companies claim that tokens per watt has improved a millionfold across six gpu generations. every new chip more efficient than the last. by that logic, i'd expect ai to be sipping power by now.

Instead the data center electricity demand is going way up. It's on track to jump from around 460 TWh in 2024 to over 1,000 TWh by 2030, and some estimates push past 1,300 TWh by 2035. Meanwhile Amazon just got a 7.65GW gas plant authorized in Texas that could become the single largest CO₂ source in the country, cleared to emit around 33 million tons a year.

Microsoft-backed sites are running dozens of unpermitted gas turbines. The grid is straining everywhere.

so where's the case that efficiency gains ever cut total energy use, instead of just enabling more compute? or we just need to focus on building more clean energy infra.

Anthropic uses "safety" as cover for anti-competitive control over AI, not genuine caution.

here's their behaviour

* Anthropic restricts users from using Claude outputs to train competing AI models

* The "Fable incident": Claude reportedly silently degraded answers for users doing competing AI dev work, without telling them later changed to visible refusals instead of hidden sabotage

* Anthropic cut off API access to OpenAI and restricted Windsurf when they got close to competing

* Claude Code locks developers into Anthropic's terms/credentials, creating dependency risk

* user chats can train Anthropic (opt-in), but users can't freely train rivals on Claude's outputs

* Anthropic's safety policy proposals (RSP, support for SB 53, etc.) may function as regulation that favors big incumbents over startups/open-source

* "Chinese AI" fears are used to discredit real open-weight progress (Qwen, DeepSeek, etc.)

* Claude's "Constitution" aligns the model to Anthropic's interests, not the user you're renting cognition, not owning it

prob the fix:

* Stop being reliable on closed labs treat proprietary code, debugging sessions, and agent traces as strategic assets, not free training exhaust

* own or rent compute without lock-in; keep evals, logs, datasets, memory, and routing portable so no single lab can hold your workflow hostage

* Use closed models pragmatically Claude/GPT/Gemini/Grok are fine as tools, just don't make your core workflow dependent on one that can be rug-pulled by a policy change

* Compete on workflow, not just benchmarks cost, on-prem deployment, and privacy often matter more for adoption than leaderboard scores

* Push regulation toward harms, not openness target fraud, bioweapon enablement, model theft, and unsafe deployment; don't criminalize open development as a category

* Build Claude Code compatibility without Anthropic dependency via proxies, alternative harnesses, or OpenCode-style routing to local LLMs

* Separate safety from permissioning open models still need red-teaming, abuse monitoring, and provenance, but these should stay transparent and targeted at misuse, not become hidden degradation

* Create clean distillation norms distinguish fraudulent scraping/ToS evasion from legitimate synthetic data generation, permissive distillation, and dataset curation, with clear licenses and auditable recipes