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SpaceX and NVIDIA are building AI data centers in orbit

SpaceX confirmed it is building its Starmind orbital AI data centers around NVIDIA's Vera Rubin NVL72 rack — 72 chips working as a single compute unit, slimmed down for radiation and heat tolerance in orbit. < cite index="17-1">Sam Altman called data centers in space "ridiculous." Elon Musk just answered with a launch timeline and a big-time hardware partner.

The details:

• < cite index="22-1">SpaceX will build its space-based Starmind data centers around NVIDIA's Vera Rubin NVL72 rack, with the first deployment targeted for late 2027. The Vera Rubin NVL72 rack combines 72 chips so they work as one large computer.

• < cite index="16-1">Musk projects that by end of 2026, SpaceX's AI computational capacity will exceed 2GW, with a further increase to nearly 10GW anticipated by end of 2027. SpaceX will use NVIDIA chips exclusively across its AI data centers — terrestrial and orbital.

• SpaceX already owns Cursor (Anysphere) and is pursuing Cognition (Devin). Combined with Starmind orbital compute and Colossus on the ground, SpaceX is assembling a full-stack AI infrastructure position — chips from NVIDIA, IDE from Cursor, autonomous.

Why it matters: The Starmind announcement is the clearest signal yet that the AI infrastructure race has outgrown terrestrial constraints. Pennsylvania blocked AI data centres. Communities are opposing power substations. Water rights for cooling are contested. SpaceX's answer is to leave the planet.

AI NEWS HIGHLIGHT

• Z.ai Ox Alpha topped OpenRouter leaderboard — GLM-series weights releasing tonight — third high-ranked Chinese open-weight model in August. Independent evals available tomorrow.
• Alibaba WAN 3.0: 30 seconds 1080p video + audio in one pass, $0.05–$0.20 per second — audio and video combined in single generation. $1.50 for a 30-second clip. Single-step production pipeline for short-form content.
• DeepSeek V4-Flash vision API live — $0.22/$0.66 off-peak, 1M context, multimodal agents near Opus 4.8 — experimental, but the vendor claim is Opus 4.8-adjacent on multimodal agent tasks. Worth benchmarking before September 1.
• Huawei proposes exporting Ascend 950-series chips to Egypt — testing US tech diplomacy on chip exports — if approved, the first documented case of Huawei exporting its most advanced AI chips to a third country under US diplomatic scrutiny.
• Caltech's Anima Anandkumar joins NVIDIA as Chief AI Scientist — one of the most cited AI researchers in the world — NVIDIA is acquiring not just companies but the researchers who set the direction of the field. The talent concentration at NVIDIA is accelerating.
• Claude Sonnet 5: 6 days left at $2/$10 — September 1 is standard $3/$15. Six days. The decision not made today is made by default on September 1.

Design your AI coding workflow to build skill — not just ship faster

Prompt: You are a senior engineering leader. The Coddy Developer Survey just found that 80% of developers describe their AI coding tool usage as feeling more like dependence than advantage — citing lost stopping points, degraded debugging instincts, and reduced ownership of their own code. The productivity gains are real. The cognitive trade is also real. The difference between a team that gets both benefits and a team that gets only the productivity hit lies entirely in how the workflow is designed.

Our team currently uses the following AI coding tools: [list your tools — Cursor, Claude Code, GitHub Copilot, Codex CLI, or others — and describe how they are integrated into your workflow].

Help me redesign our AI coding workflow to preserve skill development alongside productivity across four areas:

1. The stopping-point audit — for each AI coding tool we use: at what points does the tool remove friction that previously prompted reflection? Code review gates, debugging sessions, hard architectural decisions, and understanding why something works are the moments where learning happens. Which of these has our current AI workflow removed or shortened? For each removed stopping point: what is the minimum viable friction we can reintroduce without significantly reducing productivity?

2. The explanation gate — design a workflow rule: before any AI-generated code is merged, the developer who submitted it must be able to explain, in their own words, what the code does, why it works, and what would break if a specific line were changed. This is not a performance review — it is a learning checkpoint. Write the specific question set I should add to our code review template that tests this understanding without turning every PR into an interrogation.

3. The skill inventory — given our current AI coding tool usage, which engineering skills is our team no longer practising regularly? List the top five. For each: design a specific weekly or monthly practice that keeps the skill active — not as homework, but as part of real project work. The goal is to use AI tools for speed on known patterns while maintaining the ability to work independently on novel problems.

4. The AI-free zone — designate one category of work per sprint where AI coding assistance is not used. Not as punishment, but as deliberate skill maintenance — the equivalent of a musician practising scales. Which category of work in our current project is the best candidate for this, and why?

End with a one-paragraph workflow policy I can share with the team — explaining why we are adding friction back into specific parts of our AI-assisted workflow, what we expect them to gain from it, and what we will measure to know if it is working. The policy should make the case without being preachy. Developers are smart. Tell them the truth about the trade.

TOP TRENDING AI TOOLS

Research & Content

• Clipto — Local AI search for video, audio, meetings, and files.
• Perplexity AI — AI-powered search, $750M ARR, NVIDIA investment incoming.
• Claude — Thoughtful, context-heavy writing and deep document analysis

Software Development

• Cursor — use with stopping-point discipline to preserve debugging instincts.
• Construct Computer — Your AI coworker gets a computer.
• Murmell — Cloud canvas where your team and AI agents collaborate on shared codebases.

Agents & Automation

• AirJelly — Proactive, self-organising second brain.
• Project SKY — Ambient AI companion for Windows.
• Wispr Flow — Dictate in any app — 4x faster than typing, writes in your voice.

Video & Productivity

• Soloop — AI video editor that auto-cuts, captions, and repurposes long-form content
• ElevenLabs — Industry standard for voice cloning, text-to-speech, and audio generation

That’s a Wrap