Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Google’s new AlphaEvolve shows what happens when an AI agent graduates from lab demo to production work, and you’ve got one of the most talented technology companies driving it. Built by Google’s DeepMind, the system autonomously rewrites critical code and already pays for itself inside Google. It shattered a 56-year-old record in matrix multiplication (the core of many machine learning workloads) and clawed back 0.7% of compute capacity across the company’s global data centers. Those headline feats matter, but the deeper lesson for enterprise tech leaders is how AlphaEvolve pulls them off. Its architecture – controller, fast-draft models, deep-thinking models, automated evaluators and versioned memory – illustrates the kind of production-grade plumbing that makes autonomous agents safe to deploy at scale. Google’s AI technology is arguably second to none. So the trick is figuring out how to learn from it, or even using it directly. Google says an Early Access Program is coming for academic partners and that “broader availability” is being explored, but details are thin. Until then, AlphaEvolve is a best-practice template: If you want agents that touch high-value workloads, you’ll need comparable orchestration, testing and guardrails. Consider just the data center win. Google won’t put a price tag on the reclaimed 0.7%, but its annual capex runs tens of billions of dollars. Even a rough estimate puts the savings in the hundreds of millions annually—enough, as independent developer Sam Witteveen noted on our recent podcast, to pay for training one of the flagship Gemini models, estimated to cost upwards of $191 million for a version like Gemini Ultra. VentureBeat was the first to report about the AlphaEvolve news earlier this week. Now we’ll go deeper: how the system works,

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Unpatched systems are a ticking time bomb. Fifty-seven percent of cyberattack victims acknowledge that available patches would have prevented breaches, yet nearly one-third admit failing to act, compounding the risk. Ponemon research shows organizations now take an alarming average of 43 days to detect cyberattacks, even after a patch is released, up from 36 days the previous year. According to the Verizon 2024 Data Breach Investigations Report, attackers’ ability to exploit vulnerabilities surged by 180% from 2023 to 2024. Chronic firefighting makes manual or partially automated patching overly burdensome, further pushing patching down teams’ priority lists. Relying on manual or partially automated patching systems is considered too time-consuming, further reducing patching to the bottom of a team’s action item list. This is consistent with an Ivanti study that found that the majority (71%) of IT and security professionals think patching is overly complex, cumbersome and time-consuming. When it comes to patching, complacency kills Attackers aggressively exploit legacy Common Vulnerabilities and Exposures (CVEs), often ten or more years old. A sure sign of how effective attackers’ tradecraft is becoming at targeting legacy CVEs is their success with vulnerabilities in some cases, 10-plus years old. A sure sign that attackers are finding new ways to weaponize old vulnerabilities is reflected in the startling stat that 76% of vulnerabilities leveraged by ransomware were reported between 2010 and 2019. The misalignment between IT and security teams compounds delays, with 27% lacking cohesive patch strategies and nearly a quarter disagreeing on patch schedules. One of the unexpected benefits of automating patch management is breaking the impasse between IT and security when it comes to managing the patch workload.    “Typically, on average, an enterprise may patch 90% of desktops within two to four weeks,

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Unpatched systems are a ticking time bomb. Fifty-seven percent of cyberattack victims acknowledge that available patches would have prevented breaches, yet nearly one-third admit failing to act, compounding the risk. Ponemon research shows organizations now take an alarming average of 43 days to detect cyberattacks, even after a patch is released, up from 36 days the previous year. According to the Verizon 2024 Data Breach Investigations Report, attackers’ ability to exploit vulnerabilities surged by 180% from 2023 to 2024. Chronic firefighting makes manual or partially automated patching overly burdensome, further pushing patching down teams’ priority lists. Relying on manual or partially automated patching systems is considered too time-consuming, further reducing patching to the bottom of a team’s action item list. This is consistent with an Ivanti study that found that the majority (71%) of IT and security professionals think patching is overly complex, cumbersome and time-consuming. When it comes to patching, complacency kills Attackers aggressively exploit legacy Common Vulnerabilities and Exposures (CVEs), often ten or more years old. A sure sign of how effective attackers’ tradecraft is becoming at targeting legacy CVEs is their success with vulnerabilities in some cases, 10-plus years old. A sure sign that attackers are finding new ways to weaponize old vulnerabilities is reflected in the startling stat that 76% of vulnerabilities leveraged by ransomware were reported between 2010 and 2019. The misalignment between IT and security teams compounds delays, with 27% lacking cohesive patch strategies and nearly a quarter disagreeing on patch schedules. One of the unexpected benefits of automating patch management is breaking the impasse between IT and security when it comes to managing the patch workload.    “Typically, on average, an enterprise may patch 90% of desktops within two to four weeks,

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Surprise! Just days after reports emerged suggesting OpenAI was buying white-hot coding startup Windsurf, the former company appears to be launching its own competitor service as a research preview under its brand name Codex, going head-to-head against Windsurf, Cursor, and the growing list of AI coding tools offered by startups and large tech companies including Microsoft and Amazon. Unlike OpenAI’s previous Codex code completion AI model, the new version is a full cloud-based AI software engineering (SWE) agent built atop a fine-tuned version of OpenAI’s o3 reasoning model that can execute multiple development tasks in parallel. Starting today it will be available for ChatGPT Pro, Enterprise, and Team users, with support for Plus and Edu users expected soon. Codex’s evolution: from model to autonomous AI coding agent This release marks a significant step forward in Codex’s development. The original Codex debuted in 2021 as a model for translating natural language into code available through OpenAI’s nascent application programming interface. It was the engine behind GitHub Copilot, the popular autocomplete-style coding assistant designed to work within IDEs like Visual Studio Code. That initial iteration focused on code generation and completion, trained on billions of lines of public source code. However, the early version came with limitations. It was prone to syntactic errors, insecure code suggestions, and biases embedded in its training data. Codex occasionally proposed superficially correct code that failed functionally, and in some cases, made problematic associations based on prompts. Despite those flaws, it showed enough promise to establish AI coding tools as a rapidly growing product category. That original model has since been deprecated and turned into the name of a new suite of products, according to an OpenAI spokesperson.

Acer Gadget, a subsidiary of Acer, unveiled AI-powered wearables at the Computex 2025 trade show in Taiwan.

The Acer FreeSense Ring and Acer AI TransBuds reflect the company’s growing expansion into AI- powered wearables and real-time communication tools. Acer Gadget will also display its latest emobility solutions — including e-scooters, e-bikes, and accessories — highlighting a broad innovation strategy that spans wellness, connectivity, and mobility.

Acer FreeSense Ring

Acer FreeSense Ring

The Acer FreeSense Ring blends a sophisticated design with advanced biometric sensing to support everyday health monitoring. Crafted from ultra-lightweight titanium alloy, the ring tracks vital physiological metrics such as heart rate, heart rate variability, blood oxygen levels, and sleep quality. The data points are analyzed through a companion mobile app, which generates personalized wellness insights and provides suggestions.

With features such as sleep stage analysis and continuous tracking, the Acer FreeSense Ring helps encourage proactive health management and lifestyle awareness. Users also have full access to all health data without additional subscription fees, making the FreeSense ring a practical and reliable daily wellness companion.

Acer AI TransBuds

Acer AI TransBuds can translate languages.

The Acer AI TransBuds offer real-time AI translation for seamless communication They are lightweight and compact earbuds designed to simplify multilingual communication. By connecting to a smartphone or tablet via a plug-in receiver, the device activates real-time, two-way voice translation powered by AI-based speech recognition and semantic analysis.

Notably, only one person needs to wear the earbuds to carry out effective translation, making it ideal for casual conversations, business meetings, livestreams, or online study sessions. The earbuds also support live captioning and transcription, helping users follow conversations in real time and review them later on. The system currently supports 15 major languages spoken across Asia, the Americas, and Europe.

Acer eMobility: Expanding smart urban and outdoor mobility

Acer eMobility

Acer Gadget is also showcasing its latest e-scooters and e-bikes, designed for both urban commuting and outdoor recreation. These include the Acer eScooter Series 4 Select, Series 5 Select, Predator ES Storm, and Predator ES Thunder, each equipped with 400 W to 500 W motors, adaptive brake and suspension systems.

Riders can seamlessly monitor performance and manage security settings via the Acer eMobility App. Also on display is the Predator eRanger, a fat-tire e-bike first introduced at the Taipei Cycle Show 2025 earlier this year, demonstrating Acer Gadget’s commitment to developing more smart and versatile mobility solutions.

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. This baby boy was treated with the first personalized gene-editing drug Doctors say they constructed a bespoke gene-editing treatment in less than seven months and used it to treat a baby with a deadly metabolic condition. The rapid-fire attempt to rewrite the child’s DNA marks the first time gene editing has been tailored to treat a single individual.The baby who was treated, Kyle “KJ” Muldoon Jr., suffers from a rare metabolic condition caused by a particularly unusual gene misspelling. Researchers say their attempt to correct the error demonstrates the high level of precision new types of gene editors offer. The project also highlights what some experts are calling a growing crisis in gene-editing technology. That’s because even though the technology could cure thousands of genetic conditions, most are so rare that companies could never recoup the costs of developing a treatment for them. Read the full story.—Antonio Regalado
Access to experimental medical treatments is expanding across the US
—Jessica Hamzelou A couple of weeks ago I was in Washington, DC, for a gathering of scientists, policymakers, and longevity enthusiasts. They had come together to discuss ways to speed along the development of drugs and other treatments that might extend the human lifespan. One approach that came up was to simply make experimental drugs more easily accessible. Now, the state of Montana has passed a new bill that sets out exactly how clinics can sell experimental, unproven treatments in the state to anyone who wants them. The passing of the bill could make Montana something of a US hub for experimental treatments. But it represents a wider trend: the creep of Right to Try across the US. And a potentially dangerous departure from evidence-based medicine. Read the full story. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. Take a new look at AI’s energy use Big Tech’s appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does even a single AI query use? And what does it mean for the climate?Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O’Donnell at 1.30pm ET on Wednesday May 21 for a subscriber-only Roundtables conversation exploring AI’s energy demands now and in the future. Register here.

The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 xAI has blamed Grok’s white genocide fixation on an ‘unauthorized modification’Made by an unnamed employee at 3.15am. (TechCrunch)+ The topic is one the far-right comes back to again and again. (The Atlantic $)+ Memphis residents are struggling to live alongside xAI’s supercomputer. (CNBC) 2 Meta has delayed the launch of its next flagship AI modelIts engineers are struggling to improve its Behemoth LLM enough. (WSJ $)  3 Elon Musk is tapping up friends and allies for federal jobsIt’s creating an unprecedented web of potential conflicts of interests. (WSJ $)+ Musk is posting on X less than he used to. (Semafor) 4 The US is slashing funding for scientific researchSuch projects produced GPS, LASIK eye surgery, and CAPTCHAs. (NYT $)+ US tech visa applicants are under seriously heavy scrutiny. (Wired $)+ The foundations of America’s prosperity are being dismantled. (MIT Technology Review)5 Big Tech wants its AI agents to remember everything about you 🧠They’re focusing on improving chatbots’ memory—but critics are worried. (FT $)+ AI agents can spontaneously develop human-like behavior. (The Guardian)+ Generative AI can turn your most precious memories into photos that never existed. (MIT Technology Review) 6 People keep making anti-DEI modifications for The Sims 4And the gamemaker EA’s attempts to stamp them out aren’t working. (Wired $)
7 This chatbot promises to help you get over your ex Closure creates an AI version of ex-partners for users to vent their frustrations at. (404 Media)+ The AI relationship revolution is already here. (MIT Technology Review) 8 How this AI song became a viral megahit in JapanYAJU&U is completely inescapable, and totally nonsensical. (Pitchfork)+ AI is coming for music, too. (MIT Technology Review)
9 Your future overseas trip could be by zeppelinIf these startups get their way. (WP $)+ Welcome to the big blimp boom. (MIT Technology Review) 10 Are you a ‘dry texter’? 💬It’s a conflict-averse teen’s worst nightmare. (Vox) Quote of the day “It’s OK to be Chinese overseas.” —Chris Pereira, the CEO of iMpact, a communications firm advising Chinese companies expanding abroad, tells Rest of World that DeepSeek has given Chinese startups the confidence not to hide their origins.
One more thingWe’ve never understood how hunger works. That might be about to change. When you’re starving, hunger is like a demon. It awakens the most ancient and primitive parts of the brain, then commandeers other neural machinery to do its bidding until it gets what it wants. Although scientists have had some success in stimulating hunger in mice, we still don’t really understand how the impulse to eat works. Now, some experts are following known parts of the neural hunger circuits into uncharted parts of the brain to try and find out.
Their work could shed new light on the factors that have caused the number of overweight adults worldwide to skyrocket in recent years. And it could also help solve the mysteries around how and why a new class of weight-loss drugs seems to work so well. Read the full story. —Adam Piore 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 or skeet ’em at me.)+ Who knew—Harvard Law School’s Magna Carta may be the real deal after all.+ Early relatives of reptiles might have walked the Earth much earlier than we realised.+ New York University’s MFA Students are a talented bunch.+ The Raines sandwich sounds unspeakably awful 🥪