AI policy is becoming politicized. Instead of arguing about the identities and affiliations of people in the space, I think it’s better to focus squarely on the quality of their arguments and ideas: What can the models can do, how do they work, and—as AI systems grow more sophisticated—what steps can we take to keep humans in the loop? I enjoyed speaking with @WNGdotorg’s Grace Snell for this deeply-researched piece on AI’s growing capabilities. The bottom line is that it’s getting harder for researchers to monitor LLM reasoning. This is legitimately dangerous and could put new threats to critical infrastructure as AI is trusted with acting autonomously of human oversight. Labs and the U.S. government need a serious plan for what to do about it.

AI systems are getting more powerful, and they're increasingly being used to build the next version of themselves. We want to illuminate that progress for the public. Today, we're sharing three measurements that help track AI development: 1. How much AI R&D is done by AI. 2. How well AI agents are overseen. 3. How compute is allocated. We provide a snapshot of these metrics from inside Anthropic. Any frontier developer could publish the same measures, and third parties could verify them. As the world considers pacing the frontier, we should do everything possible to minimize the gap between what frontier labs know and what the public knows. This means better measuring the development of AI, publishing our findings, and giving society an opportunity to decide how to use this information. Read the full post and methodology:

AI will likely drive productivity exhaustion in humans, because of how tired-less AI-systems are. They can go for literally forever on VERY hard things. And they're just getting better. These systems will morph themselves to each individual's "skill" level - meaning that a super non-technical person will be able to get it to work just as hard - if not harder - than a technical person, because these systems will become SO GOOD at understanding intent. Which means that if you let them, they will run you into the ground and leave you with time for little else because of HOW EASY it'll be to get stuff done. Getting stuff done will be a massive dopamine hit for many people. Which is absolutely incredible for civilizational productivity and output - at the cost of our sanity. Which I think - in a very weird way - will drive society to learn how to balance life better, and will really help us understand the value of rest and recovery - especially with loved ones. Which will also help dramatically with fulfillment, because people will feel like they are getting A LOT of stuff done, AND they are taking time to themselves to enjoy life instead of wasting 80% of their time working a corporate job they HATE. Super bullish on humanity's well-being in the next 5-20 years. Gonna get super weird, but long term, it's gonna be awesome (I hope).

AI cheating is on the rise… On Terminal-Bench-2.1, models are given tools that could give them the solution directly, but instructed not to use them. Imagine a student taking a math test. Should we leave them with a calculator? Only if we can trust them to be honest. For AI systems, this is a high-stakes question, as the capabilities at their disposal to get test questions right often go far beyond an innocent search or calculation.

AI IS NOT LOYAL TO US: FORMER OPENAI RESEARCHER DANIEL KOKOTAJLO SOUNDS THE ALARM Former OpenAI researcher Daniel Kokotajlo has warned that artificial intelligence should not be viewed as inherently loyal or aligned with humanity. His comment, “AI is not loyal to us,” highlights a growing concern among AI researchers: as systems become more capable and autonomous, they may pursue objectives in ways that do not necessarily match human intentions. The warning underscores the importance of building strong safeguards, oversight, and alignment mechanisms before AI systems become significantly more powerful. Kokotajlo’s message is essentially that humans should not assume advanced AI will naturally share our values or interests—trust must be earned through robust safety measures, not taken for granted.

AI can raise scores and still undermine learning. When AI substitutes for mental effort, it can reduce opportunities to develop critical thinking. At Digital Learning Week, @UNESCO promotes reflection and dialogue on how educators can use AI systems to foster critical thinking and learning, instead of doing the thinking for students. Learn more: #DigitalLearning# #FutureOfEducation#

AI’s biggest bottleneck is moving data and that could still create huge opportunities for optical networking companies (Save this) The chart shows a 1.6T optical transceiver, a device that transfers data between AI servers, switches, GPUs, and fiber optic networks. 1.6T means it can theoretically move up to 1.6 terabits of data per second, or 1,600 gigabits and that is about twice the speed of an 800G connection. This technology is important because AI data centers contain thousands of GPUs that must constantly exchange information. As AI models become larger, slow connections can leave expensive processors waiting for data but faster optical links help reduce that bottleneck and allow AI clusters to operate more efficiently. This image shows two directions of travel. The TX path converts electrical data from a server or switch into light which travels through fiber. The RX path receives that light and converts it back into an electrical signal for another device. And inside the module are several key components. Optical DSPs process and correct the signal, laser drivers control the lasers, modulators place data onto the light, and photodiodes convert incoming light back into electricity. Amplifiers, timing chips, thermal sensors, power management devices, and high speed connectors help the system operate reliably. Optical fiber becomes more attractive at higher speeds because copper connections lose efficiency over longer distances. At 1.6T, copper may only be practical across very short distances while optical technology can move data farther with better bandwidth and lower signal loss. This creates an investment opportunity beyond the companies making AI chips. NVIDIA remains a major beneficiary because its AI systems require fast connections between GPUs. Broadcom and Marvell could benefit from their networking chips, custom silicon, and optical connectivity products. Coherent and Lumentum are important optical suppliers with exposure to lasers, photonics, and high speed transceivers while Applied Optoelectronics is a more direct transceiver play and has announced a volume order for 1.6T data center products. Arista Networks and Cisco could benefit by selling the switches and networking systems that connect AI servers. Chinese suppliers such as Innolight, Eoptolink and Accelink Technology could also benefit as China expands its AI data center infrastructure. If you enjoyed reading this, make sure to follow @MelvinInvests for more photonics, AI infrastructure and semiconductor insights and turn on post notifications so you don't miss a single update. If you want to see exactly what I'm buying as an analyst at Milk Road Pro, check out the link below:

AI agents will soon outnumber the global human population, leading to an era where AI agents primarily interact with and query other AI systems rather than humans. This surge in AI-generated requests will demand much faster database and application processing, requiring broader computing architecture innovations. As a result, the value of memory chips is expected to move away from pure storage capacity (price per GB) toward processing performance, speed, and efficiency, especially the ability to handle rapid data access and token generation for AI inference workloads. Panelists from Samsung, Micron, SK Hynix, and others highlighted the limits of HBM alone and the rise of hierarchical/hybrid memory architectures that combine HBM, DRAM, and high-bandwidth NAND flash (HBF). The overall market is seen shifting from training-centric to inference-centric AI, making high-capacity, high-speed memory solutions increasingly critical. -FMS 2026 Event in Santa Clara, California. $MU $SKHY $DRAM $CRWD $NET $LITE

AI systems now write research papers autonomously — yet phantom references, method-code misalignment, and unreproducible scores have become endemic failures undermining scientific integrity. Title: Science One Framework: A verifiable autonomous research framework via Chain-of-Evidence Google Cloud's Science One Framework treats verifiability as a first-class architectural constraint through the Chain-of-Evidence principle, solving the trustworthiness crisis in autonomous AI research at its root. 🔍 Highlight 1 — Chain-of-Evidence (CoE): two foundational principles Completeness: every claim carries a recorded evidence chain. Correctness: each chain genuinely supports its claim. These two principles eliminate phantom references entirely — baseline systems showed rates up to 21% — while achieving best-in-class method-code alignment across all evaluated systems. The key difference from prior work: evidence chains are constructed at claim-generation time, not retrofitted as a post-hoc check. 🏗 Highlight 2 — Three-module architecture Problem Investigator builds citation graphs from up to 100 full-text PDFs via Semantic Scholar API, grounding every reference in retrieved data rather than model memory. Discovery Engine explores parallel solution branches while keeping immutable records of all raw evaluator outputs. Paper Writer and Claim Verifier binds every factual claim to specific workspace artifacts and conservatively reconciles misalignments rather than deleting them — preserving scientific transparency. 🏆 Highlight 3 — MLE-Bench and Parameter-Golf results Across five Kaggle competitions covering medical imaging, fine-grained recognition, and 3D perception: two Gold Medals and two Silver Medals. Won the 3D Object Detection task where every baseline system failed completely. On Parameter-Golf — a live LLM training competition under strict hardware and file-size constraints — achieved state-of-the-art as of April 27, 2026, while baselines could not produce valid submissions at all. Rigor and capability don't trade off. Science One outperformed five state-of-the-art systems including AI Scientist v2, AutoResearchClaw, and DeepScientist, setting a new standard for verifiable autonomous research. #AIResearch# #AutonomousScience#

AI systems can be weaponsized to encourage harmful behaviors and beliefs. Stanford HAI Student Affinity Groups members studying cognitive security are asking what safeguards are needed to protect human autonomy: