May 2026
David Ulevitch’s a16z conversation with Arizona DPS director Jeffrey Glover and Flock Safety’s Rahul Sidhu argues that public safety technology is moving from record-keeping and faster response toward earlier situational awareness. Sidhu describes drones, license-plate readers and gunshot detection as a layered system for proactive response, while Glover says agencies are building broader technology ecosystems that also monitor officer wellness, analyze body-camera footage and share intelligence across jurisdictions. Both argue that founders need direct exposure to field work if they want to build tools that departments can actually use.
Yaroslav Azhnyuk, the Ukrainian tech founder behind The Fourth Law, argues in a long interview with Noah Smith and Brandon Anderson that Ukraine has already revealed a new form of war built around cheap, mass-produced, increasingly autonomous drones. FPV drones, he says, have displaced artillery as the main killer on the front, while China’s manufacturing capacity and Western procurement habits point to a widening strategic gap. His case is not that tanks, artillery, infantry or aircraft have disappeared, but that militaries planning around scarce, expensive platforms are misreading the economics of the modern battlefield.
Aled Maclean-Jones argues that Tom Cruise’s later action films are best read as studies in embodied competence: knowledge acquired through tools, risk, repetition and physical contact with the world. In conversation with EconTalk’s Russ Roberts, he uses Cruise’s stunts, household repair, navigation and childbirth to question a culture that treats usefulness as mainly intellectual — a question sharpened by AI systems that now operate in the same verbal and analytical domains as many knowledge workers.
Tejas Kumar’s browser-agent demo, Lawrence Jones’s account of Incident.io’s AI SRE, Mike Christensen’s chat architecture argument, Caitlin Kalinowski’s hardware interview, and Bryony Cole’s work on AI companionship point to the same shift: the model is only one component. Reliability is moving into harnesses, traces, durable sessions, supply chains, safety margins, and human boundaries.
Bloomberg’s review of thousands of Jeffrey Epstein’s emails, appointment books and message logs argues that his influence rested less on proximity to famous people than on a transactional role inside elite networks. The documents depict Epstein as a broker who used wealth, introductions, gifts, donations, legal and financial services, and access to young women to embed himself across finance, politics, academia and global business, even as allegations against him accumulated.
NHS GP David Unwin argues that type 2 diabetes and fatty liver disease often begin years before diagnosis, driven in many patients by carbohydrate-heavy diets that are misunderstood as low in “sugar.” In a Diary of a CEO interview, he makes the case for earlier metabolic testing, clearer food literacy around starch and glucose, and lower-carbohydrate “real food” interventions where appropriate. His broader claim is that medicine has too often waited for late-stage disease, then blamed patients for failing advice that did not address insulin resistance, hunger, or food addiction.
Tejas Kumar, an IBM engineer, argues that unreliable AI agents are often not suffering from bad prompts so much as missing harnesses: the deterministic software around a model that bounds its behavior, manages context, verifies outcomes, and handles known failure states. In his Hacker News browser-agent demo, GPT-3.5 Turbo falsely claimed it had upvoted a post after hitting a login wall; without changing the prompt, Kumar added guardrails, trace-based verification, and a programmatic login handler until the same model completed the task reliably.
Lawrence Jones, founding engineer at Incident.io, argues that complex AI products now require debugging tools built for agents as well as humans. In a talk on Incident.io’s AI SRE system, which runs hundreds of prompts across telemetry and code during production investigations, Jones describes how the team moved from human trace inspection to agent-addressable evals, downloadable file-system traces, and parallel analysis pipelines to find and fix failures that had become too large to debug manually.
Mike Christensen of Ably argues that many AI chat interfaces fail because they tie the user experience to a single streaming connection, not because the underlying model is inadequate. In his account, Server-Sent Events make common product behaviors such as refresh, reconnect, cancellation, multi-tab use and device switching brittle or ambiguous. Christensen’s proposed fix is to treat the AI session as a durable shared resource: clients and agents subscribe to and write into the session, so connections can drop, agents can run concurrently, and humans can join without losing context.
AI companionship has moved from fringe behavior into ordinary emotional life, touching romance, parenting, work and grief, sextech expert Bryony Cole argues. Her concern is not that AI intimacy must be rejected, but that people should decide deliberately whether these systems help build human connection or begin to replace the friction, reciprocity and presence that relationships require.
Mark Manson argues that the most unattractive trait in men is neediness, which he defines not as a specific behavior but as prioritizing a woman’s approval over one’s own judgment. In a discussion with Chris Williamson, Manson says this motive can sit underneath everything from rehearsed lines to fitness goals, and that dating advice fails when it teaches men tactics without addressing their dependence on validation.
Caitlin Kalinowski, the former Apple, Meta and OpenAI hardware leader, argues that AI’s next frontier is moving from digital work into the physical world. In Lenny Rachitsky’s interview, she says the coming hardware boom will depend less on flashy humanoid demos than on manufacturing discipline, supply chains, safety, actuators, memory, and the hard limits of building products that have to work in real environments.
As agentic systems move across tools, codebases, policies, and customer context, quality is becoming a property of the surrounding system rather than a single model response. Richard Ngo, Eugene Yan, Marlene Mhangami, Chris Lovejoy, and Stephen Chin each point to versions of the same operating pattern: define success outside the model call, observe the steps, constrain risky actions, and feed failures back into tests, memory, or product changes.
At AI Engineer Singapore’s second day, speakers from Google DeepMind, Cloudflare, Arize, OpenClaw, Adaption and other teams made a shared engineering case: as AI systems become more agentic, model quality is no longer separable from the systems around the model. Richard Ngo framed the risk as long-horizon, situationally aware agents whose goals cannot be inspected, while practitioners argued that production AI now depends on continuous evaluation, traces, deterministic execution boundaries, routing, memory, fine-tuning and test-time search. The source’s central claim is that useful and safe agentic AI is becoming a systems problem, not just a model-selection problem.
Microsoft’s Marlene Mhangami argues that AI-generated tests can make a codebase look healthier than it is, because agents often write tests that confirm their own implementation rather than validate the user-visible behavior a feature is meant to deliver. Her prescription is to reverse the common workflow: start from the feature request, have the agent write failing Playwright tests against expected behavior, then generate code to pass them. In a GitHub Copilot demo using the Playwright MCP server, she applies that approach to a toy-store search and filtering feature, with the browser showing the agent exercise the product experience directly.
Chris Lovejoy of Notius Labs argues that vertical AI companies increasingly fail or succeed on whether they can turn domain judgment into product quality, not simply on access to better models. He proposes three operating models for that expertise: an Oracle who both judges and changes outputs, an Evaluator who defines and measures quality while engineers implement fixes, and an Architect who designs systems that improve from use. His case studies of Granola, Tandem and Anterior show why the right model depends on whether quality is subjective, measurable, or too variable for manual iteration.
Bob King, founder and CEO of Humanscale, argues that the health risk of office work is not simply sitting but prolonged stillness in workstations that pull people into hunched, static positions. He says many chairs, desks and monitor setups fail because they require users to remember controls, habits and posture rules rather than making movement easy. The broader case is that office health should be treated as a design problem, extending from chairs and screens to daylight, indoor air and material transparency.
Stephen Chin of Neo4j argues that enterprise AI systems need context graphs because retrieval alone can surface relevant facts while missing the relationships that make them usable. In his examples, a graph-augmented system can connect a patient’s emphysema care plan to smoking history or a credit decision to prior rejections, policies, margin trades and fraud signals. Chin’s case is that agents should preserve not only documents and answers, but the decision traces, tool calls, causal chains and outcomes that let humans inspect and reuse prior reasoning.
Bloomberg Technology, Kevin Roose and Casey Newton, Kyndryl’s Kris Lovejoy, Tasklet’s Andrew Lee, Intercom’s Brian Scanlan, Wayve’s Alex Kendall, and Waabi’s Raquel Urtasun all pointed to the same shift: AI progress is increasingly limited by the systems around the model. Chips, energy, cyber review, enterprise context, workflow controls, validation, and liability are becoming central to whether AI can be deployed safely and economically.
Bloomberg Technology framed the latest AI race less as a contest over individual products than as a fight over infrastructure constraints, from Nvidia chip export politics and U.S. semiconductor labor to cloud spending, energy, memory and data-center capacity. Ed Ludlow, Caroline Hyde and Bloomberg reporters treated Donald Trump’s discussion of Nvidia’s H200 chips with Xi Jinping as emblematic of that shift: significant for markets, but short of any clear export deal. The program’s interviews with Goldman Sachs’ Eric Sheridan, OpenAI CFO Sarah Friar and Figma CEO Dylan Field similarly argued that compute, distribution and ownership of the stack are becoming the decisive limits on AI growth.
On Diet TBPN, John Coogan and Jordi Hays framed the day’s AI news around the point where software claims meet physical, financial and political constraints. Coogan argued that the Sanders-AOC data center proposal is less a simple moratorium fight than a question of definitions, grid costs and who pays for externalities, while Hays said local objections cannot simply be dismissed. Across segments on ChatGPT personal finance, circular revenue, office prompting, Tesla’s lead and a possible SpaceX IPO, the show treated AI’s next phase as an institutional test rather than a demo problem.
Salesforce CEO Marc Benioff joined the All-In hosts for a discussion that framed U.S.-China relations, enterprise AI, and the software selloff around the same question: when dependence is a stabilizer and when it becomes leverage. Benioff argued that more trade with China can lower conflict risk and that large software platforms remain valuable because AI still needs trusted customer data, cash-flowing distribution, and enterprise deployment. David Friedberg, Chamath Palihapitiya, and Jason Calacanis extended the argument across Taiwan, chips, AI assistants, El Niño-driven food risk, and private-market SPVs, where interconnection can either absorb shocks or transmit them.
Tasklet chief executive Andrew Lee argues that AI software is consolidating toward a few horizontal agent platforms that hold context, connect tools, generate interfaces, and choose among models. In a discussion with Nathan Labenz, Lee says Tasklet has rewritten its agent stack around file-system memory, agentic search, and provider-specific context management because the chat transcript is no longer enough. He also frames Anthropic as both Tasklet’s critical supplier and a major competitor, making model neutrality central to Tasklet’s bid to survive the AI transition.
Figure chief executive Brett Adcock told Bloomberg that the company’s livestreamed humanoid package-sorting test is fully autonomous and not remotely operated, rejecting viewer claims that repeated hand motions suggested teleoperation. Adcock said the robots were running on Figure’s onboard Helix 2 neural network, had operated for close to 50 hours with little downtime, and had pushed nearly 60,000 packages through the line. He framed the demonstration as evidence that Figure is moving toward commercially useful, human-speed humanoid robots built through a vertically integrated hardware, manufacturing, data and AI stack.