Resources – Page 5

Your Gateway to Power, Energy, Datacenters, Bitcoin and AI

Dive into the latest industry updates, our exclusive Paperboy Newsletter, and curated insights designed to keep you informed. Stay ahead with minimal time spent.

Discover What Matters Most to You

Explore ONMINE’s curated content, from our Paperboy Newsletter to industry-specific insights tailored for energy, Bitcoin mining, and AI professionals.

Discover What Matter Most to You

Featured Articles

The Download: India’s smart glasses menace and AI’s trillion-dollar gamble

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Smart glasses are already causing havoc in India When Shubnam saw an Instagram video of a Delhi protest they had attended, they realized a content creator wearing Meta smart glasses had recorded them surreptitiously. The mocking reel drew millions of views, along with transphobic abuse and AI-generated memes. Experts warn that many others will experience similar ordeals as smart glasses go mainstream. The risks are particularly acute in India, where covert recording and the circulation of images without consent are already pervasive. The bigger issue is that the smart glasses aren’t just being used to turn ordinary people into targets of viral “pranks.” They’re also becoming a tool of police surveillance.
Here’s why smart glasses are creating new privacy risks in India. —Anuj Behal
MIT Technology Review Narrated: what’s at stake in AI’s trillion-dollar gamble When Jessica Wachter, a University of Pennsylvania finance professor, wanted to assess AI’s impact on the economy over the next few years, she started with a simple fact: a handful of so-called hyperscalers are investing huge amounts of money to build AI data centers. Instead of trying to predict how widely deployed AI models will be, Wachter asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when expenditures are expected to reach nearly $1.1 trillion. The results are eye-opening. AI companies will need to achieve an extraordinary increase in productivity just to break even by 2030. This is our latest story to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Hackers claim they’ve stolen data on almost all FBI employeesThe ShinyHunters group says it seized more than 2 TB of data. (Axios)+ Including agents’ names, addresses, and phone numbers. (404 Media)+ ShinyHunters says the attack was retaliation for an FBI alert. (Reuters $)+ Now is a good time for doing crime. (MIT Technology Review) 2 Anthropic and OpenAI have both released lower-cost modelsThey face growing competition from cheaper Chinese models. (CNBC)+ Startups have been reducing their reliance on the two labs. (Bloomberg $)+ The new models are the first since their calls for an AI slowdown. (FT $)+ Their CEOs are set to brief the UN Security Council today. (Quartz)+ Could AI really kill us all? (MIT Technology Review)

3 Trump’s Treasury chief could soon become his new AI czarScott Bessent has played a central role in US-China AI talks. (Semafor)+ Other contenders include Michael Kratsios and Scott Kupor. (Gizmodo)+ Will Trump’s AI rebrand to “superintelligence” catch on? (BBC) 4 Scientists are turning renewable electricity directly into foodThe goal is to make more food with less land. (New Scientist $)+ Companies are creating food out of thin air. (MIT Technology Review) 5 A “fire amoeba” has broken the heat survival record for complex lifeThe newly discovered organism can grow and reproduce at 63°C. (NPR)+ It could inform the search for life elsewhere. (New Scientist $) 6 Meta quietly tested a “human concierge” for its Muse AI agentContractors secretly handled some calls placed by Muse. (Reuters $)+ Staff raised concerns about privacy and misleading users. (404 Media) 7 Data centres are replacing copper with light to cut energy usePhotonics can move data with less heat than electrical wiring. (BBC)+ Virtual power plants could also help. (MIT Technology Review) 8 AI models built from rat brains just got closer to realityAWS is tapping rat neurons to make video AI faster. (Wired $) 9 Uber is betting on human drivers to give its robotaxis an edgeThey can cover demand spikes while autonomous cars recharge. (Axios) 10 A humanoid robot took on an amateur fighter in a cageThe brief bout was billed as the first human-robot MMA fight. (Futurism)
Quote of the day “The use of the word artificial makes it sound fake. It’s not fake; it’s actually amazing.” —President Donald Trump tells the UN General Assembly why he wants to rename AI “superintelligence.”
One more thing COURTESY OF PAINCHEK AI is changing how we quantify pain At Orchard Care Homes, nurses used to rely on an observational scale to assess pain in residents who couldn’t communicate verbally. But agitated residents were sometimes assumed to have behavioral issues, while their pain went untreated. Then, in 2021, the care-home chain began trialing PainChek, a smartphone app that scans a resident’s face for microscopic muscle movements and uses AI to output a pain score. Within weeks, the pilot unit saw fewer prescriptions and calmer corridors. Now researchers are racing to turn pain into something a camera or sensor can score as reliably as blood pressure. But when algorithms measure our suffering, does that change how we understand and treat it? Discover how AI is changing the way we assess pain. —Deena Mousa
We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + Scientists have discovered the first new penguin species in more than 100 years.+ Discover the filmmaking skills that made Paul Verhoeven’s RoboCop a directing masterclass.+ What happens when fast food meets local tastes? These unusual international menu items offer some tasty answers.+ Dance to every track the crowd has identified at Berghain since 2024 with this media player, which replays each night in sequence.

Smart glasses are already causing havoc in India

EXECUTIVE SUMMARY Shubnam was packing boxes for a move into a new home when their friend sent them an Instagram video. The footage had only been up for a few hours, but it was days old, recorded at a Delhi protest this spring against a bill that would have narrowed the legal recognition for transgender people in India. There, a content creator known for rage-bait videos had approached Shubnam, a 38-year-old transfeminine graphic designer and educator. Shubnam, who uses a single chosen name, was dressed in a green sari with a shaved head, and the creator asked why they were wearing “female clothing.” Shubnam told him to back off. Only when the video arrived did they realize he had recorded the exchange on Meta smart glasses, styled to look like ordinary Wayfarers. Edited into a mocking reel, the footage drew millions of views, along with transphobic abuse, reaction videos, and AI-generated memes. It spread from Instagram to X and YouTube. Copies remain online today. For Shubnam, the video has caused huge harm. “All it took was one unconsented video to rewind two decades of progress that I made all on my own,” they told MIT Technology Review. “For a moment, it felt like this entire adult life I had lived was just a long lucid dream, and I was going to wake up as that clueless queer child again.” They haven’t worn a sari outside their neighborhood since.
Experts warn that many others will experience similar ordeals as smart glasses go mainstream. And they say privacy problems are becoming especially acute in India, where covert recording and the circulation of images without consent are already pervasive. Cases of cyberstalking, sextortion, deepfakes, and other forms of online abuse are growing in the country. And the chances of a crackdown on technologies such as smart glasses are slim, given that Indian law enforcement authorities are themselves increasingly adopting AI-powered surveillance tools, including facial recognition systems and their own AI-enabled smart glasses.
An unequal burden Smart glasses are among the fastest-growing devices in India’s wearables market, according to the International Data Corporation, a research firm. And Reliance Jio, one of the country’s largest telecommunications companies, says that later this year it’s set to launch a sub-$105 rival to Meta’s glasses, the latest model of which have a $420 price tag in India. The core privacy issue is that these cameras don’t look like cameras, so targets may not notice they’re being recorded. Yet Meta rejects the idea that it’s responsible for protecting people from intrusions, says Janusz Świerczyński, an Oxford Saïd Business School researcher who studies privacy and emerging technology. “More and more, the expectation is being placed on bystanders to look out for themselves,” he says. That means it’s up to them to spot a small LED in the glasses frames that activates when recording is in progress. Meta also relies on wearers to behave responsibly, says Świerczyński. Reliance Jio did not respond to our request for comment on privacy issues related to its upcoming glasses. Creators have already demonstrated ways to circumvent the recording light on Meta’s glasses. Tutorials show users how to cover or otherwise obscure the LED while continuing to record. Back in March, Wired documented a market for “stealth mode” modifications that physically disable the light or remove it entirely. All that makes covert recording easier, and the risks are not borne equally. Świerczyński says women and other marginalized groups face greater harm when inconspicuous devices capture and circulate footage without consent—as happened to Shubnam. The bigger problem, however, is that in India smart glasses aren’t just being used to turn ordinary people into targets of viral “pranks.” They’re also being adopted as a tool of police surveillance. In June, when thousands of young demonstrators gathered to protest problems with the country’s education system, Delhi police used Meta smart glasses to record them. In fact, a petition filed by former Jawaharlal Nehru University Students’ Union president Aishe Ghosh before the Delhi High Court alleged that police had filmed protesters continuously for weeks, including while they were eating and resting. It also alleged that the police had threatened to send footage of student demonstrators to their parents and colleges.

In July, India’s solicitor general dismissed the case in court as “luxury litigation.” Instead of investigating the students’ complaints, police turned on the students themselves, opening 10 criminal investigations for offenses including rioting, assault on public servants, and damage to public property. Some of the protesters, who spoke to MIT Technology Review on condition of anonymity, say they never saw the LED on the Meta AI glasses police wore during the demonstrations. Filming without consent Meta maintains that its safeguard works. The company told MIT Technology Review, “Every pair has a capture LED that blinks when you take a photo or video that you can save or share; it can’t be turned off, and if someone covers or damages the LED, the camera is disabled.” The company also says users are responsible for complying with the law and respecting people’s privacy. That approach is risky, especially in India, where norms around filming and consent are still highly disputed and digital literacy is still uneven, says Sameer Parmar, a counselor at Meri Trustline, a safety partner working with Meta and YouTube to support people harmed by online content. “The hardware is improving, but the country’s weak understanding of consent gives an invisible camera far more room to cause harm,” he says. Simply being in a public place is often treated as implicit consent to be photographed or filmed. He says the help line has been seeing an uptick in cases involving covert recording and nonconsensual footage. “Violations of this access have increased significantly and are expected to rise further in India,” he says. People are recorded in medical settings, during private encounters, and in public spaces. Those with less power to object are particularly vulnerable, he adds. “It’s either trolling or it’s playing pranks on them and then recording them” he says. Abusers “do target people who are a little more unaware or susceptible to how this technology-facilitated harm works.” The law in India has not caught up with this new kind of harm, says Apar Gupta, a lawyer and founder-director of the Internet Freedom Foundation. “India’s laws against voyeurism and the sharing of intimate images were designed largely around more recognizable forms of abuse,” he says, citing offenses such as secretly filming someone undressing, using a toilet, or engaged in a sexual act. “[The laws] offer limited protection when someone is secretly filmed in an ordinary public setting and the footage is then used to humiliate them,” he says.
Social media platforms’ rules do offer another route to removing harmful content filmed with smart glasses, but it’s not an immediate remedy. “Under India’s current rules, platforms can have up to 36 hours to act on certain complaints—a window that can be long enough for a video to be downloaded, copied, and circulated elsewhere,” he says. The problem of filming without consent seems set to grow. According to the Financial Times, some internal prototypes of new Meta glasses include AI features that continuously photograph and listen to the wearer’s surroundings, enabling the glasses to later recall what they saw or heard. These functions wouldn’t activate the external recording indicator, making it impossible for bystanders to know when information about them is being collected.
Meta told us it intends to keep the LED for active capture, such as taking photos or recording video, but not for AI features that interpret the wearer’s surroundings. Its argument is that a constantly blinking light could eventually become so commonplace that people stop noticing it. But that logic risks putting society in a perpetual game of catch-up, says Woodrow Hartzog, a professor of law at Boston University. “Tech companies have an incredible ability to push these tools out faster than society can meaningfully acclimate to the threat,” he says. “By the time society catches up, there’s already a degree of normalization.” For Shubnam, the danger is not just that people may become accustomed to being recorded. It is that they will become accustomed to seeing versions of themselves that they never chose to put out into the world. “Being comprehended along the lines of a narrative that I didn’t compose or consent to—that imposed recognition makes you feel like you’re powerless in how people know you,” they say.

How Communities Can Plan for AI Data Centers Before the Projects Arrive

The collision between AI infrastructure development and community opposition has become one of the defining data center stories of 2026. Developers are pursuing larger campuses, more power and compressed delivery schedules as AI accelerates demand for computing capacity. Meanwhile, local planning boards, elected officials and residents are increasingly being asked to make decisions about facilities whose scale, energy requirements and technological purpose may be unlike anything previously contemplated in their comprehensive plans. That gap is where Ilissa Miller believes much of the conflict begins. Miller, founder and CEO of iMiller Public Relations and a board member of the Open Infrastructure Exchange (OIX), joined the Data Center Frontier Show to discuss the OIX Digital Infrastructure Framework, an effort designed to give municipalities a more systematic way to think about data centers and other digital infrastructure before an individual development application lands in front of them. The idea is straightforward: communities routinely create long-range plans defining where homes, commercial development, industry and other land uses should go. Digital infrastructure should be part of that process as well. “Our vision for the framework was to help solve the problem by empowering communities to think about digital infrastructure,” Miller said, so municipalities can incorporate it into their comprehensive master plans and maintain control over how land is ultimately used. That distinction is key. The framework is not intended to convince communities to approve data centers. Nor does it prescribe what a town or county should decide. Instead, Miller said, it is meant to help public officials ask the right questions early enough to make those decisions deliberately. The Data Center May Not Be in the Plan One of the industry’s recurring problems is deceptively basic: many municipalities never anticipated data centers when writing their zoning codes and comprehensive plans. A parcel might already be

QatarEnergy NFE LNG Train 1 to start 1H 2027; Ras Laffan repairs to take 3 years

QatarEnergy expects the 8-million tonne/year (tpy) first train of its 32-million tpy North Field East (NFE) LNG expansion project to begin operations in first-half 2027, Reuters reported, noting that the timing of additional trains would depend on the Strait of Hormuz crisis. Speaking at the Qatar Economic Forum Special Edition in New York, QatarEnergy chief executive officer (CEO) and Qatari minister of energy affairs, Saad al-Kaabi, attributed the uncertainty to delays in delivering equipment needed for the expansion caused by the Strait of Hormuz disruption. Regarding damage to Qatar’s natural gas infrastructure sustained during the Iran war and its possible return, al-Kaabi said that repairs to the two LNG trains damaged at Ras Laffan (17% of its production capacity) would take 3 years. A damaged gas-to-liquids (GTL) train is expected to return to service first-quarter 2027. Al-Kaabi expects “a few” NFE trains to start production as 2027 progresses, and output from the 16-million tpy North Field South (NFS) expansion to begin in 2028, according to Reuters. The NFE and NFS projects are part of the overall North Field expansion program that also includes the North Field West project, which together will raise Qatar’s LNG production capacity to 142 million tpy from the current 77 million tpy. Al-Kaabi also thanked Qatar’s neighbors for being willing to allow construction of a gas pipeline across their territories to bypass Hormuz, while noting that doing so would be “redundant” and made “no economic sense” in light of the already underway North Field expansion project. “As for resuming operations,” he added, “Qatar is ready to resume normal operations within a few weeks of the reopening of the Strait of Hormuz.” More generally, al-Kaabi rejected the notion that the Strait of Hormuz was obsolete, saying that it “carries trade in all products, not only oil and

Executive Roundtable: AI Infrastructure Under Pressure

Matt Vincent is Editor in Chief of Data Center Frontier, where he leads editorial strategy and coverage focused on the infrastructure powering cloud computing, artificial intelligence, and the digital economy. A veteran B2B technology journalist with more than two decades of experience, Vincent specializes in the intersection of data centers, power, cooling, and emerging AI-era infrastructure. Since assuming the EIC role in 2023, he has helped guide Data Center Frontier’s coverage of the industry’s transition into the gigawatt-scale AI era, with a focus on hyperscale development, behind-the-meter power strategies, liquid cooling architectures, and the evolving energy demands of high-density compute, while working closely with the Digital Infrastructure Group at Endeavor Business Media to expand the brand’s analytical and multimedia footprint. Vincent also hosts The Data Center Frontier Show podcast, where he interviews industry leaders across hyperscale, colocation, utilities, and the data center supply chain to examine the technologies and business models reshaping digital infrastructure. Since its inception he serves as Head of Content for the Data Center Frontier Trends Summit. Before becoming Editor in Chief, he served in multiple senior editorial roles across Endeavor Business Media’s digital infrastructure portfolio, with coverage spanning data centers and hyperscale infrastructure, structured cabling and networking, telecom and datacom, IP physical security, and wireless and Pro AV markets. He began his career in 2005 within PennWell’s Advanced Technology Division and later held senior editorial positions supporting brands such as Cabling Installation & Maintenance, Lightwave Online, Broadband Technology Report, and Smart Buildings Technology. Vincent is a frequent moderator, interviewer, and keynote speaker at industry events including the HPC Forum, where he delivers forward-looking analysis on how AI and high-performance computing are reshaping digital infrastructure. He graduated with honors from Indiana University Bloomington with a B.A. in English Literature and Creative Writing and lives in southern New Hampshire with

ESENTIA to acquire Guadalajara-Manzanillo natural gas pipeline system

ESENTIA Energy Development SAB de CV, Mexico City, has agreed to acquire 100% of the equity interests of Energía Occidente de México S de RL de CV (EOM) from TC Energy Corp., Calgary, for a gross purchase price of $400 million. EOM owns and operates the 313-km Guadalajara-Manzanillo natural gas pipeline system, which runs from the Guadalajara area in Jalisco to Manzanillo, Colima, and is directly interconnected with ESENTIA’s Villa de Reyes-Aguascalientes-Guadalajara (VAG) pipeline system operated by Esentia Pipeline de Occidente S de RL de CV, an indirect subsidiary of ESENTIA. The pipeline transports up to 500 MMcfd of natural gas, connecting imported LNG supply near Manzanillo and continental gas supply near Guadalajara to power plants and industrial customers in Colima and Jalisco. Upon closing, the acquisition will extend ESENTIA’s pipeline network to the Port of Manzanillo on Mexico’s Pacific coast, making the company the only private operator with an integrated natural gas pipeline system connecting the Permian basin in Texas to Mexico’s Pacific coast, the company said in a release Sept. 21. The deal is part of ESENTIA’s strategy to build a cross-border transportation system and would “expand ESENTIA’s ability to serve existing and prospective customers within the combined system’s area of influence, including demand from power generation, industrial customers and potential LNG-related projects,” said Daniel Bustos, chief executive officer. ESENTIA also highlighted construction of its Aguascalientes Compression Station, which is expected to increase capacity on the VAG pipeline system beginning in early 2027. The project is part of the company’s three-phase expansion plan, which includes a total estimated investment of $680 million and an increase of 660 MMcfd in natural gas transportation capacity. For TC Energy, the transaction creates “optionality to redeploy proceeds from a mature asset towards high-value growth opportunities across our North American footprint,” said François

The Download: India’s smart glasses menace and AI’s trillion-dollar gamble

Smart glasses are already causing havoc in India

How Communities Can Plan for AI Data Centers Before the Projects Arrive

QatarEnergy NFE LNG Train 1 to start 1H 2027; Ras Laffan repairs to take 3 years

Executive Roundtable: AI Infrastructure Under Pressure

ESENTIA to acquire Guadalajara-Manzanillo natural gas pipeline system

Caturus plans to nearly double capacity of under-construction Commonwealth LNG plant

Caturus LLC plans to nearly double the expected capacity of its Commonwealth LNG plant currently under construction in Cameron Parish, La. The company is planning a five-train, 7.75 million tonnes/year (tpy) expansion, which would bring the site’s planned capacity to about 17.25 million tpy, the company noted in a release Sept. 15. Caturus LLC in May reached final investment decision (FID) on the current design of a six-train, $13.5-billion Commonwealth LNG plant. That sanction included closing of $9.75 billion in project financing and marked the start of full construction of the 9.5 million tpy plant. Phase 1 operations are expected in 2030. Base project construction is progressing on schedule with operations targeted for 2030, with about 8.5 million tpy capacity subscribed under long-term sale and purchase agreements, the company said. The expansion project is targeted to enter service in the early 2030s. Caturus said the expansion reflects strong market demand for additional US LNG volumes and long-term supply security, while capitalizing on infrastructure and commercial momentum already established at Commonwealth LNG. The expansion would leverage land already controlled by Caturus and draw on the engineering, procurement, and construction program established for the initial phase of Commonwealth LNG. The company also said its integrated upstream-to-export model, including gas production from its South Texas acreage, would support the additional liquefaction capacity.

US freezes Citgo board to protect pending $5.9-billion acquisition

The US Treasury Department Sept. 14 amended Venezuela’s sanctions license to prevent unauthorized changes to the governance of Citgo Petroleum Corp. and its two US corporate parent structures, PDV Holding Inc. and Citgo Holding Inc. The license now explicitly blocks any unauthorized “appointment, removal, or replacement of any director, officer, or other corporate governance official” at the three companies. The move comes as Venezuelan interim President Delcy Rodríguez works to regain control of the country’s overseas assets. Her administration has replaced law firms representing Venezuela and state-owned PDVSA in foreign litigation and arbitration, while opposition-appointed entities that have overseen Citgo are preparing to wind down. The license amendment freezes the current corporate boards until finalization of Amber Energy’s acquisition of PDV Holdings. The acquisition gives Amber Energy, owned by Elliott Investment Management, assets that include about 800,000 b/d of refining capacity on the US Gulf Coast and in the Midwest. It also obligates Amber to disperse $5.89 billion in a structured payout to 15 international creditors, including ConocoPhillips, whose assets were seized by the previous government. While a US federal court approved the deal, Amber still requires a US Treasury license to take title to the shares. The deal would also need to survive an appeal in the US Third Circuit Court of Appeals in Philadelphia, where the Venezuelan government is arguing the price is too low. Venezuela and state-owned PDVSA would still be on the hook for about $15 billion in remaining payouts to companies for seized assets.

S&P Global: Refined product tightness deepens

Russian refinery runs remain near July 2026 lows and are likely to recover only gradually from September onward. Russia’s ban on diesel exports has already removed 10% of waterborne supply from the global market. Further decline in Russian refinery runs could amplify the risk to global diesel markets by leading Russia to import fuel to backfill domestic needs, compounding the outright loss of the near 1 million b/d of diesel exports. Limited spare capacity raises winter risks Meanwhile, the world’s remaining unconstrained refining capacity is already running at close to maximum levels. Refinery utilization in the US has approached 97% this summer, while Europe and North America continue to operate at or near multi-decade highs in response to record margins. The industry is now approaching fall turnaround season with a significant incentive to keep pushing and little spare capacity left to offset unexpected disruptions. “The market has survived the first phase of the crisis because inventories, trade flows, and refinery flexibility absorbed much of the shock. Those shock absorbers are not disappearing, but they are becoming progressively weaker. Markets are entering winter with less room for error than they had in the spring,” said Daniel Evans, global head of fuels and refining research, S&P Global Energy. For governments and policymakers, the challenge is increasingly likely to center on balancing energy price affordability, inflation, and security of supply. High diesel prices directly impact costs for freight transport, agriculture, construction, manufacturing, and residential heating. Although the market has so far avoided an outright supply crisis, persistently low inventories, high prices, and limited spare capacity have heightened the risk of government interventions. Diesel remains the fuel most vulnerable to shortages. With a surge in demand during the harvest season, the approach of winter heating needs, depleted inventories, and no immediate prospect of supply

Saudi pipeline shutdown threatens further global oil supply losses

Saudi Arabia has shut its 1,200-km (745-mile) East-West pipeline following Sept. 11, 2026, drone attacks. Saudi officials said the drones were launched from Iraq but have not disclosed the extent of any injuries or damage or provided a timetable for repairs. Satellite imagery released Sept. 13 appeared to show substantial damage to a pumping station along the pipeline. East-West, which has a maximum design capacity of 7 million b/d, had been operating at 2 million b/d in August according to Kpler, as increased Houthi attacks in and around Bab El-Mandeb reduced Red Sea traffic. Earlier in the Iran war, flows had been 4-5 million b/d, with the pipeline serving as an alternative to the Strait of Hormuz for Saudi exports. Stay updated on oil price volatility, shipping disruptions, LNG market analysis, and production output at OGJ’s Iran war content hub. Saudi oil buyers and traders told Reuters that stocks at Red Sea terminals could support exports for 5-7 days if the pipeline remains out of service. A prolonged shutdown could remove as much as 4% of global oil supply, according to market estimates. Repair estimates have ranged from several days to as long as 6 weeks. Saudi Arabia reported to the Organization of Petroleum Exporting Countries (OPEC) that its crude production declined to 6.2 million b/d in August, down from 10.9 million b/d in February. Overall OPEC+ crude production fell 1.5 million b/d in August to 33.1 million b/d. Production from the 17 quota-bound OPEC+ members declined 980,000 b/d to 27 million b/d, 7.3 million b/d below their August target. On Sept. 6, OPEC+ agreed to maintain October production targets at September levels. Global oil prices climbed sharply Sept. 14 in the wake of the shutdown. Brent crude futures rose above $108/bbl, reaching levels not seen since May, before retreating.

Insights: Prioritizing process safety management over profits across the refining industry (Pt. 1)

In this Insights episode of the Oil & Gas Journal ReEnterprised podcast, downstream editor and lead reporter Robert Brelsford talks with Joe Barnes, principal of Barnes’ Engineering Consultants and a veteran oil and gas operations, maintenance, reliability, and major-projects leader with more than 30 years of industry experience. Barnes, who spent roughly half of his career in refining and half in exploration and production, discusses why process safety management must remain a central concern for everyone working in oil and gas—not only safety professionals. Drawing on his experience with a major refinery accident and the aftermath of a separate major offshore incident, Barnes explains why good intentions are not enough to prevent major incidents, particularly in times when the potential for high margins could tempt operator’s into promoting maximized production over execution of maintenance on processing and production installations to ensure safe and reliable operations. During the conversation, Robert and Joe examine recurring root causes identified through Barnes’ review of process-safety incidents dating back to the mid-1980s, including absence of leadership, poor communication, inadequate risk identification and mitigation, aging equipment, deferred maintenance, workforce turnover, and the loss of experienced personnel. They also discuss why the industry has sometimes failed to consistently share and apply lessons from previous incidents, particularly when investigation findings are not widely accessible. The two consider the difference between personal safety and process safety metrics, the warning signs that preceded the 2005 BP Texas City refinery explosion, and the role of management of change (MOC) in evaluating staffing, maintenance, turnaround, efficiency, and capital-reduction decisions. Barnes also outlines the questions boards and executive leaders should be asking to ensure that process safety remains a business priority across economic cycles. Robert and Joe further explore how refineries can use past incident scenarios as practical training tools, strengthen external oversight

Energy Department Launches $16 Million Prize To Grow the Mining and Critical Minerals Workforce

WASHINGTON—The U.S. Department of Energy’s (DOE) Office of Critical Minerals and Energy Innovation (CMEI) today launched the PROSPECT Planning Prize to support universities in developing the skilled workforce required to produce, process, recover, and recycle critical minerals on American soil. Each winner will receive up to $1 million from a total prize pool of $16 million. “Mining and critical minerals professionals are essential to America’s success in manufacturing, AI, energy, and national security—but this country isn’t producing enough of them,” said Assistant Secretary of Energy Audrey Robertson. “Through this prize competition, DOE will empower universities to pursue more ambitious strategies for attracting and educating the workers we need for a robust critical mineral supply chain.” This prize competition is part of DOE’s $100 million Providing Opportunities for Specialized Education in Critical Technologies (PROSPECT) initiative, which delivers on President Trump’s bold agenda to restore American energy dominance, rebuild domestic supply chains, and reduce reliance on foreign adversaries for critical minerals. The initiative’s near-term goal is to double the number of graduates with mining, minerals, and associated supply chain credentials across the United States. U.S.-based academic institutions with prize-eligible degree programs are encouraged to submit plans. Proposed programs should aim to substantially increase the number of graduates and other credentialed professionals prepared for relevant mining and supply chain careers in the next two years. Programs may target a range of beneficiaries, including early-career professionals, incumbent workers, educators, and students in university, community college, or high school. Applications are now open. The deadline for submission is Oct. 5, 2026. Learn more about this competition, including key dates and submission details, on the PROSPECT Prize page. The PROSPECT Planning Prize is funded by CMEI and managed by the National Laboratory of the Rockies under the American-Made prize program. ###

AI means the end of internet search as we’ve known it

We all know what it means, colloquially, to google something. You pop a few relevant words in a search box and in return get a list of blue links to the most relevant results. Maybe some quick explanations up top. Maybe some maps or sports scores or a video. But fundamentally, it’s just fetching information that’s already out there on the internet and showing it to you, in some sort of structured way. But all that is up for grabs. We are at a new inflection point. The biggest change to the way search engines have delivered information to us since the 1990s is happening right now. No more keyword searching. No more sorting through links to click. Instead, we’re entering an era of conversational search. Which means instead of keywords, you use real questions, expressed in natural language. And instead of links, you’ll increasingly be met with answers, written by generative AI and based on live information from all across the internet, delivered the same way. Of course, Google—the company that has defined search for the past 25 years—is trying to be out front on this. In May of 2023, it began testing AI-generated responses to search queries, using its large language model (LLM) to deliver the kinds of answers you might expect from an expert source or trusted friend. It calls these AI Overviews. Google CEO Sundar Pichai described this to MIT Technology Review as “one of the most positive changes we’ve done to search in a long, long time.”
AI Overviews fundamentally change the kinds of queries Google can address. You can now ask it things like “I’m going to Japan for one week next month. I’ll be staying in Tokyo but would like to take some day trips. Are there any festivals happening nearby? How will the surfing be in Kamakura? Are there any good bands playing?” And you’ll get an answer—not just a link to Reddit, but a built-out answer with current results. More to the point, you can attempt searches that were once pretty much impossible, and get the right answer. You don’t have to be able to articulate what, precisely, you are looking for. You can describe what the bird in your yard looks like, or what the issue seems to be with your refrigerator, or that weird noise your car is making, and get an almost human explanation put together from sources previously siloed across the internet. It’s amazing, and once you start searching that way, it’s addictive.
And it’s not just Google. OpenAI’s ChatGPT now has access to the web, making it far better at finding up-to-date answers to your queries. Microsoft released generative search results for Bing in September. Meta has its own version. The startup Perplexity was doing the same, but with a “move fast, break things” ethos. Literal trillions of dollars are at stake in the outcome as these players jockey to become the next go-to source for information retrieval—the next Google. Not everyone is excited for the change. Publishers are completely freaked out. The shift has heightened fears of a “zero-click” future, where search referral traffic—a mainstay of the web since before Google existed—vanishes from the scene. I got a vision of that future last June, when I got a push alert from the Perplexity app on my phone. Perplexity is a startup trying to reinvent web search. But in addition to delivering deep answers to queries, it will create entire articles about the news of the day, cobbled together by AI from different sources. On that day, it pushed me a story about a new drone company from Eric Schmidt. I recognized the story. Forbes had reported it exclusively, earlier in the week, but it had been locked behind a paywall. The image on Perplexity’s story looked identical to one from Forbes. The language and structure were quite similar. It was effectively the same story, but freely available to anyone on the internet. I texted a friend who had edited the original story to ask if Forbes had a deal with the startup to republish its content. But there was no deal. He was shocked and furious and, well, perplexed. He wasn’t alone. Forbes, the New York Times, and Condé Nast have now all sent the company cease-and-desist orders. News Corp is suing for damages. People are worried about what these new LLM-powered results will mean for our fundamental shared reality. It could spell the end of the canonical answer. It was precisely the nightmare scenario publishers have been so afraid of: The AI was hoovering up their premium content, repackaging it, and promoting it to its audience in a way that didn’t really leave any reason to click through to the original. In fact, on Perplexity’s About page, the first reason it lists to choose the search engine is “Skip the links.” But this isn’t just about publishers (or my own self-interest). People are also worried about what these new LLM-powered results will mean for our fundamental shared reality. Language models have a tendency to make stuff up—they can hallucinate nonsense. Moreover, generative AI can serve up an entirely new answer to the same question every time, or provide different answers to different people on the basis of what it knows about them. It could spell the end of the canonical answer. But make no mistake: This is the future of search. Try it for a bit yourself, and you’ll see.

Sure, we will always want to use search engines to navigate the web and to discover new and interesting sources of information. But the links out are taking a back seat. The way AI can put together a well-reasoned answer to just about any kind of question, drawing on real-time data from across the web, just offers a better experience. That is especially true compared with what web search has become in recent years. If it’s not exactly broken (data shows more people are searching with Google more often than ever before), it’s at the very least increasingly cluttered and daunting to navigate. Who wants to have to speak the language of search engines to find what you need? Who wants to navigate links when you can have straight answers? And maybe: Who wants to have to learn when you can just know? In the beginning there was Archie. It was the first real internet search engine, and it crawled files previously hidden in the darkness of remote servers. It didn’t tell you what was in those files—just their names. It didn’t preview images; it didn’t have a hierarchy of results, or even much of an interface. But it was a start. And it was pretty good. Then Tim Berners-Lee created the World Wide Web, and all manner of web pages sprang forth. The Mosaic home page and the Internet Movie Database and Geocities and the Hampster Dance and web rings and Salon and eBay and CNN and federal government sites and some guy’s home page in Turkey. Until finally, there was too much web to even know where to start. We really needed a better way to navigate our way around, to actually find the things we needed. And so in 1994 Jerry Yang created Yahoo, a hierarchical directory of websites. It quickly became the home page for millions of people. And it was … well, it was okay. TBH, and with the benefit of hindsight, I think we all thought it was much better back then than it actually was. But the web continued to grow and sprawl and expand, every day bringing more information online. Rather than just a list of sites by category, we needed something that actually looked at all that content and indexed it. By the late ’90s that meant choosing from a variety of search engines: AltaVista and AlltheWeb and WebCrawler and HotBot. And they were good—a huge improvement. At least at first. But alongside the rise of search engines came the first attempts to exploit their ability to deliver traffic. Precious, valuable traffic, which web publishers rely on to sell ads and retailers use to get eyeballs on their goods. Sometimes this meant stuffing pages with keywords or nonsense text designed purely to push pages higher up in search results. It got pretty bad.
And then came Google. It’s hard to overstate how revolutionary Google was when it launched in 1998. Rather than just scanning the content, it also looked at the sources linking to a website, which helped evaluate its relevance. To oversimplify: The more something was cited elsewhere, the more reliable Google considered it, and the higher it would appear in results. This breakthrough made Google radically better at retrieving relevant results than anything that had come before. It was amazing. Google CEO Sundar Pichai describes AI Overviews as “one of the most positive changes we’ve done to search in a long, long time.”JENS GYARMATY/LAIF/REDUX For 25 years, Google dominated search. Google was search, for most people. (The extent of that domination is currently the subject of multiple legal probes in the United States and the European Union.)
But Google has long been moving away from simply serving up a series of blue links, notes Pandu Nayak, Google’s chief scientist for search. “It’s not just so-called web results, but there are images and videos, and special things for news. There have been direct answers, dictionary answers, sports, answers that come with Knowledge Graph, things like featured snippets,” he says, rattling off a litany of Google’s steps over the years to answer questions more directly. It’s true: Google has evolved over time, becoming more and more of an answer portal. It has added tools that allow people to just get an answer—the live score to a game, the hours a café is open, or a snippet from the FDA’s website—rather than being pointed to a website where the answer may be. But once you’ve used AI Overviews a bit, you realize they are different. Take featured snippets, the passages Google sometimes chooses to highlight and show atop the results themselves. Those words are quoted directly from an original source. The same is true of knowledge panels, which are generated from information stored in a range of public databases and Google’s Knowledge Graph, its database of trillions of facts about the world. While these can be inaccurate, the information source is knowable (and fixable). It’s in a database. You can look it up. Not anymore: AI Overviews can be entirely new every time, generated on the fly by a language model’s predictive text combined with an index of the web.
“I think it’s an exciting moment where we have obviously indexed the world. We built deep understanding on top of it with Knowledge Graph. We’ve been using LLMs and generative AI to improve our understanding of all that,” Pichai told MIT Technology Review. “But now we are able to generate and compose with that.” The result feels less like a querying a database than like asking a very smart, well-read friend. (With the caveat that the friend will sometimes make things up if she does not know the answer.) “[The company’s] mission is organizing the world’s information,” Liz Reid, Google’s head of search, tells me from its headquarters in Mountain View, California. “But actually, for a while what we did was organize web pages. Which is not really the same thing as organizing the world’s information or making it truly useful and accessible to you.” That second concept—accessibility—is what Google is really keying in on with AI Overviews. It’s a sentiment I hear echoed repeatedly while talking to Google execs: They can address more complicated types of queries more efficiently by bringing in a language model to help supply the answers. And they can do it in natural language.
That will become even more important for a future where search goes beyond text queries. For example, Google Lens, which lets people take a picture or upload an image to find out more about something, uses AI-generated answers to tell you what you may be looking at. Google has even showed off the ability to query live video. When it doesn’t have an answer, an AI model can confidently spew back a response anyway. For Google, this could be a real problem. For the rest of us, it could actually be dangerous. “We are definitely at the start of a journey where people are going to be able to ask, and get answered, much more complex questions than where we’ve been in the past decade,” says Pichai. There are some real hazards here. First and foremost: Large language models will lie to you. They hallucinate. They get shit wrong. When it doesn’t have an answer, an AI model can blithely and confidently spew back a response anyway. For Google, which has built its reputation over the past 20 years on reliability, this could be a real problem. For the rest of us, it could actually be dangerous. In May 2024, AI Overviews were rolled out to everyone in the US. Things didn’t go well. Google, long the world’s reference desk, told people to eat rocks and to put glue on their pizza. These answers were mostly in response to what the company calls adversarial queries—those designed to trip it up. But still. It didn’t look good. The company quickly went to work fixing the problems—for example, by deprecating so-called user-generated content from sites like Reddit, where some of the weirder answers had come from. Yet while its errors telling people to eat rocks got all the attention, the more pernicious danger might arise when it gets something less obviously wrong. For example, in doing research for this article, I asked Google when MIT Technology Review went online. It helpfully responded that “MIT Technology Review launched its online presence in late 2022.” This was clearly wrong to me, but for someone completely unfamiliar with the publication, would the error leap out? I came across several examples like this, both in Google and in OpenAI’s ChatGPT search. Stuff that’s just far enough off the mark not to be immediately seen as wrong. Google is banking that it can continue to improve these results over time by relying on what it knows about quality sources. “When we produce AI Overviews,” says Nayak, “we look for corroborating information from the search results, and the search results themselves are designed to be from these reliable sources whenever possible. These are some of the mechanisms we have in place that assure that if you just consume the AI Overview, and you don’t want to look further … we hope that you will still get a reliable, trustworthy answer.” In the case above, the 2022 answer seemingly came from a reliable source—a story about MIT Technology Review’s email newsletters, which launched in 2022. But the machine fundamentally misunderstood. This is one of the reasons Google uses human beings—raters—to evaluate the results it delivers for accuracy. Ratings don’t correct or control individual AI Overviews; rather, they help train the model to build better answers. But human raters can be fallible. Google is working on that too. “Raters who look at your experiments may not notice the hallucination because it feels sort of natural,” says Nayak. “And so you have to really work at the evaluation setup to make sure that when there is a hallucination, someone’s able to point out and say, That’s a problem.” The new search Google has rolled out its AI Overviews to upwards of a billion people in more than 100 countries, but it is facing upstarts with new ideas about how search should work. Search Engine GoogleThe search giant has added AI Overviews to search results. These overviews take information from around the web and Google’s Knowledge Graph and use the company’s Gemini language model to create answers to search queries. What it’s good at Google’s AI Overviews are great at giving an easily digestible summary in response to even the most complex queries, with sourcing boxes adjacent to the answers. Among the major options, its deep web index feels the most “internety.” But web publishers fear its summaries will give people little reason to click through to the source material. PerplexityPerplexity is a conversational search engine that uses third-party largelanguage models from OpenAI and Anthropic to answer queries. Perplexity is fantastic at putting together deeper dives in response to user queries, producing answers that are like mini white papers on complex topics. It’s also excellent at summing up current events. But it has gotten a bad rep with publishers, who say it plays fast and loose with their content. ChatGPTWhile Google brought AI to search, OpenAI brought search to ChatGPT. Queries that the model determines will benefit from a web search automatically trigger one, or users can manually select the option to add a web search. Thanks to its ability to preserve context across a conversation, ChatGPT works well for performing searches that benefit from follow-up questions—like planning a vacation through multiple search sessions. OpenAI says users sometimes go “20 turns deep” in researching queries. Of these three, it makes links out to publishers least prominent. When I talked to Pichai about this, he expressed optimism about the company’s ability to maintain accuracy even with the LLM generating responses. That’s because AI Overviews is based on Google’s flagship large language model, Gemini, but also draws from Knowledge Graph and what it considers reputable sources around the web. “You’re always dealing in percentages. What we have done is deliver it at, like, what I would call a few nines of trust and factuality and quality. I’d say 99-point-few-nines. I think that’s the bar we operate at, and it is true with AI Overviews too,” he says. “And so the question is, are we able to do this again at scale? And I think we are.” There’s another hazard as well, though, which is that people ask Google all sorts of weird things. If you want to know someone’s darkest secrets, look at their search history. Sometimes the things people ask Google about are extremely dark. Sometimes they are illegal. Google doesn’t just have to be able to deploy its AI Overviews when an answer can be helpful; it has to be extremely careful not to deploy them when an answer may be harmful. “If you go and say ‘How do I build a bomb?’ it’s fine that there are web results. It’s the open web. You can access anything,” Reid says. “But we do not need to have an AI Overview that tells you how to build a bomb, right? We just don’t think that’s worth it.” But perhaps the greatest hazard—or biggest unknown—is for anyone downstream of a Google search. Take publishers, who for decades now have relied on search queries to send people their way. What reason will people have to click through to the original source, if all the information they seek is right there in the search result? Rand Fishkin, cofounder of the market research firm SparkToro, publishes research on so-called zero-click searches. As Google has moved increasingly into the answer business, the proportion of searches that end without a click has gone up and up. His sense is that AI Overviews are going to explode this trend. “If you are reliant on Google for traffic, and that traffic is what drove your business forward, you are in long- and short-term trouble,” he says. Don’t panic, is Pichai’s message. He argues that even in the age of AI Overviews, people will still want to click through and go deeper for many types of searches. “The underlying principle is people are coming looking for information. They’re not looking for Google always to just answer,” he says. “Sometimes yes, but the vast majority of the times, you’re looking at it as a jumping-off point.” Reid, meanwhile, argues that because AI Overviews allow people to ask more complicated questions and drill down further into what they want, they could even be helpful to some types of publishers and small businesses, especially those operating in the niches: “You essentially reach new audiences, because people can now express what they want more specifically, and so somebody who specializes doesn’t have to rank for the generic query.” “I’m going to start with something risky,” Nick Turley tells me from the confines of a Zoom window. Turley is the head of product for ChatGPT, and he’s showing off OpenAI’s new web search tool a few weeks before it launches. “I should normally try this beforehand, but I’m just gonna search for you,” he says. “This is always a high-risk demo to do, because people tend to be particular about what is said about them on the internet.” He types my name into a search field, and the prototype search engine spits back a few sentences, almost like a speaker bio. It correctly identifies me and my current role. It even highlights a particular story I wrote years ago that was probably my best known. In short, it’s the right answer. Phew? A few weeks after our call, OpenAI incorporated search into ChatGPT, supplementing answers from its language model with information from across the web. If the model thinks a response would benefit from up-to-date information, it will automatically run a web search (OpenAI won’t say who its search partners are) and incorporate those responses into its answer, with links out if you want to learn more. You can also opt to manually force it to search the web if it does not do so on its own. OpenAI won’t reveal how many people are using its web search, but it says some 250 million people use ChatGPT weekly, all of whom are potentially exposed to it. “There’s an incredible amount of content on the web. There are a lot of things happening in real time. You want ChatGPT to be able to use that to improve its answers and to be a better super-assistant for you.” Kevin Weil, chief product officer, OpenAI According to Fishkin, these newer forms of AI-assisted search aren’t yet challenging Google’s search dominance. “It does not appear to be cannibalizing classic forms of web search,” he says. OpenAI insists it’s not really trying to compete on search—although frankly this seems to me like a bit of expectation setting. Rather, it says, web search is mostly a means to get more current information than the data in its training models, which tend to have specific cutoff dates that are often months, or even a year or more, in the past. As a result, while ChatGPT may be great at explaining how a West Coast offense works, it has long been useless at telling you what the latest 49ers score is. No more. “I come at it from the perspective of ‘How can we make ChatGPT able to answer every question that you have? How can we make it more useful to you on a daily basis?’ And that’s where search comes in for us,” Kevin Weil, the chief product officer with OpenAI, tells me. “There’s an incredible amount of content on the web. There are a lot of things happening in real time. You want ChatGPT to be able to use that to improve its answers and to be able to be a better super-assistant for you.” Today ChatGPT is able to generate responses for very current news events, as well as near-real-time information on things like stock prices. And while ChatGPT’s interface has long been, well, boring, search results bring in all sorts of multimedia—images, graphs, even video. It’s a very different experience. Weil also argues that ChatGPT has more freedom to innovate and go its own way than competitors like Google—even more than its partner Microsoft does with Bing. Both of those are ad-dependent businesses. OpenAI is not. (At least not yet.) It earns revenue from the developers, businesses, and individuals who use it directly. It’s mostly setting large amounts of money on fire right now—it’s projected to lose $14 billion in 2026, by some reports. But one thing it doesn’t have to worry about is putting ads in its search results as Google does. “For a while what we did was organize web pages. Which is not really the same thing as organizing the world’s information or making it truly useful and accessible to you,” says Google head of search, Liz Reid.WINNI WINTERMEYER/REDUX Like Google, ChatGPT is pulling in information from web publishers, summarizing it, and including it in its answers. But it has also struck financial deals with publishers, a payment for providing the information that gets rolled into its results. (MIT Technology Review has been in discussions with OpenAI, Google, Perplexity, and others about publisher deals but has not entered into any agreements. Editorial was neither party to nor informed about the content of those discussions.) But the thing is, for web search to accomplish what OpenAI wants—to be more current than the language model—it also has to bring in information from all sorts of publishers and sources that it doesn’t have deals with. OpenAI’s head of media partnerships, Varun Shetty, told MIT Technology Review that it won’t give preferential treatment to its publishing partners. Instead, OpenAI told me, the model itself finds the most trustworthy and useful source for any given question. And that can get weird too. In that very first example it showed me—when Turley ran that name search—it described a story I wrote years ago for Wired about being hacked. That story remains one of the most widely read I’ve ever written. But ChatGPT didn’t link to it. It linked to a short rewrite from The Verge. Admittedly, this was on a prototype version of search, which was, as Turley said, “risky.” When I asked him about it, he couldn’t really explain why the model chose the sources that it did, because the model itself makes that evaluation. The company helps steer it by identifying—sometimes with the help of users—what it considers better answers, but the model actually selects them. “And in many cases, it gets it wrong, which is why we have work to do,” said Turley. “Having a model in the loop is a very, very different mechanism than how a search engine worked in the past.” Indeed! The model, whether it’s OpenAI’s GPT-4o or Google’s Gemini or Anthropic’s Claude, can be very, very good at explaining things. But the rationale behind its explanations, its reasons for selecting a particular source, and even the language it may use in an answer are all pretty mysterious. Sure, a model can explain very many things, but not when that comes to its own answers. It was almost a decade ago, in 2016, when Pichai wrote that Google was moving from “mobile first” to “AI first”: “But in the next 10 years, we will shift to a world that is AI-first, a world where computing becomes universally available—be it at home, at work, in the car, or on the go—and interacting with all of these surfaces becomes much more natural and intuitive, and above all, more intelligent.” We’re there now—sort of. And it’s a weird place to be. It’s going to get weirder. That’s especially true as these things we now think of as distinct—querying a search engine, prompting a model, looking for a photo we’ve taken, deciding what we want to read or watch or hear, asking for a photo we wish we’d taken, and didn’t, but would still like to see—begin to merge. The search results we see from generative AI are best understood as a waypoint rather than a destination. What’s most important may not be search in itself; rather, it’s that search has given AI model developers a path to incorporating real-time information into their inputs and outputs. And that opens up all sorts of possibilities. “A ChatGPT that can understand and access the web won’t just be about summarizing results. It might be about doing things for you. And I think there’s a fairly exciting future there,” says OpenAI’s Weil. “You can imagine having the model book you a flight, or order DoorDash, or just accomplish general tasks for you in the future. It’s just once the model understands how to use the internet, the sky’s the limit.” This is the agentic future we’ve been hearing about for some time now, and the more AI models make use of real-time data from the internet, the closer it gets. Let’s say you have a trip coming up in a few weeks. An agent that can get data from the internet in real time can book your flights and hotel rooms, make dinner reservations, and more, based on what it knows about you and your upcoming travel—all without your having to guide it. Another agent could, say, monitor the sewage output of your home for certain diseases, and order tests and treatments in response. You won’t have to search for that weird noise your car is making, because the agent in your vehicle will already have done it and made an appointment to get the issue fixed. “It’s not always going to be just doing search and giving answers,” says Pichai. “Sometimes it’s going to be actions. Sometimes you’ll be interacting within the real world. So there is a notion of universal assistance through it all.” And the ways these things will be able to deliver answers is evolving rapidly now too. For example, today Google can not only search text, images, and even video; it can create them. Imagine overlaying that ability with search across an array of formats and devices. “Show me what a Townsend’s warbler looks like in the tree in front of me.” Or “Use my existing family photos and videos to create a movie trailer of our upcoming vacation to Puerto Rico next year, making sure we visit all the best restaurants and top landmarks.” “We have primarily done it on the input side,” he says, referring to the ways Google can now search for an image or within a video. “But you can imagine it on the output side too.” This is the kind of future Pichai says he is excited to bring online. Google has already showed off a bit of what that might look like with NotebookLM, a tool that lets you upload large amounts of text and have it converted into a chatty podcast. He imagines this type of functionality—the ability to take one type of input and convert it into a variety of outputs—transforming the way we interact with information. In a demonstration of a tool called Project Astra this summer at its developer conference, Google showed one version of this outcome, where cameras and microphones in phones and smart glasses understand the context all around you—online and off, audible and visual—and have the ability to recall and respond in a variety of ways. Astra can, for example, look at a crude drawing of a Formula One race car and not only identify it, but also explain its various parts and their uses. But you can imagine things going a bit further (and they will). Let’s say I want to see a video of how to fix something on my bike. The video doesn’t exist, but the information does. AI-assisted generative search could theoretically find that information somewhere online—in a user manual buried in a company’s website, for example—and create a video to show me exactly how to do what I want, just as it could explain that to me with words today. These are the kinds of things that start to happen when you put the entire compendium of human knowledge—knowledge that’s previously been captured in silos of language and format; maps and business registrations and product SKUs; audio and video and databases of numbers and old books and images and, really, anything ever published, ever tracked, ever recorded; things happening right now, everywhere—and introduce a model into all that. A model that maybe can’t understand, precisely, but has the ability to put that information together, rearrange it, and spit it back in a variety of different hopefully helpful ways. Ways that a mere index could not. That’s what we’re on the cusp of, and what we’re starting to see. And as Google rolls this out to a billion people, many of whom will be interacting with a conversational AI for the first time, what will that mean? What will we do differently? It’s all changing so quickly. Hang on, just hang on.

Subsea7 Scores Various Contracts Globally

Subsea 7 S.A. has secured what it calls a “sizeable” contract from Turkish Petroleum Offshore Technology Center AS (TP-OTC) to provide inspection, repair and maintenance (IRM) services for the Sakarya gas field development in the Black Sea. The contract scope includes project management and engineering executed and managed from Subsea7 offices in Istanbul, Türkiye, and Aberdeen, Scotland. The scope also includes the provision of equipment, including two work class remotely operated vehicles, and construction personnel onboard TP-OTC’s light construction vessel Mukavemet, Subsea7 said in a news release. The company defines a sizeable contract as having a value between $50 million and $150 million. Offshore operations will be executed in 2025 and 2026, Subsea7 said. Hani El Kurd, Senior Vice President of UK and Global Inspection, Repair, and Maintenance at Subsea7, said: “We are pleased to have been selected to deliver IRM services for TP-OTC in the Black Sea. This contract demonstrates our strategy to deliver engineering solutions across the full asset lifecycle in close collaboration with our clients. We look forward to continuing to work alongside TP-OTC to optimize gas production from the Sakarya field and strengthen our long-term presence in Türkiye”. North Sea Project Subsea7 also announced the award of a “substantial” contract by Inch Cape Offshore Limited to Seaway7, which is part of the Subsea7 Group. The contract is for the transport and installation of pin-pile jacket foundations and transition pieces for the Inch Cape Offshore Wind Farm. The 1.1-gigawatt Inch Cape project offshore site is located in the Scottish North Sea, 9.3 miles (15 kilometers) off the Angus coast, and will comprise 72 wind turbine generators. Seaway7’s scope of work includes the transport and installation of 18 pin-pile jacket foundations and 54 transition pieces with offshore works expected to begin in 2026, according to a separate news

Driving into the future

Welcome to our annual breakthroughs issue. If you’re an MIT Technology Review superfan, you may already know that putting together our 10 Breakthrough Technologies (TR10) list is one of my favorite things we do as a publication. We spend months researching and discussing which technologies will make the list. We try to highlight a mix of items that reflect innovations happening in various fields. We look at consumer technologies, large industrial-scale projects, biomedical advances, changes in computing, climate solutions, the latest in AI, and more. We’ve been publishing this list every year since 2001 and, frankly, have a great track record of flagging things that are poised to hit a tipping point. When you look back over the years, you’ll find items like natural-language processing (2001), wireless power (2008), and reusable rockets (2016)—spot-on in terms of horizon scanning. You’ll also see the occasional miss, or moments when maybe we were a little bit too far ahead of ourselves. (See our Magic Leap entry from 2015.) But the real secret of the TR10 is what we leave off the list. It is hard to think of another industry, aside from maybe entertainment, that has as much of a hype machine behind it as tech does. Which means that being too conservative is rarely the wrong call. But it does happen. Last year, for example, we were going to include robotaxis on the TR10. Autonomous vehicles have been around for years, but 2023 seemed like a real breakthrough moment; both Cruise and Waymo were ferrying paying customers around various cities, with big expansion plans on the horizon. And then, last fall, after a series of mishaps (including an incident when a pedestrian was caught under a vehicle and dragged), Cruise pulled its entire fleet of robotaxis from service. Yikes.
The timing was pretty miserable, as we were in the process of putting some of the finishing touches on the issue. I made the decision to pull it. That was a mistake. What followed turned out to be a banner year for the robotaxi. Waymo, which had previously been available only to a select group of beta testers, opened its service to the general public in San Francisco and Los Angeles in 2024. Its cars are now ubiquitous in the City by the Bay, where they have not only become a real competitor to the likes of Uber and Lyft but even created something of a tourist attraction. Which is no wonder, because riding in one is delightful. They are still novel enough to make it feel like a kind of magic. And as you can read, Waymo is just a part of this amazing story.
The item we swapped into the robotaxi’s place was the Apple Vision Pro, an example of both a hit and a miss. We’d included it because it is truly a revolutionary piece of hardware, and we zeroed in on its micro-OLED display. Yet a year later, it has seemingly failed to find a market fit, and its sales are reported to be far below what Apple predicted. I’ve been covering this field for well over a decade, and I would still argue that the Vision Pro (unlike the Magic Leap vaporware of 2015) is a breakthrough device. But it clearly did not have a breakthrough year. Mea culpa. Having said all that, I think we have an incredible and thought-provoking list for you this year—from a new astronomical observatory that will allow us to peer into the fourth dimension to new ways of searching the internet to, well, robotaxis. I hope there’s something here for everyone.

Oil Holds at Highest Levels Since October

Crude oil futures slightly retreated but continue to hold at their highest levels since October, supported by colder weather in the Northern Hemisphere and China’s economic stimulus measures. That’s what George Pavel, General Manager at Naga.com Middle East, said in a market analysis sent to Rigzone this morning, adding that Brent and WTI crude “both saw modest declines, yet the outlook remains bullish as colder temperatures are expected to increase demand for heating oil”. “Beijing’s fiscal stimulus aims to rejuvenate economic activity and consumer demand, further contributing to fuel consumption expectations,” Pavel said in the analysis. “This economic support from China could help sustain global demand for crude, providing upward pressure on prices,” he added. Looking at supply, Pavel noted in the analysis that “concerns are mounting over potential declines in Iranian oil production due to anticipated sanctions and policy changes under the incoming U.S. administration”. “Forecasts point to a reduction of 300,000 barrels per day in Iranian output by the second quarter of 2025, which would weigh on global supply and further support prices,” he said. “Moreover, the U.S. oil rig count has decreased, indicating a potential slowdown in future output,” he added. “With supply-side constraints contributing to tightening global inventories, this situation is likely to reinforce the current market optimism, supporting crude prices at elevated levels,” Pavel continued. “Combined with the growing demand driven by weather and economic factors, these supply dynamics point to a favorable environment for oil prices in the near term,” Pavel went on to state. Rigzone has contacted the Trump transition team and the Iranian ministry of foreign affairs for comment on Pavel’s analysis. At the time of writing, neither have responded to Rigzone’s request yet. In a separate market analysis sent to Rigzone earlier this morning, Antonio Di Giacomo, Senior Market Analyst at

What to expect from NaaS in 2025

Shamus McGillicuddy, vice president of research at EMA, says that network execs today have a fuller understanding of the potential benefits of NaaS, beyond simply a different payment model. NaaS can deliver access to new technologies faster and keep enterprises up-to-date as technologies evolve over time; it can help mitigate skills gaps for organizations facing a shortage of networking talent. For example, in a retail scenario, an organization can offload deployment and management of its Wi-Fi networks at all of its stores to a NaaS vendor, freeing up IT staffers for higher-level activities. Also, it can help organizations manage rapidly fluctuating demands on the network, he says. 2. Frameworks help drive adoption Industry standards can help accelerate the adoption of new technologies. MEF, a nonprofit industry forum, has developed a framework that combines standardized service definitions, extensive automation frameworks, security certifications, and multi-cloud integration capabilities—all aimed at enabling service providers to deliver what MEF calls a true cloud experience for network services. The blueprint serves as a guide for building an automated, federated ecosystem where enterprises can easily consume NaaS services from providers. It details the APIs, service definitions, and certification programs that MEF has developed to enable this vision. The four components of NaaS, according to the blueprint, are on-demand automated transport services, SD-WAN overlays and network slicing for application assurance, SASE-based security, and multi-cloud on-ramps. 3. The rise of campus/LAN NaaS Until very recently, the most popular use cases for NaaS were on-demand WAN connectivity, multi-cloud connectivity, SD-WAN, and SASE. However, campus/LAN NaaS, which includes both wired and wireless networks, has emerged as the breakout star in the overall NaaS market. Dell’Oro Group analyst Sian Morgan predicts: “In 2025, Campus NaaS revenues will grow over eight times faster than the overall LAN market. Startups offering purpose-built CNaaS technology will

UK battery storage industry ‘back on track’

UK battery storage investor Gresham House Energy Storage Fund (LON:GRID) has said the industry is “back on track” as trading conditions improved, particularly in December. The UK’s largest fund specialising in battery energy storage systems (BESS) highlighted improvements in service by the UK government’s National Energy System Operator (NESO) as well as its renewed commitment to to the sector as part of clean power aims by 2030. It also revealed that revenues exceeding £60,000 per MW of electricity its facilities provided in the second half of 2024 meant it would meet or even exceed revenue targets. This comes after the fund said it had faced a “weak revenue environment” in the first part of the year. In April it reported a £110 million loss compared to a £217m profit the previous year and paused dividends. Fund manager Ben Guest said the organisation was “working hard” on refinancing and a plan to “re-instate dividend payments”. In a further update, the fund said its 40MW BESS project at Shilton Lane, 11 miles from Glasgow, was fully built and in the final stages of the NESO compliance process which expected to complete in February 2025. Fund chair John Leggate welcomed “solid progress” in company’s performance, “as well as improvements in NESO’s control room, and commitment to further change, that should see BESS increasingly well utilised”. He added: “We thank our shareholders for their patience as the battery storage industry gets back on track with the most environmentally appropriate and economically competitive energy storage technology (Li-ion) being properly prioritised. “Alongside NESO’s backing of BESS, it is encouraging to see the government’s endorsement of a level playing field for battery storage – the only proven, commercially viable technology that can dynamically manage renewable intermittency at national scale.” Guest, who in addition to managing the fund is also

The Download: AI’s trillion-dollar gamble and OpenAI’s biology data bid

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. What’s at stake in AI’s trillion-dollar gamble When Jessica Wachter, a finance professor at the University of Pennsylvania, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of uncertainties. So she started with a “remarkable fact” that is not in question: a handful of so-called hyperscalers are investing huge amounts of money to build AI data centers. Instead of trying to predict how widely deployed AI models will be, Wachter asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when expenditures are expected to reach nearly $1.1 trillion. The results are eye-opening. AI companies will need to achieve an extraordinary increase in productivity just to break even by 2030.
Take a closer look at what it will take for the AI buildout to pay off. —David Rotman
AI models need more data about biology, and OpenAI is paying to create it AI needs much more information to make important breakthroughs in curing disease. So last year Ruxandra Teslo, a policy analyst, posted an idea for supercharging medical AI systems: use data from failed biotech companies. By bidding at bankruptcy proceedings, she argued, it might be possible to obtain detailed regulatory filings, manufacturing strategies and safety data, creating what she called “biotech’s lost archive.” The OpenAI Foundation, the nonprofit parent of OpenAI, announced this week that it will fund her idea, paying to create “high-quality scientific datasets.” Learn more about their new effort. —Antonio Regalado Our Roundables on AI’s extinction threat is now available on demand As frontier models become more capable, warnings about AI extinction have become widespread in Silicon Valley. But are the threats really as dangerous as they’re presented? In the latest MIT Technology Review Roundtable, executive editor Niall Firth, senior AI editor Will Douglas Heaven and AI reporter Grace Huckins took a closer look at the arguments behind those warnings. They discussed what AI extinction could actually mean, how seriously we should take the risks and what, if anything, can be done to reduce them. Subscribers can now watch an exclusive recording of the discussion. Want to join the next conversation? Subscribe to MIT Technology Review for exclusive access to all our future Roundtables, and recordings of previous ones.

MIT Technology Review Narrated: a startup claims it’s found a drug to make your blood young Generation Lab says its new rejuvenation treatment “blocks the systemic spread of aging in the bloodstream, reawakens the body’s own repair mechanism, and restores health and youth to multiple tissues.” The approach is based on research by the company’s scientific founder, Irina Conboy. She found that joining the circulatory systems of old and young mice improved the old animals’ ability to heal from injury. Conboy now says she has found a combination of two existing drugs that can produce youthful effects without the need for any bodily fluid exchange. But there’s a snag: Generation Lab won’t reveal what the drugs are.This is our latest story to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Nvidia and Meta CEOs have rejected calls for a coordinated AI slowdownJensen Huang and Mark Zuckerberg pushed back on the proposals. (FT $)+ Huang says new AI safety laws are unnecessary. (Axios)+ Zuckerberg claimed competition will push AI labs toward safety. (Reuters $)+ What’s next for AI after its doomer turn? (MIT Technology Review) 2 The FTC chair has warned against giving AI companies antitrust waiversHis comments follow Anthropic’s call for a safety exemption. (Reuters $)+ Nvidia’s CEO also slammed the calls for new antitrust laws. (CNBC)+ The US is divided over AI regulation. (MIT Technology Review) 3 A Chinese hacking firm has used AI to analyze stolen secretIts tools turn hacked government data into intelligence reports. (WSJ $)
4 “Smart” nanoparticles delivered mRNA to tumors in a cancer studyThe treatment reprogrammed cells to attack tumors in mice. (Wired $)+ Federal health agencies are abandoning mRNA. (MIT Technology Review) 5 A digital fly brain is taking on an extraordinary range of tasks onlinePeople have taught it to drive, trade bitcoin, and play Doom. (NYT $)+ The simulated brain is a map of a fruit fly’s 166,000 neurons. (404 Media)
6 The Senate has blocked new crypto rules amid a fight over TrumpIt demanded tougher ethics rules around Trump’s crypto holdings. (AP)+ The move is a major blow to the crypto industry. (NYT $) 7 Chinese firms allegedly used Binance to launder Iranian oil moneyProsecutors say they laundered more than $1.5 billion. (Quartz)+ Hackers are selling tools to bypass banks’ facial checks. (MIT Technology Review) 8 An AI agent platform is reinventing spam to flood inboxes worldwideiLand says its agents have sent 1.6 million messages. (404 Media) 9 ByteDance founder Zhang Yiming has become Asia’s richest personHis fortune has risen above $105 billion as AI booms. (Bloomberg $) 10 A fully AI-generated sitcom has arrived—and it’s terribleA reviewer called the characters “dead-eyed waxworks.” (Guardian) Quote of the day
“The only institution that Americans might trust less than Washington these days is Silicon Valley.” —Patrick Hillman, the chief operating officer of Logical Intelligence, a San Francisco–based startup chaired by Yann LeCun, says in a statement that people have little faith in tech companies to act in the public interest. One more thing Why Trump’s “golden dome” missile defense idea is another ripped straight from the movies In 1940, a fresh-faced Ronald Reagan starred in Murder in the Air, a movie centered on a “superweapon” that could stop enemy aircraft. More than 40 years later, the concept became a real-life centerpiece of Reagan’s presidency with the Strategic Defense Initiative (SDI), better known as “Star Wars.” Now Donald Trump has revived the dream. In 2024, Trump announced plans to build the “Golden Dome,” a system of sensors and interceptors on the ground, in the air and in space. It’s often compared to SDI for its futuristic sheen, its aggressive form of protection and the idea that an impenetrable shield is the cheat code to global peace.

Introducing Gemini 3.8 Live and 3.8 Live Extended Thinking

Today, we’re introducing two new models that bring advancements in near real-time reasoning to more effectively enable voice agents and make conversing with AI feel more intuitive and intelligent.Gemini 3.8 Live: Built for scale and cost efficiency, combining conversational intelligence with fluid dialogue and visual grounding.Gemini 3.8 Live Extended Thinking: Built for high-complexity tasks, with increased intelligence and multi-step reasoning.For developers and enterprises, these models deliver the building blocks for reliable, production-ready voice agents. They also make speaking with Gemini across the Gemini app, Google Workspace, and Search more fluid and collaborative — helping you tackle complex tasks using just your voice.Experience more fluid, intelligent conversationsGemini 3.8 Live Extended Thinking provides enterprise-grade task completion and intelligence, capturing the #1 overall spot on Artificial Analysis’ Speech to Speech Quality Index (82.6), and leads in agentic task completion with 68.6% on τ-Voice and 35.1% on Sierra’s τ-Voice-banking benchmark. It also provides strong reasoning capabilities, scoring 97.7% on Big Bench Audio, while maintaining a highly competitive price point compared to other frontier models.Gemini 3.8 Live has shown a high preference among users, securing a second place in the Speech Agent Arena. In addition to this performance, it remains highly cost-effective — providing developers and enterprises with a capable and efficient model built for scale.

AI models need more data about biology, and OpenAI is paying to create it

Last year, the clinical trial policy analyst Ruxandra Teslo posted an idea for super-charging medical AI systems: use data from failed biotech companies. By bidding at their bankruptcy proceedings, she proposed, it might be possible to obtain detailed regulatory filings, manufacturing strategies, and safety data—information usually kept hidden as valuable trade secrets. She called these documents “biotech’s lost archive” and said they could be used to help train AIs that would act as powerful co-pilots in the often opaque drug approval process. Today, the OpenAI Foundation, the nonprofit parent of OpenAI, said it would fund her idea as part of a new effort it calls Data for Public Health, which aims to help artificial intelligence make big leaps in medicine by funding the creation of “high-quality scientific datasets.”
The basic idea is that AI isn’t going to be capable of making important breakthroughs in curing disease unless researchers can feed the models much more information than they have so far. “Everyone is recognizing that data is the biggest bottleneck in successfully applying AI to biology,” says Morgan Levine, a former vice president for computation at Altos Labs, a longevity company.
In its initial round of data grants, the OpenAI Foundation announced it would give $40 million to a program to collect data about novel cancer vaccines at the University of North Carolina, Chapel Hill, and will also support OpenAdmet, a group that runs competitions in which researchers try to predict drug effects. Teslo’s biotech archive idea received $500,000 and will be pursued by 1Day Sooner, an advocacy group representing clinical trial volunteers that she advises. “We expect many remaining breakthroughs in preventing and curing disease to come from pairing the intelligence of new models with more observations of the world—in other words, more data,” the OpenAI Foundation said in a statement. OpenAI started as a nonprofit, but leader Sam Altman forked its personnel into a for-profit corporation that develops new models, launches products, and which is now planning an initial public offering of stock that could value it at $1 trillion. Because the foundation holds a 26% equity stake in OpenAI, it is now be on track to become the richest charitable organization on the planet, potentially sitting on $250 billion in stock value. (By comparison, the Gates Foundation and a trust associated with it held about $180 billion at the end of 2025.) Making good use of that kind of money will not be easy. The foundation, based in San Francisco, is still hiring for many key roles and only started ramping up its grantmaking this year. Its largest single gift so far, of $100 million, was awarded in August to the Common Health Coalition, an organization that helps patients get access to hepatitis C drugs. OpenAI’s charitable efforts come even as apocalyptic fears have broken out about the possibility that runaway AI could wipe out all human life. Those fears, which include the possibility of a deadly bioweapon, have been stoked by AI company insiders, some of whom say the chance of human extinction is 10% or more within the next decade. Last week, Altman and xAI founder Elon Musk both endorsed a call by Anthropic CEO Dario Amodei to “slow the pace at which we improve the capabilities of AI models” so that risk prevention can catch up.

Jacob Trefethen, an executive at the foundation, says it essentially operates separately from OpenAI, but shares an official mission of ensuring that artificial intelligence “benefits all of humanity.” “We’re starting grantmaking when we think the best way to achieve that mission is to make grants to external non-profits, research institutions, and other third parties,” Trefethen said in an interview. He says the foundation hopes to give away $1 billion by the end of the year. The $500,000 grant to 1Day Sooner will help the group prove it can obtain the data troves of bankrupt companies, says organization’s president and co-founder Josh Morrison. He thinks non-exclusive copies of company datasets could be acquired for only “a few tens of thousands of dollars” each. His organization is currently in possession of three datasets, two of them donated by Lumen Bioscience, a biotech that previously used the Chapter 11 strategy to gain insights into another company’s drug development efforts. Morrison says two other attempts to obtain drug company files this year proved unsuccessful, after 1Day Sooner’s bids were not accepted. Bankruptcies could become what some are calling a “new land grab” for AI training. Last month, Google won a bid to take over the corporate data of failed carrier Spirit Airlines, including 100 million emails. That led to objections from flight attendants and others who worried that private or proprietary data could be exposed. The drug company files that 1Day Sooner is seeking are known as common technical documents. They typically contain the back-and-forth between companies and regulators as well as detailed scientific and medical measurements and essentially provide everything that is known about a drug. A stockpile of such files, Teslo thinks, could help turn an AI into a regulatory expert, which in her view could be one of the main ways AI helps speed cures to market. “People say ‘We will invent AI, and AI will cure cancer,’ but that’s very removed from the messy reality and the regulatory process,” says Teslo, who is a writer for Works In Progress and a non-resident fellow at the Institute for Progress, a think tank in Washington, DC. “About 70% of the money and time in drug development is spent in clinical development—organizing the trials and testing the drug—but despite that, the process is basically a black box, especially for small biotech companies generating the innovations.”

The Download: AI doomers, whistleblowing agents, and de-aged livers

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The AI industry has taken a doomer turn. What now? AI chiefs Dario Amodei, Sam Altman, Elon Musk, and Demis Hassabis are suddenly all in agreement: the latest generation of LLMs aren’t safe and everyone needs to figure out what to do about it. It’s easy to be cynical. With trillion-dollar IPOs in their sights, OpenAI and Anthropic need to reassure investors that they’re the grown-ups in the room while at the same time hinting at the power of the monsters they have created and intend to tame. Calling for a slowdown does both. Still, the vibe at the top of these firms really does appear to have shifted. But what does a slowdown actually mean, and how much should we trust the companies calling for one?
Read the full story about what could come next. —Will Douglas Heaven
This article is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday. Roundtables: could AI really kill us all? AI extinction fears have gone from a fringe idea to a serious concern among people working at the world’s leading AI labs. But how credible are those fears, and what should we make of the warnings? Today, MIT Technology Review executive editor Niall Firth, senior AI editor Will Douglas Heaven and AI reporter Grace Huckins will unpack the debate in a subscriber-only Roundtable. They’ll look at where AI extinction fears come from, whether they hold any water and what we should do if they do. Tune in today at 16:00 BST / 11:00am EST / 8:00am PST. Want to join the conversation? Subscribe to MIT Technology Review for exclusive access to all our Roundtables. AI agents blew the whistle on their cheating colleagues A group of AI agents asked to solve a series of math problems split into rival factions—when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep swarms of autonomous AI agents in line. The experiment offers a glimpse of how AI agents might police one another. But it also shows how quickly things can go off the rails when they’re left to interact on their own.

Find out what happens when AI agents start enforcing their own rules. —Amit Katwala Donated livers can be made biologically younger Once an organ is removed from a donor’s body, the clock starts ticking. Surgeons usually flush it with a preservative solution, bag it and put it on ice, where it immediately starts to degrade. The team has a matter of hours to get it into a recipient’s body. But there’s another option: machines that pump donated organs with nutrients and remove waste products, essentially giving them a chance to be back in a body. Now, scientists have found that livers kept on these systems seem to get younger, at least at a molecular level. The finding could help explain why organs kept on these machines tend to do better after transplantation. It could also lead to new ways to test the health of donated organs and potentially repair ones that might otherwise be discarded. Here’s what scientists discovered about making donated livers biologically younger. —Jessica Hamzelou The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Trump has called AI safety fears a “hoax” and rejected more safeguardsHe says stronger guardrails could undermine America’s AI advantage. (NBC)+ Trump has united against AI doomerism with Nvidia’s Jensen Huang. (Axios)+ Anthropic’s co-founder says AI kill switches may need to be mandatory. (BBC)+ Bill Gates says we’ve passed AI’s risk thresholds. (MIT Technology Review)
2 OpenAI contractors are reading people’s ChatGPT chats And you can bet the vast majority of its 900 million users haven’t got a clue. (404 Media)+ LLMs could supercharge mass surveillance. (MIT Technology Review) 3 The US military has confirmed it has weapons in orbitIt’s the first time the Pentagon has disclosed this. (Ars Technica)+ Officials have not disclosed what the weapons are. (BBC) 4 A new brain implant can translate speech and gestures at the same timeThe system converts brain activity into words and avatar movements. (Nature)+ It helps people with paralysis communicate more naturally. (New Scientist $)+ Eventually, they could control robots or exoskeletons. (Economist $)+ China has approved the first invasive BCI. (MIT Technology Review) 5 New York has seized a dozen celebrity deepfake websitesIt’s the biggest-ever legal action against harmful deepfake sites. (CNN)+ Deepfakes have targeted at least 138 women MEPs. (Wired $) 6 US environmental regulators are scrapping limits on power plant emissionsThe move could lead to dirtier power amid surging AI demand. (Verge)+ Trump’s EPA says the rollback will save hundreds of billions. (Gizmodo)+ New technology is changing nuclear power. (MIT Technology Review) 7 The EU plans to restrict social media and AI chatbots for kidsUnder-15s would require parental supervision. (Politico)+ The rules would also cover video platforms and games. (Reuters $)
8 Chinese researchers have mapped a path to the “last AI built by humans”Their five-stage plan aims for genuine recursive self-improvement. (SCMP)+ But it might take a while to get there. (MIT Technology Review) 9 The real AI economy is being built by ordinary peopleWorkers are using cheap AI to expand what they can do. (Rest of World) 10 Two strange new forms of ice could exist inside Uranus and NeptuneThey could help explain the planets’ magnetic fields. (New Scientist $) Quote of the day
“The only control or ‘guardrails’ that AI needs is a STRONG AND SMART (High IQ!) PRESIDENT, and the U.S.A. has that, in spades!” —President Trump proclaims in a social media post that he’s the only protection that the US needs from AI. One more thing INSTITUTE OF PERSONALITY AND SOCIAL RESEARCH, UNIVERSITY OF CALIFORNIA, BERKELEY/THE MONACELLI PRESS How creativity became the reigning value of our time —Bryan Gardiner Americans don’t agree on much these days, but there remains at least one quintessentially modern value we can all still get behind: creativity. We teach it, measure it, envy it and endlessly worry about its death. Given how much we obsess over it, creativity can feel like something that has always existed. But the concept is surprisingly young. The first known written use of the word didn’t occur until 1875, and before about 1950 there were “approximately zero” articles, books, or essays dealing explicitly with the subject. In his book The Cult of Creativity, Samuel Franklin explores how creativity became an unimpeachable value and why tech leaders have embraced it so enthusiastically. I spoke to him about why we’re so fascinated by creativity, how Silicon Valley became the supposed epicenter of it, and how AI might reshape our relationship with it. Read the full interview. We can still have nice things

What must happen for AI’s trillion-dollar gamble to pay off

When Jessica Wachter, a finance professor at the University of Pennsylvania’s Wharton School, wanted to assess AI’s impact on the economy over the next few years, she faced a long list of business and technical uncertainties. So she started with what she calls a “remarkable fact” that is not in question: A handful of so-called hyperscalers are investing huge amounts of money to build AI data centers. Instead of trying to predict how useful and widely deployed AI models will be, she simply asked how fast the hyperscalers’ earnings will need to grow to justify their spending through 2027, when—she and her collaborator estimate—expenditures will reach nearly $1.1 trillion. It’s a no-nonsense accounting approach to making sense of today’s historical AI buildout. The results are eye-opening: The AI companies will need to increase their own productivity by a factor of 2.7 to break even by 2030, accounting for the cost of capital and a 15% return, and depreciation of the assets. Not impossible, says Wachter. The result would lead to the kind of economic growth that we saw during the US IT boom over a period of about 10 years starting in the mid-1990s. But, she says, for it to happen by 2030 “that’s a lot of growth compressed into a few years.” And if the hyperscalers cannot meet such profit goals? “Then they will fall behind on their interest payments, and that risks bankruptcy,” says Wachter, who was previously the SEC’s chief economist and director of its division of economic and risk analysis. If a productivity boom “fails to materialize,” she and her coauthor conclude in their research paper, “the current buildout will be the largest misallocation of capital in history.”
It doesn’t take superintelligence to realize that today’s large investments in the infrastructure for artificial intelligence come with huge risks. The hyperscalers will spend about $750 billion this year, building massive data centers scattered across the country. And the spending spree shows no signs of slowing. According to some projections, total AI capital investments from the hyperscaler companies—Alphabet, Microsoft, Amazon, Meta, and Oracle (which partners with OpenAI)—could be more than $5 trillion over the next four years. It’s one of the largest capital investments by any industry in history. But there’s a problem that’s obvious to anyone paying attention.
While the hyperscalers plan to spend trillions, total AI revenues will be around $150 billion to $200 billion this year, says Gary Gensler, who ran the SEC during the Biden administration and is now a professor at MIT’s Sloan School. “The challenge is that the spending does not have commensurate revenues yet. That’s a fact,” he says. “And then the question is, is that an investment that will be paid off in the future?” At stake in that trillion-dollar question is the financial health of the giant AI companies and the overall US economy—the investments could soon balloon to around 3% of GDP. The answer could also determine the fate of the hugely expensive data centers themselves. No one really knows how profitable and useful these multibillion-dollar behemoths will be down the road. Though AI models have made dazzling progress over the last few years, it’s anyone’s guess how much compute capacity we will need. The technology could become more efficient and therefore less dependent on raw computational power. Or demand for AI products could slow, or customers could turn to cheaper models. The risks, both to investors and to the economy, have become even greater this year, as these AI companies have begun borrowing large amounts of money to build more and more data centers. Free cash flow—operating cash flow minus capital expenditures—is expected to soon dip into negative territory for the group. Even Alphabet, known for generating and hoarding huge amounts of cash, reports in the latest quarter that its impressive revenues of nearly $120 billion were devoured by AI infrastructure spending, leaving it with a free cash deficit of some $5.9 billion—its first shortfall since Google went public in 2004. In the near term, it’s not a big financial worry for most of the companies. They make a lot of money and have very deep pockets. But debt is expensive, and some investors are losing patience. If future demand for the data centers’ computation power drops, the companies will still be on the hook to pay back the borrowed money. What’s more, the risks are spreading to the rest of the economy as the loans get passed along via various financial mechanisms. It won’t be enough to simply cover the enormous price tags of the new data centers. Hyperscalers will also have to pay for the rising costs of capital as they borrow more money. They will need returns that are impressive enough to justify all their spending to investors and creditors. And to add to those concerns, they will have to make up for the depreciation of billions of dollars in chips housed within the facilities—a ticking time bomb buried in the investments. Performance of the expensive GPU chips at the core of the data centers—such compute electronics represent some 60% of costs—is roughly doubling every two years or so. The pace of progress helps explain the increasing wizardry of the AI models, but it comes with a cost. Owners of AI data centers that come online this year and next will need to spend billions more on the next generation of chips by the end of the decade if they want to stay competitive. Without the investments, says Mihir Kshirsagar at Princeton’s Center for Information Technology Policy, the data centers risk becoming “hulks,” stranded assets “scattered all over the place.” To put it bluntly: The AI companies need to start making a lot more money. And they need to do it fast. But juicing their earnings alone still won’t be enough to sustain their data-center investments for the long term.

Productivity is everything At some point, AI is also going to have to create broad economic growth to justify continuing the hyperscalers’ spending spree. Sloan’s Gensler describes today’s large investments into AI infrastructure as “a parlay bet by the capital markets and the economy.” That means success will require winning three related but independent wagers: Hyperscalers must generate massive revenues, AI must boost widespread economic growth, and both must happen while the powerful but expensive so-called frontier models that rely on the data centers fend off cheaper versions, which many businesses might find good enough. What makes this so tricky is that each wager depends on the other two but also poses its own challenges. If the hyperscalers continue to spend huge amounts of money on data centers into the next decade, revenues will need to skyrocket into the trillions. Stijn Van Nieuwerburgh, a finance professor at Columbia Business School, bases his estimates on a scenario in which about 183 gigawatts of planned AI compute capacity is built between 2025 and 2032; he calculates that each gigawatt costs about $41 billion. Assuming a 10% return—the minimum that would be acceptable to most investors—“required” annual revenues will be roughly $3.7 trillion by 2032, he says. Others get a similar number. Winning the second part of the bet—productivity growth across the economy—will be crucial to achieving such numbers. For a few years, AI companies could likely boost their revenues by simply selling subscriptions and tokens to all the businesses clamoring to get into AI. But eventually—and this might be happening already—those paying customers will need to justify their expenses by seeing bottom-line benefits from the technology. AI will need to fulfill its promise of making workers more productive and making businesses more efficient and profitable while expanding their products and services. In economic jargon, that means customers will need to see productivity growth. Taken together, these results will mean the country is prospering and growing.
“If you don’t get the productivity gains, at some point people are going to sour on AI, and that will bring down investments and it would also limit revenue growth,” says Daron Acemoglu, an MIT economist and 2024 Nobel laureate. For the investments to be sustainable over, say, the next five to 10 years, we definitely “need to see productivity gains,” he says. Most economists who watch the numbers closely agree that, for now, the economy-wide statistics show little or no productivity growth from AI. There are some hopeful signs it’s on the way, though. In a recent survey of some 6,000 senior business executives in the US, the UK, Germany, and Australia, the vast majority—around 90%—report no increase in productivity over the last three years. But they expect a boost of around 1.45% in total over the next three years; US executives anticipate a 2.25% bump over that time.
In a follow-up survey, the respondents also reported plans for their businesses to spend more on AI, leading the authors to anticipate some $280 billion in private-sector AI expenditures by the end of 2026. That’s good news for the hyperscalers. But it comes with a dose of bad news for those worried about AI’s impact on jobs. The executives expect to increase the productivity of their companies by increasing their sales while significantly cutting the number of employees. If AI improves productivity by destroying jobs, public backlash to the technology—the kind we have seen around data centers, for example—will likely get worse. Perhaps it’s worth adding one more wager to the parlay bet described by Gensler: The public and local communities must feel that they are also benefiting from the massive investments in AI. And let’s not forget how interdependent these wagers are; if productivity growth comes from companies running models like DeepSeek, then the hyperscalers’ revenues could collapse. If productivity comes from cutting jobs, a public backlash could block many of the planned investments—and stunt anticipated revenues. We will need to win all the wagers for the hyperscalers’ bet to pay off. We’re all part of the AI gamble now It was one thing when the AI companies were spending cash they had accumulated over the years to build their own data centers. Then the risk was largely limited to their own balance sheets and shareholders. But it’s a higher-stakes game when much of the money is borrowed. Morgan Stanley, for one, calculates that more than half of the $2.9 trillion that hyperscalers will spend between 2025 and 2028 to build AI data centers will be financed with “external capital.” The borrowing is leading some of the companies to engineer complex webs of financing that are becoming intertwined with much of the rest of the economy. “A lot of financial institutions, directly or indirectly, are exposed to these data centers either as lenders, or as guarantors of some of the debt, or as backers of the private credit funds who are funding these data centers,” says Columbia’s Van Nieuwerburgh. “People don’t even know they’re holding this stuff. It’s somewhere deep inside their pension fund. Ultimately, it’s backing their life insurance policies. And that risk is getting distributed everywhere in places that are invisible.”
As the investments in data centers have spiked, the financial engineering has become more byzantine. Take, for example, Meta’s so-called Hyperion data center under construction in Richland, Louisiana. When the company announced the two gigawatts of compute capacity at a price tag of some $10 billion in late 2024 it was Meta’s largest planned data center. Greeted with much enthusiasm by state and local politicians, the project, located in the rural northeast corner of the state, was seen as a boon to the community. Entergy Louisiana, the state’s largest utility, rushed forward with proposals to build three large natural-gas power plants to service the massive data center. Then last fall—the projected cost was now $30 billion—the financing got a lot more complex and, to some in the community, a lot more disconcerting. Meta transferred an 80% stake to the large (and troubled) private-credit firm Blue Owl Capital, forming a joint venture called Beignet (like the famed New Orleans pastry) to raise financing for the data center. Meta then signed a series of four-year leases with the joint venture, an arrangement that the company says gives it “long-term strategic flexibility.” To backstop the agreement, Meta provides the venture with what is called a residual value guarantee, in which it will make a cash payment to cover the value of the facility “following any non-renewal or termination of a lease.” Got all that? I hope so. The financial wheeling and dealing is actually even more convoluted, with a cast of wholly owned subsidiaries and LLCs. Beignet has set up Laidley LLC, which owns and operates the site as the landlord. In turn, Laidley leases the facilities to Meta’s wholly owned subsidiary Pelican Leap LLC, which is the tenant. And there is a series of four-year leases that cover the different buildings that make up the data center campus.
It’s not a coincidence, says Van Nieuwerburgh, that the length of the leases matches the expected lifetime of the data center’s GPUs. While Meta has to pay off its loan if it terminates the leases early, that will still leave its investors “with an empty building and no cash flow,” he says. “And then they need to find a new tenant for a huge data center, and good luck with that.” Meanwhile, Meta is doubling down on its bet. In July, the company announced it was expanding the data center to five gigawatts of compute capacity. The total price tag is now $50 billion (so far, Meta hasn’t said whether Blue Owl will be involved in financing the expansion). Meanwhile, Entergy is now planning to build seven more gas-fired power plants, bringing the total capacity of the facilities to around 7.5 gigawatts—some six times the amount of electricity used by New Orleans. An aerial view of the construction of Meta’s data center in Richland Parish, Louisiana.SCOTT BALL/THE NEW YORK TIMES VIA REDUX PICTURES If the complex financing is a puzzle to many investors and even financial experts, it is even more baffling to those directly affected by the construction of the data center. The main worry concerns how Entergy’s spending on the natural-gas power plants will affect electricity prices, and who will be left paying the bill for the power if Meta walks away. Entergy says it has a 20-year guarantee from Meta that the company will purchase electricity over that period to cover the costs of the power plants and related infrastructure. But there are skeptics, especially given how fast the fortunes of the AI industry are changing. “In four years, is Mark Zuckerberg still going to be interested in this? Or is he going to throw in the towel?” asks Paul Arbaje, a senior analyst at the Union of Concerned Scientists, which has been advocating, largely unsuccessfully, for the Louisiana Public Service Commission to provide more transparency around the data center and its financing. Even if the 20-year deal holds, consumer advocates are worried that Meta or its partners won’t fully cover all the costs, including those associated with operating and maintaining the power plants—and those additional costs that could be passed on to residential ratepayers. What’s more, says Logan Burke, the executive director of the Alliance for Affordable Energy, if Meta doesn’t end up needing as much power as Entergy planned (these projections are not public), consumers could be left paying for the surplus produced by the plants. And if Meta terminates its leases early? “It gets complicated very quickly,” says Burke, who questions whether the shifting roster of financial entities will honor existing agreements. “That everybody is going to do what they’re saying they’re going to do over the next 20 years is just hard to believe.” For UCS’s Arbaje the bottom line is this: “They’re making huge bets that these data centers will be worth it. Bet with your own money, not with ratepayer money.” After the bubble Predicting when the AI investment bubble will burst is a fool’s errand. But there is little doubt a day of reckoning is coming, given the irrational exuberance that has overtaken the hyperscalers and their investors. Of course, you might argue that this time is different, and that the rules of accounting and lessons of economic history don’t apply—that AI is too transformative. Maybe, but don’t count on it. “History tells us that at some point you get a retrenchment, and it’s just a question of when and how severe,” says Sloan’s Gensler. It could be that today’s $750 billion spending rate “goes flat” or decreases next year. Or, he suggests, “we’re now in 2028 or 2029, and then all of sudden they’re retrenching because they’ve got enough capacity.” But, he adds, “you can be pretty assured there’ll be a retrenchment.” Though a so-called retrenchment might be inevitable, it’s worth keeping in mind that the fates of the financial bubble and the underlying AI technology revolution could be very different. Already, some Silicon Valley insiders are rooting for a crash; in a recent blog post the longtime venture capitalist Vijay Pande wrote that “the coming crash would be the best thing that happens to this technology.” The argument makes some sense. A crash could make AI investments more rational, calm the impulse to build billion-dollar data centers on every vacant field that CEOs fly over, and refocus investors on how to use the technology to create sustainable value. But we should probably be careful what we wish for. After the bursting of the dot-com bubble at the beginning of the 2000s, hundreds of thousands lost their jobs, large and small companies alike went bankrupt, the economy of Silicon Valley and San Francisco was decimated (at least for a while), and the shocks sent the US into a mild recession in 2001. For the financial community and many tech workers, it was no fun. Even more devastating for the economy and the average American was the great recession that began in late 2007. Comparing the financial engineering leading up to it and the methods deployed by hyperscalers today is sobering. So-called special purpose vehicles (SPVs) are back! If Columbia’s Van Nieuwerburgh is right about the dangers of letting investments from the hyperscalers get entangled throughout the economy, the fallout could be severe. But technologies survived and even prospered in the aftermath of both downturns. The early 2000s, even in the face of the dot-com fiasco, were a time of great innovation and tech optimism. The froth came off the spending on silly technologies, helping to focus investments on more promising ones. It’s no coincidence that each of the hyperscalers rose out of the ashes of the crash or started up shortly after. The fiber-optic infrastructure built during the feverish telecom bubble that ran parallel to the dot-com one is still the backbone of much of today’s communication infrastructure; we wouldn’t have Facebook or Amazon or Google without it. This time, however, we’re facing a unique risk: The huge financial investments by the hyperscalers have ensnared the future of AI itself with the fortunes of the massive data centers spreading around the country. The logic is founded on a deeply held belief about the power of scaling in AI; the bigger you build it, the smarter it gets. That might be true, but it’s unproven and a risky bet. There are already plenty of red flags, from strong public opposition to the construction of new data centers to the competitive threat from cheaper, good-enough AI models to the rapid improvement of small, local AI models. None of these trends point toward a future dominated by frontier models housed in massive, billion-dollar data centers. The financial bubble around the colossal spending by the hyperscalers will likely burst eventually—or maybe soon. It might be financially painful, but we’ll survive. Wall Street will survive. AI itself will survive, though it may look different and lose some of today’s hubris. The financial fate and future utility of the massive data centers fueled by trillions of dollars of spending, on the other hand, are far less certain.

The AI industry has taken a doomer turn. What now?

This story appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. This weekend, Dario Amodei, CEO of Anthropic, posted an essay calling for a brake on the pace of development of LLMs. Amodei cites the looming dangers he sees from the technology, from its use in cyberattacks and bioterrorism to its potential to wreck the economy. The heads of the other three top US AI labs—OpenAI CEO Sam Altman, Google DeepMind chairman Demis Hassabis, and SpaceXAI CEO Elon Musk—voiced their support. “Dario is right,” Musk wrote on X. Think about how surreal that agreement is for a moment. Just a few months ago, Musk and Altman sat in court attacking each other’s reputations in a (failed) lawsuit that Musk brought against his former OpenAI colleague that was—on paper at least—about whether or not Altman was a trustworthy steward of such dangerous technology. Amodei’s rift with OpenAI is even deeper. Anthropic was founded in 2021 because Amodei didn’t think Altman took the risks of the technology they were building seriously enough. Anthropic and OpenAI have been competing in a winner-takes-all race ever since. (Hassabis has stayed out of the drama, but his company remains a rival.) Now, it seems, they’re all in agreement: The latest generation of LLMs aren’t safe and everyone needs to figure out what to do about it. The public messaging from the top AI labs has taken a doomer turn.
It’s easy to be cynical. It’s not at all clear what any of them mean by a slowdown or how it would work. These companies also care a lot about how they come across. With trillion-dollar IPOs in their sights, OpenAI and Anthropic need to reassure investors that they’re the grown-ups in the room while at the same time hinting at the power of the monsters they have created—and intend to tame. Calling for a slowdown does both. And yet the vibe at the top of these firms really does appear to have shifted. Amodei’s latest post landed six days after OpenAI published an essay by Jakub Pachocki, the firm’s chief scientist, in which he also laid out why he’s concerned about what will happen if the pace of development of LLMs continues unchecked. In short, Pachocki is worried that OpenAI’s ability to build powerful models now far outstrips its ability to monitor and control them.
Amodei and Pachocki each cite the cyberattack against AI firm Hugging Face by a swarm of OpenAI’s agents in July—a hack that OpenAI did not even realize had taken place until days after it was all over—as a wake-up call. But their exact position is hard to pin down. Pachocki both calls for a slowdown and highlights an urgent need to stay ahead: “The strongest argument I see for continuing to train much smarter models quickly is the need to build defensive systems against the dangers posed by other AI,” he writes. As Pachocki frames it, AI firms are locked in a literal arms race. Slowing down is good, winning is better. (Don’t forget: OpenAI just spent millions of dollars and a staggering amount of computer power to rush out a controversial math result a few days ahead of Anthropic.) But let’s assume a slowdown happens. Top labs agree to spend more time and resources on finding ways to monitor and control existing models instead of making more capable ones. They invite outside auditors in to help evaluate those models. What might this coordinated effort actually achieve? Consider the Hugging Face attack again. OpenAI has said that the model that drove most of the rogue agents was a “highly persistent” next-generation model that it was testing in-house. Their implication appears to be that OpenAI has built a model so good it’s dangerous. But if you read the reports about the Hugging Face hack published by OpenAI and METR, a third-party firm that OpenAI called in to help them understand what happened, what you come away with is the impression not of a model that was too powerful for OpenAI to keep up with, but of a broken model that OpenAI failed to train properly. The agents did what they did—including leaving messages for one another, delegating work to other agents, and scouring their environment for any means possible to complete their tasks—because they had been rewarded during training for doing exactly those things. There were also errors in the training setup, such as tasks that were impossible to complete, which pushed the models to find unexpected workarounds that were also rewarded. At the time, many of these issues went overlooked or unreported. OpenAI says it has stopped training this new model and locked it down. That makes it sound like it has caged a dangerous beast. In fact, OpenAI has shelved a faulty product.

That’s not to say a faulty product can’t be dangerous. Broken software has even killed people in the past. But as the discussion of a slowdown gathers steam, it’s worth remembering that all of this is self-inflicted. A slowdown might have some altruistic side effects. But it’ll mostly give these tech titans a chance to clean up the mess on their own assembly lines. Transparency from these frontier labs will be key to any meaningful effort to reform, restrain, or regulate AI. Otherwise, the rest of us will still only have their word for exactly what they’ve built and how safe it is—whatever pace they’re going. To continue this discussion about AI’s latest doomer moment, join me and my colleagues for a subscriber-exclusive Roundtable discussion tomorrow, September 15, at 11 a.m. US eastern time. We hope to see you there!

Stay Ahead with the Paperboy Newsletter

Your weekly dose of insights into AI, Bitcoin mining, Datacenters and Energy indusrty news. Spend 3-5 minutes and catch-up on 1 week of news.

Your Gateway to Power, Energy, Datacenters, Bitcoin and AI

Discover What Matters Most to You

AI

Bitcoin:

Datacenter:

Energy:

Discover What Matter Most to You

Featured Articles

Stay Ahead with the Paperboy Newsletter

Smarter with ONMINE

Streamline Your Growth with ONMINE