May 2026
OpenAI’s Steve Coffey and Nish Singaraju present the updated Agents SDK as a way to move long-running agent work out of hand-built orchestration loops and into a model-native harness. Their case is that production agents increasingly need durable state, file-system access, tools, skills, sandboxing, and resumability, while the actual compute environment should remain replaceable and ephemeral. Coffey distinguishes this from one-shot Responses API calls and hosted shell use, arguing that the SDK is meant for agents operating across files, systems, and multi-step workflows.
NASA Administrator Jared Isaacman tells Bloomberg’s Tim Stenovec that the US lunar program is no longer a question of ambition but of execution. He argues that NASA must turn Artemis into a workable sequence of tests, landings and industrial demand signals quickly enough to beat China, which he describes as a true peer moving at SpaceX-like speed. The moon base, in Isaacman’s account, is both a geopolitical objective and a proving ground for the commercial systems, nuclear technologies and Mars capabilities NASA wants next.
Benchmark partner Eric Vishria told Bloomberg Technology that demand for AI inference and compute remains strong enough that companies such as Cerebras benefit from the financing flexibility of public markets. He argued that the current venture environment is sharply divided: frontier AI companies can still access abundant capital, while many businesses outside that investor focus face little available funding. Vishria said timing helped Cerebras’s May 2026 IPO, but framed the outcome as the product of a decade of company-building rather than market conditions alone.
Abridge’s Chaitanya Asawa says GPT-5.5 improved the company’s clinical decision-support system as it added more tools and context, a signal that the model could better synthesize information under complexity. His case is that stronger reasoning and tool use can turn patient context, live clinical conversation, and trusted medical guidance into denser point-of-care support, while leaving clinicians to review answers and accept or reject proposed note edits.
Cognition co-founder and CPO Walden Yan and OpenInspect creator Cole Murray argue that software engineering is moving from IDE-based, step-by-step prompting toward background agents that can turn a specification into a tested pull request. Their case is that Devin’s rise from 16% to 80% of non-merge commits across three Cognition repos is not mainly a model benchmark, but evidence of a production workflow built on cloud sandboxes, scoped permissions, repo setup, testing, integrations, memory, and code review. Both warn that autonomy without those systems can degrade a codebase as quickly as it accelerates output.
Bloomberg Technology framed Snowflake’s 34% stock surge less as a reaction to its $6 billion Amazon Web Services deal than as a repricing of its AI software position. Snowflake chief executive Sridhar Ramaswamy pointed to stronger product revenue, higher retention and adoption of tools such as Cortex, while Bloomberg’s Brody Ford argued the AWS agreement mainly helps answer how Snowflake can manage the infrastructure costs of building AI features.
Bloomberg’s Jo Constantz reports that Anthropic’s intense hiring process has created a coaching market in which applicants are paying an average of $4,600 to prepare for interviews. The central pressure point, she says, is not the technical screen but a culture interview candidates describe as unusually introspective, reflecting a company trying to scale quickly while preserving a sharply defined internal culture.
Navier CEO Sampriti Bhattacharyya told Bloomberg Technology that the company’s plan to deploy 100 electric vessels in the Maldives is intended to prove electric marine transport as a standardized inter-island network, not a resort novelty. The rollout will begin with five vessels and expand over three years, linking airports, resorts, and local communities while testing the infrastructure, routes, and software needed to operate Navier’s hydrofoil boats at commercial scale.
Stanford adjunct lecturers Afshine Amidi and Shervine Amidi argue that evaluating text-to-image models starts with separating aesthetic quality from prompt adherence, then choosing metrics suited to the failure being tested. In Lecture 7 of Stanford’s CME296 course on diffusion and large vision models, they treat human ratings, FID, CLIPScore, reference-based measures, multimodal judges, and benchmarks as imperfect instruments rather than substitutes for a universal image-quality score. Their central warning is practical: automated and qualitative evaluations can be useful, but only when their assumptions, calibration, and failure modes are made explicit.
Dan Loeb, founder of Third Point, argues that investing has become inseparable from technology, with AI, semiconductors and energy now overriding much of the usual macro framework. In a conversation with Patrick O’Shaughnessy, Loeb traces Third Point’s shift from event-driven credit and deep-value situations toward quality businesses, thematic technology investing, activism and cross-capital-structure credit, while maintaining that markets still misprice companies because humans, governance failures and structural trading constraints have not gone away.
Joe Sullivan, the former federal cybercrime prosecutor and security executive at Facebook, Uber and Cloudflare, uses a Stanford CS153 lecture to argue that modern technology leadership now turns as much on governance and transparency as on technical response. Drawing on his prosecution over Uber’s 2016 security incident, Sullivan says companies need to assign disclosure authority, document cross-functional decisions, and build executive trust before a crisis, because the legal and reputational failure around an incident can become as consequential as the breach itself.
OpenAI’s Joyce Ruffell presents the company’s collaboration with Chip Ganassi Racing as an effort to turn an already data-rich IndyCar operation into a faster decision-making system. The case made in the source is not that AI replaces race judgment, but that it can connect historical, test, race, pit-stop, and strategy data quickly enough to matter in the narrow windows between sessions and during a race. At Long Beach, the argument is illustrated through Alex Palou’s win: a late pit-strategy adaptation, precise crew execution, and trusted information flow produced the margin.
Bloomberg’s Mark Gurman says Apple is preparing a broad Siri overhaul for iOS 27 that would turn the assistant into a system-wide AI interface rather than a voice tool. The changes, expected to be announced at Apple’s June 8 Worldwide Developers Conference, include a standalone chatbot-style Siri app and a “Search or Ask” interface for typing requests, searching the device and web, and invoking AI tools across the iPhone. Gurman argues Apple’s advantage is distribution across more than two billion devices, even as Siri trails ChatGPT and Gemini in AI credibility.
ElevenLabs is introducing Dubbing v2 alpha as an AI dubbing model built around preserving the original speaker’s performance, not just translating a transcript. The company says the system conditions directly on source audio so tone, pacing, emphasis and emotional delivery can carry across more than 90 languages, with sync-aware translation adapting phrasing to fit the timing of the original. ElevenLabs is positioning the launch for creators, marketers and studios that want automated localization without building a separate dubbing pipeline.
Former U.S. Navy chief and influence specialist Chase Hughes argues that modern manipulation works less by changing minds directly than by engineering the conditions in which certain choices feel automatic. In a wide-ranging conversation with Chris Williamson, Hughes says social media, interrogation, leadership, body language and shame all turn on the same mechanics: attention, fear, context, pressure and permission. His central claim is that people become easier to move when they are destabilized, performing for imagined judgment, and offered a simple release from uncertainty.
Behavioral scientist Nicholas Epley argues in a TED talk that people routinely avoid social connection because they misjudge how warmly others will respond. Drawing on experiments involving more than 30,000 people, he says this “misplaced pessimism” leads people to skip conversations, compliments, gratitude and offers of support that are usually received better than they expect. His prescription is modest: treat social fear as a forecast to be tested, and when in doubt, reach out.
Elad Gil argues that generative AI is changing the basic unit of enterprise technology from software seats to “human labor equivalents” — work product, labor hours and cognition that buyers can purchase directly. In a Tim Ferriss interview, the investor says that shift is reopening markets that once looked structurally unattractive, from legal software to other white-collar categories, because AI is giving companies something materially different to sell. Gil’s broader case is that this is a rare consensus moment: buyer openness is high, language models plug into existing commercial workflows, and weak growth from an AI company is therefore a sign that something is wrong.
Snowflake CEO Sridhar Ramaswamy told Bloomberg that the company’s stronger outlook reflects AI-driven demand for its data platform, not a threat to its software model. He argued that Snowflake’s $6 billion multiyear Amazon agreement will lower infrastructure costs, support cheaper AI pricing for customers and strengthen joint selling, while product adoption and revenue metrics show AI increasing consumption on the platform.
Luiz Domingos, chief technology officer of Mitel, argues that enterprise AI has moved past pilots and into communications workflows where latency, compliance, auditability and human oversight determine whether systems can be deployed. In a conversation with Craig Smith, Domingos says cloud-only AI will not meet the needs of real-time voice and regulated industries, and that edge and hybrid deployments will become central. His larger prediction is that enterprise AI will increasingly be accessed by voice rather than screens, especially for frontline workers whose jobs do not fit a desktop interface.
In a Neo4j talk, Zaid Zaim and Andreas Kollegger argue that AI agents need more than language models, tools, and retrieval if they are to make consequential decisions. Zaim frames context graphs as a way to store the policies, prior decisions, causal links, and reasoning traces behind an action; Kollegger extends that into a five-stage decision workflow in which agents frame the case, check rules and precedent, assess risk, act only within authority, and write the outcome back to the graph as future precedent.
Neuralink co-founder and president DJ Seo told Sequoia partner Shaun Maguire at AI Ascent 2026 that the company has moved from a single human implant demonstration to more than 20 patients, while still treating its current work as restoration of lost function rather than elective enhancement. Seo argued that Neuralink’s larger aim is not faster computer control but a higher-bandwidth interface between brains and AI, eventually enabling direct, multimodal transfer of concepts. The path there, he said, depends less on a single implant breakthrough than on scaling surgery, robotics, manufacturing, clinical evidence and neural-data models.
Greg Brockman, Sachin Katti, Amin Vahdat, Tatsunori Hashimoto, Maxim Kogan, Phil Hetzel, Scott Wu, Priscila Oliveira, and Pete Koomen each point to the same shift: model capability is only one part of deployment. The applied-AI question is becoming whether companies can secure useful compute, shape model behavior, govern agent actions, and turn organizational context into reliable work.
OpenAI co-founder Greg Brockman argues that compute has become the central bottleneck in AI, turning data centers into a strategic advantage and a public allocation problem. In a Knowledge Project interview with Shane Parrish, Brockman says the question is no longer just how powerful AI systems become, but where scarce capacity should go — consumer access, business productivity, scientific discovery or problems such as cancer research — and how the benefits can be felt broadly rather than concentrated.
Maxim Bar Kogan, founder and CEO of Onyx Security, argues that enterprise AI security is shifting from policing chatbot data leaks to controlling autonomous agents that can use credentials, call APIs, edit code and alter production systems. In a conversation with Sarah Guo, he makes the case for an independent AI control plane that can judge whether an agent’s actions match its assigned intent, rather than relying on traditional permissions, proxies or the model vendors themselves. Kogan says the hard problem is doing that supervision cheaply and quickly enough for enterprise deployment.