May 2026
Dario Gil, the U.S. Department of Energy’s Under Secretary for Science, used his AI+Science keynote to argue that AI is shifting scientific advantage from access to instruments and computing toward the throughput of integrated discovery systems. He presented DOE’s Genesis initiative as the national-scale architecture for that shift, linking data, AI models, high-performance computing, experimental facilities, and industry partners into closed-loop workflows. Gil’s case was that the test is not more papers, but whether faster scientific cycles can produce measurable gains in productivity, security, and industrial capability.
Stanford’s AI+Science Conference opened with James Landay announcing that the university is merging the Human-Centered AI Institute and Stanford Data Science into a single institute for AI and data science across Stanford. Landay, president Jonathan Levin, Surya Ganguli and Risa Wechsler framed the move around a common argument: AI is becoming a scientific instrument, but one that will require open research, domain-specific rigor, uncertainty-aware methods and human judgment about which questions matter.
In a Stanford AI+Science lightning-talk session introduced by Surya Ganguli, four young researchers made a common case: AI-for-science is useful only when paired with rigorous evaluation. Aishwarya Mandyam, Amar Venugopal, Steven Dillmann and Alda Elfarsdóttir each treated AI systems or outputs as claims to be tested — through uncertainty estimates for clinical policies, causal checks on generated text, executable benchmarks for scientific agents, and empirical links between corporate climate language and later emissions.
John Coogan and Jordi Hays frame Cerebras’s IPO as a public-market test of whether AI customers will pay heavily for faster inference, while noting that the company’s wafer-scale architecture still faces limits around memory, context windows and large-model serving. In their account, the same standard of evidence runs through the day’s other stories: Kevin Warsh’s narrow Fed confirmation, Figure’s robot demo and Musk’s case against OpenAI all turn less on rhetoric than on whether technical, institutional or legal claims can be substantiated.
Abridge executives Janie Lee and Chaitanya “Chai” Asawa argue that the patient-clinician conversation is becoming healthcare’s core intelligence layer, not merely an input for automated notes. In a discussion with Redpoint’s Jacob Effron, they describe Abridge’s move from ambient documentation into clinical decision support, prior authorization and other workflows that depend on EHR data, payer rules, medical literature and local guidelines. Their case is that healthcare AI will be judged less by chatbot fluency than by whether it can deliver accurate, low-latency, privacy-preserving support inside clinical workflows without adding to clinicians’ alert burden.
TBPN’s John Coogan and Jordi Hays use Cerebras’s first day as a public company to frame a narrower AI hardware argument: the market is beginning to price low-latency inference as a product in its own right. Cerebras founder Andrew Feldman argues that fast inference will eventually consume demand for slow AI responses, while SemiAnalysis’s Doug O’Laughlin cautions that the company’s wafer-scale SRAM architecture may be limited by memory scaling and model size. The result is a public-market test of whether owning a valuable slice of the AI compute stack is enough.
Bloomberg’s Mark Gurman reports that Apple’s partnership with OpenAI has deteriorated because OpenAI expected deep ChatGPT integration across Apple software and a multibillion-dollar annual opportunity, but received a narrower set of features. Gurman says OpenAI has tried to renegotiate, believes talks have stalled, and is preparing possible legal action while still seeking an out-of-court resolution. Apple has not commented, but Gurman says it has its own concerns about OpenAI’s privacy practices, durability, leadership, and recruitment from Apple hardware teams.
In Defending Taiwan, Eyck Freymann argues that U.S. strategy is too narrowly focused on deterring a Chinese invasion and is underprepared for a gray-zone crisis that could isolate Taiwan without open war. Freymann’s case, developed in discussion with Hoover Institution participants including Philip Zelikow, is that Beijing’s most plausible path may be legal, commercial, and coercive control over Taiwan’s external ties. Deterrence, he argues, will require Washington and its allies to integrate military power with political discipline, economic planning, technological leverage, and diplomatic coordination before such a crisis begins.
Cerebras raised $5.55bn in the year’s largest US IPO while Cisco shares jumped on a higher hyperscaler-orders forecast, putting both a new AI compute listing and an incumbent networking supplier in the market’s AI infrastructure trade. Cerebras CEO Andrew Feldman argued that the company’s wafer-scale systems, OpenAI deal and AWS engagement show it can become a major compute supplier; Bloomberg reporters pressed the harder question of how much of today’s AI infrastructure demand will turn into broad, durable revenue.
Codex is moving from a coding assistant toward an agent for delegated knowledge work, according to Thibault Sottiaux, OpenAI’s head of Codex. In an OpenAI Forum conversation with Chris Nicholson of OpenAI Global Affairs, Sottiaux argues that as models have become more reliable and better connected to workplace context, Codex is being used to research, organize information, create files and presentations, coordinate across tools, and run background tasks. That shift, he says, makes delegation, trust and access controls central as agents act across files, communications tools and company systems.
Laurie Voss of Arize argues that agentic applications need the same engineering discipline as other production software: instrumentation, inspectable traces, targeted evals, and controlled experiments, not a handful of prompts that “look right.” In a hands-on workshop using a financial analysis agent, Voss shows how teams should read traces before writing evals, classify failures by root cause, and combine deterministic checks, LLM judges, custom rubrics, and human-labeled meta-evaluation. His central warning is that the choice of eval can dominate the result: the same agent scored 0 out of 13 on a correctness eval and 13 out of 13 on a faithfulness eval because the first judge was asking the wrong question.
Ericsson chief executive Börje Ekholm told Bloomberg Technology that competing with China in telecoms requires more than excluding Chinese vendors: Western companies have to match China’s scale, technology curve and cost discipline. He described China as both a market Ericsson needs to be in and the benchmark for competition, while arguing that the company’s hedge is to build strength in the U.S., India and Japan and maintain flexible manufacturing and R&D. Ekholm also cast AI as a future network-demand story, saying physical-world AI will require low-latency connectivity at the edge.
Michelle Giuda, chief executive of Purdue’s Krach Institute for Tech Diplomacy, told Bloomberg Technology that Xi Jinping’s warning to Donald Trump over Taiwan was serious but did not mark a new position from Beijing or Washington. She argued that Taiwan remains the central pressure point in U.S.-China relations because of both security commitments and semiconductor dependence, while Iran and an unusual tech CEO delegation showed the summit’s mix of incremental diplomacy and improvisation.
Oura chief executive Tom Hale told Bloomberg Technology that the company’s AI work is not a new response to the current market cycle but an extension of years of prediction work in wearables. His argument is that Oura can move from near-term wellness signals, such as illness or menstrual-cycle alerts, toward longer-range health guidance, provided the science and regulatory validation support it. Hale said the company is still stopping short of diagnosis while it works with the FDA, including on blood-pressure submissions, and framed Oura’s hardware as an advantage in an AI market where software is easier to copy or generate.
OpenAI product lead Adele Li and researcher Kenji Hata argue that Images 2.0 marks a shift from novelty image generation to a working visual layer inside ChatGPT. In a podcast discussion with Andrew Mayne, they point to 1.5bn images generated weekly, sharper text rendering, stronger photorealism, broader aspect ratios and more consistent characters as evidence that the model is moving into education, internal communication, marketing assets, software mockups and other practical creative work.
Amy Boyd and Nitya Narasimhan of Microsoft argue that agent observability has to track the widening gap between what an AI agent is meant to do and what it actually does as models, prompts, tools and user behavior change. Their walkthrough of Microsoft Foundry frames observability as a loop of OpenTelemetry tracing, trace-linked evaluations, monitoring, optimization and red teaming. The central demonstration is an observe skill that can generate an evaluation dataset, run batch tests, optimize prompts, compare versions and roll back to the best-performing agent version from a sparse starting point.
Microsoft Research’s presentation on energy-based fine-tuning argues that language-model post-training can be aimed at whole responses rather than next-token imitation. Carles Domingo-Enrich presents EBFT as a middle path between supervised fine-tuning and reinforcement learning: it samples model completions, compares them with ground-truth answers in a model-derived feature space, and turns that comparison into a policy-gradient update without a separate reward model or verifier. The reported results show gains over SFT on several coding and translation measures, with performance often comparable to RLVR while avoiding explicit correctness rewards.
David Rothschild argues that AI’s largest economic effects will come less from better models than from whether workflows and markets are rebuilt for agents rather than humans. In his Microsoft Research Forum talk and related work on agentic markets, he says the key question is architectural: open systems could reduce communication friction and spread welfare gains, while closed platforms could use the same capabilities to reinforce incumbency. The transition, in his account, depends on choices about delegation, monitoring, auditability, and market access that are being made before the full disruption is visible.
Microsoft Research’s Amit Sharma presents Interwhen as a framework for moving AI agents from post-hoc checking to verified execution while they are still acting. The open-source library uses LLMs to turn natural-language instructions, policies, and partial responses into smaller verifiable properties, then applies symbolic or model-based verifiers to tool calls and intermediate behavior. Sharma argues that this lets agents continue normally when checks pass but interrupts them when a verifier detects a violation, addressing risks that final-output review may catch too late.
Microsoft Research’s Peli Halleux and Yash Lara present GitHub Agentic Workflows as a move from AI-assisted coding to repository-level process automation. Their argument is that agents should be embedded inside GitHub Actions to research, plan, assign, and open pull requests under human review, rather than operate as unconstrained swarms. The system’s promised scale depends on orchestration, sandboxing, limited permissions, and Microsoft-hosted models on Azure.
Microsoft Research is presenting MagenticLite as a full-stack agentic system designed to make small language models usable for multi-step work across a browser and local files. Weili Shi, Harkirat Behl and Hussein Mozannar argue that the capability comes from specializing the stack rather than relying on frontier-scale models: MagenticBrain handles planning, coding and delegation, while Fara 1.5 controls the browser. The release also emphasizes user oversight, with the agent pausing for credentials, approvals or other points where the user needs to take control.
Cerebras chief executive Andrew Feldman used the AI chipmaker’s $5.55 billion IPO to argue that public investors are valuing the company as a fast-inference infrastructure supplier, not merely another semiconductor listing. In a Bloomberg Technology interview before trading began, Feldman said demand is concentrated around speed, claimed Cerebras is about 15 times faster than its nearest competitor, and pointed to large relationships with OpenAI and AWS as evidence of commercial traction, while acknowledging that the AWS agreement is still being finalized.
At the first public taping of Hoover’s GoodFellows, John Cochrane, Niall Ferguson and H.R. McMaster used the Constitution as a text for argument rather than commemoration. Cochrane warned against treating it as scripture and stressed the civic “spirit” behind it, McMaster described an imperfect founding grounded in principles, and Ferguson stated a broad First Amendment position. Their clearest split came over amendment: Ferguson said the process no longer works in practice, while Cochrane argued its history shows it has sometimes been too available.
Jonas Templestein of Iterate argues that an agent harness can be reduced to an append-only event stream plus processors: synchronous reducers to derive state, and post-append hooks to perform side effects. His design puts model chunks, tool calls, errors, schedules, subscriptions and even processor deployment into the log, so a restarted agent can replay state without replaying old LLM calls. The larger claim is that agents and third-party services can compose by reading and appending to the same durable stream, with bounded waits and circuit breakers replacing tighter, blocking plugin interfaces.