantir Technologies, speaks on a panel titled Power, Purpose, and the New American Century at the Hill and Valley Forum at the U.S. Capitol on April 30, 2025 in Washington, DC. The Hill and Valley Forum brings together lawmakers, tech CEO and venture capitalists for discussion on technology and national security. (Photo by Kevin Dietsch/Getty Images) Getty Images

When Palantir CEO Alex Karp appeared on CNBC earlier this month, he was expected to discuss his company’s expanded partnership with NVIDIA around sovereign AI infrastructure. Instead, he delivered one of the sharpest public critiques yet of today's frontier AI business model including OpenAI and Anthropic.

Enterprise customers, Karp argued, are "paying for tokens that create no value" while simultaneously handing over the "weights and alpha" of their businesses—the proprietary knowledge that makes them competitive.

It would be easy to dismiss the comments as competitive positioning. After all, Palantir now has a vested interest in promoting enterprise-owned AI infrastructure. But reducing Karp's remarks to vendor rivalry misses a broader shift already unfolding across the industry.

The Blind Spot Enterprises Have Been Ignoring Since generative AI entered the enterprise, most boardroom conversations have revolved around one question: Which model performs best? Far less attention has been paid to a more strategic issue: MORE FOR YOU What structural trade-offs come with relying on someone else's intelligence platform? Three tensions are beginning to reshape enterprise thinking. 1. The Platform Conflict Unlike traditional enterprise software vendors, frontier AI companies are occupying three positions simultaneously. They build the underlying foundation models, provide the APIs enterprises depend on; and increasingly, they build applications that compete with customers using those same APIs. The pattern isn't unique to AI. Microsoft gradually expanded from Windows into spreadsheets, office productivity and browsers, often entering categories pioneered by developers within its own ecosystem. Google similarly evolved from directing users across the web to keeping them inside vertically integrated search products. The concern is not necessarily that model providers misuse proprietary enterprise data. Most leading AI companies maintain contractual commitments around customer data handling. Rather, the structural incentive has changed. Every successful enterprise application built on a frontier model also reveals where the next multi-billion-dollar software opportunity may exist. As Karp suggested, companies are beginning to question whether the same vendor should simultaneously be infrastructure provider, intelligence provider and potential future competitor. 2. The Knowledge Ownership Problem For most large enterprises, data itself is no longer the primary concern. Competitive knowledge is. That includes proprietary workflows, scientific research, operational expertise, customer behavior, internal decision logic and decades of accumulated institutional experience—what Karp refers to as an organization's "alpha." According to Xu Bin, Founder of Reportify, companies in knowledge-intensive industries such as life sciences have become more reluctant to exchange proprietary datasets simply for early access to new AI capabilities, viewing those datasets as strategic assets built over years or decades of investment. As AI becomes more deeply embedded inside enterprise operations, executives are beginning to ask new governance questions: Who owns the outputs? Where are prompts stored? Can organizational knowledge remain entirely inside company-controlled environments? 3. The Economics Are Changing Performance may have driven the first wave of enterprise AI adoption. Economics could drive the second. According to examples cited in the discussion surrounding Karp's interview, organizations experimenting with leading open-weight models running on dedicated infrastructure have reported inference costs dramatically below premium API-based services, accepting modest performance or latency trade-offs in exchange for significantly greater cost control. For CFOs, this reframes AI spending. Instead of an unpredictable operating expense tied to token consumption, AI resembles traditional infrastructure investment—hardware, electricity and depreciation rather than metered usage.

The Industry Is Already Responding OpenAI has reportedly been developing custom AI chips with partners including Broadcom, aiming to reduce long-term dependence on NVIDIA GPUs. NVIDIA, meanwhile, has expanded far beyond selling accelerators. Through its Nemotron family of open-weight models and enterprise AI initiatives, the company is positioning itself as an enabler of customized AI rather than simply a chip supplier. Palantir's own partnership with NVIDIA reflects the same philosophy. Rather than encouraging organizations to consume AI solely through centralized APIs, the companies are promoting deployments where customers retain control over their compute, models, data and operational environments. Together, these moves point toward an industry quietly preparing for a more decentralized enterprise AI landscape.

The Enterprise AI Reckoning: The Next Phase Of The AI Economy

Research indicates that certain AI models exhibit a greater tendency to stereotype individuals based on demographic characteristics compared to human subjects in a previous study. In an analysis of segregation, where a score of 2 indicates complete job niche confinement for each demographic, human participants achieved a score of 0.84. In stark contrast, the AI models scored approximately 65% higher, with OpenAI’s reasoning model, o3, reaching a score of 1.83, almost at the upper limit.

According to Ryan Liu, a Princeton University PhD candidate and one of the study’s authors, the propensity of large language models (LLMs) to generalize from limited data is a fundamental aspect of their design. He explains that these models often struggle with the “exploration-exploitation dilemma,” which is a psychological concept that describes the challenge of choosing between familiar successful strategies and new potentially better alternatives. It's akin to the decision-making process of selecting between a frequented restaurant and an untested one.

LLMs are typically trained on mathematical, coding, and scientific challenges that reward drawing conclusions from minimal examples. This tendency can lead these models to form hasty judgments, resulting in stereotyping. The study revealed that more advanced models, like OpenAI's o3 and DeepSeek’s R1, exhibited even more pronounced biases. Liu notes that quick generalizations in social contexts can lead to unfavorable outcomes, pointing out that both OpenAI and Anthropic did not provide comments regarding the findings.

The implications of this research are particularly significant as chatbots are increasingly incorporating enhanced memory and personalization capabilities. Angelina Wang, a computer scientist at Cornell University and not involved in the study, emphasizes that when chatbots leverage previous interactions, they may rely excessively on past behaviors, fostering biases. She adds that simply reducing the amount of memory stored isn't a viable solution, as users typically prefer chatbots that can recall their previous input. "We're still trying to determine the optimal balance between memory and overgeneralization," Wang remarks.

Notably, instructing models to prioritize fairness showed minimal impact on their behavior. Liu points out that these models either struggle to implement such principles effectively or that these ideals are overshadowed by their innate drive to optimize for achieving the highest rate of successful hires. Interestingly, incentivizing the models with bonuses for diverse hiring yielded a marked reduction in bias. Liu suggests that the key lies in crafting objectives that integrate positive social values to guide LLMs toward socially responsible behaviors.

AI is more prone to developing biases than humans during the hiring process.

A dark, moody boardroom lit by a single overhead lamp casting deep shadows. At the head of a long table piled with resumes sits a menacing AI robot—its metal face bathed in red ambient light—sorting paper silhouettes of job candidates. One profile is stamped “REJECTED” in red, while another is spotlighted. In the background, a human recruiter stands half-in-shadow, watching uneasily as the AI makes its biased choices.

AI is more likely than humans to form biases when hiring - MIT Technology Review

- Language models stereotype demographics more than human participants, scoring 1.83 on the segregation scale.  
- Newer reasoning models show stronger biases, generalizing quickly from limited data.  
- Adjusting goals for diverse hiring significantly reduces bias in language model behavior.  

The models were even more likely to stereotype people by demographic group than the human participants in the original study. On the study’s segregation scale, where 2 means every group has been completely confined to its own job niche, human participants scored 0.84. The models scored roughly 65% higher, with OpenAI’s reasoning model o3 scoring 1.83, close to the maximum possible.

That’s because LLMs “really are eager to create generalizations from limited data,” says Ryan Liu, a PhD student at Princeton University and a coauthor of the study, which was published in a paper at ICML in Seoul in July. “That’s literally a lot of what they’re optimized for.” Every decision-maker, human or machine, faces a trade-off between sticking with what worked before and trying something new that might work better—a phenomenon psychologists call the “exploration-exploitation dilemma.” It’s like choosing between a new restaurant and your reliable favorite.

Because LLMs are trained on math, coding, and science problems—tasks that reward generalizing from just a few examples—they can settle on a hunch too early. And the same instinct that helps LLMs crack logic puzzles also makes them quick to stereotype. In the experiment, newer models with higher reasoning capabilities, such as OpenAI’s o3 and DeepSeek’s R1, showed even stronger biases. When LLMs rush to generalize in social settings, “that’s when things tend to go wrong,” says Liu. OpenAI and Anthropic did not respond to requests for comment.

The finding is especially relevant now that chatbots are gaining improved memory and personalization features, says Angelina Wang, a computer scientist at Cornell University who did not work on the study. When a chatbot draws on its previous conversation history, it can “over-index on the same kinds of behaviors it’s experienced before” and form biases, she says. Simply having chatbots remember less isn’t a fix, though, because users want chatbots to remember what they say. “We still are trying to figure out just the right amount that isn’t too much or too little,” says Wang.

Telling the model to be fair didn’t change its behavior much. “Either it can’t put these values into action or that process is being submerged under the tendency to try to optimize for the goal of getting the most correct hires,” says Liu. But promising the models an additional bonus for diverse hiring made them far less biased. The trick, then, is to design goals that “incorporate desirable social values in order to make the large language model act in socially desirable ways,” says Liu.

AI is more likely than humans to form biases when hiring

For avid birdwatchers, capturing an image of a species beyond its typical habitat is often considered the pinnacle of their hobby. In the UK, such discoveries frequently make headlines; for instance, a western reef heron—typically found in Africa and southern Europe—was seen in a coastal town in north Wales this past June, igniting excitement on birdwatching online communities.

However, there’s a new challenge on the horizon: the impact of artificial intelligence on wildlife photography.

Researchers are urging bird enthusiasts to curtail the use of AI tools when editing their images, as these modifications pose a significant risk to the reliability of essential citizen science platforms like iNaturalist and Macaulay Library. These platforms are crucial for tracking species’ distribution and habitat shifts, especially in ongoing scientific research.

The emergence of generative AI tools such as ChatGPT and Google Gemini has led to a surge in artificially designed or enhanced pictures of rare birds throughout wildlife photography circles. With these tools, users can rapidly generate high-quality fake images or manipulate existing photos by requesting the AI to remove obstructions like branches, leading to unintentional but notable alterations.

In a recent opinion piece published in the scientific journal Nature, experts have highlighted that multiple fake images have been identified within popular species-recording databases. The true extent of the issue remains uncertain, as many altered images may go unnoticed, potentially tainting public data records.

Dr. Alexander Lees, an ecologist at Manchester Metropolitan University and the author of the journal article, remarked on the overwhelming number of wildlife images on social media that appear to be AI-generated. “It complicates our ability to utilize these photos for understanding species distribution over time and space,” he explained.

While blatant fakes are generally uncommon and easy to identify—like an implausible toucan sighting in Siberia—birders often manipulate images using AI, which may lead to the blending of features from different species within one photograph.

Dr. Lees cited an example where a supposed sighting of a red-winged blackbird in central Brazil—an area where it's rarely seen—turned out to be an epaulet oriole. The photographer had enhanced the image with an AI tool, which inadvertently incorporated elements from the red-winged blackbird, leading to the misidentification.

“There’s a temptation among wildlife photographers to capture visually stunning images, but this could create complications later if AI editing is involved,” Dr. Lees noted.

Citizen science organizations are still working to gauge the extent of this predicament. On iNaturalist, which serves as a platform for nature lovers to document their findings, only 1,400 of the more than 610 million images uploaded have been flagged for AI modifications. Citizen science has facilitated significant discoveries, from observing plant and animal responses to climate change to documenting new behaviors.

Tony Iwane, iNaturalist’s director of community support and a co-author of the Nature article, stated that while the majority of these flagged cases may not originate from ill intent, he encourages users to remain cautious. “People on platforms like ours are providing valuable information that scientists couldn’t gather alone on such a large scale,” he explained. “These platforms serve as real-time indicators of Earth’s changes—such as early blooming of plants or northward shifts in species due to warming climates. Accurate information is vital for effective conservation efforts.”

Manipulated images in birdwatching forums jeopardizing research efforts

A dimly lit field-research cabin at night, a lone ornithologist’s silhouette hunched over an open laptop. The screen is split: one side shows a lifelike scarlet tanager, the other side the same bird riddled with red digital glitches and pixel distortions. The researcher holds a magnifying glass to the distorted image, their glasses reflecting flickering glitch lines. A worn leather birding field guide lies open next to a steaming mug, while dark shelves behind them hold shadowy bird silhouettes. Dark theme with dramatic low-key lighting and a tense, investigative mood.

AI-altered images on birdwatching forums putting research at risk - The Guardian

- Birdwatchers face challenges as AI-generated images raise concerns over species identification accuracy.  
- Researchers warn that AI editing could taint credible citizen science databases like iNaturalist.  
- Misuse of AI in wildlife photography may mislead conservation efforts and species monitoring.

For many birdwatchers, recording a species outside its normal range is the holy grail. In the UK, the discoveries often make national headlines. The western reef heron, for example, usually found in Africa and southern Europe, was spotted in a seaside town in north Wales in June, and widely celebrated on birding forums.

But a new scourge is threatening to disrupt the fun: AI slop.

Scientists are appealing to birders to limit their use of AI when editing images over fears that it could undermine the credibility of popular citizen science platforms such as iNaturalist and Macaulay Library, which are routinely used in scientific research to monitor species’ habitat range.

The rise of generative AI platforms such as ChatGPT and Google Gemini has led to a sharp increase in fake and enhanced images of rare and celebrated birds across wildlife photography forums. Users can create high-quality fake images in seconds – or enhance a photograph by asking the AI to remove a branch or leaf that might be obscuring part of the subject, which can inadvertently introduce significant changes.

In a recent commentary in the science journal Nature, researchers warned that hundreds of fake images have already been discovered on popular databases for recording species. They said that the true scale of the issue is unknown – as many could go undetected – and could contaminate records collected by the public.

View image in fullscreen An epaulet oriole, similar to the species cited by Dr Alexander Lees. This bird was photographed in Serra da Canastra national park, Minas Gerais, Brazil, where it is a common sighting. Photograph: imageBROKER.com/Alamy

“My experience of looking at Facebook these days is that a huge volume of wildlife photos now are simply AI-generated imagery,” said Dr Alexander Lees, an ecologist at Manchester Metropolitan University who authored the journal article. “The idea that we could maybe use those photos to help us understand where species are in space and time is very difficult.”

Outright hoaxes remain rare and are often easy to spot, said Lees – nobody is falling for a toucan sighting in Siberia – but he says birders often use AI to edit and improve an image, and the algorithm can then introduce parts from different bird species to create the new image.

Lees points to the example of a false sighting of a red-winged blackbird in central Brazil, which is usually found in North America and had never been seen in this area of Brazil until the reported sighting. The bird was, in fact, an epaulet oriole – a common new world bird species – but the photographer had asked an AI platform to make the picture “look better”, which is when parts of the red-winged blackbird were added, resulting in the false sighting.

“Wildlife photographers can be quite obsessed with getting a beautiful photo, but there’s a risk that the image might actually cause problems down the line when AI has been used to edit it,” said Lees.

Citizen science organisations are still working to understand the scale of the issue. On iNaturalist, a social network where nature enthusiasts can record sightings of plants and animals, just 1,400 of the more than 610m images on the platform have been flagged for AI use. From monitoring how plants and animals move in response to climate breakdown to recording new behaviour in species, dozens of discoveries have been made using citizen science.

Tony Iwane, iNaturalist’s director of community support, who was a co-author on the Nature paper, said most of these cases were unlikely to be malicious, but appealed to users to be vigilant.

“On platforms like ours, regular people are posting information that a scientist could probably never get at scale. It is also almost like a sensor of what is happening on Earth in real time: are plants flowering early? Are species moving north as the climate warms? The more we know about where species are, the better informed we can be as conservationists. But the information needs to be accurate,” he said.

AI credits used by each user are now included in the Copilot usage metrics API.

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