Phoenix

Phoenix AI

Phoenix AI

Phoenix AI

Phoenix AI

Phoenix AI

Phoenix AI

Phoenix AI

Phoenix AI

Phoenix AI

Phoenix AI

Good AI-assisted development happens at the systems level, not the task level

Every time I add a new feature to my Phoenix app, my AI coding agent ships the feature... but doesn't add a menu item for it. The page exists, the functionality works, but there's no way for a user to actually get there. My first instinct, like everyone's, is to go tell the model "add the button." And that works. But think about what just happened: I noticed a problem, diagnosed it, and told the model exactly what to do. I'm doing the thinking. The model is doing the typing. I'm pedaling the Peloton so Anthropic can give me free tokens. That's the promise of "prompt engineering" — you get better at telling the model what to do. But you're still working for the model. We want the model working for us. Here's the difference. Instead of telling the model to add the button, I ask: how do I make this mistake impossible in the future? I use BDD specs that define what my app should do at its boundaries. The Phoenix LiveView test helpers have a navigate function that lets the agent jump directly to any page — which means it can make tests pass without ever touching the UI. So here's what I did: I wrote a linter rule that prevents the agent from calling navigate. Now there's an allowed fixture that drops the test on a known starting route, and the only way the agent can reach my new feature is by clicking through the UI — which forces it to add the menu item to make the test pass. I will never have this problem again. Not because I wrote a better prompt. Because I changed the system so the correct behavior is the only possible behavior. That's the shift. Stop fixing the model's output. Start constraining its environment so the right output is the path of least resistance. Every mistake is a chance to design out the next one, not a chance to write a better prompt. submitted by /u/johns10davenport [link] [comments]

Claude Code helped me bring my dead passion project back to life

**TL;DR: Claude Code took a half-finished HeroMachine conversion and helped me complete it over a long weekend. I'm the creator of HeroMachine, a free Flash-based character creator that's been around since 1998. Over 25 years I and a handful of other artists hand-drew nearly 10,000 items (heads, bodies, weapons, capes, the works) so people could assemble their own superhero illustrations. It found a real audience in tabletop gamers, writers, teachers, kids who just wanted to see their character come to life, and middle-aged dudes like me who once dreamed of a career in comics. At its peak HeroMachine 3 had tens of thousands of active users. Then Flash died in 2020, and HeroMachine died with it. I tried to rebuild. I really did. I hired a developer, spent thousands of dollars, and got back an unfinished product. I tried redoing it myself, but the sheer scope was paralyzing and I just didn't have the energy any more after working my day job every day. HeroMachine 3 has thousands of hand-drawn items across 30+ equipment slots, each with three-channel coloring, transforms, layering, masking, and more. Rebuilding all of that from scratch while also converting every item from Flash's internal format to SVG? I burned out. Real life got in the way. After a while it just felt like I'd failed, and I stopped trying. Fast forward to earlier this year. In my day job as a web developer, I started using Claude Code to automate tedious migration work like taking old WordPress sites and converting their content into our modern custom-built blocks. The kind of work where you know exactly what needs to happen, it's just painfully repetitive. One Friday night I had the thought: "If it can convert old WordPress content, maybe it can help convert those old HeroMachine items, too." Five days later I had a working app. I want to be real about what that means, because I have the same genuine concerns about AI I know a lot of you do. What AI did NOT do: Draw a single item. Every piece of art is still hand-drawn by me and a small group of human artists over the past 25 years. Every creative decision, from what to draw, how to draw it, and what looks right, is still mine. Design the application. HeroMachine's logic — the architecture, feature set, how items and colors and transforms work together — was designed and written by me in ActionScript over 10+ years. Claude Code helped me translate that existing design into a modern stack, but every decision about what the app should do came from me. What AI did do: Help me translate my existing ActionScript code into modern JavaScript and Svelte. I'd point it at the decompiled ActionScript code, explain how something worked, and it would produced the refactored result. Automate the conversion of thousands of Flash-format items into clean SVGs. Help me debug when I got stuck and build new features quickly when I had ideas. Eliminate the parts that were actually stopping me: the tedium, the unfamiliar syntax, the sheer volume of conversion work that made the whole project feel impossible. I got more done in five days than in the previous five years. Not because the AI is smarter than me, but because it removed the wall between "I know exactly what this should be" and "I can actually ship it." I'll be honest, I find AI companies' business practices troubling. I have real concerns about what AI will do to my own industry and my actual job, not to mention the huge data center being built less than an hour from where I live that could have a massive impact on our environment. I hate that it's positioned to take over the fun, creative parts of work while leaving us with the grunt work. Am I sharpening the axe that will ultimately be used on people like me? Maybe. I've sat with that, and I don't have a clean answer. What I can tell you is that I sunk 25 years into HeroMachine and it was dead. Now it lives again, and I have a hard time convincing myself that's an altogether bad thing. HeroMachine 3 "Phoenix Edition" (it rose from the ashes!) is free and live now if you want to check it out. I'm happy to answer questions about the process, the tech, or the ethics of it. I don't think this is a simple story, but at least it's an honest one. submitted by /u/AFDStudios [link] [comments]

I built a persistent graph memory MCP for Claude (open source, 2-min setup)

Claude is brilliant in-session but forgets everything between conversations. Project memory and CLAUDE.md help, but they don't scale to actual structured knowledge: who works on what, why a decision was made, what depends on what, across hundreds of facts. I open-sourced Sandra two weeks ago, a graph + vector memory backend with a native MCP server. It started 15 years ago as our internal memory layer at EverdreamSoft (it still powers Spells of Genesis in production), and turned out to fit LLM agents really well. Concrete example : I tell Claude in one session « we're building Phoenix with Marie and Tom, it runs on Postgres ». A week later in a fresh chat I ask « who's on Phoenix? » and Claude returns Marie and Tom. Even better, Tom opens his own Claude session connected to the same Sandra instance and asks « what DB does Marie's project use? ». Claude traverses Marie → works_on → Phoenix → uses → Postgres to answer. Same graph, any teammate, no manual handoff between people or chats. Vector memory typically returns the original sentence as a chunk and loses the link when queried through a different path, plus most setups are per-user only. What it gives you in Claude : Persistent memory across sessions, structured as a graph (subject, verb, target) Claude reads AND writes to it through MCP tools, no manual updates Exact, fuzzy, and semantic search exposed as MCP tools Long-text storage per entity (notes, full documents) on top of structured refs Setup, 2 minutes : git clone https://github.com/everdreamsoft/sandra && cd sandra docker compose up -d claude mcp add sandra --transport http --url http://127.0.0.1:8090/mcp Then ask Claude to remember something, query it, or build the graph as you talk. Benchmark for the curious : 0.89 on Structured Recall Bench (130 deterministic questions, no LLM judge). Vector stores cluster between 0.25 and 0.48 on the same bench. Methodology and raw JSON: https://sandraeds.everdreamsoft.com/lp/bench-claudeai AI involvement : core PHP engine hand-written over 15 years, predates LLMs. MCP layer and recent tooling were Claude-assisted, human-reviewed before merge. This post was drafted with Claude and edited by me. Repo: https://sandraeds.everdreamsoft.com/lp/claudegit Live demo (interactive MCP request from a public Claude session): https://sandraeds.everdreamsoft.com/lp/sandraclaudeai MIT. Curious what people here would actually use a persistent graph memory for, drop ideas in comments. submitted by /u/yodark [link] [comments]

Claude is going to cause me to stroke out

I'm asking Claude to help me set up Docker and some containers and such, since I don't understand any of this at all and my ability to "just figure it out" is virtually gone thanks to anxiety and some issues with my meds. Things have been going ok for the most part, but we've reached this sort of stage where he's giving me instructions on the next step and then in the same response, he'll say, "No, wait, nevermind, this is better" and tell me to do something else entirely... I called him on it and he said it's his fault for getting ahead of himself and he'd do better, but it keeps happening and seems to be getting worse.. At one point he told me to make a Shared Folder, then said "Wait, make a regular one" and I asked which it should be. He said "Regular, it won't change again". I made the folder, looked for the setting he told me to change, I told him it wasn't there, and he said "Oh, yeah, that setting doesn't really matter. Nevermind". Then we went on to some other settings and eventually hit a roadblock, at which point he said, "The phoenix_downloads folder won't show up because File Station's Mount Remote Folder can only mount to folders inside shared folders, not plain folders under /volume1. That's why we needed a shared folder after all — I was wrong to correct myself earlier." Is there any way to mitigate this? I don't have much hair left and he's making me want to pull it out and my anxiety is spiking like a mofo. The amount he's been able to help me with other things has blown my mind and I couldn't be more thrilled with the overall results, but I'm at a loss right now. submitted by /u/inkandchalk [link] [comments]

I've created an MCP to build automations using Claude Code.

Hey there! Over the past few days, I’ve been building an MCP Server for my side project (Hooklistener), which lets you create any kind of automation. I’ve built all of this using Claude Code (it’s worth noting that I have a technical background). The backend is primarily Elixir and Phoenix. The workflow is always as follows: Planning mode Implementation Phase (using specific agents; for example, I have some with specific instructions for working with Elixir code). Once that’s done, I run the code-simplifier skill and perform a couple of rounds of validation. The interesting thing about this is that it lets you create simple automations without even touching a UI. For example, imagine you need to send GitHub notifications to Telegram: you could do this directly from Claude Code. I'd appreciate your feedback! https://reddit.com/link/1sgpde0/video/1sn6rsx306ug1/player submitted by /u/absoluterror [link] [comments]

sparX: Phoenix-powered X content skills/agents specifically for Claude Code

sparX is a collection of Claude Code skills, agents, and deep reference material on the X algorithm (phoenix) that transforms claude into a full X content studio: drafting, optimizing, scoring, scheduling, trend research, performance review, and visual content creation. I created this to draft optimized X posts with the help of claude. Its completely open source MIT Licensed Feedback is very welcome! submitted by /u/RealEpistates [link] [comments]

11.7B Claude tokens in 45 days. Here's every project it built — and what actually happened.

People kept asking what 9.3B tokens actually builds. The number is now 11.7B over 45 days. Here's the honest answer. **What's real and running:** **Phoenix Traffic Intelligence** — Live traffic system on ADOT's AZ-511 feed. 8 Phoenix freeway corridors monitored 24/7. Cascade risk detection, weighted incident scoring (construction zones separated from real incidents), AI-generated crew dispatch recommendations, 2-minute sweep cycle. Already in conversation with City of Phoenix Office of Innovation and AZTech about a pilot. **Expression-Gated Consciousness** — A formal mathematical model for the gap between what people know and what they express. 44+ subjects, Pearson r=0.311, three discrete response types confirmed by data. Cold emailed Joshua Aronson (NYU, co-author of the foundational 1995 stereotype threat paper). He replied. Call is pending. **LOLM** — Custom transformer architecture built from scratch. Not fine-tuned. Original architecture targeting 10B–100B parameters on Google TPU Research Cloud. **Codey** — AI coding platform in development. Structural codebase analysis across 12 LLM providers. $8,323 estimated API-equivalent compute. No team. No university. No funding. Phoenix, Arizona. Full breakdown of how the tokens were used, what it cost by day, and how it compares to other documented heavy users: theartofsound.github.io/claude-usage-dashboard Portfolio showing everything live: theartofsound.github.io/portfolio If you want to talk about how I'm actually structuring sessions at this scale — multi-agent setups, context management, what burns tokens vs what doesn't — happy to get into it. submitted by /u/OGMYT [link] [comments]

Built a GUI overlay on native Claude Code terminals

I've been experimenting with Claude Code this week and built a GUI overlay on top of it – not a wrapper, not a chat layer. Looking for honest feedback before I open-source it. Full disclosure: some of what I built is probably already possible directly in Claude. I'm not claiming this is the only way to do it – I was just curious to see how far I could push the interface and what becomes possible when you add a visual layer on top. This is the result of a week of tinkering. What it is: Claude Code terminals run natively underneath. The GUI listens to structured JSON returned from external API calls and renders dynamic visual screens on top – tables, editors, dashboards – without replacing the terminal. All visual screens are configurable by JSON format / style. What I've built so far: Lead research – Describe your ICP, fetch leads, review in a visual table, run ICP scoring as a skill, push selected contacts to your CRM. All in one place. Landing page editor – Build and edit ad landing pages visually without leaving the interface. More like a wordpress feeling here. SEO/GEO analysis – Results rendered as a browsable overview. Draft and edit blog articles in a side panel from the same screen. Ad creative + campaign launcher – Load your brand workspace, preview generated ad variants, select, and launch a campaign directly from the GUI. Live meeting & call analysis – Toggle record during a prospect call or meeting and get live feedback as the conversation unfolds: talk ratio, objection signals, topic tracking, suggested next steps. No waiting for a post-call summary. Voice analyzed with Deepgram here. Website intelligence + auto brand context – An external API extracts everything from a client's website: active ads, page content, assets, copy tone. It auto-generates a brand voice skill and branding skill from that data. Switch workspaces and those skills are already loaded – every prompt is immediately in the right brand context. Team skill sync + auto-evolution – Skills are shared and synced across team members automatically. As the team works, skill files adapt based on internal feedback and real process outcomes – they update themselves over time rather than staying static. Multi-workspace switcher – Each workspace carries its own skills, tools, and MCP configs (Google Ads, Meta, LinkedIn, mailing accounts, etc. - configurable from external API or internal systems). Built with agencies in mind: 20–100 clients, clean context switching, no mess. Who it's for: Sales, marketing, and customer support – specifically non-technical people who want to run serious AI-powered workflows with claude code but who are missing a interface to review data/ What I'm genuinely trying to figure out: Is any of this solving a real problem for you or your team? The live call analysis and auto-evolving team skills – useful in practice or over-engineered? What's the one thing that would make you actually use this daily? Planning to open-source this. Not pitching anything - just collecting feedback before the release. Happy to drop a screen recording in the comments if there's interest. https://preview.redd.it/unzjvcxt6drg1.png?width=4582&format=png&auto=webp&s=7c997544d563c52de20d29215c9716ab44413d49 submitted by /u/Few_Earth_1001 [link] [comments]

The Luminous Vanguard of the Imperial Dominion

Forged in unbreakable carbon steel and illuminated by the empire’s sacred energy, the Luminous Vanguard represents the highest echelon of the Imperial Army—fourteen commanders chosen not only for their strength, but for their unyielding loyalty and symbolic purpose. Each officer bears a distinct armor set, infused with radiant light-strips that pulse like a living force—signifying rank, specialization, and battlefield authority. Their right-arm insignias are not mere decoration, but ancient emblems of power: dragons, phoenixes, beasts, and mythical creatures that embody the spirit of their command. Together, they form an unstoppable war council: • The Black Dragon Commander – Master of annihilation and fear, striking from shadows with ruthless precision. • The White Phoenix Marshal – Symbol of rebirth and strategy, rising stronger from every defeat. • The Silver Hawk Overseer – Eyes of the empire, unmatched in reconnaissance and aerial dominance. • The Golden Sovereign – The embodiment of imperial authority, leading with absolute command. • The Emerald Serpent General – Specialist in stealth warfare and silent elimination. • The Crimson Flame Warden – Bringer of devastation, wielding overwhelming offensive force. • The Azure Tide Commander – Controller of fluid tactics and battlefield adaptation. • The Infernal Beast Captain – Aggression incarnate, thriving in chaos and close combat. • The Obsidian Lion Sentinel – Guardian of the empire, unbreakable and immovable. • The Violet Revenant – A ghost of war, feared for relentless pursuit and silent judgment. • The Radiant Gold Executor – Enforcer of imperial law, delivering swift and absolute justice. • The Shadow Eclipse Knight – Operates beyond sight, mastering deception and psychological warfare. • The Scarlet Wolf Lord – Leader of elite strike packs, fierce and loyal to the end. • The Amethyst Warlord – The final authority in battle, cloaked in mystery and unmatched power. Bound by honor, enhanced by technology, and driven by a single purpose—the expansion and protection of the empire—these fourteen stand as living legends. Where their lights shine… resistance falls. submitted by /u/Dark-Rose19 [link] [comments]

Weekly Report Mar 2 -- Mar 9, 2026

# Weekly Report: Mar 2 -- Mar 9, 2026 ## Quick Stats | Metric | Count | |--------|-------| | Merged PRs | 47 | | Open PRs | 24 (11 draft) | | Open issues | 61 | | New issues this week | 33 | | Issues closed this week | 6 | | CI runs on main | 30 | ## Highlights An exceptionally active week with 47 merged PRs. Key themes: - **Realm migration**: Keycloak master-to-kagenti realm migration landed (#764), with follow-up fixes (#851, #863) - **Platform hardening**: Podman support (#861), Docker Hub rate limit fixes (#844), PostgreSQL mount fix (#852) - **CI/CD improvements**: OpenSSF Scorecard 7.1->8+ (#807), stale workflow permissions (#859), HyperShift cluster auto-cleanup (#854) - **New capabilities**: CLI/TUI for Kagenti (#835), Istio trace export to OTel (#795), RHOAI 3.x integration (#809) - **Dependency updates**: 8 Dependabot PRs (Docker actions major bumps, CodeQL, Trivy) - **Authorization epic**: 7 new issues (#787-#794) laying out a comprehensive authorization and policy framework - **Agent sandbox epic**: New epic (#820) for platform-owned sandboxed agent runtime ## Issue Analysis ### Epics (active initiatives) | # | Title | Owner | Status | |---|-------|-------|--------| | #862 | AgentRuntime CR — CR-triggered injection | @cwiklik | New, design phase | | #820 | Platform-Owned Sandboxed Agent Runtime | @Ladas | Active, PR #758 in progress | | #828 | Migrate installer from Ansible/Helm to Operator | @pdettori | New, planning | | #787 | Authorization, Policies, and Access Management | @mrsabath | New, 6 sub-issues filed | | #841 | Org-wide orchestration: CI, tests, security | @Ladas | Active, PRs #866-#868 open | | #767 | Migrate from Keycloak master realm | @mrsabath | Mostly done (#764 merged), close candidate | | #619 | Tracing observability PoC | @evaline-ju | Active (#795 merged) | | #621 | OpenSSF Scorecard to 10/10 | @Ladas | Active (#807 merged, now 8+) | | #523 | Refactor APIs for Compositional Architecture | @pdettori | Active, PR #770 open | | #518 | OpenShift AI deployment issues | @Ladas | Active (#809 merged) | | #309 | Full Coverage E2E Testing | @cooktheryan | Ongoing | | #440 | Multi-Team Deployment on RHOAI | @Ladas | Ongoing | | #439 | Namespace-Based Token Usage Quotas | @Ladas | Ongoing | | #614 | Feedback review community meeting | @Ladas | Stale (>30d no update) | | #623 | Identify Emerging Agentic Deployment Patterns | @kellyaa | Stale | | #612 | Agent Attestation Framework | @mrsabath | Stale, PR #613 still draft | ### Security-Adjacent Issues | # | Title | Status | Recommendation | |---|-------|--------|----------------| | #822 | Keycloak configmap should be secret | Open | High priority — credentials in configmap | | #106 | Replace hardcoded secret with SPIRE identity | Open | Long-standing, PR #769 in draft | | #333 | SPIFFE ID missing checks | Open | Stale, needs triage | | #267 | Replace hard-coded Client Secret File path | Open | Good first issue, needs assignee | ### Bug Reports | # | Title | Still affects main? | PR exists? | Recommendation | |---|-------|---------------------|------------|----------------| | #856 | Warnings during Kagenti install | Likely yes | No | Triage — install warnings | | #855 | Can't checkout source on Windows | Yes (skill naming) | PR #869 | In progress | | #829 | Deleting A2A agent doesn't delete HTTPRoute | Likely yes | No | Needs fix | | #826 | No way to log out of Kagenti | Yes | No | UX bug, needs fix | | #825 | Build failures lead to stuck state | Likely yes | No | Needs investigation | | #738 | UI drops spire label on 2nd deploy | Likely yes | No | Stale (>30d) | | #486 | Installer issues (Postgres/Phoenix) | Partially (#852 fixed PG) | Partial | Re-verify Phoenix | | #781 | kagenti-deps fails on OCP 4.19 | Unknown | No | Stale, needs triage | | #606 | Unsupported Helm version | Unknown | No | Stale, needs triage | | #655 | Duplicated resources between repos | Unknown | No | Stale, needs triage | ### Issues Closed This Week (good velocity) | # | Title | Fix PR | |---|-------|--------| | #833 | UI login fails after realm migration | #834 | | #831 | --preload fails when images cached | #832 | | #819 | Remove deprecated Component CRD refs | #818 | | #813 | Import env vars references bad URL | #821 | | #810 | Import env vars silently fails on dup | #821 | | #804 | OAuth secret job SSL error on OCP | #805 | ### Feature Requests | # | Title | Priority | Recommendation | |---|-------|----------|----------------| | #858 | Use new URL for fetching Agent Cards | Medium | Good first issue | | #836 | AuthBridge sidecar opt-out controls in UI | Medium | Tied to #862 epic | | #824 | Help text for UI fields | Low | Good UX improvement | | #823 | Examples as suggestions in UI | Low | Nice-to-have | | #817 | Auto-add issues/PRs to project board | Medium | PR #870 open | | #814 | Mechanism to update agent via K8s | Medium | Operator feature | | #786 | Register MCP servers from UI | Medium | UI feature | | #783 | Agent card signing/verifica

Arizona’s water is drying up. That’s not stopping the data center rush.

It’s no secret that Arizona is worried about its water. The [Colorado River is drying up](https://grist.org/politics/colorado-river-deal-trump-burgum/), [in part due to climate change](https://www.youtube.com/watch?v=AzpYHXgfbbI), and groundwater aquifers are running dry. Some of the state’s biggest industries are suffering as a result: Many farmers have been forced to rip up their cotton and alfalfa fields, and some home developers have been blocked from building new subdivisions. A state with hydrologic woes of this magnitude would seem an unlikely place to attract new factory-scale industries, which often have substantial water appetites themselves, but over the past year that’s exactly what’s happened. So-called hyperscaler tech companies like Microsoft and Meta have swarmed in to build the data centers fuelling the artificial-intelligence boom, and the Taiwan Semiconductor Manufacturing Company has spent billions of dollars on a factory complex outside Phoenix. This [rapid](https://www.reuters.com/sustainability/climate-energy/desert-storm-can-data-centres-slake-their-insatiable-thirst-water--ecmii-2025-12-17/) [development](https://fortune.com/2024/04/08/tsmc-water-usage-phoenix-chips-act-commerce-department-semiconductor-manufacturing/) has [triggered](https://www.azcentral.com/story/money/business/tech/2024/11/04/phoenix-provides-water-to-a-new-chipmaker-any-cause-for-worry/75917812007/?gnt-cfr=1&gca-cat=p&gca-uir=true&gca-epti=z1104xxe1104xxv004275d--47--b--47--&gca-ft=198&gca-ds=sophi) [fears](https://www.apmresearchlab.org/10x/data-centers-resource) that the industry will suck up the finite water supplies available to residents of Phoenix and Tucson. So far, however, these predictions have not come true. Even though Arizona will soon be home to nearly 200 data centers and chip factories, these facilities have not yet caused a major bump in the state’s water consumption. The companies’ precise effects on water supply are hard to discern due to their own secrecy about their water usage, but the aggregate picture suggests they have found ways to minimize their impact, whether through new cooling technologies or by recycling water on-site. And despite [local](https://news.azpm.org/s/102502-marana-data-center-vote-sparks-backlash-three-residents-launch-council-runs/) [backlash](https://www.theguardian.com/us-news/2025/oct/15/tucson-arizona-ai-data-center-project-blue), water experts and many local officials appear to have largely made their peace with the industry’s arrival — and with the Phoenix region’s emergence as one of the nation’s largest AI infrastructure clusters. “There’s not a hair-on-fire context right now,” said Sarah Porter, a fellow at Arizona State University’s Kyl Center for Water Policy. “We just don’t see it.” Arizona is home to [more than 150 data centers](https://www.datacentermap.com/usa/arizona/), according to an analysis from the Data Center Map, an industry resource. Each of these buildings contains thousands of servers that need to stay cool in the desert heat as they process computational queries. This cooling can be done with air conditioners, but it’s more efficient to surround them with pipes full of cold water, or to use evaporating mists to draw out hot air. Cooling systems like these *can* consume a huge amount of water, but [no one knows](https://www.azcentral.com/story/money/business/tech/2026/02/04/arizona-data-centers-water-power-use/88054536007/?gnt-cfr=1&gca-cat=p&gca-uir=true&gca-epti=z119875p003550c003550e1185xxv119875d--55--b--55--&gca-ft=206&gca-ds=sophi) how much they *are* consuming. Independent estimates suggest that an average data center can use anywhere from [50,000](https://www.eenews.net/articles/states-push-to-end-secrecy-over-data-center-water-use/) to [5 million](https://www.eesi.org/articles/view/data-centers-and-water-consumption) gallons of water per day. An [analysis](https://www.ceres.org/resources/reports/drained-by-data-the-cumulative-impact-of-data-centers-on-regional-water-stress) from the sustainability advocacy organization Ceres estimated that the data centers active in Phoenix last summer used around 385 million gallons of water per year. Ceres projected that the metropolitan’s data center water consumption could grow tenfold to around 3.8 billion gallons per year. But even that worst-case-scenario would make data center usage equivalent to just around 1 percent of total [residential water consumption](https://www.azwater.gov/adwr-data-dashboards) in the Phoenix area — and less than half a percent of the region’s total 2024 water usage. (A comparison with agricultural usage is even more stark: Agriculture uses [more than 70 percent](https://environment.arizona.edu/news/where-does-our-water-come) of the state’s water, and still accounts for around 35 percent of water consumption even in the Phoenix metro, the state’s most urban region.) Furthermore, there’s some evidence that Ceres’ estimates may be too high. State data show that

Arizona’s water is drying up. That’s not stopping the data center rush.

It’s no secret that Arizona is worried about its water. The [Colorado River is drying up](https://grist.org/politics/colorado-river-deal-trump-burgum/), [in part due to climate change](https://www.youtube.com/watch?v=AzpYHXgfbbI), and groundwater aquifers are running dry. Some of the state’s biggest industries are suffering as a result: Many farmers have been forced to rip up their cotton and alfalfa fields, and some home developers have been blocked from building new subdivisions. A state with hydrologic woes of this magnitude would seem an unlikely place to attract new factory-scale industries, which often have substantial water appetites themselves, but over the past year that’s exactly what’s happened. So-called hyperscaler tech companies like Microsoft and Meta have swarmed in to build the data centers fuelling the artificial-intelligence boom, and the Taiwan Semiconductor Manufacturing Company has spent billions of dollars on a factory complex outside Phoenix. This [rapid](https://www.reuters.com/sustainability/climate-energy/desert-storm-can-data-centres-slake-their-insatiable-thirst-water--ecmii-2025-12-17/) [development](https://fortune.com/2024/04/08/tsmc-water-usage-phoenix-chips-act-commerce-department-semiconductor-manufacturing/) has [triggered](https://www.azcentral.com/story/money/business/tech/2024/11/04/phoenix-provides-water-to-a-new-chipmaker-any-cause-for-worry/75917812007/?gnt-cfr=1&gca-cat=p&gca-uir=true&gca-epti=z1104xxe1104xxv004275d--47--b--47--&gca-ft=198&gca-ds=sophi) [fears](https://www.apmresearchlab.org/10x/data-centers-resource) that the industry will suck up the finite water supplies available to residents of Phoenix and Tucson. So far, however, these predictions have not come true. Even though Arizona will soon be home to nearly 200 data centers and chip factories, these facilities have not yet caused a major bump in the state’s water consumption. The companies’ precise effects on water supply are hard to discern due to their own secrecy about their water usage, but the aggregate picture suggests they have found ways to minimize their impact, whether through new cooling technologies or by recycling water on-site. And despite [local](https://news.azpm.org/s/102502-marana-data-center-vote-sparks-backlash-three-residents-launch-council-runs/) [backlash](https://www.theguardian.com/us-news/2025/oct/15/tucson-arizona-ai-data-center-project-blue), water experts and many local officials appear to have largely made their peace with the industry’s arrival — and with the Phoenix region’s emergence as one of the nation’s largest AI infrastructure clusters. “There’s not a hair-on-fire context right now,” said Sarah Porter, a fellow at Arizona State University’s Kyl Center for Water Policy. “We just don’t see it.” Arizona is home to [more than 150 data centers](https://www.datacentermap.com/usa/arizona/), according to an analysis from the Data Center Map, an industry resource. Each of these buildings contains thousands of servers that need to stay cool in the desert heat as they process computational queries. This cooling can be done with air conditioners, but it’s more efficient to surround them with pipes full of cold water, or to use evaporating mists to draw out hot air. Cooling systems like these *can* consume a huge amount of water, but [no one knows](https://www.azcentral.com/story/money/business/tech/2026/02/04/arizona-data-centers-water-power-use/88054536007/?gnt-cfr=1&gca-cat=p&gca-uir=true&gca-epti=z119875p003550c003550e1185xxv119875d--55--b--55--&gca-ft=206&gca-ds=sophi) how much they *are* consuming. Independent estimates suggest that an average data center can use anywhere from [50,000](https://www.eenews.net/articles/states-push-to-end-secrecy-over-data-center-water-use/) to [5 million](https://www.eesi.org/articles/view/data-centers-and-water-consumption) gallons of water per day. An [analysis](https://www.ceres.org/resources/reports/drained-by-data-the-cumulative-impact-of-data-centers-on-regional-water-stress) from the sustainability advocacy organization Ceres estimated that the data centers active in Phoenix last summer used around 385 million gallons of water per year. Ceres projected that the metropolitan’s data center water consumption could grow tenfold to around 3.8 billion gallons per year. But even that worst-case-scenario would make data center usage equivalent to just around 1 percent of total [residential water consumption](https://www.azwater.gov/adwr-data-dashboards) in the Phoenix area — and less than half a percent of the region’s total 2024 water usage. (A comparison with agricultural usage is even more stark: Agriculture uses [more than 70 percent](https://environment.arizona.edu/news/where-does-our-water-come) of the state’s water, and still accounts for around 35 percent of water consumption even in the Phoenix metro, the state’s most urban region.) Furthermore, there’s some evidence that Ceres’ estimates may be too high. State data show that

December 22, 2025

*David Sathuluri is a Research Associate and Dr. Marco Tedesco is a Lamont Research Professor at the Lamont-Doherty Earth Observatory of Columbia University.* **As climate scientists warn that we are approaching irreversible tipping points in the Earth’s climate system, paradoxically the very technologies being deployed to detect these tipping points – often based on AI – are exacerbating the problem, via acceleration of the associated energy consumption.** The UK’s much-celebrated £81-million ($109-million) [Forecasting Tipping Points programme](https://www.theguardian.com/environment/2025/feb/18/early-warning-system-for-climate-tipping-points-given-81m-kickstart) involving 27 teams, led by the Advanced Research + Invention Agency (ARIA), represents a contemporary faith in technological salvation – yet it embodies a profound contradiction. The ARIA programme explicitly aims to “harness the laws of physics and artificial intelligence to pick up subtle early warning signs of tipping” through advanced modelling. We are deploying massive computational infrastructure to warn us of climate collapse while these same systems consume the energy and water resources needed to prevent or mitigate it. We are simultaneously investing in computationally intensive AI systems to monitor whether we will cross irreversible climate tipping points, even as these same AI systems could fuel that transition. ## The computational cost of monitoring Training a single large language model like GPT-3 consumed approximately 1,287 megawatt-hours of electricity, resulting in 552 metric tons of carbon dioxide – equivalent to driving 123 gasoline-powered cars for a year, according to a recent [study](https://arxiv.org/ftp/arxiv/papers/2104/2104.10350.pdf). GPT-4 required roughly [50 times](https://www.weforum.org/stories/2024/07/generative-ai-energy-emissions/) more electricity. As the computational power needed for AI continues to double approximately every 100 days, the energy footprint of these systems is not static but is exponentially accelerating. > **[UN adopts first-ever resolution on AI and environment, but omits lifecycle](https://www.climatechangenews.com/2025/12/12/un-adopts-first-ever-resolution-artificial-intelligence-ai-environment-lifecycle-unea/)** And the environmental consequences of AI models extend far beyond electricity usage. Besides massive amounts of electricity (much of which is still fossil-fuel-based), such systems require advanced cooling that consumes enormous quantities of water, and sophisticated infrastructure that must be manufactured, transported, and deployed globally. ## The water-energy nexus in climate-vulnerable regions A single data center can consume up to [5 million](https://utulsa.edu/news/data-centers-draining-resources-in-water-stressed-communities/#%3A%7E%3Atext=Unfortunately%2C+many+data+centers+rely+on+water-intensive%2Cto+supply+thousands+of+households+or+farms.) gallons of drinking water per day – sufficient to supply thousands of households or farms. In the Phoenix area of the US alone, more than [58 data centers](https://utulsa.edu/news/data-centers-draining-resources-in-water-stressed-communities/) consume an estimated 170 million gallons of drinking water daily for cooling. The geographical distribution of this infrastructure matters profoundly as data centers requiring high rates of mechanical cooling are disproportionately located in water-stressed and socioeconomically vulnerable regions, particularly in Asia-Pacific and Africa. At the same time, we are deploying AI-intensive early warning systems to monitor climate tipping points in regions like Greenland, the Arctic, and the Atlantic circulation system – regions already experiencing catastrophic climate impacts. They represent thresholds that, once crossed, could trigger irreversible changes within decades, scientists have warned. > **[Nine of our best climate stories from 2025](https://www.climatechangenews.com/2025/12/22/nine-of-our-best-climate-stories-from-2025/)** Yet computational models and AI-driven early warning systems operate according to different temporal logics. They promise to provide warnings that enable future action, but they consume energy – and therefore contribute to emissions – in the present. This is not merely a technical problem to be solved with renewable energy deployment; it reflects a fundamental misalignment between the urgency of climate tipping points and the gradualist assumptions embedded in technological solutions. The carbon budget concept reveals that there is a cumulative effect on how emissions impact on temperature rise, with significant lags between atmospheric concentration and temperature impact. Every megawatt-hour consumed by AI systems training on climate models today directly reduces the available carbon budget for tomorrow – including the carbon budget available for the energy transition itself. ## The governance void The deeper issue is that governance frameworks