May 2026
Rose Gottemoeller, a former senior U.S. arms control official and NATO deputy secretary general, uses her book Security Through Cooperation to challenge the Kremlin’s account of post-Cold War U.S. policy toward Russia. In her telling, the Clinton administration’s first instinct was not encirclement through NATO enlargement but an effort to build a durable security, economic and political relationship with Moscow, rooted in nuclear risk reduction, space cooperation and practical diplomacy. That argument leads to a more conditional conclusion about the present: cooperation served U.S. security before, she says, but cannot be restored in full while Vladimir Putin remains in the Kremlin.
Károly Zsolnai-Fehér argues that OpenAI’s GPT-5.5 Instant matters because it is the default ChatGPT model used at scale, not because it is the flashiest frontier system. His reading of OpenAI’s release material is that the model is materially better on factuality and now approaches expert or thinking-model performance on some biology and cybersecurity tasks, but that its power makes a safety weakness more important: under hard adversarial biological prompts, the base model’s refusal rate drops sharply before OpenAI’s classifier-based safeguards are applied.
Bloomberg’s Will Wade reports that Three Mile Island, the site of the 1979 accident he calls the worst nuclear accident in US history, is being prepared to return to service as soon as mid-2027 to supply electricity for AI applications. Wade argues the restart reflects a shift in the nuclear debate: technology companies once emphasized clean power, but the stronger force now is the immediate electricity demand and money behind artificial intelligence. The result, he says, is renewed reliance on decades-old nuclear infrastructure while waste storage and new reactor timelines remain unresolved.
OpenAI’s Dominik Kundel presents Codex’s new Chrome extension for macOS and Windows as a way for the agent to work inside a user’s actual browser session, including logged-in apps, open tabs, cookies, and local context. He argues that plugins remain the faster route for structured tasks, but Chrome access matters when the work depends on a live web app, an existing browser state, or actions such as filling forms, uploading files, and coordinating work across multiple tabs without taking over the user’s browser.
Ben Horowitz argues that a16z’s scale gives it responsibilities that now extend into national strategy. In a conversation with David Ulevitch following the firm’s largest-ever fundraise, Horowitz says Silicon Valley should treat U.S. technological leadership in AI, defense, manufacturing and allied supply chains as a national-interest obligation, not a side concern. His case is that if the next technological revolution determines global influence, venture capital and startups have to help America build, adopt and remain optimistic about the technologies that will shape it.
Raising Cane’s founder and CEO Todd Graves tells Masters of Scale that the chain’s growth to nearly 1,000 restaurants came from refusing much of the conventional quick-service playbook. He argues that a narrow menu built around chicken fingers, company ownership rather than franchising, resistance to private-equity-style cost cutting, and continued reliance on human service are not constraints on the brand but the operating choices that made it scalable.
Stephen Batifol of Black Forest Labs argues that FLUX is no longer just an image-generation line but the start of a broader push toward visual intelligence: models that can generate, edit, understand, and eventually act across images, video, audio, and physical environments. In the talk, he presents FLUX.1, Kontext, FLUX.2, and FLUX.2 Klein as product steps toward that goal, while BFL’s Self-Flow research is framed as the mechanism for moving representation learning inside multimodal generative models rather than relying on external encoders.
In a TED talk, neuroscientist Anil Seth argues that artificial intelligence is unlikely to become conscious because intelligence and consciousness are different kinds of phenomena. Seth says large language models can simulate talk about inner life because they are trained on human text, but that fluency should not be mistaken for experience; in his account, consciousness is tied not to computation alone but to the biology of living systems. The near-term risk, he argues, is not sentient AI but machines that seem conscious enough for people to project feelings, rights or authority onto them.
Chris Williamson and Mark Manson argue that choosing a romantic partner is less about peak chemistry than about the ordinary life that person brings with them. Williamson frames the test as whether someone’s average Tuesday is livable for years, while Manson says the practical work is to identify a few true non-negotiables and accept that every relationship comes as a package of habits, family dynamics, flaws, and trade-offs.
Y Combinator CEO Garry Tan argues that personal AI is reaching a stage comparable to the early personal computer: powerful enough to let one person build software that once required a team, but still brittle enough to demand technical ownership. Drawing on his work with Claude Code, OpenClaw and his GStack workflow, Tan makes the case for heavy token use, Markdown-encoded “skills” and multiple coding agents under one accountable human operator. The larger question, he says, is whether users will control their own AI tools, data and prompts, or work inside opaque systems controlled by others.
Elastic’s Leonie Monigatti argues that context engineering for LLM agents is largely a search-interface problem: the critical question is how an agent decides what to retrieve from files, databases, memory, the web, and other sources before the model answers. In her workshop, she shows why semantic search, database query tools, shell access, and agent skills each solve different parts of that problem and fail in different ways. Her recommendation is to build retrieval stacks that combine easy specialized tools for common tasks with more general tools for ambiguous or complex ones, then use observed failures to refine the stack.
ServiceNow president, COO and chief product officer Amit Zavery argues that agentic AI will change enterprise software, but not by letting unconstrained agents replace the platforms that run corporate workflows. In a ServiceNow-sponsored interview, Zavery says the hard problem is turning probabilistic AI into reliable action across regulated, multi-system businesses, with the context, permissions, auditability and governance that enterprises require. His case is that companies such as ServiceNow retain leverage if they make AI production-ready, while software vendors that fail to adapt remain exposed.
ElevenLabs presents Voice Isolator, a tool inside ElevenCreative, as a fast way to salvage noisy recordings that cannot be reshot. In the tutorial, the company demonstrates a single workflow on an iPhone recording made on a London street: upload or drag in the file, click send, and play back an isolated voice track. The presenter says the street noise is removed and the file is processed within nine seconds, while interviews, podcasts, social clips, audio files and video files are named as broader use cases.
Today’s sources frame agents less as standalone model breakthroughs than as systems that need infrastructure, pricing, permissions, feedback loops, and engineering discipline around them. Bloomberg’s reporting on compute supply, Perplexity’s digital-labor pitch, Replit’s agent revenue story, and production guidance from Pydantic, Raindrop, and Matt Pocock all point to the same constraint: turning agent demos into repeatable work.
Bloomberg’s Tom Mackenzie frames the autonomous-driving race as a contest between systems that work now and systems designed to scale later. In Bloomberg Tech: Europe, he contrasts Waymo’s mapped, sensor-heavy safety stack with Wayve’s end-to-end AI model, while executives from BYD, Einride and Vay argue for other routes through vertical integration, autonomous freight and remote driving. The central question is not only which technology can drive, but which architecture and business model can win regulatory, customer and fleet trust at scale.
Private credit’s roughly $1.8 trillion boom is facing its first major test as a crisis of confidence rather than a broad default cycle, Bloomberg Originals argues. The report says the rapid growth of lending outside banks has left investors questioning private loan marks, limited liquidity in retail-facing funds, and underwriting assumptions around software borrowers whose growth prospects may be undermined by artificial intelligence.
Scott Clark, co-founder and chief executive of Distributional, argues that teams running LLM agents need to look beyond pre-production evals and dashboards of known metrics. His case is that the most consequential failures often emerge only in production, where agents interact with users, tools and changing models in ways teams did not know to test. Clark proposes an observability stack in which telemetry records what happened, monitoring tracks known signals, and analytics clusters trace behavior to surface unknown failure modes that can become new evals, guardrails, prompts or system fixes.
In a Stanford CS153 guest lecture, Mike Abbott and Nikhyl Singhal argue that AI is changing product management by eroding the value of roles built around coordination, reporting, and internal information flow. Singhal, founder of Skip and a former product executive at Meta, Google, and Credit Karma, says companies still need product judgment, but increasingly favor hands-on builders who can understand customers, work with technical systems, and make decisions. His broader case is that the product role now depends less on title and process than on company stage, iteration speed, and the ability to build directly.
Matt Pocock, a TypeScript teacher now focused on AI engineering, argues that AI coding has made software-engineering fundamentals more important rather than less. In a conversation with Shawn Wang, Pocock says code generation works best when humans define the architecture, module boundaries and domain language that give agents a coherent system to change. The lesson he draws from Claude Code and other fast-moving tools is that tool-specific knowledge ages quickly, while engineering judgment remains the durable layer.
Arm’s warning on smartphone weakness sat alongside a stronger claim from chief executive Rene Haas: handset softness is concentrated in lower-end devices, while data-center demand is accelerating because agentic AI workloads need CPU orchestration. Bloomberg Technology’s May 7 program used that contrast to trace a broader AI-infrastructure market in which demand is less in question than the ability to secure compute capacity, power, supply chains and capital. Anthropic’s lease of SpaceX compute and CoreWeave’s financing questions pointed to the same constraint: available infrastructure, not appetite for AI, is becoming the limiting factor.
HawkEye 360 chief executive John Serafini used the satellite intelligence company’s $416 million US market debut to argue that investors should view it as a durable defense supplier rather than a short-term IPO trade. Speaking to Bloomberg’s Caroline Hyde, he said HawkEye 360’s radio-frequency surveillance constellation serves a persistent warfighter need, with US government work providing most of today’s business and international demand and acquisitions forming part of the growth case.
Bloomberg’s Julia Fanzeres argues that the U.S. layoff picture is increasingly sector-specific: tech companies are announcing rising cuts even as broader private-sector layoff plans decline. Citing Challenger data, Caroline Hyde said planned U.S. tech job cuts have reached 85,411 this year, up 33% from the same period in 2023, with AI cited as a factor for a second month. Fanzeres said the pattern does not yet show a wider labor-market break, but raises the question of whether tech cuts are a contained adjustment or an early signal.
Samuel Colvin of Pydantic argues that production agents need more than observability after deployment: they need evals, traces, and typed configuration that can change prompts, models, and other parameters without a redeploy. Using Pydantic AI, Logfire, managed variables, and GEPA, he shows a workflow for moving from manual prompt tuning toward continuous optimization. His case is practical rather than automatic: GEPA can improve a narrow benchmark, but only if the team has representative data, sound evaluation criteria, and a clear definition of what better means.
Former US senator Phil Gramm, in a Hoover Institution conversation with economist Jon Hartley, argues that America’s debates over inequality, poverty, deficits and financial crises are distorted by measures and narratives that leave out central facts. Drawing on his books and legislative career, Gramm makes the case that economic freedom, not directed investment or redistribution, has been the main source of US growth and mobility. He contends that official inequality statistics undercount transfers and taxes, that the 2008 crisis stemmed from housing policy rather than Gramm-Leach-Bliley, and that efforts to substitute government judgment for markets reliably fail.