The premium in electricity for big AI sites is the "tolerable megawatt"—power that fits into a congested grid without driving local prices or waiting years for substation upgrades. Nearby price spikes force solutions inside the fence, so campuses that bring batteries, inverters, switchgear, and orchestration get built. Anthropic pledged to fund grid...

Companies win by building replayable failure environments that turn tacit work into trainable feedback loops. Replayable failure environments—unit tests, scoring functions, adversarial dialogues, and runbooks—convert risky generation into bounded search. TetrisBench and Shopify showed that when tasks are rewritten as a scoreable loop, model behavior...

Cities’ building-performance rules are making "negawatts with paperwork" the easiest and most valuable way to comply. With higher rates and local power constraints, many owners choose low‑capex packages that clear standards: controls, recommissioning, sensors, sequencing, partial equipment swaps, and embedded financing that turns avoided fines and bills...

The public web is becoming a low-trust archive. That flips the frontier: the biggest AI gains will come from operators who run closed-loop systems that act, observe outcomes, and iterate. Cheap model output contaminates public corpora, expert retrieval mixes confident knowledge with speculation, and open contribution channels become expensive to screen....

AI is making company structure the real moat: coherence becomes scarce, so small, autonomous product cells win. Execution gets cheap and code generation is abundant, which shifts the bottleneck to integration, testing, maintenance, and cross-team coordination. That coordination tax compounds when agents juggle many tools and when review chains are...

AI is forcing software to split into a public, machine-readable read layer and a permissioned write layer. Projects that win publish official Skills, tests, and benchmarks so agents can operate the code, and they lock contribution paths because cheap generation produces unlimited plausible diffs. Agents need executable truth—Skills show how to use a...

Buildings that can prove site-level flood exposure, controllable power quality, and documented hardening become financeable adaptation infrastructure. NVIDIA shows coarse models miss local extremes; Google’s Groundsource turned sparse reporting into 2.6 million flood events while still capturing 85–100% of severe floods, proving observation is...

Smarter models act as scribes while deterministic runtimes hold the keys. Semantic guardrails operate where models reason but exploits live in execution details: a harmless-looking command became remote malware via shell process substitution in Snowflake’s Cortex Agent. Apple enforces its camera indicator inside a secure exclave with tiny APIs. The...

Executable advertising is collapsing while advertising itself adapts into legible sponsorships. Consumer AI hits a subscription ceiling: products that want billion-user reach need an ad-supported tier. The legacy web ad stack depended on client-side execution, which enabled pervasive tracking and time-on-page monetization. That execution is revocable —...

AI’s biggest returns come from repetition—workflows that repeat often enough to let agents reuse computation, distill solved runs into skills, and resume long jobs with preserved state. Every successful run leaves behind cacheable prompt prefixes, a validated remediation sequence, test harnesses, and resumable task state. Those artifacts compound:...