AI Is Literally Going to the Moon

Welcome back apprentices! 👋

Hey {{first_name|friend}},

Welcome to week 6 of 2026.

This week, AI didn’t get smarter.

It got… strategic.

While everyone’s been arguing about prompts and productivity hacks, the real shift happened somewhere quieter — and way more important.

If you use AI for work (or plan to), this is the part you actually need to pay attention to.

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🏈The Super Bot Bowl. Anthropic, OpenAI, Google, Amazon, Meta, and a wave of startups turned the Big Game into a high-stakes showdown to become your go-to AI assistant, device, or agent. From Alexa+ and Gemini to AI-powered glasses — and even a vodka ad generated mostly by AI — the tech wasn’t just the product, it was the producer. As tech’s share of Super Bowl ads climbs to ~10%, the race for everyday AI dominance just went prime time.

💰 Domain Name or Power Move? Crypto.com CEO Kris Marszalek just bought AI.com for a record-breaking $70M — nearly double the previous domain sale — and unveiled it with a Super Bowl ad. The new platform promises a no-setup personal AI agent that can trade stocks, manage schedules, send messages, and automate your workflows. Marszalek’s bigger vision? A network of self-improving agents that share upgrades and push us closer to AGI. It’s a headline-grabbing launch — but now comes the real test: can it compete with the fast-moving agent tech from established AI labs?

⏳ AI Was Supposed to Save Time. It Didn’t. A new Harvard Business Review study tracking ~200 tech employees over eight months found that AI tools didn’t shrink workloads — they expanded them. Workers took on broader responsibilities, logged longer hours, multitasked more, and even fired off prompts during breaks, while engineers spent extra time reviewing AI-assisted code. AI made unfamiliar tasks feel doable — which meant people simply did more of them. Productivity rose, but so did the pace and pressure.

AI should work for you, not the other way around. Yet most AI tools still make you do the work first—explaining context, rewriting prompts, and starting over again and again.

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Week two of February 2026 was all about power moves. Not better prompts. Not benchmark scores. Power. The kind that shapes incentives, infrastructure, and who ultimately controls the interface to intelligence.

Across video models, lunar compute plans, and developer tooling, one pattern emerged: AI has officially shifted from a model race to a business model race, an infrastructure race, and a trust race — all at once.

🎬 China’s video acceleration is real — multi-modal, 2K, audio-synced generation is moving from flashy demo to production-ready capability in record time.

🌕 AI’s infrastructure ambitions just left Earth — consolidation, internal shakeups, and talk of lunar-powered compute signal how serious the energy + scale battle is becoming.

🛠️ The “AI wrote this” problem is now a startup category — investors are betting big that tracking and auditing agent-generated code will be as critical as generating it.

Seedance 2.0 isn’t just sharper pixels. It signals maturity:

These are production features, not novelty features.

The fact that it followed Kling 3.0 within days suggests rapid iteration cycles in China’s AI video sector. That compresses competitive timelines globally.

If models can reliably generate:

…then creative bottlenecks shift from production to direction.

Musk’s plan reframes AI as an energy + materials problem:

It’s ambitious — and consistent with his pattern of vertical integration.

But here’s the tension:

Rapid restructuring plus deep ambition creates execution strain.

You can’t scale compute to the Moon if you can’t ship stable model updates on Earth.

Vision matters. But execution velocity matters more.

Entire’s $60M seed round says something loud:
AI code governance is now investable infrastructure.

When AI writes code:

Logging prompts and reasoning isn’t bureaucracy — it’s traceability.

If AI writes 40–60% of internal tooling (as many dev teams report informally), audit trails become as critical as CI/CD pipelines.

Creative advantage is moving from pure production to direction and judgment. 

Operational advantage is moving from “we use AI” to “we track, review, and govern AI output.” 

And competitive advantage is increasingly tied to infrastructure — who can scale reliably, not just experiment quickly. 

In 2026, the edge belongs to those who combine speed with oversight, ambition with execution, and automation with accountability.

So… don’t chase the flashiest demo.

Audit your tools. Understand the incentives. Log the outputs.

And if someone promises the Moon — check if they shipped version 1.0 on Earth first.

ChatGPT is a superpower if you know how to use it correctly.

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A playground for your imagination (and low-key prompt skills).

Each send, we give you a customizable DALL·E prompt inspired by a real-world use case — something that could help you in your business or job if you wanted to use it that way. But it’s also just a fun creative experiment.

You tweak it, run it, and send us your favorite. We pick one winner to feature in the next issue.

Bonus: you’re secretly getting better at prompt design. 🤫

Last week, we challenged you to test GPT-4o’s visual generation skills with this prompt.

Here’s the WINNER:

Congrats to Sebastian for his creation🥳

Want to be featured next? Keep those generations coming!

On a rooftop at sunset, a single table overlooks a glowing city skyline washed in warm orange and electric pink light. At the center sits [your object], just revealed from an open box, catching the last rays of daylight. The materials are ultra-detailed and photorealistic — glass reflections, brushed metal edges, soft shadows stretching across the surface. The camera captures a cinematic three-quarter angle with shallow depth of field, rich color grading, and crisp high-resolution clarity — like a premium launch moment frozen in time.

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