Articles

Anthropic’s Mythos is a frontier AI model so capable at offensive cybersecurity that the company is withholding a public release and limiting access through Project Glasswing, a restricted defensive-security program. The move signals a turning point where advances in general-purpose coding and reasoning are creating real-world cyber risks, forcing new debates over access, safeguards, and disclosure norms.

AI may advance quickly by 2030, but the economy is likely to absorb it more slowly, limiting near-term GDP gains even in rapid progress scenarios. The bigger impact may come first through work, wages, and wealth distribution, with lower labor-force participation, a falling labor share, and rising concentration at the top.

AI-driven productivity gains could look like either 40% unemployment or a universal three-day workweek, even if the total amount of work is the same. The real issue is not the math of fewer labor hours, but how income, status, and social inclusion are distributed when work declines.

Gas Town is Steve Yegge’s multi-agent coding workspace manager, designed to turn disposable AI chat into persistent, auditable issue tracking across repos with named workers, task dependencies, and git-backed handoffs. This practical guide explains how Gas Town works, who it is for, and when it makes sense over a simpler editor-plus-chat setup.

Jeffrey Emanuel’s Agent Flywheel is a planning-first framework for multi-agent coding that turns vague software goals into coordinated execution through durable artifacts like markdown plans, task graphs, and operating manuals. Its promise is faster parallel development through better repo legibility, though the strongest evidence so far comes from Emanuel’s own examples rather than independent benchmarks.

Paperclip is an open-source control plane for managing teams of AI agents with task ownership, budgets, approvals, and audit trails. It helps founders and enterprises orchestrate local and remote agents more like employees, though questions remain around security, audit fidelity, and multi-human governance.

AI labs have pricing power, but mostly outside commodity token pricing. The strongest moats come from workflow integration, enterprise controls, reserved capacity, and premium use cases where better model performance materially boosts customer output, even as massive infrastructure costs make profitability much harder to sustain.

A new AI-driven labor market is emerging where jobs do not disappear, but become weaker: fewer entry-level roles, slower promotions, and less bargaining power for workers.

METR's time-horizon chart is the most cited benchmark in AI, but reading it correctly means stacking it against GDPval, RLI, SWE-CI, and labor data — and understanding that benchmark progress and deployable judgment are not the same thing.

StrongDM's dark factory pattern reveals how the best teams are shifting from writing code to designing validation harnesses, specs, and scaffolding around AI agents — and where the approach breaks when teams skip the rigor.

A breakdown of the social mechanics behind AI job panic — the fallacies, rhetorical moves, and personality types that turn productivity demos into existential dread, and the habits that help you keep your balance.

Software now serves two users: people and agents. The products that design for both will unlock latent demand by making valuable work cheap enough to delegate.

AI job-loss panic is outrunning the evidence. The best data points to task reshuffling and tighter entry-level hiring, not mass replacement. Here's how to read the signals and protect what matters.

Anthropic shipped Dispatch to let you reach Cowork from your phone. OpenClaw lets an AI agent run on its own schedule from your messaging apps. Same surface, different architectures, different tradeoffs.

OpenClaw shows what people are actually buying in AI: not a solution, but an easy action that feels like progress. The scarce skill is still judgment.

Latent demand is the missing variable in most AI strategies. People want outcomes; friction keeps them from acting. AI strips away enough friction that behavior finally becomes worth it — and demand expands.

Vibe coding fails when the plan is weak. Iterative planning with specialized review rounds catches the expensive mistakes before code hardens around them.