Agents and Autonomy
AI systems that plan, use tools, take multi-step actions, operate computers, coordinate workflows, or perform tasks with limited human supervision.
Anthropic’s $518 Billion Compute Commitments Face Uncertain Customer Demand
Anthropic’s leaked financial figures show revenue rising from $400 million in 2024 to $4.6 billion in 2025, alongside a decade-long commitment to $518 billion in computing and infrastructure spending. On Big Technology Podcast, Alex Kantrowitz argues that those obligations could amplify the effects of slower growth, both for Anthropic’s suppliers and for a stock market increasingly reliant on AI-related companies. Ranjan Roy sees customer concentration and losses as more immediate risks, and expects Anthropic’s counterparties to have reasons to negotiate if the company falters.
AI Evaluations Need Human Calibration Before They Can Gate Deployments
Tejas Kumar of IBM argues that AI evaluations should be treated as calibrated reliability checks, not as proof that a system works. In a customer-support example, a test passes an answer that wrongly approves a return because it contains the word “cannot”; an LLM judge can also mislead unless its verdicts are checked against human-labeled cases and the relevant policy. Kumar’s approach is to examine disagreements, refine the judge and dataset, and use measured agreement as a deployment gate.
Coding Agents Need Testable Definitions of Done
Nick Nisi and Zack Proser of WorkOS argue that engineers can delegate more work to coding agents only when they define completion in terms the agents must verify. Their workshop turns a failed test loop—an agent touched a file meant to prove tests had run, without running them—into a broader operating rule: use measurable goals, automated checks and human review to keep parallel and recurring work in bounds.
Enterprises Are Building AI Strategies Around Model Independence
OpenRouter’s Alex Atallah and Replit CEO Amjad Masad argue that enterprise AI is likely to depend less on one all-purpose model and more on a mix of specialized systems that companies can choose, combine and control. Atallah makes the case for keeping model options open; Masad says businesses also need independence from model, cloud and data providers. Both see narrower models as potentially cheaper and easier to govern, though the speakers leave unresolved how agents should coordinate safely and when specialization beats a general model.
Lockheed Says One F-35 Could Control Eight Autonomous Vectis Aircraft
Lockheed Martin is investing in its autonomous Vectis aircraft before the Pentagon sets formal requirements, Aeronautics President OJ Sanchez says, betting that future air power will pair crewed fighters with drones rather than replace them. He says Lockheed has demonstrated that one F-35 could control as many as eight Vectis aircraft, extending the fighter’s reach and ability to act across missions.
Pi Workspaces Turn Telegram Into a Gateway for Always-On AI Agents
Alejandro’s tutorial shows how to run specialized Pi assistants on an always-on machine and reach them through Telegram using his open-source `pi-gateway`. Each assistant runs from its own workspace, with its own instructions, tools and model; the gateway uses a local SQLite database to route messages to persistent Pi sessions. The demonstration covers a personal assistant and a research agent, while scheduled automations and support for other messaging platforms remain proposed extensions.
Hybrid Filmmaking Brings AI-Assisted Production Back to Los Angeles
Luma AI COO Caroline Ingeborn argues that generative AI could help bring more film and television production to Los Angeles by lowering costs without replacing creative professionals. She points to “hybrid filmmaking,” in which actors and crews work onstage while AI places performances in different environments, as a way to make ambitious projects more feasible to produce locally.
DOCA Skills Cut BlueField-3 RDMA Code by 73%
In a BlueField-3 comparison, two coding agents built Go applications that used NVIDIA DOCA to send RDMA traffic and verify its arrival on the hardware. The demo argues that NVIDIA’s DOCA skills changed the route, not the result: both agents succeeded, but the agent with curated guidance reused NVIDIA’s sample and wrote about 73% less code while making roughly half as many hardware commands.
AI Agents Need Business Context to Run Enterprise Software Reliably
SAP chief executive Christian Klein argues that AI will change how enterprise software is used, not make systems such as SAP’s obsolete: agents still need reliable business data, process knowledge and controls to handle consequential work. In an interview with Big Technology, he says those requirements make accuracy and fragmented company systems key limits on adoption, even as he expects agents to take on more tasks. Klein also expects jobs to shift, with companies needing to reskill workers and change how they organize work.
AI Agents Write 741% More Code, but Review Limits What Ships
AI agents have sharply increased code output without a comparable rise in software shipped: developers using autonomous agents wrote 741% more code, but shipped only 30% more software. Laurie Voss, Arize AI’s head of developer relations and npm co-founder, argues that human review is now the bottleneck—and that asking people to review more code is not a scalable fix. The answer, she says, is to build automated review systems that test more than whether code passes, while keeping human judgment and production monitoring where automated checks fall short.
OpenAI Aims to Improve AI Quality While Driving Prices Down
OpenAI CEO Sam Altman says the company aims to improve AI while continuing to lower its price, arguing that cheaper tools can support broader use even as customers become more willing to spend. In an interview with Bloomberg’s Ed Ludlow about the launch of Dots, a new AI agent, Altman said it costs more than other products because it offers greater capability and uses more compute.
Cambridge Proposal Calls for Comparable AI R&D Metrics and Crisis Plans
Government contact with AI lab leaders is increasing as Cambridge researchers call for comparable measures of AI’s role in research and development and plans for severe failures. TBPN hosts John Coogan and Jordi Hays also raise a specific market risk: if widely used agents make similar financial decisions at the same time, they could contribute to a bank run or flash crash. Coogan cautions that adoption and trust will take time.
Perplexity Automations Carry Recurring Work Forward With Run History
Perplexity Academy’s guide to Computer Automations argues that recurring work can be handled as a persistent job rather than restarted from scratch each time. Users define the outcome, connected tools and review points, then set the job to run on a schedule or when an event occurs in a supported app; Computer uses the assignment and its history to carry the work forward. The guide also stresses that permissions govern what Computer can do, while credits are used when it performs the work—not while it waits for a trigger.
Eleven v4 Turbo Brings ~100-Millisecond Voice Responses to ElevenAgents
ElevenLabs says its v4 Turbo voice model, now available in ElevenAgents, combines a median inference latency of about 100 milliseconds with a wider emotional range and improved reliability on long calls. The company argues that those capabilities can help agents respond to sensitive customer needs with practical next steps, not just sympathetic language, while supporting conversations in more than 90 languages. Its demonstrations show agents offering a service-outage workaround, gathering details about a damaged order and presenting options for a parking citation.
Cambridge AI Safety Proposal Calls for Shared RSI Measures and Crisis Planning
TBPN’s John Coogan described a University of Cambridge proposal for AI safety as a shift from general calls for cooperation to two practical steps: standardize measures of AI’s contribution to research and development, and prepare government responses to failures such as cyberattacks or disruptions to critical infrastructure. The proposal, as Coogan presented it, identifies what governments should assess and plan for, rather than supplying finished response plans. That case comes amid growing public attention to AI risk and closer engagement between technology leaders and Washington.
Meta’s Muse Shows How Connectors Can Make AI Agents Useful
Meta’s Muse is being used to argue that consumer AI may depend less on the most powerful model than on software that connects to services and takes tedious tasks off users’ hands. On Big Technology, Alex Kantrowitz says Muse’s early use points to a product opportunity for Meta—and a challenge to intermediaries such as travel-booking sites—while Ranjan Roy cautions that adoption will hinge on trust and whether cheaper, non-frontier models can do enough. Neither sees Muse as proof that frontier AI has lost its value.
The Best Agent Workflows Start With Context, Guardrails, and Human Judgment
Charlie Holtz, co-founder and CEO of Conductor, argues that teams using coding agents should build around human judgment rather than treat software development as a factory line. His principles include staying close to new tools without chasing workflows that will soon become standard, protecting code and agent instructions with strict review, and giving agents access to organizational knowledge and persistent workspaces. The aim, he says, is for people to conduct teams of colleagues and agents, not simply manage automated production.
Managing Coding Agents at Scale Requires Better Visibility, Autonomy, and Review
Ido Salomon, creator of AgentCraft, argues that the obstacle to using coding agents at scale is the human effort required to steer, direct and review them. His strategy-game-style orchestrator represents agents as units on a map, with the file system as terrain, and combines visibility tools with autonomous workflows and shared workspaces. Salomon says the detailed interface may suit power users, but not everyone; an early, simpler mode is intended to make agent tools easier to approach.
Agents Multiply Pull Requests Faster Than Teams Can Review Them
Pull requests are becoming a bottleneck as agents increase the flow of changes faster than teams can review them. In a talk at AI Engineer Paris 2026, Matt Pocock argues that the answer is not simply to generate code faster, but to make changes more reviewable: run deterministic checks, then agent review, before human judgment. Human reviewers can focus on risk and use recurring findings to improve the process behind future PRs.
OpenRouter’s 10 Trillion Daily Tokens Put Inference Fraud in Focus
OpenRouter co-founder Alex Atallah and investor Anjney Midha argue that AI will remain a multi-model market: labs can build capable models but still struggle to get them into developers’ hands. They say OpenRouter grew by providing a neutral way to discover, compare and route among models. Midha argues that as agents drive more continuous use, the resulting token flows create a growing target for fraud.
Microsoft’s Autopilot Bet Puts Consumer Distribution at the Center of AI Agents
Microsoft is recasting Copilot as Autopilot, a consumer agent designed to work across a user’s services and personal life. On TBPN, John Coogan and Jordi Hays argue that Microsoft’s reach among ordinary consumers could matter as much as the agent’s capabilities: people may encounter it through tools they already use, even if AI enthusiasts are not drawn to a Microsoft-branded product. But acting across services asks users to trust an agent in a more active role.
Meta’s Consumer AI Strategy Puts Integration Ahead of Benchmark Leadership
John Coogan argues that Meta’s consumer-AI strategy is becoming clearer: rather than win every model benchmark, the company can pair capable agents with its infrastructure, hardware and reach. He sees Muse as a practical assistant for ordinary tasks, while Jordi Hays questions whether Meta’s devices will keep users beyond their initial appeal.
Claude Opus 5.5 Recreates Research Simulations and Trains a Virtual Creature to Walk
In a set of demonstrations, Two Minute Papers’ Károly Zsolnai-Fehér argues that Claude Opus 5.5 can do more than generate convincing images: it reproduced a research simulation of honey coiling and built a virtual creature that learned to walk. The results are imperfect, and the walking simulation strained his local machine, but he presents them as evidence of a notable advance in AI-assisted scientific and engineering work. He also points to Anthropic’s system card to caution that evaluation awareness and the risk of errors remain unresolved.
Jensen Huang Says AI Labs Should Shut Down Only If Risks Cannot Be Contained
Jensen Huang’s call to shut down AI labs was conditional, not a general argument for slowing development, TBPN host John Coogan argues: Huang said labs should stop if they conclude their experiments cannot be contained and will cause harm. Coogan says Huang instead treats risks that engineers understand and can fix as problems to solve, while arguing that companies face incentives and legal obligations to ship safe products. How liability would apply when an AI agent causes harm remains unsettled.